Reviewerly/exHarmony huggingface dataset:
Applying domain knowledge and academic information to enhance unknown-item search in OPAC

Many students usually use the unknown-item search strategies, including subject and keyword searches, to retrieve books or other materials provided in library catalogs. However, the success rates for unknown-item searching is relatively low compared with the known-item search strategies, i.e., title or author searches. In this paper, a framework for improving the unknown-item search is proposed. The main contributions of our framework concern both user's keywords and book indexing: (i) To enhance a user's keyword, the framework will select other relevant terms in a domain-related ontology; (ii) Topics expressed in course description are used as book indexing. A preliminary experiment shows that the proposed framework gives satisfactory results in terms of the numbers and the precision scores of retrieved books. Furthermore, the proposed interesting-score measure can facilitate to lift the precision levels.
Poverty rate in Africa and information access: The case of the Internet

Information is key to basic survival in society. No civilization ever left that out in its existence. In fact, those who had greater access to information were known to have been more successful in war, infrastructures, commerce, and trade. However, we’re not living in the ancient times, and this fact brings both relief and worries. Relief because there are greater and more advanced infrastructures put together these days to transmit information. Worries because even at this point of development around the world, these infrastructures aren’t accessible to most people for one reason or the other. And this is exactly the case of the Internet in Africa today.
Securing Virtual Machines on Cloud through Game Theory Approach

With the ever so growing boundaries for security in the cloud, it is necessary to develop ways to prevent from total cloud server failure. In this paper, we try to design a Game Strategy Block that sets up rules for security based on a tower defence game to secure the hypervisor from potential threats. We also try to define a utility function named the Virtual Machine Vitality Measure (VMVM) that could enlighten on the status of the virtual machines on the virtual environment.
Sensitivity Analysis of Economic Variables using Neuro-Fuzzy Approach

Sensitivity analysis (SA) is a vital task for decision making in economic management. In this paper, a novel fuzzy sensitivity analyzer (FSA) is proposed to analyze the sensitivity of economic variables. The proposed FSA algorithm consists of an adaptive neuro-fuzzy inference system (ANFIS) that is adjusted for forecasting economic time series. Based on the output of ANFIS, FSA can determine the importance degree of parameters. In the numerical studies, the proposed method is applied for the sensitivity analysis of oil and gold time series. According to the results, FSA indicates that oil price is highly dependent upon the inflation rate, dollar index and market index while OPEC production level and gold price have less impact. Furthermore, in the gold price modeling, the highest sensitivity is obtained from silver price while demand for gold is more a function of market index and inflation rate. The proposed method can be used in many SA applications.
Parallel-to-Axis Uniform Cubemap Projection for Omnidirectional Video

Omnidirectional video can provide an immersive visual experience by presenting a 360° video content. For omnidirectional video, pixels on the sphere need to be mapped onto a two-dimensional plane to adapt to the existing coding standards. Equirectangular Projection (ERP) and Cubemap Projection (CMP) have excessive sampling density on some sampling regions, which results in unnecessary sampling pixels. In this paper, Parallel-to-Axis Uniform Cubemap Projection (PAU) is proposed. After projecting the sphere to a cube like CMP, an equal-angle mapping position adjustment is used to sample the sphere more uniformly. After adjustment, one chosen axis is equal-angle sampled and each line parallel to the other axis is sampled equiangularly in the proposed format. Experimental results show that our proposed projection format can achieve 10.68% bitrate gain compared to ERP.
Assisted Music Score Reading Using Fixed-Gaze Head Movement

Eye-tracking has a very strong potential in human computer interaction (HCI) as an input modality, particularly in mobile situations. However, it lacks convenient action triggering methods. In our research, we investigate the combination of eye-tracking and fixed-gaze head movement, which allows us to trigger various commands without using our hands or changing gaze direction. In this instance, we have proposed a new algorithm for fixed-gaze head movement detection using only scene images captured by the scene camera equipped in front of the head-mounted eye-tracker, for the purpose of saving computation time. To test the performance of our fixed-gaze head movement detection algorithm and the acceptance of triggering commands by these movements when the user's hands are occupied by another task, we have designed and developed an experimental application known as EyeMusic. The EyeMusic system is a music reading system, which can play the notes of a measure in a music score that the user does not understand. By making a voluntary head movement when fixing his/her gaze on the same point of a music score, the user can obtain the desired audio feedback. The design, development and usability testing of the first prototype for this application are presented in this paper. The usability of our application is confirmed by the experimental results, as 85% of participants were able to use all the head movements we implemented in the prototype. The average success rate of this application is 70%, which is partly influenced by the performance of the eye-tracker we use. The performance of our fixed-gaze head movement detection algorithm is 85%, and there were no significant differences between the performance of each head movement.
PRIVACY PROVISION FOR TIP ATTRIBUTES IN NTTP BASED LBS SYSTEMS

Location Based Services (LBS) are mainly concerned to protect privacy of users because privacy is a core need of any user who deals with any computing system. In LBS systems, privacy is concerned with three fundamental metrics such as temporal information, user identity & spatial information. According to variance of multiple scenarios these three attributes must be protected because losing one's privacy attribute can lead to full privacy intervention of user. Different strategies are being used to deal these metrics including TTP and NTTP. Therefore the purpose is to bring the privacy at user's satisfaction level in LBS for user identity, temporal & spatial information attributes by using NTTP (Non-Trusted Third Party). In current study, we have conducted a comprehensive survey on approaches of NTTP in LBS systems. The purpose of survey was to focus on further advancements of these proposed models.
Multidimensional Correlation Hierarchical Differential Privacy for Medical Data with Multiple Privacy Requirements

In recent years, mobile devices have become increasingly more popular with the rapid development of the Internet and information technology. A large amount of high-dimensional data, which is stored in a distributed manner in multiple agencies, has become a very important resource on the current Internet. Collecting these data for analysis and applications contributes to great social and economic value. Therefore, the privacy problem of high-dimensional data publication in distributed multiparty settings has drawn progressively more attention and become one of the popular issues in current research. The existing anonymous methods mainly adopt the k-anonymous model or differential privacy, which apply a uniform threshold to add noise. However, different attributes in the dataset often have different sensitivities. Additionally, the correlation between attributes also increases the risk of privacy leakage. Moreover, the allocation of a privacy budget in differential privacy is another problem. To solve these problems, this paper proposes a multidimensional correlation hierarchical differential privacy (MuCH-DP) method in the medical data publication domain with multiple privacy requirements. In a distributed multiparty setting, the correlation between attributes is quantified through mutual information and established by the relevant Bayesian networks. To guarantee privacy and improve data utility, this paper designs a personalized privacy budget allocation strategy for the different sensitivities and assigns personalized privacy budgets for multiple participants. Finally, the feasibility and utility of the multidimensional correlation hierarchical differential privacy (MuCH-DP) method are verified by the experiments.
Pedagogical mastership of the higher school teacher of XXI century: sinergy of virtual and real in the educational space

The article studies the essence and peculiarities of the development of pedagogical mastership of a higher school teacher in the synergetic combination of virtual and real in the educational space. It is substantiated, that transformation processes of the information-digital educational space of Ukraine need new scientific studies of the pedagogical mastership development. That is why the pedagogical mastership development of a high school teacher in the synergetic combination of virtual and real in the educational space is a very urgent task, because it is associated with social self-organization in changing conditions of modernity. Today the post-non-classic psycho-synergetic studying methodology of the human psychics gains its new development in the socio-humanitarian sphere. It is proved, that the specificity of psycho-measuring environments from the aspect of the post-non-classic methodology of psycho-synergy is in the internal psychic character and is conditioned by states of psychics. It is explained by the fact that external irrigators don’t directly determine human reactions – they are mediated by “internal conditions”. It is revealed, that pedagogical mastership provides self-organization of the high level of professional activity on the reflexive base in the synergetic combination of virtual and real in the modern educational space. Under modern conditions of virtual and modern worlds pedagogical mastership remains the “extremely felt activity of planning the future word of mankind”. There are concretized peculiarities of the influence of transformation processes of the information-digital educational space on the modern generation. Most important requirements to pedagogical mastership under conditions of transformation processes of the information-digital educational space that will influence the modern generation are concretized: the modern teacher is an active user of the Internet, who must understand his/her own responsibility at interactions in social networks; an aim of the “school of future” is to adapt education to unique characteristics of each pupil, developing moral-ethic qualities; introduction of on-line education reduces financial costs at intensifying the emotional-positive influence of feedback in education; integral study of the educational space in the triad “past – present – future”. Directions of the development of pedagogical mastership of a higher school teacher of XXI century are explained on the example of the work of the Educational-consulting Center of ONPU “Pedagogical mastership of a higher school teacher”
Five Times Extended Reed-Solomon Codes Applicable in Memory Storage Systems

In this letter it is proved that five times extended Reed-Solomon codes contain an infinite subset of almost MDS codes with parameters: [(q - 1) + 5, (q - 1), 5] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GF(2m)</sub> which are defined over a finite field GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) where m ≥ 3 is a positive odd integer. The first three of these codes reach an upper bound for linear block code distance for the corresponding codeword lengths and number of information symbols in codewords in existing tables for optimal code parameters [1]. The relevant parts of weight spectra for the first four codes confirm the code parameters.
A Teaching Model for Undergraduate Students

Mathematics, along with the need for logic and thinking, is becoming more important in many fields. Therefore, many universities in Korea have opened and operated a college basic math course to improve basic math skills for freshmen in science and engineering. The new generation of digital generation is creative, familiar with cooperation and active. They are already rapidly changing and ready for new education, and education needs a new paradigm to evolve. Flipped Learning is being suggested which is well known as a teaching method which lets students learn the contents they will learn in advance through the advance online video and have a discussion through the team interaction in the main class for them to solve the assignment through the cooperation in a self-initiated way. In this study, we have taken a merit of flipped learning, made a model of Gauss Jordan elimination method in matrix that students cannot easily understand in the lecture. Here, we will introduce a teaching model that combines flipping learning and existing lecture methods.
A Legal Perspective on Social Media Use and Employment: Lessons for South African Educators

Abstract&#x0D; In this article, the author provides a legal perspective on the interplay between social media use and employment. The unique characteristics of social media are identified in order to frame the article before a number of considerations with regard to employment relationships that impact on deciding social media misconduct cases are deliberated upon. Thereafter, the author reflects on the implications of social media transgressions for educators' professions and contemplates the forms that social media misconduct by South African educators can take, with specific focus on defamation. Before the article reaches its conclusion, social media evidence and the impact of the unique characteristics of social media on users' legitimate expectations of privacy are also considered. The article concludes with a few guidelines on how educators can avoid the pitfalls that make social media use potentially hazardous to their employment.&#x0D; &#x0D;
The Effectiveness of Teaching Writing Descriptive Text by Using Social Media “Instagram” to Improve Students’ Writing Ability at Junior High School Students

Teaching writing is one skill that is taught by English teacher in school. It became important skill that student have to master writing skill. The researcher chooses Descriptive Text, because it is one of type text to describe something such as place, animal, people and things. The researcher use social media of “Instagram” to teach student in writing descriptive text to improve students writing ability. The researcher use experimental research in quantitative method which two classes as experimental and control group. The sample of the data is taken from Barunawati Junior High School students. The researcher use snowball sampling. The result has been calculated by using SPSS 20.0. The result showed that teaching writing descriptive text using social media“Instagram” to improve students writing ability is effective.
REMOTE BANKING FRAUD DETECTION FRAMEWORK USING SEQUENCE LEARNERS

The reliability and performance of fraud detection techniques has been a major concern for the financial institutions as traditional fraud detection models couldn’t cope with the emerging new and innovative attacks that deceive banks. This paper proposes a conceptual fraud detection framework that can detect anomalous transaction quickly and accurately and dynamically evolve to maintain the efficiency with minimum input from subject matter expert. Based on the proposed framework,we implement Long Short-Term Memory (LSTM) based Recurrent Neural Network model for detecting fraud in remote banking and evaluate its performance against SVM models. Two novel features for remote banking fraud are evaluated, i.e., the time spent on a page and the time between page transitions. Modeling is performed on an anonymised real-life dataset, provided by a large financial institution in Europe. The results of the modeling demonstrate that given the labeled dataset both the LSTM and SVM model can detect payment fraud with acceptable accuracy, though overall the LSTM models perform slightly better than the SVM models. The results also prove the hypothesis that the events across banking channels can be modeled as time series data and then sequence-based learners such as Recurrent Neural Network (RNN) can be applied to improve or reduce the False Positive Rate (FPR) and False Negative Rate (FNR).
Techniques, Tools and Applications of Graph Analytic

Graphs have acute significance because of polytropic nature and have wide spread real world big data applications, e.g., search engines, social media, knowledge discovery, network systems, etc. Major challenge is to develop efficient systems to store, process and analyze large graphs generated by these applications.Graph analytic is important research area in big data graphs dealing with efficient extraction of useful knowledge and interesting patterns from rapidly growing big data streams.Tremendously huge and complex data of graph applications requires specially designed graph databases having special data structures and effective features for data modeling and querying.The manipulation of large size of data requires effective scalable and distributed computational techniques for efficient graph partitioning and communication.Researchers have proposed different analytical techniques, storage structures, and processing models.This study provides insight of different graph analytical techniques and compares existing graph storage and computational technologies.This work also assesses the performance, strengths and limitations of various graph databases and processing models.
Marketing in higher education: promoting educational services and programmes

Purpose – the purpose of the article is to investigate current marketing and branding trends in higher education, and to discuss some popular marketing tools incorporated into the marketing strategy of a contemporary university. The rele-vance of the research topic is determined by the fact that managers working in higher education have to become more market-oriented as they are facing new challenges in an extremely competitive international environment. Research methodology – the article is based on the analysis of theoretical literature and previous research on the topic, OECD and EU documents on the basic trends in higher education, and marketing plans of randomly chosen higher edu-cation institutions. Findings – the analysis performed in the article has allowed the author to identify the main challenges experienced by contemporary universities in the agenda of promoting their educational services and programmes, and some novel mar-keting tools applied for enhancing the university’s brand awareness and reputation. Research limitations – the limitations of the study are mainly related to the research base, which includes a few higher education institutions, so future study with a broader research base is recommended to generalize the findings. Practical implications – the research results may be useful for higher education managers responsible for developing a competitive marketing strategy of the university aimed at promoting their educational services and programmes. Originality/Value – the research is based on the analysis of working marketing plans of some universities having good marketing traditions.
Impact of Organizational Citizenship Behavior on Organizational Commitment on Jordanian Frontline Employees of Arabic Bank

This study aims to investigate the impact of Organizational Citizenship Behavior on Organizational Commitment on employees of Arabic Bank The study population included of all Managers, assistant Managers and head of department in Arabic Bank branches at Amman Capital totaling (52) branches.The study sampling unit consists of individuals working at three managerial level (Managers, assistant Managers and head of department) in these branches totaling (128) individuals.To achieve the study objectives, the study used quantitative (descriptive analytical approach) through many statistical tools, most notably multiple regression analysis.The study reveals a number of results namely: The organizational citizenship behavior (altruism, courtesy, Conscientiousness, sportsmanship, and civic virtue) have Impact on organizational commitment (Affective Commitment, Continuous Commitment &Normative Commitment) on employees of Arabic Bank.The study is proposed a few recommendations for Arabic branches; The management of Arabic bank should be considering to critical factors that influences citizenship behavior and employee commitment to improve the efficiency of the organization, not concentrating only on improving say infrastructure, rewards, improving technologies and their likes.
Deep Skip-Gram Networks for Text Classification

Text classification is one of the indispensable tasks for natural language processing, which has many real applications. However, existing methods for text classification still cannot well effectively capture long-range and local-pattern features within texts due to the huge variation of text expression. Motivated by this, we propose skip-gram convolution to extract non-consecutive local n-gram patterns, which provide much more comprehensive information for varying text expressions, and help us to understand the human text better. We also employ the recurrent neural network to extract the long-range features from localized level to sequential and global level via the chain-like architecture. To demonstrate the effectiveness of our deep skip-gram networks, we conduct comprehensive experiments on eight large-scale datasets that are widely used for the text classification task. Experimental results show that our deep skip-gram networks can outperform most of competing state-of-the-art methods, especially significant on more complex and challenging datasets. Moreover, our model is very robust and can be generalized very well on different datasets, even without tuning the hyper-parameters for specific dataset.
Pengenalan Nomor Ruangan Menggunakan Kamera Berbasis OCR Dan Template Matching

Most important room parameters to easily recognize with provide numbering of room, the number having function as room address. Camera have function to getting room number picture. To recognize of room number used OCR (Optical Characters Recognition) and Template Matching method. The system having purpose to make it easy the room number recognize process using camera. The first process Camera capture result will be identified using OCR system, and cahange image to be ASCII trought several stages, between segmentation, normalisation, feature extraction and recognition process. The image result of OCR, used input as template matching method, this method having several stages between resize, gray, edge canny, histogram, classification, identification image process, and matching with storage image. The result of system tested, getting several parameters should be maximized to increase success rate in room number recognition proccess, between using a standart room number, angle of camera capture is 90o, and luminance 224-230. From setting paramteters, this system can detect and recognize the room number of 93,75% success rate.