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FlashSyn addresses these challenge with its novel counter-example driven approximation refinement technique. Instead of attempting to extract symbolic expressions to exactly match the logic of actions, FlashSyn collects data points to approximate the effect of actions with polynomials and interpolations. FlashSyn then uses the approximated expressions to drive the synthesis. If the synthesis fails because of a large deviation caused by the approximations, FlashSyn collects the corresponding data points as counter examples to iteratively refine the approximations. This novel technique allows the underlying optimizer of FlashSyn to work with more tractable expressions to avoid the awkward timeout situation. It also decouples the two difficult tasks, finding the action sequence and finding the action parameters. When working with a set of coarse-grained approximated expressions, FlashSyn can filter out unproductive action sequences with a small cost.

The EDU-Attention is simple and effective in handling hard sentences in ACSA. Experiments show that our model achieves better accuracy than BERT based models on hard sentences with a much smaller model size and faster inference time.

For amodal completion, we compute the mean intersection-over-union, mIOU, between the predicted and ground-truth amodal masks, as well as invisible mIoU, inv-mIoU, for the predicted and ground-truth occluded regions.

Then we further generalize the lottery ticket hypothesis into recursive lottery ticket hypothesis, and by this hypothesis and other inferences of lottery ticket hypotheses, we will further explore the juvenile state of neural networks beyond the luckiness of initialization to try to explain what determines the learning potential and convergence speed of neural networks.

For pushing, robots often rely on physics models. These models are computable but are only approximations of physical phenomena. For instance, Yu et al.  present a dataset of planar pushing experiments to study how reliable these models are, benchmark motion prediction methods, or for model learning. It shows that pushing can be seen as a stochastic process even if a highly precise manipulator is used to perform the pushes, highlighting the importance of learning to tackle such tasks. This is investigated in which, as in , uses the concept of Residual Physics, i.e., augments analytical models with data-driven techniques to compensate for the imperfections of the models. In , the trained neural networks not only correct the model predictions but also provide distributions over possible outcomes of actions. Hogan et al.  further address the problem of pushing an object on a plane along a desired trajectory or to pass through a sequence of via points. The proposed method consists of using Model Predictive Control and a family of mode sequences that have been designed by the authors. The main limitation of is the necessity of hand-designing a specific family of mode sequences for each task the robot has to solve. In addition, all the above works only deal with single objects.

Englehardt et al. looked for trackers on the homepages on the Alexa Top 1M sites, and measured cookie syncing between trackers on a single website . This study did not look for cookies getting shared across different websites, and their crawler did not interact with the websites studied.

We develop an end-to-end deep learning approach to multi-modal retinal disease recognition. Extensive experiments on a real-world dataset support the following conclusions. As the efficacy of color fundus photography and OCT scans is disease-dependent, the ability of being both selective and interpretable is important for multi-modal fusion. The proposed MM-MIL module possesses both properties, as demonstrated by its superior performance against the prior art. Moreover, MM-MIL has substantially fewer parameters than the prevalent Multi-Head self-Attention module, and thus can be trained on relatively small-sized data. All this makes MM-MIL attractive for AI-assisted retinal disease diagnosis.

In this section, we focus on research efforts related to our work from two lines separately, TEE-enforced confidential smart contract and MPC-enabled smart contracts. Table REF shows the difference between Tenet and representative related works.

All LCC HVDC links have transformers with load tap changers and reactive shunts at both sides. Shunts have capacities equal to half of the converter's rated power. They are activated with five identical steps and a time constant of 0.5 s to support the voltage profile after faults or disturbances.

The paper is organized as follows. First, in Section II, the three main building blocks of any ML radio propagation model are introduced. These are the input to the ML model, the model itself, and its output. Then, the challenges associated with each one of them, as dealt with by various ML-based propagation modeling papers, are discussed. The key ideas drawn from these papers are presented in the next three sections. Section III identifies several ways to specify the input to the ML model. Section IV highlights key points regarding the various ML models that have been used for propagation modeling, while Section V presents the types of output data that have been derived through these models. Section VI presents the main conclusions of the paper.

For the other proposed approaches, we did not perform grid search but tried some combinations of data augmentation strategies. If we performed this, we anticipate our results would improve slightly, but the relative performance of the approaches would likely stay the same.

In summary, we obtain a substantial model performance without the use of in-domain training data for Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The transfer to or the difficulty of these concepts appears not to be the same, the performance for Conscientiousness is substantially higher than for Neuroticism. These results can only be partially compared to previous work due to the differences in the evaluation setup. However, it should be noted that the concepts that appear to be more challenging in our setup show also lower evaluation measures in related work .

All results are reported as the average accuracy together with standard deviation of twenty training runs, where each training uses a different randomly selected labeled subset in the target domain. The standard deviation is therefore mainly measuring the influence of different labeled positive subsets and not so much the influence of stochasticity in the learning optimization process itself.

Concerning the undersampling approach, the proposed OPF-based framework for data imbalance introduces the Optimum Path Forest-based approach for undersampling, the so-called OPF-US, which concerns in removing samples that are more likely to lead the classifier to ill decision boundaries. To such an extent, the supervised OPF is employed to capture the importance of the majority class samples via the k-fold cross-validation approach considering five splits of the training set. After the training phase, a rank score representing the prediction of a validation sample is assigned to each training sample in a k-fold cross-validation iteration. At the final stage, the training samples are ordered by the ascending order of their average rank scores, being the majority class samples with lower ranks removed from the training set until it is fully balanced. Furthermore, this work also introduces three variants of OPF-US, which adopt different policies regarding the samples to be pruned. Besides, it also proposes three hybrid approaches considering the tasks of data undersampling and oversampling.

We evaluate our framework on the drive-by BHM application using lab-scale experiments with two structurally different bridges and three vehicles of different weights. In the evaluation, we train our framework using labeled data collected from a vehicle passing over one bridge to diagnose damage in another bridge with unlabeled vehicle vibration data. Our framework outperform five baselines without UDA, MTL, the new loss function, or hierarchical structure.

The paper is structured as follows. In section 2 we discuss the evaluation methods and algorithms used in this research. The design of experiments, parameters and performance measures are described in section 3. The results are presented in section 4, and the discussions and concluding remarks are shown in section 5.

In this paper, we have proposed a game-theoretic machine learning approach to deal with the revenue optimization in sponsored search auctions. Specifically, we have proposed a Markov model to describe how advertisers change their bids, and then use the model to learn the auction mechanism that optimizes search engine's revenue on the predicted bids. The experimental results demonstrate the effectiveness of our proposal. As for the future work, we plan to consider other factors in our learning process, e.g.,the reserved price. We also plan to investigate on more comprehensive advertiser behavior models.

Furthermore, the planning problem is challenging because the motion planner has to account for the dynamic object and thus plan with time as one of the planning dimensions. It should generate a valid trajectory that avoids collision with the environment around it and also with the target object to ensure that it does not damage or topple it during the grasp. Avoiding collisions with the object requires precise geometric collision checking between the object geometry and the geometry of the manipulator. The robot arms also have kinodynamic constraints such as torque and velocity limits that the motion planner may have to account for while computing the plans, especially when the robots must move at high speeds. The resulting complexity of the planning problem makes it infeasible to plan online for this task.

We organize this paper as follows. Firstly, we will establish our new characteristic finite element method for the growth-mediated autochemotactic pattern formation in self-propelling bacteria in Section 2. Secondly, we consider the convergence of the novel method, and derive the corresponding error estimate in Section 3. And then, we find several wave pattern formation in the chemorepulsion regimes in Section 4. At last, we draw some conclusions in Section 5.

Constructing a counterfactual plan, however, is not a straightforward task because of the many competing criteria in the design process. By definition, the plan should be valid: by committing to any counterfactual in the plan, the application should be able to flip his current unfavorable outcome to a favorable one. However, each possibility in the plan should be in the proximity of the covariates of the applicant so that the modification is actionable. Further, the plan should consist of a diverse range of recourses to accommodate the different tastes and preferences of the population.

The reasoning for the scores was discussed in the interviews that followed. In total, we recorded 122 minutes of interview audio. We analyzed the interview results while focusing on the successful and unsuccessful usage of patterns. We further discuss design recommendations generated from the analysis and our work's limitations in the following sections.

Recently, the Pathfinder has gained hardware performance counters. Future work will use these to drill down into the timings and provide evidence for some of our hypotheses. Additionally, the counters should help diagnose the variance in average time per BFS seen in Table REF .

With our SOS framework, these and other low level operations are readily extended to higher-order surfaces. SOS geometry processing alleviates concerns, for example, that curved surface continuous collision detection might miss collisions due to linearization error, leading to unrealizable states. Similarly, SOS-based closest projection does not suffer from local optima that could affect processes down the line. SOS programming transforms these and other problems on a huge variety of curved patch types into problems where a user can confidently certify a global optimum.

Departing from , , , , , , , this paper develops a cyber insurance mechanism that focuses on a specific and realistic load-altering attack vector stemming from electric vehicle charging and uses real-world charging and power grid data from Manhattan, NY. The paper makes the following contributions:

We also test UDALM, which first does MLM pre-training on the target corpus, and then runs multitask learning with MLM objective and supervised training on MS MARCO. The results show that UDALM in this case greatly harms the performance by 12.2 points in average, when compared with the MLM-pre-training approach. We suppose this is because unlike text classification, the dense retrieval models usually do not have an additional task head and the direct MLM training conflicts with the supervised training.

In our study, the robot was placed at a certain location in the room. After a participant had approached the robot, the interaction started. The robot remained in its position during the interaction for safety reasons, while the robot arm moved back and forth depending on the human's movements.

This paper takes a step in this direction, by addressing, for the first time, the problem of quantifying and studying Wikipedia readers' engagement with citations. More specifically, we ask the following research questions,

In this paper, we propose a GCT to distill the awareness of potential inter-image absence of the common salient object into the existing CoSOD models, improving their robustness to the presence of noisy images that do not share the group-wise co-salient object.

Something interesting this dataset provides is the characterization of the hate speech; that is, having the attacked characteristics for each hateful tweet. Using this, we can assess the incidence of context for each protected characteristic. Contextual information seems to have more impact on some characteristics; for instance, when the attack is against LGBT people. Moreover, we can observe that the dataset has complex and compositional examples of discriminatory language for some specific characteristics.

This paper proposes a novel backdoor attack mechanism on deep neural networks. Unlike existing approaches that inject backdoors through training, this paper shows that a robust, flexible backdoor can be assembled as a malicious payload and directly injected into the victim model through bytecode rewriting. The approach was evaluated with experiments on photos collected from 30 users, 5 state-of-the-art models, and 116 mobile deep learning apps collected from Google Play. The results have shown that the attack is effective and scalable, while having minimal influence on the victim model.

The inheritance process is partly a social issue. As such every technological proposition should be made with that caveat in mind. In particular, it means that solely technical solutions are not going to make it. Any proposition should be such that it includes new possibilities for the blockchain to interact with the outside world. Those interactions should be more sophisticated than what the current implementation of Oracles offers. Typically, the fact that heirs may not necessarily be users of a blockchain implies that there are tools that have to be developed in order to allow interactions from the blockchain towards the real world. Which is a dual problem than the traditional problem that is addressed by Oracles. By its very nature the inheritance process is a very long time problem. The time horizon counts in decade. Very few issues have this property, but as the move towards a digital society accelerate, more and more aspects of our lives will be tackled using digital technologies. And so more and more digital products will accompany us throughout our lives. How to manage such a kind of products? How can they be tested? Are some of the questions that will have to be answered.

Next, we show the results of all the ten submodels in terms of the DST training and standard independent training. By independent training, we mean each submodel is trained independently without sharing its model weightsFor the independent training for UNITER, each submodel is first initialized with a specific portion of the model weights from the pretrained model and then finetuned independently.. From the results in Fig. REF , we can see that all the ten submodels achieved by DST training deliver better performance than their counterparts obtained by independent training. This corroborates with the observations from Table REF .

Router buffer size was varied in steps of 50 pkts from 50 pkts to 300 pkts. Preliminary experiments with ns2 suggested that increasing buffer size beyond 300 pkts does not significantly affect packet loss and so 300 pkts was chosen as an upper limit on the buffer size. Conversely, for buffer sizes below 50 pkts packet loss increases dramatically, which makes it a reasonable lower bound.

While this study is limited in its scope and the sample size too small to obtain accurate predictive logistic regression classifiers for other datasets, we believe that the result is an interesting first step. By itself, it already offers insights in the characteristics of the 3-D Secure decision making in the back-end, normally shrouded from the user.

Integration of additional semantic properties of AC symbols is done on a case by case basis by modifying critical pair constructions but avoiding extension rules or complex AC termination orderings compatible with these semantic properties. A general approach based on the properties of the semantic properties that avoids extension rules and sophisticated machinery due to the AC properties, needs further investigation.

A board-certified radiologist was given access to the chest X-ray image, the full radiology report, the radiology report impression section, the image ground truth across all conditions, and the radiologist report labels across all conditions for each of the 500 examples in the CheXpert test set. The radiologist then explained examples where radiologists labeling reports disagree with radiologists labeling X-ray images. We also calculated the counts of disagreements between radiologists labeling reports and radiologists labeling X-ray images for each condition on the CheXpert test set. A board-certified radiologist explained why there were large numbers of disagreements on certain conditions.

For robot-environment interaction, robots need to have a sophisticated adaptation of their end-effector pose and stiffness. The importance of controlling pose and stiffness for the successful accomplishment of robotic tasks makes them fundamental research topics in the field of robot manipulation.

Any hyperbolic partial differential equation can be reduced to an ordinary differential equation by projecting the solution along the characteristic curves. One can obtain the solution of the ODE along these characteristic curves and transform it back to physical space for the final solution. The data matrix generated from the hyperbolic PDEs contains information about the characteristic curves and the physics of the problem. Passing this data matrix through a deep neural network architecture successively transforms the data matrix and hierarchically extracts the significant spatial and temporal features. Particular deep neural network architectures have certain biases to extract a certain type of features, e.g. CNN with max-pooling extracts translational invariant features. The stochastic and randomness in the denoising convolutional autoencoder allows the AB-CRAN architecture to further identify the translational invariant low-dimensional manifold similar to projecting the hyperbolic PDE along the characteristic curves. Attention-based sequence-to-sequence modeling learns the trajectory along these curves and transposes convolution projects the solution back to physical space. The physical priors endowed in the AB-CRAN neural network architecture allow it to predict wave propagation for large time horizons.

Table REF summarises some of the main works done in the field so far. These studies reveal that exoplanet detection issue has been approached using standard artificial intelligence techniques but it remains a challenge that deserves more attention. Hardly any exploration has been done that reveals the use of novel deep learning techniques in astronomy. We capitalize the usage of the irrefutably appealing efficiency of semi-supervised generative adversarial networks and auxiliary classifier generative adversarial networks to tackle the issue. These are already proved to be useful in biomedical applications and other fields.

For the performance evaluation of the modeling and the pruning points detection algorithms, we considered two different setups. The first setup, in a controlled environment, using either ground-truth or inferred segmentations by the grapevine segmentation neural network, of grapevine plants pictures acquired inside the lab. The second setup, is based on the simulated vineyard, using the segmentation masks and the detected grapevine items inferred by the grapevine segmentation neural network.

Table REF summarizes the results for all tasks evaluated, where the BERTIN models exhibited good performance overall, and the Gaussian models in particular even outperformed the strong baselines established by BETO and BNE for NER and PAWS-X.

To fill in this gap, several new DST approaches, which involve the relations among domains and slots, have been proposed. Some of them leverage a graph structure to capture the slot-domain membership relations , , , , . Specifically, a predefined schema graph is employed to represent the slot-domain membership relations. However, they fail to incorporate the dialogue-aware dynamic slot relations into the schema graph. The other approaches utilize the attention mechanism to learn dialogue-aware dynamic slot relation features in order to facilitate information flow among slots , , , , . However, these approaches ignore the slot-domain membership relations defined by prior knowledge. Since both the prior slot-domain membership relations and dialogue-aware dynamic slot relations can enhance DST performance, our approach is developed to combine them in an effective way.

Performance Metrics Trade-offs. During on-board model execution, an IoT application that interacts with the loaded model may demand high performance on a particular metric over others. For example, a real-time IoT device would require ultra-fast inference, while a low-memory device would require the highest model size reduction. So, the challenge is how to perform optimization that favors particular metrics over others?

In order to quantitatively evaluate the effectiveness, generality and efficiency of our proposal, we conduct three sets of experiments to compare our topic segmentation approach against a variety of baselines and previous models. Namely, we assess the performance of our model in regards to the Intra-Domain Segment Inference Performance, Domain Transfer Segment Inference Performance, and conduct an additional Efficiency Analysis.

We also find that the proposed discriminative modeling strategy performs well for both large and small models. In Fig. REF , we show model ITR as a function of parameter count. Discriminative models outperform generative models across a wide range of model sizes. Among the discriminative models, there is also a positive trend of model performance with increasing model size, indicating that increasing model size may provide additional benefit.

In this paper, we conducted a systematic investigation of 24 contact tracing apps based on the GAEN framework in the US. All the apps were implemented and deployed by the official health departments of the respective US states. We discovered that the considered apps are over-privileged, they violate their own privacy policies, and contain vulnerabilities that can be exploited by malicious users to cause harm to the app's users.

Super-resolution algorithms can be categorized into two types: traditional and deep learning based methods. In this section, we will focus on the second category as the most successful in computer vision.

The recent emergence of large language models raises the question of how to evaluate their capabilities. We survey three different approaches: perplexity, large complex benchmarks, and simple tasks, the latter of which includes LMentry.

Surrogate models play a significant role in modern optimization, prediction, modeling or simulation tools. In recent years various types of surrogate models have been proposed in the machine learning literature and integrated as options in machine learning libraries. It remains however difficult for users to select the right method for the given data-set and often this model selection problem is solved by experimenting with different models, looking at the training error.

Convolutional Neural Networks have proven to be efficient feature extractors which serve well for text classification. Kim Yoon  suggested training a CNN architecture with filters of multiple sizes, on top of pre-trained Word2Vec  embeddings. The feature maps from the different filters, are concatenated after a max-over-time pooling operation, and passed to a Softmax layer. Zhang et al.  expanded on Kim Yoon's  model, with an empirical identification of hyper-parameter settings. Kim's CNN for text  motivates a portion of our work, and inspires a critical part of the proposed method in this paper. DPCNN  employs deepening of word level CNNs to capture global representations of text, and proposes a deep pyramid convolutional neural network that achieves the best accuracy by increasing the network depth, without increasing computational cost.

The deep learning algorithms need more data to learn from the deep representative features. Hence, MLP and 1DCNN show remarkable improvement in the metrics. The performance of SPCAGAN augmented data for the anomaly detection is evident from the striking improvement in the metrics as shown in Table REF . The results offer compelling evidence for supporting the efficiency of our proposed method.

We share a summary of our experiment results on different MoE model architecture and data augmentation techniques in Table REF . First, with the baseline DistilBERT model, we improves the F1 by simply including the Out-of-Domain examples in the training set.

As our method learns a decoupled neural 3D representation of the dynamic and static scenes, we start this section with a review of scene representations, and then focus on methods for object motion decoupling. We also review prior works for 2D segmentation of moving objects.

As shown in  REF , the sensor data collected during walking follow different distributions. From  REF , we can see data of the adult is more stable than data of the elderly when walking. And  REF demonstrates that there exist differences between data collected from two positions of one person at the same time. Therefore, directly applying a model trained from existing data to a new environment may suffer from dramatic deterioration caused by distribution shifts.

Universality. As shown in fig:univ, our local label propagation is a universally safe choice for few-shot inference under both transductive and non-transductive settings. This is in contrast to existing methods such as global label propagation, where the user needs to make decisions depending on the amount of unlabeled data that is available.

The model reduction methods discussed till now yield good reduced-order models for infinite time-horizon. In specific settings, one may have access to simulation data over a finite time horizon, or one might be interested in approximating the output trajectory of the original system only over a limited time interval. In such cases, the model order reduction problem is restricted over a finite time interval. Accuracy outside the time interval is not essential.

To generate 3D mesh from a single free-hand sketch, we first propose a baseline approach, that works the same way as single-view reconstruction for real images. Then we extend the baseline architecture by decomposing image features into a latent view space and a latent shape space, and condition the generation process on the choice of viewpoint.

In the field of Chinese information extraction, little work has been performed on Chinese predicate head recognition. Related works can be divided into two categories according to whether it defines the predicate head as the structural center of a sentence.

In Section REF we focus on the resulting auto-encoder, and the properties of its latent-space. We inspect its smoothness and disentanglement. Smoothness is shown through well-behaved interpolations, even between distant motion. Disentanglement is demonstrated using latent space arithmetic; by adding and subtracting various motion embeddings, we achieve compositionality and semantic editing. Lastly, we leverage our latent structure to perform action recognition over the trained encoder. The latter setting is also used for ablation study. In the following, we first lay out the data used, and other general settings.

In the experiments, the methods are applied on the fly by computing the envelopes, neighboring point distances, and bounding boxes of the query series at runtime as the query arrives. All runtime overhead is counted in the performance report of all the methods. The reported times are the average of 10 repetitions.

In this section, we perform extensive experiments to evaluate existing OOD detection methods, including the standard and adversarially trained ones, and our ATD method against an end-to-end PGD attack. To this end, we first give details about the setting of the experiments. Next, we compare all the methods, which shows that ATD significantly outperforms the other methods. Toward the end, we conduct some additional experiments to investigate some aspects of our solution.

However, none of the previous work utilize dialogue acts with a non-recurrent based LM such as Transformer-XL nor optimize towards improving robustness of in-domain slot entities. In this paper we experiment and study the impact of utilizing dialogue acts along with a masked language model fusion to improve contextualization and domain adaptation. Additionally, we also propose a novel multi-task architecture with TXL LM that improves the robustness towards in-domain slot entity detection.

We described our system for the sixth CHiME challenge for distant multi-microphone conversational speaker diarization and speech recognition in everyday home environments. We explored several methods to incorporate multi-microphone and multi-array information for speech enhancement, diarization, and ASR. For track 1, most of the improvements in WER were obtained from data selection and augmentation, and language model rescoring. Through careful training data selection, we reduced the training time of the system 3-fold while also improving its performance. In track 2, array fusion and overlap handling in the diarization module provided more accurate speaker segments than the Challenge baseline, resulting in improved speech enhancement via multi-array GSS. The gains from acoustic modeling and RNNLM rescoring developed in track 1 also largely carried over to track 2.

Among those problems, we investigate the effect of video encoding quality on action recognition performance in this study. Existing action recognition models are not designed for low-quality videos; thus, it is not clear how they perform on such low-quality videos. There are following two potential situations where low-quality videos are used.

Note that our segmentation method described in Section REF is more straightforward compared to DatasetGAN and DatasetDDPM since it does not require auxiliary steps of the synthetic dataset generation and training the segmentation model on it.

This section presents some of the related works about remote healthcare systems, secured healthcare systems, and SVM-based intrusion detection systems. The basic concepts of this section are the prerequisite of our framework.

In the previous section we presented the results as themes we found in our analysis. Some of these presented common characteristics and some issues were reported in multiple themes. We now summarise the results, highlight the key points and suggest important questions for future research.

There are still some shortcomings in our method, for example, we would like to use the GLCNet to better learn general temporal invariance features. However, at present we only simulate temporal transformations by randomly enhancing the images in terms of colour and texture due to the lack of multi-temporal image data. This cannot really imitate the complex transformations caused by seasons, imaging conditions, etc. So, the true temporal features might not be learnt sufficiently, which can subsequently be complemented by using real multi-temporal images. In the future, the method of this paper will be further improved and then applied to large-scale image data to alleviate the critical lack of labeling in tasks such as global land cover.

All in all, the results and preliminary explanations presented in this chapter do not suffice to arbitrate, in light of the intricacies that arise. Further systematic analysis of datasets and errors are required in order to properly guide reflection. Those analysis will be presented in Chapter , and further discussed in Chapter .

We are the first to provide a study on generating veracity explanations. We show that the generated explanations improve veracity prediction performance, and find that jointly optimising the veracity explanation and veracity prediction objectives improves the coverage and the overall quality of the explanations.

Table REF shows the efficacy of the individual HTC and Grid R-CNN detectors, and their ensembles. We observed that a single HTC detector outperforms an ensemble of HTC and Grid R-CNN in our experiments. This indicates that a careful selection of integrated detection models and parameter tuning are necessary to bring about an effective ensemble model, thus further exploration is required. We examined MC dropout as the uncertainty estimation technique on the HTC and Grid R-CNN detectors individually, and also their ensembles. In practice, this method is equivalent to performing several stochastic forward passes through the network and then taking an average of the results. We chose to sample three passes by adding a dropout rate of 0.3 to the second shared fully-convolutional layer of HTC's region of interest head. After testing a large number of post-processing parameters, we found that models using dropout consistently demonstrated better quality when compared to models without dropout. The result of an ensemble model with dropout outperforming an ensemble without dropout demonstrates the effectiveness of our idea for integrating these two approaches. The results of the models with dropout are displayed in Table REF .

Simple 'said that' baseline: To relate every utterance to it's speaker, speaker name followed by'said that' was used. Removing redundant utterances : We removed the sentences with particular tags that had no contribution to what the conversation was about. Realizing common actions : We found words like agreeing, denying, etc. and replaced utterances with sentences like 'Speaker1 agreed'. Joining Questions and Answers : Identification of question and their answers in dialgues by word matching.

Table REF shows the BLEU score achieved by the models trained with smaller training sets that are randomly sampled from full IWSLT 2014 training set. We observe that the performance of all methods increases with an increase in the training set size, and DHICM achieves a much higher performance compared to T-base for all training set sizes. The performance of T-optimal and DHICM is similar for larger datasets, however, for low-resource datasets, our approach outperforms T-optimal by a large margin.

Overall, our experimental results indicate that TPE is the most suited HPO method for GNN as applied to our molecular property prediction problems given limited computational resources. Meanwhile, RS is the simplest method but can achieve comparable performance against TPE and CMA-ES. In our future work, facing molecular problems on small datasets, the use of CMA-ES also deserves further investigation, and we believe that CMA-ES, RS, and TPE will have very similar performance given more computational budget. Furthermore, as mentioned in Section REF , the selection of the "meta-parameters" for HPO methods deserve more research; we will investigate the impact of HPO methods' meta-parameter values on their performance.

It is often the case that new methods are presented as having clear advantages over existing ones, based on empirical evidence. The inventors of these methods have little incentive to explore the underlying reason for the performance gap. Without a dedicated effort to do so, the literature can quickly become misleading.

To our knowledge, pLUTo is the first work to propose a mechanism to enable the efficient storage and querying of LUTs inside DRAM to enable the in-memory execution of complex operations. In this section, we describe relevant prior works.

In summary, this work aims to understand the role of language in visual learning from a holistic perspective, which goes beyond the high performance of VLP. To achieve this goal, we borrow the idea of probing from the NLP field, and probe the visual representation in pretrained models on a broad spectrum of tasks that measure various properties of the representations. As shown in fig:intro, our probing results suggest that training with language helps vision models learn better semantics, but not localization. We hope our findings provide insights for improving vision models with multi-modal knowledge.

Throughput: Ping and throughput benchmarks are ideal for testing the quality and strength of a signal once a communication channel is formed. At least two nodes that are able to share data are required for this benchmark. Ping should be used initially to confirm that each node is able to communicate with the interface at every other node it should have access to. This will also give an initial indicator to the stability of the channel based on the variance of the latency reported by the ping process. To test the maximum capacity and stability of the channel, iperf is used to utilize all the resources available. Iperf will attempt to pass as much data as possible through the channel. This benchmark can be done in simultaneously or individually in multiple directions to test several channels and their hardware. The stability of a channel is also indicated based on the consistency of the throughput measured. Ping and throughput tests are ideal for testing the effectiveness of handovers. A ping test will help determine the amount of latency introduced, and a throughput test will determine how much data is lost during the handover.

Online Deployment: After the MOCC model was offline trained in the simulator, it needs to be online deployed with the real Internet applications. For better portability, we encapsulate all MOCC's functions into one library. Our library provides three main functions:

Many papers have been published last year proposing some protocol for contact tracing or exposure notification. Martin martin2020demystifying-covid19-tracing offer an excellent overview of the state of the art. We highlight the main Bluetooth-based approaches here and compare them with our proposal.

There is a line of works on automated program repair for compilation errors and context-aware program repair. We also briefly introduce some works that use deep learning techniques for different software engineering applications.

We perform deterministic simulations for arbitrage and peak demand shaving in Section REF . Section REF presents numerical results for energy storage performing backup along with arbitrage and peak shaving. Section REF compares forecast plus MPC with results for a week with respect to the deterministic results.

In this section we give an overview of related work in LA and EDM specifically targetting MOOCs. After discussing the approaches most relevant to FutureLearn, we take a more detailed look at questions of understanding learner behaviour and then more precisely at questions about video usage.

We evaluate Farm by leveraging different variations of Gpt-3. In the classification setting, we compare our methodAll reported results use Google as an external knowledge source, where the top three most relevant sources are used to augment the rationale generation task for in-context inference. Results using other external sources and contextualization schemes will be reported in a future edition. to the existing SafeText benchmark. In the rationale generation setting, we compare Farm to a Gpt-3 baseline that leverages the same 16-shot prompting without external knowledge. Results are partitioned by the safe and unsafe scenarios containing 1096 and 370 examples, respectively.

Related work primarily pertains to app analyses that have been summarized by the concept of security code smells, data transmissions with a particular interest in web communication, and public service audits that improve the app server security. We present relevant literature in each of these three research areas in the remainder of this section.

Experience analysis. For normally distributed data, we employed one-way repeated-measures ANOVAs with Interaction Techniques as the within-subjects variable. For data that were not normally distributed, like the performance analysis, we first processed the data through ART and then use repeated measure ANOVAs with the transformed data.

Using extensive quantitative experiments on five public detest and also qualitative results, we show that although our transformation corrupts the neighbourhood of data, the final binary codes obtained using our method, preserve more neighbourhood compared with many other linear hashing methods.

The rest of the report is described as follows. In Chapter 11, we perform a series of tests to compare our proposed tool to current state of the art competitors. Finally, we sum up our work and propose interesting future improvements in Chapter 12.

The results shown in the previous section lead to several interesting conclusions. First, the results of the identical twin match experimentation further highlight the difficulty of identical twin pairs when presented to facial recognition tools. This conclusion is drawn from the fact that the non-mated distribution of identical twin matches lies close to the mated distribution and is in fact a good estimator of the left tail of the mated distribution for both tested matchers. Second, the occurrence of non-mated look-alikes in the tested datasets is quite rare. This is drawn from the low occurrence of scores falling above the twin comparison score threshold T, and the small percentage of identities having at least one look-alike as determined by the worst-case similarity baseline measurement shown in Figure REF . Finally, the results shown in Figures REF and REF lead to the conclusion that the determination of a comparison score from a facial recognition tool may not be directly correlated with perceived facial similarity. It is shown that the comparison score returned from each of the matchers does show a positive trend with the similarity score returned by the proposed network, but that the similarity of the faces in question may not be the chief determining factor of the comparison score. It is hypothesized that the comparison score returned by a facial recognition tool is instead optimized for peak recognition performance, and that the determination of a quantitative measure of facial similarity is a distinct task from that of facial recognition.

The aforementioned crowd counting models use accurately dot annotated localization maps as ground truth for the crowd images to train the model. All these works focus on investigating the accuracy improvement, but none of these explore the model performance over imperfectly labelled data.

Among the various categories of pruning methods, we mainly use unstructured pruning since it is the most effective in finding highly compressed networks that have order-of-magnitude smaller parameters, which is a critical factor for 3D convnet compression. Also, it can be applied to generic network architectures due to its simplicity, is simpler to implement, and can provide insights into structural properties from the final pruning patterns.

We have introduced a few improvements on the legacy estimators, from the proposition of new binning schemes to the use of heuristics to automatically pick relevant values for hyperparameters of estimators of the ECE. On top of this, a novel approaches has been built to define properly the notion of local calibration error, which produces novel estimators for the ECEs. By testing all approaches on a synthetic experimental setup for which we had access to very precise estimates of the theoretical ECE, we have been able to compare all candidate estimators. This systematic evaluation, which had never been done until now, allowed us to formulate some recommendations on which estimator to use in what context.

In this paper, we attempted to obtain knowledge about how research is conducted, especially how journal articles are produced, by comparing preprints with journal articles that are finally published.

In this work, we propose a new domain adaptation framework for semantic segmentation with annotated points via active selection. First, we conduct an unsupervised domain adaptation of the model;from this adaptation, we use an entropy-based uncertainty measurement for target points selection. Finally, to minimize the domain gap, we propose a domain adaptation framework utilizing target points annotations. We present the experiments on synthetic data to real data in traffic scenarios. Experimental results on benchmark datasets shows the effectiveness of our approach against other domain adaptation approaches.

In future work, we plan to investigate new neuronal cyberattacks with different action mechanisms and impacts. Additionally, we aim to explore the possibility of having realistic topologies, which are currently very limited, to simulate existing and prospecting cyberattacks. Finally, we want to focus our efforts on designing and implementing detection mechanisms to identify the initiation of a neuronal cyberattack and propose mitigation techniques to reduce their impact or even suppress it.

In this paper, we propose a new cross supervision based semi-supervised semantic segmentation approach, uncertainty-guided self cross supervision. Our method achieves self cross supervision by imposing the consistency between the subnetworks of a multi-input multi-out model. In order to alleviate the problem of noise accumulation and propagation in the pseudo label, we proposed uncertainty-guided learning, utilizing the uncertainty as guided information to reduce the effects of wrong pseudo labeling. Experiments show our approach dramatically reduces training costs and achieves powerful competitive performance.

Dietary apps for nutritional assessment are developed to assist users with their diet-related issues or keep track of their dietary intake. Such apps tend to act as guides and enable users to choose healthier alternatives to improve their nutritional habits in the long term. Therefore, due to the vital importance of diet-related apps, this SLR analyzed a wide range of existing literature on mHealth apps from scientific databases of CINAHL, Science Direct, and PUBMED and shortlisted almost 56 studies. We have investigated the apps' comprehensiveness in terms of critical features, general issues, and usability challenges from general users' reference frames. We have further examined the strength and weaknesses of the existing freely available diet-related apps and summarized concerns and gaps for future work. Our findings show that the credibility of database resources, comprehensive information about macronutrients and micronutrients, validation of database, data privacy, use of AI for food logs, and automated portion size estimation from the pictures are foremost challenges. Addressing the challenges mentioned above will improve the usability and comprehensiveness of diet-related apps. Therefore, making them more substantial for patients, general users, and dieticians. Moreover, implementing blockchain technology and health standards for data security, exploring recent trends in continual learning for food recognition, and outlining standard guidelines for regulating apps are essential future topics that can be explored.

Our project aims to explore how DS workers create their presentation slides, and then build human-centered AI systems to support such task. Thus, in this section, we organize the literature review into three subsections: Communication in Data Science Teams, Data Science Work in Computational Notebooks, and Human-centered AI for Data Science.

We perform a set of computational experiments using previously addressed unsupervised methods GraphSAGE, Node2Vec, and Attri2Vec. In the following, we briefly discuss each of them to clarify their usage as the feature extractors.

However, a proper examination of the performance of the SOA approach is still lacking in the field. In this study, we manually crafted a dataset of 1,416 static code warnings and their evolution status from two real-world open-source systems and used it to identify potential for improvement in the SOA approach.