Reviewerly/exHarmony huggingface dataset:
Effects of Rounding and Truncating Methods of Quantization Error and SQNR for Sine Signal

Within the Analog to Digital Conversion (ADC), quantization noise is a duplicate of a Quantization Error (QE) which is introduced by quantization. In signal processing and telecommunication systems, the noise is non-linear and depends on the signal type. During the analog, Sine signal converts to the digital (ADC) process, the two methods are used Rounding and Truncating in-order to eliminate the error produced in the digitization process. The rounding method quantize assigns each sample of sine signal to the nearest quantization level. However, making the Truncating would have assigned each sample of sine signal to the quantization level below it. This paper compares the rounding and truncating methods of QE for sine signal, signal to quantization noise ratio, correlation coefficient, and regression equation of a line for both methods. Then, it calculates the residual sum of squares and compares it to the regression equations of the lines.
Trust Based Fuzzy Linguistic Recommender Systems as Reinforcement for Personalized Education in the Field of Oral Surgery and Implantology

The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. In this framework, personalized services are necessary. Recommender systems can be used in an academic environment to assist users in their teaching-learning processes. In this paper, we present a trust based recommender system, adopting a fuzzy linguistic modeling, that provides personalized activities to students in order to reinforce their education, and applied it in the field of oral surgery and implantology. We don’t take into account users with similar ratings history but users in which each user can trust and we provide a method to aggregate the trust information. This system can be used in order to aid professors to provide students with a personalized monitoring of their studies with less effort. The results obtained in the experiments proved to be satisfactory.
Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the required number of function evaluations for BO without compromise in solution quality. We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework. The LF data can arise from previous evaluations of an LF approximation of the BOF or a related optimization task. An extra GP model called LF-GP is trained to fit the LF data. We develop an operator termed dynamic weighted product of experts (DW-POE) fusion. The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism. Extensive experimental results on benchmark BOF optimization tasks demonstrate the superior performance of the proposed algorithm over state-of-the-art.
Application of Data Science within the Army Intelligence Warfighting Function: Problem Summary and Key Findings

Army Intelligence operates in a data rich environment with limited ability to operationalize exponentially increasing volumes of disparate structured and unstructured data to deliver timely, accurate, relevant, and tailored intelligence in support of mission command at echelon. The volume, velocity, variety, and veracity (the 4 Vs) of data challenge existing Army intelligence systems and processes, degrading the efficacy of the Intelligence Warfighting Function (IWfF). At the same time, industry has exploited the recent growth in data science technology to address the challenge of the 4 Vs and bring relevant data-driven insights to business leaders. To bring together the lessons from industry and the data science community, the US Army Research Laboratory (ARL) has collaborated with the US Army Intelligence Center of Excellence (USAICoE) to research these Military Intelligence (MI) challenges in an Army AR 5-5 Study entitled, "Application of Data Science within the Army Intelligence Warfighting Function." This paper summarizes the problem statement, research performed, key findings, and way forward for MI to effectively employ data science and data scientists to reduce the burden on Army Intelligence Analysts and increase the effectiveness of data exploitation to maintain a competitive edge over our adversaries.
Usability Design Support Uniformly Applicable through Upstream to Downstream Design Phases

As a method of evaluating usability, usability tests are widely used, in which designers observe users using products. However, conventional usability tests take efforts on designers because designers need to manually input and analyze operation data of users when using tests. This paper proposes methods that can be applied uniformly through upstream to downstream design phases by using motion capture and Dynamic Time Warping (DTW). These methods enable designers to automatically acquire operation data of users and to calculate similarity of operation locus between designers and users. As the first step, an experiment was conducted that assumed the upstream design phases, using a triple digital timer. Consequently, it is revealed that it is possible to detect operations which are considered to have usability problems by using methods which this paper proposes.
SIMULASI RANGKAIAN SEVEN SEGMENT MENGGUNAKAN MULTISIM PADA PEMBELAJARAN RANGKAIAN ELEKTRONIKA ANALOG DAN DIGITAL DI SMKS INFORMATIKA SUKMA MANDIRI

Electronic components are the most complex and sensitive components, not infrequently damage occurs if the components used are not appropriate, for example, excess power, current, short and others. Therefore simulation software can help the process of designing electronic circuits before being patented directly on the components. Not only that, we can also use electronic devices that we cannot reach (buy and meet) like the oscilloscope in the simulation software. The use of a simulation device is used one of them to build a concept in terms of teaching and learning. NI Multisim 11.0 software can be used in the teaching and learning process in this case is the subjects of a series of analog and digital electronics on the seven segment material. Seven segment is a display that is formed from 7 groups of LED (Light Emitting Diode) segments that function to emit light when passing through an electric current through which it is arranged in such a way as to form numbers from 0 (zero) to 9 (nine). Where in this simulator there is a simulation that shows the process of the formation of hex numbers as output. This research discusses how to design schematic circuits, analyze, simulate, check errors, and make layouts on a PCB. The results of this study concluded that the use of Multisim NI software is very good for learning media so that the material presented will be easier to understand.
Designing User Experience Mobility Assistant Application for the Physically-Disabled Using the Wheel Method

This research was conducted to design a mobility assistant application for persons with physical disability using The Wheel method. This application can assist mobile activities for people with disability. The application called Kuygo was made in four stages: analysis, design, prototype, and evaluation. Analysis of user needs is carried out at Loka Bina Karya (LBK) Bogor through observation and interviews. The interaction design requirements are generated in the form of requirements statements and inventory tasks with three main tasks, namely mobility preparation, mobility comfort, and providing a review of a location. Afterwards, discussions were held in the form of design thinking and ideation sessions, with people who care about people with disabilities and the Senyum Difabel community, which produced sketches, storyboards, and wireframes. Furthermore, the design implementation is carried out by making a prototype of medium-fidelity and the results are tested using cognitive walkthrough techniques. Of the four questions that must be answered with cognitive walkthrough techniques, the average success rate of all the tasks tested is 94.62%. Based on these results, no major usability errors were found in the prototype medium-fidelity and the Kuygo application can be further developed.
Hyperspectral Processing Scripts for the HydReSGeo Dataset 1.0.0

This repository includes the processing scripts of the HydReSGeo dataset for the hyperspectral, LWIR, and soil moisture data.
Deep convolutional neural network for hand sign language recognition using model E

An image processing system that based computer vision has received many attentions from science and technology expert. Research on image processing is needed in the development of human-computer interactions such as hand recognition or gesture recognition for people with hearing impairments and deaf people. In this research we try to collect the hand gesture data and used a simple deep neural network architecture that we called model E to recognize the actual hand gestured. The dataset that we used is collected from kaggle.com and in the form of ASL (American Sign Language) datasets. We doing accuracy comparison with another existing model such as AlexNet to see how robust our model. We find that by adjusting kernel size and number of epoch for each model also give a different result. After comparing with AlexNet model we find that our model E is perform better with 96.82% accuracy.
Reproducibility of AOD Algorithm: An Experimental evaluation for Key-Predictors Identification

INTRODUCTION: Today surveillance systems are widespread across the globe for monitoring of various activities.Abandoned Object Detection (AOD) and identifying its location is one of them.In this paper, we evaluated the reproducibility of an existing AOD algorithm on benchmark video datasets.OBJECTIVES: The purpose of the study is to identify the key predictors for developing a generalized AOD algorithm.METHODS: The algorithm selection is performed by a detailed exploration of repositories through various research questions (RQs).RESULTS: After the study video summarization, Correct Detection Rate (CDR), generalized Region of Interest (ROI), background learning, and interaction factor considered for enhancing the AOD algorithm.CONCLUSION: Identification of suspiciousness has various measures depending upon perception, on the basis of results explored the existing algorithm can be improved using key-predictors with observational parameters.
The impact of immersion level and virtual reality experience on outcomes from navigating in a virtual environment

Navigating a virtual environment (VE), while maintaining situational awareness, and learning the environment are important in military training. As head mounted displays (HMDs) and virtual reality (VR) become more prevalent in military training, it is important to investigate outcomes with different levels of immersive technologies and experience with VR. We conducted a within-subjects experiment (61 participants returned for all sessions), which measured selfreported VR experience and varied level of immersion (high, Oculus HMD; medium, NVIS HMD; low, Monitor) during VE navigation. After a task to find target objects, participants engaged in transfer tasks to determine what they learned from the VE. These tasks examined if items from the environment were recognized (yes/no and multiple choice identification), or generated from memory without cues, which suggests deeper processing (recall of target/non-target items). Level of immersion impacted recognition without interacting with previous VR experience, with high immersion performing significantly better than medium and low. Immersion impacted recall of target objects, with high recalling significantly more than medium. For incidental (non-target) recall, immersion did not have an impact, but previous VR experience resulted in significantly better recall of non-target objects. As this was found only for incidental recall, it suggests those with previous VR experience retained more information from the environment in general. The results suggest that immersion may have different impacts depending on type of information to be learned, and previous VR experience may improve performance on deeper learning tasks. These outcomes can be applied in the design of VEs and navigation tasks.
Assessing Architectural Patterns Trade-offs using Moment-based Pattern Taxonomies

Large software systems are designed to satisfy or accommodate many requirements; architectural patterns are a well-known technique to reuse design knowledge. However, requested quality attributes (QA) may be inconsistent at times; e.g., high security typically hampers performance and scalability. Thus, a key concern of systems architects is understanding trade-offs among alternative solutions; e.g., a pattern may favor performance at the expense of scalability or security, another may privilege scalability, and yet another may push security. This article argues that the usual organization of individual patterns in topic-related pattern languages is not too helpful to identify trade-offs, and proposes to borrow a taxonomic principle of architectural tactics, organizing the patterns for each QA into “moments”. This enables architects to use simple tradeoff highlighting techniques to understand trade-offs in complex systems. The approach was used in the systematic design of a SCADA-to-ERP secure bridge, where moment-oriented pattern taxonomies for availability, confidentiality, and performance were used. This approach offers the promise of enabling the trade-offenabled, pattern-driven design of large systems by supporting the systematic exploration of trade-offs among patterns for specific QA's.
Visualizing dynamic and uncertain battlefield information: lessons from cognitive science

The Army aims to enhance Soldier Situational Awareness (SA) and performance through the development of technologies, including sensors to collect novel sources of information, AI-driven algorithms to integrate information sources and recommend courses of actions, and display technologies like augmented reality to communicate this information to Soldiers. However, the operational effectiveness of these technologies relies on Soldiers' ability to quickly and accurately perceive, interpret, and make decisions with the displayed information. How do we determine whether and when one visualization method is more beneficial than another? We propose a research approach that leverages foundational cognitive science to identify cognitively informed trade-off spaces for different visualization methods, given Soldier needs and the dynamics of battlefield and operational information across mission phases. This approach will also enable understanding of how variables such as stress and time pressure moderate these trade-off spaces. The goal is to derive recommendations for visualization design, including novel methods of visualization that align with and support both the abilities and limitations of human cognition. We use the context of spatial knowledge and navigation as an illustrative example, exploring how different visualizations may effectively accommodate or overcome phenomena of human decision-making (e.g., biases and heuristics) given dynamic and uncertain battlefield information. In this review paper, we discuss the types of tactical information used to perform specific tasks, the underlying cognitive processes supporting these tasks, and the implications that aspects of human cognition have for visualization methods, ultimately motivating the research approach above.
Situational and game technologies of training in students’ professional competence development

The transition from traditional educational paradigm to competence one has marked new ways of higher educational institutions development. The implementation of modern Federal state educational standards requirements has determined the need to use modern specialist methods in training that develop students’ competence and the ability to carry out highly qualified professional activities. Higher education institutions face the task of implementing technologies that contribute to the development of practical orientation of students, their independence in this process. The purpose of the article is to reveal the experience of using situational-game technologies of training, as one of the most practice-oriented, aimed at activating students’ students, contributing to professional competence development. The concept of "technology" came to education from the sphere of production. Technologization implies a clear subordination to a certain algorithm of actions, so the technology allows you to achieve specific results in the allotted amount of time. In the field of professional education, technology has changed, but its meaning remains the same-the regulation of the activities of the subjects of the educational process to achieve the planned goals. The article defines the essence of educational technologies in the training of University students, reveals the features of their application, the role and advantages of situational and game technologies of training in the conditions of implementation of the competence approach. The research given in this article shows the importance of game educational technologies and the prospects of their use in the training a highly qualified, creative specialist who can quickly solve professional problems in non-standard ways.
CORPUS TRIANGULATION: COMBINING DATA AND METHODS IN CORPUS-BASED TRANSLATION STUDIES

Review of the book: Corpus triangulation: combining data and methods in corpus-based translation studies , by Sofia Malamatidou, London and New York, Routledge, 2018, 190 pp., £105.00 (hardback), ISBN 9781138948501
Implementasi RFID dan NodeMCU Untuk Data Kunjungan Perpustakaan Berbasis IoT

Radio Frequency Identification (RFID), automatic identification by storing and sending data wirelessly via RFID card or transponders. A variety of recent applications of RFID have been developed in various sectors of life, especially related to identification via radio waves both in the field of automotive, biomedical, educational institutions, police, banks, transportation, and others. The purpose of this research is to design an RFID application in the field of libraries, so that RFID can be used as an alternative solution other than the barcode as the media identification data stored neatly in a book database program through the support of electromagnetic waves. At present we know that the national university library still uses paper to fill in the visit, to resolve the issue data from testing using the RFID method and the NodeMCU microcontroller, it can send data to a web application and can be stored in a database, It is expected that from the results of testing this application can solve the problems that exist in the library that previously members wrote a visit data to enter the future only need to tap the card to enter and do not need paper as a registration medium
Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection

Surveillance cameras are expected to play a large role in the development of ITS technologies. They can be used to detect abnormally behaving individuals which can then be reported to drivers nearby. There are multiple works that tackle the problem of abnormal behavior detection. However, most of these works make use of appearance features which have redundant information and are susceptible to noise. While there are also works that make use of pose skeleton representation, they do not consider well how to handle cases with occlusions, which can occur due to the simple reason of pedestrian orientation preventing some joints from appearing in the frame clearly. In this paper, we propose a skeleton trajectory representation that enables handling of occlusions. We also propose a framework for pedestrian abnormal behavior detection that uses the proposed representation and detect relatively hard-to-notice anomalies such as drunk walking. The experiments we conducted show that our method outperforms other representation methods.
Geological Survey of New South Wales: MinEx CRC data acquisition kicks off in New South Wales

The Geological Survey of New South Wales (GSNSW) and Geoscience Australia (GA) coordinated the largest airborne electromagnetic (AEM) survey ever flown in NSW in September – October 2019. The surve...
Secure SMS System using RSA Encryption Based on Android platform

To send text messages free of charge using an existing mobile number, you can use this system like SMS messenger and install it on your mobile device. This apk is an application that enables you to text and sends small images to other this apk’s users without charge. To send a secure message, secure sessions must exist between the sending device and all the recipient’s devices. Asymmetric key cryptography algorithm RSA is used for encryption and decryption of the message. The user can obtain the security of text messages using RSA. This system provides a secure message on the mobile phone. Every SMS packet contains 140 bytes of effective data. Mobile users send short messages to the short message center through mobile terminals, and then the short message center transmits these short messages to target users.
Relative Array Analysis of Visual Servo System

Feedback data from the environment regulate the control systems in characterization and decision making in the applications of robotics and automation in industries. Machine vision being non-touching in nature, provides useful information about the targets and surroundings, catering to a variety of applications. Closed loop robotic applications find visual servoing as a potent and efficient tool interactions with the environment. This paper aims at developing a process control-like model for visual servoing. The inherent drawbacks of image based visual servoing can be explained with variable interaction and Relative Gain Array (RGA) is a useful tool for evaluating the system.
Static and Moving Object Detection and Segmentation in Videos

This paper presents static object detection and segmentation method in videos. In this context, background subtraction BS technique based on the frame difference concept is applied to the identification of static objects. First, we estimate a frame differencing foreground mask by computing the difference of each frame with respect to a static reference frame image. The Mixture of Gaussian MOG method is applied to detect the moving particles and then outcome foreground mask is subtracted from frame differencing mask. Pre-processing techniques are applied to reduce the noise from the scene. Finally, morphological operation and largest connected component analysis are applied to segment the object. The proposed method was effectively validated with two public data sets. The results demonstrate the proposed approach can robustly detect, and segment the static objects without any prior information of tracking.