Reviewerly/exHarmony huggingface dataset:
Defeasible Justification Using the KLM Framework

The Kraus, Lehmann and Magidor (KLM) framework is an extension of Propositional Logic (PL) that can perform defeasible reasoning. The results of defeasible reasoning using the KLM framework are often challenging to understand. Therefore, one needs a framework within which it is possible to provide justifications for conclusions drawn from defeasible reasoning. This paper proposes a theoretical framework for defeasible justification in PL and a software tool that implements the framework. The theoretical framework is based on an existing theoretical framework for Description Logic (DL). The defeasible justification algorithm uses the statement ranking required by the KLM-style form of defeasible entailment known as Rational Closure. Classical justifications are computed based on materialised formulas (classical counterparts of defeasible formulas). The resulting classical justifications are converted to defeasible justifications, based on the input knowledge base. We provide an initial evaluation of the framework and the software tool by testing it with a representative example.
Parallel algorithm for calculating the learning processes of an artificial neural network

Nowadays, the digital processing of graphic data is undergoing unprecedented changes. Recently, the use of artificial intelligence has become more and more important in all areas. In this article, an algorithm for parallelizing the learning processes of a neural network has been developed. The algorithm identifies an area of a person’s face from an image and performs parallel labor-intensive processes associated with training a neural network. Parallelization of the processes of organizing neural nodes (layers) is carried out in the Python programming language.
Development of University Students’ Teamwork Skills in Distance Learning Education

Developed teamwork skills are one of the highest demand skills among employers. However, self-isolation and the transition to distance learning made it much more difficult to develop these skills. Teachers have to look for new technologies, methods and types of activities in order to be able to develop students' teamwork skills in conditions of distance education. The purpose of the article is to assess the development of teamwork skills among students of Moscow universities and to identify the main restrictions in the development of these skills in the conditions of distance learning. The following tasks were set: 1) to analyze the research of domestic and foreign scientists on the issues of teamwork skills development; 2) to conduct an initial testing of students in these skills; 3) to identify the main restrictions of teamwork in distance learning; 4) to analyze the data obtained and to formulate conclusions. The study involved first- and second-year students (Moscow State University of Food Production, n=246; Russian State Social University, n=251). The results showed that the university students who had taken part in the research did not have well developed teamwork skills. The differences between the students of these universities are not significant according to the method of Student's t-distribution. The method of expert assessment revealed the following restrictions of teamwork in the conditions of distance learning: the difficulty of establishing emotional contact between teamwork participants; increasing the labor intensity of the educational process in the conditions of distance learning; low level readiness of some teachers to use ICT (information and communication technologies); the differences in the social and economic environment and available facilities for students, which make it difficult for them to be involved in teamwork. The need for purposeful activities to develop teamwork skills in conditions of distance learning was proved. (Abstract)
Development of an Algorithm for Searching for a Mark, Determining its Center and Shift from the Initial Position for Software and Hardware for Processing Signals for Video Inspection of a Vision System

The study deals with the development and experimental testing of the algorithm for the software for collecting and processing the results of the movement of a control point (marker) from the initial position. The analysis of existing methods for searching for objects in the image is carried out. The main principles and approaches to the development of an image processing algorithm are presented, and the preferred solutions for its implementation are outlined.
Data Dimensionality Reduction Technique Based on Preservation of Hellinger Divergence

In this paper, we investigate the issue of applying the Hellinger divergence in dimensionality reduction of hyperspectral data. We use two approaches in constructing dimensionality reduction techniques. The first approach is based on approximating the Hellinger divergence by Euclidean distances in a lower-dimensional space, while the second approach is based on preserving the Hellinger divergence when mapping data into a space of reduced dimensionality. In our experiments, we study the efficiency of the proposed techniques using known hyperspectral scenes both in terms of data mapping error and the quality of subsequent classification.
Modeling and Problem Formulation

This chapter presents the formulation and models of the datacenter capacity provisioning problem. We will derive competitive online algorithms with proven competitive ratios to solve the datacenter capacity provisioning problem effectively in the subsequent chapters. First, we adopt the models of datacenters from the extant literature [1–8]. Our models can realistically capture several key features of datacenters, including workload, server energy consumption, cooling and power conditioning systems, and dependence of power grid. But these models are not meant to be completely extensive. Some features are dropped for the sake of brevity. Nonetheless, in the discussion section (Sect. 3.2.3), we will present further extensions of the datacenter model.
Adversarial Attacks on Radar Target Recognition Based on Deep Learning

Synthetic aperture radar (SAR) image classification is a challenging problem due to the complex imaging mechanism as well as the random speckle noise, which affects radar image interpretation. Recently, deep neural networks (DNNs) have been shown to outperform previous state-of-the-art techniques in computer vision tasks owing to their ability to learn relevant features from the data. However, the fragility of these models has received far less academic attention in the remote sensing community, which limits our understanding of the security of remote sensing image classification models. To explore the basic characteristic of adversarial examples of SAR images, we compare several mainstream adversarial methods and evaluate the securities of used DNNs from the perspective of attention. We subsequently perform other attempts. The experimental results provide data support and an effective reference for the defense capabilities of various DNNs regarding attack in SAR image classification models.
Mobile and safe e-cash system based on blockchain

My system is applied to mobile terminals for electronic payment. This system can circulate in multi bank. My electronic cash circulates in the blockchain, the bank is no longer the bottleneck of the e-payment. This thesis researches on the agreement of the payment agreement, the verification agreement, the search agreement .These agreement are encrypted by the elliptic curve cryptography (ECC).This system is uncomplicated, efficient, security, and the system suits the mobile electronic payment terminals of which power, memory, network bandwidth, operand capacity accommdate are extraordinary constrained. This system can prevent from repeating use, non-frameability, wiretap, misrepresenting and “perfect crime” too. It can secure against misidentification assault, secure against anonymity assault, secure against association assault. Anonymity of the e-transaction initiator and recipient can be ensured. The receiver cannot repudiates the e-transaction.
Semantic Inferences Towards Smart IoT-Based Systems Actuation Conflicts Management

IoT-based systems have long been limited to collecting field information via sensors distributed at the edge of their infrastructure. However, in many areas such as smart home, smart factory, etc. these systems include devices that interact with the physical environment via common actuators. Throughout the lifecycle of these systems, from design, to deployment to operation, the ability to avoid actuation conflicts, both in terms of the commands that actuators receive (direct conflicts) and the effects that they produce (indirect conflicts), is a new challenge in the realm of trustworthy Smart IoT-based Systems (SIS). As part of the European project ENACT, which aims to provide full DevOps support for trustworthy SIS, we present a lightweight ontology that provides SIS designers with (1) a semantic metamodel to formally describe SIS subsystems and the actuators they interact with, and (2) a set of SWRL (Semantic Web Rule Language) inference rules to automatically identify and semi-automatically resolve actuation conflicts. Consistent with the best practices of the DevOps approach, a particular emphasis is placed on facilitating the use and interpretation of inference results. To provide insight into the appropriateness of the proposed approach in the context of SIS, rule processing times for different actuation conflict configurations are provided.
The Model of an Explanation of Self and Self-awareness Based on Need Evolution

The nature of the self has always been an unsolved problem. This study combines a biological theory of the essential features of life with a review of views of the self in philosophy, brain science, mathematics and artificial intelligence. Combining the primary needs of living things with evolutionary theory, a new explanation of the fundamentals of life is provided. The primary needs of life are centred on energy and the transmission of information about oneself. These two intrinsic needs combine with the environment to form a new universal need and a necessary mechanism. And driven by the needs, the organism’s perception of its own needs and state gives rise to self-awareness. Based on this new theory, an iterative model of the evolution of needs and an evolutionary model of the self are proposed. These models are alternative interpretations of the self and self-awareness, and offer improved directions for machine simulation of artificial intelligence.
Subverting Deniability

Deniable public-key encryption (DPKE) is a cryptographic primitive that allows the sender of an encrypted message to later claim that they sent a different message. DPKE’s threat model assumes powerful adversaries who can coerce users to reveal plaintexts; it is thus reasonable to consider other advanced capabilities, such as being able to subvert algorithms in a so-called Algorithm Substitution Attack (ASA). ASAs have been considered against a number of primitives including digital signatures, symmetric encryption and pseudo-random generators. However, public-key encryption has presented a less fruitful target, as the sender’s only secrets are plaintexts and ASA techniques generally do not provide sufficient bandwidth to leak these. In this article, we give a formal model of ASAs against DPKE, and argue that subversion attacks against DPKE schemes present an attractive opportunity for an adversary. Our results strengthen the security model for DPKE and highlight the necessity of considering subversion in the design of practical schemes.
The Effectiveness Of Online Learning Activity Through Students’ Speaking Ability At Ibrahimy University

Online learning activity is one of the most effective lecture methods during the covid-19 pandemic. It can be done using a technology connecting internet by Android smartphone or a computer. Through this lecture is applying an online teaching learning process on the students’ speaking ability. The research objective is to determine the effect of online learning activity method on the university student on speaking at Ibrahimy University of Situbondo East Java. This research uses pre experimental research with one group pre-posttest design. The number of respondents was 07 persons who take an online learning activity. The data was taken using an application of kinemaster as pretest and posttest scores. The final data that can be conclude by comparing a mean score of the pretest and posttest. The average of pretest shows 49,3 and the average of posttest score shows 63,6.
A method of VAE-based video colorization

In this paper, we propose an automated method to colorize old black and white videos. The model consists of several trainable components, containing a coloring module for video frame coloring and a refinement module for spatiotemporal color refinement. Video coloring is a multimodal problem. We satisfy the need for diversity in the coloring results of video frames by using a video frame coloring network to generate diverse coloring results. Then we use the color refinement module to enhance the coloring effect of the initial coloring results. We also introduce the optical flow method to constrain the consistency of coloring between video frames. By predicting the creation of multiple different coloring results, the user has control over these coloring results. This is essential in practical engineering applications. Experimental results show that this method has better result performance than existing video coloring methods.
Introduction to Multi-Robot Coordination Algorithms

This passage mainly focuses on the path-planning algorithms and avoidance collision algorithms in the swarm robot field. The evolution of the path planning method of swarm robots is mentioned. It describes how the Firefly algorithm, Artificial bee colony algorithms, and Bacteria foraging optimization algorithms are derived, what theories support them to work, and the current application in a wide area. It divides collision avoidance algorithms into two parts, which are decentralized and centralized methods. Virtual Force Field, Reciprocal Velocity Obstacles (RVO), Virtual Force Field (VFF), and Optimal Reciprocal Collision Avoidance are decentralized collision avoidance solutions, and Multi-Agent Reinforcement Learning is a centralized collision avoidance algorithm. Muti-sensor-based collision avoidance is a method that uses the sensor to get the information to solve the problems. These methods are created to increase the precision and efficiency of the swarm-robot missions. For applications, these mentioned algorithms are already used in areas including air, land, and water working robot.
GCD-Filter: Private Set Intersection Without Encryption

A private set intersection (PSI) protocol is a protocol to get the intersection of two sets, each of which belongs to one party, without disclosing extra information of each party’s set to the other party. In this paper, we propose a novel semi-honest PSI protocol without using any encryption primitive in a semi-honest security model. Specifically, we first slice all elements in the set and inject dummy slices. Then we utilize the greatest common divisor (GCD) to find the common parts of the two parties’ slice set product, which is the product of all slices. Finally, we filter the elements by utilizing the GCD to find out the intersection. Different from most previous PSI protocols, we get the intersection by calculation rather than comparison. Our protocol has many advantages over other state-of-the-art PSI protocols, such as robust security against quantum attacks, low communication cost, high computation efficiency when the bandwidth is low, etc. Through extensive experiments, we find the optimum parameters in our setting and demonstrate the performance of our protocol. Different from previous PSI protocols, the communication cost of our protocol varies with the cardinality of the intersection. In comparison, the communication cost of our protocol is the lowest, which gains an over $$200\%$$ improvement than the communication-optimized PSI protocol spot-low (Pinkas et al., CRYPTO’19). In addition, the running time of our protocol is the lowest when the communication bandwidth is about 1 Mbps.
Time Series and Stationary Processes

This chapter introduces basic concepts such as time series, stationary process and covariance function. Subsequently, the time domain of a stationary process, which is a subspace of the Hilbert space of square integrable random variables, is presented. This Hilbert space approach allows for nice geometric interpretations and offers useful tools e.g. for prediction. The penultimate section describes important classes of stationary processes: white noise, moving average processes, autoregressive processes and harmonic processes. The final section discusses examples of non-stationary processes.
Channel-Spatial Mutual Attention Network for 360° Salient Object Detection

In this work, we conduct 360° panoramic salient object detection by taking advantage of both the global and local visual cues of 360° images, with a novel channel-spatial mutual attention network (CSMA-Net). The key component of the CSMA-Net is the proposed CSMA module, which cascades channel-/spatial-weighting-based mutual attentions. The objective of our CSMA module is to refine and fuse the bottleneck features from two separate encoders with different planar representations of 360° panorama as inputs, i.e., equirectangular image and cube map. Our CSMA-Net outperforms 10 state-of-the-art segmentation methods based on the proposed 360° SOD benchmark where multiple fine-tuning and testing strategies are applied to the widely-used 360° datasets. Extensive experimental results illustrate the effectiveness and robustness of the proposed CSMA-Net <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Method for Accurate 3-D Gaze Estimation With a Single Camera and Two Collinear Light Sources

The simplest configuration for estimating 3D line-of-sight based on corneal reflection and pupil refraction uses a single camera and two non-collinear light sources. The corneal radius and 3D corneal center can be determined by solving a system of six equations with six unknowns. However, when two light sources are collinear with the camera, the reflection planes of the two light sources are coplanar. In this case, the estimation of 3D corneal center is under-determined in the absence of any pre-known eyeball parameters (e.g., corneal radius). To solve this issue, this paper proposes to calibrate the corneal radius with corneal reflections from two light sources combined with an iris imaging model, so that the 3D corneal center is estimated with a known corneal radius. Then, the pupil features or iris features can be selected to estimate the 3D pupil center or 3D iris center, which uses an optimization solution for only the center parameters to avoid unnecessary errors. Thus, the optical axis of the eyeball is constructed with two spatial centers, and the kappa angle calculated in real time is used to convert the optical axis of the eyeball to the 3D line-of-sight. We simulated this gaze estimation process using pupil and iris features, and performed error analysis on the proposed method. In practical experiments, the proposed method was verified using iris features, and the gaze accuracy was 1.22° and 1.13° in the x- and y-directions, respectively, which is comparable to that of more sophisticated systems. A single-camera-two-collinear-light-source system is the most typical and common system configuration, and the realization of 3D gaze estimation under such a system configuration can effectively promote the wide application of eye-tracking interaction.
Fault diagnosis of High Voltage Circuit Breaker using Random Forest and PSo-KELM

High voltage circuit breaker (HVCB) is an essential and important device of power grid, while any faults occurred in operation mechanism of HVCB will greatly jeopardize reliability and safety of power system. In order to improve fault diagnosis performance, a new fault diagnosis method using random forest (RF) and kernel extreme learning machine (KELM) is presented in this paper. At first, RF is applied to select the critical features derived from coil current. Then, the selected features are applied as inputs of KEML to establish fault diagnosis model. Next, particle swarm optimization (PSO) is introduced to turn crucial parameters of KELM to enhance diagnosis accuracy. Finally, practical samples are used to assess fault diagnosis performance of the presented method. Experimental results indicates the proposed RF-KELM method is capable of providing higher diagnosis accuracy than conventional approaches, which indicates promising future.
Transformation of the right to higher education in the conditions of digitalization of the educational environment: Regional experience

Global challenges are intensifying the emergence of qualitatively new forms of delivery and content of the educational process. The modern educational space is impossible without international cooperation and the introduction of the latest digital learning methods. There is an urgent need for a systematic analysis of the achievements and disadvantages of digitalization of education in the framework of the implementation of the right to higher education. The article presents a study of the educational process at Kuban State Technological University, on the basis of which the authors note that in the conditions of a distance format, there is a need for additional social and legal guarantees for obtaining higher education through access to external information resources, further methodological improvement of the educational process is required. Qualitatively new, “digital” approaches to the system of higher education, the formation of a “digital” educational environment of the university should be aimed at the maximum implementation of the principle of accessibility of higher education.
Density Peaks Clustering Based on Natural Search Neighbors and Manifold Distance Metric

The famous density-based clustering approach Density Peaks Clustering (DPC) is getting more and more popular recently. However, DPC algorithm is vulnerable to the parameter <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d<sub>c</sub></i> and is incapable of obtaining desired clustering results when dealing with manifold data sets. Furthermore, the allocation strategy of the remaining data points can easily lead to domino chain reaction which means that when a data point is allocated improperly, the points around it will be allocated incorrectly too. To solve these deficiencies, in this paper, we put forward an algorithm named density peaks clustering based on natural search neighbors and manifold distance metric (DPC-NSN-MDM). In the beginning, we apply natural search method (NSM) to identify the natural neighbors and further calculate the local density ρ of each data point. Secondly, when calculating distance δ of each data point, we put the manifold distance metric with a scaling factor to replace the traditional Euclidean distance metric. At last, manifold distance is also used in the allocation strategy of remaining data points to reduce the effect of the domino chain reaction. The proposed DPC-NSN-MDM algorithm succeeds in getting superb experimental performance on both synthetic data sets and real-world data sets.