Reviewerly/exHarmony huggingface dataset:
Electronic voting through DE-PBFT consensus and DAG data structure

Electronic voting systems are one of the most important issues in today's world. At the present time, most systems are traditional and centralized, and the counting of votes is done by a central party, and security depends on cryptographic techniques. Therefore, the need to create decentralized voting systems is increasing day by day, so in this way, preservation and counting of votes are done directly with the help of a consensus algorithm. Having the difficulties mentioned above, this paper presents an unbiased electronic voting model in which the votes from ballot boxes are sent to the candidates, and the candidates should confirm the received votes, after candidates' confirmation, they agree on the received votes by running an DE-PBFT consensus, and then add votes in their DAG data structure . In this way, all candidates are responsible for maintaining, validating and counting votes, and the trust through consensus protocols is provided in a decentralized system. The voter's anonymity has also been preserved through ring signature.
Privacy-Preserving Collaborative Data Anonymization with Sensitive Quasi-Identifiers

Collaborative anonymization deals with a group of respondents in a distributed environment. Unlike in centralized settings, no respondent is willing to reveal his or her records to any party due to the privacy concerns. This creates a challenge for anonymization, and it requires a level of trust among respondents. In this paper, we study a collaborative anonymization protocol that aims to increase the confidence of respondents during data collection. Unlike in existing works, our protocol does not reveal the complete set of quasi-identifier (QID) to the data collector (e.g., agency) before and after the data anonymization process. Because QID can be both sensitive values and identifying values, we allow the respondents to hide sensitive-QID attributes from other parties. Our protocol ensures that the desired protection level (i.e., k-anonymity) can be verified before the respondents submit their records to the agency. Furthermore, we allow honest respondents to indict a malicious agency if it modifies the intermediate results or not following the protocol faithfully.
Determine the Tonality of of News Media Reports by Conceptual Analysis

The article describes the solution of the task of creating linguistic tools and the methodology of automatic determination of the tonality of news reports related to the quality of life of an ordinary citizen of the Republic of Kazakhstan. The approach for solving the problem was defined, software and methods of automated creation of object dictionaries and evaluation predicate dictionaries, as well as evaluation measure modifier dictionaries were developed. The experiment confirmed the correctness of the proposed methodology for assessing events covered in news reports and the operability of the software complex. This technique, with appropriate selection of event assessment objects, can be used in creating tonal portraits of specific authors on the set of their publications, as well as tonal portraits of various news aggregates on the set of events they cover in a particular time interval.
Design of a New Automated Fault Detector based on artificial intelligence and Big Data Techniques

Concerns over energy and environmental issues around the world have led to the worldwide focus on energy use reduction. Studies in each area of energy have shown that residential and commercial construction sectors consume more power than other sectors such as industry, agriculture, services, and transportation. Studies of energy consumption in building sectors have reported that energy savings of 10% to 30% can be obtained by using artificial intelligence (AI), the system would be capable of detecting and analyzing anomalies in energy usage pattern assessing, diagnosing and suggesting the best solution in suitable time. This paper proposes to integrate and hybridize between AI techniques and big data algorithms which can enhance monitoring and controlling building systems, increasing comfort and decreasing efficiently the running costs. In addition, the authors suggest a tool which aims to automatically detect abnormal energy consumption by using AI and big data which are produced by the Building Management System (BMS). This happens by designing a software application that is called Fault Detection Tool (FDT) which automatically detects the abnormalities of energy consumption, optimizes the use of different resources and analyzes faults, complaints and time taken to terminate them. Experimental results show that with the proposed approach, it is possible to accurately detect anomalous patterns in building energy consumption. This tool will be a part of an artificial intelligent decision-making system.
Graph-regularized feature selection based on spectral learning and subspace learning

Feature selection is an important approach for reducing the dimension of high-dimensional data. In recent years, many feature selection algorithms have been proposed. However, most of them only exploit information from the data space. They often neglect useful information contained in the feature space, and typically do not exploit information about the underlying geometry of the data. To overcome these problems, we introduce new unsupervised feature selection methods based on the feature selection framework of joint embedding learning, sparse regression, and subspace learning, and extend the framework by introducing the feature graph. The framework of NSSRD comprises four main parts: dual-graph nonnegative spectral learning, dual-graph sparse regression, feature selection, and optimization. The framework of SGFS comprises three main parts: sparse subspace learning, local structure preserving, and update rules.
A Pulse Convolution Jamming Method Based on Multiple Phases Sectionalized Modulation

To solve the problem of multi-target barrage jamming, a method of pulse convolution jamming based on multiple phases sectionalized modulation (MPSM) is proposed in this paper. Firstly, the received signal is divided into multiple subsections and modulated by multiple phases. Then, the jamming signal is modulated by pulse convolution. Finally, amplify and transmit the jamming signal. This method utilizes MPSM to expand the jamming range, and pulse convolution modulation to control the number, location and amplitude of the jamming, thus achieving the effect of local barrage of jamming for multi-target. The theoretical analysis and simulation results show that this method can not only obtain the gain of pulse compression process, effectively reduce the jamming power, but also control the jamming range, number, location and amplitude.
A Study on the Impact of Data Characteristics in Imbalanced Regression Tasks

The class imbalance problem has been thoroughly studied over the past two decades. More recently, the research community realized that the problem of imbalanced distributions also occurred in other tasks beyond classification. Regression problems are among these newly studied tasks where the problem of imbalanced domains also poses important challenges. Imbalanced regression problems occur in a diversity of real world domains such as meteorological (predicting weather extreme values), financial (extreme stock returns forecasting) or medical (anticipate rare values). In imbalanced regression the end-user preferences are biased towards values of the target variable that are under-represented on the available data. Several pre-processing methods were proposed to address this problem. These methods change the training set to force the learner to focus on the rare cases. However, as far as we know, the relationship between the data intrinsic characteristics and the performance achieved by these methods has not yet been studied for imbalanced regression tasks. In this paper we describe a study of the impact certain data characteristics may have in the results of applying pre-processing methods to imbalanced regression problems. To achieve this goal, we define potentially interesting data characteristics of regression problems. We then conduct our study using a synthetic data repository build for this purpose. We show that all the different characteristics studied have a different behaviour that is related with the level at which the data characteristic is present and the learning algorithm used. The main contributions of our work are: i) to define interesting data characteristics for regression tasks; ii) to create the first repository of imbalanced regression tasks containing 6000 data sets with controlled data characteristics; and iii) to provide insights on the impact of intrinsic data characteristics in the results of pre-processing methods for handling imbalanced regression tasks.
A functional proof pearl: inverting the Ackermann hierarchy

We implement in Gallina a hierarchy of functions that calculate the upper inverses to the hyperoperation/Ackermann hierarchy. Our functions run in Θ(b) for inputs expressed in unary, and in O(b2) for inputs expressed in binary (where b = bitlength). We use our inverses to define linear-time functions—Θ(b) for both unary-represented and binary-represented inputs—that compute the upper inverse of the diagonal Ackermann function A(n). We show that these functions are consistent with the usual definition of the inverse Ackermann function α(n).
Image Edge Detection Based on the Canny Edge and the Ant Colony Optimization Algorithm

In order to solve the problem of edge discontinuity in traditional edge detection methods, a new edge detection method is proposed, which combines the Canny operator and improved the ant colony optimization algorithm. In this method, firstly, the edge of the image is extracted by the traditional Canny operator. Then the endpoint of the edge is calculated as the initial position of the ant. The fuzzy triangle membership function is introduced by the gray value in the neighborhood. The fuzzy membership value of each pixel between the edge endpoints is calculated as the heuristic matrix of the ant colony. The heuristic matrix promotes ants to search along the real edge to detect continuous and complete edge lines. The experimental results show that the proposed method effectively improves the accuracy of contour extraction of target objects in images, and the edge information extracted by this method is clearer.
Multi-User System for Virtual Interaction of a Pasteurization Process

This article presents the development of a virtual Multi-user system of an industrial process, of a pasteurizing plant. The system will have a realistic and intuitive environment, in order to train and train operators in tasks of visualization, monitoring and control, through immersion and interaction with the devices and instrumentation present in the industrial process. The development of the virtual environment is carried out by means of CAD design software for the modeling of all the elements together with the Unity 3D graphic engine, in addition to the MATLAB tool that provides information on the simulation of the process, while virtual reality devices are used for immersion. The characteristic of Multi-user is given by the creation of a server which synchronizes and allows the exchange of information between several users at the same time. The experimental tests allow the operator to interact with the virtual environment and acquire monitoring and control skills with the different stages of pasteurization of the dairy industry without risks and labor problems.
Improvement of dehazing algorithm based on dark channel priori theory

The dehazing algorithm based on dark channel priori theory is an effective method for dehazing of single image. However, it still has shortcomings in some of special cases. In this work, a method is proposed to impair these disadvantages. By considering local smoothness of the sky region, pixels in the bright regions (such as light source, white object, etc.) of the haze image are first excluded from being mistaken as reference pixels, so that the estimation of subsequent parameters is more accurate. Meanwhile, the pixels of the most occurrences are selected to replace the ones in the neighborhood centered on the edge pixels, and the operation is carried out pixel by pixel along the edge contour in replace of the minimum filter. This makes the estimation of transmission near the edge in better agreement with the actual situation, and can effectively avoid the “white haze” which is an artifact caused by the minimum filtering in the areas with the depth of field changing dramatically. Compared with the previous methods, the proposed algorithm in this paper can restore images more clearly, with more image edge details retained, and effectively improve visual effect of the scene in haze weather.
ON THE INNOVATIVE DEVELOPMENT OF CRIMINALISTIC SCIENCE IN THE DIGITAL AGE

The article considers the problems of innovative development of criminalistic science in the context of global digitalization. The unity of criminalistics as a science having its own subject, system, tasks, objects is emphasized. The concept of the theory of the information-computer support criminalistic activities theory is considered. A system of this criminalistic theory has been proposed, including the concept of theory of information-computer support for criminalistic activities; doctrine about computer crime; digital traces as sources of criminalistic significant computer information; on information-computer criminalistic models of types of computer crimes; criminalistic investigation of computer tools and systems; on information-computer criminalistic software for investigative and judicial actions; on information-computer criminalistic support of computer crime investigation techniques; on the digitalization of the system of criminalistic registration, including the reference and information funds of criminalistic and forensic expertise, including the relationship and delineation of the digitalization of criminalistic and forensic expertise; on information and computer criminalistic support of civil and administrative proceedings. The main directions of development for the theory of information-computer support of criminalistic activities and elements of its system are outlined.
A Framework Designing for Story Sharing of the Elderly: From Design Opportunities to Concept Selection

In this paper, we describe how we translated contextual inquiry into design opportunities from which a framework regarding storytelling and eight design concepts were emerged, and adopted Pugh’ matrix and user consultation to evaluate these concepts. To be specific, based on the findings of contextual inquiries with older adults and caregivers, a framework of story sharing of the elderly was constructed. Eight design opportunities were then proposed. The concept selection included two rounds: Pugh’s matrix, evaluating the concepts against the baseline. User consultation, getting feedback from elderly users.
Combination of k-means with naïve bayes classifier in the process of image classification

Abstract In this paper, the data which has been tested is the image of bananas extracted based on the colour characteristics and statistics, namely red, green, blue, hue, saturation, value, average, variance, skewness, kurtosis, and energy. Image data is extracted as much as 60 data for training data, and images for test data as many as 105 data. From the 105 image data has been tested, the number of correct test data is 89 data and the number of incorrect test data is 16 data. The total percentage obtained from the test is 85%. The results obtained are a collection of images based on the level of maturity that is mature, medium, or raw.
Multi-weighted nuclear norm minimization for real world image denoising

The noise in real world images is much more complicated than additive Gaussian white noise that most existing denoising methods are designed for. The performances of the denoising methods aiming at additive Gaussian white noise on real world images are not satisfactory. A major feature of noise in real world images is that the noise levels vary with regions. We propose a denoising model named multi-weighted nuclear norm minimization according to the characteristic of the noise in real images which is the noise levels varying with regions. In our model, the objective function is divided into two parts: the data fidelity term and the regularization term. The regularization term is a weighted nuclear norm. We use two weight matrices on the data fidelity term to balance the data between channels and between regions, respectively. Since the objective function has no analytical solution, we use alternating direction method of multipliers to decompose the objective function into sub-problems with analytical solutions. We prove the effectiveness of the proposed model by experiments.
Effects of a computer-based learning environment that teaches older adults how to install a smart home system

In this study, we examined the teaching effectiveness of three strategies (trial-and-error, textbook, and combination) via a computer-based learning environment (CBLE) that teaches smart-home installation (SHiB). One hundred and twenty-five participants were randomly assigned to one of the strategies and tested with SHiB CBLE. Findings revealed that participants in the combination condition performed significantly better than those in the textbook (control) group with medium effect-size (g = 0.70). Senior participants in the trial-and-error group performed significantly better than those in the control condition with large effect-size (g = 0.89). Younger participants in the combination condition performed significantly better than those in the control condition with medium effect-size (g = 0.70). Results suggest that the teaching strategies had differential effects due to age groups.
Blockchain Based Efficient and Accurate Property Title Retrieval and Secured Storage System

A future-oriented, blockchain real estate market could help with making new plans of action of allying potential buyers and sellers. It is difficult to assume how properties of numerous sorts could be liquified, tokenized and exchanged. This blockchain paper will considerately influence transaction times as property sellers find entrusted buyers and the other way around, instead of hunting down a certifiable buyer and seller. It is more than likely that the entire real estate industry will gain advantage by this move, by bringing down the obstacle of real estate speculation and likewise eradication of center men. New meanings of property ownership and rental agreement will emerge from this move in the real estate business. The ownership of property need to be clearly stated and historically defined by documentation. There are many conflicts in ownership which is challenged in judicial/legal systems. The provision of maintaining the clarity of ownership is the responsibility of identified advocates and provided as Certificate of Title. There is no system taking care systematically of the overall transactions. The proposed will provide the secured transaction maintenance system for land/property transactions to support the timely verification of titles of said property. Also the implementation would be based on Blockchain Technology making the system transfer proof in storage of transactions.
LSD Based Vision Detection System for Industrial Robot under Complex Illumination Conditions

In this paper, we present an object vision detection and localization method approach to the industrial robotic system under complex illumination conditions. In industrial applications of machine vision, many of feature extraction algorithms depend on edge detection, which can be affected by ambient illumination variation and loses efficacy. We propose a robust method that combines image enhancement, edge detection and feature information extraction to detect and locate target components. The key idea is to use a histogram key point constrained homomorphic filtering to enhance the image followed by line and arc segments feature detection. Finally, the experimental results on probe end-face image dataset and steelmaking automation manufacture demonstrate the ability of the proposed method to perform more robust and accurate effectiveness under illumination variant conditions.
An Optimal Algorithm to Resolve the Impact of Big Data in IoT Application using Bayesian Network

Internet of Things (IoT) becomes the hot cake in all technological fields. IoT applications concurrently generate the huge amount of data that need to be handled. In this paper, an optimal mechanism is proposed to handle big data for decision making. Based on the classical decision-making process in the field of Big Data, we design an adaptive knowledge-based method through IoT application. A Bayesian network model is used to manage knowledge formation in all direction for the decision-making process. Knowledge of Bayesian networks is habitually emitted as an optimal solution, where the analysis job is to locate a formation that exploits a statistically motivated score. Generally, accessible knowledge tools deal with this optimal solution by means of ordinary search methods. As it required big amount of data space, therefore it is a time consuming procedure that should be avoided. The situation becomes decisive once big data involve in searching for optimal solution. An algorithm is introduced to accomplish faster processing of optimal solution by limiting the search data space. The proposed recursive algorithm will limit the search space. The result shows that the optimal mechanism of the decision process is able to handle big data by reducing processing time, computational complexity and a higher percentage of prediction rates.
Towards Language Service Creation and Customization for Low-Resource Languages

The most challenging issue with low-resource languages is the difficulty of obtaining enough language resources. In this paper, we propose a language service framework for low-resource languages that enables the automatic creation and customization of new resources from existing ones. To achieve this goal, we first introduce a service-oriented language infrastructure, the Language Grid; it realizes new language services by supporting the sharing and combining of language resources. We then show the applicability of the Language Grid to low-resource languages. Furthermore, we describe how we can now realize the automation and customization of language services. Finally, we illustrate our design concept by detailing a case study of automating and customizing bilingual dictionary induction for low-resource Turkic languages and Indonesian ethnic languages.
Semantically-Aware Statistical Metrics via Weighting Kernels

Distance metrics between statistical distributions are widely used as an efficient mean to aggregate/simplify the underlying probabilities, thus enabling high-level analyses. In this paper we investigate the collisions that can arise with such metrics, and a mitigation technique rooted on kernels. In detail, we first show that the existence of colliding functions (so-called iso-curves) is widespread across metrics and families of functions (e.g., gaussians, heavy-tailed). Later, we propose a solution based on kernels for augmenting distance metrics and summary statistics, thus avoiding collisions and highlighting semantically-relevant phenomena. This study is supported by a thorough theoretical evaluation of our solution against a large number of functions and metrics, complemented by a real-world evaluation carried out by applying our solution to an existing problem. Some further research venues are also discussed. The theoretical construction and the achieved results show the soundness, viability, and quality of our proposal that, other being interesting on its own, also paves the way for further research in the highlighted directions.