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While these efforts are helpful in evaluating the quality of style transfer algorithms, they do not separately evaluate content preservation and stylization quality. Many, potentially all, of the evaluation procedures described would give high scores to an algorithm that simply returned the style input image. Motivated by this chapters and describe forced choice user studies which evaluate both stylization quality and content preservation.

DVF tightly fuses image predictions with LiDAR voxel features and is not strictly capped by image predictions. DVF is trained directly with ground truth 2D bounding boxes, avoiding noisy, detector-specific 2D predictions while enabling LiDAR ground truth sampling to simulate missed 2D detections and to accelerate training convergence.

In addition to non-GNN based node embedding methods, GNN based methods have achieved huge success in generating node representations. GNNs are an application of deep learning on graph data and use information from the particular graph mining task of interest in learning the node representations. In this survey, we summarized the general framework of GNNs and their categories including static GNNs, spatio-temporal GNNs and dynamic GNNs.

Currently, there are three scenarios in which a continual learning experiment can be configured. Task-incremental learning is the easiest of the scenarios, as a model receives knowledge about which task needs to be processed. In this scenario, models with task-specific components are the standard, where the multi-headed output layer network represents the most common solution.

In this paper, both transfer learning and active learning are key components of the design methodology, and the combination of transfer learning and active learning allows leveraging small amounts of labelled data to improve the performance of the training process of the selected deep learning model.

Prior research mainly studies the updates of ad libraries, the cost of ad libraries and the security and privacy issues surrounding ad libraries. Our study is the first to investigate the integration practices of ad libraries. We briefly highlight the related works as follows:

The results suggest that imposing ontology-based knowledge as structural inductive bias in the model helps to mitigate the difficulties of high-dimensionality, by limiting the number of connections in the neural network architecture. This can be useful for clinical tasks when little is known about the influence of different genes to a particular target bio-marker. In such scenarios, where all available data has to be put in use, the structural inductive that limits spurious connections proves beneficial. However, if there is enough knowledge about the task to do feature selection, then limiting the number of inputs and applying a simpler machine learning model may be a better way forward.

The research topics related to our work include: warping and morphing with implicit surfaces; level set methods; implicit surface representations using neural networks; differential geometry; exterior calculus; partial differential equations and physics-based methods.

the conference must be sponsored by the Brazilian Computer Societysbc.org.br. This criteria aims to avoid local or regional and small workshops. the conference should have had at least 20 editions. This criteria was used in order to select more consolidated conferences in Brazil. the conference should be indexed in either the Scopus or DBLP databases. Finally, this criteria was chosen to identify papers that have more scientific visibility.

The remainder of this paper is organized as follows: Section  describes mathematical formulations of the stochastic MILP model. To demonstrate this model, Section  presents a case study and simulation results. Finally, Section  concludes the paper and discusses future work.

The structure of the paper is as follows. In Section we summarize the main research directions in the field of motion prediction for autonomous driving and discuss the applicability for our purpose regarding the aforementioned prerequisites. In Section we introduce our approach of the MixNet and additional features that cover the velocity fusion, safety checks and interaction-awareness. Besides that, we present our recorded dataset and the training procedure. Then in Section we evaluate its performance compared to the baseline model and analyze its robustness against noisy input and the superpositioning weights. In Section we discuss future research directions based on the shown results. Finally, in Section , we conclude our work and outline the scope of the paper.

A drawback of this approach, however, is that we can't integrate the generated links within the article text, thus the links would have to be displayed separately on the site. The effectiveness of this approach also depends on the size ratio of the used Wikipedias.

For evaluation of sound designs in the DareFightingICE Competition, we proposed two metrics that are the win ratio and the average HP difference when fighting against the aforementioned opponent AI. Our experiment results showed that the sound design of DareFightingICE was more effective than the sound design of FightingICE. This confirms that the proposed two metrics can be used in evaluation of entry sound designs in the competition.

Multi-attribute classification is a more general form of classification where each data example has a set of labels. Models for multi-attribute prediction can be implemented similarly to models for single-attribute prediction, but face additional difficulties. Some attributes may be rare, causing issues related to severe class-imbalance. Attributes may be missing or only a subset may be observed for each example in the dataset. In these settings, making calibrated predictions and quantifying uncertainty is especially important.

Our work aimed at determining a list of promising techniques and models to be used for business intelligence. Thanks to all the computing resources provided by Total SE, we were able to massively perform experiments with state-of-the-art transformer models and different parameter configurations.

The remainder of the paper is organized as follows. Section II elaborates on our proposed RL-based ICN framework for stretch reduction. Section III consists of the performance evaluation, results, and discussion, and we concluded our paper in Section V with some future research directions.

With this work, we demonstrated that the fundamental security implication of disturbance errors still persists even in emerging neuromorphic technologies. As neuromorphic hardware has the potential to become a key component of modern computing systems, evaluating its basic security aspects is essential to providing a stepping stone to building secure next-generation devices.

With the development of IT, social media has become more and more popular for people to express their views and exchange ideas publicly. However, some people may take advantage of the anonymity in social media platform to express their comments rudely, and attack other people verbally with offensive language. To keep a healthy online environment for the adolescences  and to filter offensive messages for the users , it is necessary and significant for technology companies to develop an efficient and effective computational methods to identify offensive language automatically.

For RQ1, we present some general performance of our model for GUI rendering inference and the comparison with state-of-the-art baselines. For RQ2, we carry out experiments to check if our tool can speed up the automated GUI testing, without sacrificing the effectiveness of bug triggering. For RQ3, we integrate AdaT with DroidBot as an enhanced automated testing tool to measure the ability of our approach in real-world testing environments.

Based on the literature review, some related topics for future research can be discussed as follows. As mentioned previously, the classification setting is usually a classic closed-set problem. In this setting, we face the risk of open space and misclassification of an unknown sample falling into over-occupied space divided for the known classes. An appropriate understanding of the nature and the underlying structure of the data can help us to arrange the known classes in a more compact form and limit the open space. The clustering technique is to create meaningful groups of the given samples based on the similarity that can improve the exploration of the data information and the generalization ability of classification learning. Thus, designing a learning framework that combines clustering and classification tasks can overcome the problem of over-occupied space. However, all current existing simultaneous learning clustering and classification algorithms are designed for closed-set problems. Therefore, designing such a framework under open-set assumptions could be a promising direction.

In the third column, we compare the regret and running time between the batched and the sequential versions of NeuralUCB and NeuralGCB. We plot the regret incurred against time taken for different training schedules for 10 different runs. For all functions, the regret incurred by the batched version is comparable to that of the sequential version while having a significantly less running time. Furthermore, NeuralGCB has a smaller regret compared to Batched NeuralUCB for comparable running times.

Additional experimental results in the appendix. We also compare area under precision recall curve with SOTA results and report it in the appendix. Logistic regression for assigning weights to the uncertainty scores is trained on a small subset of the iD and OOD samples. We show that these weights can also be learned by only using iD and adversarial samples generated from the iD as a proxy for OODs. All these results, along with ablation studies on indicators of high AU and high EU composing individual detectors as well as individual detectors are included in the appendix. In all the results, we achieved the performance that is similar to the one reported in Table REF .

We believe that both parameters are mathematically natural. Theorem REF indicates that the chord visibility width can be exponentially larger than the point visibility width. Thus we would expect that chord visibility width is potentially more profitable. We would expect that both parameters are equally relevant as the example that we give is fairly contrived. The remainder of this paper is dedicated to proving Theorem REF .

In our default blocked representation, the first element of a block is represented uncompressed, and the rest of the elements are compressed relative to the previous element. In addition to delta-encoding, CPAM also supplies an interface for the user to define their own form of compression for each block. For example, they can quantize values, or use other variable length codes when keys are known to be small. CPAM uses a reference counting garbage collector to manage the memory for both the internal nodes and the compressed leaf nodes, which can be of variable size due to compression.

Our model showed immense promise in its ability to outperform the best models in its training and validation accuracies. However, due to a combination of over-fitting and the difficult nature of the test dataset, we failed to achieve a significant increased in performance. However, given that our model was only trained on one dataset and the extent to which our results improved when data was augmented, there is reason to believe that training on more data will allow our model to generalize and replicate its impressive training and validation performances. Thus, future development of our deep model which incorporates more examples and is not unlucky to have its test data be the most difficult subset of the data might significantly outperform existing methods. Additionally, using more datasets in varied locations will allow the extracted room features to be of more use, instead of simply indicating when we rotated the room. One downside to a convolutional or deep model as opposed to algorithmic methods proposed is that it becomes more difficult to work with varied room sizes. For different room sizes, future work will need to add padding to smaller room images so that spacial dimension are not affected. To eventually switch to a practical deep model to be incorporated in a social robot, many more datasets of different sizes must be used and the ability for variable-sized rooms must be implemented.

In this section we introduce key concept needed to understand our contributions. We first describe the FFAI framework and the Blood Bowl game. We then provide an overview of the reinforcement learning problem. Finally, we introduce imitation learning, with a focus on behavioural cloning.

Note that we require no human labelled data such as labels for sentences or similarity ratings for creating sentence-level meta-embedding from the above-described proposed method. Therefore, we denote our unsupervised proposed method by UNSUP in the remainder of the paper.

Patch-sampling based methods. These approaches extract local features from the patch-based CNN intermediate representations. Gong et al. designed a multi-scale CNN framework to sample the local patch features densely, and then encoded them via VLAD . Some other methods , represented the scene image with multi-scale local activations via the FV encoding. Depth image patches were exploited in the work of Song et al. . They first trained the model with densely sampled depth patches in a weakly-supervised manner, and then fine-tuned the model with the full image. Nevertheless, densely sampled patch features may contain noise, which limits the scene recognition performance.

chrupala2015learning simulate visually grounded human language learning in face of noise and ambiguity in the visual domain. Their model predicts visual context given a sequence of words. While the visual input consists of a continuous representation, the language input consists of a sequence of words. The aim of this study is to take their approach one step further towards multimodal language learning from raw perceptual input. Kdr2016RepresentationOL develop techniques for understanding and interpretation of the representations of linguistic form and meaning in recurrent neural networks, and apply these to word-level models. In our work we share the goal of revealing the nature of emerging representations, but we do not assume words as their basic unit. Also, we are especially concerned with the emergence of a hierarchy of levels of representations in stacked recurrent networks.

This study aims to detect Monkeypox disease by developing CNN model using transfer learning approaches. In this work, we have used pre-trained deep learning architectures to extract essential features that are practically difficult to initially identify by visual inspection due to their similarities with other infectious diseases such as chickenpox and measles. We then fed our data through several layers, where the top most dense layer is used to detect the Monkeypox disease.

In this paper, we introduce a new multi-level surgical activity annotations for the LRYGB procedures, namely phases and steps. We proposed MTMS-TCN, a multi-task multi-stage temporal convolutional network that was successfully deployed for joint online phase and step recognition. The model is evaluated on a new dataset and compared to state-of-the-art methods in both single-task and multi-task setups and demonstrates the benefits of modeling jointly the phases and steps for surgical workflow recognition.

We propose a method to improve corruption robustness and domain adaptation of models in a fully test-time adaptation setting. Unlike entropy minimization, our proposed loss functions provide non-vanishing gradients for high confident predictions and thus attribute to improved adaptation in a self-supervised manner. We also show that additional diversity regularization on the model predictions is crucial to prevent trivial solutions and stabilize the adaptation process. Lastly, we introduce a trainable input transformation module that partially refines the corrupted samples to support the adaptation. We show that our method improves corruption robustness on ImageNet-C and domain adaptation to ImageNet-R on different ImageNet models. We also show that adaptation on a small fraction of data and classes is sufficient to generalize to unseen target data and classes.

Based on the experimental results, we aim to assist data scientists in e-commerce scenarios by focusing on two additional research questions, which concern the choice of a suitable model for e-commerce scenarios:

In the future, we would like to explore the cross-lingual capabilities of the transformer models in the MWE detection task. Cross-lingual transformer models such as xlm-roberta can be used to transfer knowledge between languages so that a model can be trained only on English data but can be used to predict on other languages. Since the xlm-roberta-based performed best in this study, we believe that this model can be further explored to detect MWEs in different languages.

Two search algorithms were implemented to extract a task tree to prepare dishes that exist in FOON. The general process requires starting from the goal node and search for candidate functional units. For each candidate unit, a search is performed on its input nodes to determine if they exist in the kitchen. If the node does not exist in the kitchen, it is explored. The search concludes when all nodes are already in the kitchen. Based on the algorithm, different candidate units can be selected and the resulting task tree will vary per dish.

As has been discussed above, current GNN-based face clustering algorithms could be very memory-consuming, and rely on expert knowledge to set proper thresholds to determine the connectivities between faces. To address such challenges, we propose a face clustering method based on pairwise classification. We formulate the face clustering task as a classification task between faces and train a classifier to directly determine whether two faces should belong to the identical class. Furthermore, to generate face clusters more efficiently, we select face pairs sent to the classifier based on a novel rank-weighted density manner, which turns out to be more insensitive to outliers. Figure REF illustrates an overview of our proposed method.

In this work, we seek to design a practical solution to secure legacy ICS against stealthy sensor attacks. The solution should be based on assumptions that even advanced attackers cannot easily bypass, while making minimal changes to a legacy ICS. Furthermore, the solution should have a negligible impact on the functioning of all legacy devices in the ICS.

Some static checks remain in DSI: Does the MongoDB server log file contain errors or stack traces, or are there core files on the cluster hosts, etc? The scope of DSI is therefore everything that concerns a single system performance task execution. Anything that requires historical data as input is in the signal processing project.

This section is organized in three parts. We first discuss works that address the issue of limited data in HSRS image classification. Then, we review convolutional neural networks for HSRS image classification. Third, we introduce prior work on Bayesian neural networks.

An analysis of the predictions of the three models across all three data sets highlights their behavioural characteristics, as seen in the table above. LTDDM is verbose; it makes many predictions, often multiple for the same target event, producing much higher recall when target events occur infrequently. In contrast, LSTM is terse and precise. When presented with temporally sparse data, LSTM prefers to err on the side of the sequence mean and remain silent.

Other downstream applications like coherence evaluation of language model generated text and tasks such as chat disentanglement are also good candidates for testing coherence models. It would be worthwhile to build a coherence testset that is independent of the training tasks and similar to downstream applications, which could be used by the community to test the generalization ability of their models. In future work, we also hope to investigate the possible training scenarios that will result in more generalizable coherence models which can be used for evaluating downstream tasks.

Our primary contribution is a novel representation for musical material, which when coupled with state-of-the-art transformer architectures, results in a powerful and expressive generative system. In contrast to previous work, which represents musical material as a single time-ordered sequence, where the musical events corresponding to different tracks are interleaved, we create a time-ordered sequence of musical events for each track and concatenate several tracks into a single sequence. Although the difference is subtle, this enables track-level inpainting, and attribute control over each track. We also explore variations on this representation which allow for bar-level inpainting. Unto our knowledge, both inpainting and attribute control have not been integrated into a single model.

Overall, one can see that the task of predicting the country from the tweet text is more difficult than from the user-provided meta-data. Combining both feature types yields the highest accuracy. In all cases, the minimal accuracy across the day is substantially lower than the average. This indicates that the difficulty of the classification task varies over time, presumably due to changes in the label distribution.

Regarding the FL learning, it can be seen from the learning curve presented by Figure REF that the training was regular. The averaged loss of the clients learning over 200 communication rounds shows that the learning is indeed progressing and able to converge. More so, a significant standard deviation can be observed on the client-side. This trait is likely due to the large number of dissimilar clients and the heterogeneity where the results show that such training with heterogeneous data is indeed challenging.

Automatic detection of road-closures is the topic of another group of studies pietrobon2019algorithm, cheng2017automatic. Cheng et al. cheng2017automatic presented a high-efficiency road closure detection framework based on multi-feature fusion. Their framework had two parts, an offline road closure feature modeling part and an online identification part. For the offline modeling, they first partitioned the road-network into grids, and then extracted their road closure features of these grids and the roads within them from historical data. The online component screened out closed grid candidates based on the plunge in traffic flow. They also identified sections with road closures based on variations in turning behavior by drivers on these roads. Their framework was evaluated on three real-world datasets - from Chengdu, Shanghai, and Beijing.

We find that the presence or absence of Support Devices is a statistically significant predictor of misclassification for three models detecting Cardiomegaly, for four models detecting Pleural Effusion, and for two models detecting Consolidation.

A hybrid BCI application with a deep learning classification system has many future potentials with increased computation capabilities. One of the most important aspects of deep learning-based models is handling the raw data without much pre-processing. These models can find the features by themselves rather than following the traditional feature extraction and feature selection approaches. By using raw data, more complex deep learning architectures may be required for classification tasks. With the complexity of the classifier will require more computational resources will also be required. In this particular instance, the raw data based classifier performed poorly than the others. This result does not mean that future classifiers should abandon raw data based classifiers. Much better tuning and novel architectures can be a solution to improve the performance of such classifiers.

The remainder of this paper is organized as follows. We first describe the present control architecture in Section , as summarized in Figure REF . Section elaborates the drivers of change that are happening in the electric energy systems. In Section we summarize the key research challenges on designing a control architecture for the evolving power grid with increased renewables. Sections and describe the new control loops in tranmsission and distribution systems, respectively. Section provides some concluding remarks.

Network Reconstruction. Our goal is to see how accurately a model can capture the interaction patterns among nodes and generate embeddings exhibiting their temporal relationships in a latent space. In this regard, we train the models on the residual network and generate sample sets as described previously. The performance of the models is reported in Table REF . Comparing the performance of PiVeM against the baselines, we observe favorable results across all networks, highlighting the importance and ability of PiVeM to account for and detect structure in a continuous time manner.

We conducted 30 semi-structured interviews with ML industry practitioners specializing in assessing and mitigating ML ethics risks, from six companies. The research proposal, the interview protocol, and consent forms were reviewed and approved within one of the institutions represented in this study. Here, we describe the participants, recruiting, data collected, analysis, and study limitations.

In the future, diagnosing and treating lymphoma methods based on deep learning have very broad application prospects. Especially since the outbreak of the new crown epidemic in 2019, medical image analysis technology has received more and more attention. At present, medical image analysis still has great limitations. Therefore, it is very necessary to develop a system that requires a small amount of calculation, small memory, and interpretability.

We first present previous work on self-supervised learning using one task or a combination of surrogate approaches. Then we introduce curriculum learning procedures and discuss meta-learning for deep neural network.

There are many challenges to accurately identifying figures in scanned ETDs. The image resolution and scanning quality may vary across the collection. OCR output is often error-ridden. Most older ETDs were typewritten. In very old documents, figures and tables may have been hand-drawn or rendered in a separate process and literally cut-and-pasted into typewritten documents. Further, since ETD collections are cross-disciplinary, the documents in them present a variety of layout styles.

BLEU score and decoding time increase only slightly, when we use more encoder layers. The bulk of the decoding time is consumed by the decoder, since it works in an auto-regressive manner. We can substantially cut down decoding time by using fewer decoder layers which does lead to sub-optimal translation quality.

With SciFact-Open, we introduce a challenging new test set for scientific claim verification that more closely approximates how the task might be performed in real-word settings. This dataset will allow for further study of claim-evidence phenomena and model generalizability as encountered in open-domain scientific claim verification.

As these methods have been adopted from image domain applications, their approach of mixing two different spectrograms has to be tailored for audio signals to preserve data distribution within a spectrogram structure for maintaining salient time-frequency correlations. Therefore, a different masking policy that preserves salient frequency features for MSDA is needed. Inspired by the previous data augmentation strategies in the speech and vision domains, we propose a novel audio data augmentation strategy, named SpecMix, for training with time-frequency domain features. The proposed method expands the idea of Cutmix, which attempted to cut-and-mix two data samples. However, their masking policy is based on image data, therefore it is not necessarily suitable for time-frequency domain features. To address this problem, we modified the masking policy tailored to time-frequency domain features. The proposed method can be integrated to ResNet, U-Net, and other state-of-the-art architectures for acoustic scene classification, sound event classification, or speech enhancement tasks.

Despite the popularity of backgammon and academic interest in the computational complexity of games, to the best of our knowledge our work is the first to address the complexity of backgammon. One possible explanation is the apparent ambiguity in generalizing backgammon. We contend, however, that the backgammon generalization we use is as natural as those for other games like checkers or chess. Another explanation is the difficulty in forcing backgammon moves as needed for a reduction.

In the past few years, a large number of approaches have been proposed to solve the problem of water-body extraction from remote sensing images. Generally, these methods can be divided into two categories: methods based on manual features and methods based on deep learning.

Comparison of SDR Gains: Comparing SDR gains is most useful when multiple SDR types are expected to be used or a transition of types for a node is expected. When performing an experiment, this benchmark can also be useful for ensuring SDRs of the same type are producing the same output when given the same input. This benchmark will also require a spectrum analyzer or an SDR for monitoring. To compare either Tx or Rx gains of a set of devices, the devices should be co-located and using the same front end components. To compare Tx gains, an appropriate Tx gain should be selected for the devices and operated one at a time with the result recorded on the monitoring device. By doing so, the difference in power can be determined on the monitoring device and the Tx gains can be changed to determine the pair that produces most similar output. Similarly, Rx gains can be compared by flipping the roles from the Tx gain comparison. It is likely for handovers to be performed over different SDR devices based on the fixed vs. mobile nature of different nodes. Therefore, proper gains should be selected on each device for optimal output from each device.

In this paper, we presented CorAl, a principled and intuitive quality measure and self-supervised system that learns to detect small alignment errors between pairs of previously aligned point clouds. CorAl uses dual entropy measurements found in the separate point and in the joint point cloud to obtain a quality measure that substantially outperforms previous methods on the task of detecting small alignment errors within a benchmarking lidar dataset, and within a large-scale urban dataset for spinning radar.

Other related work includes different approaches in manifold learning , , , manifold regularization , , , , , graph diffusion kernels and kernel methods widely used in computer graphics , for shape detection. Most methods assume that signals defined over the graph nodes exhibit some level of smoothness with respect to the connectivity of the nodes in the graph and therefore are biased to capture local similarity.

blackThe rest of the paper is organized as follows. Sections  and present the optimization problem, solution and training algorithm for energy storage arbitrage. Section  illustrate our algorithm with real data numerical simulations. Section  concludes this paper.

This paper is related to four research domains, including remote sensing scene classification, few-shot natural image classification, few-shot remote sensing scene classification, and graph matching techniques, respectively. Next, we review them in detail respectively.

However, the aforementioned solutions for traffic matrix completion operate on the two-dimensional traffic matrices whose columns are stacked. The multi-ways nature of such matrices is unfortunately ignored. Consequently, the matrix representation is simply not enough for efficient data recovery solutions.

Future work will investigate the capability of predicting numeric values associated to unseen triples. We will also extend our approach to support multiple numeric attributes associated to the same triple.

In the IID experiment, ResNet-18 is used as the backbone network. On CIFAR-10, batchsize is set to 256, 80 epochs are set for classification network training. In each epoch, the initial learning rate is set to 0.01, and the learning rate decreased ten times at the 30th and 60th epochs. Momentum and weight decay are set to 0.9 and 0.0005, respectively. For Mini-ImageNet, the batchsize is set to 128, 120 epochs are set for classification network training, with an initial learning rate of 0.1 in each epoch and a ten-fold reduction in the learning rate at the 30th, 60th and 80th epochs. The momentum and weight decay settings are the same as above. In the case of the increase sample experiment, we first initialize the base class dataset using 5000 randomly selected images. For each samples selection cycle, we select 5000 images from the pool of samples to be selected and place them in the train set until the train set increased to 50000. We use a similar method in the reduction experiment.

Table REF shows the results of account ranking on the bipartite news-sharing network. CoCred performs the best, although its accuracy is not very high. The other algorithms fail to exploit the signals from news source labels, yielding near random performance. These results suggest that source credibility alone is not a very strong signal to infer the reliability of unknown accounts.

We argued that for a given service rate, the optimal revenue rates in server farms with non-exponential service times generally exceed those in server farms with exponential service times. But, the optimal prices in the former systems depend on the elapsed service times in the busy servers and cannot be obtained using the Markov control framework as we have done in this work. Similarly, optimal pricing for processor sharing systems and systems with queues where customers can wait for service are also challenging problems. These are potential topics for future research.

While a more robust and elaborate analysis remains for future work, this case study demonstrates the potential use of the process to automatically detect spread of visual content across social media platforms.

Relations to Plug and Play Environments: Based on the three main requirements of a plug and play environment, physics-based reinforcement learning approaches fail to fit into this setting as they only develop a global transition model of the environment. Whilst some of the object-oriented reinforcement learning approaches construct local transition models, but they develop a single reward model that is incompatible with the plug and play approach. Additionally, local transition models of objects in such methods are not reusable in new environments as they are environment-specific. Model-based multi-agent reinforcement learning methods generally work with similar agents with same classes of attributes and the focus of such methods is on improving the co-operation of agents with the help of global transition model to achieve the highest return. Consequently, such approaches cannot be applied into a plug and play environment.

In the future, we plan to implement an approach to identify the semantic similarity of entities using a classifier based on the Siamese network. Moreover, alternative names or synonyms will be used for expanding the list of candidates.

In this section, we briefly discuss the most related previous work falling into two categories: how to train a better individual deep video classification model and how to fuse these individual models to achieve best accuracy.

This work demonstrated the role of kernel alignment in learning dynamics, both through experiment and theory. We showed empirically that learning is accelerated through rapid feature alignment early in training and that this acceleration cannot be accounted for by a simple increase in the scale of the kernel over time, but is a consequence of the top eigensystem of the kernel evolving to match the task. Varying the depth allowed us to demonstrate that architectural features in the neural network control the rate of feature learning and consequently alter the kernel alignment curve. We identified the new phenomenon of kernel specialization, where the kernel for each output channel aligns preferentially to its own target function. We show that such alignment does not occur in linear networks but happens rapidly in non-linear networks. A normative model of feature evolution, where features are updated to to reduce the predicted error one step in advance reproduces many of the empirical phenomena we documented above. First, we find that the feature learning rate controls the asymptotic value of the alignment metric as well as the dynamics as well as the timescale of feature learning: faster feature learning implies higher asymptotic alignment. We also theoretically study the final kernel alignment of linear networks and show that their feature learning rate depend on depth and initial weight scale. The final kernel for two layer linear networks indicates. Lastly, in a theoretical analysis of the specialization phenomenon we demonstrate why nonlinearity is necessary to account for the class-specific alignment of sub-blocks of the rank-4 kernel tensor with the target functions corresponding to each class.

Unfortunately, the current technological development does not yet allow the full potential of quantum computers to be expressed, which will only reach maturity in the next few decades. At present, however, it is possible to use quantum computers in feedback circuits that mitigate the effect of various noise components , . VQAs, which utilise a conventional optimiser to train a parameterised quantum circuit, have been considered an effective technique for addressing these restrictions.

The remainder of this paper is organized as follows: In section we discuss our domain decomposition method, which is based on a space-filling curve. We first give a short overview on domain decomposition methods and their properties for elliptic PDEs. Then we discuss space-filling curves and their peculiarities. Finally, we present our algorithm and its features. In section we deal with algorithmic fault tolerance. Here we recall its close relation to randomized subspace correction for our setting. Then we present a fault-tolerant variant of our domain decomposition method. In section we discuss the results of our numerical experiments. We first define the model problem which we employ. Then we give convergence and parallelization results. Furthermore we show the behavior of our method under failure of processors. Finally we give some concluding remarks in section .

As shown, multiple publications highlight the need for technical-legal interaction. Still, there is no one systematic understanding of it based on the practice of the implementation of technical measures for data privacy compliance.

We repeat every experiment run for 10 times, and for every run we redo both the pre-training and the fine-tuning. The repetitions differ in the random initialization of the pre-training phase, as well as in the split into training and validation data for the fine-tuning phase. We run the fine-tuning for a fixed budget of 10 training epochs and apply no early stopping or other regularization. Although the 10 epochs are enough to reach a sufficient convergence, we also run extended experiments for 100 epochs for which results can be found in Appendix.

FLS displays are inspired by today's indoor and outdoor drone shows that use illuminated, synchronized, and choreographed groups of drones arranged into various aerial formations. An FLS display is similar because each FLS is a drone and a motion illumination is rendered by computing FLS flight paths that synchronize FLSs as a function of time and space.

Apart from example in Figure REF , we provide more examples of different predictions between models trained with independent and compound objective in Table REF . In general, by doing manual analysis of errors, we noticed three types of errors being fixed by the compound objective model in BERT:

RQ1 Can RealiT  improve the single token bug localization and repair performance in comparison to techniques purely trained on mutants? RQ2 Is pre-training on mutants necessary for achieving a high localization and repair performance? RQ3 Can training with mutants alone be sufficient for achieving a high performance? RQ4 Are real bug fixes still helpful if the number of real bug fixes available for training is further limited?

We present the evaluation of the Coconut threshold credentials scheme; first we present a benchmark of the cryptographic primitives described in sec:coconut:construction and then we evaluate the smart contracts described in sec:coconut:applications.

However, there is a significant limitation in the social commerce scenario for applying approaches based on word embeddings. While the amount of textual content users produce in their social interaction is large and suitable for this approach, the amount of text that describes the products is much smaller. This imbalance makes the vector representations of the words in the customer corpus of good qualityblack, while those obtained from the product descriptions blackproduce noisy blackvectors.

Table II proves that adding a statistical machine learning classifier after the feature extractor and class activation heatmap seems to increase the overall accuracy of the model for all of the four welds. The target accuracy is acheived by the four machine learning classifiers, with a tiny advantage to XGboost classifier.

We notice that the literature on stochastic non-convex optimization is huge and cannot cite all of them in this section. We will focus on methods requiring only a general unbiased stochastic oracle model.

In this section we present the results obtained with the paired model and the proposed unpaired model. Paired model performance serves as an upper bound on the unpaired model's performance, since the paired model has access to more information.

Table REF shows the results of comparing our method to several video stabilization approaches. As can be seen, our method presented the best values on average in comparison to all the baselines in terms of stability average, distortion, cropping ratio, and success rate. Even though our method did not surpass the baselines at each class individually, it still achieved competitive results. Every baseline performs badly in at least one class, while our method is more robust across classes, hence holding the final best results on the VSAC105Real.

In our ODLAE algorithm, we adopt two different data fusion strategies to make full use of the information in the different hidden layers of the autoencoder. Finally, devising objective function balance the prediction and reconstruction loss, and constantly adjusting the ratio coefficient of prediction and reconstruction loss through the continuous learning on the streaming data. This combination of the prediction and reconstruction loss gives us better prediction performance.

We introduced a framework for collecting datasets to improve the robustness and interpretability of detecting machine generated text in the scientific domain. By developing a comprehensive dataset, SynSciPass, we were able to show that models trained on it were not only more robust under domain shifts but also that those models were able to detect the generic type of text generation technology such as for translation, paraphrase, or novel generations which could help understand if a passage was generated by appropriate or inappropriate means. Despite these findings, our work has also shown that current models, including our own, do not perform well in realistic scenarios that change the distribution of text seen. Because of this lack of robustness, we suggest that future work concentrate on formulating both datasets and approaches that comprehensively test machine generated text detectors in a wide variety of realistic and unseen scenarios.

Satisfying these conditions in a single design is a difficult challenge. The design choices concerning one requirement, such as size, produce additional constraints to others, such as sensing and powering. Consequently, the design process should simultaneously take all of these constraints and find convenient design solutions for multi-purpose applications.

The pattern-based calibration method used gives adequate results in terms of the resulting reprojection error. Empirically, we have found that the overall calibration quality depends largely on the quality of the calibration target, especially its flatness.

To enable nighttime vision-based navigation,  have proposed to use an intensity-based lidar to replace cameras for the VTR framework. While this work demonstrates that this system is resilient to low illumination conditions, it suffers from motion distortion issues. Using headlights, proposed a bag-of-word approach to prioritize experiences most relevant to live operation. This in turn allows a growing number of robot experiences while limiting computation requirements. In this work, the authors have successfully repeated paths over a 31 period, including day and night driving relying on headlights. Extending the experimental evaluation of VTR,  have logged over 140 of autonomous navigation in an untended gravel pit, also including nighttime navigation. have used a deep neural network to learn a nonlinear color transform mapping that maximizes vision-based localization resiliency to appearance change. In this work, they successfully localized on routes over a 30 period by relying only on a single experience. However,  still observed vision-based localization failures in low illumination, even when using headlights. In this work, the authors proposed to fuse vision-based localization with GNSS measurements to enable VTR systems to function in areas where vision localization fails.

We define and identify the most suitable set of parameters in exaggerated corrective pronunciation feedback in both audio and visual modalities from three critical aspects. We propose personalized exaggerated feedback according to English proficiency of the learner. We design the audio-visual corrective CAPT system, PTeacher, which includes a pronunciation training course. The course can dynamically evaluate learners' English pronunciation proficiency in a life-long manner. We support all of our findings and analysis with extensive user studies conducted on 100 second-language English learners and 22 professional native teachers. Comprehensive results demonstrate the advantage of our proposed exaggerated training system as well as the effectiveness of each module.

The management of scenarios in which the system variables are numerous and change rapidly assumes an automatic, and in some cases intelligent, adaptation by the calculation structure , , . Recently, the rapid rise of Artificial Intelligence techniques has enabled us to increase the responsiveness of decision-making processes, as well as to introduce forms of automation and relational empathy with machines at previously unthinkable levels , .

In this work, we presented a novel frame-work that is able to automatically assign the proper weight for each of the given demonstrations and exclude the adversarial ones from the data-set. Our algorithm can achieve superior performance and sample efficiency than BC and IRL approaches in case of the presence of adversarial demonstrations. For future work, it would be enticing to use better optimization approach and extend the frame-work to handle continuous action space.

To fill the gap between supervised and unsupervised IDS, the authors in proposed a two-stage deep neural network: the first classifier is trained in a supervised manner, whereas the second one is a discriminator in a GAN network and is used for detecting unknown attacks. They evaluated the two classifiers separately, and the combined result was not reported. A new idea presented in is to generate attack samples by an LSTM-based GAN model, and then the generated samples and available normal samples are fed into a DCNN model. The study is promising but achieved low accuracy and needs to be further developed. The authors from used tree-based machine learning algorithms and focused on developing a complicated data preprocessing framework to improve the accuracy.

Neither of these groups is, or should be, expected to be security experts, but the decisions they make can still have serious security impacts. In an effort to better support these software creators, several tools and libraries have been proposed such as OpenSSL, PyCrypto, and cryptography.io which encapsulate many of the security decisions, theoretically making development easier.

On one hand, we compute the BLEU and METEOR scores of the generated claims with respect to the ground-truth claims. On the other hand, we compute the likelihood that the generated claims possess textual features that reflect the input user's beliefs. We do so by measuring the accuracy of predicting user's stances on big issues given the generated claims. We compute this accuracy for each of the 48 big issues individually and report the results for all of them. To this end, we carry out the following three steps for a given approach.

The empirical evaluation shows that SuMMs are comparable and often more suitable than the closest baselines, showing flexibility in fitting a variety of event sequence datasets with the additional benefit that they can identify influencers.

Our work lies in the field of curriculum learning in reinforcement learning. CL comprises three key elements: transfer learning, task sequencing, and task generation. In this work, we focus on transfer learning and task sequencing.

Scores for 12 BERT-SQuAD models and 4 benchmarks are shown in Figure REF . These results are summarized in Figure REF . Adding more features to BERT-SQuAD improves F-scores. Table REF shows that models with more features are more confident.