Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Verifiable DSSE with Forward Security in Multi-server Environments

With big data and cloud computing development, data owners begin to outsource their large-volume data to remote servers to reduce local storage costs. Researchers have currently proposed some Dynamic Searchable Symmetric Encryption (DSSE) schemes that support efficient keyword searches on encrypted data with dynamic update operations. Unfortunately, most of them are constructed based on a single server architecture, which means that they cannot be used directly when multiple servers participate in the search process. Some schemes assume that the server is honest-but-curious, which cannot be guaranteed, especially in the multi-server scenario. This paper proposes a Blockchain-based Verifiable DSSE scheme with forwarding security in Multi-server environments. In the scheme, the data owner encrypts the partitioned files and randomly stores them in different servers from which a single server cannot recover any complete file. The use of blockchain and smart contracts to achieve reliable and verifiable search can detect malicious servers’ improper behaviors. Experimental evaluation show that the scheme achieves verifiable search, forward privacy with less leakage, and high search efficiency.
Death Prediction in the Current Pandemic Scenario and Cluster Classification Using Soft Computing Techniques

Coronavirus is a profoundly irresistible infection and has ruinous impacts far and wide. This virus affects both the economic and health sectors of all nations. It also freezes the day-to-day life in the world. In this paper, predictive and classification models for COVID-19 in Indonesia using machine learning, deep learning, and genetic algorithm techniques are developed. In predictive models, we used machine learning algorithms like Logistic Regression, Support Vector Machine, and Recurrent Neural Network. Among the machine learning algorithms, feature scaling with Logistic Regression (94.75%) performed well. Decision Tree Classifier and Genetic algorithms are used to classify the clusters like green, yellow, orange, and red conforming to the new death of each province in which Genetic Algorithm gave better accuracy (93%).
Reconstruction of DES in Order to Reduce Memory Constraints Found on IoT Devices

Security and memory constraints are the most problematic topics when Internet of Things (IoT) is considered. IoT devices need strong cryptographic algorithms to secure data and information. Most strong cryptographic algorithms need a lot of memory for installation, which is impossible to some IoT devices, which have limited memory space. One of the most common cryptographic algorithms needed on IoT devices is Data Encryption Standard (DES). In this study, DES was reconstructed to generate new algorithm called Rec_DES which has less memory constraints and has approximately the same encryption strength compared to an original DES. The method of reconstruction was to analyze all functions on DES, which do not add value when memory and strength are considered. All functions that do not add value were reconstructed to be strong or totally removed. The method of reconstructing DES to generate Rec_DES was regarded as a novel approach, since no one had ever reconstructed original DES to generate an algorithm which operates the same way like Rec_DES. In this study, new names were coined since the method used was a novel approach. This novel approach was coined “reconstruction method of DES” since DES was reconstructed to generate new algorithm and a new reconstructed DES algorithm was coined Rec_DES. This approach worked successfully on DES.
A Globally Optimal Label Selection Method via Genetic Algorithm for Multi-label Classification

Multi-label classification addresses a special pattern classification problem where an instance could belong to multiple class labels simultaneously. Nowadays, a pivotal challenging issue is to deal with high-dimensional label space case, which generally deteriorates classification performance and increases computation burdens for traditional multi-label classifiers. To this end, dimensionality reduction originally for feature space is also applied to label space, deriving two kinds of techniques: label embedding and label selection. Boolean matrix decomposition (BMD) approaches to the original binary matrix via two low-rank binary matrix Boolean multiplication. Without any constraint on low-rank matrices, this BMD could result in a label embedding method. When the left matrix consists of a column subset of original matrix, interpolative decomposition is implemented, which corresponds to a label selection technique. In this paper, we propose a globally optimal label selection approach, which consists of two stages: to remove a few uninformative labels via an exact BMD without any approximated loss, and to select most informative labels of fixed number via globally optimal genetic algorithm. Our experiments on four benchmark data sets with more than 100 labels show that our proposed method is superior to three existing approaches, according to two performance evaluation metrics (precision and discounted gain@n) for high-dimensional label space.
Branching Frequency and Markov Entropy of Repetition-Free Languages

We define a new quantitative measure for an arbitrary factorial language: the entropy of a random walk in the prefix tree associated with the language; we call it Markov entropy. We relate Markov entropy to the growth rate of the language and the parameters of branching of its prefix tree. We show how to compute Markov entropy for a regular language. Finally, we develop a framework for experimental study of Markov entropy by modelling random walks and present the results of experiments with power-free and Abelian-power-free languages.
Implementing Autonomic Internet of Things Ecosystems – Practical Considerations

Development of next generation Internet of Things ecosystems will require bringing in (semi-)autonomic behaviors. While the research on autonomic systems has a long tradition, the question arises, are there any “off-the-shelf” tools that can be used directly to implement autonomic solutions/components for IoT deployments. The objective of this contribution is to compare real-world-based, autonomy-related requirements derived from ASSIST-IoT project pilots with existing tools.
iPMDS: Interactive Probabilistic Multidimensional Scaling

Dimensionality reduction is often used for visualization without considering their understanding by users. Multidimensional scaling, for instance, provides an arbitrarily-oriented visualization. However, users can be integrated into the loop to provide clues about their understanding of the visualization. In this paper, we propose an interactive probabilistic multidimensional scaling (iPMDS) approach to compute the visualization with the lowest information loss while taking the information provided by users into account. We show that a more interpretable visualization can be obtained after interacting with the visualization while keeping a good dimensionality reduction accuracy.
Integrating PPN into Autoencoders for Better Information Aggregation Performance

The Pulse-in Pattern-out Network (PPN) presented by Yonekura et al. in 1991 has gained a reputation of providing good decoders whereas it does not provide any encoders. It may be possible by integrating the PPN into the standard neural network models with encoding and decoding capabilities to enhance their decoding capability and, thus, improve their overall performance. In this study, we integrate the PPN into the four-layer autoencoder model so that the two network models share the same information aggregation layer and the latter ones, and we let either one of the input layer in the autoencoder model or that in the PPN accept the input data in turn. It was clarified that the integration of PPN into autoencoders improves the information aggregation performance measured by the restoration errors especially in the cases that the information aggregation layer has fewer nodes and that the third layer has many nodes.
Feature extraction of online English learning behavior based on genetic algorithm

In order to solve the problem of low accuracy and efficiency of traditional online English learning behavior feature extraction methods, a method of online English learning behavior feature extraction based on genetic algorithm is proposed. This paper analyzes the generation mechanism of online English learning behavior data, and collects online English learning behavior data. Based on the collected data, K-means algorithm is used to cluster and mine online English learning behavior data. Based on the preprocessed data, the process of genetic algorithm is analyzed, and the feature extraction function of online English learning behavior is constructed to complete the feature extraction of online English learning behavior. Experimental results show that, compared with the traditional feature extraction method, the accuracy and efficiency of the proposed method are significantly improved.
ISLHelper: A web based helper plugin for Indian Sign Language

Lack of learning tools is a hindrance in acquiring knowledge for deaf people. People associated with them find it difficult to communicate because of the non availability of learning tools. Scale of research and availability of assistive software tools for deaf people is much less compared to technology based solutions for hearing peers. Getting to know sign equivalent of a word is not that easy when browsing a web page. User should use a multimedia dictionary to find the sign equivalent of a spoken language word. ISLHelper provides a solution to this problem by displaying Indian Sign Language(ISL) signs within a web page without navigating away. ISLHelper is available as a Firefox browser plugin. Results of usability study indicates that ISLHelper is favoured by participants.
Automatic Detection of Storage-Based Covert Channels

Network packets can be used to hide information in space or in time, a steganographic technique known as a covert channel. Steganalysis is the science of finding the presence of covert channels. Re-embedding steganalysis has been shown to be effective in detecting both the presence and the amount of hidden information. The technique, however, is impractical because it required manual analysis of every network flow. This paper proposes methods to automate the analysis process effectively. The experimental results suggest that automatic detection is possible and that re-embedding steganalysis is practical.
Blockchain Technology for Property Sales and Registration

Due to involvement of a lot of untrusted actors in the real estate market, real estate cannot be trusted blindly. A blockchain-centered policy has been proposed in this work for real estate operations which prevents property fraud cases and also ensures transparency, safe traceability, and trust. Since blockchain is not a centralized system, use of blockchain avoids forgery or tampering of the real estate documents as the information or data will be stored in form of blocks. We use smart contracts for property registration process.
A Study on the Structural Equation Modeling of the Relationships among Career Barrier, Self-esteem, Self-efficacy, and Career Preparation Behavior through the Feature of Busan Regional College

The purpose of this study is to find the structural relationships among career barrier, self-esteem, self-efficacy, and career preparation behavior with the feature of Busan regional college students. For investigating this topic, 1,543 college students were selected from the 13<SUP>th</SUP> of YP(Youth Panel) of Korea Employment Information Service. In order to find out the structural relationships among career barrier, self-esteem, self-efficacy, and career preparation behavior with variables, structural equation modeling was used. Followings were the results of the research: (a) Career barrier effected significantly on the self-esteem, whereas self-efficacy was not statistically significant. (b) Self-esteem effected significantly on the self-efficacy, whereas career preparation behavior was not statistically significant. (c) Self-efficacy effected significantly on the career preparation behavior. (d) Busan regional feature effected significantly on the career barrier, and female was higher than male in self-efficacy. Based on the results of this study, implications were discussed and some recommendations were suggested.
REVIEW OF SCIENTIFIC WORKS ON TECHNOLOGIES FOR EXTRACTION OF BIOCOMPONENTS FROM VEGETABLE RAW MATERIALS. SUPERCRITICAL CO2 EXTRACTION IS AN EFFECTIVE NEW METHOD FOR SOLVING THE GLOBAL PROBLEM OF UTILIZATION AND QUALITY OF PLANT AND ORGANIC RAW MATERIALS

A brief review and assessment of methods for the extraction (extraction) of active biocomponents from plant raw materials is presented. The overview contains a number of tables and diagrams to facilitate the initial perception of this topic. Examples of stationary and mobile installations developed in the world are shown. The prospects of the Supercritical CO2-extraction method, as the most effective for the extraction of valuable biomedical components, have been analyzed. The schemes and samples of the used CO2-extractors are shown. The schemes and external view of the author’s experimental setup for extraction with supercritical CO2 developed by our team are presented.
Naive Bayes Algorithm for Sentiment Analysis on Twitter

To analyze the tweets by various users of Twitter across the globe and provide a detailed analysis of the positive and negative discussion of the topic under a given heading. This is done using sentiment analysis algorithm which applied in the Python environment, which is also certainly used for statistical analysis of the data and a web user interface has been developed for the overall output of the data. Using API Access, the Access Token is retrieved. Then the sentiment algorithm is applied step by step to gather the positive and negative in the tweets of applied section and the percentage is reported.
ODCA: An Outlier Detection Approach to Deal with Correlated Attributes

Datasets from different domains usually contain data defined over a wide set of attributes or features linked through correlation relationship. Moreover, there are some applications in which not all the attributes should be treated in the same fashion as some of them can be perceived like independent variables that are responsible for the definition of the expected behaviour of the remaining ones. Following this pattern, we focus on the detection of those data objects showing an anomalous behaviour on a subset of attributes, called behavioural, w.r.t the other ones, we call contextual. As a first contribution, we exploit Mixture Models to describe the data distribution over each pair of behavioral-contextual attributes and learn the correlation laws binding the data on each bidimensional space. Then, we design a probability measure aimed at scoring subsequently observed objects based on how much their behaviour differs from the usual behavioural attribute values. Finally, we join the contributions calculated in each bidimensional space to provide a global outlierness measure. We test our method on both synthetic and real dataset to demonstrate its effectiveness when studying anomalous behaviour in a specific context and its ability in outperforming some competitive baselines.
Social-Interaction GAN: Pedestrian Trajectory Prediction

With the increasing number of intelligent autonomous systems in human society, the ability of such systems to perceive, understand and anticipate human behaviors becomes increasingly important. However, the pedestrian trajectory prediction is challenging due to the variability of pedestrian movement. In this paper, we tackle the problem with a deep learning framework by applying a generative adversarial network (GAN) and introduce a model called Social-Interaction GAN (SIGAN). Specially, we propose a novel Social Interaction Module (SIM) to dispose the human-human interactions, which combines the location and velocity features of the pedestrians in a local area. Extensive experiments show that our proposed model can obtain state-of-the-art accuracy.
A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models

This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values. One class of variational methods, the Bethe/Kikuchi approximate algorithm, is combined with Expectation-Maximization (EM) to learn BN model parameters from data using an upper limit on cluster size. Our proposed method is novel since the Bethe/Kikuchi algorithms are typically only used to approximate marginal distributions. We review popular Bethe/Kikuchi approximate algorithms, including Belief Propagation (BP), Iterative Join Graph Propagation (IJGP), and Fundamental Cycle Base (FCB) algorithms for discrete BNs, and conduct experiments to test their learning performance using a wide range of discrete BNs in practice. Experiments show that by using IJGP or FCB algorithms, with EM, we can obtain more accurate results than the conventional approximate algorithms, such as Evidence Pre-propagation Importance Sampling and Variational Inference. When exact methods are intractable the IJGP/FCB with EM can be an effective alternative solution.
CMMI Compliant Workflow Models to Track and Control Changes

The Capability Maturity Model Integration (CMMI) is a renowned Software Process Improvement (SPI) framework.Research studies have revealed that CMMI adoption needs a lot of resources in terms of training, funds, and professional workers.However, the software SMEs (SSMEs) have few resources and cannot adopt CMMI.One of the challenges of adopting CMMI is that CMMI tells "What to do?" as requirements to be met, and leaves "How to do?" to the implementers.The software industry especially SSMEs faces difficulties in successfully implementing various process areas (PAs) particularly Configuration Management Process Area (CM-PA).SG-2 (Track and control changes) is one of the important Specific Goals (SGs) required by CMMI to successfully implement CM-PA.As a starting point, we have achieved this SG by implementing its two contributing Specific Practices (SPs).The proposed WFMs were validated through an Expert Panel Review (EPR) process.In addition, a case study approach was used for the evaluation.The results showed that the models are useful, easy to use, supportive in the achievement of SG-2, and applicable to SSMEs.It is worth mentioning that this research work has not only contributed to the implementation studies but also added to the empirical software engineering body of knowledge.
Fractional-Order Adaptive <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> </math>-Laplace Equation-Based Art Image Edge Detection

In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> </math> -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.
Skeleton-based Action Recognition with Multi-scale Spatial-temporal Convolutional Neural Network

The skeleton data convey significant information for human action recognition since they can robustly accommodate cluttered background and illumination variation. Early convolutional neural networks (CNN) based method mainly structure the skeleton sequence into pseudo-image and feed it into image classification neural network such as Resnet, which can not capture comprehensive spatial-temporal feature. Recently, graph convolutional networks (GCNs) have obtained superior performance. However, the computational complexity of GCN-based methods is quite high, some works even reach 100 GFLOPs for one action sample. This is contrary to the highly condensed attributes of skeleton data. In this paper, a Multi-scale Spatial-temporal Convolution Neural Network (MSST-Net) is proposed for skeleton-based action recognition. Our MSST-Net abandons complex graph convolutions and takes the implicit complementary advantages across different scales of spatial-temporal representations, which are often ignored in the previous work. On two datasets for action recognition, MSST-Net achieves impressive recognition accuracy with a small amount of calculation.