Reviewerly/exHarmony huggingface dataset:
SensAI+Expanse Adaptation on Human Behaviour Towards Emotional Valence Prediction

An agent, artificial or human, must be continuously adjusting its behaviour in order to thrive in a more or less demanding environment. An artificial agent with the ability to predict human emotional valence in a geospatial and temporal context requires proper adaptation to its mobile device environment with resource consumption strict restrictions (e.g., power from battery). The developed distributed system includes a mobile device embodied agent (SensAI) plus Cloud-expanded (Expanse) cognition and memory resources. The system is designed with several adaptive mechanisms in a best effort for the agent to cope with its interacting humans and to be resilient on collecting data for machine learning towards prediction. These mechanisms encompass homeostatic-like adjustments such as auto recovering from an unexpected failure in the mobile device, forgetting repeated data to save local memory, adjusting actions to a proper moment (e.g., notify only when human is interacting), and the Expanse complementary learning algorithms' parameters with auto adjustments. Regarding emotional valence prediction performance, results from a comparison study between state-of-the-art algorithms revealed Extreme Gradient Boosting on average the best model for prediction with efficient energy use, and explainable using feature importance inspection. Therefore, this work contributes with a smartphone sensing-based system, distributed in the Cloud, robust to unexpected behaviours from humans and the environment, able to predict emotional valence states with very good performance.
RESEARCH OF THRESHOLD TECHNOLOGY OPTIMIZATION PROPERTIES

To classify objects, you need to get an image description with so many possible descriptions that it is pointless to take each as a separate class.A partial task of this problem is to divide the image into segments. Since there is no common method for all cases, we consider methods that allow us to determine new subsets using already selected subsets.The paper considers the main approaches to identify new subsets using already selected subsets. The publication contains the following sections: partition of an image of any type; quantization of the input image; simultaneous application of smoothing and quantization; application of consistent filter and quantization.
A Comparative Study of Sentiment Analysis on Mask-Wearing Practices during the COVID-19 Pandemic

COVID-19 has become one of the most highly orated subject matter in these days. Countries have taken many viable actions to prevent the spread of the virus directed by international recommendations, which led to many disputes concerning wearing a face mask as a preventive measure against the virus. This study aims to assess and compare the overall accuracy, macro precision, macro F-measure and macro recall of the different decision models towards the COVID-19 mask-wearing practices via sentiment analysis. Tweets are labeled and text pre-processing techniques are applied as stemming, normalization, tokenization, and stop-word removal. Subsequently, the tweets are transformed into master feature vectors by applying various feature extraction, feature representation, feature selection and word embedding techniques with five supervised machine learning decision models to predict mask wearing practices reinforced from Twitter tweets. Moreover, the highest macro F-measure and macro precision are found with feature extraction as hybrid-grams, feature representation as TF-IDF, feature selection as Chi-Squared Test, and highest macro recall with feature extraction as BOW, feature representation as TF-IDF, feature selection as ANOVA F-value. Hence, this study concludes that the Naive Bayes (NB) algorithm outperforms other decision models with master feature vectors applied. In addition, it also outperforms word embedding techniques.
Cybercrime-Emerging Issue

With the rise of social distancing, security of the Internet and online communication become essential issue and cybercrime emerging challenge.
Utilization of Duo lingo Application used and Practice Drilling Vocabulary Technique for Up-Grading Students’ Reading Comprehension

English language has been commonly accepted as an active universal language in the world. People who study English language consider it as their personal proficiency. Therefore, nowadays many people, particularly students, ought to master English language. Mastering English language is an important value for the students to have. Refers to the condition, the researcher interested to help student to master English specially reading comprehension, and this research would find the following questions; Is there any influence of duolingo application used to the students’ reading comprehension at SMA Muhammadiyah Tanjung Redeb?, (1) Is there any influence of vocabulary enhancement to the students’ reading comprehension at SMA Muhammadiyah Tanjung Redeb?, (1) Is there any influence of duolingo application used and vocabulary enhancement to the students’ reading comprehension at SMA Muhammadiyah Tanjung Redeb?. The method of this research is partial correlation method, then to analyze the data used normality, homogeneity, linearity, partial correlation and regression equation.
Managerial and ICT Skills Possessed by Business Education Graduating Students for Entrepreneurial Success in Universities in South-South Nigeria

The study examined managerial and ICT skills possessed by Business Education graduating students for entrepreneurial success in Universities in South-South Nigeria. Two research questions were answered while two null hypotheses were tested at 0.05 level of significance. Descriptive survey research design was adopted in carrying out the study. Â  Â  The population and sample of the study comprised 1,037 final year (400 Level) Business Education students of 2019/2020 academic session from which 285 final year students of Business Education were sampled as the respondents. The instrument used to collect data was a 30-item structured questionnaire designed by the researcher and face-validated by three experts. The reliability of the instrument was ascertained using Cronbachâ€™s Alpha reliability method which yielded a coefficient of 0.87. Out of the 285 copies of the questionnaire administered, 274 copies were completely filled and retrieved representing 96%. Data collected were analyzed using mean for answering the research questions while t-test statistics was used for testing the two null hypotheses. Based on the data collected and analyzed, the result showed that the level of managerial and ICT skills possessed by Business Education graduating students in Universities in South-South Nigeria, is quite low. Hence, the study among others recommended that Government should make funds available for the provision of required ICT infrastructure for effective teaching and learning of Business Education students in tertiary institutions in Nigeria and that proactive school-industry linkage arrangement for quality internship experience of Business Education students for acquisition of relevant managerial and ICT skills for gainful employment in Nigerian labour market and for entrepreneurial success, should be sustained.
Best Computer Monitor For Low Vision

Constant and prolonged use of a computer built without eye care technologies can cause severe damage to your eyes, leading to eye strain and other ...
Using deep perceptual embeddings as a quality metric for synthetic imagery

The potential of synthetic image generation in machine learning applications has a myriad of both benefits and drawbacks. Synthetic datasets are valuable when real datasets are costly to acquire or difficult to share. Although synthetic imagery has improved in quality over the years, automated evaluation of the synthetic data remains challenging. For example, the Fréchet inception distance does not always correlate with perceptual quality and the perceptual path length operates at the model level rather than the data level. In this work, we propose a new evaluation metric that both correlates with perceptual quality and operates at the data level so that it can function on datasets from any domain. We do this by mapping high dimensional images into a lower dimensional, perceptually disentangled embedding space and computing the distance between distributions of embeddings within this space. We utilize a convolutional autoencoder trained with a linear combination of pixelwise and perceptual losses to perform the mapping and use existing metrics to measure the distance between the distributions of embeddings. We demonstrate efficacy of this metric on the CIFAR-10 dataset.
Late Fusion Multi-view Clustering with Learned Consensus Similarity Matrix

Multiple kernel algorithms in a late fusion manner have been widely used because of its excellent performance and high efficiency in multi-view clustering (MVC). The existing MVC algorithms via late fusion obtain a consensus clustering indicator matrix through the linear combination of the base clustering indicator matrix. As a result, the optimal consensus indicator matrix’s searching space reduces, and the clustering effect is limited. To learn more information from the base clustering indicator matrices, we construct a consensus similarity matrix as the input of the spectral clustering algorithm. Furthermore, we design an effective iterative algorithm to solve the new resultant optimization problem. Extensive experiments on 11 multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed algorithm.
PRODUCTION CHARACTER OF UNIVERSITY PROCESSES

The modern tendencies in the Russian system of education, the emergence of competition and market relations in this sphere facilitated the reconsideration of the scientific grounds for the higher educational establishments activity. The suggested theoretical and methodological approaches to the domestic higher educational institutions’ management are subject to debate. The paper analyses the most essential terminology for the theoretical grounds of production management applicable for higher education processes. The terms "production", "activity", "production process" are clarified. The frequently used term "the education services production" is proved to be incorrect. The paper specifies the educational service in the context of the higher educational establishment activity, taking into consideration that the educational service should provide the student with the appropriate knowledge, training management, teaching support and student’s own educational activity control. The educational production process with which the student interfaces is divided into three subsequent stages: preparatory, main and final. The essence of each stage is defined. The students’ and teachers’ educational activity subject and result identification ways are suggested. These ways are aimed at making ultimate decisions for educational establishment production management optimization and other aspects improvement.
Selection of Computer Private College to Use Elimination Methods and Options of Expressing Reality (ELECTRE)

Higher education is a continuation of secondary education which is prepared for students to become members of society who have academic and professional abilities who can apply, develop and create science, technology and arts. Higher education contains background problems, problem formulations and research objectives. Choosing a college has many aspects that must be considered, such as the location of the college, tuition fees, distance from residence, accreditation, levels, and choice of computer program majors. The hesitation in choosing a college to major in computer is often an obstacle for every prospective student. Especially with the many universities that exist today, both private and public universities. This makes prospective students often feel unfamiliar, confused, lack of information about tertiary institutions so that they are wrong in making choices. So that the solution from researchers designed information systems that are useful for assisting prospective students in choosing private universities for web-based computer programs, using the ELECTRE mysql database method. The results of research on private universities with the Electre method, it can help and provide recommendations and the best alternative decision solutions to prospective students in making decisions with many criteria.
Deep Neural Networks Techniques using for Learning Automata Based Incremental Learning Method

Surprisingly comprehensive learning methods are implemented in many large learning machine data, such as visual recognition and visual language processing. Much of the success of advanced training in recent years is due to leadership training, which requires a set of information for specific tasks, before such training. However, in reality, selected tasks related to personal study are gradually accumulated over time as it is difficult to collect and submit training data manually. It provides a way to continue learning some information columns and examples of steps that are specific to the new class and called additional learning. In this post, we recommend the best machine training method for further training for deep neural networks. The basic idea is to learn a deep system with strong connections that can be "activated" or "turned off" at different stages. The approach you suggest allows you to reduce the distribution of old services as you learn new for example new training, which increases the effectiveness of training in the additional training phase. Experiments with MNIST and CIFAR-100 show that our approach can be implemented in other long-term phases in deep neuron models and achieve better results from zero-base training.
Teaching Information Architecture in South Africa: In Conversation with Terence Fenn

An interview with Terence Fenn, a senior lecturer at the University of Johannesburg and frequent author of information architecture and design papers, talks and presentations, where he discusses how his interest in information architecture developed over time, how he teaches information architecture to his students in the Art, Design and Architecture school, the social and cultural importance of design, and how he has been using information architecture to redefine how to teach design in a post-colonial, post-Apartheid South Africa.
Handwritten Recognition of Rajasthani Characters by Classifier SVM

The entirely distinct pattern recognition technologies have been proposed over recent years and thus the different research teams focus on the effects of popularity. Because of its use in many areas, such as pattern recognition and machine learning, handwritten character recognition has found great success. In online handwritten character recognition, the basic field is for use. The various character recognition techniques were suggested in the offline handwritten recognition process. Although the techniques for transforming textual content are established by some empirical studies and publications. This textual material has been translated from a paper file into a machine-readable form. The character recognition system could help produce a paperless document as a key in the coming days. The key aspect was the digitization of paper documents and the retrieval of existing paper records as well. In this job, we took out offline samples of some Rajasthani handwritten characters. The proposed average recognition rate for machine archives is 89.82 percent, using histogram oriented gradient features and support vector machine classifiers.
Pemanfaatan Sistem Informasi Dalam Pengelolan Data Masjid Al-Ikhlas Deli Tua

The internal activities of mosques throughout Indonesia have been stipulated in Regulation No. 54 of 2006 Decree of the Minister of Religion Number 54 of 2006 concerning the Organizational Structure and Work Procedures of Mosque Welfare Bodies. Mosque Welfare Agency (BKM). With the regulations that have been set by the government, residents around the mosque are willing and ready to accept the mandated tasks when appointed by the relevant government as mosque administrators. In managing mosque data, it is time to link it with a digital information system so that people can easily get information about mosques. To facilitate this data collection, with the development of internet network infrastructure and information system services that are increasingly accountable and sharing programming languages that are increasingly supportive. With this service, it can provide information anytime and anywhere as long as it is connected to the internet network with smartphone and computer media.
The danube river as an activation factor of the ethnopedagogical component of general cultural competence of bachelors in natural sciences

In accordance with the European parameters of professional training of the new formation of educators, it is mandatory to emphasize the problem of forming in the future teacher the basic competencies of modern human, including an ethnocultural one. The value of this format of professional training is the focus of competence-oriented content of education on the formation of life and worldview of the future teacher, as well as his/her professional self-development. Ethnopedagogical component of training of future bachelors of natural sciences is of particular importance in the implementation of the concept of specialized training in high school and is one of the indicators of professional readiness to implement the multicomponent component of the content of modern natural education. Ethnopedagogy as a component of modern pedagogical science is a powerful resource for the formation of general cultural competence of the future teacher, which reflects a certain level of his/her professional readiness for teaching in the context of interaction of natural and social environments, to emphasize semantic and ideological aspects, to highlight the dominance of new values.
Guides: Subject Guide: Management and Marketing: Newspapers

A guide to getting the most out of the Library and Collections resources for Management and Marketing
Designing of three regions de-blocking filters in BDCT coded images at low bit rate

Compression of images and video frames at a low bit rate significantly degrades the visual quality decoded with JPEG and MPEG standards. This paper focuses on an efficient pre and post-processing technique to address and rectify the blocking artifact. This method differentiates the blocked images or frames into different regions based upon the activity function. It efficiently removes the artifacts while preserving the edge details with the help of different adaptive filters. The proposed technique is implemented on various images and videos and results are compared with multiple existing methods using performance metrics like SSIM and GBIM. The simulation results show significant improvement in subjective quality for different images and video frames. It outperforms existing de-blocking methods.
Critical Event Simulation Method for Assessing Security Leader Candidates: Hazard Control Competency

Choosing a leader candidate for a security officer requires a proper method. Assessment using behavioral based is a suitable approach for selecting the right candidate. To find out the competence possessed, using a critical event simulation.. This study aims to measure the competencies prospective security officers to face dangerous and hazard situations.Critical event simulation measuring individual ability to deal with the issues to protect hazard and a potential danger. In the simulation method presented potential hazard situation. This study aimed to measure hazard control competencies. Hazard control competence is measured by looking at six dimensions. The six competencies referred to are: problem-solving skills, customer service orientation, discipline, willingness to follow the procedure, and teamwork. These six competencies signify the ability to deal with hazardous situations in an acceptable way.The critical event simulation a method purpose to see hazard control competency will emerge and have variability when carried out on 50 candidates involved in this study.participants divided into five groups. Each group will get 5 cases of Critical event simulation.The ability to face and overcoming danger and hazards measure by looking hazard control competency. The responses of each candidate observed by two observers.The study included 50 security officer to see the effectiveness of the methods Simulation of the Critical event to unveil competence. Results showed Critical Event Simulation is aneffective method for measuring hazard control competency. The measurement results obtained show consistency with the assessment supervisor. Five competency assessment using critical event simulation has significant correlation with the competency. Through this study obtained results that show a critical situation simulation assessment method to effectively measure hazard control competency.
The Lib2Life Platform – Processing, Indexing and Semantic Search for Old Romanian Documents

Preserving the cultural heritage of a nation throughout generations is essential in a continuously developing society.This paper introduces the Lib2Life platform powered by advanced Natural Language Processing techniques, focusing on the processing, indexing and semantic search of old documents from the Central University Libraries in Romania.Our platform enables the upload and text preprocessing of scanned documents by librarians, who can afterwards manually correct the extracted content and corresponding metadata.In addition, Lib2Life ensures the exploration of the collection of books using a semantic search engine to retrieve documents fitted to the users' interests.The platform was evaluated using an usability questionnaire which pinpointed out that Lib2Life is a modern and user-friendly smart search engine for old documents written in the Romanian language.Improvements in terms of server response time and functionality were suggested.The platform proved to be intuitive and easy to use, having the potential to become an analytical system incorporating a rich and diverse collection of books.
Confidential data sharing in cloud with secured IOT Assistance

The always developing number of Internet associated gadgets represents a few network safety chances. A large portion of the traded information between the Internet of Things (IoT) gadgets are not enough gotten because of asset limitations on IoT gadgets. Quality Based SignCryption (ABSC) is an incredible cryptographic system reasonable for dispersed conditions, giving adaptable access control and information mystery. Be that as it may, it forces high plan grave particle costs, and doesn't uphold access strategy update (client expansion/disavowal). This paper presents PROUD, an ABSC arrangement, to safely re-appropriate information design cryption cycle to edge workers to diminish the calculation overhead on the client sides. Pleased permits end-clients to offload the vast majority to an edge worker and check the accuracy of the got halfway design crypted information from the edge worker. The entrance strategy update include in PROUD doesn't influence the size of the message got by the end customer which decreases the information move limit and the limit purposes. Our complete hypothetical, trial investigation demonstrates that the beats existing plans regarding usefulness, correspondence and calculation.