Reviewerly/exHarmony huggingface dataset:
DESIGN OF DASHBOARD FOR WEBSITE BASED TEACHER PERFORMANCE MONITORING (CASE STUDY: SMAN 2 SUMBAWA BESAR)

State Senior High School (SMAN) 2 Sumbawa Besar is one of the educational institutions in Sumbawa Besar which has been established since July 1977 and has been accredited A. SMAN 2 is also one of the favorite schools. SMAN 2 Sumbawa Besar is located at Jl. Garuda 102 Sumbawa Besar. In the current monitoring implementation process, school supervisors have to come to the school and check the teacher performance assessment team on teacher performance so that it takes a long time to obtain total information. From the current monitoring process, the problem is that supervisors and principals need a long time to find out information on the performance of all teachers. The proposed solution given to solve the problem is the Dashboard application to monitor the performance of web-based teachers at SMAN 2 Sumbawa Besar. This dashboard will provide a display of graphic information including: 1) the number of teachers who are Civil Servants (PNS) and Honorary. 2) Number of teachers by gender. 3) The number of teachers with the latest education. 4) The average score of all teachers per period to see the development of teacher performance from the initial period to the last period. 5) The average performance score of all teachers based on the competencies per period (the teacher performance competencies in question are Pedagodic, Personality, Social, Professional). 6) The acquisition of each teacher's score based on the competence of each teacher 7) The acquisition of teacher scores based on the indicators of teacher performance competence. 8) Average teacher attendance per period. Based on the results of trials conducted with the school, this application can display teacher performance results in graphical form, easy to use and easy to access without having to install other applications, and shorten the time for principals and supervisors to monitor teacher performance.
SERTIFIKAT TOEFL ITP

TOEFL ITP is an English Test proficiency test held by ETS. It aims to know the score of the English proficiency and competence.
Usability Testing Experiment Applied to Redesign on Point-of-Sale Software Interfaces Using User-Centered-Design Approach

In this paper, we present a crossover experiment with a remote usability testing to validate a User Centered Design process applied to a point-of-sale user interfaces redesign, in which the original software product interfaces had a low grade of usability. It was developed as a structured usability remote test plan applied with two samples of users through a crossover experiment. Each sample was composed by 6 participants that had close knowledge about information systems features and other personality attributes obtained by the cashier profile designed through the personas technique. Cashiers are a fundamental role, since they use this software many hours per day, and are very rotative employees, that tend to be young people between 18 and 25 years old. We obtained favorable results, as each sample decreased their execution time in completing the 10 given tasks in more than one minute, which is a valuable time in sales, and it reduced their dependency to ask for help or making operational mistakes in the sale’s process. They emphasized some improved points such as “more understandable labels”, “more striking and pleasant colors”, “better visualization of the components”, and “much easier process to use”.
Automatic Detection and Segmentation of Liver Tumors in Computed Tomography Images: Methods and Limitations

Liver tumor segmentation in computed tomography images is considered a difficult task, especially for highly diverse datasets. This is demonstrated by top-ranking results of the Liver Tumor Segmentation Challenge (LiTS) achieving ~70% dice. To improve upon these results, it is important to identify sources of limitations. In this work, we developed a tumor segmentation method following automatic liver segmentation and conducted a detailed limitation analysis study. Using LiTS dataset, tumor segmentation results performed comparable to state-of-the-art literature, achieving overall dice of 71%. Tumor detection accuracy reached ~83%. We have found that segmentation’s upper limit dice can reach ~77% if all false-positives were removed. Grouping by tumor sizes, larger tumors tend to have better segmentation, reaching a maximum approximated dice limit of 82.29% for tumors greater than 20,000 voxels. Medium and small tumor groups had an upper dice limit of 78.75% and 63.52% respectively. The tumor dice for true-positives was comparable for ideal (manual) vs. automatically segmented liver, reflecting a well-trained organ segmentation. We conclude that the segmentation of very small tumors with size values < 100 voxels is especially challenging where the system can be hyper-sensitive to consider local noise artifacts as possible tumors. The results of this work provide better insight about segmentation system limitations to enable for better false-positive removal development strategies. Removing suspected tumor regions less than 100 voxels eliminates ~80% of the total false-positives and therefore, may be an important step for clinical application of automated liver tumor detection and segmentation.
A Bayesian hierarchical score for structure learning from related data sets

Score functions for learning the structure of Bayesian networks in the literature assume that data are a homogeneous set of observations; whereas it is often the case that they comprise different related, but not homogeneous, data sets collected in different ways. In this paper we propose a new Bayesian Dirichlet score, which we call Bayesian Hierarchical Dirichlet (BHD). The proposed score is based on a hierarchical model that pools information across data sets to learn a single encompassing network structure, while taking into account the differences in their probabilistic structures. We derive a closed-form expression for BHD using a variational approximation of the marginal likelihood, we study the associated computational cost and we evaluate its performance using simulated data. We find that, when data comprise multiple related data sets, BHD outperforms the Bayesian Dirichlet equivalent uniform (BDeu) score in terms of reconstruction accuracy as measured by the Structural Hamming distance, and that it is as accurate as BDeu when data are homogeneous. This improvement is particularly clear when either the number of variables in the network or the number of observations is large. Moreover, the estimated networks are sparser and therefore more interpretable than those obtained with BDeu thanks to a lower number of false positive arcs.
Addressing Transparency Vis-a-Vis Privacy in Portability of Health Insurance Through Blockchain

A blockchain is a community ledger deal out in excess of a set-up that testimony operations (memorandums send commencing individual system knot to a further) carry out in the middle of network contributors. Every operation is confirmed by network nodes according to a mainstream agreement method prior to being added to the block chain. Recorded information is able to not be revolutionized or wipe away, and the past of each operation can exist re-formed by the side of in the least time. Authors have been proposed a system using block chain mechanism for health insurance entails institution of burly procedures for health data collection and compilation right from grass root level. The Recommended technique bestows the precise to the policyholder of health insurance to relocate the credit gained him for pre-existing conditions and time vault, keeping outs in case he chooses to switch the insurance provider.
A Comparison between Machine Learning Researches that use Arabic Text: A Case Study of Social Media Datasets

The world is directed to use the huge data and use it in a beneficial way, this allowed researchers to think about how to classify these data, which have many shapes, speeds and sizes, as it was important to study the data that are in social media, analyse and benefit from it through its classification. We used more than 20 papers to compare how the researchers use different algorithms to classify the huge data that comes through social networking sites and the most important language they used to classify the text was the Arabic language. The researchers concluded that deep learning and convolution neural networks speed up the classification process.
Sentiment Polarity Classification using Minimal Feature Vectors and Machine Learning Algorithms

Recently, social media users can comment as texts to describe their opinions. These texts can be analyzed to classify them into either positive or negative attitude. Feature vectors for representing the texts must be designed and prepared before building a classifier. Generally, texts are represented by vectors of weights or frequencies of terms that appear in the text. The length of the feature vector is equal to the number of terms in the dictionary derived from the possible words in all texts. The large amount of words in dictionary leads to the high dimensional vector for representing text and bring about the long processing time to training and testing the text classification models. This paper, the low-dimensional vectors, V8D, were proposed for representing the texts. The set of positive and negative words including the words of negation which have the significant meanings were considered as information to create these vectors. Four machine learning algorithms to solve the classification problem, i.e., k-Nearest Neighbors, Naïve Bayes classifier, Artificial Neural Networks and Support Vector Machine, were applied to classify the opinion texts. By experimenting on eight data sets with various domains, the proposed V8D vectors were compared with the traditional TF-IDF vector in term of the predictive correctness. The experimental results show that representing text as our V8D vector for opinion text classification can provide the best efficiency in both of space usage and processing time.
Machine Learning Mitigants for Speech Based Cyber Risk

Statistical analysis of speech is an emerging area of machine learning. In this paper, we tackle the biometric challenge of Automatic Speaker Verification (ASV) of differentiating between samples generated by two distinct populations of utterances, those of an authentic human voice and those generated by a synthetic one. Solving such an issue through a statistical perspective foresees the definition of a decision rule function and a learning procedure to identify the optimal classifier. Classical state-of-the-art countermeasures rely on strong assumptions such as stationarity or local-stationarity of speech that may be atypical to encounter in practice. We explore in this regard a robust non-linear and non-stationary signal decomposition method known as the Empirical Mode Decomposition combined with the Mel-Frequency Cepstral Coefficients in a novel fashion with a refined classifier technique known as multi-kernel Support Vector machine. We undertake significant real data case studies covering multiple ASV systems using different datasets, including the ASVSpoof 2019 challenge database. The obtained results overwhelmingly demonstrate the significance of our feature extraction and classifier approach versus existing conventional methods in reducing the threat of cyber-attack perpetrated by synthetic voice replication seeking unauthorised access.
AN AUTOMATED METHOD TO CONDUCT IMPORTANCE-PERFORMANCE ANALYSIS OF PRODUCT ATTRIBUTES FROM ONLINE REVIEWS - AN EXTENSION WITH A CASE STUDY

Abstract With the growth of online marketplaces and social media, product designers have been seeing an exponential growth of data available, which can serve as an extremely valuable source of information communicated from customers without geographical limitations. The data will reveal customers’ preferences, which can be expensive and slow to obtain via traditional methods such as survey and questionnaires. While existing methods in the literature have been proposed to extract product information and make inference from online data, they have limitations, especially in providing reliable results and in dealing with data sparsity. Therefore, this paper proposes a method to conduct an Important-performance analysis from online reviews. The major steps of this method involve using latent Dirichlet allocation (LDA) to identify product attributes, using IBM Watson Natural Language Understanding tool to perform aspect-based sentiment analysis, and using XGBoost model to infer product attribute importance from the collected dataset. In our case study, we have collected over 150,000 text reviews of more than 3,000 laptops from Amazon.
Theorizing the Virtual: An Unfinished Conversation with Thomas Elsaesser

I would like to begin with a set of simple provisos to avoid confusion. Even at this point in mid 2021, when both the technology and the medium are still being referred to as VR, there can be some ...
Predictive Analysis of the Cryptocurrencies’ Movement Direction Using Machine Learning Methods

Cryptocurrencies are among the most interesting financial instruments of recent years. Unlike the classical understanding that money exists as a means of change from one hand to another, this digital economy has begun to attract people's attention. The most popular currency emerging from this concept of cryptocurrency is “Bitcoin”. As the popularity of Bitcoin started to increase since 2016, the number of academic studies on cryptocurrencies has increased in parallel. In light of these developments, our study proposes predictive models of price change directions of high market value cryptocurrencies. Bitcoin, Ethereum and Litecoin were selected as cryptocurrencies and daily opening, closing, high and low prices of these currencies were collected from financial websites. Preprocessing was performed on the collected data to create input vectors. These vectors were given regression algorithms which are Multiple Linear, Polynomial, Support Vector, Decision Tree and Random Forest Regression. As evaluation metrics, R-square Method (R2) and Root Mean Square Error (RMSE) were chosen. After doing experiments with different parameter settings, it was found out that the chosen machine learning models showed satisfactory performances in predicting the directions of then mentioned cryptocurrencies.
Execution Repair for Spark Programs by Active Maintenance of Partition Dependency

Spark programs typically codify to reuse some of their generated datasets, called partition instances, to make their subsequent computations complete in a reasonable time. At runtime, however, the underlying Spark platform may independently delete such instances or accidentally cause these instances inaccessible to the program executions. Those instances will invalidate the computation assumption made in writing these programs that such depending instances are present, which leads performance bloat and even breaks the executions. In this paper, we present FAR, a novel and effective framework to handle such performance bloat and actively repair the executions by maintaining the instance dependencies in Spark program executions. FAR monitors the partition instance lifecycle activities at all levels, and determines from the execution plan of the current Spark action in the current program execution on whether a partition instance will have a dependency relation with a later one underlying the computation of that action. The experimental results showed that with the active execution repair mechanism of FAR, when some dependency partition instances were inaccessible, programs can achieve 7.3x to 67.0x speedup in re-generating them. The results also interestingly revealed that the program executions actively repaired by FAR can run to successful completion in environments with 1.7x-2.0x fewer available memory.
Evaluating Contact Tracing Apps for Privacy Preservation and Effectiveness

During the COVID-19 pandemic, digital technology has been massively investigated to automate and augment the traditional manual Contact Tracing (CT) process. This results in a variety of smartphone-based CT apps, symptom tracking tools, dashboards, and analytical tools to support the comprehensive digital CT program, where CT apps act as the central instrument. However, the variety of digital CT solutions, especially the CT apps, contain different levels of privacy threat and technical capabilities that are directly linked with effectiveness. Sometimes, the choice of a particular method of the digital CT has an opposite effect to privacy and effectiveness, only to adding uncertainty to decision making regarding digital CT. In this paper, we present a novel approach to quantitatively pre-assess CT apps on privacy preservation and effectiveness. This assessment of CT apps would facilitate developing a shared understanding among policymakers. We also implemented a digital evaluation toolkit and show evaluation of CT apps of a variety of specifications using our toolkit. Our research aims to help addressing the issues of selecting the appropriate digital CT currently faced by national policymakers around the world.
Haptic Interaction for VR: Use-Cases for Learning and UX, Using the Example of the BMBF Project SmartHands

VR is becoming increasingly popular and is developing from a niche market to a mass market currently experiencing thriving. Highly developed technology at affordable prices and a spectrum of applications ranging from gaming to learning to e-business are shaping the VR landscape in 2021. There are attempts to address many human senses through software and hardware to make the scenarios even more realistic. The integration of haptics is becoming the focus of current trends and developments. This paper attempts to do justice to this trend and looks at haptic interaction from different angles. Besides a brief excursus to the development of haptic devices, we will also discuss physiological grounds to grasp better the possibilities offered by integrating the sense of touch. Also, practical examples from the BMBF project SmartHands will illustrate how and when haptic feedback in the form of haptic gloves can be usefully integrated into a VR learning application. We will also attempt to build a direct bridge to application-oriented e-business by outlining promising use cases to embed haptic feedback in e-commerce.
Design and Implementation of Conversion of Gesture to Voice Using OpenCV and Convolution Neural Network

Communication is the most important aspect of society that people are dependent upon. Today everyone can communicate with the help of their native languages but there is a small part of society who cannot speak and faces a lot of problems in communicating. These people are the one who are suffering from speaking impairment. Such people are also members of this society and have equal right to enjoy their life and communicate with the external environment in an easy and professional manner. In today’s world, technologies have made life easy. Everyday there is a new innovation, and a new device is ready. But there is a certain section of the community who still cannot take benefit of these technologies and gadgets for his or her communication. These people not able to speak and cannot convey their message and society calls them mute. This work will help such people to speak easily without the involvement of any third person or mediator. Mute people do the action ahead of the screen of the device, since this device is going to be user-friendly, an individual can feed action and its meaning into it. Person feeds his own action and gestures alongside its name or title, and therefore, the camera will detect the gesture, then print it on the screen and convert it into audio output. People working during a professional environment can cash in on it.
Point-based Value Iteration for VAR-POMDPs

Partially observable Markov decision processes have been widely adopted in the automatic planning literature since it elegantly captures both execution and observation uncertainties. In our previous paper, we proposed a model called vector autoregressive partially observable Markov decision process (VAR-POMDP) which extends the traditional POMDP by considering the temporal correlation among continuous observations. However, it is a non-trivial problem to develop a tractable planning algorithm for the VAR-POMDP model with performance guarantees as most existing algorithms need to explicitly enumerate all possible observation histories, which is in an unbounded continuous space. In this paper, we extend the famous point-based value iteration algorithm to a double point-based value iteration and show that the VAR-POMDP model can be solved by dynamic programming through approximating the exact value function by a class of piece-wise linear functions. Meanwhile, we prove that the approximation error is bounded. The effectiveness of the proposed planning algorithm is illustrated by an example.
Combining Different Knowledge Sources for Text Understanding

We describe a project under development whose objective is to build a model of natural language text understanding. This model has a form of a converter of a natural language text to its semantic structure. The project basically consists in enriching the ETAP-4 linguistic processor, developed at the Institute for Information Transmission Problems (IITP) of the Russian Academy of Sciences, with a new module - that of semantic analysis. An important feature of this module is that it uses not only linguistic knowledge incorporated in the grammar and the combinatorial dictionary, but also extralinguistic knowledge stored in the ontology and contextual information accumulated in the repository of individuals. We developed an ontology that serves as the semantic metalanguage for semantic structures. This knowledge permits to make different types of inferences, which extract implicit information. Several examples are given that show how all these knowledge sources can be used, how the meaning is represented and what inferences can be made.
Hand Motion and Face Recognition System for Visually Disabled People

Keen security framework has gotten essential in current everyday life. The proposed security framework has been developed to prevent breakdowns in deeply secure regions, such as home conditions with less use of force and a more dependable independent safety device for both intruder recognition and entryway security. The past techniques have seen the utilization of accelerometers, which are fixed on the hand as they are viewed as the best device for completing such practices. In our work, the visitor is identified by a standard web camera to capture the image. If the face recognizes the people, they will be greeted by name and the door will be unlocked. Doors will not be unlocked if they are not identified. For Door Access, the system will perform detection and recognition. Home Automation Model utilizing the online site is intended to give simplicity of control of home apparatuses for individuals, especially the old or those genuinely unfit to perform everyday exercises effectively. Electronic computerization discusses straightforwardly with raspberry pi to control home appliances. The proposed technique, permits clients to deftly and conveniently control various family apparatuses with basic signals.