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We present two experiments, one small preliminary experiment trained on two
months of data to compare our approach to Bayesian networks; and another
large-scale experiment trained on two and a half years of data and tested on
three months of data.This distinction is made due to the conclusive
results of the preliminary experiment, and computation considerations for the
training of Bayesian networks.
Our model is coded in PyTorch, and will be available online in the final
version of the paper, as described in Appendix REF .
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The two adversarial cases considered in this work include the injection of ADD and JMP instructions, respectively. An ADD instruction consumes one CPU cycle, while a JMP instruction takes three cycles. Naturally, the injection of these malicious instructions causes a displacement of one and three cycles.
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With modern technology having reached a stage where video cameras are everywhere, thanks to the ubiquitous laptop and tablet, it is the perfect time to develop new musical instruments for that particular hardware. Naturally, an ulterior motive exists for this paper: the author wished to design a musical toy that people easily could try out and have fun with, and perhaps be inspired by in their own creative endeavours.
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Systems are generally considered as complex if the sum of the individual behaviors of its subparts cannot explain the overall behavior of the ensemble. This consideration encourages the modeling of the inter-relationships between subparts, instead of modeling them as independent behaviors. Our results reinforce this perspective, as we report this modeling approach to be more predictive of users' decisions. Moreover, our approach has the advantage to be interpretable, as described in the following section.
| 1 |
Near-infrared detection experimentation will hasten the use of drone-based early detection if such sensors are made based on the results of these experiments. Also, experimentation for the creation of a portable Laser-induced breakdown spectroscopy-based technology would be a handy tool in the early detection of RPW on the ground. Furthermore, in this matter, high-frequency radar and X-ray technology experiments have some promises based on preliminary experiments.
| 1 |
Metrics obtained for large-sized objects have been omitted since there are no samples in the established dataset. As shown in each of the tables obtained for the images that make up the defined dataset, there is a clear improvement in the mAP measure.
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Fig. REF shows the averaged equivalent number of looks by a non-local filter for the test site. From this figure, we can see that the elevation is estimated in a spatially adaptive manner. The number of looks at the buildings is quite lower than in the homogeneous area, which indicates that pixels chosen by a non-local filter should have similar properties, such as similar elevation, reflectivity, scattering characteristics, and so on.
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We conduct both quantitative and qualitative analysis.
For NCBI Disease, BC5CDR Disease, and BC5CDR Chemical datasets, we compare our proposed model's score with previous researches.
Bioinformatics datasets are reported by top one recommendation accuracy.
Given the biomedical entity in the train set, entities are matched with the most similar entities in datasets.
If the query entity and target entity share the same concept ID, it is considered correct.
The financial NEN dataset is a pairwise NEN matching corpus.
For evaluations on the financial NEN dataset, models that are used in evaluations distinguish whether two named entity pairs share identical meanings or not.
We also perform the qualitative analysis to assess models' weaknesses.
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Performance.
Table REF shows the performance for each outcome task. All models performed well in pneumonia detection and in all tasks VGG16 outperforms its ResNet18 counterpart. For the remaining outcomes the robust models have less sensitivity than their standard counterparts.
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We implemented the core consensus logic for all protocols, view change for Multi-Paxos and Raft, and all optimizations of Mencius. We implemented all the attack scenarios we present in this paper. We did not implement snapshot and replica reconfiguration, which are outside the scope of this paper.
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Finally, we evaluated various PLM on our new benchmark and provide baseline model and human performance metrics. Our experimental results show that current LM need further improvements to attain human-level language ability. We hope our new benchmark can contribute to advancements in the Korean NLP field.
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While the results show a slight advantage to the TDW for the target searches, it was not as significant as expected. This is in part because the experiment deliberately spanned a range of difficulty, and thereby is inclusive of both the SDD and TDW advantages. However, results obtained from the post experiment survey indicate that participants decidedly preferred the TDW experience over the SDD, even if their performance results did not show a marked difference. Indeed, several participants believed themselves to have performed better with the TDW when they had in fact performed better with the SDD.
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In this section, we investigate the performance of the proposed model for both computer vision and language processing tasks. However, due to limited computing resources, we use smaller models for comparing the performance between the baseline transformer architecture and the proposed model. Therefore, the reported results of baselines may not be comparable to the state-of-the-art results.
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We have observed that most of the considered community metrics are heavily biased with respect to cluster size. While this does not mean that they are useless for cluster quality evaluation, it makes them inadequate for a simplistic approach based on either of them individually. They do however characterize properties that are expected of good clusters, and can complement other methods on a more qualitative analysis. And considering that there isn't a single universal definition of what constitutes a good clustering, being able to evaluate each of these properties separately can be valuable. Also note that these metrics can be particularly useful when comparing partitions of the same number of elements, because in that case the potential of bias related to cluster size is not a concern.
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Existing approached for integrating neural and symbolic techniques can be categorised into two main types. Firstly, there are a set of approaches that leverage neural networks for learning and symbolic components for reasoning. Secondly, there are approaches that leverage both neural networks and symbolic components together to perform a joint learning task.
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Our B-M model relies on subsets of Facebook pages for training, which were chosen according to their performance on the development set as well as on the observation of emotions distribution on different pages and in the different datasets, as described in Section . The feature set we use is our best on the development set, namely
all the features plus Google-based embeddings, but excluding the lexicon. This makes our approach completely independent of any manual annotation or handcrafted resource.
Our model's performance is compared to the following systems, for which results are reported in the referred literature. Please note that no other existing model was re-implemented, and results are those reported in the respective papers.
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The QG model is responsible for generating questions about the objects in the image and acquiring knowledge that is useful for novel object recognition.
To this end, we conditioned the QG with partial knowledge, which masks part of the knowledge.
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We contribute to lessening this gap by presenting our work which is concerned with presenting the latest up-to-date research around time series GANs, their architecture, loss functions, evaluation metrics, trade-offs and approaches to privacy preservation of their datasets.
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In this section, we firstly introduce the three datasets that we report the results on, followed by the quantitative metrics we use. Secondly, we present our experimental setup in detail. Further, the quantitative and qualitative results together with ablation analysis on various clustering algorithms as baselines are shown.
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Architectures. For both Omniglot and MiniImageNet experiments, we utilize a 4 layer convolutional neural network with 64 filters, followed by BatchNorm and ReLU nonlinearities. Each of the four layers is followed by a max-pooling layer which results in a vector embedding of size 64 for Omniglot and 1600 for MiniImageNet. Exact architectures can be found in Appendix REF . Protonet-like models use BatchNorm with statistics tracked over the training set, and MAML-like baselines use transductive BatchNorm as the original works do. As spectral normalized models require residual connections to maintain the lower Lipschitz bound in equation REF , we add residual connections to the CNN architecture in all Protonet based models.
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We then focus our attention on evaluating the finetuning performance, i.e., when the entire pretrained model is optimized using the available labels. We compare against different initialization schemes: random initialization, which covers the case of training a model in a fully-supervised manner; initialization from a supervised pretraining of the ResNet-18 backbone on ImageNet, a popular choice despite the domain gaps with remote sensing data; initialization from SSL via SimSiam; initialization with the proposed SSL pretraining. Tables REF and REF report the results for the DeepLab and Dranet segmentation architectures, respectively. It can be noticed that the proposed approach is the only one that is able to significantly improve over random initialization. In general, this can be explained by the fact that there exists a semantic gap between the pretraining process and the labels of the final task. The finetuning process has thus to use the precious labels to partially undo what has been learned during pretraining, losing efficiency. In particular, pretraining on ImageNet does not allow to learn feature extractors that are aware of the spectral information, or that can effectively process SAR data, being their statistics so different from optical data. On the other hand, SimSiam is also not very effective because the mostly geometric augmentations used by the SSL do not lend themselves to learning features that correlate with materials and thus do not generalize well beyond the limited improvements seen in the linear protocol. Finally, notice, once again, how the observed gains are agnostic to the specific segmentation architecture. These results suggest that the proposed method is highly effective to improve the performance of end-to-end deep learning models for land cover classification when SAR and multispectral data are jointly used.
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We then turn our attention to the detection results for separating Adv from ID in the right-hand side in Figure REF .
We see that for all adversarial attacks used for generating the Adv samples, the detection performance is miserable if we use the features from the early layers of the text classifier.
However, the situation twists in the later layers of the network, with a burst in AUROC starting in layer 10 and lasting until the last layer for most Adv samples.
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Hence, both quantitative and qualitative results demonstrate that highlighting certain product features in the item representations for making outfit combinations is meaningful and can be achieved with attention.
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All our models were trained on a machine with 8 NVIDIA Tesla V100 GPUs, each with 16GB of memory.
When using pretrained encoders, we leveraged gradual unfreezing to effectively tune the language model layers on our datasets.
We used the "Base" variant of BERT and RoBERTa encoders, which uses 768-dimensional embeddings, 12 layers, 12 heads, and 3072 hidden units.
When training from scratch, we used a smaller encoder consisting of 512-dimensional embeddings, 6 layers, 8 heads, and 1024 hidden units.
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discuss, in the context of medical Statistics, issues to consider when designing a simulation study; in particular, by exposing how the study will be performed, analysed and reported in details.
Moreover, design decisions are discussed such as the procedures for generating datasets and the number of simulations to be performed.
The authors also suggest a checklist of important design considerations.
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We find that component-wise convergence speed divergences is the key challenge in fine-tuning stability, based on the case study of the gradual unfreezing method.
Based on our observation, we propose a new simple component-wise gradient clipping method to help stabilize fine-tuning, which achieves empirical improvements of fine-tuning stability over previous methods.
| 1 |
Our approach relies primarily on federation and differential privacy. Federated learning approaches to speech recognition are relatively popular and common, but the main criticism lobbed at such approaches to privacy is that the resulting model can be subjected to a membership inference or model inversion attack. These attacks could result in revealing private information about the speakers used in training. However, combining federation with differential privacy effectively blocks such attacks. Therein lies the novelty of our work.
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In this section, we will introduce our method in detail. First, we exhibit the model architecture, and then introduce the multi-stage distillation strategy for the model training. An overview of our approach is shown in Figure REF .
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In previous studies, researchers pointed out that social contexts influence people's daily-life fashion. To our knowledge, our study is the first to discuss how much social contexts affect what people wear in a quantitative manner. Our findings in Section 5.1 suggest that our approach with CAT STREET can validate other fashion phenomena observed and discussed qualitatively by providing objective indices about daily-life fashion.
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Benaloh and Tuinstra proposed a receipt-free voting system able to guarantee coercion resistance, ballot privacy, and verifiability via interactive zero-knowledge proofs. Unfortunately, their system requires a trusted central authority that continuously interacts with the voters during the voting process. Moreover, voters must not be able to communicate with external entities during
the voting process. Compared with classic paper ballot voting, the overhead of this system in terms of deployment effort and actions required from the voters is unfortunately not trivial. The advantages provided are the determinism and quick aggregations of the result. Remote voting is hardly achievable.
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The main focus of this paper has been the characterization of the spectrum and the singular values of the coefficient matrix stemming from the approximation with space-time grid for a parabolic diffusion problem and from the approximation of distributed order fractional equations.
For this purpose we employed the classical GLT theory and the new concept of GLT momentary symbols. The first has permitted to describe the singular value or eigenvalue asymptotic distribution of the sequence of the coefficient matrices. The latter has permitted to derive a function, able to describe the singular value or eigenvalue distribution of the matrix of the sequence, even for small matrix-sizes, but under given assumptions. blackIn particular, we exploited the notion of GLT momentary symbols and we used it in combination with the interpolation-extrapolation algorithms based on the spectral asymptotic expansion of the involved matrices.
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In this paper, we propose MixSiam, a mixture-based approach upon the siamese contrastive. By investigating the state-of-the-art siamese-based method, we find it difficult to deal with large intra-class variations as it only learns from augmented views with little variance. To this end, we present a mix-based feature learning strategy upon the siamese structure. On the one hand, the hard virtual samples generated by mixup are introduced to enhance the robustness. On the other hand, we capture the discriminative representation of an instance from its corresponding augmented views. By learning to minimize the similarity between the hard virtual sample and the discriminative representation, the model learns to be invariant to the hard samples. Experiments are conducted on standard ImageNet and several small datasets, demonstrating our superiority comparing with the state-of-the-art methods. The consistent improvement over the siamese baseline further verifies the effectiveness of our method. In the future, the augmentation strategies will be investigated to select the appropriate ones for generating the mixed images. More experiments will be conducted on more downstream tasks such as object detection and object segmentation to further validate the generalization ability of the proposed model.
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This section describes the studies related to author name disambiguation which are further divided into rule-based approaches, machine learning based approaches, and more specifically neural network based approaches. It also details the studies using KGEs for scholarly data.
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Bamler et al. presented a language model for probabilistic temporal text data which tracks the semantic evolution of individual words over time. The model uses Word2Vec to create embeddings, which are connected through inference algorithms that allow training the model jointly over all time periods. Word embedding trends are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices. This method is used to evaluate the change in the semantics of each word over time.
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To examine whether people adapt the amount of planning to its cost and benefits, we analysed their number of clicks as the decision to click corresponds to a decision to plan more because additional planning is necessary to get any benefit out of the additional information. We hypothesised that in each condition participants would gradually learn to adapt the amount of planning they perform. Concretely, we predicted that in high-variance conditions the number of clicks would increase over time because the large range of possible rewards makes planning unusually beneficial and at the same time causing not planning to be very costly. By contrast, in low-variance conditions planning is less beneficial and therefore the number of clicks should decrease. Furthermore, we predicted that people would adapt to the high cost of planning by reducing their number of clicks more strongly when the benefit of planning is low and increasing it less strongly when the benefit of planning is high.
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In this paper, we consider Faster R-CNN and YOLOv3, which are the state of the art algorithms of CNN for object detection. We selected them due to their excellent performance and our objective is to compare between them in the context of the car detection problem. In this next section, we will present a theoretical overview of the two approaches.
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In this section, we discuss previous approaches that are relevant to our method. As there is no prior work on parametric reshaping of portraits in videos, we organize this section according to the two stages of our method by reviewing related works on video-based reconstruction and video deformation, respectively.
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The COVID-19 pandemic surprised the world, generating an enormous health crisis with profound social and economic implications. Reopening the economy becomes a latent issue as many countries are vaccinating considerable portions of their populations .
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The remaining paper is organized as follows. Section 2 presents the problem statement, section 3 presents the proposed solution, section 4 presents the experiments and section 5 presents the discussion.
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In general, our experiments confirmed what one would typically expect, namely that the best performance is obtained by running a specification that was native to a particular solver on that solver. However, the experiments also showed that, for a number of benchmarks, our translation-based approach is actually able to match or even, in rare cases, outperform the native approaches. This demonstrates the usefulness of our translation also from a computational perspective: a specification that performs poorly with one solver, may be more efficient when translated to the input language of another solver.
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In this work,
we propose the FusionDeepMF model, where MF integrates a linear kernel to model the latent feature vectors based on users' rating activity and reliability score and on the other hand MLP uses a non-linear kernel to learn users' latent feature vectors.
This model uses a tuning-parameter determining the trade-off between the dual embeddings that are generated from the linear and non-linear kernels.
We observe that the new way of modeling users' reliability score not only characterizes the users' latent feature vectors accurately but also presents a better interpretation of how the similarity of users' reliability score affects rating prediction.
The raters also play a vital role to notify the reliability of a reviewer's review and help the model to learn the latent factor of the reviewer more accurately to predict rating.
| 2 |
In contrast to existing methods, ILD avoids learning intermediate signals by computing the analytical learning gradient directly from the expert demonstration through differentiable physics. The analytical gradients carry rich information about both the expert intentions, i.e., the reward, and the specifications of the environment dynamics. Meanwhile, ILD dynamically selects local optimization goals for each state in the rollout trajectory, which gives a simpler optimization landscape for policy learning.
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Figure REF shows that, as with KITTI, initializing the model with depth pre-trained weights significantly improves semantic segmentation accuracy with both ResNet18 and ResNet50 encoders. Initializing by ImageNet pre-trained weights also improves from random initialization. Note that, with both encoders, pre-training by depth trains faster, which matches with our observations on KITTI. This may be due to that depth provides a pre-trained decoder, while for ImageNet pre-train, the decoder needs to be randomly initialized.
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The proposed method is a 2 step training process. First, the model is pre-trained by contrastive self-supervised learning. Then the model is fine-tuned for the task of video-based FER. Following we present our method in detail.
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Although chart-based parsers technically share the above features to an extent, they parallelize over multiple paths of composition instead of straightforwardly composing multiple constituents in a single path. Overall, chart-based parsers still need to recurse over the entire sequence length. In contrast, CRvNN can instead halt early based on the induced tree-depth albeit at the cost of more greediness.
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The rest of the paper is organized as follows: In Section 2, we give a brief overview of the existing dynamic saliency approaches. In Section 3, we present the details of our proposed deep architecture for video saliency. In Section 4, we give the details of our experimental setup, including evaluation metrics, datasets and the competing dynamic saliency models, and discuss the results of our experiments. Finally, in the last section, we offer some concluding remarks.
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In Section , we give a brief overview of the rule-based graph programming language GP 2 along with the previous semantics. We propose the new semantics in Section and give examples of transition sequences. In Section we prove several properties of the new semantics, including non-blocking as well as finite nondeterminism, and define the semantic function along with semantic equivalence. In Section , we compare the new and previous semantics by showing the new semantic function is an extension of the previous one, and that they are equivalent excluding divergence.
| 0 |
Current approaches for verifying floating-point code include abstract interpretation,
interactive theorem proving and decision procedures, which we survey in this section.
We are not aware of work that would automatically integrate reasoning about
uncertainties.
| 4 |
We also describe for the first time some techniques so far present only as
source code in various systems without any explicit documentation and present
some of our original work.
One problem in the field of version control is that since most innovation
and research happens not in the academia or even classical industry, but the
open community, very few of the results get published in the form of a
scientific paper or technical report and many of the concepts are known only
from mailing list posts, random writeups scattered around the web or merely
source code of the free software tools. Thus, another aim of this work is tie
up all these sources and provide an anchor point for future researchers.
| 0 |
This section first presents the results of our analysis of hybrid OA
uptake in Elsevier's journal portfolio with a view to licensing,
disciplinary differences, and citation impact. Then, we present a
descriptive analysis of Elsevier's invoicing data, highlighting
licensing and disciplinary differences for invoicing channels and invoice recipients.
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The models utilizing merged streams are easiest to extend as they do not require
additional signalling. A merged stream may be shared with participants before
any streams have been added, making the
MCUMulti model very easy to implement for an unlimited number of parties.
You simply share the merge in the offer and then add the stream received in the answer to
the merge when the connection has been established.
| 2 |
The U-Net was initially trained with the real world dataset of 750 images. The resulting model was tested on the test dataset containing 250 images composed of all the 10 categories listed in Table REF . This evaluation provides the insight into the ability of deep learning based crop row detection methods in handling external variables present in a real world crop field.
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In this section, we first briefly introduce the common probabilistic model for classification in Section REF , and then present the proposed latent variable model for learning part-whole hierarchies and classification in Section REF .
| 0 |
In this paper, we have shown how multilevel techniques can be used to improve existing methods for stochastic trace estimation. We have derived general error bounds for our multilevel trace estimator, and through numerical experiments have demonstrated the efficacy of the multilevel estimator as compared with single-level methods.
| 1 |
In addition, we perform an ablation study of our system to analyze the importance of each individual component of our parser. The first row in the second sub-tables illustrates the results when only organizational features are used, while the second row shows the impact of removing the features and only using RoBERTa for the action classification. Finally, the third row contains the performance of our system with organizational features and a randomly initialized RoBERTa model component.
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Self-supervised learning: In self-supervised learning for images, we learn rich features by solving a pretext task that needs unlabeled data only. The pseudo task may be colorization , inpainting , solving Jigsaw puzzles , counting visual primitives , and clustering images .
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It begins with the calculation of pairwise linguistic distances for the given database of words. A Phonetic Substitution Table is used to assign weights during the calculation and could possibly be modified. The result is a new distance table which is analysed in the following ways:
| 2 |
Lastly in Figure 5. we can see the compression ratios for each codec, size and
entropy. This is mostly just a sanity check. NPY is always at 1, since it is a
plain serialization format. Bloscpack gives better compression ratios for low
entropy data. NPZ and ZFile give better compression ratios for the medium
entropy data. And all serializers give a ratio close to zero for the high
entropy dataset.
| 3 |
We optimize the models with the ADAM algorithm over the categorical crossentropy loss with an initial learning rate of 0.001. We allow up to 150 epochs for training and implement early stopping observing the validation loss and patience of five epochs for CNN5 and 15 epochs for the other models. For the three bigger models, we apply dynamic learning rate scheduling, multiplying the learning rate by 0.3 after a patience period of twelve epochs. We run all experiments five times with different random seeds and report the average values. All experiments are implemented with Tensorflow 2.2.0 and Foolbox 3.0.0 .
| 2 |
As Bifrost is a secure file sharing system using an untrusted cloud,
and is proposed based on DD, it is natural to look at
the current literature about both these topics.
As far as our knowledge, there is no work that has combined the aspects of
secure file sharing and DD until now.
Therefore, in this Section, we look at some of the state-of-the-art
research on the topics that are the main focus point of Bifrost.
| 4 |
Based on our results, we conclude that most appropriate initial application of this signed area-based method appears to be causal discovery in low dimensional, coupled time series with unidirectional causal relationships. We believe the chief contribution here is a totally model-free, data-driven approach to uncovering causal relationships in coupled time series that provides a useful alternative to traditional methods such as Granger causality, convergent cross mapping, and causal graph discovery algorithms such as PCMCI.
| 1 |
In this section, we introduce key concepts and terminology that we use in our work to map signs of exploration and explanation in computational notebooks to corresponding shifts within the sensemaking loop.
| 0 |
settles2010active provides a general active learning survey,
summarizing the prevalent AL scenario types and query strategies.
They present variations of the basic AL setup like
variable labeling costs or alternative query types,
and most notably, they discuss empirical and theoretical research investigating the effectiveness of AL:
They mention research suggesting that AL is effective in practice
and has increasingly gained adoption in real world applications. However, it is pointed out that empirical research also reported cases in which AL performed worse than passive learning and that the theoretical analysis of AL is incomplete.
Finally, relations to related research areas are illustrated, thereby connecting AL among others to reinforcement learning and semi-supervised learning.
| 4 |
sec:analysis presented theoretical results which demystified the layer-by-layer view in value network, in this section we will verify these theoretical results in experiments. We then show that DLGN recovers major part of performance of state-of-the-art DNNs on CIFAR-10 and CIFAR-100.
| 3 |
This paper addresses the problem of generalization under distribution shifts. We first cover work in this problem setting, as well as the related setting of unsupervised domain adaptation.
We then discuss test-time training and spatial autocending, the two components of our algorithm.
| 0 |
green
In this section, we survey blockchain-based secure storage in section REF and blockchain-based secure sharing in section REF .Although existing models and schemes achieve secure storage and sharing of medical information, they fail to realize fine-grained access to medical information, which will undoubtedly reduce the user experience. In addition, most existing studies have not considered the storage bottleneck of blockchain.orange
In order to make up for the deficiency of existing studies, this paper not only achieves the safe storage and sharing of medical information, but also optimizes the access control operation of medical information, and alleviates the storage pressure of blockchain to a certain extent, which is also the difference between the proposed scheme in this paper and the existing model.
| 0 |
The manuscript is organized as follows, Section explains in formal terms, what is the video captioning task and which are its parts or components. Section incorporates all the elements involved in evaluating the proposed methods or approaches, that means the datasets and the performance metrics. The analysis and discussion are done in Section , some aspects to be improved are described in Section as well as some possible applications. Finally, Section gives some conclusions derived from our literature revision and comparison. In Appendix REF we present tables with all our raw reviewed data.
| 0 |
In practical programming, how do you describe your demand through NL of NL2Code? As is well known, describing one demand is not unique, which may be extremely diverse. For example, for a demand, NL can be described in various languages like English, French, and Chinese. Also, NL can be formatted as code comment, competition problem, and so on. Moreover, it can be described with various granularities, i.e. you can describe a demand in terms of its background knowledge, functionality, algorithmic process, etc. In addition, you should specify some programming constraints for the demand, such as programming language, code library, time-space complexity, etc. As we can see above, the NL2Code task is inherently highly versatile, diverse and complex, which would be detrimental to the evolution of the research direction if not combed. Such situation motivates us to do the survey of NL2Code.
| 0 |
This section demonstrates the start-of-the-art performance of UniMVSNet with
comprehensive experiments and verifies the effectiveness of the proposed Unification
and UFL through ablation studies. We first introduce the datasets and
implementation and then analyze our results.
| 3 |
For conditional generation, we show evaluation results from the original BigGAN setting and the proposed objective in Table REF . The results indicate the proposed framework can also be applied in the more sophisticated training setting and obtain competitive performance.
| 3 |
Specifically, we predict the probability of belonging to an object for the points in the encoder embedding, and select the top-scored points as the reference points.
The transformation that contains the scale information of the object is predicted from the corresponding encoder embedding. The image-dependent box query helps locate the object and improve the performance.
| 2 |
Modern advances in computing hardware are enabling new opportunities for the manner in which large volumes of imagery data are processed. Over the past decade, Hadoop has emerged as an early experimental testbed for several big data applications due to its excellent large-scale data-handling capability, high fault tolerance, reliability, and low cost of operation . Hadoop provides distributed data storage and analysis solutions which previously have been exploited for implementing large scale mean-shift based image segmentation algorithms . In , an optimization effort on the Hadoop file storage system was studied to elicit better performance for large scale computing with image data. The authors of study a Hadoop and MapReduce based implementation of the parallel K-means algorithm to reduce the computational time taken for executing parallel data clustering on a large number of satellite images. Pursuing content mining on digital images, the authors in introduced an approach for large-scale scene retrieval on massive image databases.
| 4 |
Also, an adversarial loss is applied by using a generated anomaly map and an input image. Unlike the existing GAN, the discriminator of AnoSeg determines whether the image is a normal class and whether the anomaly map is focused on the normal region. Since the anomaly map learns the normal sample distribution, AnoSeg has high generalization for unseen normal and anomaly regions even with a small number of normal samples.
| 2 |
Fig. REF shows a qualitative visual comparison between the neural solver and the semi-implicit solver in contrast to the reference solution. While both the neural solver and the semi-implicit solver took the same runtime, the neural solver shows faster convergence as the time step increases.
| 3 |
The algorithm presented above describes a single-head unit.
As with self-attention, this layer can be split into a set of Dispatcher heads to improve performance.
The number of heads then becomes another hyper-parameter to tune during training.
| 2 |
They integrated a special extension in the TM-aware compiler to insert
pre-validation hooks in front of dangerous operations. Dangerous
operations are basically in-place stores, whether on stack or shared data, and
indirect branches which result from shared reads according to the data flow.
Every read from shared data is considered to be inconsistent unless it was
validated. If the data read is used in a subsequent dangerous operation, it
requires a pre-validation to prevent possible errors. Hence, the compiler
extension analyses the data flow and instruments every in-place store and
indirect branch with pre-validation code which are the first accessing data read
from shared location in terms of data flow. The inserted code decides at runtime
whether a validation of the reads in a particular data flow branch is necessary
or not and aborts in case of inconsistencies.
| 2 |
A manual evaluation of hand-written lede paragraphs with the generated ledes showed that the majority of differences are due to grammatical errors: the addition or subtraction of a few non-crime supporting part-of-speech tokens or special characters like dash, quotes etc in any token.
| 3 |
The UCF-QNRF dataset is a new crowd counting dataset consisting of 1,525 images with a total of 1.25 million annotations, of which crowd counts between 49 and 12,865. It is splitted into training and testing subsets consisting of 1201 and 534 images, respectively.
| 2 |
In this section, we propose a novel set of tools to explain the face image quality and its effect on FR model behavior, independent of the underlying working principles of the FIQA methods itself, by focusing on the response of the FR models to samples labeled with different qualities.
We leveraged the process of activation mapping of a visualization network to display the scaled activation weighted by the face embedding of the FR network. This mapping links the content of face embeddings with the pixels in the input face image. Based on the activation mapping, we draw statistic characteristics for images with different face qualities and thus enable analyses of the response of FR models to low and high face image qualities and the quality interpretation of single samples based on FR model responses.
| 2 |
Each subject demonstrates a
different facet of the bFuzzer algorithm. json demonstrates how using a trie,
we can recover tokens efficiently when there are no further constraints imposed.
The case of tinyC demonstrates how precise feedback can help when there
are other syntactic constraints imposed on the token. The case of mjs demonstrates how backtracking can help. Finally for ini, any string is
a valid prefix, and for csv, any string is a valid input.
These demonstrate how the random choice of
a byte is sufficient to cover the input language effectively.
| 3 |
Future investigations might include further examining linguistic feature in video understanding and exploring the VL Encoder in other video analysis problems such as event proposal generation to extend VPC to end-to-end DVC using shared feature.
| 1 |
The difference between the MARE baseline and both MARE approaches shows the positive effect of pretrained transformer networks. Despite the general weaker performance of DARE, which uses an automatically selected rule-set, the original benchmark has the highest MRE precision score. This indicates a high certainty in extracted relations and a high number of false negatives because of the low recall score.
| 1 |
In this paper, we propose a lightweight self-supervised non-blind deconvolution module that embeds a classic iterative deconvolution algorithm into a deep learning application. By integrating convolutional layers into the iterative Landweber deconvolution algorithm we are able to efficiently reconstruct the sharp image. We also explore constraints added to the data fidelity term, Hessian and sparse prior. We show that our model is able to obtain very competitive quantitative results when evaluated on computer vision benchmark datasets, without leveraging any large pre-trained networks and having only a fraction of parameters compared to other SOTA methods. Moreover, we evaluate our method on real microscopy images obtained by our EDOF underwater imaging system showing the capabilities of our model in a real-world application.
| 0 |
Overall, Tab. REF , REF and REF comprehensively show that proposed PSUMNet achieves state of the art performance, generalizes easily across a range of action datasets and uses a significantly smaller number of parameters compared to other methods.
| 1 |
Denoising autoencoders can be enhanced by utilizing PCA to eliminate outliers and noise without access to any clean training data. While dimensionality reduction and a denoising component could potentially improve accuracy and remain a consideration for future work, it is not directly applicable to the present model which demonstrates success with minimal reductions to the input data.
| 1 |
Automated detection features: Not all features in our RF-based classifier are novel. Many have been used in automated detection approaches before. However, we focused on features that could heavily contribute to the detection of FHD-based phishing attacks, ignoring others that are non-conclusive or counter-active towards the detection of these websites, which in turn, allowed our model to get higher performance. We recommend building task-specific classifiers for detecting different types of phishing attacks.
| 1 |
CRAFT was created in order to facilitate research on understanding and closing the gap between the capabilities of human intelligence and artificial systems in grasping and reasoning about physical relationships between different objects in an environment.
| 0 |
Given the critical nature of the medical domain, the pace of adoption of modern deep learning models did not gain enough traction for impactful change. Instead, it was used to address mundane medical procedures. On the other hand, summarization is a long-studied problem in text processing. Earlier, most of the focus and improvements were on extractive summarization, with a drastic shift to abstractive summarization recently. Some of the work done in the medical health domain is discussed below, followed by a section on technical progress in summarization methods.
| 4 |
As can be seen, several approaches aim to control for length, stance, audience, or similar. However, all of them still compare argumentative texts with different content and meaning in terms of the aspects of topics being discussed. In this work, we assess quality based on different revisions of the same text. In this setting, the quality is primarily focused on how a text is formulated, which will help to better understand what influences argument quality in general, irrespective of the topic. To be able to do so, we refer to online debate portals.
| 0 |
Validation is made flexible and entirely parametrable by users who can perform any kind of cross validation scheme on any subpart of the train datasets.
Test datasets however are sanctuarized and can never be used for training. Test performance evaluation is constrained to a rigorous methodology.
| 2 |
Super-resolution has shown vastly on recent works, and also reaffirmed in this thesis, both its ability to enhance the clarity and visual aspects of images and its potential to improve the accuracy performance of face recognition systems.
| 0 |
Remarking that there is a lack of publicly available research for tracking ice hockey players making use of recent advancements in deep learning, in this paper we track and identify hockey players in broadcast NHL videos and analyze performance of several state-of-the-art deep tracking models on the ice hockey dataset. We also annotate and introduce a new hockey player tracking dataset on which the deep tracking models are tested.
| 0 |
In future work, we hope to expand upon the findings presented here by applying our policy model to health environments beyond the ICU, such as in general inpatient hospital wards or in the outpatient setting. The heterogeneity of care trajectories is wider in these settings, and so the potential ambiguity regarding patient risk forecasts is amplified. In addition to expanding to new datasets, we would like to explore a number of improvements to the quality and granularity of our intervention policy models, such as shifting to continuous predictions and increasing the number and sophistication of intervention patterns forecasted. It will also be imperative to conduct user testing with clinicians to determine clinical benefit and potential for integration into real-world workflows.
| 1 |
We proposed a technique for secure location-based alerts that uses searchable encryption in conjunction with variable-length location encoding. Specifically, using Huffman compression codes, we showed that it is possible to significantly reduce the overhead of searchable encryption for cases where alert zones are compact and sparse, which is the case we believe to be most likely in practice. Extensive analytical and empirical evaluation results prove that our proposed approach significantly outperforms existing fixed-length encoding techniques, with only a small overhead in terms of additional encryption time.
| 2 |
To supplement our recommendations we provide the first public implementations of all stopping criteria we evaluate, making it simple for practitioners exploit active learning to train machine learning models more cheaply, or in cases they otherwise couldn't have been used. Additionally we provide our evaluation framework so that researchers have a detailed baseline upon which to develop future stopping criteria, and suggest promising directions for future development to advance the field of active learning.
| 1 |
Popular graph algorithms such as BFS, SSSP, PageRank are widely used in real world server workloads. The demand of graph processsing workloads is increasing rapidly with the increase in internet usage.
| 0 |
In this section, we briefly introduce controversy detection on different social media platforms, emotion detection, and a few recent works that have laid the groundwork by combining emotions and controversy detection.
| 4 |
At the same time, DVML gets rid of the triplet constraints for the generator by considering the class manifold as a Gaussian distribution.
By estimating the parameters of the distribution, the sampling of new examples is performed using a variational approach.
Because there is no adversarial training, examples tend to be mostly sampled at the center of the Gaussian distribution.
As such, they only slightly contribute to the DML loss.
| 2 |
To the best of the authors' knowledge, this work presents the first example of an eye-in-hand RGBD dataset for object detection gathered during actual tree crop harvesting. Unlike much existing literature, multiple current generation object detector architectures are benchmarked and publicly released, along with day and night datasets.
| 4 |
In this section, several simulation studies are presented to justify our theoretical discoveries for regression, classification, and stability of the interpolated-NN algorithm, together with some real data analysis.
| 2 |
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Dataset Card for IMRAD Classification Dataset (100k Rows)
Dataset Name: IMRAD Classification Dataset (100k Rows)
Dataset Description:
This dataset contains approximately 100,000 sentences extracted from scientific research papers and labeled according to their corresponding IMRAD (Introduction, Methods, Results, and Discussion) sections. The data was initially sourced from the unarXive_imrad_clf dataset on Hugging Face and expanded using data augmentation techniques. This dataset is ideal for training and evaluating machine learning models for IMRAD classification, a crucial task in automating scientific text analysis.
Dataset Structure:
- Format: CSV
- Columns:
text: The sentence extracted from the research paper.label: The corresponding IMRAD section label:Introduction: 0Discussion: 1Methodology: 2Results: 3Related Work: 4
Dataset Creation:
- Source Data:
unarXive_imrad_clfdataset ([https://huggingface.co/datasets/saier/unarXive_imrad_clf]) - Data Augmentation: Synthetic data was generated using a large language model (e.g., 'gemini-1.5-flash') to increase the size and diversity of the dataset.
Dataset Statistics:
- Number of Sentences: 100,000 (approximate)
- Label Distribution:
- Introduction: [15862]
- Discussion: [26001]
- Methodology: [15148]
- Results: [24247]
- Related Work: [19298]
Potential Uses:
- Training and evaluating machine learning models for IMRAD section classification.
- Developing automated tools for scientific text analysis, such as information retrieval, summarization, and content recommendation.
- Research on natural language processing (NLP) techniques for scientific text.
License: [MIT, CC BY-SA 4.0]
Citation Information:
[Sid Ali Assoul]. (2024). IMRAD Classification Dataset (100k Rows). Hugging Face Dataset.
[https://huggingface.co/datasets/stormsidali2001/IMRAD-sections-clf-gemini-augmented](https://huggingface.co/datasets/stormsidali2001/IMRAD-sections-clf-gemini-augmented)
Contact:
[Sid Ali Assoul] - [[email protected]]
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