Reviewerly/exHarmony huggingface dataset:
Full-Field Modal Analysis by Using Digital Image Correlation Technique

At the state of art, the design validation of mechanical products in automotive, aerospace, and other advanced manufacturing industries is made by using vibration measurements and experimental modal analysis. The structures’ dynamic behavior is investigated mostly using contact transducers (accelerometers, strain gauges, load cells, …). Optical methods such as Digital Image Correlation (DIC) have recently received special attention in the structural dynamics field because it can be used to obtain full-field measurements. However, DIC can only measure vibrations in the camera field of view, which causes problems for hidden parts or in case the structure is too large to fit into the field of view. Therefore, it is of paramount importance to combine DIC with other traditional sensors such as accelerometers and strain gauges. Complex 3D test articles and structures need to be measured from many different directions. The knowledge gained by developing a heterogeneous sensor measurement system can significantly reduce the time and cost associated with pointwise sensor instrumentation. In this paper, DIC technique is used to get the full-field displacement. The sensor merging, DIC + pointwise sensors, is investigated. This information is then used to derive the modal characteristic of the structure (e.g. natural frequencies, damping ratios, and full-field mode shapes) that can be used for a more reliable FE model validation.
Analysis of the Influence of Extracurricular Knowledge on College Students’ Values Based on Knowledge Mapping Theory

As the main force of the future society, college students are deeply concerned by the cultivation and development of their values. However, the cultivation of college students’ values is mostly at the present stage. In addition to extracurricular activities, college students are exposed to a variety of things, which affects the values of college students as much as the classroom. Therefore, the cultivation of values cannot be limited to the classroom. The influence of extracurricular knowledge on the values of college students is also extremely important. This paper analyzes the influence of college students’ extracurricular knowledge on values based on knowledge graph theory. Research shows that the influence of extracurricular knowledge on college students’ values is a double-edged sword, which has both positive and negative effects. Whether it is negative or positive, it depends on the values that college students choose to be exposed to outside the classroom. That is to say, the influence of extracurricular knowledge on college students’ values depends on the values of extracurricular knowledge itself.
Using Verb Frames for Text Difficulty Assessment

This paper presents the first investigation on using semantic frames to assess text difficulty. Based on Mandarin VerbNet, a verbal semantic database that adopts a frame-based approach, we examine usage patterns of ten verbs in a corpus of graded Chinese texts. We identify a number of characteristics in texts at advanced grades: more frequent use of non-core frame elements; more frequent omission of some core frame elements; increased preference for noun phrases rather than clauses as verb arguments; and more frequent metaphoric usage. These characteristics can potentially be useful for automatic prediction of text readability.
CDVA/VCM: Language for Intelligent and Autonomous Vehicles

Intelligent transportation is a complex system that involves the interaction of connected technologies, including Smart Sensors, Intelligent and Autonomous Vehicles, High Precision Maps, and 5G. The coordination of all these machines mandates a common language that serves as a protocol for intelligent machines to communicate. International standards serves as the global protocol to satisfy industry needs at the product level. MPEG-CDVA is the official ISO standard for search and retrieval applications by providing Compact Descriptors for Video Analysis (CDVA). It is robust and enables efficient implementations on embedded systems. CDVA is the first generation language for images/videos. MPEG-VCM is developing advanced features beyond CDVA to the new generation as Video Coding for Machines (VCM). With the wide availability of low-power AI chips, CDVA and VCM are ready to deploy and serve as the language for intelligent and autonomous vehicles. In this paper, we demonstrate the use of the SuperCDVA and Closed Captioning CDVA algorithms for intelligent and autonomous vehicles. Concepts are borrowed from the Super Characters algorithm in Natural Language Processing. In order for intelligent and autonomous vehicles to understand events on the road, the CDVA vectors are organized into an image to represent the story of the video.
An Adaptive Thread Partitioning Approach in Speculative Multithreading

Thread partition is a core part of Speculative Multithreading (SpMT) technique. The existing thread partition approaches mostly adopt one unique thread partitioning scheme for unknown programs, resulting in high misspeculation ratio, restricting the programs’ speedup improvement due to inappropriate partitioning schemes. This paper which introduces an adaptive thread partition approach (AdapTPA), takes the relationship between program complexity and thread partitioning scheme as the research entry point, and uses the irregular programs as the research carrier, and utilizes formal analysis, probability statistics, mathematical modeling and simulation experiments to reveal the rule that program’s characteristics affect speedup performance, and generates a compound thread partitioning scheme for one program, and selects and executes the most suitable thread partitioning scheme according to the runtime context and the program’s complexity, so to achieve the expected maximum speedups. With the method of path statistics on one program’s control flow graph, the program’s complexity calculation model is set up; A candidate thread partitioning scheme set is constructed on the foundation of classical thread partitioning approaches; Using expert knowledge to guide production rules, a scheme selection mechanism that complies with program complexity is explored. Compared to the heuristic rules-based (HR-based) thread partitioning method, the experiment results show that AdapTPA delivers an average 18.24% performance improvement.
A Comparison the Effectiveness of Data Mining Techniques in Predicting Career Advice to Promote Education for People with Disabilities (Thailand)

The purpose of this research was to study and compare the effectiveness of data mining techniques used to predict career advice given to promote education to people with disabilities in Thailand. The study used data about job positions and the anonymized individual competencies of 907 disabled people who are currently studying in the Thai education system. The data consisted of 28 features of individual competencies, including proficiencies, occupations, special abilities, gender, and work positions, including major field and position recruited for. The study showed that: (i) random forest, decision tree, and bagging techniques were the most appropriate data mining techniques; and (ii) the three techniques showed precision levels of respectively 86.42%, 81.29%, and 81.07%, and so random forest methods were the most accurate.
Lagrangian decomposition for neural network verification

A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. Previous methods have either used off-the-shelf solvers, discarding the problem structure, or relaxed the problem even further, making the bounds unnecessarily loose. We propose a novel approach based on Lagrangian Decomposition. Our formulation admits an efficient supergradient ascent algorithm, as well as an improved proximal algorithm. Both the algorithms offer three advantages: (i) they yield bounds that are provably at least as tight as previous dual algorithms relying on Lagrangian relaxations; (ii) they are based on operations analogous to forward/backward pass of neural networks layers and are therefore easily parallelizable, amenable to GPU implementation and able to take advantage of the convolutional structure of problems; and (iii) they allow for anytime stopping while still providing valid bounds. Empirically, we show that we obtain bounds comparable with off-the-shelf solvers in a fraction of their running time, and obtain tighter bounds in the same time as previous dual algorithms. This results in an overall speed-up when employing the bounds for formal verification. Code for our algorithms is available at https://github.com/oval-group/decomposition-plnn-bounds.
CodeCubes: Coding with Augmented Reality.

CodeCubes is interface that uses Augmented Reality to stimulate Computational Thinking in young students. The visual programming blocks are replaced by paper cubes that have an Augmented Reality marker on each face. Each marker represents a programming instruction. The game is composed of three levels. It consists of programming a car course in a racetrack, driving from the start to the final goal.Code Cubes takes advantage of the physicality offered by Augmented Reality technology. We present the design and development of the game, focusing on its main characteristics and describing the various development stages. We also present the first results obtained by exploring Code Cubes. The results were positive, showing the potential of Augmented Reality interfaces in learning scenarios.
Trust and Access Controls in IoT to Avoid Malicious Activity

This chapter discusses computing data in a cloud, current challenges to store, retrieve, process, as well as security requirements, and possible solutions. It frames the trust-based access control model that incorporates a digital signature to minimize the malicious activity for storage, retrieval, updates, and processing of sensitive data. The chapter provides the trust framework for the user and access control algorithms for data processing in the cloud environment. Cloud computing is a paradigm with unlimited on-demand services that is software as a service. It can virtualize hardware and software resources, with high processing power, storage, and pay-per-usage. Big Data technology solves many problems irrespective of volume, velocity, and source of generation. It is a continually changing technology, and many industries, customers, and government agencies are involved in usage and managing. Vulnerability assessment of the devices and software supporting these devices are essential to trust the functionality of these devices.
PERANCANGAN SISTEM INFORMASI TRACER STUDY PADA UNIVERSITAS BANTEN JAYA

Banten Jaya University is a tertiary institution located in Serang City, Banten Province. Which has three faculties including education, computer science and engineering. In a university, information about alumni data is needed, starting from the department, graduation year, place of work and other information related to the alumni. Tracer Study is useful as a link between universities and the business world and industry. The tracer study process at Banten Jaya University is still not optimal, because the data collection is still done manually by distributing questionnaires to alumni and lack of information dissemination to alumni, because it is limited only through social media. This is why the information system Tracer Study is built using the PHP programming language and MySQLi as the database, to collect alumni information. This information system uses web-based technology and the waterfall method in the development. From the results of testing this system is in accordance with the algorithm that was made,users can fill out questionnaires online, see other alumni information, and managing job openings, as well as displaying graphs of alumni who had worked after graduation and the number of graduates each year.
Use cases of blockchain technology for humanitarian engineering

The aims of this chapter are twofold. First, it describes the primary building blocks of blockchain technology. Second, it illustrates various use case scenarios of blockchain technology in the fields of agriculture, energy health and others.
RAD: Reinforced Attention Decoder Model On Question Generation

Question Generation (QG) aims to construct questions from given text automatically. Recently, QG has received widely concerned. The mainstream method is still based on the fixed sequence generation model of Seq2Seq model, and few people consider the influence of generation order in the result. In this paper, we present a novel Reinforced Attention Decoder Neural Network for the QA-SRL task. First, our model draws on the idea of the reinforcement learning algorithm with baseline, which is using the accuracy of each slot and sentence as the reward, updating the Policy Network to predict the optimal generation order. Second, we apply the Attention mechanism on the baseline to get more relevant information about the entire sentence. Addition experiments explore that distilling knowledge from RAD (the teacher) model to guide RAD-Reborn model (the student) training can achieve better performance. Extensive experiments on QA-SRL Bank 2.0 show that our model outperforms previous systems of all of the evaluation metrics. In particular, the metric EM (Exact Match) increased significantly by over 3%.
Smart Home Stress Assist: A Real-Time Intervention for PTSD

35th Annual Assistive Technology Conference Scientific/Research Proceedings, San Diego, 2020
DCM: A Dense-Attention Context Module For Semantic Segmentation

For image semantic segmentation, a fully convolutional network is usually employed as the encoder to abstract visual features of the input image. A meticulously designed decoder is used to decoding the final feature map of the backbone. The output resolution of backbones which are designed for image classification task is too low to match segmentation task. Most existing methods for obtaining the final high-resolution feature map can not fully utilize the information of different layers of the backbone. To adequately extract the information of a single layer, the multi-scale context information of different layers, and the global information of backbone, we present a new attention-augmented module named Dense-attention Context Module (DCM), which is used to connect the common backbones and the other decoding heads. The experiments show the promising results of our method on Cityscapes dataset.
Capacitive-Based Gesture Recognition System for Human-Machine Interface in Automotive Applications

This paper presents a gesture recognition system for automotive applications. The system consists of four self-capacitance sensors, an Analog Front-End (AFE) designed in standard 0.18 μm CMOS technology and a digital core for signal processing and gesture identification. All blocks are taken into account during the design process and therefore, the most demanding tasks during the data acquisition and processing chain are implemented in the most appropriate and optimal block, minimizing the resources needed to perform the tasks. This leads to an optimized system design of high performance and reduced cost. A proof-of-concept system is designed for architecture verification reaching promising results.
Security, Availability, and Multiple Information Sources: Exploring Update Behavior of System Administrators

Experts agree that keeping systems up to date is a powerfulsecurity measure. Previous work found that users sometimesexplicitly refrain from performing timely updates, e.g., dueto bad experiences which has a negative impact on end-usersecurity. Another important user group has been investigatedless extensively: system administrators, who are responsiblefor keeping complex and heterogeneous system landscapesavailable and secure.In this paper, we sought to understand administrators’ behavior, experiences, and attitudes regarding updates in a corporate environment. Based on the results of an interviewstudy, we developed an online survey and quantified commonpractices and obstacles (e.g., downtime or lack of informationabout updates). The findings indicate that even experiencedadministrators struggle with update processes as the consequences of an update are sometimes hard to assess. Therefore,we argue that more usable monitoring and update p
MODEL FEED FORWARD NEURAL NETWORK (FFNN) DENGAN ALGORITMA PARTICLE SWARM SEBAGAI OPTIMASI BOBOT (Studi Kasus : Harga Daging Sapi dari Bank Dunia Periode Januari 2007 – Desember 2018)

Beef is one of the important food commodities to fulfill the nutritional adequacy of humans. The World Bank notes the beef prices that are exported worldwide every month. For this reason, those data becomes a predictable series for the next period. Feed Forward Neural Network is a non-parametric method that can be used to make predictions from time series data without having to be bound by classical assumptions. The initiated weight will be evaluated by an algorithm that can minimize errors. Particle Swarm Optimization (PSO) is an optimization algorithm based on particle speed to reach the destination. The FFNN model will be combined with PSO to get predictive results that are close to the target. The best architecture on FFNN is obtained with 2 units of input, 1 unit of bias, 3 hidden units, and 1 unit of output by producing MAPE training 11.7735% and MAPE testing 8.14%. According to Lewis (1982) in Moreno et. al (2013), the MAPE value below 10% is highly accurate forecasting. Keywords: Feed Forward Neural Network (FFNN), Particle Swarm Optimization (PSO), neurons, weights, predictions.
PENERAPAN ANALISIS KLASTER K-MODES DENGAN VALIDASI DAVIES BOULDIN INDEX DALAM MENENTUKAN KARAKTERISTIK KANAL YOUTUBE DI INDONESIA (Studi Kasus: 250 Kanal YouTube Indonesia Teratas Menurut Socialblade)

YouTube is one of the most popular online platforms today. The popularity of YouTube has makes it an effective advertising medium. In April 2019, Socialblade released the top 250 YouTube channels in Indonesia based on their gradations with various characteristics. YouTube channel data will be grouped into several clusters to make it easier for advertisers to choose channels with characteristics as needed. The purpose of this study is to determine the best number of clusters and determine their characteristics. The method used is the k-Modes cluster analysis with values k = 3, 4, 5, ..., 8. The k-Modes method can group objects that have categorical type variables into relatively homogeneous groups. The best number of clusters (k) can be checked using the Davies Bouldin Index (DBI). Based on the analysis carried out, obtained the best number of six clusters with a Davies-Bouldin Index value of 1.080509. The most recommended cluster for advertising is cluster 6, which has grade A characteristics, gold title, and has an estimated annual income of 5 million USD &lt; income ≤ 10 million USD. Keywords: Youtube, Cluster Analysis, k-Modes, Categorical Data, Davies-Bouldin Index
Unlocking Student's Preference on Two BINUS Mobile Learning Management System

Many universities in the World have their own unique way for informing their students. The methods may vary for each university, but most of the modern universities today use a Learning Management Systems (LMS). The LMS application is widely used by the lecturers and students of Bina Nusantara University called BINUMAYA. The system was originally created as a website application, but now its mobile version is also available and is widely used by amongst the students and the lecturers. However, the existing student version of the mobile application have two separate applications, that function almost identically with the official BINUMAYA system. We are interested in finding out the students' perceptions about the two applications. Therefore, we ran a user survey on BINUS students that were randomly selected by convenience sampling. Respondents were asked several questions related to performance, trustworthiness and preferability using the applications. The data collected was analysed using the two mean difference tests for each variable. Our findings showed that the students have a few reasons on why they prefer to choose one application compared to the other and, why they only trust the information presented by the official mobile LMS.
Unknown mixing times in apprenticeship and reinforcement learning

We derive and analyze learning algorithms for apprenticeship learning, policy evaluation, and policy gradient for average reward criteria. Existing algorithms explicitly require an upper bound on the mixing time. In contrast, we build on ideas from Markov chain theory and derive sampling algorithms that do not require such an upper bound. For these algorithms, we provide theoretical bounds on their sample-complexity and running time.
INFORMATION DISORDER & THE ONLINE’S GATEKEEPING MECHANISM STRUGGLE IN POST TRUTH ERA

Online journalistic practice in Indonesia has become a singular phenomenon in the digital era. Along with the rapid development of communication technology, the news portals in this country are also growing. The ease of access to any information; which is able to be accessed by anyone and anywhere has emerged to be one of many factors why online news sites tendecially being used as the main reference for any kind of information. Oppositely, there has been a wide spreading of misinformation, malinformation as well disinformation, which are circulated on social media and micro-blogging media. The concepts authors initiate in this research are the gatekeeping mechanism and disorder information. This has become the focal point of the paper to observe how the gatekeeping mechanism struggle with the occurrence. This paper method is qualitative, preserverving the interview data collecting technique. Informants in this paper are the editorial team of Liputan6.com and Detikcom. The conclusions of this article are : (a) Liputan6.com implemented traditional gatekeeping yet a gatewatching for the reader(s), Detikcom performing a processing journalism, (b) both media stated Information selection based on certain criteria, (c) Liputan6com expending SCTV East Java Bureau for disseminating the local news, Detikcom has representative in the back office in East Java for the local news, (d) both media having the awareness whether the selecting information leading to online news term’s and phenomenon nowadays. Keywords: Information Disorder, Gatekeeping Mechanism, Online News Portal, Post Truth Era, Online Journalism