Reviewerly/exHarmony huggingface dataset:
Enhancing Learning Systems using Interactive Intelligent components

Following the invention of the teaching machine in 1924 by Sidney Pressey, there has been an evolutionary trend in the creation and promotion of software systems categorized as Learning Management Systems(LMS).Functionally, a typical LMS could be on-premise or cloudbased, however, one common feature canvassed is increased effectiveness.This paper focusses on enhancing effectiveness using intelligent components (e.g.algorithms, logic, etc,) to ease decisions on vital academic fronts.Emphasized here is the role of academic heads of departments in HEIs in effectively drawing the course allocation plan for a learning period.In large departments with many academic programs, courses, and lecturers, drawing a plan that fairly reflects capacity as to 'which teacher teaches what course', is an issue.This paper presents a systematic design of an intelligent component from the grounds up using the rapid application development(RAD) approach.The system underscores a posteriori knowledge on the part of the authors as to having significant experience on the modalities around academic activities across the different horizons and explores such to drive home the interestingness in enhancing the traditional practice through the deployment of intelligent interactive software.The prototype system is web-based with an android option and implemented using C#, ASP.Net Core, Xamarin forms; to enhance multiplatform support.The intelligent engine is driven by naïve bayes algorithm.The system is validated using two human experts who are presented with the histories of two courses and a handful of lecturers in a given department in a Nigerian HEI.The result of the validation shows that the advice given by the system is in tandem with the best decision an expert could have taken but would outperform a human expert when faced with scenarios as in large academic highlighted above.The paper concludes that the solution portends immense potentials and would ease the pressures and stress encountered by decision makers in fathoming capabilities of teaching staff vis-a-vis course cofferingsand with regard to the dynamic nature of academic activities in a typical HEI.
Interaction of a three-level atom and a field with a time-varying frequency in the context of triangular well potentials: An exact treatment

We introduce a model of interaction between a three-level atom (3LA) and a one-mode field whose frequency evolves with the time in the context of triangular potentials. We consider a new class of cat states described as a superposition of the coherent states that are associated with these kinds of potentials. Mathematical and physical consequences of the obtained results are analyzed and discussed in detail by using the exact analytical treatment of the quantum system-state at subsequent times. We investigate the nonlocal and nonclassical properties of different system states in terms of the main parameters of the model. Interestingly, we present the dynamical behavior of the entanglement, second order correlation function, quantum Fisher information, and geometric phase of the considered bipartite quantum system. We show that the physical quantities for the proposed scheme are very sensitive through the choice of the time-varying frequency of the fields, at either coherent states or their superposition, and we compare the results to the case of fields that are associated to harmonic well potentials. Finally, we explore the relationship and dependence of the physical quantifiers on the main parameters of the model.
Tube-Based Taut String Algorithms for Total Variation Regularization

Removing noise from signals using total variation regularization is a challenging signal processing problem arising in many practical applications. The taut string method is one of the most efficient approaches for solving the 1D TV regularization problem. In this paper we propose a geometric description of the linearized taut string method. This geometric description leads to the notion of the “tube”. We propose three tube-based taut string algorithms for total variation regularization. Different weight functionals can be used in the 1D TV regularization that lead to different types of tubes. We consider uniform, vertically nonuniform, vertically and horizontally nonuniform tubes. The proposed geometric approach is used to speed-up TV regularization processing by dividing the tubes into subtubes and using parallel processing. We introduce the concept of a relatively convex tube and describe the relationship between the geometric characteristics of tubes and exact solutions to the TV regularization. The properties of exact solutions can also be used to design efficient algorithms for solving the TV regularization problem. The performance of the proposed algorithms is discussed and illustrated by computer simulation.
Holistic Approach to Information Security Risk Management

Risk management is a very important area in information security.Risk management comprises of risk identification, risk assessment and risk treatment.Risk identification on current security infrastructure helps organizations to reveal vulnerabilities, threats and identify the risks that these two factors pose to their security infrastructure.Risk assessment helps in analyzing the identified risks using aspects like probability and impact and risk treatment helps in reducing the impact of the risks to an acceptable level [1].Risk management is mandatory requirement of ISO 27001:2013 and ISO 22301:2012 standards and the organization going for these certifications must comply with it.The eventual goal of a risk management activity is to define appropriate safeguards tailored to your company's risk profile and priorities.Risk management activity usually precedes and help define audit plans and facilitate the development of a corporate security plan.Qualified team members from information security department of an organization perform risk assessment at predefined period or after any change to provide reasonable assurance to the top management about the organization's risk profile.Risk management allows organizations to adopt security strategies that are tailored to their unique operating environment, threat landscape and business objectives.Riskbased security strategies deliver value to an organization by allowing it to understand the impact of risk and the efforts required to mitigate the risks.These security strategies help organizations in complying with regulations, thwart security attacks etc.Risk-based security strategies may differ markedly from the approaches currently adopted by many organizations.
Analysis and Implementation of AES and RSA for cloud

Cloud computing is recently a vogue in IT cooperate world.The cloud server provides various services to customers like software, storage, and infrastructure.Every Start-Up, small company or big company needs a space where they can store their data and use it whenever it is required.Cloud provides you one of the most important services that are storage, where security is necessary aspect.This paper analyze the most popular symmetric algorithm AES and asymmetric algorithm RSA, It also suggested a hybrid model for achieving data confidentiality and key privacy by combining these famous algorithm.Additionally it implements the various modes of AES and also implements RSA on various key sizes in java.Finally generate the graph on the basis of implementation which shows the performance of AES and RSA.
Social Studies Learning With Numbered Head Together Model Improves Learning Outcomes Viewed From Student Learning Motivation

The lack of optimal learning outcomes of social studies for fifth-grade elementary school students in Cluster XI Buleleng District is the main reason for this research. This study aims to analyze the effect of numbered head together (NHT) learning models, assisted by audio-visual media on social studies learning outcomes in terms of the learning motivation of fifth-grade elementary school students. Types of this research is a quasi-experiment with a non-equivalent post-test only control group design. The population in this study was grade V elementary school. The sample in this study was the VB class at SD Singaraja Lab of 35 students and VA class at SD Lab Singaraja of 35 students. Data collection methods used were of multiple-choice tests and non-test methods in the form of questionnaires. Learning outcomes data obtained were analyzed using Anava two pathways. The results showed that there were differences in social studies learning outcomes between students who were taught with the NHT model assisted by audiovisual media and students who were taught conventional learning models, there was an interaction effect between the learning model of NHT assisted with audiovisual media with learning motivation towards students' social studies learning outcomes, there was the difference in social studies learning outcomes between students who are taught with the NHT model assisted by audio-visual media and students who were taught conventional learning models to students who have high learning motivation, there was the difference in social studies learning outcomes between students who are taught with the NHT model assisted by audio-visual media and students who were taught conventional learning models to students who have low learning motivation. Based on the results of the study it can be concluded that the numbered head together learning model assisted by audiovisual media has a positive effect on social studies learning outcomes in terms of the learning motivation of students.
Design of Navigation System for Liver Surgery Guided by Augmented Reality

The traditional surgical navigation system combining preoperative CT and intraoperative ultrasound is widely used in open hepatectomy, but the problem of this system is that there are some errors in terms of the precision of the time and the space during the surgery. In order to solve the above problems, this paper introduces augmented reality technology into the surgical navigation system. In order to accurately describe the biomechanical characteristics of the liver and let the navigation system perform more accurately, this paper uses Tetgen to perform tetrahedral partition on the triangle mesh data obtained after the three-dimensional CT was reconstructed, and then the surface mesh data and the internal tetrahedral data were obtained and they were used to describe a whole liver model. The surface triangle mesh data is used to render graphics and describe the surface topology change, and the internal tetrahedral data combined with mass-spring theory is used to simulate deformation. Subsequently, the ex vivo pig liver was used to experimentally verify the accuracy of gravity deformation of the liver model, and the results show that the error was mainly distributed between −2mm and −2.5mm. At the same time, this paper uses the NDI Polaris infrared tracking system to carry out precision experiments on the augmented reality module, and the measured error is 1.55± 0.29mm. Finally, various modules of the system are integrated to finish the experiment in which the ring-shaped lesions are cut from the ex vivo pig liver with the aid of augmented reality. The experimental error is 0± 1.26mm, and with the assistance of the general purpose graphics processing unit (GPGPU), the refresh rate is above 200FPS. The results prove that the liver surgery navigation system proposed in this paper is excellent in terms of real-time performance and accuracy, which can help doctors accurately locate the tumor during surgery and perform ideal resection.
Vision-based vibration measurement by sensing motion of spider silk

Vibration measurement of operating machines is significant to condition monitoring and fault diagnostics. In this paper, we proposed a novel vision-based vibration sensor prototype to measure vibrations in conjunction with spider silk. The spider silk possesses superior physical properties which endow the silk with the capability of responding to the vibrating object over a wide frequency range, and thus it can be regarded as a type of vibration sensor. Using high-speed camera to sense the motion of spider silk, we can finally recover the vibration of the object from videos. The phase-based video motion processing method and time-varying motion filtering method are utilized to estimate the video motion signals and reduce the environment noise. In addition, experiments are designed and performed to demonstrate the performance of the proposed sensor prototype.
Mediating Effects of Avatar Realism on Motivation inGame Based Learning - Review

Virtual environments and digital games are being increasingly used as educational tools in current education settings.For the past few decades there were tremendous amount of studies involving digital game based learning (DGBL) in many subject matters to identify best design approach to increase its effectiveness.DGBL have been expanded in several ways by referring to digital entertainment games such as in using avatar which would assist the students to revise and learn on their own.Usage of avatars as an educator in the virtual learning environment is increasing as its importance increases where interactivity, learner engagement, cultural factors, communication, embodiment and motivation are crucial design considerations in a virtual learning environment.Nevertheless, question arises as what type of avatar whould be preferred by the learners in DGBL.There were dearth studies conducted on how the realism level of an avatar in DGBL would influence the emotions of a learner since researchers stated that emotions practiced throughout a learning method offer clues not solely to the effectiveness of a learning activity however additionally to the method of learning.Likewise, there were scarcity in identifying the relationship between level of realism with different elements of emotions (valence and arousal) and differ ent theories of motivation.Thus, this paper, critically reviews the literature related to usage of avatar in DGBL.This paper also discusses the finding related to motivation and emotion factors in DGBL, relation between realism in avatar and emotion, relation between motivation, emotion and realism of avatar in DGBL.
Action Prediction Based on Partial Video Observation via Context and Temporal Sequential Network With Deformable Convolution

Predicting activity motion form video is of great importance with multiple applications in computer vision. From the self-driving cars field to the health system, the earlier the anticipation the higher the classification probability success. The main challenge of prediction is accurate information of the object of interest in the frame as compared to the full-frame, from the partial observation. To this end, we propose an end-to-end two-stage architecture model that leverages pixel-level features awareness of spatiotemporal information of the object of interest. The first stage of our model is a classification block composed of 3 blocks layers: a background subtraction layer that enables the model to focus on the subject of interest followed by Deformable Convolution layers for feature extraction and finally an additive Softmax for the final classification. Learned information from the first stage is then transferred to the second stage composed of Long Short-Term Memory layers and a final loss function for prediction. The pervasive evaluation on the UT-Interaction, the HMDB51 as well as on the UCF-Sports benchmarks show the betterment of our model performance over threshold probability difference as compared to other solutions. And demonstrate an early action prediction at a lower observation ratio.
Sentiment Analysis with Supervised Learning Techniques: A Survey

Objectives: This study aims two main goals; one is to provide complete notions relevant to sentiment analysis by SA mechanisms, its categorization, and its techniques.The second goal is to make a comprehensive study of supervised learning techniques used in SA classification to summarize the different works conducted in this area and track the recent developments.Methods: To achieve the first goal, several important survey studies, including modern and relevant works presented would be analyzed for full concepts around SA.As for the second objective of the study, the most important reports would be investigated, analyzed, and compared in the use of supervised learning techniques in SA from the previous to the recent researches till 2019.Findings: This study also made a comprehensive research of the supervised machine learning classifiers used in SA, its recent techniques and enhancement methods and the suggestion future works.There are still some open challenges in this area such as mining the complex reviews and implicit aspect identification.The sentiment language is also a challenge; thus, addressing each language according to its attributes is a difficult task and so the sentiment domain issue.Application/improvements: The information provided is used in assessing opinions and analyzing sentiment that could be used by researchers and institutions, and to identify different trends besides recommending the future research directions.
A Crypto Scheme Using Data Obfuscation of Entity Detection and Replacement for Private Cloud

Cloud has been rising, renown, and extremely demanding innovation now a day.Cloud has wide ubiquity with its advanced features, like web access, more stockpiling, easy setup, programmed refreshes, low cost, and resource provisioning on a rent basis.Disregarding many advantages, security is viewed as increasingly significant and drew the consideration of numerous researchers.The information storage is drastically increasing, and there are many occasions that cloud doesn't ensure that data/information that has been placed in the cloud is secured from unauthorized access.Many experts are attempting to guarantee data security in the cloud, yet tragically they don't give satisfactory results.Hence we attempted to propose an effective crypto-scheme with obfuscation and cryptography for unstructured information.The scheme attempts to safeguard the secrecy of information at two phases.In the first phase, it obfuscates the file by supplanting the keywords (obfuscation), and at the subsequent phase, the obfuscated file is encoded by using the conventional RSA (Rivest Shamir Adleman) encryption algorithm for high security.Investigation results show that the proposed mechanism yields great outcomes.
Construction of Audit Internal Control Intelligent System Based on Blockchain and Cloud Storage

Construction of audit internal control intelligent system based on Blockchain and cloud storage is conducted in this paper. At present, the development of artificial intelligence in various fields is getting better and better, and the artificial intelligence information technology is slowly beginning to converge towards the auditing field. The blockchain storage application system requires each node to store a complete data, causing waste of the storage resources; at the same time, it does not make full use of the network routing traffic resources. Based on this background, this paper proposes the novel AI and BC based intelligent system for the audit internal control. The system is designed and validated through the simulation. The proposed intelligent system can improve the overall efficiency.
Does Training and Employee Commitment Predict Employee Retention?

Employee retention is evidently linked with an organization's competitive advantage.A very limited attention is given to the predictors of employee retention in UAE.Training and employee commitment are directly and/or indirectly linked with employee retention.In this study how training and employee commitment predict employee intentions to stay has been examined.Data was collected from 124 employees working at different levels in private sector using convenience sampling method.Hierarchical regression analysis was conducted to assess the strength of predictors.Findings of the study have managerial implications across different industries.
Speaking Skill in Correlation with English Speaking Learning Habit and Self Confidence of Vocational High School Students

The purpose of conducting this study is based on problem identification asking whether there are correlations among English speaking learning habits and self-confidence toward English speaking skills in promoting local tourism and culture of Bangka Belitung. The first-grade students of SMKN 3 Pangkalpinang in the academic year of 2018/2019 majoring in Tourism Travel Business Agent became the population in this study, and it took 50 students as the sample using a purposive random sampling technique. This correlational study used questionnaires and oral tests in collecting the data. The oral test was used to obtain English speaking skill data, while the questionnaires were used to obtain English speaking learning habits and students’ self-confidence data. The data were then analyzed using Simple and Multiple Correlation and Regression. The results of this study revealed that (1) there was a relationship between English speaking learning habit and English speaking skill, (2) there was a correlation between students’ self-confidence and English speaking skill, and (3) English speaking learning habit, confidence, and English speaking skill was simultaneously and positively correlated. In short, this study leads to a recommendation to the teachers and students to be aware of having good English speaking learning habits and high self-confidence to enhance English speaking skills.
Higher Education Teachers’ Needs Concerning with the Moodle and the Impact of Training Received in the use of the Platform : Case Study in Higher Education

The fact that some researchers emphasize that there is an underuse of the potencial of Learning Management Systems (LMS) has increased the interest in verifying what was happening in the institution where i teach. As a result of these studies carried out and published previously, was found that teachers often referred to the need they felt for technical, pedagogical and didactic training for their work with the Moodle, in an expectation of optimizing the use of the Moodle platform. The present paper has as its object the presentation of the study on the impact of training carried out by the teachers of a Polytechnic Institute of Higher Education in their pratices with the Moodle Platform.
A new selection operator for genetic algorithms that balances between premature convergence and population diversity

The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics.