Reviewerly/exHarmony huggingface dataset:
An Experimental Study of Class Imbalance in Federated Learning

Federated learning is a distributed machine learning paradigm that trains a global model for prediction based on a number of local models at clients while local data privacy is preserved. Class imbalance is believed to be one of the factors that degrades the global model performance. However, there has been very little research on if and how class imbalance can affect the global performance. class imbalance in federated learning is much more complex than that in traditional non-distributed machine learning, due to different class imbalance situations at local clients. Class imbalance needs to be re-defined in distributed learning environments. In this paper, first, we propose two new metrics to define class imbalance -- the global class imbalance degree (MID) and the local difference of class imbalance among clients (WCS). Then, we conduct extensive experiments to analyze the impact of class imbalance on the global performance in various scenarios based on our definition. Our results show that a higher MID and a larger WCS degrade more the performance of the global model. Besides, WCS is shown to slow down the convergence of the global model by misdirecting the optimization.
Collaborative Embedding for Knowledge Tracing

Knowledge tracing predicts students’ future performance based on their past performance. Most of the existing models take skills as input, which neglects question information and further limits the model performance. Inspired by item-item collaborative filtering in recommender systems, we propose a question-question Collaborative embedding method for Knowledge Tracing (CoKT) to introduce question information. To be specific, we incorporate student-question interactions and question-skill relations to capture question similarity. Based on the similarity, we further learn question embeddings, which are then integrated into a neural network to make predictions. Experiments demonstrate that CoKT significantly outperforms baselines on three benchmark datasets. Moreover, visualization illustrates that CoKT can learn interpretable question embeddings and achieve more obvious improvement on AUC when the interaction data is more sparse.
High-dimensional distribution generation through deep neural networks

Abstract We show that every d -dimensional probability distribution of bounded support can be generated through deep ReLU networks out of a 1-dimensional uniform input distribution. What is more, this is possible without incurring a cost—in terms of approximation error measured in Wasserstein-distance—relative to generating the d -dimensional target distribution from d independent random variables. This is enabled by a vast generalization of the space-filling approach discovered in Bailey and Telgarsky (in: Bengio (eds) Advances in neural information processing systems vol 31, pp 6489–6499. Curran Associates, Inc., Red Hook, 2018). The construction we propose elicits the importance of network depth in driving the Wasserstein distance between the target distribution and its neural network approximation to zero. Finally, we find that, for histogram target distributions, the number of bits needed to encode the corresponding generative network equals the fundamental limit for encoding probability distributions as dictated by quantization theory.
Knowledge Tracing with Exercise-Enhanced Key-Value Memory Networks

Knowledge tracing is a critical component of an intelligent tutoring system. It can track students’ knowledge states and skill mastery in order to provide a more helpful learning environment for them (for example, personalized exercise recommendations). People have been trying to apply the deep learning framework in recent years to tackle the challenge of tracing knowledge. Although these deep learning models produce impressive outcomes, they do have significant drawbacks. In existing prediction models, each exercise is often represented by a basic sequence number code, and the semantic information contained in the exercise text description has not been fully mined. We introduce a new profound education method, Knowledge Tracing with Exercise-Enhanced Key-Value Memory Networks (EKVMN) in this study, that fully utilizes the text information of questions as well as the memory function of the existing deep knowledge tracing model to predict students’ performance. Experiments on real-world knowledge tracing datasets reveal that our proposed model outperforms the baselines in terms of prediction performance. It also demonstrates the significance of context semantic information in the knowledge tracing task.
A Hierarchical Approach to Multi-Agent Path Finding

Solving Multi-Agent Path Finding (MAPF) instances optimally is NP-hard, and existing optimal and bounded suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP is able to solve as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.
A Comprehensive Extraction of Relevant Real-World-Event Qualifiers for Semantic Search Engines

In this paper, we present an efficient and accurate method to represent events from numerous public sources, such as Wikidata or more specific knowledge bases. We focus on events happening in the real world, such as festivals or assassinations. Our method merges knowledge from Wikidata and Wikipedia article summaries to gather entities involved in events, dates, types and labels. This event characterization procedure is extended by including vernacular languages. Our method is evaluated by a comparative experiment on two datasets that shows that events are represented more accurately and exhaustively with vernacular languages. This can help to extend the research that mainly exploits hub languages, or biggest language editions of Wikipedia. This method and the tool we release will for instance enhance event-centered semantic search engines, a context in which we already use it. An additional contribution of this paper is the public release of the source code of the tool, as well as the corresponding datasets.
Enhanced Real-Time RGB-T Tracking by Complementary Learners

This paper focuses on integrating information from RGB and thermal infrared modalities to perform RGB-T object tracking in the correlation filter framework. Our baseline tracker is Staple (Sum of Template and Pixel-wise LEarners), which combines complementary cues in the correlation filter framework with high efficiency. Given the input RGB and thermal videos, we utilize the baseline tracker due to its high performance in both of accuracy and speed. Different from previous correlation filter-based methods, we perform the fusion tracking at both the pixel-fusion and decision-fusion levels. Our tracker is robust to the dataset challenges, and due to the efficiency of FFT, our tracker can maintain high efficiency with superior performance. Extensive experiments on the RGBT234 dataset have demonstrated the effectiveness of our work.
A Checklist for Explainable AI in the Insurance Domain

Artificial intelligence (AI) is a powerful tool to accomplish a great many tasks. This exciting branch of technology is being adopted increasingly across varying sectors, including the insurance domain. With that power arise several complications. One of which is a lack of transparency and explainability of an algorithm for experts and non-experts alike. This brings into question both the usefulness as well as the accuracy of the algorithm, coupled with an added difficulty to assess potential biases within the data or the model. In this paper, we investigate the current usage of AI algorithms in the Dutch insurance industry and the adoption of explainable artificial intelligence (XAI) techniques. Armed with this knowledge we design a checklist for insurance companies that should help assure quality standards regarding XAI and a solid foundation for cooperation between organisations. This checklist extends an existing checklist that SIVI, the standardisation institute for digital cooperation and innovation in Dutch insurance.
Auto-attentional mechanism in multi-domain convolutional neural networks for improving object tracking

Purpose Multi-domain convolutional neural network (MDCNN) model has been widely used in object recognition and tracking in the field of computer vision. However, if the objects to be tracked move rapid or the appearances of moving objects vary dramatically, the conventional MDCNN model will suffer from the model drift problem. To solve such problem in tracking rapid objects under limiting environment for MDCNN model, this paper proposed an auto-attentional mechanism-based MDCNN (AA-MDCNN) model for the rapid moving and changing objects tracking under limiting environment. Design/methodology/approach First, to distinguish the foreground object between background and other similar objects, the auto-attentional mechanism is used to selectively aggregate the weighted summation of all feature maps to make the similar features related to each other. Then, the bidirectional gated recurrent unit (Bi-GRU) architecture is used to integrate all the feature maps to selectively emphasize the importance of the correlated feature maps. Finally, the final feature map is obtained by fusion the above two feature maps for object tracking. In addition, a composite loss function is constructed to solve the similar but different attribute sequences tracking using conventional MDCNN model. Findings In order to validate the effectiveness and feasibility of the proposed AA-MDCNN model, this paper used ImageNet-Vid dataset to train the object tracking model, and the OTB-50 dataset is used to validate the AA-MDCNN tracking model. Experimental results have shown that the augmentation of auto-attentional mechanism will improve the accuracy rate 2.75% and success rate 2.41%, respectively. In addition, the authors also selected six complex tracking scenarios in OTB-50 dataset; over eleven attributes have been validated that the proposed AA-MDCNN model outperformed than the comparative models over nine attributes. In addition, except for the scenario of multi-objects moving with each other, the proposed AA-MDCNN model solved the majority rapid moving objects tracking scenarios and outperformed than the comparative models on such complex scenarios. Originality/value This paper introduced the auto-attentional mechanism into MDCNN model and adopted Bi-GRU architecture to extract key features. By using the proposed AA-MDCNN model, rapid object tracking under complex background, motion blur and occlusion objects has better effect, and such model is expected to be further applied to the rapid object tracking in the real world.
Implementation of English Online Course Teaching System Based on Virtual Reality Scene

At present, most of the developed virtual classrooms are difficult to create realistic teaching scenes. Therefore, this paper discusses the design objectives and ideas of the web-based virtual teaching system, and describes the basic architecture of the virtual teaching system. Firstly, the scene is designed and rendered by Unity3d, and the script code is written to access the recognition map database and its corresponding video. Then, using 3dsmax modeling and Virtools to realize the behavior interaction function, the experimental teaching courseware based on virtual scene is created. Finally, the overall functional structure of the virtual teaching system is studied, and the system including users and business processes are analyzed. The survey results show that the scene fidelity and interactivity basically achieve the expected goal, which has a good teaching effect in the process of practical teaching.
Steganalysis of the methods of concealing information in graphic containers

Aim. Today, there is a pressing matter of protection against steganography-based attacks against information systems. These attacks present a danger as they use the most common data files – especially graphics files – as containers that deliver malicious code to a system or cause a leak of sensitive information. Developing methods of detecting such hidden information is the responsibility of a special subsection of steganography, the steganalysis. Such methods should be extensively used in computer forensics as part of security incident investigation, as well as in automated security systems with integrated modules for analyzing data files for malicious or dangerous information. An important feature of such activities is the need to examine a wide variety of elements and containing files. In particular, it is required to verify not only the colour values of the pixels in images, but their frequency characteristics as well. This raises a number of important questions associated with the best practices of applying steganalysis algorithms and making correct conclusions based on the outputs. The paper aims to briefly analyse the most important and relevant methods of steganalysis, both spatial and frequency, as well as to make conclusions regarding their performance and ways to analyse the outputs based on the test results of the software that implements such methods. Methods. The steganalysis of concealment within the least significant bits of an image’s pixels uses Pearson’s Chi-square statistical analysis, as well as the Regular-Singular method that involves signature analysis of pixel groups and analytical geometry tools for estimating the relative volume of the hidden message. The Koch-Zhao method of steganalysis is used for the purpose of detecting information embedded in the frequency-domain image representation. It also allows identifying the parameters required for extracting the hidden message. Results. A software suite was created that includes the software implementations of the analysed methods. The suite was submitted to a number of tests in order to evaluate the outputs of the examined methods. For the purpose of testing, a sample of images of various formats was compiled, in which information was embedded using a number of methods. Based on the results of the sample file analysis, conclusions were made regarding the efficiency of the analysed methods and interpretation of the outputs. Conclusion. Based on the test results, conclusions were made on the accuracy of the steganalysis methods in cases of varied size of the embedded message and methods of its concealment. The patterns identified with the help of the analysis outputs allowed defining a number of rules for translating the outputs into conclusions on the identification of the fact of detection of hidden information and estimation of its size.
Data Selection as an Alternative to Quality Estimation in Self-Learning for Low Resource Neural Machine Translation

For many languages, the lack of sufficient parallel data to train translation models have resulted in using the monolingual data, source and target, through self-learning and back-translation respectively. Most works that implemented the self-learning approach utilized a quality estimation system to ensure that the resulting additional training data is of sufficient quality to improve the model. However, the quality estimation system may not be available for many low resource languages, restricting the implementation of such approach to a very few. This work proposes the utilization of the data selection technique as an alternative to quality estimation. The approach will ensure that the models will learn only from the data that is closer to the domain of the test set, improving the performance of the translation models. While this approach is applicable to many, if not all, languages, we obtained similar and, in some implementations, even better results (\(+\)0.53 BLEU) than the self-training approach that was implemented using the quality estimation system on low resource IWSLT’14 English-German dataset. We also showed that the proposed approach can be used to improve the performance of the back-translation approach, gaining \(+\)1.79 and \(+\)0.23 over standard back-translation and self-learning with quality estimation enhanced back-translation respectively.
Post-quantum Online Voting Scheme

We propose a new post-quantum online voting scheme whose security relies on lattice assumptions. Compared to the state-of-the-art, our work does not make use of homomorphic primitives nor mix-nets, that are more traditional ways to build electronic voting protocols. The main reason is that zero-knowledge proofs, mandatory in the two aforementioned frameworks, are far to be as efficient as in “classical” cryptography, leading us to explore other approaches.We rather base our work on a framework introduced by Fujioka et al. at Auscrypt 1992 that makes use of a blind signature scheme as the main building block. We depart however from this seminal work by allowing threshold issuance of blind signatures (to prevent ballot stuffing by malicious authorities) and by using a threshold post-quantum public key encryption scheme (rather than a commitment scheme) to allow voters to “vote and go” and to prevent “partial results”. We instantiate all the required primitives with lattice-based constructions leading to the first online voting scheme that simultaneously provides post-quantum public verifiability and everlasting privacy (information-theoretic ballot anonymity). Another advantage of our protocol is that it can, contrary to recent proposals, efficiently handle elections with multiple candidates or with complex ballots (and not only referendums or single member plurality voting) without weakening the whole voting protocol by increasing the parameters size as with previous post-quantum voting schemes.
Evaluation of professional competence of future chemistry teachers in the current context

The implementation in the higher school of such new normative documents as Federal State Educational Standard, unit standard and others, set the goal for realization of competence approach in education. At the same time, such competence as the expected results serve as the evidentiary base for guaranteeing the quality of graduates training. There is currently no unified standard for assessing the quality of education overall. The subject of this research is the formation and assessment of formedness of professional competences of students on the discipline 44.03.01 Pedagogical Education with major in Chemistry and 44.03.05 Pedagogical Education with major in Biology and Chemistry. &amp;nbsp;The goal of this research consists in formation of professional competence of students in the conditions of a classical university through creation of the system of occupational assignments. For the development of theoretical platform of research, special attention is given to the theoretical questions of formation of professional competence of students majoring in pedagogy. The author describes the requirements to evaluation tools and the structure of evaluation tools. The article employs the general theoretical methods of summarization, systematization, analysis, and comparison. The scientific novelty consists in outlining approaches towards solution of the problem of formation of professional competence of the students majoring in pedagogy in the conditions of a classical university; determination of criteria and indicators of professional training of future chemistry teachers in the context of implementation of the new educational policy. The conclusion is made that the practice-oriented educational institutions are one of the means of formation and evaluation of the formedness of professional competences of the students majoring in pedagogy.
On the Diophantine Equation n^x + 13^y = z^2 where n = 2 (mod 39) and n + 1 is not a Square Number

The purpose of the present article is to prove that the Diophantine equation n^x + 13^y = z^2 has exactly one solution (n, x, y, z) = (2, 3, 0, 3) where x, y and z are non-negative integers and n is a positive integer with n ≡ 2 (mod 39) and n + 1 is not a square number. In particular, (3, 0, 3) is a unique solution (x, y, z) in non-negative integers of the Diophantine equation 2^x + 13^y = z^2
Analysis and Recognition Method of Internet Image Public Opinion Based on Partial Differential Equation

This article comprehensively and systematically expounds the development trends and basic theory of partial differential methods, analyzes the characteristics of sampling multiscale transformation in detail, and deeply studies the network image denoising and network image restoration methods that perform partial differential diffusion in the pixel domain and the transform domain. An adaptive diffusion method of partial differential equations is proposed. Among them, the key parameters can be adaptively changed according to the curvature and gradient of the local geometric information of the network image, and the diffusion direction and intensity of the diffusion can be controlled. First, using the principle of variation, we derive the Euler equation corresponding to the diffusion method of partial differential equations and analyze its diffusion ability using the local orthogonal coordinate system of the network image. Based on the theoretical analysis of public opinion, this article applies opinion mining technology to the online public opinion early warning system to achieve the purpose of grasping the opinions of netizens in time and guiding the trend of public opinion. Opinion mining is the use of natural language processing technology to automatically extract the emotional tendencies and evaluation objects contained in the subjective text. In the edge area of the network image, the diffusion along the edge direction should have a large diffusion coefficient, and the diffusion along the vertical edge direction should have a small diffusion coefficient; in the flat area of the network image, it diffuses to the surrounding with equal intensity, and the diffusion intensity value is relatively high. Secondly, based on the analysis of the adaptive partial differential equation diffusion method, using the half-point difference format, a numerical method for network image recognition is designed. Both theoretical analysis and experimental results show that the network image recognition model based on adaptive partial differential equation diffusion is more effective than the model based on partial differential equation recognition; at the same time, experiments show that the network image recognition model based on adaptive partial differential equation diffusion is more effective than the network image recognition model based on ordinary diffusion. The network image recognition model based on constant partial differential equation diffusion is more effective in improving the quality of network image recognition.