Reviewerly/exHarmony huggingface dataset:
EVOLUTION ALGORITHM FOR PARTITION BY METHOD OF CRYSTALLIZATION OF ALTERNATIVES FIELD

The operation of the partitioning algorithm is based on the use of collective evolutionarymemory, which means information that reflects the history of the search for a solution and isstored independently of individuals. The algorithm associated with evolutionary memory seeks tomemorize and reuse ways to achieve better results. The collective evolutionary memory of thepartitioning algorithm is a set of statistical indicators that reflect, for each implemented alternative,the number θ of its occurrences in the best solutions at previous iterations of the algorithmand the number δ indicating the usefulness of the implemented alternative when constructing solutionsat previous iterations of the algorithm. The team does not have centralized management, andits features are the presence of indirect exchange of information. Indirect exchange consists inperforming certain actions, at different times, during which some parts of evolutionary memorychange by one agent. In the future, this changed information is used by other agents in these parts.First, at each iteration, a constructive algorithm generates nk solutions Qk. Each solution Qk is amapping Fk=V→X, is represented as a bipartite subgraph Dk and is formed by sequentially assigningelements to nodes. The formation of each solution Qk is performed by the set of agents A,by means of the probabilistic choice by each agent ai of the node vj. The process of assigning anelement to a node involves two stages. In the first stage, agent ai is selected, and in the secondstage, the node. In this case, the restriction must be fulfilled: each agent of the set A correspondsto one unique node of the set V. The estimate ξk of the solution Qk and the utility estimate δk of theset of alternatives implemented by the agents in the solution Qk are calculated. At the second stage,the agents increase the integral utility of the set of alternatives in the integral placer of alternativesR* by the value δk. At the third stage, the utility estimates δk of the integral placer of alternatives arereduced by μ. The paper uses the cyclic method of forming decisions. In this case, the building up ofestimates of the integral utility δk of the set of positions P is performed after the complete formationof the set of solutions Q at iteration l. Experimental studies were carried out on the basis of formedtest cases with the optimal solution obtained earlier. The results obtained were compared with theresults obtained by other well-known algorithms for dividing circuits into parts. For comparison, aset of standard benchmarks was formed. After analyzing the results, we can conclude that the proposedmethod allows you to get 4–5 % better solutions than its analogues.
Discriminative Context-Aware Correlation Filter Network for Visual Tracking

In recent years, discriminative correlation filter (DCF) based trackers using convolutional features have received great attention due to their accuracy in online object tracking. However, the convolutional features of these DCF trackers are mostly extracted from convolutional networks trained for other vision tasks like object detection, which may limit the tracking performance. Moreover, under the challenge of fast motion and motion blur, the tracking performance usually decreases due to the lack of context information. In this paper, we present an end-to-end trainable discriminative context-aware correlation filter network, namely DCACFNet, which integrates context-aware correlation filter (CACF) into the fully-convolutional Siamese network. Firstly, the CACF is modeled as a differentiable layer in the DCACFNet architecture, which can back-propagate the localization error to the convolutional layers. Then, a novel channel attention module is embedded into the DCACFNet architecture to improve the target adaption of the whole network. Finally, this paper proposes a novel high-confidence update strategy to avoid the model corruption under the challenging of occlusion and out-of-view. Extensive experimental evaluations on two tracking benchmarks, OTB-2013 and OTB-2015, demonstrate that the proposed DCACFNet achieves the competitive tracking performance.
Orbit Structure of Grassmannian <i>G</i> <sub>2,<i>m</i> </sub> and a Decoder for Grassmann Code <i>C</i> <sub>(2, <i>m</i>)</sub>

In this article, we consider decoding Grassmann codes, linear codes associated to the Grassmannian and its embedding in a projective space. We look at the orbit structure of Grassmannian arising from the multiplicative group Fqm* in GLm(q) . We project the corresponding Grassmann code onto these orbits to obtain a subcode of a q -ary Reed-Solomon code. We prove that some of these projections contain an information set of the parent Grassmann code. By improving the decoding capacity of Peterson's decoding algorithm for the projected subcodes, we prove that one can correct up to ⌊(d-1)/2⌋ errors for Grassmann code, where d is the minimum distance of Grassmann code.
Flexible Eye Design for Japanese Animation

Eyes are crucial in depicting the emotions and psychology of a character. In this talk, we present the eye system we developed at OLM Digital to design and control eyes for Japanese animation where they typically come in a lot of different shapes and colors. Our tool is straightforward and provides a lot of control to the users. Our artists can create elongated irises and pupils, add the cornea bump and refraction and add lighting effects like caustics and catchlights. The render pass exports the necessary information to provide the ability to adjust the final appearance at compositing time, giving a lot of creative freedom to the artists.
Breast Cancer Recognition Based on BP Neural Network Optimized by Improved Genetic Algorithm

In recent years, the incidence of breast cancer is increasing and becomes one of the main causes of female death. The BP neural network optimized by standard genetic algorithm has slow convergence speed and is prone to local optimization, which makes the diagnosis accuracy of breast cancer decrease. This paper uses the improved genetic algorithm to optimize BP neural network by improving the selection operator of the standard genetic algorithm. The population diversity was first increased, and the probability of crossover and mutation was adaptively adjusted. Then deep optimization was executed on the initial weight threshold of BP network to speed up the network's convergence, and the number of iterations was reduced. Finally breast cancer diagnose was performed. The experiment results show that both the fitness of the improved genetic algorithm and the recognition accuracy of breast cancer are improved. The shortcomings of the standard genetic algorithm optimized BP neural network algorithm in breast cancer diagnosis are well solved.
Trusted Virtual Machine Model Based on High-Performance Cipher Coprocessor

In the cloud computing environment, with the complex network environment, the virtualization platform faces many security problems. At the same time, trusted computing can greatly enhance the architecture security of virtualization platform systems, but there are many problems when trusted computing is deployed directly in the cloud environment. Therefore, this paper proposes a trusted virtual machine model based on high-performance cipher coprocessor to solve the security problems such as the isolation and insufficient performance of virtual TPM (vTPM) on the existing virtual platform. In this model, virtio technology was used to realize the virtualization of TPM, and a management architecture was designed to manage the life cycle of vTPM. The analysis shows that the model can complete the isolation of vTPM, and protect the security of vTPM during the migration process through the migration control server, and can strengthen the security of the virtualization platform. Finally, the simulation results show that the model is more feasible and suitable for cloud platform than hardware TPM.
Securing Blockchain with Quantum Safe Cryptography: When and How?

Blockchain and quantum computing are trending topics in nowadays scientific communication, and they increasingly attract the attention of academia, but also industry stakeholders and policymakers. In this communication, we address the conundrum related to the quantum menace and the deployment of blockchain solutions. As any cryptographic product, blockchain security is affected by the advent of universal quantum computers. Besides the more or less certainty about its immediacy, we underline that blockchain is not substantially different from other secure and resilient platforms in such regard. On this ground, we discuss the main points of the roadmap that the research community in cryptography is currently taking to tackle the quantum challenge, and we highlight how these points should be properly integrated in the design and implementation of the life-cycle of blockchain products.
Learning algorithm with noisy labels for video frame classification

Abstract In this paper, we propose Recycling+, an algorithm that uses temporal information for video frame classification with noisy label. With the proposed algorithm, the network can be trained robustly through not only clean samples with original labels but also noisy samples with corrected labels obtained by temporal average predictions.
Reliable Distributed Clustering with Redundant Data Assignment

In this paper, we present distributed generalized clustering algorithms that can handle large scale data across multiple machines in spite of straggling or unreliable machines. We propose a novel data assignment scheme that enables us to obtain global information about the entire data even when some machines fail to respond with the results of the assigned local computations. The assignment scheme leads to distributed algorithms with good approximation guarantees for a variety of clustering and dimensionality reduction problems.
@PAD

In this work, we study the vulnerabilities of protection systems that can detect cyber-attacks in power grid systems. We show that machine learning-based discriminators are not resilient against Denial-of-Service (DoS) attacks. In particular, we demonstrate that an adversarial actor can launch DoS attacks on specific sensors, render their measurements useless and cause the attack detector to classify a more sophisticated cyber-attack as a normal event. As a result of this, the system operator may fail to take action against attack-related faults leading to a decrease in the operation performance. To realize a DoS attack, we present an optimization problem to determine which sensors to attack within a given budget such that the existing classifier can be deceived. For linear classifiers, this optimization problem can be formulated as a mixed-integer linear programming problem. In this paper, we extend this optimization problem to find attacks for more complex classifiers such as neural networks. We demonstrate that a neural network, in particular, with RELU activation functions, can be represented as a set of logic formulas using Disjunctive Normal Form, and the optimization problem can be used to efficiently compute a DoS attack. In addition, we propose a defense model that improves the resilience of neural networks against DoS through adversarial training. Finally, we evaluate the efficiency of the approach using a dataset for classification in power systems.
Recent News Recommender Using Browser’s History

Identifying users’ interests to use in a recommender system is a difficult process because it needs a huge amount of data to analyze. On the other hand, each user has many historical data stored by the browser in his computer such as the visited pages including its title, visit date, URLs…etc. However, these data are not manipulated or analyzed properly to be used in recommender systems. In this research, we are proposing to use natural language processing techniques on the browser’s historical data, which collected from user’s actions on the browser to improve the recommendation systems in a way that helps to recommend and select news from recent news site based on the user’s interests and history. We experimented with our technique by developing an application that extracts and analyzes the historical data from the browser’s history. In addition, use the previously analyzed data in a recommender system. The experiments showed good results as discussed in the implementation part of this paper. The use of the browser’s history in recommender systems will enhance the recommendation process. This data can be used in the recommendation of news and other domains such as the advertisements, research topics, etc.
Design Optimization of Two Stage Operational Amplifiers using Chaotic Antlion Optimization

Formulation of assorted category circuits will be the major requirement of competent circuit parameters along with involuntary method. Analog circuits require appropriate handling and utilization of strategies since it involves up surging difficulties along with chaos existing in formulation of integrated circuits design, a suitable strategy for determining the best solution with minimal or nil faults is necessary. Processing difficulty involved in Integrated circuits due to characteristic issues involved in formulation and need of managing many contradictory and interrelationships are in concern. In this work a novel chaotic antlion optimization strategy is established in identification of dimensions of equipment with respect to integrated circuits that might be analog in nature that adopts the strategy of determining the best solutions. Our new strategy called novel chaotic Antlion optimization suggests designing two stage Operational Amplifier which is assured to yield better results than multi-objective antlion optimization since it can handle chaos behavior and randomness.
Estimated Influence of Online Management Tools on Team Management Based on the Research with the Use of the System of Organizational Terms

The paper aims at presenting results of the research which allows estimating a level of this influence and which concerns team management. The research problem in the paper concerns a possibility of replacing human managers with robots in the field of team management. If the managerial actions should be recorded by the management tools in order to launch machine learning of robots, there comes a research question: to what extent does a managerial tool influence the team management launched by a manager? The research problem entails a hypothesis: (H1) the use of the online management tools creates much less primal organizational terms than all actions taken in human-computer interaction and (H2) the use of the online management tools changes an order of managerial actions in team management. The paper contains theoretical foundations of human-computer interaction research (Sect. 2), theoretical foundations of the system of organizational terms (Sect. 3), research results (Sect. 4) and conclusions (Sect. 5).
Theoretical basis of the research of the problem of forming the value attitude in specialized institutions

It is important to determine what opportunities exist in the processes of acquisition, preservation and realization of value constellations, relative to another person in the boarding school environment compared to other educational institutions regarding the use of the most important existential of practical life. Teachers pay attention to a number of factors that prevent the effective education of value attitude towards the person. There are crime, anger, sense of permissibility, low level of personal communication, irresponsible, life pessimism and etc. among them. Analysis of theoretical works and synthesis of scientific provisions suggest that value attitude towards another person is an integrated neoplasm, which is determined by humanistic ethics and morality and is based on moral values such as respect, dignity, equality, benevolence, balance, self-sufficiency, readiness for constructive dialogue. Therefore, the formation of the value of another person is a specially organized educational process, which defines the attitude towards another person, as the highest value of the subject of life, aimed at combining knowledge about such moral values as respect for dignity, equality, benevolence, balance, self-sufficiency, readiness for constructive dialogue, ideas about the ways of their manifestation and ability to guide them in their own activities. Thus, philosophical, psychological and pedagogical studies show that the value attitude towards another person is an important characteristic of the moral sphere of the individual and is most confirmed by humanistic ethics and morality and is the basis of domestic and international legislation, which needs the close attention and development of optimal pedagogical conditions for the formation of a value attitude towards another person in pupils of boarding schools, as the most impressionable category of children with a view to their socialization and full integration into society.
Maximum Likelihood and Least Squares Methods, Algorithms and Experiments for “Sensing Waves and Seeing Images”

Our eyes see light waves and our ears hear sound waves, while what we recognize is images. With lens focusing and frequency analysis, waves sensed by eyes and ears are reflected as images in human brains. From waves to images is a domain transformation. The extension of vision and hearing is the theory and method of image formation.Communication and observation are the two reflections of human intelligence.We all known that communication is guided by information theory, while image formation is attributed to observation, and whether information theory can guide observation is worth studying. In this paper, the model of information-theoretic image formation is given, referring to the information-theoretic communication model. Likelihood information and entropy information are analyzed emphatically, and then the Maximum Likelihood (ML) and Least Squares (LS) methods are derived. The iterative implementation algorithms of ML and LS named Expectation Maximization (EM) and Recursive Least Squares (RLS) are derived, respectively. Finally the applications of ML/EM and LS/RLS to the passive image formation of three-dimensional double helix array are developed.
Variable Transformation to a $$2\times 2$$ Domain Space for Edge Matching Puzzles

The Eternity II (E2) challenge is a well-known instance of the set of Edge Matching Puzzles (EMP), which are examples of combinatorial problem spaces of the worst-case complexity. Transformation of the domain space to consider pieces at the $$2\times 2$$ level increases the total number of elements but is shown to result in orders of magnitude smaller search spaces. While the original domain space has uniform cardinality, the transformed space exhibits statistically exploitable features. Two heuristics are proposed and compared to both the original search space and the raw transformed search space. The efficacy of the two heuristics is empirically demonstrated. An explanation of how the mapping results in an overall decrease in the number of nodes in the solution search space of the transformed problem is outlined.
Heterogeneous Sensor Fusion With Out Of Sync Data

This paper tackles sensor fusion when data from different sensors are out of sync - observations may be sampled asynchronously and there may be no timing information at the fusion center for observations from some sensors. This often happens when sensors are heterogeneous in nature hence may employ different and even time varying sampling cycles. Furthermore, observations may need to go through wireless channels when tagging each observation with a timestamp may be too expensive. Treating the timing information in out of sync data as a hidden variable, this paper employs the expectation-maximization (EM) algorithm to recover the posterior distribution of the timing information. Data fusion utilizing the out of sync data can subsequently be carried out. Using a simple two-sensor off-line state estimation experiment where one sensor's data is available but is out of sync at the other sensor, we demonstrate that the performance of the EM based approach improves on the fusion performance when the out of sync data is discarded. Extensions to high dimensional observations to systems involving multiple out of sync data streams, and to online state tracking with out of sync data are discussed.
A Family of Multipartite Entanglement Measures

We construct a family of additive entanglement measures for pure multipartite states. The family is parametrised by a simplex and interpolates between the R\'enyi entropies of the one-particle reduced states and the recently-found universal spectral points (Christandl, Vrana, and Zuiddam, STOC 2018) that serve as monotones for tensor degeneration.
Recommendation Algorithm Based on Improved Convolutional Neural Network and Matrix Factorization

The traditional collaborative filtering algorithm uses the user rating information as a recommendation basis, but the ratings matrices are usually sparse and cannot reflect users' preference exactly, so the recommendation results are not very accurate. Therefore, this paper proposes an improved convolutional neural network for collaborative filtering (CNNCF), using the deep learning model to deeply mine the hidden feature information. then implicit the semantic model, Then the extracted explicit feature information was replaced by the implicit feature information in the LFM to further improve the prediction accuracy, and finally personalized recommendation through the user-item preference matrix. Experimental results on the MovieLens dataset show that the model can overcome data sparse, and recommendation accuracy is better than the traditional collaborative filtering model.
Empirical Study of College Teachers’ Attitude Toward Introducing Mobile Learning to Classroom Teaching

With the rapid development of information technology and high broad reach of internet, mobile learning is becoming increasingly popular. In this paper, a mobile-learning method is proposed to explore college teachers' attitude toward introducing mobile learning to classroom teaching. An experiment was implemented to investigate teachers' attitude and classroom teaching activities. The results show that they hold an affirmative attitude toward mobile learning, and different mobile learning platforms were applied into almost every step of their classroom teaching. Compared to traditional teaching method, mobile learning has prominent advantages, such as instant and interactive way of teaching, fast updating knowledge, simple and convenient assessment function, various teaching approaches and improvements on students' learner autonomy. When introducing mobile learning to classroom teaching, teachers' main concern falls on that students use mobile devices to do other things in the name of study and can't balance their time spent on mobile learning. Finally, a suggestion is given that it is better for teachers to combine mobile learning with traditional teaching methods to achieve the best teaching effect.
Critical path planning

This chapter describes the critical path methods. Two fundamentally different critical path methods emerged from the earliest days, with one system using the arrows to represent project tasks or activities. Every circle in the diagram represents some recognizable event in the project, and every linking arrow represents the task or activity needed for the project to progress from a preceding event to the succeeding event. Activity-on-node networks are known as precedence diagrams. These networks are far more readily understood by engineers and technicians than their arrow diagram equivalents because they resemble the process flowcharts used in many projects. The precedence diagram for the garden workshop project really needs very little explanation. The key given on the diagram explains the significance of all the data entered in each activity box. In network diagrams containing a few activities it should be easy to avoid errors. The most risk of errors occurs when the network data are entered into a computer for processing.