Reviewerly/exHarmony huggingface dataset:
Multi-parameter Coordinated Public School Admission Model by using Stable Marriage

School admission is a very important process in improving the education quality.Meanwhile, one problem in the school admission system is the mismatch.There are unassigned applicants and unallocated seats.In Indonesia, zone-based model is adopted in the public-school admission system.Students are assigned to their nearest school.Besides location, student's academic performance and economic level are also concerned.Based on it, this work proposes coordinated public school admission model that accommodates flexible number of the concerned parameters.It is built based on the stable marriage algorithm or the deferred-acceptance algorithm as its derivative.The proposed model is a combination between the mandatory approach and the school choice approach.The concerned parameters are school-home distance, student national exam score, school rank, applicant poor status, and applicant's preference.The simulation is conducted to investigate the performance of the proposed model compared with the previous models: the zone-based model and the two-step model.The prioritization of the concerned parameters is proven easily adjusted.The simulation result shows that in the over-demand condition, the proposed model creates higher average student national exam score and higher average school-home distance rather than the previous models.When the number of applicants is twice of the number of seats, the proposed model creates 6.6 percent higher in the average student national exam score and 71.4 percent higher in the average school-home distance.The simulation result also shows that the mismatch is solved.
Equivariant Siamese Tracking Algorithm Based on SEACT

Recently, Siamese trackers are most popular algorithms for single object tracking and have been attempted to infrared tracking. Since the dependence on the initial target template, Siamese network could not handle the scenes in the complex and changing infrared environment well, where similar interferent with various sizes makes these trackers run to drift or even tracking failure. In our previous work, we introduced the reinforcement learning model into the derivation stage of the tracker to improve the accuracy of the tracker. However, our tracker still cannot solve the problem of positional deviation led by the non-translational equivalent structure of the Siamese network when training. In this paper, we design an equivariant Siamese tracking algorithm SEACT guided by DRVGG, which is more adaptable to transformation scenarios including target scaling with the built-in scale equivariance of Siamese network. We conduct experiments on VOT-TIR2016 benchmark. The algorithm achieved excellent performance and can accurately generate zoom targets in time when the object scaling. Meanwhile, we test some sequences collected by ourselves, and there is a little improvement for some occlusion situations.
A Survey on Depth Estimation for 3D Reconstruction of Images

Depth estimation plays an important role in many applications of computer vision. Depth can be inferred from multiple images of same scene taken from different station points or from monocular cues associated with a single image. Stereo vision, an extensively used method for gathering depth information from 2D scene endures with view synthesis, which is 3D scene reconstruction. The automated systems such as robotics, constructing a 3D spatial model of a scene and tracking object in 3D space are some of the applications that rely on stereo vision. Stereo vision concept is exploited by many vision based remote control systems to access the machine in a touch-free environment. Most of the existing approaches use multiple images to perceive depth such as stereopsis, structure from motion, depth from defocus/focus, deep learning, machine learning.
The Dark Data Quandary

The digital universe remains a black box. Despite attaining high-technology capabilities like artificial intelligence and cognitive computing, “Big Data” analytics have failed to keep pace with surging data production. At the same time, the falling costs of cloud storage and distributed systems have made mass data storage cheaper and more accessible. These effects have produced a chasm between data that is stored and data that can be readily analyzed and understood. Enticed by the promise of extracting future value from rising data stockpiles, organizations now retain massive quantities of data that they cannot presently know or effectively manage. This rising sea of “dark data” now represents the vast majority of the digital universe.Dark data presents a quandary for organizations and the judicial system. For organizations, the inability to know the contents of retained dark data produces invisible risk under a spreading patchwork of digital privacy and data governance laws, most notably in the medical and consumer protection areas. For courts increasingly confronted with Big Data-derived evidence, dark data may shield critical information from judicial view while embedding subjective influences within seemingly objective methods. To avoid obscuring organizational risk and producing erroneous outcomes in the courtroom, decision-makers must achieve a new awareness of dark data’s presence and its ability to undermine Big Data’s vaunted advantages.
Design of Management Information Systems Memorandum of Understanding (MoU) And Memorandum of Agreement (MoA) Universitas Darussalam Gontor

The Memorandum of Understanding (MoU) at Universitas Darussalam Gontor (UNIDA) is an important activity that can be proven by the number of MoUs that have been carried out. However, because the management of the MoU is still manual, there is a risk of damage, loss, and duplication of the MoU data. In this research, an information system for the Bureau of Cooperation, Alumni, and International Affairs UNIDA has been built to meet the bureau's needs in managing data correctly so that students and lecturers can utilize the data. The system is made in the form of a website using the concept of Model-View-Controller (MVC) and Single Page Application (SPA) by applying the SDLC (System Development Life Cycle) methodology. The study results show the satisfaction value of the respondent's assessment is at 4.11 on a scale of five, which means that this information system provides satisfaction to users. In the subsequent project research, this system can develop an authentication site for user security.
Initiatives Management's Role in Improving Work Efficiency: A Field Study On Workers in Salalah Methanol Company in Dhofar Governorate

This quantitative study discussed the role of initiatives management in improving the work efficiency of the employees of the Methanol Company in the Dhofar Governorate in the Sultanate of Oman. The problem emerged that some Omani institutions suffered from administrative and economic crises as a result of fluctuations in oil prices, the absence of scientific bases adopted in the selection and appointment of people to jobs, and the absence of criteria for evaluating achievement and results. Therefore, the study aimed to; Diagnosing the level of initiatives management at the Methanol Company in the Dhofar Governorate, and the level of improving the company's work efficiency The study adopted the descriptive analytical approach, and the study population consisted of all employees of the Salalah Methanol Company in the Governorate of Dhofar, who numbered (160) employees. A random sample of (113) employees was selected according to the table of Gresty and Morgan (1970). The researcher designed a questionnaire by making use of the theoretical literature. It consisted of (48) phrases distributed in (8) axes. The following statistical methods were adopted: descriptive analysis, Pearson correlation coefficient, alpha consistency coefficient, T-test for two independent samples, one-way analysis of variance, multiple regression analysis, Scheffe test for dimensional comparisons. The research found a set of results, including: There is a direct relationship between initiatives management and improving work efficiency. There is a statistically significant effect at the level of significance (a ≤ 0.05) between managing initiatives and improving work efficiency. There were no statistically significant differences at the level of significance (a ≤ 0.05) about managing initiatives and improving work efficiency due to demographic variables.
Using Mind Mapping to Improve Students’ ReadingComprehension at SMK BPS&K II Bekasi

Various studies have shown mind mapping is a powerful tool to help learners develop their skills, including reading comprehension. This two-cycled action research wasconducted to enhance students’ reading comprehension by implementing mind mapping. The participants of this study were 25 students of X Accounting class in SMK BPS&K II Bekasi. The results show that mind mapping improved students reading comprehension. It can be seen from the increase of mean scores of the three tests administered during the action research, i.e. 54.4 to 70 to90. Moreover, the students’attitudes towards reading comprehension also changed from a negative one to a positiveone.
Privug: Using Probabilistic Programming for Quantifying Leakage in Privacy Risk Analysis.

Disclosure of data analytics results has important scientific and commercial justifications. However, no data shall be disclosed without a diligent investigation of risks for privacy of subjects. Privug is a tool-supported method to explore information leakage properties of data analytics and anonymization programs. In Privug, we reinterpret a program probabilistically, using off-the-shelf tools for Bayesian inference to perform information-theoretic analysis of the information flow. For privacy researchers, Privug provides a fast, lightweight way to experiment with privacy protection measures and mechanisms. We show that Privug is accurate, scalable, and applicable to a range of leakage analysis scenarios.
Smart solar panel monitoring system Using image processing

In the present study, the dust is being deposited on the Panel where the efficiency and the output power decreases. So in order to increase the efficiency, we are proposing a system based on Image Processing.  Image processing uses a camera and senses the dust which triggers the motor to clean the Solar Panel using Wiper. In addition to this, a webpage is also developed in order to check the Power moni]toring. In case if the automated cleaning process isn't responding, a operating button will also be present in the webpage to clean the Solar Panel manually.
Proof sheet of the Endowment for Excellence plaque handover at Postgraduate and Research Students Association [PARSA]

ANU Reporter Photos, Proof Sheets - Endowment for Excellence Plaque at PARSA, Tsui Lecture, Glass Workshop - Prof. Ronald Dore, Deane Terrell & others
Open Intent Extraction from Natural Language Interactions (Extended Abstract)

Accurately discovering user intents from their written or spoken language plays a critical role in natural language understanding and automated dialog response. Most existing research models this as a classification task with a single intent label per utterance. Going beyond this formulation, we define and investigate a new problem of open intent discovery. It involves discovering one or more generic intent types from text utterances, that may not have been encountered during training. We propose a novel, domain-agnostic approach, OPINE, which formulates the problem as a sequence tagging task in an open-world setting. It employs a CRF on top of a bidirectional LSTM to extract intents in a consistent format, subject to constraints among intent tag labels. We apply multi-headed self-attention and adversarial training to effectively learn dependencies between distant words, and robustly adapt our model across varying domains. We also curate and release an intent-annotated dataset of 25K real-life utterances spanning diverse domains. Extensive experiments show that OPINE outperforms state-of-art baselines by 5-15% F1 score.
INSPEKTA: INFRASTRUKTUR SIRKULAR PENGELOLAAN LIMBAH RUMAH TANGGA GUNA MEMBANGUN SMART ENVIRONMENT CITY

The development of Indonesia's new capital city in Kalimantan Timur has the potential to cause waste and energy problems. Waste production has increased every year. Therefore, the waste problem has a high urgency because it can cause environmental and health problems. In addition, the Ministry of Energy and Mineral Resources estimates that electricity infrastructure development will be needed and an additional capacity planning of around 864 MW to supply electricity in Kalimantan Timur. This study aims 1) To determine the innovation and design of INSPEKTA, 2) To find out the effectiveness of INSPEKTA in the development in Kalimantan Timur. Type of this research is written idea which is done by designing, simulating, estimating the electrical energy obtained, as well as feedback on INSPEKTA’s innovations. The sample area used as a reference is Kalimantan Timur. The work procedures carried out are: 1) Initial data collection using literature studies, questionnaires, and interviews, 2) Designing and prototyping of INSPEKTA, 3) Testing the work system and the effectiveness of INSPEKTA using quantitative calculations and simulating innovation mechanisms. From this research it can be concluded as follows: 1) INSPEKTA can utilize household waste for recycling, and converted it into electrical energy, 2) INSPEKTA can support zero-waste and green energy programs, 3) People get access to free electrical energy from the INSPEKTA system, 4) INSPEKTA can create a smart environment city with features in a mobile application such as tree planting, donations, shopping at regional MSMEs and information about waste that has been collected and converted.
COVID-19 Contact Tracing in a Pediatric Hospital: Maximizing Effectiveness Through Specialized Team and Automated Tools

Background: In their interim infection prevention and control recommendations for the coronavirus disease 2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) recommend that healthcare facilities have a plan to identify, investigate, and trace potential COVID-19 exposures. In an academic hospital, the scale of such tracing is substantial, given that medically complex patients can have dozens of staff contacts across multiple locations during an encounter. Furthermore, the family-centered care model employed by pediatric institutions precludes visitor exclusion, further complicating tracing efforts. Despite this complexity, tracing accuracy and timeliness is of paramount importance for exposure management. To address these challenges, our institution developed a contact-tracing system that balanced expert participation with automated tracing tools. Methods: Our institution’s contact-tracing initiative includes positive patients, parents and/or visitors, and staff for the enterprise’s inpatient, procedural, and ambulatory locations at the main campus and 4 satellites. The team consists of 11 staff and is overseen by an infection preventionist. For positive patients and parents and/or visitors, potentially exposed staff are automatically identified via a report that extracts staff details for all encounters occurring during the patient’s infectious period. For positive staff, trained contact tracers call the staff member to determine whether mask and distancing practices could result in others meeting CDC exposure criteria. Any potentially exposed healthcare workers (HCWs) receive an e-mail that details exposure criteria and provides follow-up instructions. These HCWs are also entered into a secure, centralized tracking database that (1) allows infection prevention and occupational health staff to query and identify all epidemiologic links between traced patients, parents and/or visitors, and staff, and (2) initiates staff enrollment in a twice-daily symptom tracking system administered via REDCap. Potentially exposed patients and parents and/or visitors are contacted directly by a hospital representative. The contact tracing team, infection prevention staff, and occupational health staff meet daily to review positive staff cases in the last 24 hours. Results: To date, the team has traced ~1,300 patients, 15 parents and/or visitors, and 700 staff. Since the start of the pandemic, tracing and contact notification for all positive cases has been conducted within 24 hours. Through these proactive tracing efforts and other institutional infection prevention initiatives, the institution only experienced 1 staff cluster (N &lt; 15) and &lt;5 hospital-onset patient cases. Conclusions: Equipping a trained group of contact tracers with automated tracking tools can afford infection prevention and occupational health departments the ability to achieve and sustain timely and accurate contact tracing initiatives throughout a large-scale pandemic response. Funding: No Disclosures: None
Penerapan Ontology Berbasis Protégé Untuk Mengestimasi Nilai Ekonomi Cryptocurrency

Cryptoccurency is a digital currency that is used as a medium of exchange as well as rupiah and dollars. And just like currency, Cryptocurrency also experiences value volatility or commonly reffered to as fluctuation. The purpose of this study is to estimate the fluctuating cryptocurrency value by implementing it into ontology using protégé tools. With the ontology, it can make it easier for users to find information about cryptocurrency. The results of this study indicate that ontology is one of the bases of Knowledge Managament Systems which can make it easier to systematizem, improve and accelerate knowledge management so that it is easy to understand for cryptocurrency users. Another result of this research is the creation of an ontology cryptoccurency with the blockchain subclass, users, currency, economu and factors. Each of these subclasses has more subclasses in a structured manner, and the results of making computer technique ontology in estimating the economic value of cryptocurrency are useful for users or people who want to find out more information about cryptocurrency.
English Learning Assistance in the Era Pandemic COVID-19 for Students in Mendiro

This paper aims to provide information about English learning assistance in the era pandemic Covid-19 for students in Mendiro through the community service. This activity was done to respond the community’s need to minimize the problems of learning English which were appeared during Covid-19 pandemic. The participants of the activity were 25 students which consisted of primary schools, Junior High School and Senior High School. The main problem of the students were difficulties in understanding English lessons as they were lack of interaction with their teachers since they were study from home (BDR). They have limited strategies to do self study at home. To overcome the problems, a team of lecturers of English Language Education conducted a community service in Mendiro village, Ngombol subdistric of Purworejo. Through sharing of knowledge and strategies to learn English skills through internet media, the participants could get knowledge and took opportunity to practice their English. By adopting the strategies given by team of lecturers, the participants are expected to be able to learn English easier with more enjoyable and enjoy simple literary works such as short films, short stories, short stories videos and songs. Finally, they can obtain better English skills in the future
Accounting in the fourth industrial revolution: Exploration of digital currency exchanges using AHP method

Abstract Purpose: The present study aimed to analyze and select the best exchange in the field of digital currencies with a futuristic perspective. Research methodology: The research method in this article is to use Analytic Hierarchy Process (AHP) to identify the strengths and weaknesses of each of these exchanges and analyze their coefficients according to the criteria of security, support, commission, number of currency codes, authentication, and trading volume. Results: The most important criteria in choosing the best digital currency exchange are: security, support, commission, number of currency codes, authentication and trading volume. Limitations: The limitations of this research can be the characteristics of digital exchanges in the field of providing digital currency cryptographic services in accordance with the framework of the Fourth Industrial Revolution. Contribution: According to the weight of the obtained criteria will be prioritized, which from the results and analysis obtained in this research can be used to invest in the field of currency cryptography and select the top exchange.
Parameter Based Survey of Recommendation Systems

As the amount of data is increasing, showing a user the information they are seeking becomes important. This has led to the increasing popularity of recommendation systems. A recommendation system aims to understand the needs of a customer and the different types of products to best suggest a product to the user. It studies the relationship between users, between items and between an item and user to best identify the next product to be recommended. Thus, studying the different types of recommendation systems and identifying their strengths and weaknesses becomes imperative. This paper focuses on explaining the concept of a recommendation system, its types and its uses. It also compares and discusses them with respect to identified parameters like methodology used for recommendation, similarity factor, performance metric, dataset and parameters of the dataset considered for making recommendations. Through the survey a lack of use of variable selection algorithms and the lack of incorporation of trendanalysis and time was observed.
An improved algorithm of Ground Moving Target Tracking based on Obstacle Information

Standard Interactive Multimodal Model Algorithms (IMM) have problems such as poor model probability matching, slow model switching and poor tracking accuracy due to the use of fixed transition probability matrices. This paper presents an IMM algorithm to update the transition probability matrix adaptively. First, the prior information of the ground obstacles is added to the model probability update phase to improve the matching degree of the model probability. Secondly, the Markov transfer probability is corrected in real time by using the model likelihood function value to enhance the matching model and weaken the influence of the mismatch model. The simulation results show that the tracking accuracy of this method is significantly better than that of traditional IMM algorithm.
USE OF APPLICATION BASED MODULE ON ADDITIVE AND ADDICTIVE MATERIALS FOR LEARNING ACTIVITIES DURING PANDEMIC COVID -19 (Study at a Student of Junior High School Muhammadiyah 1 Sleman)

The use of modules as a medium for learning at the school level is commonly used. The module serves to assist educators in delivering material. Therefore, the module must have good quality in terms of material and design so that students can more easily understand the material presented. At this time, the module is expected to be able to help students understand the material, because during the Covid-19 pandemic, students were asked to do learning activities at home. The material is delivered by the teacher online through the WhatsApp group during the learning hours. This study examines the material additives and addictive substances delivered to class VIII students. The object of this research is the VIII grade students of junior high school Muhammadiyah 1 Sleman which consists of three classes. The purpose of this study was to determine the level of students' understanding of additives and addictive substances.Keywords: , ,