Reviewerly/exHarmony huggingface dataset:
Recognizing Human Falls and Routine Activities Using Accelerometers

Detecting falls and other mishaps using data from sensors worn by individuals is an important task with applications in healthcare. A related task is using such sensor data to detect routine activities of daily living. This paper models such detection of falls and routine activities as a classification problem. Using a publicly available dataset of real accelerometer traces generated by participants performing intentional falls and other activities, the efficacy and performance of several classifiers are studied experimentally.
R-LDA: Profiling RDF Datasets Using Knowledge-Based Topic Modeling

Recently, Linked Open Data (LOD) has experienced an exponential growth via publishing huge volume of datasets on the Web. This vast amount of information needs to be searched, queried, and interlinked easier than before. It is recommended that potential data publishers provide recapitulative information about their datasets published on the Web. This information, which functions as metadata, will facilitate those datasets to be discovered easily. As it is not always the case, we are faced with a large number of datasets without a proper profile, leading to a high demand for different data profiling techniques. In this paper, we focus on RDF dataset profiling utilizing unsupervised machine learning techniques, namely knowledge based topic modeling. We also investigate the use of Wikipedia categories to represent the topics identified in an RDF dataset. In the proposed model, we extract a number of representative topics for an RDF dataset and annotate them with Wikipedia categories. The union of the assigned categories serves as a profile of the dataset, in a sense that it provides an overall characterization of the content of the dataset.
Computer Aided Measurement of Smart City Multi-source Information Synergy Structure Dependent on Adaptive Artificial Immune Network Algorithm

In view of the low efficiency in the measurement of multi-source information synergy structure for the smart city and other problems, a kind of computer aided measurement of smart city multi-source information synergy structure (hereinafter referred to as CAMMISS for short) that is dependent on the adaptive artificial immune network algorithm (hereinafter referred to as AAINA for short) is put forward. Firstly, the number of the input and output nodes in the multi-source information synergy structure (hereinafter referred to as MSISS for short) is determined in accordance with the number of the situation input index and the value of the output situation. The connection weight value and the threshold value of each layer are randomly initialized, and the floating number encoding method is used to encode the weight value and the threshold value into the adaptive artificial immune network algorithm. The experimental results show that compared with the methods of the MSISS and the genetic algorithm for the optimization of the multi-source information, the method put forward in this paper has relatively fast convergence speed and relatively high prediction accuracy.
Discovering All-Chain Set in Streaming Time Series

Time series chains discovery is an increasingly popular research area in time series mining. Previous studies on this topic process fixed-length time series. In this work, we focus on the issue of all-chain set mining over the streaming time series, where the all-chain set is a very important kind of the time series chains. We propose a novel all-chain set mining algorithm about streaming time series (ASMSTS) to solve this problem. The main idea behind the ASMSTS is to obtain the mining results at current time-tick based on the ones at the last one. This makes the method more efficiency in time and space than the Naïve. Our experiments illustrate that ASMSTS does indeed detect the all-chain set correctly and can offer dramatic improvements in speed and space cost over the Naive method.
STUDENTS OF NET GENERATION: LATERAL PROFILES AND DIGITAL SKILLS

The aim of the research is to study the lateral organization of the brain of modern students, which we position as the Generation Y+ and determine their level of knowledge of modern computer technologies. The authors examined the following research questions: whether the Generation Y+ is different from previous generations in terms of neuropsychology; as far as Generation Y+ has developed skills in using the capabilities of the digital environment. To study the profile of the lateral organization of the brain, we used the method of E. D. Khomskaya to determine the leading arm and the leading eye (motor and sensory systems). This technique allows a person to be attributed to one of the 16 profiles of the lateral organization of the brain. We assumed that the new “digital generation” would have a more pronounced hemispheric lateral organization of the brain than in people who participated in the studies of the 1960s–1980s. The object of the study were first-year undergraduate students of two leading economic universities in Moscow and Prague, studying in different fields (psychology, information technologies, management). The study demonstrates the emerging generational changes in the lateral profile and computer skills of the younger generation, which for these reasons can be attributed to the Generation Y+. The main conclusion can be considered that the computer literacy of the younger generation is a confirmed fact, but the final transition to “digital reality” has not yet taken place, and the possible qualitative changes of the next generation require scientific confirmation based on comprehensive studies.
THE USE OF MASSIVE OPEN ONLINE COURSES IN TEACHING PHYSICS TO BACHELORS OF GENERAL ENGINEERING

Problems and tasks. The article analyzes the results and suggests the forms of application of e-learning in the course of physics. The results of using mass open online courses (MOOC) in the study of physics at a number of engineering areas of the Siberian Federal University are presented, limited possibilities of using the available MOOC are noted. Positive opportunities to use online courses arise in the organization of independent work of students and the implementation of a differentiated approach to learning. The purpose of the article is to identify and justify the possibility of using MOOC in physics courses of engineering program tracks. The methodology of our research is based on a deductive generalization of research work by other authors and professional- experience of the authors of this article in the use of MOOC for students’ independent work and the implementation of a differentiated approach in teaching students. Results. The article deals with methodological approaches to the use of MOOC in higher education, their value for the organization of modern educational process. The authors make a conclusion that there is a need for a differentiated approach in the use of MOOC for students with different levels of training. The paper highlights the problems in teaching physics to be solved and the tools to solve them. &nbsp;Conclusion. The use of massive open online courses in combination with classroom work allows you to organize independent work of students, to teach physics of an increased level of difficulty, to broaden the possibilities of differentiated training. The authors recommend the colleagues to use Coursera provider courses “Physics in experiments” and “Application of derivative and integral in general physics course”. Differences in the content of physics courses make it difficult to use other good massive online courses in general engineering education.
A Faster R-CNN Approach for Partially Occluded Robot Object Recognition

Many objects in household and industrial environments are commonly found partially occluded. In this paper, we address the problem of recognizing objects for use in partially occluded object recognition. To enable the use of more expensive features and classifiers, a region proposal network (RPN) which shares full-image convolutional feature with detector network is needed. We build our approach based on the recent state-of-the-art Faster R-CNN to increase the recognition capability of partially occluded object. We evaluate our approach on the real-time object recognition and robot grasping. The results demonstrate the effectiveness of our proposed method.
Introspection of Virtual Machine Memory Resource in the Virtualized Systems

Nowadays, data center and cloud servers have commonly adopted virtualization technologies to consolidate multiple servers into a physical one. It is because cloud service providers can achieve low server maintenance cost by improving resource utilization and reducing power consumption through server consolidation with virtualization technologies. However, unlike the physical system resources such as CPU and storage which can be flexibly utilized and shared by using time-based schedulers, memory resource is not easy for flexible utilization and sharing in that the memory size of each virtual machine is fixed by initial configuration. For this reason, sufficient understanding on memory resource usage of each virtual machine is essential in analyzing the existing memory management techniques such as memory ballooning and virtual machine migration. In this paper, we introduce a novel virtual machine memory monitoring tool, called SELF-e, which is developed for tracing the page accesses of each virtual machine in real-time and collecting necessary information on shared pages. Experimental results show that SELF-e efficiently announces the information on classified pages without significant performance degradation.
Subassembly Retrieval of 3D CAD Assembly Models with Different Layout of Components Based on Sinogram

We propose a method to find assembly models contained in another assembly model given as a query from a set of 3D CAD assembly models. A 3D CAD assembly model consists of multiple components and is constructed using a 3D CAD software. The proposed method distinguishes assembly models which consist of a subset of components constituting the query model and also whose components have the same layout as the subset of the components. We compute difference between the shapes and the layouts of the components from the sinograms which are constructed by the Radon transform of their projections from various angles. We evaluate the proposed method experimentally using the assembly models which we prepare as a benchmark. The proposed method can also be used to find the database models which contains a query model.
MESH: A Flexible Distributed Hypergraph Processing System

With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, however, social interaction takes place not only between pairs of individuals as in the graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through a more general hypergraph model, resulting in the need to build scalable hypergraph processing systems. In this paper, we present MESH, a flexible distributed framework for scalable hypergraph processing. MESH provides an easy-to-use and expressive application programming interface that naturally extends the think like a vertex model common to many popular graph processing systems. Our framework provides a flexible implementation based on an underlying graph processing system, and enables different design choices for the key implementation issues of partitioning a hypergraph representation. We implement MESH on top of the popular GraphX graph processing framework in Apache Spark. Using a variety of real datasets and experiments conducted on a local 8-node cluster as well as a 65-node Amazon AWS testbed, we demonstrate that MESH provides flexibility based on data and application characteristics, as well as scalability with cluster size. We further show that it is competitive in performance to HyperX, another hypergraph processing system based on Spark, while providing a much simpler implementation (requiring about 5X fewer lines of code), thus showing that simplicity and flexibility need not come at the cost of performance.
Automatic Speech Recognition Dataset Augmentation with Pre-Trained Model and Script

In this paper, we present a method of enhancing automatic speech recognition dataset with an immature pre-trained model and script. Comparing the chunks obtained from the pre-trained model with the ground truth script, we produce the pair of an audio and its script. In each pair, the audio has exact beginning and end of an utterance, and the script is clear since we use the human-written script. In the experiments on news videos and scripts, it is shown that our method extract automatic speech recognition dataset in exact and effective manner. In addition, the new dataset can be used to train speech synthesizing model.
A Demonstration of Core-Model Independence in Evolutionary Model Consistency Checking

Evolutionary Model Consistency Checking (EMCC) is a previously introduced adjunct to Evolvable and Adaptive Hardware (EAH) methods. In EMCC, the objective function evaluations already being conducted to inform an evolutionary search for a system repair are dual-purposed to also enable model-based inference of the very fault that necessitated the repair. In previous work, we demonstrated the efficacy of the approach inserting two EAH oscillators inside of a simulated Flapping-Wing Micro Air Vehicle (FW-MAV). Each oscillator would, while the system was in service, new oscillatory wing gait patterns that would enable the existing high-level controller to properly control the vehicle after one or both wings had become damaged. We additionally used the wing gait evaluations that were doing anyway to infer very accurate estimates of the damage produced lift and drag force deficits that necessitated the repair. In those earlier works, we had claimed, without detailed justification, that the ability to make accurate estimates was independent of the specific nature of the core models so long as the core models related a history of system commands to an outcome. For the FW-MAV, this would mean that EMCC should work for any wing force model that related a wing gait pattern to a lift and drag force. In this paper, we will more carefully consider that important claim and demonstrate that EMCC still functions when we swap out the previously used simplistic wing force model for a more capable and laboratory verified wing force model.
Limiting the Influence to Vulnerable Users in Social Networks: A Ratio Perspective

Influence maximization is a key problem in social networks, seeking to find users who will diffuse information to influence a large number of users. A drawback of the standard influence maximization is that it is unethical to influence users many of whom would be harmed, due to their demographics, health conditions, or socioeconomic characteristics (e.g., predominantly overweight people influenced to buy junk food). Motivated by this drawback and by the fact that some of these vulnerable users will be influenced inadvertently, we introduce the problem of finding a set of users (seeds) that limits the influence to vulnerable users while maximizing the influence to the non-vulnerable users. We define a measure that captures the quality of a set of seeds, as an additively smoothed ratio between the expected number of influenced non-vulnerable users and the expected number of influenced vulnerable users. Then, we develop greedy heuristics and an approximation algorithm called ISS for our problem, which aim to find a set of seeds that maximizes the measure. We evaluate our methods on synthetic and real-world datasets and demonstrate that ISS substantially outperforms a heuristic competitor in terms of both effectiveness and efficiency while being more effective and/or efficient than the greedy heuristics.
Performance Analysis of Data Encryption Algorithms for Secure EHR Transmission

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Multi-user distributed information-control systems as an element of modern digital educational environment

Introduction: One of the key deterrents to the quality training of engineers for digital economy is the lack of access to modernlaboratory equipment in Russian regional universities. An effective approach to solving this problem is organizing remote access toexperimental equipment based on the concept of multi-tenancy. Purpose: Developing a method to organize a high-tech digital educationalenvironment, providing laboratory research in a remote multi-user mode for the training of engineers with contextual learningtechnology. Methods: Creating a unified structure for a digital educational environment segment, based on multi-user distributedmeasuring-control systems with a unified structure and special methods of adapting such systems to various conditions of their operation.Results: A unified structure for a digital educational environment segment has been developed and tested. Its main elements area web laboratory providing regulated access of trainees to the laboratory studies, and multi-user distributed measuring-control systemsimplementing these studies by providing the trainees with parallel access to their shared resources: automated laboratory layouts/stands/installations. The created software complex for the collection and processing of statistical information about the functioning ofmulti-user distributed measuring-control systems as mass service systems underlies the proposed method of their adaptation to variousoperating conditions. The proposed segment of digital educational environment allows you to implement a number of contextual trainingdirections, including: application in experimental research of modern computer measuring technologies; studying the characteristicsof a digital twin of a laboratory layout/installation using standard techniques; identification and verification of mathematical modelsof objects, devices, processes and phenomena under study, or samples of multi-user systems in the form of appropriate mass servicesystems; approbation of various shared resource dispatch algorithms. Practical relevance: The proposed structure of an educationalenvironment segment provides training of engineers using innovative hardware and software solutions; operation of experimentalequipment on the basis of multi-tenancy concept, reducing the cost of equipping a trainee workplace in dozens of times; system-levelapplication of contextual learning technology.
A Practical and Insider Secure Signcryption with Non-interactive Non-repudiation

Signcryption with non-interactive non-repudiation is a public key primitive which aims at combining the functionalities of encryption and signature schemes, while offering to a judge the ability to settle a repudiation dispute without engaging in a costly multi-roundprotocol. We propose a new RSA based identification scheme together with a strongly unforgeable signature scheme. We derive a practical and efficient signcryption scheme with non-interactive non-repudiation we show to be insider secure, under the RSA assumption and the Random Oracle model. The communication overhead of our signcryption scheme, compared to the corresponding signature scheme is one group element.
Secret Sharing Using Near-MDS Codes

We propose a generalized secret sharing scheme based on NMDS codes. The proposed scheme is efficient and the computational complexity for setup and reconstruction phase is only $$O(n^3)$$ , where n is the number of participants. The scheme admits an access structure based on two mutually exclusive sets of participant combinations of sizes t and $$t-1$$ respectively. The parameter t for the access structure is independent of the field size. The proposed scheme is ideal and perfect and has desirable security features of cheating detection and cheater identification. We also provide a cryptanalysis of the $$(t+1, n)$$ threshold secret sharing scheme based on NMDS codes proposed in [12]. We show that their scheme is insecure and that there always exists a set of m participants, where $$m <t+1$$ , which can reconstruct the secret.
Problem of Cost Function Synthesis for Mobile Robot’s Trajectory and the Network Operator Method for its Solution

In mobile robotic systems are designed for patrolling and protecting large areas, in difficult conditions, when environmental constraints severely limit the space of acceptable motions preferences, a state space sampling strategy is more effective, than sampling in the space of controls. Although this has been evident for some time, the practical question is how to achieve it while also satisfying the severe constraints of vehicle dynamic feasibility. The paper presents a new the network operator method for state space sampling utilizing a model-based trajectory generation approach
Legal risk and criminal imputation of weak artificial intelligence

At present, the legal risk of artificial intelligence is mainly manifested by the three risks of weak artificial intelligence: the risk of weak artificial intelligence, the risk of the weak artificial intelligence to infringe on personal information and the use of the weak artificial intelligence to carry out the risk of illegal crime. The criminal law should properly eliminate the principle of technology neutrality and expand the application of criminal law to solve legal risk of weak artificial intelligence.
Adaptive Fuzzy Logic Traffic Signal Control Based on Cuckoo Search Algorithm

Traffic congestion becomes a big problem to perplex the current society. Effective traffic signal control can alleviate traffic congestion, especially for real-time traffic signal control. To improve the control efficiency, fuzzy logic control based on cuckoo search algorithm is applied to solve the problem of real-time traffic signal control. Research object is multi-lane four-phase single intersection which is also the commonly intersection in reality. Vehicular evaluation index model is established firstly. Then, the appropriate green time is given by the cuckoo search algorithm and fuzzy logic control according to the number of real-time road vehicles. Through simulation experiments, the proposed method based on the fuzzy logic control optimized by cuckoo search algorithm can be verified to obtain a good effect. This method also suits for other complex nonlinear systems.