Reviewerly/exHarmony huggingface dataset:
An Efficient Approach to Vectorize the Hybrid Breadth-First Search

Graph, as a powerful data structure, is widely used in numerous applications. Among plenty of algorithms in graph analysis, Breadth-First Search (BFS) is one of the most basic and important kernels. As the size of the graph increases rapidly, how to speed up the processing remains a meaningful task. Meanwhile, modern processors are commonly equipped with vectorization (Single Instruction Multiple Data, SIMD) processing units, which support processing multiple data elements in a single instruction. These processing units show the potential to accelerate data-intensive applications. In this paper, we evaluate the vectorized implementations of a direction-optimizing BFS, which consists of both the classical top-down and a novel bottom-up methods. We analyze the previous representative implementations, and against the defect and inefficiency they exhibit, we propose a new approach, which features compacting valid data elements in the SIMD vectors and supplementing new input data to replace the invalid ones regularly. Our experiments using the AVX2 instruction set show a maximum speedup of 1.67x on synthetic graphs and 19. 73x on real-world graphs by comparing to the state-of-the-art work.
Prospects for the development of distance learning within the system of professional education

The object of this research is distance learning, while the subject is the organization of distance learning in professional educational institutions with a long-term view. The author emphasizes the relevance of this research in the real and hypothetical epidemic conditions. The current stage is viewed as a period of contemplation or reconsideration of distance learning. The article employs the methods of theoretical and empirical analysis. The results of sociological surveys conducted among the students, pedagogues, rectors and vice-rectors of the Russian universities are analyzed. The &amp;nbsp;author indicates the conservatism of the modern system of education, and the importance of interaction between the main actors of the educational process. The fundamental difficulties in organization of distance learning are outlined. The scientific novelty of consists in creation of the model of educational process based on the empirical data underlying the development of distance learning. It is demonstrated that the conduct scientific research, development of information technologies, and improvement of the normative framework open new opportunities for creating a more effective learning environment. The key moment in increasing the effectiveness of the new form of learning consists in reaching the full presence effect, which allows implementing the traditional pedagogical technologies. Description is given to the possibilities of telepresence technology. The conclusion is formulated that distance learning can be transformed into a new form in accordance with the status of technological and technical development.
Demonstrating High-Precision and High-Fidelity Digital Inking for Virtual Reality

Pen computing has become popular with tablet and wall-screen computers for digital precision tasks such as writing, annotating, and drawing. Digital pens have been made possible by the developments in input sensing technologies integrated into such screens. Virtual Reality systems, however, largely detect input using cameras, whose update rates are insufficient for capturing pen input with the necessary fidelity. In this demonstration, we showcase a digital pen for VR that accurately digitizes writing and drawing, including small and quick turns. Our prototype Flashpen repurposes an optical flow sensor from gaming mice, which digitizes minute motions at over 8 kHz when dragged across a surface. We demonstrate several use-cases for Flashpen during interaction in VR, including sketching, selecting, annotating, and writing.
Research of the methodology of the development of the demo exam in Russia and its aspects in the competence Digital metrology

Abstract Nowadays, the demo exam is becoming a frequently used way to additionally test the knowledge of students in technical schools and colleges. However, due to the difficult epidemiological situation, this process is carried out remotely. Let’s consider the features of the demo exam in this format. The demo exam changes the perception of learning, because the traditional final qualifying work becomes only a small part of the main exam. On the frontier of successful completion of education, there is the ability to work with measuring equipment, the skills of perceiving production tasks and the possibility of solving them in the shortest possible time. It is important to note in this case the complexity of the transition to this type of exam for many students who are accustomed to perceiving the defense of their graduation work as a formal process associated with retelling a pre-written text. The demo exam is a bridge between theory and practice, which must be introduced gradually into the education system in order to eliminate a negative effect.
Adaptive Learning Rate for Dealing with Imbalanced Data in Classification Problems

This research modified a backpropagation learning algorithm in order to increase its ability to deal with imbalanced data problems. We used the backpropagation algorithm and a concept of multiple adaptive learning rates to train the feedforward neural network. Using multiple adaptive learning rates allowed us to achieve a classification model that had fewer problems when dealing with an imbalanced dataset. The experimental results showed that the proposed method performed significantly better than the conventional backpropagation neural network in all tests.
Design and Implementation of Digital Transaction System Based on Blockchain Environment

In recent years, block chain has gradually become a hot spot of technological innovation, and the common operation of bitcoin wallets can also leak some user information, which has been exploited in the past. Combining different outputs into a single transaction input by wallets is a common form of privacy leakage. This article proposes a new type of distributed, decentralized, and trustless technical solution. Firstly, the design and implementation of the blockchain are studied from a technical perspective. Then, the security of the blockchain is analyzed from both the blockchain itself and the application. Finally, the blockchain security system and traditional centralized security system are compared from the perspective of security architecture. This article introduces a new distributed data storage technology, which has led to changes in system and program design concepts, and may subvert the current organization model of business society.
STUDY OF DIFFERENTIAL EQUATIONS WITH CONTROL

Earlier in several papers the question on minimizing of the entropy increment in the achievement of some goals was arisen. Based on the analysis of the processes discussed in this paper and in the problem of minimizing the increment of entropy the hypotheses on entropy have been put forward. An accurate estimate of the entropy increment is obtained with limited acceleration and braking of the point.
Ranked Communication Channel Confidence for Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning systems have received increasing attention in these years. Existing works have proved that communication between agents can accelerate the training process and improve the final performance. However, in real-world tasks, the communication bandwidth is always restricted and unstable either in the training or test process, which is usually neglected in previous works. In this paper, we propose a method called Ranked Communication Channel Confidence Multi-agent Reinforcement Learning (RC3MARL), which exploits a non-uniform information encoding mechanism to discriminate the message in finer granularity. RC3MARL is more flexible and can achieve better performance in various environments with different communication bandwidths than the methods with fixed communication mechanism. In concrete, when the bandwidth is limited, RC3MARL can guarantee an acceptable lower-bound performance, and when the bandwidth is adequate, RC3MARL will make full use of the bandwidth and provide better performance. The experimental result demonstrates that our method can achieve better performance with same training samples.
Dynamic Domain Sizes in Temporal Probabilistic Relational Models

Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.
CARDINAL: Contextualized Adaptive Research Data Description INterface Applying LinkedData

In the publishing process for research data, common user interfaces for gathering descriptive structured metadata traditionally rely on static free-text input elements. This constitutes an obstacle for interdisciplinary, unambiguous, fine-grained data descriptions. Reusing already existing domain-specific metadata models based on semantic ontologies are a more promising approach, but the careful selection and presentation of relevant properties is not trivial. In this paper, we present the CARDINAL approach, which takes the current research context into consideration to request additional but only meaningful domain-specific characteristics. It generates and presents an adaptive user input interface to the user that allows the structured input of knowledge-domain specific descriptive metadata based on existing ontologies. We show in a proof-of-concept the feasibility of such a contextualized web form for research metadata and discuss challenges in the selection process for relevant ontologies and properties. A web-based survey experiment with 83 participants of varying research domain and expertise shows, that the CARDINAL approach allows to collect additional relevant metadata in a structured way without overstraining the user.
Using Descriptive Statistics and Graphs

This chapter discusses the use of descriptive statistics and graphs to present to policymakers and conduct exploratory data analysis (EDA). Descriptive statistics, that include measures of central tendency and dispersion, are discussed and demonstrated using real data. The utilization of graphs, which includes histograms, box charts, and scatter plots, are also presented.
Fuzzy World: A Tool Training Agent from Concept Cognitive to Logic Inference

Not like many visual systems or NLP frameworks, human generally use both visual and semantic information for reasoning tasks. In this paper, we present a 3D virtual simulation learning environment Fuzzy World based on gradual learning paradigm to train visual-semantic reasoning agent for complex logic reasoning tasks. Furthermore our baseline approach employed semantic graphs and deep reinforcement learning architecture shows the significant performance over the tasks.
Problems of Information in Dialectical Materialism (Translated from English)

The article introduces a complete translation of Professor P. P. Kirschermann's "Problems of Information in Dialectical Materialism". It was originally published in the respectable sovietological journal "Studies in Soviet Thought" in 1968. Professor P. P. Kirschermann's article is a preview of his monograph "Information und Widerspiegelung. Zu Problemen der Kybernetik und des Diamats", published in 1969. Its English translation appeared a year later and was published by D. Reidel Publishing Company as "Information and Reflection: On Some Problems of Cybernetics and how Contemporary Dialectical Materialism Copes with Them". Professor P. P. Kirschenmann worked under the guidance of the famous Polish philosopher J. M. Bochenski in the framework of a special program implemented by the Institute for Eastern European Studies at the University of Fribourg, Switzerland. In his article, he identified the main features of dialectical and materialistic reflection of information and demonstrated their connection with the fundamental principles of the dialectical materialism. Professor P. P. Kirschenmann focused on the existence of internal collisions, which impeded the implementation of a consistent conceptualization of information. This critical understanding of the dialectical and materialistic philosophy of information is of significant interest for several reasons. First of all, it proves the involvement of dialectical and materialist philosophy in the context of European thought of the XX century. In addition, it demonstrates the relevance of P. P. Kirschenmann's provisions for modern domestic contexts of the philosophy of information.
Improving face recognition in surveillance video with judicious selection and fusion of representative frames

Face recognition in unconstrained surveillance videos is challenging due to the different acquisition settings and face variations. We propose to utilize the complementary correlation between multi-frames to improve face recognition performance. We design an algorithm to build a representative frame set from the video sequence, selecting faces with high quality and large appearance diversity. We also devise a refined Deep Residual Equivariant Mapping (DREAM) block to improve the discriminative power of the extracted deep features. Extensive experiments on two relevant face recognition benchmarks, YouTube Face and IJB-A, show the effectiveness of the proposed method. Our work is also lightweight, and can be easily embedded into existing CNN based face recognition systems.
Human identification using micro-motion and LPCP-Cov neural networks

A method of human identification using micro-doppler data based on a lightweight neural network is proposed. It can identity various gaits and different person effectively, and the amount of computation and the number of parameters of the model can be reduced also. The principle like that used in linear phase finite impulse response filter in digital signal processing is utilized here. The proposed LPCP-Conv kernel can lower the storage cost and the computational complexity of the model. The results of experiments show that the proposed algorithm can decrease the quality of computation and the number of parameters effectively, as well as improving the accuracy. The LPCP-Conv kernel is friendly to hardware accelerator with low power design for reducing the cost of memory access.
Innovative approaches in the training of engineering personnel

Abstract The article is devoted to the problems of using innovative technologies in the process of training future specialists in the field of applied physics and engineering. The necessity of development and implementation of innovative technologies in higher education is substantiated. The effectiveness of the proposed technologies is evaluated in terms of the formation of professional competencies of bachelors. The article considers innovative approaches and the corresponding educational technologies. The experience of the development and integration of innovative technologies in the educational process of a technical university is described on the example of teaching mathematical subjects for students. The article describes the experience of the effective use of electronic educational environment of the university to create original elements of electronic educational resources, considers the possibility of introducing students to VR \ AR technologies in the framework of project activities. The article discusses the results of a training experiment in which students of technical training areas participated.
Constant Approximation Algorithm for Minimizing Concave Impurity

Partitioning algorithms play a key role in many scientific and engineering disciplines. A partitioning algorithm divides a set into a number of disjoint subsets or partitions. Often, the quality of the resulted partitions is measured by the amount of impurity in each partition, the smaller impurity the higher quality of the partitions. Let M be the number of N-dimensional elements in a set and K be the number of desired partitions, then an exhaustive search over all the possible partitions to find a minimum partition has the complexity of O(K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> ) which quickly becomes impractical for many applications with modest values of K and M. Thus, many approximate algorithms with polynomial time complexity have been proposed, but few provide the bounded guarantee. In this paper, we propose a linear time algorithm with bounded guarantee based on the maximum likelihood principle. Furthermore, the guarantee bound of the proposed algorithm is better than the state-of-the-art method in [1] for many impurity functions, and at the same time, for K ≥ N, the computational complexity is reduced from O(M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(M).
Aligning the training and evaluation of unsupervised text style Transfer

In the text style transfer task, models modify the attribute style of given texts while keeping the style-irrelevant content unchanged. Previous work has proposed many approaches on the non-parallel corpus (without style-to-style training pairs). These approaches are mostly motivated by heuristic intuition and fail to precisely control texts’ attributes, such as the amount of preserved semantics, which leaves discrepancies between training and evaluation. This paper proposes a novel training method based on the evaluation metrics to address the discrepancy issue. Specifically, the model first evaluates different aspects of the transferred texts and provides the differentiable quality approximations by employing extra supervising modules. Then the model is optimized by bridging the gap between approximations and expectations. Extensive experiments conducted on two sentiment style datasets demonstrate the effectiveness of our proposal compared with some competitive baselines.
An Algorithm of Object Detection Based on Regression Learning for Remote Sensing Images

Abstract Saliency detection method is widely used in the field of object detection. It can greatly improve the efficiency of detection by extracting the region that may contain the objects and reduce the processing of background area. It is very difficult to detect the airports in remote sensing image for its changeable structure and complex background. Considering this situation, this paper proposes a object detection method of saliency model based on regression learning, this method extracts salient regions that may contain airports, and then detects salient regions through feature bag model. Our method uses super-pixel segmentation, minimization of absolute shrinkage and selection operator and hierarchical learning theory based on back-propagation. The salient regions that contain potential objects are extracted and sent to the feature bag model for classification. and it can location the final objects. This paper constructs the airport training dataset and test dataset, and the experimental results fully prove the effectiveness and superiority of this method.
φ comp: An Architecture for Monitoring and Enforcing Security Compliance in Sensitive Health Data Environment

Even if electronic health records have revolutionise the efficiency and quality of delivering healthcare, security concerns remain one of its major setback. For this reason, compliance to security regulations when processing, storing, and transmitting health data is mandatory in many countries. This paper reports the design and implementation of φcomp, an approach that automates the monitoring and enforcing of security compliance at runtime in sensitive health environment. It is composed of a distributed architecture which analyses the behaviour of e-health environments and subsequently enforces security rules to ensure compliance with respect to health security regulations such as HDS and HIPAA. The results of our experiments in a production environment showed that it is highly customisable with respect to different use case application, can easily and rapidly be deployed, and induces very low overhead on the third party infrastructure for compliance management.
Computer analysis of differential systems stability based on linearization and matrix multiplicative criteria

Abstract This article proposes a method of stability analysis in the sense of Lyapunov for systems of ordinary differential equations. The method is based on stability criteria in the form of necessary and sufficient conditions. The criteria are obtained from matrix multiplicative transformations of difference numerical integration schemes. The form of the criteria entails the possibility of their cyclical software implementation as a cycle based on the number of cofactors. It is mathematically substantiated that the replacement of an infinite matrix product by a finite product, which is necessary in the programming process, preserves the reliability of the stability analysis according to the proposed criteria. We studied the influence of inaccuracy of the difference solution of the system on the reliability of the computer analysis of stability.
Procedural Democracy and Electronic Agents

In recent years, firms offering political campaigning and fundraising services have claimed that artificial intelligence is used to maximize their clients' interests. Numerous news reports suggest that artificial intelligence capacities have influenced high‐profile voting events; publications decrying the threat posed to democratic procedures are proliferating. This article takes a skeptical view of such claims, with an argument in six sections. First, the received principles of procedural democracy in relation to voting events are outlined. Second, the kind of services in question are considered. Third, to what extent these services actually involve artificial intelligence is pondered. Fourth, how such technological capacities may bear upon the tenets of procedural democracy are raised. Fifth, it is argued that it is unlikely that those tenets are indeed significantly undermined in practice. And sixth, this article concludes by considering the implications of it being proven that such technological capacities do play a decisive role in voting events.
Experimental QND measurements of complementarity on two-qubit states with IonQ and IBM Q quantum computers

We report the experimental nondemolition measurement of coherence, predictability and concurrence on a system of two qubits. The quantum circuits proposed by De Melo et al. are implemented on IBM Q (superconducting circuit) and IonQ (trapped ion) quantum computers. Three criteria are used to compare the performance of the different machines on this task: measurement accuracy, nondemolition of the observable, and quantum state preparation. We find that the IonQ quantum computer provides constant state fidelity through the nondemolition process, outperforming IBM Q systems on which the fidelity consequently drops after the measurement. Our study compares the current performance of these two technologies at different stages of the nondemolition measurement of bipartite complementarity.