Reviewerly/exHarmony huggingface dataset:
A Study on the Influence of Organizational Innovation Climate on Employee’s Innovation Performance

In the era of knowledge-driven economy, employees' innovation performance is the decisive factor of enterprise competitiveness.In order to further enrich the research achievements of the innovation performance of employees, this paper tests and verifies the relationship between the organizational innovation climate, thriving at work, inclusive leadership and the innovation.The results establish that the organizational innovation climate is significantly and positively connected to employee innovation performance, and thriving at work plays a moderating role.In addition, inclusive leadership mediates the influence of organizational innovation climate on employees thriving at work, thus mediating the moderator role of thriving at work of the model.
Rancang Bangun Sistem Informasi Akuntansi Pada Perusahaan Jasa Reparasi

An industry that is engaged in repair is an industry that is engaged in goods and special services, namely serving hardware, IT Consulting, network installation, and CCTV. The problem that occurs is accounting difficulties in managing transaction data because accounting is required to recap transaction information taken from different Microsoft Excel sheets or files. Not only that, but the manual process in making accounting reports also takes time so there can be delays in making accounting reports and errors when structuring accounting reports. Based on the above background, the formulation of the problem in the research is How to Design and Build an Accounting Data System in an Industry that is engaged in repairs, on the other hand, the research objective to be achieved in the reporting process is being able to design and build an industrial accounting information system. The result of this research is to create a system that can digest customer information, supplier information, account information, universal daily information, transaction information, user information, and accounting reports. This system is expected to help accountants in making accounting reports to identify the state of industry accounting
RANCANG BANGUN PENERAPAN CVT VARIO 110 PADA MESIN CUCI PENGGERAK MEKANIS KAPASITAS MAKSIMAL 7 KG

Washing is an activity that is done every day by housewives, young people, and is done by anyone, so it takes time to do washing activities, especially housewives who have so many jobs at home that washing is quite a heavy job for housewives. ladder. The design of the application of the CVT Vario 110 on a mechanical driving washing machine with a maximum capacity of 7 kg is expected to be a solution to the problems of housewives in washing. The method used in this research is by design, Design Analysis and continued with the fabrication process of the design results, and testing the washing machine that has been made. The results of the calculation of washing and drying rotations at the front sprocket are 91.6 rpm, the rear sprocket is 436.3 rpm, and the tube rotation is 127.1 rpm. Then tested using a tachometer with the results of testing the front sprocket 86.3 rpm, rear sprocket 294.6 rpm, primary pulley (CVT) 294.6 rpm, secondary pulley (CVT) 145.5 rpm, and tube (tube) 127, 1 rpm. Based on the test results, this washing machine is capable of washing up to a capacity of 7 kg so it is suitable for use in helping housewives wash their jobs.
Rancang Bangun Sistem Informasi Stok Barang Berbasis Website

The arrangement of the stock of goods is a vital component in the trading business, because the availability of goods owned by the number of goods that come out is recorded. This recording is one part of the stock inventory component. A stock of goods is said to be balanced if, incoming goods, outgoing goods and the rest of the goods have a total amount equal to the total number of goods at the time of entry into the warehouse. The main component of other than recording is the flow of goods turnover has a clear track record. The use of information systems can easily keep track records of per unit of goods. This study takes a case regarding the stock of goods at a smartphone shop, the flow of the goods turnover process at the store is unique from other stores, because every day goods or units of goods must be circulated. From these problems, an Inventory System for Web-Based Mobileshops was built, there are several steps of data flow to describe the flow of information and transformations that are applied when data moves from input to output, namely, Event List, Data Flow Diagram, CDM, PDM, and Design. The system user interface is built. PHP and MySQL programming languages ​​are used to build the System. Based on the BlackBox testing carried out, it can be concluded that the Inventory System Design for Web-Based Mobileshop has been successfully built. The features of this system are managing loan application data, managing brand data, cellphones, outgoing goods and incoming goods as well as managing DO and producing output reports of goods going out and goods coming in.
Correction to: A Beginner’s Guide to Statistics for Criminology and Criminal Justice Using R

This book was inadvertently published without including the ESM information in the proofs. The corrections have now been carried out to include the datasets to be used with each chapter of the textbook.
Rancang Bangun E-Commerce Pada Toko Bravo Sports

Bravo Sports Store is one of the shops or businesses engaged in the trade of sports equipment. In the process of selling goods, today the store is still using a conventional system that consumers must come directly to the store to buy the desired goods. Stores have been using technological advances to help disseminate information about goods sold by using social media such as BBM, Instagram, Facebook, and twitter. However, it is considered inefficient because of the large number of consumers. Social media is also not able to distinguish notifications between consumers who want to ask about goods and consumers who want to buy goods. In addition, the owner must also make continuous renewal of information items such as examples of quantities of goods and prices of goods which are then published back to the public. To overcome this the authors will design and build an e-commerce web. The result of e-commerce is a system can be used to process goods data, employee data, transaction data, order data, employee data, consumer data, and reports. Based on blackbox testing done can be concluded that has been successfully built a web e-commerce.
Perancangan Pengontrol Otomatis dan Pengatur Posisi Tempat Duduk pada Kursi

A wheelchair is a tool used by people who have difficulty walking due to illness, injury or disability. This tool can be driven by being pushed by someone else, moved by using a hand, or by using an automatic machine. To support the development in the world of health, it is necessary to have technological innovations in wheelchair products to increase user convenience and comfort. The purpose of this paper is to provide ideas and innovative contributions in the form of automatic control in a wheelchair. The methodology applied includes the design, manufacture, testing, and selection of components for the automatic controller and seat position control in a wheelchair.
APLIKASI E-COMMERCE BERBASIS WEB MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE DAN K-MEDOIDS

Sugi Jaya Mandiri, Ltd. is a company specializing in sales of industrial equipments, such as cables, pipes, construction materials, machine spareparts et cetera. The company processes data manually by using Microsoft Excel. The process complicates data recapitulation, especially concerning large amounts of data. Another issue is the lack of advertising towards customer. This application aims to solve the problems by implementing data processing feature with Weighted Moving Average timeseries method and favourite product feature to better market products to prospective customers using k-Medoids clustering method.Preliminary evaluations using k-Medoids method shows two products fit to advertise by considering attributes such as sold amount, mean sold amount and transaction amount. Data evaluation using Weighted Moving Average method acquires sales forecast with Accuracy rate of 92.456%.
Administrasi Keuangan

School financial administration explains how to manage finances that exist in schools, so as not to be misused by certain parties. Correct financial administration also creates a conducive atmosphere in school. The role of the teacher is very important in financial administration to manage the income and expenses of the school.
SISTEM PENUNJANG KEPUTUSAN UNTUK MENENTUKAN PENERIMA BEASISWA PENINGKATAN PRESTASI AKADEMIK DI UNIVERSITAS TARUMANAGARA

The distribution of scholarship sometimes is not relevant. The main cause of this problem is because the selection process of the scholarship awardee is not using the right method and tools. The purpose of this research is to develop a decision support system to determine the Peningkatan Prestasi Akademik scholarship (PPA Scholarship) awardee using a few criteria from Students and Alumni Directorate. The methods that are being used is Simple Additive Weighting (SAW). A few criteria that are being used in this system are GPA score, student’s total credit, parents earning, and student’s achievement. The calculation using SAW methods have increase the speed and accuracy in determining the right scholarship awardee.
The management team of educational organization: approaches to its formation and development

The article examines the approaches of foreign and domestic authors to the definition of a management team in a modern educational organization. Particular attention is paid to the role and characteristics of management teams in relation to the requirements of the education of the future. The article presents the categories of teams and their distinctive features, shows the conditions and prerequisites for effective teamwork.
PERANCANGAN APLIKASI “BAGI BAGASI” DENGAN MENGGUNAKAN WEB DAN MOBILE

Service Application for Baggage using Mobile and Web is an application created aimed at making it easier for people to buy the needs of the community that are abroad or domestically. Often when traveling, we have excess baggage, from there BagiBagasi will help find potential buyers or prospective sellers and entrust the transaction process in this application. This application was designed using PHP and Android programming languages. The application design method uses the System Development Life Cycle. The test results are performed using the User Acceptance Test method and user satisfaction testing using the questionnaire method. With this application, the community is expected to be helped in meeting their needs for goods that cannot be bought by themselves.
Research on Eye Pupil Location and Eye Tracking by Designing a Fully Conventional Neural Network (FCNN)

Eye-tracking has become a significant tool in various areas, incorporating interaction of humans with a computer, computer vision, psychology, and medical diagnostics.Various protocols have been utilized to track the gaze.Although, some may not be accurate in the real world, while others may need explicit custom calibration, which can cause problems.Few of these techniques are associated with low image grades and varying lighting situations.The latest success and popularity of deep studying has dramatically increased the effectiveness of eye-tracking.The convenience of huge datasets additionally improves the function of deep learning-depend techniques.This technical paper introduces the latest deep studying-depend gaze assessment technology with a concentration on Fully Convolution Neural Networks (FCNN).This technical paper also gives an overview of other machine-based eye assessment methods.This research objective to enable the research population to generate significant and useful horizons that can improve the structure and growth of better, more effective deep learning depends on eye-tracking methods.This paper also gives information on different pertained models, network structures, and open origin datasets to help train deep learning models.
Boosting 3D Adversarial Attacks with Attacking On Frequency

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial attacks. Recently, 3D adversarial attacks, especially adversarial attacks on point clouds, have elicited mounting interest. However, adversarial point clouds obtained by previous methods show weak transferability and are easy to defend. To address these problems, in this paper we propose a novel point cloud attack (dubbed AOF) that pays more attention on the low-frequency component of point clouds. We combine the losses from point cloud and its low-frequency component to craft adversarial samples. Extensive experiments validate that AOF can improve the transferability significantly compared to state-of-the-art (SOTA) attacks, and is more robust to SOTA 3D defense methods. Otherwise, compared to clean point clouds, adversarial point clouds obtained by AOF contain more deformation than outlier.
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning

Keeping track of and managing Self-Admitted Technical Debts (SATDs) is important for maintaining a healthy software project. Current active-learning SATD recognition tool involves manual inspection of 24% of the test comments on average to reach 90% of the recall. Among all the test comments, about 5% are SATDs. The human experts are then required to read almost a quintuple of the SATD comments which indicates the inefficiency of the tool. Plus, human experts are still prone to error: 95% of the false-positive labels from previous work were actually true positives. To solve the above problems, we propose DebtFree, a two-mode framework based on unsupervised learning for identifying SATDs. In mode1, when the existing training data is unlabeled, DebtFree starts with an unsupervised learner to automatically pseudo-label the programming comments in the training data. In contrast, in mode2 where labels are available with the corresponding training data, DebtFree starts with a pre-processor that identifies the highly prone SATDs from the test dataset. Then, our machine learning model is employed to assist human experts in manually identifying the remaining SATDs. Our experiments on 10 software projects show that both models yield a statistically significant improvement in effectiveness over the state-of-the-art automated and semi-automated models. Specifically, DebtFree can reduce the labeling effort by 99% in mode1 (unlabeled training data), and up to 63% in mode2 (labeled training data) while improving the current active learner's F1 relatively to almost 100%.
Neural Grapheme-to-Phoneme Conversion with Pre-trained Grapheme Models

Neural network models have achieved state-of-the-art performance on grapheme-to-phoneme (G2P) conversion. However, their performance relies on large-scale pronunciation dictionaries, which may not be available for a lot of languages. Inspired by the success of the pre-trained language model BERT, this paper proposes a pre-trained grapheme model called grapheme BERT (GBERT), which is built by self-supervised training on a large, language-specific word list with only grapheme information. Furthermore, two approaches are developed to incorporate GBERT into the state-of-the-art Transformer-based G2P model, i.e., fine-tuning GBERT or fusing GBERT into the Transformer model by attention. Experimental results on the Dutch, Serbo-Croatian, Bulgarian and Korean datasets of the SIGMORPHON 2021 G2P task confirm the effectiveness of our GBERT-based G2P models under both medium-resource and low-resource data conditions.
Variational Quantum Optimization with Multi-Basis Encodings

Despite extensive research efforts, few quantum algorithms for classical optimization demonstrate realizable quantum advantage. The utility of many quantum algorithms is limited by high requisite circuit depth and nonconvex optimization landscapes. We tackle these challenges by introducing a new variational quantum algorithm that benefits from two innovations: multi-basis graph encodings and nonlinear activation functions. Our technique results in increased optimization performance, a factor of two increase in effective quantum resources, and a quadratic reduction in measurement complexity. While the classical simulation of many qubits with traditional quantum formalism is impossible due to its exponential scaling, we mitigate this limitation with exact circuit representations using factorized tensor rings. In particular, the shallow circuits permitted by our technique, combined with efficient factorized tensor-based simulation, enable us to successfully optimize the MaxCut of the nonlocally connected $512$-vertex DIMACS library graphs on a single GPU. By improving the performance of quantum optimization algorithms while requiring fewer quantum resources and utilizing shallower, more error-resistant circuits, we offer tangible progress for variational quantum optimization.
Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili and Magahi

In Natural Language Processing (NLP) pipelines, Named Entity Recognition (NER) is one of the preliminary problems, which marks proper nouns and other named entities such as Location, Person, Organization, Disease etc. Such entities, without a NER module, adversely affect the performance of a machine translation system. NER helps in overcoming this problem by recognising and handling such entities separately, although it can be useful in Information Extraction systems also. Bhojpuri, Maithili and Magahi are low resource languages, usually known as Purvanchal languages. This paper focuses on the development of a NER benchmark dataset for the Machine Translation systems developed to translate from these languages to Hindi by annotating parts of their available corpora. Bhojpuri, Maithili and Magahi corpora of sizes 228373, 157468 and 56190 tokens, respectively, were annotated using 22 entity labels. The annotation considers coarse-grained annotation labels followed by the tagset used in one of the Hindi NER datasets. We also report a Deep Learning based baseline that uses an LSTM-CNNs-CRF model. The lower baseline F1-scores from the NER tool obtained by using Conditional Random Fields models are 96.73 for Bhojpuri, 93.33 for Maithili and 95.04 for Magahi. The Deep Learning-based technique (LSTM-CNNs-CRF) achieved 96.25 for Bhojpuri, 93.33 for Maithili and 95.44 for Magahi.
DroTrack: High-speed Drone-based Object Tracking Under Uncertainty

We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-dimensional space, causes high uncertainty. The uncertainty problem leads to inaccurate location predictions and fuzziness in scale estimations. DroTrack solves such issues by discovering the dependency between object representation and motion geometry. We implement an effective object segmentation based on Fuzzy C Means (FCM). We incorporate the spatial information into the membership function to cluster the most discriminative segments. We then enhance the object segmentation by using a pre-trained Convolution Neural Network (CNN) model. DroTrack also leverages the geometrical angular motion to estimate a reliable object scale. We discuss the experimental results and performance evaluation using two datasets of 51,462 drone-captured frames. The combination of the FCM segmentation and the angular scaling increased DroTrack precision by up to $9\%$ and decreased the centre location error by $162$ pixels on average. DroTrack outperforms all the high-speed trackers and achieves comparable results in comparison to deep learning trackers. DroTrack offers high frame rates up to 1000 frame per second (fps) with the best location precision, more than a set of state-of-the-art real-time trackers.
Hardness of Random Optimization Problems for Boolean Circuits, Low-Degree Polynomials, and Langevin Dynamics

We consider the problem of finding nearly optimal solutions of optimization problems with random objective functions. Two concrete problems we consider are (a) optimizing the Hamiltonian of a spherical or Ising $p$-spin glass model, and (b) finding a large independent set in a sparse Erd\H{o}s-R\'{e}nyi graph. The following families of algorithms are considered: (a) low-degree polynomials of the input; (b) low-depth Boolean circuits; (c) the Langevin dynamics algorithm. We show that these families of algorithms fail to produce nearly optimal solutions with high probability. For the case of Boolean circuits, our results improve the state-of-the-art bounds known in circuit complexity theory (although we consider the search problem as opposed to the decision problem). Our proof uses the fact that these models are known to exhibit a variant of the overlap gap property (OGP) of near-optimal solutions. Specifically, for both models, every two solutions whose objectives are above a certain threshold are either close or far from each other. The crux of our proof is that the classes of algorithms we consider exhibit a form of stability. We show by an interpolation argument that stable algorithms cannot overcome the OGP barrier. The stability of Langevin dynamics is an immediate consequence of the well-posedness of stochastic differential equations. The stability of low-degree polynomials and Boolean circuits is established using tools from Gaussian and Boolean analysis -- namely hypercontractivity and total influence, as well as a novel lower bound for random walks avoiding certain subsets. In the case of Boolean circuits, the result also makes use of Linal-Mansour-Nisan's classical theorem. Our techniques apply more broadly to low influence functions and may apply more generally.
Private Information Retrieval Over Gaussian MAC

Consider the problem of Private Information Retrieval (PIR), where a user wishes to retrieve a single message from $N$ non-communicating and non-colluding databases (servers). All servers store the same set of $M$ messages and they respond to the user through a block fading Gaussian Multiple Access Channel (MAC). The goal in this setting is to keep the index of the required message private from the servers while minimizing the overall communication overhead. This work provides joint privacy and channel coding retrieval schemes for the Gaussian MAC with and without fading. The schemes exploit the linearity of the channel while using the Compute and Forward (CF) coding scheme. Consequently, single-user encoding and decoding are performed to retrieve the private message. In the case of a channel without fading, the achievable retrieval rate is shown to outperform a separation-based scheme, in which the retrieval and the channel coding are designed separately. Moreover, this rate is asymptotically optimal as the SNR grows, and are up to a constant gap of $2$ bits per channel use from the channel capacity without privacy constraints, for all SNR values. When the channel suffers from fading, the asymmetry between the servers' channels forces a more complicated solution, which involves a hard optimization problem. Nevertheless, we provide coding scheme and lower bounds on the expected achievable retrieval rate which are shown to have the same scaling laws as the channel capacity, both in the number of servers and the SNR.