Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Web News: A Systematic Mapping Study

The number of news articles published on the Web has had a dramatic increase. News websites are overwhelmed daily with articles, and their processing and classification is a challenge. Reading news from the web has become an important citizen’s information source, and its classification can show relevant information about social or cultural patterns on society. In this context, techniques that can automatically analyze and classify news articles are essential. In particular, data mining and machine learning techniques have been applied for the classification of web news, as they can detect structural patterns based on documents characteristics. Their use requires specialized text processing and summarizing techniques. The objective of this study is to characterize data mining and machine learning techniques used for the web news classification, the datasets used, and the evaluation metrics. We performed a systematic literature mapping of 51 primary studies published between 2000 and 2019. We found that the most used techniques fall into these paradigms: clustering, support vector machines and generative models. Also, 33 studies used online data extracted from Internet’s news web pages, while 25 downloaded a previously published dataset. The most common metric is the F-measure, with 25 reports. In summary, several data mining and machine learning techniques have been applied to the automatic classification of web news, showing some trends regarding the techniques, datasets, and metrics.
Explainable and Argumentation-based Decision Making with Qualitative Preferences for Diagnostics and Prognostics of Alzheimer's Disease

Argumentation has gained traction as a formalism to make more transparent decisions and provide formal explanations recently. In this paper, we present an argumentation-based approach to decision making that can support modelling and automated reasoning about complex qualitative preferences and offer dialogical explanations for the decisions made. We first propose Qualitative Preference Decision Frameworks (QPDFs). In a QPDF, we use contextual priority to represent the relative importance of combinations of goals in different contexts and define associated strategies for deriving decision preferences based on prioritized goal combinations. To automate the decision computation, we map QPDFs to Assumption-based Argumentation (ABA) frameworks so that we can utilize existing ABA argumentative engines for our implementation. We implemented our approach for two tasks, diagnostics and prognostics of Alzheimer's Disease (AD), and evaluated it with real-world datasets. For each task, one of our models achieves the highest accuracy and good precision and recall for all classes compared to common machine learning models. Moreover, we study how to formalize argumentation dialogues that give contrastive, focused and selected explanations for the most preferred decisions selected in given contexts.
Improvement of Association Rule Algorithm Based on Hadoop for Medical Data

Data mining technology and association rule mining can be important technologies to deal with a large amount of accumulated data in the medical field, and can reflect the value of large medical data. According to the characteristics of large medical data, aiming at the problem that the traditional Apriori algorithm scans the database too long and generates too many candidate itemsets, a method of digital mapping and sorting of itemsets is proposed. The method of the base model and generation model was used to generate superset, which can improve the efficiency of superset generation and pruning. By using open source framework Hadoop and transplanting the improved algorithm to the Hadoop platform combined with the MapReduce framework, the idea of parallel improvement was introduced based on database partition. Experimental results show that it solves the redundancy of large-scale data sets and makes Apriori algorithm have good parallel scalability. Finally, an example was given to demonstrate the possibility of improving the algorithm.
[Cross-modal retrieval method for thyroid ultrasound image and text based on generative adversarial network].

Ultrasonic examination is a common method in thyroid examination, and the results are mainly composed of thyroid ultrasound images and text reports. Implementation of cross modal retrieval method of images and text reports can provide great convenience for doctors and patients, but currently there is no retrieval method to correlate thyroid ultrasound images with text reports. This paper proposes a cross-modal method based on the deep learning and improved cross-modal generative adversarial network: ①the weight sharing constraints between the fully connection layers used to construct the public representation space in the original network are changed to cosine similarity constraints, so that the network can better learn the common representation of different modal data; ②the fully connection layer is added before the cross-modal discriminator to merge the full connection layer of image and text in the original network with weight sharing. Semantic regularization is realized on the basis of inheriting the advantages of the original network weight sharing. The experimental results show that the mean average precision of cross modal retrieval method for thyroid ultrasound image and text report in this paper can reach 0.508, which is significantly higher than the traditional cross-modal method, providing a new method for cross-modal retrieval of thyroid ultrasound image and text report.超声检查是甲状腺病变检查中的常用手段，其检查结果主要由甲状腺超声图像和检查所见文本报告组成。实现医疗图像与文本报告的互相检索（简称：互检）可以为医生及患者提供极大的便利，但目前尚未有将甲状腺超声图像与文本报告相关联的互检方法。本文提出一种基于深度学习的跨模态甲状腺图文互检方法，并在跨模态生成对抗网络的基础上提出改进方法：① 将原网络中用于构建公共表示空间的部分全连接层之间的权重共享约束改为余弦相似度约束，能使网络更好地学习不同模态数据的公共表示；② 在跨模态判别器前加入全连接层，将权重共享的原网络中图像和文本全连接层合并在一起，在继承了原网络权重共享的优点基础上实现语义正则化。实验结果表明，本文方法的甲状腺超声图像与文本报告互检平均精度均值可以达到 0.508，较传统跨模态方法有较大提升，为甲状腺超声图像和文本报告的跨模态检索提供了新手段。.
C’Meal! the ChatBot for Food Information

Conversational systems are growing their success among users thanks to their ability to collect and rank users’ preferences and provide them relevant information in a simple way. In this paper we present C’Meal, a chatbot-based conversational framework which, given some ingredients and other requests entered by the user as input, researches the most appropriate recipe. Two categories of people are addressed in the program: those who want to discover new recipes, and those who want to create a recipe with the few ingredients left in the fridge to reduce food waste. The user must formulate his request in both cases by inserting the desired ingredients and other specific requests as a meal type or method of cooking.
Decoding Cognitive Function with Magnetoencephalography

Decoding of brain activity can perform feats of mind-reading by revealing what a person is seeing, perceiving, or remembering. Decoding can assess the information contained in magnetoencephalography (MEG) neural patterns, offering a principled approach to characterize differences in cognitive function between the normal and abnormal brain. Since their introduction, multivariate decoding methods have transformed cognitive neuroscience. They are now increasingly complementing traditional univariate methods for the analysis of neuroimaging data, in part owing to the higher sensitivity afforded by these techniques. However, deriving information from distributed MEG neural patterns requires special analytical approaches. The aim of this chapter is to introduce decoding techniques and their application in MEG. It reviews the different ways to extract MEG multivariate patterns and perform decoding analyses. It also highlights challenges and limitations in the interpretation of decoding results.
Coverless Text Hiding Method Based on Improved Evaluation Index and One-Bit Embedding

In the field of information hiding, text is less redundant, which leads to less space to hide information and challenging work for researchers. Based on the Markov chain model, this paper proposes an improved evaluation index and onebit embedding coverless text steganography method. In the steganography process, this method did not simply take the transition probability as the optimization basis of the steganography model, but combined it with the sentence length in the corresponding nodes in the model to gauge sentence quality. Based on this, only two optimal conjunctions of the current words are retained in the method to generate sentences of higher quality. Because the size of the training text dataset is generally large, this leads to higher complexity of the steganographic model; hence, fewer repetitions of the generated steganographic sentences occur. Different datasets and methods were selected to test the quality of the model. The results indicate that our method can achieve higher hiding capacity and has better concealment capability.
Brand Equity and Semiotics

Marketers generally agree that consumers make brand choices based upon intangible benefits such as status, belonging, or sex appeal, rather than product attributes alone, such as ingredients. Intangible benefits are delivered to consumers through communications or brand semiotics, which include the brand’s cultural positioning, service and packaging design, media content, and technological innovations. The semiotic dimensions of brands are not just the icing on the cake, or a “value added” to the brand’s functional benefits; they form the foundation and sine qua non of brand equity. The brand equity equation banks on the strength of brand meanings to build and differentiate the brand in an increasingly cluttered market, prompting consumers to choose one brand over the other. This opening chapter introduces readers to the semiotic dimensions of brands and methods used by semioticians to leverage competitive difference in a given market. In order to understand the basic semiotic methods presented here, analysis is limited to advertising campaigns rather than design or consumer research. Marcel Danesi contributes an essay on the importance of brand names for value creation.
ICT and challenges of agricultural extension education

After the novel coronavirus outbreak, many countries closed universities.This situation urges to implement online delivery as an alternative method.This study aimed assessing the access to and use of ICT by students, the ICT competencies possessed by the students, and assesses the current level of students' readiness for online education methods in the developing country like Ethiopia.Sample of 106 undergraduate students were selected.Semi-structured survey questionnaire was used for data collection.The findings revealed that most of the students have very limited access to and use of different types of ICTs.Mobile phones are the most popular ICT tool used by students.Poor ICT using competencies is another problem observed.ICT experts in the field of online education need to plan smartphone-based technologies, and it is recommended to offer zero-rated access to specific educational websites, and offer free or discounted mobile internet packages to all students who need it to switch to online classes.
Dealing with Fake Online Reviews in Retailing

Learning Outcomes Appreciate the scope and pervasiveness of fake reviews in retailing Recognise the causes of fake reviews in retailing Understand consumer responses to fake reviews in retail Understand how retailers can and should manage fake reviews Understand better the expected future of retail with fake reviews
Satellite Tracking Using the Space-Based Optical Sensor and Shifted Rayleigh Filter

Space-based optical sensors play an increasingly important role in space surveillance. Due to the limited computation resource on a satellite, an accurate and efficient onboard target tracking algorithm is desirable. Traditionally, the extended Kalman filter (EKF) is preferred because of its low complexity. However, the EKF may become divergent when the initial target state error is large. In this paper, a novel satellite tracking algorithm is proposed. It adopts the linearized satellite dynamics to propagate the state prior mean and covariance, and adopts the shifted Rayleigh filter (SRF) to perform the data assimilation. The SRF changes the conventional measurement model to a displacement vector plus noise form. By carefully choosing the noise covariance, the measurement probability density functions of this new and the traditional models are matched with each other. A numerical simulation is conducted and the results show that the proposed algorithm has superiorities in tracking accuracy and filter consistency compared with the EKF, at the cost of slightly increased computation time. Thus, the proposed algorithm is of practical value.
Comparative analysis of inpainting techniques based on sparse models and isophote comparison

One of the most commonly used image processing tasks in photo editing is to improve the quality of the picture when it contains scratches, smudges or any unwanted object or drawing. The restoration method used to remove them is inpainting, which consists of filling in the areas, where the unwanted information is found, in an imperceptible way. Inpainting has been used since ancient times, where the concept of removing or replacing an object in an image has been developed through the painting of photographs. Recently, new inpainting techniques have emerged based on sparse models that offer a solution to this problem. A sparse model is a system of linear equations that involves the use of a dictionary together with a vector &alpha; to make the reconstruction or improvement of the images. Orthogonal Matching Pursuit (OMP) and K-SVD are the techniques used to obtain the dictionary and the vector &alpha;. These inpainting techniques provide fairly realistic results but have not been evaluated against other techniques. Therefore, in this work we compare the results obtained with sparse modelling against those obtained with two other techniques, the first one based on bilinear interpolation and the second one, called Isophote continuation, initially identifies the area to be reconstructed, then from the adjacent neighbours creates new layers within the region to be reconstructed and repeats the process until the area to be restored is completely filled. Initially, the results of the techniques were visually contrasted with the original images. Then the difference between the original and the resulting image was calculated taking into account only the areas of interest to find the number of non-zero pixels and the root mean square error (RMSE). The techniques based on dispersed models based generated good results as well as Inpainting by Continuation of Isophotes.
Approximated algorithm and low cost VLSI architecture for edge enhanced image scaling

Scaling on the image is to be performed if the resolution of the target device and source device is not the same. Image interpolation is obtaining the unknown pixel from the known pixels. Bilinear interpolation is a commonly used interpolation technique for scaling the image because of its low complexity. Blurring effects on the scaled image due to bilinear interpolation is reduced by adding spatial sharp filter as a prefilter. To preserve the losses of edges, adaptive edge detection is used. In this paper, an approximated algorithm and its low-cost VLSI architecture is proposed for edge enhanced image scaling. Quality of the scaled images are evaluated and found to be good and comparable to that of the existing image scaling methods. The proposed architecture requires less area when compared to the existing architectures which can scale images without compromising the quality.
Ambidexterity as a New Research Paradigm: Examining the Mediating Role of Employees Goal Orientation, Exploitation and Exploration

The aim of this study was to empirically validate the impact of ability-enhancing HR bundles on organizational ambidexterity through mediation of employees cognitive factors and employees exploration and exploitation. The multi-level data was collected from 600 employees of the software companies and analyzed through MEDTHREE analysis and SEM using AMOS software. Data was collected in two-time waves and results revealed positive relationship between ability-enhancing HR bundles and organizational ambidexterity. This study contributes to literature by providing new insights and by investigating links of ability-enhancing HR bundles with organizational ambidexterity directly and indirectly.
SECURING GENERALIZED DATA USING RB23 ALGORITHM

Adv. Math. Sci. J. Vol. 9, No. 9 Open Access SECURING GENERALIZED DATA USING RB23 ALGORITHM R. Jaichandran, K. Shantha Shalini, S. Leelavathy, J. Johnson Rajaguru and Kuruva Pradeep DOI: https://doi.org/10.37418/amsj.9.9.25 Full Text Abstract Abstract The security of ChaCha stream cipher family design from Salsa stream cipher design. The ChaCha design also ovolo … page AMSJ9-N9-25 Read More »
HYBRID BIOINSPIRED ALGORITHM FOR ONTOLOGIES MAPPING IN THE TASKS OF EXTRACTION AND KNOWLEDGE MANAGEMENT

The article is devoted to solving the problem of mapping ontological models in the processesof extracting and knowledge management. The relevance and significance of this task are due tothe need to maintain reliability and eliminate redundancy of knowledge during the integration(unification) of various origins structured information sources. The proximity and consistency ofthe conceptual semantics of the combined resource during the mapping is the main criterion forthe effectiveness of the proposed solutions. The article considers the problems of choosing appropriatesolution approaches that preserve semantics when displaying concepts. The strategy ofchoosing bio-inspired modeling is substantiated. The aspects of the effectiveness of various decentralizedbio-inspired methods are analyzed. The reasons for the need for hybridization are identified.The paper proposes to solve the problem of mapping ontological models using a bio-inspiredalgorithm based on hybridization of bacterial and cuckoo search algorithms optimization mechanisms.The hybridization of these algorithms allowed us to combine their main advantages: a consistentbacterial search that provides a detailed study of local areas, and a significant number ofthe cuckoo agent during the implementation global movements of Levy flights. To evaluate theeffectiveness of the proposed hybrid bio-inspired algorithm, a software product was developed andexperiments were performed on the mapping of different sizes ontologies. Each concept of anyontology has a certain set of attributes, which is a semantic vector of attributes. The degree of thesemantic vectors similarity for the compared concepts of displayed ontologies is a criterion fortheir integration. To improve the quality of the display process, a new encoding of solutions hasbeen introduced. The quantitative estimates obtained demonstrate time savings in solving problemsof relatively large dimension (from 500,000 ontograph vertices) of at least 13 %. The timecomplexity of the developed hybrid algorithm is O (n 2). The described studies have a high level oftheoretical and practical significance and are directly related to the solution of classical problemsof artificial intelligence aimed at finding hidden dependencies and patterns on a multitude ofknowledge elements.
Data Visualization Techniques and Algorithms

Data visualization plays an important role in gaining insight from data. Generally, traditional methods are used to systematically create graphical formats of data attributes of either numeric or textual data. However, these traditional methods are very time-consuming computationally when they must display data points of big data sources. It is significant to explore new methods and algorithms that require less computational time while taking into consideration the volume of data attributes involved. In this chapter, the behavior of animals is explored to help create a method and an algorithm for data visualization suited for big data visualization.
Pedagogical Capabilities Of A Teacher’s Personal Page In Social Network

The article presents an analysis of the pedagogical capabilities of social networks in solving modern educational problems. The advantages of a social network as a tool for distance learning, a platform for student collaboration and organization of pedagogical interaction are singled out. The article reveals the pedagogical potential of the social network as a whole, as well as a teacher’s personal page in it, in regards to the individualization of the educational process, selection of the education content, organization of pedagogical communication, involvement in activities. Educational capabilities of services and applications of the social network “VKontakte” are analyzed. The paper dwells on the results of a diagnostic study of students’ attitudes to the communication activities of teachers on their personal pages in networks. The author describes and analyzes the experience of interacting with students on the social network platform, which confirms the positive impact on the students’ attitude to the subject and educational motivation of the possibility to communicate and connect with the teacher through the social network. The article also identifies restrictions (increased requirements for the content and design of the personal page) and possible negative consequences of interaction with students through social networks (non-compliance with the framework and blurring of the boundaries of communication, the interpenetration of professional and personal spheres of life).
How the Experience of Workplace Violence Influences the Turnover Intention of Social Workers: Focused on the Mediated Effect of Job Satisfaction

본 연구는 사회복지사가 경험한 직장 내 폭력이 이직의도에 미치는 영향을 파악하고, 직무만족의 매개효과를 검증하는 것이다. 이를 위해 2019년 1월부터 4월까지 서울지역 사회복지기관 종사자 450명을 대상으로 설문 조사를 진행했다. 회수 후 불성실하게 응답 된 설문지를 제외하고 420명에 대한 결과를 SPSS Statistics 25.0을 사용하여 매개분석을 실시하였다. 연구결과, 사회복지사는 현장에서 다양한 폭력을 경험하였다. 또한 직장 내 폭력은 이직의도에통계적으로 유의미한 영향을 미쳤고, 두 변수 사이에서 직무만족은 부분 매개하는 것으로 확인되었다. 이에 따라 사회복지사의 인권 존중을 위한 법적 규정 마련, 직장 내 폭력 예방을 위한 사회복지기관 내 법적 의무교육 강화, 신입 사회복지사 대상의 직무별 특화 교육 시행, 근로자지원프로그램의 도입을 제언하였다.This study determines the influence that workplace violence experienced by social workers has on their turnover intention and seeks to examine the mediated effect of job satisfaction. A survey was conducted with 450 participants who worked at social welfare institutions in the Seoul metropolitan area from January to April 2019. For a total of 420 responses from the surveys, excluding the ones that have been undependably completed, a mediation analysis was conducted using SPSS Statistics 25.0. The results of the analysis revealed that social workers experienced a variety of violence in the workplace. The results also found that workplace violence had a statistically significant influence on social workers’ turnover intention, and job satisfaction partially mediated in between the two main variables. As such, this study proposes the arrangement of legal regulations to ensure the respect of the human rights among social workers, the strengthening of legally compulsory training within social welfare institutions for the prevention of workplace violation, the implementation of specialized training for each job function for new social workers, and the introduction of Employee Assistance Programs (EAPs) particularly designed for the group.
Research on the application of block chain technology in electricity trading system

As the technical basis of Bitcoin, block chain has the characteristics of decentralization ， openness and transparency. In order to promote the development of security and openness of power trading system and solve the trust problem among participants in power market，a new research direction of power trading method based on block chain is proposed. Therefore，this paper outlines the block chain，explains the definition and characteristics of block chain in detail，discusses the application of block chain in power trading system，and summarizes the challenges faced by block chain technology in power trading system.