Reviewerly/exHarmony huggingface dataset:
Hyperspherical Quantization: Toward Smaller and More Accurate Models

Model quantization enables the deployment of deep neural networks under resource-constrained devices. Vector quantization aims at reducing the model size by indexing model weights with full-precision embeddings, i.e., codewords, while the index needs to be restored to 32-bit during computation. Binary and other low-precision quantization methods can reduce the model size up to 32×, however, at the cost of a considerable accuracy drop. In this paper, we propose an efficient framework for ternary quantization to produce smaller and more accurate compressed models. By integrating hyperspherical learning, pruning and reinitialization, our proposed Hyperspherical Quantization (HQ) method reduces the cosine distance between the full-precision and ternary weights, thus reducing the bias of the straight-through gradient estimator during ternary quantization. Compared with existing work at similar compression levels (~30×, ~40×), our method significantly improves the test accuracy and reduces the model size.
Planar Object Tracking via Weighted Optical Flow

We propose WOFT – a novel method for planar object tracking that estimates a full 8 degrees-of-freedom pose, i.e. the homography w.r.t. a reference view. The method uses a novel module that leverages dense optical flow and assigns a weight to each optical flow correspondence, estimating a homography by weighted least squares in a fully differentiable manner. The trained module assigns zero weights to incorrect correspondences (outliers) in most cases, making the method robust and eliminating the need of the typically used non-differentiable robust estimators like RANSAC. The proposed weighted optical flow tracker (WOFT) achieves state-of-the-art performance on two benchmarks, POT-210 [23] and POIC [7], tracking consistently well across a wide range of scenarios.
FORECASTING THE NUMBER OF DEATH DUE TO TRAFFIC ACCIDENTS IN MEDAN CITY USING EXPONENTIAL SMOOTHING METHOD

This study aims to determine the prediction of the number of deaths due to traffic accidents in the city of Medan in 2009 - 2018. This study was conducted with quantitative forecasting, namely forecasting based on quantitative data in the past. The data of this study is data on the number of deaths due to traffic accidents in the city of Medan in 2009 - 2018 with a total of 10 years. Data were analyzed using Microsoft Excel with Exponential Smoothing method. The number of deaths due to traffic accidents in Medan City in 2019 was 244 people, in 2020 it was 219 people, and in 2021 it was 197 people with a MAPE of 0.1536659 or 15.36% with an alpha constant (α = 0.1). This shows that the level of accuracy at MAPE &amp;lt;20, meaning that the forecasting ability is good using the Exponential Smoothing method while the MAD value is 41.02 and the MSE value is 2677.54
IMPLEMENTATION OF NORTH WEST CORNER METHOD AND MODIFIED DISTRIBUTION METHOD IN OPTIMIZING FISH DISTRIBUTION COSTS USING MATLAB PROGRAM

&lt;p class="Abstract"&gt;Calculating distribution costs using the transportation method seeks to solve the problem of goods sent from source to destination so that the most optimal amount of transportation costs will be obtained to maximize profits. CV. Horizon Sibolga Group is a company that provides raw fish for sale to fish processing consumers. The company is located at Gatot Subroto street Number. 108 Pondok Batu Sarudik, Central Tapanuli Regency, North Sumatra Province. In this study, the North West Corner method will be used to find an initial feasible solution, and the Modified Distribution method will be used as an optimization test. If the test results show an optimal solution, then the allocation has been optimal and can be said to have reached the most profitable value. These results were obtained using Matlab software. Before the calculation, the company incurs distribution costs for a year of Rp. 963,000,000. From the research results, the total cost obtained after using the North West Corner method as an initial feasible solution is Rp. 928,831,000, and after using the Modified Distribution method as an optimization test, the total distribution cost is Rp. 923,058,000. So that the method used can save costs of Rp. 39,942,000, with a percentage of 4.14%.&lt;/p&gt;&lt;p class="Abstract"&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: Optimization, Transportation Problems, North West Corner, Modified Distribution, Matlab.&lt;/p&gt;
PEMBANGUNAN SISTEM INFORMASI MONITORING KARYAWAN PADA BAGIAN LOGISTIK DI PT. MOWILEX INDONESIA.

Mowilex Indonesia is a company engaged in manufacturing with more than 1000 employees. In managing employee data, this company does not yet have an integrated information system so that data management for employee activities has not been effective and efficient. Therefore, an information system is needed that can monitor all employee activities ranging from attendance, leave, mutation, promotion and other activities. With this system, companies can monitor employee activities easily and quickly, especially in making reports. The objectives of this study are 1) to create employee monitoring information system, 2) to provide convenience in making monitoring performance reports for management needs. This research uses waterfall development methods and Unified Modeling Language (UML) modeling as system design methods, while its implementation uses the PHP programming language with the help of sublime text and MYSQL databases with the help of navicat.
Daily Posture Behavior Patterns Derived From Multitime-Scale Topic Models Using Wearable Triaxial Acceleration for Assessment of Concern About Falling

Concern about falling is prevalent in older population. This condition would cause a series of adverse physical and psychological consequences for older adults’ health. Traditional assessment of concern about falling is relied on self-reported questionnaires and thus is too subjective. Therefore, we proposed a novel multitime-scale topic modeling approach to quantitatively evaluate concerns about falling by analyzing triaxial acceleration signals collected from a wearable pendent sensor. Different posture segments were first recognized to extract their corresponding feature subsets. Then, each selected feature related to concern about falling was clustered into discrete levels as feature letters of artificial words in different time scales. As a result, all older participants’ signal recordings were converted to a collection of artificial documents, which can be processed by natural language processing methodologies. The topic modeling technique was used to discover daily posture behavior patterns from these documents as discriminants between older adults with different levels of concern about falling. The results indicated that there were significant differences in distributions of posture topics between groups of older adults with different levels of concern about falling. In addition, the transitions of posture topics over daytime and nighttime revealed temporal regularities of posture behavior patterns of older adult’s active and inactive status, which were substantially different for older adults with different levels of concern about falling. Finally, the level of concern about falling was accurately determined with an accuracy of 71.2% based on the distributions of posture topics combined with the mobility performance metrics of walking behaviors and demographic information.
Deep Associative Classifier

Deep neural networks architecture provides a powerful technique for solving various problems including classification. They owe their performance to the complex and layered data representation and processing built upon neural networks. The success of deep neural networks in various fields has resulted in less focus on other techniques like rule-based models, especially associative classifiers. Associative classifiers are competitive models to deep neural networks on tabular data but suffer from certain limitations i.e., require proper threshold values that differ for different datasets. Even though deep neural networks have resulted in huge success, they have complex and lengthy hyper-parameter tuning. In recent years, attempts to develop models that can compete with deep neural networks using deep representations with decision trees while reducing hyper-parameters have been tried. In this study, we propose a Deep Associative Classifier (DAC), an ensemble of associative classifiers that transforms features in a deep model representation. This model has deep neural network like architecture with associative classifiers as a base learner and overcomes some of the limitations of deep neural network architecture as well as associative classifiers. We use 10 UCI datasets and compare our approach with other existing deep neural network models, a gcForest approach, and associative classifiers. Our proposed model outperforms various state-of-the-art classifiers not only in terms of accuracy but also memory requirement and has fewer hyper-parameters to tune.
Evolutionary Constrained Optimization with Dynamic Changes and Uncertainty in the Objective Function

Many real-life optimization problems involve dynamic changes with uncertain parameters and data, which make the decision-making process challenging. Although there are some studies on solving dynamic or uncertain problems, there is limited work on solving problems with both dynamic and uncertain characteristics. Therefore, this paper proposes an evolutionary framework for solving constrained optimization problems where the objective function's coefficients are uncertain and changing over time. In the algorithm, a mechanism is proposed for detecting a change and predicting the magnitude of uncertainty, which helps to generate better initial solutions for the evolutionary search process that improves its performance after a dynamic change. It is evaluated on 13 benchmark problems, with the reported results demonstrating its efficiency in terms of the quality of its solutions.
SISTEM INFORMASI AKUNTANSI PENDAPATAN PADA KOPERASI SEDANA MERTA JATI DI DESA DARMASABA

Accounting information system is a series of procedures in collecting various data, processing the data into information and distributing it to interested parties. With the accounting information system, it is hoped that it can provide a network of procedures that are arranged in an integrated manner in carrying out smooth company activities. The cash income cycle describes the activities of a company in selling goods and services to customers starting from receiving orders, sending goods/services, billing, receiving payments to depositing cash and recording it. Sedana Merta Jati Cooperative is one of the business entities engaged in credit or savings and loans owned and managed by its members located in Banjar Bersih, Darmasaba Village, Abiansemal District, Badung. The Sedana Merta Jati Cooperative has used accounting information systems in its operational processes to achieve effective and efficient performance. The cash income of the Sedana Merta Jati Cooperative comes from cash receipts from receivables and deposits. Based on the results that have been observed, it can be concluded that the Cash Revenue Cycle from Receivables and Deposits is obtained by implementing the procedures for notification of receivables, billing procedures, procedures for cash receipts, and procedures for recording cash receipts. The income cycle accounting information system at the Sedana Merta Jati Cooperative has been carried out effectively in accordance with the assignment functions, namely administrative and general functions, collection functions (collectors/accompaniments), financial functions/cashier functions and computerization functions.
PENGARUH KECANGGIHAN TEKNOLOGI INFORMASI, KEMAMPUAN PERSONAL DAN PARTISIPASI MANAJEMEN TERHADAP EFEKTIVITAS SISTEM INFORMASI AKUNTANSI

This study aims to determine the effect of Information Technology Sophistication, Personal Ability and Management Participation on the Effectiveness of Accounting Information Systems at PT. Perkebunan Nusantara I Langsa). The population in this study is all parts of the field of work at PT. Perkebunan Nusantara I Langsa using SAP ERP. The sampling technique used the Proportionate Stratified Random Sampling method, data collection was carried out by distributing questionnaires to 79 respondents with criteria who were familiar with using the system and related to the system. The data analysis method uses Multiple Linear Regression (multiple regression analysis). The results of this study indicate that the sophistication of information technology has a significant effect on the effectiveness of accounting information systems, personal abilities have no significant effect on the effectiveness of accounting information systems and management participation has a significant effect on the effectiveness of accounting information systems. Based on the experience of researchers in the research process, there are several limitations in this study, such as the number of research respondents who are less, there are only 8 fields of work and the object of research is only one company so that it is still not enough to answer the actual situation and when collecting information and data through questionnaires. there are some employees who do not want to fill out the questionnaire. Other weaknesses experienced and become several factors that must be considered again for further researchers in order to improve their research by adding several more variables such as the perception variable about ease of use in order to further increase the effectiveness of the accounting information system.
Analisis Bimbingan Belajar Siswa Berkesulitan Membaca

Analysis of Student Guidance with Difficulty Reading. Qualitative research with a descriptive study approach. Thesis of Elementary School Teacher Education Study Program, Faculty of Teacher Training and Education, Pakuan University, Bogor, 2020. The objective is to describe the forms of learning difficulties to read, factors of reading difficulties and tutoring for students with reading difficulties. The subjects of this study were 3 students of class IIIA &amp; IIIB Cimayang State Elementary School 01. The research was carried out in the odd semester of the 2020-2021 school year. In collecting data, researchers used observation, interview and documentation methods. Researchers used credibility, transferability, dependability, and confirmability tests to obtain data validity. The results showed that the form of reading difficulty students could not distinguish letters that were almost the same shape, students did not recognize diphthong and digraphs, students stammered reading syllables, students could not read words, could not pronounce letters in one sentence and still spell letters. The factors that cause student reading difficulties are students lack of interest in reading, the family environment lack of parental attention to their children's learning activities at home, lack of enthusiasm for students in learning, teaching methods and media used by teachers when the teaching and learning process is inadequate does not vary, disturbances in health. Guidance for students having difficulty learning to read at SD Negeri Cimayang 01 is still not optimal. The role of the school in providing guidance for students with difficulty learning to read is also not optimal. In addition, the ability of students to have difficulty learning to read in overcoming their learning difficulties is still lacking.
The Impact Cloud Computing Services in It Industry of Malaysia

This particular study is based on impact of cloud computing in IT industry of Malaysia. Globalisation and industrialisation have enhanced the importance of technological advancements in all business sectors. These can foster business financial growth. On the other hand, pandemic situation has brought different adversities in social conditions of local people. This has increased the importance to implement this technology that has ability to resolve the financial and social conditions as well. This is the reason, developing countries such as Malaysia has adapted this approach. This study has been accomplished with “positivism philosophy, deductive approach and secondary data collection method”. Proper justification with the research objectives has been provided in the last section of this study.
Incremental Learning Method for Data with Delayed Labels

Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting.
COVIDware: Designing Interactive Everyday Things as Tangible Homeware for Social Isolation

This paper describes our collaborative journey of creating everyday interactive artefacts to help us think, reflect, and live through self-isolation. Through a co-design approach, we designed interactive homeware objects (that we collectively refer to as ‘COVIDware’) to address the challenges of isolation during the pandemic. Five artefacts were developed by self-isolated designers as interactive art installations. We discuss how each creator reflected on her design concept, process, and encounter through concepts of critical making, speculation, and engagement via in-the-isolated-wild deployments. By empowering early researchers/enthusiasts to design ‘with’ smart-materials, and off-the-shelf items, we reflect on how these homey interfaces can enhance people’s wellbeing beyond screen-based interactions. Despite not collaborating in the making process, our findings from the designer’s making process show how all the designed artefacts shared attributes of biophilic design, imperfection, and unconventional interactions with the overarching goal of promoting wellbeing, and meaningful connection with nature, self, and others.
ANALYSIS OF ONLINE LEARNING MOTIVATION IN STUDENTS XI SMA NEGERI 1 JENEPONTO

The purpose of this study was to determine the motivation for online learning and to identify the factors that hinder and support the implementation of online learning in Class XI students of SMA Negeri 1 Jeneponto. The type of research used by the researcher is qualitative research. The data comes from the words or actions of the researchers' online interviews, voice recordings, and student learning documentation at SMAN 1 Jeneponto. In this case, the subjects are also Class XI students who are also part of the resource persons in this study, whose criteria are determined through purposive sampling technique. The results of the study show that for various reasons, the learning motivation of SMAN 1 Jeneponto students with online learning has decreased during the COVID-19 pandemic. In terms of inhibiting factors, one of them is that students have difficulty accessing the internet caused by network or signal disturbances when participating in online learning, while the other side is being able to reduce the spread of COVID-19.
A Classless Analysis of Italian Nouns and their Theme-Vowel Alternations

The decompositional, non-lexicalist, approach to word-structure and the theory of roots have proven highly rewarding. One grey area, however, comes from Romance since, rather than roots, the word structure primitive appears to be the stem: root + ‘stem-formative/theme vowel’. Regardless, there have been perspicacious decompositional accounts of Italian, however these are still marred by the large number of morphological/item-specific irregularities, motivating arbitrary noun classes. Additionally, there are roots that do not inflect: consonant-final and vowel-final forms when these are oxytonic or loanwords. Given these irregularities, previous analyses in Italian have included the use of lexical exceptions and class features. We challenge the use of class features in generating the attested patterns (and their exceptions). Instead, we propose a new categorisation of root-shapes, which, when combined with the exponents of nominal inflection, produce the correct surface pairings, as well as the non-alternating forms. In our analysis, there is no diacritic or special marking of lexical exceptions, all forms inflect regularly in accordance to their phonological shape. This requires the innovation of one new mechanism (Inhibition), but we back it up by showing that it leads to an unexpected beneficial prediction that solves a long-standing problem associated with Raddoppiamento Sintattico (RS).
Learning Media Application of Knowledge Sharing Room to Improve Student and Community Literacy

The development of technology that is increasingly fast and sophisticated makes it easy for everyone to obtain or disseminate information, discuss and share thoughts between one person and another. Information Technology has an important role in all fields. Electronic learning or distance learning is a new breakthrough in the development of information technology in the field of education, e-learning is a teaching and learning process using the Internet as a learning system. Building a digital literacy culture needs to involve the active role of the community together. The success of building digital literacy is one indicator of achievement in the fields of education and culture. In an effort to improve literacy for students and the community, this research will develop a digital-based literacy resource called the Mobile Web-Based Knowledge Sharing Application. The system development method used in this study uses the System Development Life Cycle (SDLC) method, and the research flow is described using the Fishbone Mapping Chart model. In realizing the Knowledge Sharing Room application, the design stage is used using a structured model using Use Case Diagrams, Entity Relationship Diagrams (ERD) with website design using Hypertext Preprocessor programming, HTML, Java Script as a programming language, and My Structured Query Language (MySQL) as a database storage medium.