Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Cloud Storage Options for Diverse Application Requirements

Cloud Storage is the concept of combining and sharing of storage resources through the Internet. Each cloud service provider (CSP) offers universal data storage services using its geographically distributed datacenters. Businesses and consumers are increasingly reliant on cloud-based storage solutions instead of in-house, on-premises local storage hardware in order to save on initial expenditures to build and maintain the hardware infrastructures. Cloud Storage provides enormous levels of data protection and important data can be restored in case of missing local copies. Selecting the right public cloud provider has become critical to long term business success. Depending on the different business needs and requirements of these storage services, we compare a few of the storage services provided by three market giants like Amazon Web Services, Microsoft Windows Azure and Google Cloud Platform.
Multi-Range Fusion for X-ray Image Enhancement

Digital medical images are stored in the Digital Imaging Communications in Medicine (DICOM) standard where a 12-bit integer represents each pixel. However, commercial display devices are only designed to work with an 8-bit standard such as JPEG or PNG. Traditional methods compress the high dynamic range (HDR) to a low dynamic range (LDR) to visualize a DICOM image. The compression process is a linear transformation and the LDR is represented in an 8-bits format. Due to a quantization error the contrast is limited. Therefore, in this article, we propose a method to enhance X-ray images in term of contrast. Firstly, we estimate an information range that captures valuable information of an x-ray image. The range is represented by an upper and lower threshold. Within these thresholds, LDR images will be extracted to capture information from a specific sub-range. These images are then fused to create an enhanced LDR image that has a higher contrast but retains information from each sub-range. Experimental results demonstrate that the proposed method helps achieve a better contrast.
Interim Analysis and Further University Digitalization Prospects Definition

During almost two last decades, Samara State Technical University is transitioning its learning and administrative processes into digital form. This paper presents an overview of current results and points targets for ongoing and further development.
Intelligentization of film and television post-production based on complex image processing and scene switching algorithms

Intelligentization of the film and television post-pro duction based on the complex image processing and scene switching algorithms is studied in the paper. At present, the film and television media is in a period of rapid development, and new media formats are constantly emerging. The emergence of high-definition television HDTV, the rapid development of the network puts forward new requirements for film and television post-production, in order to adapt to these changes, various software manufacturers continue to improve software functions and keep pace with the market. The development of computer virtual technology and the application of the design field have caused a fundamental change in the traditional design process. Therefore, the handover procedure caused by any delay should be minimized, which has attracted the attention of many researchers and seeking new solutions. The novel solutions are proposed model improve the complex image processing and scene switching algorithm performance. Through the verification, the performance is guaranteed.
Technology-Assisted smart sports guiding framework implementations based on smart data acquisition hardware

Technology-assisted novel smart sports guiding framework implementations based on the smart data acquisition hardware is studied in this paper. In terms of characteristics, evolution hardware is a hardware system with self-organization, self-adaptation, and self-repair characteristics. Although the form of definition is slightly different, the inner essence is the same, that is, the evolved hardware is autonomous hardware. This feature makes the hardware no longer just passively accept the role of the outside world, but can actively adjust its own structure. Adapt to the environment. Repair the damage caused to them by the environment. Corresponding to this, CPLD also has to complete two functions: the primary function is to receive and register the command control word of the data acquisition module from the host, and then these models are used to construct the efficient smart analysis model and the applications on the sports guiding is discussed. The performance is tested in final.
Quantum Aware Distributed Ledger Technology for Blockchain-Based IoT Network

A digital ledgerDistributed ledger which is based on distributed technology will help to address cybersecurity and secrecy problems in the Internet of Things Architecture, but combining these two technologies poses some difficulties. In order to preserve a tradition of financial transactions, cryptocurrencies introduced DL technology. The DL size in a cryptocurrency addresses hundreds of GBs, while the storage of IoT nodes is limited. Similarly, cryptocurrencies implement costly processes of consensus, while IoT nodes in calculation and energy are restricted. Moreover, classic distributed ledger technology (Bitcoin) are not based on quantum. The chapter aims a distributed ledger security based on quantumQuantum digital signature technology, specifically distributed ledger for Internet of Things, for IoT architectures. One cornerstone of the chapter is a new signature creation scheme which is named as Single time signature based on time(STS)Single time signature, Blockchain-STS, a compact scheme. Compared with the famous Winternitz-OTS+ system Blockchain-STS offers a 75% reduction in signature size and a 76% reduction in signature generation time.
A General Model for Long-short Term Anomaly Generation in Sensory Data

Anomaly detection algorithms play an important role in Internet of Things (IoT) where a significant amount of data is processed every second. The abnormal data can seriously affect the decision-making of data analysts that may lead to system failure. Hence, anomaly detection algorithms are useful tool to identify anomaly. However, detection accuracy of these algorithms is affected by the amount and quality of training data. In fact, the well-known-published datasets are limited. Moreover, they are not labeled and are hard to use for training. In this paper, we propose a general model for artificial anomaly generation. The proposed model can generate six typical forms of anomalies in IoT time-series data including stuck-at, offset, drift, noise, outlier, and spike. The model allows users not only to straightforwardly generate anomalies under various parameters but also generate combined anomalies which are the combination of those six typical forms of anomalies.
Application of Hybrid Sampling Method in the Prediction of Telecom Customer Churn

In the telecommunications industry, there is a widespread problem of data imbalance. This problem seriously affects the prediction results, making it impossible for telecommunications operators to accurately find potential lost customers, causing a lot of losses. Aiming at the problem of economic loss caused by the imbalance of telecommunications customer data that affects model prediction performance, this paper proposes two hybrid algorithms DB-QCS (DBSCAN Quadrilateral centroid SMOTE) and KM-QCS (K-Means Quadrilateral centroid SMOTE) to solve the above problems, The hybrid algorithm mainly solves the problem of further increasing the marginalization of the sample distribution and introducing noise when the SMOTE algorithm synthesizes new samples. The main idea is to first use the under-sampling method to delete outliers or edge samples in most classes of samples, thereby reducing the number of synthesized new samples to solve the problem of introducing excessive noise. Then, the problem of marginalization of the sample distribution is solved by limiting the synthesis area of the new sample during oversampling, and finally the sampled data set is used for classification training. A large number of experiments on 5 unbalanced telecom customer data sets show that the hybrid algorithm achieves higher F-measure, G-mean and AUC values compared with the SMOTE algorithm.
MADFAM: MicroArchitectural Data Framework and Methodology

In the aftermath of Spectre and Meltdown researchers have proposed a variety of attack detection solutions by applying machine learning to data collected from hardware performance monitoring units. Although many microarchitectural attack detection systems provide high-accuracy detection results, the behavior of the underlying data collection mechanisms is not well described or understood. This research introduces the MicroArchitectural Data Framework And Methodology (MADFAM) to prescribe a systematic approach to collecting and preserving the information available in sequences of microarchitectural data. The proposed framework focuses on hardware performance counters (HPCs) as the primary data source. HPC configuration is complex, which makes it difficult for others to reproduce results or advance the state-of-the-art. This framework includes a description of design decisions that HPC research must consider across an array of problem domains, including information security. MADFAM proposes a data collection architecture and evaluation criteria to improve the discussion about the experimental settings and design decisions used in HPC research. The proposed framework evaluation criteria are then used to establish a baseline characterization of time series data that future research can use to compare alternative framework implementations.
Instance Selection from Skewed Class Distributions by Using the multi-objective optimizer

Instance selection aims to improve the performance of a model by removing uninformative and noisy data. Two issues to consider during instance selection are: (1) the volume of data reduction, and (2) conservation of generalization performance. Since maximizing dataset reduction leads to poor performance, both issues conflict. Regarding this issue, a chaos-based evolutionary algorithm is proposed which aims to keep only the boundary instances by a combined fuzzy weighted average distance-based decision surface. The performance has been evaluated on real-world datasets by the 10-fold cross-validation method. Evaluations manifest the competitive performance for instance selection in terms of error rate, reduction rate, and Gmean.
&lt;b&gt;Recent Research and Development for Improvement of Earthquake Resilience&lt;/b&gt;

In order to increase the earthquake resilience of railways, it is important to simultaneously improve "strength" and "recovery" in a well-balanced way. "Strength" is defined as the performance to prevent the decrease in railway functionality during earthquakes and "recovery" is the capacity to rapidly restore functionality after earthquakes.This paper first introduces recent research and development for improving the strength and capacity to recover of railways after earthquakes with common core technologies. The paper then summarizes recent essential trends in research, focusing particularly on recovery, which "utilize external information," "merge estimation and observational information," and "combine of strength and recovery."
The Classic Television Dramas Restoration Based on Super Resolution

Super-resolution is a widely used technology in many applications, such as video repair. Aiming at the insufficiency of the method Fast Super-Resolution Convolutional Neural Networks (FSRCNN), an image super-resolution reconstruction method based on multi-scale syndication network is proposed in this study. Based on multi-scale structures, a feature sampling model is firstly proposed to extract the features of Low-Resolution image (LR); Secondly, the features are enhanced with the help of feature fusion and sub-pixel convolutional layer; Finally, a joint loss function involving MSE loss and PSNR loss is proposed to improve the optimization of the networks training. Comparison experiments are implemented on the sets of Set5, Setl4, and BSD100. The experimental results suggest that the method shows superiority against the state-of- the-art methods. As an application, the proposed method is applied to increase the resolutions of the television dramas "Journey to the West" and "The Dream of Red Mansion". The repair results improved by 4.67dB and 5.37dB respectively.
IN MEMORIAM: J. MICHAEL DUNN, 1941–2021

The history of relevance logic cannot be written without mentioning J. Michael Dunn who played a prominent role in shaping this area of logic.In the late twentieth century, he was a doyen with a world-class reputation in the field of philosophical logic.Dunn's research also encompassed logics outside of relevance logic, from 2-valued first-order logic to quantum logic and substructural logics such as the Lambek calculi, intuitionistic logic, linear logic, etc.Each of the disciplines of philosophy, mathematics, and computer science has been impacted in intrinsic ways by some of the theorems proved by Dunn.
A Neural Network Approach For Evaluating Levelized Cost Of Electricity For Generating Electricity Across Various Generation Technologies

This paper proposes a novel method to determine the LCOE of energy sources based on uncertain parameters. Generally, to determine the global sensitivity analysis of energy technology, Monte Carlo simulation (MCS) is used. The drawback of MCS is its computational expense to generate a large number of random variables. So an alternate method of using the NARX network is proposed. Instead of generating many random variables, the NARX network is trained, and the network directly estimates the Levelized cost of Electricity (LCOE). Thus the repetitive approach of calculating the LCOE based on the uncertain variables is no longer needed. Thereby dramatically reducing the computational burden of the process. For the study, coal-based thermal energy technology and solar PV are considered. This particular technology is chosen due to the large number of uncertain variables associated with LCOE. The global sensitivity analysis results for both MCS and NARX are compared. The results showed that NARX requires less time than MCS and generates the same result as MCS with low error. Also, the computation time has is reduced with almost replicating results of MCS
Research on Cooperative Reconnaissance Strategy of the Unmanned Aerial Vehicle Group in Uncertain Environment

In the scenario of cooperative reconnaissance mission with multiple unmanned aerial vehicles (UAVs), the reconnaissance objects usually camouflage their tracks, and the reconnaissance equipment carried by the UAVs is defective, which brings uncertainty to reconnaissance mission. The paper proposes a cooperative reconnaissance strategy of the UAV group based on particle swarm optimization in uncertain environment. This strategy includes three parts: global search, local search, and cooperative search. In the simulation experiment, we can realize that the probability of discovering the areas where a certain number of reconnaissance objects is located exceeds 99%, and the obtained minimum value of the reconnaissance objective function differs from the actual minimum value by 0.14%. When the two UAV groups cooperate in reconnaissance, the time to complete the reconnaissance mission is reduced by 9.46% with no cooperation.
Implementing Recommender System-based Approach for Health Management Mobile Application

Health management is one of the essential tasks in human life since our daily activities depend on our health. Also, depending on our health, our bodies’ actions and reactions are changed. If we are healthy, we are pleased to work. Since healthy life is essential in human life, maintaining a healthy life and for that purpose doing the right health management is necessary. Since we already have the widespread use of smartphone devices, using a smartphone-based health management applications can help people maintain their health by recommending food, nutrition, exercise, entertainment, etc. In this paper, we present our design and possible implementations of a recommender system-based approach for health management. Here we should also consider that, some people have certain health conditions that limit their food choices. The presented approach classified users based on their profile then applied a recommendation algorithm to recommend food, nutrition, and exercise. Our application automatically ignores food and exercises based on users’ risk of diseases. By using this application, users can easily maintain their health.
Medical CT Image Enhancement System Based on Swarm Intelligence Optimization Algorithm

With the development of electronic technology and computer technology, the application field of medical images will continue to expand, and medical image processing technology will also attract more and more attention. CT images are increasingly used for clinical diagnosis and treatment. The purpose of this work is to study the CT image enhancement system based on swarm intelligence optimization algorithm. Integrate medical image enhancement algorithms based on swarm intelligence optimization algorithms and common medical image processing and analysis algorithms into the medical image processing system to form a powerful and easy-to-use medical image enhancement system. Based on the research of adaptive gray-scale image enhancement technology based on intelligent optimization algorithm, a new target fitness function is proposed, which uses GA, PSO and QPSO to achieve adaptive gray-scale image enhancement. The results show that the suitability value of the QPSO group intelligent optimization algorithm is 9.5241, the robustness is strong, and the enhancement effect is slightly better.
Explainable Hybrid CNN and FNN Approach Applied on Robotic Wall-Following Behaviour Learning

Fuzzy Neural Network (FNN) applied to robotic control tasks has proved to be effective by previous researchers. However, FNN has an inherent deficiency in dealing with inputs of large dimensions, such as images. Therefore, this research utilizes a Convolutional Neural Network (CNN) model to convert image into distance values and delivers these values to FNN based robot controller as inputs. The proposed hybrid CNN+FNN are tested with both a regression model and a multi-task model. Results show that the multi-task method performs better with less information loss from input images. This paper also proved that the proposed hybrid approach can be generalized into an unknown robotic simulation environment and performs better than its FNN counterpart. By utilizing state of the art explainable analysis method, both the CNN part and the FNN part of the hybrid approach can be explained in a human-understandable way.
Features of Manifestation of Cognitive-Activity Style of Students of Natural-Scientific and Humanities Profiles

The relevance of the article is proved by the fact that up-to-date educators have to find new teaching approaches, focused on developing students’ cognitive activities in the conditions of increasing information gain in a rapidly developing high-tech society. The purpose of the article is to determine cognitive and activity-related characteristics of students specializing in natural science and humanitarian profiles. The methodology “Determining cognitive and activity-related style (L. Rebecca)” was used as the leading research method, which made it possible to identify certain parameters of students’ cognitive activity. The article presents the results obtained on the basis of using the tests proposed by the author of this methodology to study physical feelings in the learning process (vision, hearing, kinesthetics); communication (extroverted personality, introverted personality); individual abilities (emotional-intuitive, logical); approach to work (tightly regulated, unregulated); handling students’ ideas specializing in natural science and humanitarian profiles based on the comparative analysis (synthesis, analysis). Getting to know the identified features of the cognitive and activity-related style of students specializing in natural science and humanitarian profiles can be used by teachers of vocational schools to increase students’ cognitive activity.
Multi-scale Multi-clue Crowd Counting Network

At present, crowd counting under complex background is still a big challenge, but a meaningful task for public safety. We focus on this problem and propose a multi-scale multi-clue crowd counting network (MMNet), which is composed of a feature encoder backbone and four stacked multi-clue crowd estimation modules (MCEM) under multiple scales as decoders. Each module consists of three predictors, including a shared attention predictor (SAP), a density map predictor (DMP) and a local counting map predictor (LCMP). DMP utilizes the information of each pixel on the image, while LCMP divides the image into patches and counts the number of people on these patches, focusing on the number in each patch. These two predictors solve the problem of inaccurate crowd counting under complex background from the perspective of training target. They use the microscopic information and macro information of the image for model training, respectively. SAP helps them concentrate more on the human head region in the image by generating multi-scale shared attention maps from the perspective of feature extraction. Furthermore, we design a multi-task joint training strategy that automatically adjusts the loss weights of different tasks to promote training and the robustness of the model. Extensive experiments on three challenging datasets (ShanghaiTech, UCF_CC_50, UCF-QNRF) show the superior performance of MMNet.
Telecom Operator Knowledge Graphs Reasoning for Question Answering

Telecom operators generate complex business systems to meet different needs of users, which causes difficulties for users to obtain relevant information. To address this problem, this paper proposes a KGQA method for complex queries in the telecom operator domain, which semantically understands the input query, converts the multi-constraint problem into a multi-constraint query graph, and thus obtains structured query statements. In the process of constraint binding, the beam search mechanism is used to effectively reduce the search space of relations and entities and improve the parsing efficiency. Finally, the model uses reinforcement learning based optimization to maximize the expected payoff in the absence of inference path annotations. Experiments on real dataset shows, compared with existing methods, the method proposed in this paper has better performance.
The Distance Function Optimization for the Near Neighbors-Based Classifiers

Based on the analysis of conditions for a good distance function we found four rules that should be fulfilled. Then, we introduce two new distance functions, a metric and a pseudometric one. We have tested how they fit for distance-based classifiers, especially for the IINC classifier. We rank distance functions according to several criteria and tests. Rankings depend not only on criteria or nature of the statistical test, but also whether it takes into account different difficulties of tasks or whether it considers all tasks as equally difficult. We have found that the new distance functions introduced belong among the four or five best out of 23 distance functions. We have tested them on 24 different tasks, using the mean, the median, the Friedman aligned test, and the Quade test. Our results show that a suitable distance function can improve behavior of distance-based classification rules.