Reviewerly/exHarmony huggingface dataset:
Multi-Agent Uncertainty Sharing for Cooperative Multi-Agent Reinforcement Learning

Cooperative multi-agent reinforcement learning has been considered promising to complete many complex cooperative tasks in the real world such as coordination of robot swarms and self-driving. To promote multi-agent cooperation, Centralized Training with Decentralized Execution emerges as a popular learning paradigm due to partial observability and communication constraints during execution and computational complexity in training. Value decomposition has been known to produce competitive performance to other methods in complex environment within this paradigm such as VDN and QMIX, which approximates the global joint Q-value function with multiple local individual Q-value functions. However, existing works often neglect the uncertainty of multiple agents resulting from the partial observability and very large action space in the multi-agent setting and can only obtain the sub-optimal policy. To alleviate the limitations above, building upon the value decomposition, we propose a novel method called multi-agent uncertainty sharing (MAUS). This method utilizes the Bayesian neural network to explicitly capture the uncertainty of all agents and combines with Thompson sampling to select actions for policy learning. Besides, we impose the uncertainty-sharing mechanism among agents to stabilize training as well as coordinate the behaviors of all the agents for multi-agent cooperation. Extensive experiments on the StarCraft Multi-Agent Challenge (SMAC) environment demonstrate that our approach achieves significant performance to exceed the prior baselines and verify the effectiveness of our method.
Few-shot learning for E2E speech recognition: architectural variants for support set generation

In this paper, we propose two architectural variants of our recent adaptation of a ‘few shot-learning’ (FSL) framework ‘Matching Networks’ (MN) to end-to-end (E2E) continuous speech recognition (CSR) in a formulation termed ‘MN-CTC’ which involves a CTC-loss based end-to-end episodic training of MN and an associated CTC-based decoding of continuous speech. An important component of the MN theory is the labelled support-set during training and inference. The architectural variants proposed and studied here for E2E CSR, namely, the ‘Uncoupled MN-CTC’ and the ‘Coupled MN-CTC’, address this problem of generating supervised support sets from continuous speech. While the ‘Uncoupled MN-CTC’ generates the support-sets ‘outside’ the MN-architecture, the ‘Coupled MN-CTC’ variant is a derivative framework which generates the support set ‘within’ the MN-architecture through a multi-task formulation coupling the support-set generation loss and the main MN-CTC loss for jointly optimizing the support-sets and the embedding functions of MN. On TIMIT and Librispeech datasets, we establish the ‘few-shot’ effectiveness of the proposed variants with PER and LER performances and also demonstrate the cross-domain applicability of the MN-CTC formulation with a Librispeech trained ‘Coupled MN-CTC’ variant inferencing on TIMIT low resource target-corpus with a 8% (absolute) LER advantage over a single-domain (TIMIT only) scenario.
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning

We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents. This problem assumes two environments: a simulator environment with semantics, that is, low-dimensional and essential information, as the state space, and a real-world environment with images as the state space. By learning mapping from images to semantics, we can transfer a policy, pre-trained in the simulator, to the real world, thereby eliminating real-world on-policy agent interactions to learn, which are costly and risky. In addition, using image-to-semantics mapping is advantageous in terms of the computational efficiency to train the policy and the interpretability of the obtained policy over other types of sim-to-real transfer strategies. To tackle the main difficulty in learning image-to-semantics mapping, namely the human annotation cost for producing a training dataset, we propose two techniques: pair augmentation with the transition function in the simulator environment and active learning. We observed a reduction in the annotation cost without a decline in the performance of the transfer, and the proposed approach outperformed the existing approach without annotation.
Application of NF-method for vibroacoustic control of tightness of pipe line fittings gate valve

The article is devoted to the study of the possibility of using the NF-method for vibroacoustic control of the tightness of the gate valves of pipe fittings. The method makes it possible to reveal the statistical stationary properties of random signals. Experimental studies are carried out, the main results are presented. The algorithm of the NF-method, the experimental setup, and the experimental technique are described.
An Optimal Control Approach to Particle Filtering on Lie Groups

We study the filtering problem over a Lie group that plays an important role in robotics and aerospace applications. We present a new particle filtering algorithm based on stochastic control. In particular, our algorithm is based on a duality between smoothing and optimal control. Leveraging this duality, we reformulate the smoothing problem into an optimal control problem, and by approximately solving it (using, e.g., iLQR) we establish a superior proposal for particle smoothing. Combining it with a suitably designed sliding window mechanism, we obtain a particle filtering algorithm that suffers less from sample degeneracy compared with existing methods. The efficacy of our algorithm is illustrated by a filtering problem over SO(3) for satellite attitude estimation.
Comparison of Influence of Various Proposals of Transforming Nominants into Stimulants on Linear Ordering and Grouping of Listed Companies

The paper is a continuation of the authors’ research on the transformation of nominants with a recommended range of values into stimulants normalised in the interval $$\langle \mathrm{0,1}\rangle $$ , which is a necessary condition for the use of linear ordering methods for multivariate objects. The present article is empirical in nature. The research was aimed to test to what extent the selected formulas for transformation of nominants into stimulants influence the final result of linear ordering and grouping of objects on the basis of different variable nominants, and therefore whether there are significant differences in the final ranking of the objects obtained using these formulas. The study focused on symmetric nominants and used four formulas of transformation nominants into stimulants: Kukuła's proposal and three authors’ proposals. The results of linear ordering were evaluated in terms of the discriminatory properties of the obtained synthetic measures, the consistency of the rankings of objects and the concordance of their grouping. The study uses data on financial ratios characterising the financial situation of companies in the Machinery Industry sector listed on the Warsaw Stock Exchange in 2019.
Using Online Courses in the Educational Process

The experience of St. Petersburg Electrotechnical University is considered. The characteristics of models for embedding elements of e-learning and distance learning technologies into the educational process are given. The technology of information and technical support of the educational process using online courses is given. The principles and stages of examination of online courses developed at the university are presented. An analysis of a sociological survey of students on their perception of online courses is given. An example of a technology for developing an online master’s program is given. The data of a comparative analysis of full-time education and training based on online courses are presented.
Named Entity Recognition Method for Power Equipment Based on BERT-BiLSTM-CRF

Power equipment text has the problems of multiple entity names and inadequate extraction of feature information. In view of the low accuracy of power equipment text recognition by commonly used named entity recognition methods, this paper proposes a method for power equipment domain name recognition based on BERT + BiLSTM + CRF (Bidirectional Encoder Representations from Transformers +Bi-directional Long Short-Term Memory + Conditional Random Field) model. This method carries out entity recognition based on sequence annotation for defect related entity types, including defects, equipment, components and so on. First, the method of rule and dictionary is used to distance supervision for named entity recognition and form the initial corpus. Secondly, carry out model training based on pre training BERT language model, BiLSTM model and CRF model, and obtain the CRF feature template set by word co-occurrence and co-occurrence of words in this domain. Finally, based on the results of model training to improve the recognition accuracy. The research shows that this method improves the accuracy of named entity recognition in power equipment domain. It is an effective named entity recognition method, which can provide new ideas for named entity recognition in other domains.
Collusion Attack Analysis and Detection of DPoS Consensus Mechanism

With the development of blockchain technology, the increasing safety accidents result in huge economic losses in blockchain systems. Delegated Proof of Stake (DPoS) selects the witness nodes to produce blocks by voting, leading to the quick confirmation of transactions. As one of the widely used consensus mechanisms in public blockchain, DPoS is still threatened by attacks. In this paper, an analysis method for collusion attacks of DPoS consensus mechanism is proposed. Meanwhile, we analyze the behavioral motivations of malicious nodes and detect the attacks that exist in the voting process of DPoS. First, the coalitional game is the basic form of cooperative game, which can be used to analyze the structure, strategy and benefits of cooperative game. We build a coalitional game model to analyze motivations of DPoS nodes that launched collusion attacks. And then we use the Shapley-Shubik power index and Banzhaf power index in weighted voting games of DPoS, which calculated different values that DPoS suffered attacks during the voting phase. Experimental results show that collusion attacks in DPoS can be effectively detected by this method. In addition, the analysis results can further contribute to the security of the DPoS blockchain system.
Request for information as an auxiliary measure in intellectual property litigation concerning electronic works and the protection of personal data

The purpose of introducing a new procedure on intellectual property into the Code of Civil Procedure was to ensure the efficiency of the procedure and to develop uniform practices. At the same time, the solutions adopted so far in Directive 2004/48 / EC on the enforcement of intellectual property rights caused doubts and was subject of criticism. According to this solution, the competent judicial authorities may order that information on the origin and distribution networks of the goods or services which infringe an intellectual property right be provided by the infringer or any other person. It is very important to assure such protection especially in case of electronic works. The regulation of a uniform request for information in the Code of Civil Procedure has not been interpreted yet, as obtaining information on the scope of infringements simultaneously discloses information about third parties. Hence, it is justified to analyze whether it is acceptable to disclose personal data of electronic works in the light of the Directive, the jurisprudence of the Court of Justice and the current new regulation. The regulation of a uniform request for information in the Code of Civil Procedure has not been interpreted yet, as obtaining information on the scope of infringements simultaneously discloses information about third parties. Hence, it is justified to analyze whether it is acceptable to disclose personal data of electronic works in the light of the Directive, the jurisprudence of the Court of Justice and the current new regulation.
Research and Accomplishments in Applications of Non-negative Matrix Factorization

Since its introduction in the 1990s, non-negative matrix factorization (NMF) has captured a great amount of attention due to its capability and effectiveness in processing data in such a way that few earlier methods could perform, partly due to its non-negative constraint. This paper first briefly presents the basic NMF algorithm and concerns with the algorithm itself, then demonstrates its power with three applications in three different fields, namely face recognition in Computer Vision, distance prediction in Networking and molecular pattern discovery in Genetics. The paper ends with a quick look at other applications of NMF and recent developments that researchers have made.
Learning Graph Signal Representations with Narrowband Spectral Kernels

In this work, we study the problem of learning graph dictionary models from partially observed graph signals. We represent graph signals in terms of atoms generated by narrowband graph kernels. We formulate an optimization problem where the kernel parameters are learnt jointly with the signal representations under a triple regularization scheme: While the first regularization term aims to control the spectrum of the narrowband kernels, the second term encourages the reconstructed graph signals to vary smoothly on the graph, and the third term enforces that similar graph signals have similar representations over the learnt dictionaries. Once the graph kernels and signal representations are learnt, the initially unknown values of the signals are estimated based on the computed model. Experimental results show that the proposed method gives significant improvements in the estimation performance compared to reference approaches.
Hardware Implementation of Epidermis Segmentation in Skin Histopathological Images

The histopathological analysis of tissue specimens is the gold standard to diagnose many kinds of cancer. Recently, a few computer-aided diagnosis (CAD) techniques have been proposed for the automated diagnosis of a skin cancer type, known as melanoma, by analyzing whole slide images (i.e., biopsy slides). The first step in the automated melanoma diagnosis is the epidermis segmentation of the biopsy images. The whole slide images are typically very large and hence require a significant amount of processing time. In this paper, we propose a hardware accelerator for the epidermis segmentation in a skin whole slide image. Experimental results show that the proposed system provides a significant speedup over a software-only implementation.
Developing the Evidence-Informed Digital Competence Assessment Instrument and Platform for Educators

Digital competence is considered as one of the key competences for active participation in the technologically fast-evolving society. Additionally, digital competence has become national-level indicators for implementing digital innovation in education, but there is a lack of common understanding on how to carry out methodologically sound digital competence assessment and how to motivate educators to assess the competence and reflect on their teaching practices. Digital competence assessment has been largely conducted externally by researchers or national agencies with different survey-type tools, so that respondents rarely own, re-use or revisit the collected data. Such an approach reduces the motivation of educators to use this data for continuous professional development (PD) purposes. The paper introduces an approach and a tool that potentially enables digital competence assessment with the focus on supporting professional development while combining different assessment approaches. The paper explores the results of expert group interview.
Serverless Machine Learning Platform: A Case for Real-Time Crisis Detection over Social Media

Serverless computing is an emerging execution model in the cloud in which all required infrastructure and resources are fully managed by cloud providers. The promising features of serverless technology have created a new research focus on building the next generation serverless machine learning. In this paper, we propose a serverless machine learning platform for serving models in real-time by leveraging Google serverless cloud services. We evaluate the proposed platform by serving an NLP model built for crisis detection over Twitter platform. Our experimental results substantiate that serverless machine learning provides lightweight and cost effective cloud services for building highly scalable applications for real-time ML model predictions.
Zero-shot Scene Graph Generation with Relational Graph Neural Networks

Existing scene graph generation (SGG) methods are far from practical, primarily due to their poor performance on predicting zero-shot (i.e., unseen) subject-predicate-object triples. We observe that these SGG methods treat images along with the triples in them independently and thus fail to consider the complex and hidden information that is inherently implicit in the triples of other images. To this effect, our paper proposes a novel encoder-decoder SGG framework to leverage the semantic correlations between the triples of different images into the prediction of a zero-shot triple. Specifically, the encoder aggregates the triples in each image of training set into a large knowledge graph and learns the entity embeddings that capture the features of their neighborhoods with a relational graph neural network. The neighborhood-aware embeddings are then fed into the vision-based decoder to predict the predicates in images. Extensive experiments on the popular benchmark Visual Genome demonstrate that our proposed method outperforms the state-of-the-art methods in popular zero-shot metrics (i.e., zR@N, ngzR@N) for all SGG tasks.
TLD-WF: Surfing Freely Under Website Fingerprinting Attacks

Anonymous networks hide communication content and communication relationships by encrypting the content and transmission path of traffic. But the website fingerprinting attack can determine the websites a user visits by analyzing the user’s traffic patterns and website fingerprints without decrypting the traffic. To defend against website fingerprinting attacks, most of the existing methods reduce the accuracy of analysis by increasing the access delay and background traffic. Unfortunately, such methods not only degrade the user experience, but also affect the performance of anonymity networks. By adopting the Tor pluggable transmission technology, this paper proposes a new targeted, lightweight defense to against website fingerprinting attack (TLD-WF). TLD-WF uses the TLD-flow module to obtain the analyzed flow information with an accuracy rate of over 90%, and conducts targeted dynamic scrambling. TLD-WF not only effectively guarantees the anonymity and security of Tor users, but also has the advantages of zero latency overhead and low bandwidth overhead. The experimental results show that TLD-flow can reduce the analysis accuracy of the CNN classifier from 93.2% to around 8.3% under normal conditions. With guaranteed zero latency and 45% bandwidth overhead, the analytical accuracy of the CNN classifier will be reduced by about 50%.
ASNN: Accelerated Searching for Natural Neighbors

How to set k value for k-nearest neighbors is a primary problem in machine learning, pattern recognition and knowledge discovery. Natural neighbor (NaN) is an adaptive neighbor concept for solving this problem, which combines k-nearest neighbors and reverse k-nearest neighbors to adaptively obtain k value. It has been proven effective in clustering analysis, classification and outlier detection. However, the existing algorithms for searching NaN all use a global search strategy, which increases unnecessary consumption of time on non-critical points. In this paper, we propose a novel accelerated algorithm for searching natural neighbor, called ASNN. It is based on the fact that if the remote objects have NaNs, others certainly have the NaNs. The main idea of ASNN is that it first extracts remote points, then only searches the neighbors of remote points, instead of all points, so that ASNN can quickly obtain the natural neighbor eigenvalue (NaNE). To identify the remote objects, ASNN only searches the 1-nearest neighbor for each object with kd-tree, so its time complexity is reduced to $$\boldsymbol{O(nlogn)}$$ from $$\boldsymbol{O(n^2)}$$ , and the local search strategy makes it run faster than the existing algorithms. To illustrate the efficiency of ASNN, we compare it with three existing algorithms NaNs, kd-NaN and FSNN. The experiments on synthetic and real datasets tell that ASNN runs much faster than NaNs, kd-NaN and FSNN, especially for datasets with large scale.
Towards a Unifying Logical Framework for Neural Networks

Neural networks are increasingly used in safety-critical applications such as medical diagnosis and autonomous driving, which calls for the need for formal specification of their behaviors. In this paper, we use matching logic—a unifying logic to specify and reason about programs and computing systems—to axiomatically define dynamic propagation and temporal operations in neural networks and to formally specify common properties about neural networks. As instances, we use matching logic to formalize a variety of neural networks, including generic feed-forward neural networks with different activation functions and recurrent neural networks. We define their formal semantics and several common properties in matching logic. This way, we obtain a unifying logical framework for specifying neural networks and their properties.
Towards Analyzing the Robustness of Deep Light-weight Image Super Resolution Networks under Distribution Shift

Deep light-weight image super resolution networks that provide a high performance have numerous real-life applications, such as mobile devices and multimedia systems. Hence, analyzing the capability of such deep networks in providing a similar performance between the cases that they are applied to the images with and without distributions similar to that of the training is crucial. In this paper, we carry out the robustness analysis of the deep state-of-the-art light-weight super resolution networks by proposing and using three metrics that are based on the statistical information of the super resolved images in both pixel level and feature level. The results of our metrics for the deep state-of-the-art light-weight super resolution networks demonstrate the behavior of such networks against realistic distribution shift in the test dataset.
A Conceptual Model Supporting Systematic Design of Service Governance Systems

Digital service systems have dominated our daily lives for satisfying various demands with almost no time and location constraints. Service providers need to take necessary actions to change specific behaviors of service systems to optimally comply with continuously-changing internal and external factors, e.g., new-issued laws and regulations, temporal and spatial distribution of user demands. This is called governance of service systems. Although there have been many approaches for guiding service system design, there are no systematic approaches for the design of the corresponding governance systems. By unifying existing studies on service governance, we propose a conceptual model and the corresponding design process of governance systems. A case study on the governance of an e-Business service systems is briefly demonstrated. Several challenges and opportunities are listed for future research. To sum up, this paper attempts a doable approach of "governing digital service systems by digital governance systems".