Reviewerly/exHarmony huggingface dataset:
Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Human hands engage in interactive activities in many practical working scenarios, among which the interactions between human hands and objects are the most common.Tracking the movement of the human hand during hand-object interactions is an important research task that is also challenging due to the high-dimensionality and occlusions.In this paper, we track hand-object interactions from depth observations with a model-based method.To overcome the difficulties of optimum searching in the handobject high-dimensional space, we propose a new algorithm -collaborative differential evolution filtering (CoDEF) -for tracking hand-object interactions.The proposed CoDEF algorithm integrates the differential evolution (DE) algorithm into a particle filtering (PF) framework to accelerate the convergence of particles.Particles are driven to the regions with a high probability by optimizing the matching error under the current observation with DE.To decompose the state space and decrease the complexity of optimum searching, CoDEF tracks the movement of the hand and object by using two collaborative trackers.Based on the proposed CoDEF algorithm, we develop a model-based tracking system with 3D graphic techniques.According to the experimental results, the proposed CoDEF algorithm can achieve robust tracking of hand-object interactions using fewer particles.
Approach for Identifying Phishing Uniform Resource Locators (URLs)

Phishing attacks are still very rampant and do not show signs of ever stopping.According to Santander Bank Customer Service, reports of phishing attacks have doubled each year since 2001.This work is based on identifying phishing Uniform Resource Locators (URLs).It focuses on preventing the issue of phishing attacks and detecting phishing URLs by using a total of 8 distinctive features that are extracted from the URLs.The sample size of study is 96,018 URLs.A total of four supervised machine learning algorithms: Naive Bayes Classifier, Support Vector Machine, Decision Tree and Random Forest were used to train the model and evaluate which of the algorithms performs better.Based on the analysis and evaluation, Random Forest performs best with an accuracy of 84.57% on the validation data set.The uniqueness of this work is in the choice of the selected features considered for the implementation.
Categorizing 2019-n-CoV Twitter Hashtag Data by Clustering

Unsupervised machine learning techniques such as clustering are widely gaining use with the recent increase in social communication platforms like Twitter and Facebook.Clustering enables the finding of patterns in these unstructured datasets.We collected tweets matching hashtags linked to COVID-19 from a Kaggle dataset.We compared the performance of nine clustering algorithms using this dataset.We evaluated the generalizability of these algorithms using a supervised learning model.Finally, using a selected unsupervised learning algorithm we categorized the clusters.The top five categories are Safety, Crime, Products, Countries and Health.This can prove helpful for bodies using large amount of Twitter data needing to quickly find key points in the data before going into further classification.
IMPLEMENTASI PENCATATAN AKTIVITAS MAHASISWA MENGGUNAKAN WEB SERVICE PADA FEEDER PDDIKTI DENGAN METODE EXTREME PROGRAMMING

One of the features in the Feeder PDDIKTI application is the student activity feature. The feature is used to record student activities with participants' activities, supervisors and examiners. The problem with the Feeder PDDIKTI application when recording student activities is that the operator must input data one by one to the application. Therefore DIKTI also provides web services that can be used so that the recording process can be better.Implementation of recording student activities using web services on the Feeder PDDIKTI with extreme programming methods is carried out in accordance with the stages. In the planning part, an analysis is carried out on the business needs and user stories, in the design, use case diagrams and activity diagrams are made, coding is done by implementing the design using the PHP programming language, and testing is done using black box testing.In running the application, upload excel files are used for student activity data, activity participants, supervisors and examiners. After the student activity is successfully uploaded an excel file is generated from the system which contains student activities that already have the id_akt_mhs attribute. These attributes will be used to link student activity data with activity participants, supervisors, and examiners when filling in data in an excel file. Applications made are not yet connected to the Academic Information System Application so that for future development it is expected to be able to connect and retrieve data directly from the Academic Information System Application.
An Intelligence Perspective on Privacy and Data Protection Risks in Social Media

Abstract Since the emergence of Internet and social media, new Intelligence branches have flourished, like CYBERINT (Cyber Intelligence), OSINT (Open Source Intelligence) or SOCMINT (Social Media Intelligence), with the aim to exploit different dimensions of the virtual world. These Intelligence-related disciplines may inquire personal information, statements and conversations posted voluntarily on websites or social platforms in order to profile people, identify social networks and organizational structures, and uncover vulnerabilities and threats/risks that can jeopardize the security of individuals or organizations. In this respect, the Internet - as environment - can provide valuable information from both technical and social side. This is why the World Wide Web is and will remain an important place to search for data and information that can be processed into Intelligence, and represents the reason why people working in sensitive domains (e.g. Intelligence) should be aware of their vulnerabilities and the risks and threats posed by this environment. DISCLAIMER : This paper expresses the views, interpretations, and independent position of the authors. It should not be regarded as an official document, nor expressing formal opinions or policies, of NATO or the HUMINT Centre of Excellence.
USING EDUCATIONAL DIGITAL TOOLBOX IN E-LEARNING tO BOOST STUDENTS’ READING MOTIVATION IN COVID-19 PANDEMIC

This study aimed at investigating the use of educational digital toolbox in e-learning to motivate college students’ reading motivation, such as recognizing ideas, comprehending texts using graphics, images, and charts, audio, audio-visual and video to personal experience, opinion or evaluation. The study attempted the qualitative method. The instrument used was a questionnaire which were prepared for the purpose and which was administered to 84 students at Indraprasta university, majoring in English education department in South Jakarta, during March and April 2020. The result of this study shows most of students boost their reading motivation by using educational digital toolbox in e-learning process. Give the new perspective that language students can be motivated to use long distance learning more active and more interactive, as well as enrich their learning experiences. Moreover, using appropriate pedagogical implementations such as effective digital toolboxes, interesting reading topics and students’feedback can boost students’ reading motivation with the changing learning trend.
Modeling The Psychological Competence Of Higher Education Leaders (In The Example Of Mid-Level Management Staff)

This article discusses the modeling of psychological competence of higher education management staff.A number of political and social events taking place in the world community today show that the 21st century has been predicted to be a century of high information technologies and a sharp rise in intellectual level.It is precisely these details that require the reform of human relations, including in the field of management, the choice of subtle and unique ways of dealing with people of high intellectual level and influencing them effectively.
Corporate Social Responsibility and Financial Performance Amongst Rural and Community Banks in Ghana

This study examines the connection between corporate social responsibility and firms' financial performance through competitive advantage and access to finance as mediating variables and the role of corporate governance as a moderating variable in such relationship using rural banks as study case.A sample of 126 banks were selected and data from these banks were analysed using structural equation modeling and hierarchical regression with moderation.It was found that embarking on corporate social responsibility leads to improvement in competitive advantage and gaining access to capital.These are both related to increase in corporate financial performance.The study found that business organisations that practise CSR activities turn to gain a defensible competitive position in their industry.The practice of CSR tends to create access to finance for business strategies, resulting in positive impact on financial performance depending on the efficiency of their corporate governance practices.The study recommends that activities of CSR should be integrated into firms' corporate and business level strategies.It also recommends that rural banks and the banking industry at large should adopt proper corporate governance structures to ensure judicious use of firm resources in order to enjoy improved financial performance.
The Suitability Evaluation of Procurement Information Systems to the Needs of Users and Management Using Human, Organization, Technology-Fit (HOT-Fit) Framework

Management Information System (MIS) in government institutions was a crucial thing in supporting good services to the community. The established MIS often provided less optimum benefits since the MIS development did not go through a good planning and was not supported by a clear IT Masterplan so that an evaluation of the MIS implementation is needed, especially from 3 (three) main aspects, including aspects of Human, Organization and Technology. The procurement unit of Malang City has a MIS for procurement submission called siBaja which has been established since 2016. This study aimed to evaluate the suitability of siBaja to the needs of users and management using Human, Organization, Technology-Fit (HOT-Fit) framework with some modifications.To obtain the data, a survey method was used by distributing questionnaires to the application users.The data analysis model used was SEM-PLS by using SmartPLS 3.0 software. This study found that the implementation of siBaja was not fully fit for the relationship between Human-Technology that was indicated by the absence of a significant effect between US variable and SQ variable with a significance value of 0.899 at α = 0.05 and was not fully fit for the relationship between Organization-Technology factor which was characterized by the absence of a significant effect between SC variable and IQ variable with a significance value of 0.221 at α = 0.05.
CALIBRATING A LENS WITH A “LOCAL” DISTORTION MODEL

Abstract. This paper is about camera calibration where an abnormal systematic effect was discovered. The effect was first encountered in a multi-camera system used for close range 3D photogrammetric reconstruction. The objectives for this research were two-fold. The first objective is to identify the source of the systematic error, and the second objective is to model the error as rigorously as possible. The first objective was met after acquiring several calibration data sets where the camera bodies, the lenses, and the image formats were varied. It was concluded that the source of error is the lens system. The second objective was also met. The so called “local” lens distortion was modelled using second order polynomials as the plots of the residuals vs. the image coordinates resembled parabolic shapes. Overall, the final room mean square error for the residuals after applying radial and “local” lens distortion was reduced from 1/2 to 1/6 of a pixel or a 200% relative estimated error improvement.
Antenatal Education Model Based Gamification in Terms of Knowledge and Attitude Among Pregnant Women in the Antenatal Classes

The maternal mortality rate remain high in Indonesia. MMR in Indonesia around 80% of obstetric problems. Indonesia has developed the antenatal class program to improving knowledge and attitude for pregnant women. Antenatal class is an important component for pregnant women to improve their knowledge and skills during pregnancy, childbirth and the postnatal period. Educational methods using conventional methods are becoming obsolete and innovative educational methods have been developed. The aim of the research is to create antenatal education model based gamification to improve knowledge and attitude of pregnant women.The research we implemented Research and Development (R&amp;D) method and quasi experimental with a control group pre-test and post-test design. Research subjects pregnant women were given education by ladder snakes and the control group by pregnancy booklets for 10 days meeting. The results show that antenatal education model is worthy of a education media with p-value of 0,000 and the application of this education based gamification after 10 days is effective in increasing knowledge (p=0,001) and attitudes (p=0,001) better than control group. According of the resultsAn antenatal education based on gamification is reliebel to improving knowledge and attitudes of pregnant women. An antenatal education based on gamification can be applied to antenatal classes
Learning to Reason in Round-Based Games: Multi-Task Sequence Generation for Purchasing Decision Making in First-Person Shooters

Sequential reasoning is a complex human ability, with extensive previous research focusing on gaming AI in a single continuous game, round-based decision makings extending to a sequence of games remain less explored. Counter-Strike: Global Offensive (CS:GO), as a round-based game with abundant expert demonstrations, provides an excellent environment for multi-player round-based sequential reasoning. In this work, we propose a Sequence Reasoner with Round Attribute Encoder and Multi-Task Decoder to interpret the strategies behind the round-based purchasing decisions. We adopt few-shot learning to sample multiple rounds in a match, and modified model agnostic meta-learning algorithm Reptile for the meta-learning loop. We formulate each round as a multi-task sequence generation problem. Our state representations combine action encoder, team encoder, player features, round attribute encoder, and economy encoders to help our agent learn to reason under this specific multi-player round-based scenario. A complete ablation study and comparison with the greedy approach certify the effectiveness of our model. Our research will open doors for interpretable AI for understanding episodic and long-term purchasing strategies beyond the gaming community.
An Automated Process for the Repository-Based Analysis of Ontology Structural Metrics

Quantitative metrics are generally applied by scientists to measure and assess the properties of data and knowledge resources. In ontology engineering, a number of metrics have been developed to analyse different features of ontologies in the last few years. However, this community has not generated any standard framework for studying the properties of ontologies or generated sufficient knowledge about the usefulness and validity as the measurement instrument of these metrics for evaluating and comparing ontologies. Recently, 19 ontology structural metrics were studied using the OBO Foundry and AgroPortal ontology repositories. This study was based on how each metric partitioned the two datasets into five groups by applying the k-means algorithm. The results suggested that the use of five clusters for every metric might be suboptimal. In this paper, we propose an automated process for the study of ontology structural metrics by including the selection of an optimal number of clusters for each metric. This optimal number is automatically obtained by using statistical properties of the generated clusters. Moreover, the cosine similarity is used for estimating the similarity of two repositories from the perspective of the behaviour of the same set of metrics. The results on the two datasets allow for a more realistic perspective on the behaviour of the metrics. In this paper, we show and discuss the difference observed in the comparative behaviour of the metrics on the two repositories when using the optimal number with respect to a predetermined number of clusters for every metric. The proposed method is not specific for ontology metrics and therefore, can be applied to other types of metrics.
E-Learning’s “De Facto” Implementation Probabilities in Educational Sector: A Preliminary Estimation if Confinement Should Be Extended in Covid-19 Crisis Context

Background: Before the Covid-19 breakout, some debates' subjects were left hanging in the education area: the E-Learning integration. With the pandemic impact (making closed almost all schools in the same month), the debate gained again importance. Among the temporal Anti-Covid-19 measures adopted, was the replacement of classic education by distance-online education.Purpose: This article attempted a preliminary estimate related to the probabilities of E-learning’s ‘‘de facto’’ implementation in different countries groups of the world if the confinement should be extended in Covid-19 crisis context.Methods/Approach: The probabilistic method was adopted to analyze 143 countries including (49 High income, 40 upper-middle-income, 33 lower-middle-income, and 21 low-income economies). The main variables (related to E-learning), collected from official sources in the Covid-19 period were: Access-to-electricity, Access-to-Internet, Access-to-Multimedia-Devices (Personal-Computer, Tablet and/or Smartphone).Results: Results showed that the probabilities of E-learning's ''de facto'' implementation would be around 0.6502 in the world, 0.8731 in High-Incomes, 0.7075 in Upper-Middle-Incomes, 0.5272 in Lower-Middle-Incomes, and 0.2950 in Low-Income economies.Originality/value: (1) In the past, "suddenly closing almost all schools in the world" would be inadmissible. Everyone can henceforth believe in that eventuality after the experience with Covid-19. In this context, this research shows the need to reconsider E-learning or at least, a new hybrid education model as a "plan B" for future times. Thanks to technological integration, online and traditional education could go hand in hand, even after the Covid-19. (2) This study reminds the importance of speeding up the resolution of certain thorny (pending) problems such as access to energy and the development of educational infrastructure in certain regions of the world. The quality, performance and success of the educational component depend on it.
Rapid Diagnosis of Malaria using Images of Stained Blood Smear

This paper presents a technique to distinguish malaria parasite during blood checks recolored by Giemsa to improve the precision of distinguishing, on the preliminary step, the red platelet veil is extricated.It is owed toward the way that the big majority of malaria parasites live within pink platelets.At that factor, recolored components of blood, for example, pink platelets, parasites, and white platelets be extorted.On the subsequent level, pink platelet cowl is situated at the extracted recolored additives to isolate the potential parasites.By utilizing thick blood smear, quite a few blood smears can be analyzed hastily and no trouble in any respect.This work manages the programmed evaluation of parasite thickness in 'parasites according to a microliter of blood' from minuscule images of Giemsa-recolored thick blood smear.The principal factor of this examination is to look at the microscopic images of recolored dainty blood spreads using an assortment of processor apparition methods, reviewing malaria parasitemia on free factors (RBC's morphology). The projected process depends on the inductive method, shading of malaria parasites via versatile calculation of the Gaussian mixture model (GMM). Evaluation precision ofRBCs is progressed, parting impediments of RBCs by separation alternate with close by maxima.Additionally, the characterization of tainted with non-contaminated RBCs has prepared for accurately evaluating parasitemia.Instruction along with assessment had completed on image dataset concerning floor facts, finding out the extent of infection with the affectability of 98 %.The exactness and skill ability of the proposed conspire about being programmed are demonstrated tentatively, outperforming other condition-of-the-art plans.Furthermore, this examination tended to the procedure with autonomous elements (RBCs' morphology).
LINE SEGMENT MATCHING ALGORITHM BASED ON FEATURE GROUPING AND LBD DESCRIPTOR

Abstract. This paper proposes a line-matching algorithm based on feature grouping and a line band descriptor (LBD) to address the insufficient reliability of individual line descriptors for line matching. First, the algorithm generates line-pairs according to geometrical relationships such as the distances and angles between line segments extracted from a single image. Subsequently, the algorithm employs the epipolar line of intersection between two lines in a reference line-pair to constrain candidate pairs corresponding to the reference line-pair. Thereafter, each line in the reference line-pair is considered individually, and its support region and the corresponding support region of each candidate line in the candidate pairs are established, following which an affine transformation is used for unifying the sizes of the reference support region and the candidate support region. Moreover, the LBD descriptor is then used for describing the reference and candidate lines. The Euclidean distances between the reference line and each candidate line descriptors are calculated, and the nearest neighbor distance ratio (NNDR) is used as a criterion for determining the final matching. Finally, the one-to-many and many-to-one line correspondences in matching results are transformed into one-to-one line correspondences by fitting multiple lines to the new line; simultaneously, incorrect matches are eliminated. The experimental results show that the proposed algorithm yields reliable line-matching performance for close-range images.
A Real-time Deep Convolution Image Recognition Method Based on Data Mining

The traditional convolution neural networks(CNN) usually have slow target recognition speed due to its complex models and computational complexity. To improve the recognition speed, two mining data methods are employed to optimize the deep convolution recognition structure in this paper. Features of image which are obtained by deep CNN are firstly screened based on the dispersion ratio algorithm then genetic algorithm is adopted to get less useful features. These data mining methods greatly reduce the feature dimensions so that the recognition computations are reduced tremendously. To verify this idea, synthetic aperture radar (SAR) images are recognized by the proposed models. The correct recognition rate and the complexities of the proposed method are analyzed and compared to the traditional CNN. The experimental results show that the correct recognition rate gets better and the complexities reduce immensely so the target recognition speed is greatly improved.