Reviewerly/exHarmony huggingface dataset:
Exploring Multilingual Syntactic Sentence Representations

We study methods for learning sentence embeddings with syntactic structure.We focus on methods of learning syntactic sentenceembeddings by using a multilingual parallelcorpus augmented by Universal Parts-of-Speech tags.We evaluate the quality of the learned embeddings by examining sentencelevel nearest neighbours and functional dissimilarity in the embedding space.We also evaluate the ability of the method to learn syntactic sentence-embeddings for low-resource languages and demonstrate strong evidence for transfer learning.Our results show that syntactic sentence-embeddings can be learned while using less training data, fewer model parameters, and resulting in better evaluation metrics than state-of-the-art language models.
Modeling Evolving User Behavior via Sequential Clustering

In this paper we address the problem of modeling the evolution of clusters over time by applying sequential clustering. We propose a sequential partitioning algorithm that can be applied for grouping distinct snapshots of streaming data so that a clustering model is built on each data snapshot. The algorithm is initialized by a clustering solution built on available historical data. Then a new clustering solution is generated on each data snapshot by applying a partitioning algorithm seeded with the centroids of the clustering model obtained at the previous time interval. At each step the algorithm also conducts model adapting operations in order to reflect the evolution in the clustering structure. In that way, it enables to deal with both incremental and dynamic aspects of modeling evolving behavior problems. In addition, the proposed approach is able to trace back evolution through the detection of clusters’ transitions, such as splits and merges. We have illustrated and initially evaluated our ideas on household electricity consumption data. The results have shown that the proposed sequential clustering algorithm is robust to modeling evolving behavior by being enable to mine changes and update the model, respectively.
Adaptive Mobile Learning Framework Based On IRT Theory

Advances in remote systems and skilled online tools have made great enthusiasm for adaptable learning lately.Patterns show that diverse mobile learning is the next achievement of meaningful innovation.In any case, adaptable mobile learning progresses through its different perspectives and the links between them must be integrated.In this article, we will conduct a baseline survey of specific writing with mixed-use learning in the light of the fact that there is still a need for extensive research on disrupting classroom innovation, particularly on how the performance of various tasks affects the instructor function in the classroom as a media coach and learning facilitator.The easily accessible writing identified with the adaptable mobile adaptation unequivocally highlights that the robust adaptable learning system is missing and that few scientists have explained the existence of a permanent structure.To address this problem, a systematic framework is proposed in this paper to discover diversity that adapts to the individual characteristics of students as described in each student's learning preparation.This paper also presents the theory of material responsiveness and how it is generally applied in a test case for any online course for the time of flexible exams within the course [for the development of model information] of elements (evaluation questions), which are available in online vaults.Being a probability theory, the article depicts the variables to consider and how to detect probability that a student answers an appropriately defined element, known as the level of absorptive capacity, taking into account the prediction test and their previous answers.
Research on Path Planning and Tracking Method of Auto-driving Vehicles Under Complex Constraints

To solve the problems of path planning and path tracking in the field of automatic driving, the path planning and tracking methods applicable to the actual environment under complex constraints are studied. Firstly, the improved double-tree RRT algorithm (RRT-Connect) based on bidirectional extended balance was introduced. Under the advantage of avoiding the modeling of space, the target preference function and the metric function were introduced in combination with the environmental constraints and the constraints of the vehicle itself. At the same time, combined with the regression detection and collision detection mechanism to solve the local minimum problems in motion planning, and greatly improve the effectiveness of path planning. Then generate a smooth continuous executable track based on the cubic spline interpolation function. Finally, the heading angle is obtained according to the vector field method and converted into a tangential angle to continuously track the path. The effectiveness, correctness and practicability of the algorithm are verified by simulation experiments.
Modeling Physical Operating Principles During Search Design of Cooling and Refrigerating Systems

The chapter describes a new method of search design of cooling and refrigerating systems, the basis of which is represented by a graph model of the physical operating principle based on the thermodynamic description of physical processes. The method can be applied as a means of enhancing the labor efficiency of designers at the early stages of designing owing to reduction in labor expenditures when choosing the concept of an engineering system for refrigeration and also as a methodical support for the development of computer-aided design systems. The mathematical model of the physical operating principle has been substantiated, and the basic abstract theorems of a relatively semantic load applied to nodes and edges of the graph have been represented. The graphic representations of the physical operating principle model of physical phenomena for cooling systems have been developed. The necessity and the physical operating principle, enough for the given model and intended for the considered device class, were demonstrated by the example of an absorption cooling and refrigerating plant. The sequence of drafting of the POP model has been presented. The structures of data have been shown in the form of relative tables.
Prototype of classifier for the decision support system of legal documents

We propose a prototype of the classifier of electronic documents for the decision support system in the field of economic justice.The system uses both wellknown text analytics algorithms and an original algorithm based on an artificial neural network.A text mining model has been developed to classify court documents to determine the category (class) of a statement of claim.A preliminary analysis of court documents and the selection of significant features were carried out.To choose the best way of solving problem of document classification we implemented Bayesian classification algorithm, k nearest neighbor algorithm and decision trees algorithm.All used algorithms show results with errors on the same sample corpus of texts.To improve the accuracy of classification, an original model based on an artificial neural network was developed, which shows an unmistakable determination of the type of document on a test sample for a number of classes of lawsuits in arbitration proceedings.
Learning Representations in Reinforcement Learning:An Information Bottleneck Approach

The information bottleneck principle is an elegant and useful approach to representation learning. In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms. %by accelerating the process of discarding irrelevant information when the %input states are extremely high-dimensional. We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound. Then, we maximize this lower bound with the Stein variational (SV) gradient method. We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO). Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly. Finally, we study the information bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE) . We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process. We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.
Conceptual model of the VR module for “Virtual plaza for interactive presentation of Bulgarian cultural heritage”

In the last years, Virtual Reality (VR) has been established itself as a highly promising media technology for reconstruction and presentation of cultural heritage sites to a wide audience. Virtual Reality enables not only an exploration of a single artefact but of many virtual objects spatially and temporally arranged into a single scene. The users enter an artificially created space and get a feeling of being a part of it. They can perceive and interact with the objects in the virtual environment in natural way. In this paper we propose a conceptual model of the VR module for the “Virtual Plaza for Immersive Representation of Bulgarian Cultural Heritage Sites”. It contains an analysis of the possible input data and the developed workflows to help experts from the cultural heritage domain to implement various scenarios for presentation of single artefacts and complete scene par of cultural heritage site using the benefits of Virtual Reality as visualization medium.
Temper and temperament of prehistoric craft: Temper type evolution and clay body 'workability'

Abstract Temper is an additive incorporated into clay during the formation of a ceramic vessel, and may consist of various materials. In a number of previous experiments over the past several decades, archaeologists have experimentally demonstrated that tempers used by prehistoric craftspeople would have imparted important post-firing use-life properties to ceramic vessels. However, although widely touted, the notion that prehistoric temper types would have aided in pre-firing vessel formation has never been systematically tested. Here, we experimentally assess whether calcium carbonate-based tempers, like limestone and burnt shell, would have made clay bodies more workable relative to silicate-based grit temper, as has been previously proposed. In this study, participants were asked to build five simple and challenging three-dimensional forms using grit-, limestone- and shell-tempered clay bodies, and then rank these conditions in terms of workability. Our statistical and qualitative assessments of these data were unambiguous: contrary to claims in the scientific literature, the calcium carbonate tempers did not make clay bodies more workable, and were consistently, sometimes significantly, ranked lower than silicate grit-tempered clay bodies in terms of workability. Our results have several implications for temper selection and evolution in prehistory, specifically during the widespread silicate grit to calcium carbonate transition during the Late Woodland period (AD 500‐1400) of the North American Midwest.
Learning How to Play Bomberman with Deep Reinforcement and Imitation Learning

Making artificial agents that learn how to play is a long-standing goal in the area of Game AI. Recently, several successful cases have emerged driven by Reinforcement Learning (RL) and neural network-based approaches. However, in most of the cases, the results have been achieved by training directly from pixel frames with valuable computational resources. In this paper, we devise agents that learn how to play the popular game of Bomberman by relying on state representations and RL-based algorithms without looking at the pixel level. To that, we designed five vector-based state representations and implemented Bomberman on the top of the Unity game engine through the ML-agents toolkit. We enhance the ML-agents algorithms by developing an Imitation-based learner (IL) that improves its model with the Actor-Critic Proximal-Policy Optimization (PPO) method. We compared this approach with a PPO-only learner that uses either a Multi-Layer Perceptron or a Long-Short Term-Memory network (LSTM). We conducted several pieces of training and tournament experiments by making the agents play against each other. The hybrid state representation and our IL followed by PPO learning algorithm achieve the best overall quantitative results, and we also observed that their agents learn a correct Bomberman behavior.
Improved cryptanalysis of the AJPS Mersenne based cryptosystem

Abstract At Crypto 2018, Aggarwal, Joux, Prakash and Santha (AJPS) described a new public-key encryption scheme based on Mersenne numbers. Shortly after the publication of the cryptosystem, Beunardeau et al. described an attack with complexity 𝓞(2 2 h ). In this paper, we describe an improved attack with complexity 𝓞(2 1.75 h ).
Software Trustworthiness Evaluation using Structural Equation Modeling

Software trustworthiness evaluation results can provide guidance for existing software development.Therefore, it is of great significance to study software trustworthiness evaluation.Weights and the relationship of trustworthy attributes have a huge effect on the results of software trustworthiness evaluation.However, few studies have considered weights and the quantitative relationship among trustworthy attributes at the same time.Structural equation modeling (SEM) provides a more scientific and reasonable method to obtain weights, where the objectification of subjective weights is guaranteed.In this paper, we propose a SEM-based method to evaluate software trustworthiness.Firstly, we establish the trustworthy evaluation indicator system for software.Based on the survey data, we construct the SEM for software trustworthiness evaluation to obtain the weights of trustworthy attributes and the relationship among them.Lastly, we apply the trustworthy measurement model to calculate the trustworthiness value of software to be surveyed.These trustworthiness values demonstrate the reasonability of our method.
A Weakly-Supervised Deep Network for DSM-Aided Vehicle Detection

With the breakthrough of the spatial resolution of optical remote sensing images at the sub-meter level and the explosive development of deep learning, geospatial object detection has achieved a growing interest in remote sensing community. However, labeling large training datasets in object level is still an expensive and tedious procedure. This might lead to the poor model generalization and degraded network learning ability. To this end, a weakly-supervised deep network (WSDN) is developed for geospatial object detection by applying a digital surface model (DSM)-aided auto-labeling and a pre-trained network learned from the task-independent dataset. Experimental results conducted on the stereo aerial imagery of a large camping site are performed to demonstrate that the proposed WSDN yields better detection results, with 62.78% precision and 55.13% recall.
Dirichlet-Multinomial Counterfactual Rewards for Heterogeneous Multiagent Systems

Multi-robot teams have been shown to be effective in accomplishing complex tasks which require tight coordination among team members. In homogeneous systems, recent work has demonstrated that “stepping stone” rewards are an effective way to provide agents with feedback on potentially valuable actions even when the agent-to-agent coupling requirements of an objective are not satisfied. In this work, we propose a new mechanism for inferring hypothetical partners in tightly-coupled, heterogeneous systems called Dirichlet-Multinomial Counterfactual Selection (DMCS). Using DMCS, we show that agents can learn to infer appropriate counterfactual partners to receive more informative stepping stone rewards by testing in a modified multi-rover exploration problem. We also show that DMCS outperforms a random partner selection baseline by over 40%, and we demonstrate how domain knowledge can be used to induce a prior to guide the agent learning process. Finally, we show that DMCS maintains superior performance for up to 15 distinct rover types compared to the performance of the baseline which degrades rapidly.
Design of a Privacy-Preserving Algorithm for Peer-to-Peer Network Based on Differential Privacy

In the peer-to-peer network (P2P), the private information of individual users faces the risks of being tracked, identified or leaked.This paper attempts to develop a security technique that fully protects the privacy of P2P users.Firstly, the node data were collected and analyzed from the P2P.Then, a privacy-preserving algorithm was proposed for the P2P based on the differential privacy model.The proposed algorithm adds noise to the degree distribution of the nodes, and solves the high sensitivity under the Laplace mechanism, which arises from the unique structure of the P2P.Finally, the proposed algorithm was verified through experiments.The results show that our algorithm protected the privacy of individual data in the storage and dissemination process, controlled the sensitivity to a low level through noise addition, and allocated the privacy budget rationally.Thus, the proposed algorithm is available and reliable for P2P operations like credit verification, transmission and storage, and enjoys excellent effectiveness and robustness.
An Improved Focused Web Crawler based on Hybrid Similarity

Web crawler is an efficient strategy for downloading data automatically from the Internet.Focused web crawler is a special k ind of web crawler that is responsible for getting certain information from webpages and making them available to users.The most important problem of focused web crawler is to confirm the similarity between the target webpages and the topics.Therefore, this paper proposes an improved focused web crawler algorithm, whose similarity calculating methods derive from three aspects: anchor text, content, and structure of the webpages.This improved algorithm is called hybrid similarity.If the anchor text similarity is bigger than the threshold, the target webpages are downloaded directly; otherwise, the target webpages' similarity is analyzed by using the TF-Gini feature weighting algorithm and the improved cosine similarity algorithm.The experimental results in this paper have pr oven that the hybrid similarity algorithm is more effective than the traditional algorithm.The precision increases by nearly 10% compared with the traditional algorithm.
Rotation Estimation: A Closed-Form Solution Using Spherical Moments

Photometric moments are global descriptors of an image that can be used to recover motion information. This paper uses spherical photometric moments for a closed form estimation of 3D rotations from images. Since the used descriptors are global and not of the geometrical kind, they allow to avoid image processing as features extraction, matching, and tracking. The proposed scheme based on spherical projection can be used for the different vision sensors obeying the central unified model: conventional, fisheye, and catadioptric. Experimental results using both synthetic data and real images in different scenarios are provided to show the efficiency of the proposed method.
File encryption based on reduced-round AES with revised round keys and key schedule

The continuing advancement of technology had provided security issues in protecting the confidentiality of information. The need to protect unauthorized access of a third party is warranted. In this paper, the reduced-round modified AES with revised round keys and key schedule is proposed to ensure file confidentiality. The modifications to the AES cipher round was the reduction of the round iterations from 10 to 6, and additional key permutations were added in between states; while in the key schedule, additional byte substitution process was appended. Time and throughput were utilized to measure the performance of the application's encryption/decryption process; while the avalanche effect and randomness tests were used to measure the security of the modified AES algorithm. The results of evaluations have shown that the encryption and decryption time have improved by 1.27% and 1.21% respectively while the throughput has similarly improved by 1.29% and 3.19% for both encryption and decryption respectively. Whereas the avalanche effect of the modified AES was 50.06% which was more than the ideal value of 50% and it was also better than the standard AES which was 49.94% using the sample dataset. Finally, all the ciphertext outputs of the modified AES passed the randomness tests.
Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences

We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the set to its "head subject" node in the higher ranks of the taxonomy tree. The head subject is supposed to "tightly" cover the query set, possibly bringing in some errors referred to as "gaps" and "offshoots". Our method, ParGenFS, globally minimizes a penalty function combining the numbers of head subjects and gaps and offshoots, differently weighted. Two applications are considered: (1) analysis of tendencies of research in Data Science; (2) audience extending for programmatic targeted advertising online. The former involves a taxonomy of Data Science derived from the celebrated ACM Computing Classification System 2012. Based on a collection of research papers published by Springer 1998–2017, and applying in-house methods for text analysis and fuzzy clustering, we derive fuzzy clusters of leaf topics in learning, retrieval and clustering. The head subjects of these clusters inform us of some general tendencies of the research. The latter involves publicly available IAB Tech Lab Content Taxonomy. Each of about 25 mln users is assigned with a fuzzy profile within this taxonomy, which is generalized offline using ParGenFS. Our experiments show that these head subjects effectively extend the size of targeted audiences at least twice without loosing quality.
THE MODEL OF FORMATION OF SELF-DIRECTED LANGUAGE LEARNING OF UNIVERSITY STUDENTS IN A UNIFIED HIGHER EDUCATION AREA

Purpose of the study: The article aims to analyze the problem of modeling self-directed language learning of university students in a Unified Higher Education Area. Methodology: Our study is based on the theoretical research method – the analysis of foreign and national philosophical, pedagogical literature on a problem; analysis and generalization of modern pedagogical experience; theoretical modeling; empirical – research and analysis of pedagogical experience; observation, interviews with teachers and students. Results: Modeling of self-directed language learning of university students is considered by the author in terms of systemic, student-centered and competence-based approaches. The model of self-directed language learning is represented as an integrative construct, which includes the target, content, technological and productive blocks. The main components of the model are the goal, tasks, components of self-directed learning, modern technologies, methods, forms, criteria, and indicators. Applications of this study: The materials of this research can be used by teachers to develop special courses of lectures and seminars on the organization of self-directed language learning of university students. Novelty/Originality of this study: In this research, the model of Formation of Self-Directed Language Learning of University Students in a Unified Higher Education Area is presented in a comprehensive and complete manner.