Reviewerly/exHarmony huggingface dataset:
Three-dimensional shape measurement for the steep surface using DMD camera

Three-Dimensional shape measurement has been confronted with the ambiguity on the steep surface. To address the problem, compressed sensing theory is employed to reconstruct phase shifting images in DMD camera. Specially, every CCD pixel in the region of interest is aligned to N × N DMD mirrors to construct DMD camera. The one-dimensional measurement matrices are chosen to collect the measured values on CCD pixel according to the directional judgement of original gradient image. Due to the enhancement of the spatial sampling frequency and the noise robustness, the reconstructed sinusoidal stripe images are utilized to obtain the three-dimensional model of the steep surface accurately. We measure the planes with various slopes to discuss the measurement capability. The comparative experiments show that our proposed method can correct the deformed phase and repair the defect on the steep surface.
Identification of Fraudulent Online Transactions and Protection: State-of-art Techniques

In this digital era, the trend of online transactions for E-commerce sites and banking services is increasing. By using different online transaction methods users can make payments directly from their bank accounts. But along with the increase of online transactions, there is an increase in fraudulent transactions. These fraudulent transitions have identical features and characteristics of online transactions, so there is a need for the development of frameworks or technologies to detect fraudulent transactions. In this context, this paper represents a survey of the latest frameworks and techniques proposed by the researchers for the identification of fraudulent transactions and securing online transactions.
RVCar: An FPGA-Based Simple and Open-Source Mini Motor Car System with a RISC-V Soft Processor

Deep Neural Network (DNN) is widely used for computer vision tasks, such as image classification, object detection, and segmentation. DNN accelerator on FPGA and especially Convolutional Neural Network (CNN) is a hot topic. More research and education should be conducted to boost this field. A starting point is required to make it easy for new entrants to join this field. We believe that FPGA-based Autonomous Driving (AD) motor cars are suitable for this because DNN accelerators can be used for image processing with low latency. In this paper, we propose an FPGA-based simple and open-source mini motor car system named RVCar with a RISC-V soft processor and a CNN accelerator. RVCar is suitable for the new entrants who want to learn the implementation of a CNN accelerator and the surrounding system. The motor car consists of Xilinx Nexys A7 board and simple parts. All modules except the CNN accelerator are implemented in Verilog HDL and SystemVerilog. The CNN accelerator is converted from a PyTorch model by our tool. The accelerator is written in C++, synthesizable by Vitis HLS, and an easy-to-customize baseline for the new entrants. FreeRTOS is used to implement AD algorithms and executed on the RISC-V soft processor. It helps the users to develop the AD algorithms efficiently. We conduct a case study of the simple AD task we define. Although the task is simple, it is difficult to achieve without image recognition. We confirm that RVCar can recognize objects and make correct decisions based on the results.
GRAPHULY: GRAPH U-Nets-Based Multi-Level Graph LaYout

Graph layout is a critical component in graph visualization. This paper proposes GRAPHULY, a graph u-nets-based neural network, for end-to-end graph layout generation. GRAPHULY learns the multi-level graph layout process and can generate graph layouts without iterative calculation. We also propose to use Laplacian positional encoding and a multi-level loss fusion strategy to improve the layout learning. We evaluate the model with a random dataset and a graph drawing dataset and showcase the effectiveness and efficiency of GRAPHULY in graph visualization.
Holmes: A Hardware-Oriented Optimizer Using Logarithms

Edge computing, which has been gaining attention in recent years, has many advantages, such as reducing the load on the cloud, not being affected by the communication environment, and providing excellent security. Therefore, many researchers have attempted to implement neural networks, which are representative of machine learning in edge computing. Neural networks can be divided into inference and learning parts; however, there has been little research on implementing the learning component in edge computing in contrast to the inference part. This is because learning requires more memory and computation than inference, easily exceeding the limit of resources available for edge computing. To overcome this problem, this research focuses on the optimizer, which is the heart of learning. In this paper, we introduce our new optimizer, hardware-oriented logarithmic momentum estimation (Holmes), which incorporates new perspectives not found in existing optimizers in terms of characteristics and strengths of hardware. The performance of Holmes was evaluated by comparing it with other optimizers with respect to learning progress and convergence speed. Important aspects of hardware implementation, such as memory and operation requirements are also discussed. The results show that Holmes is a good match for edge computing with relatively low resource requirements and fast learning convergence. Holmes will help create an era in which advanced machine learning can be realized on edge computing.
The Automatic Generation of Smart Contract Based on Configuration in the Field of Government Services

With the development of blockchain technology, the automatic generation of smart contract has become a hot research topic. The existing smart contract automatic generation technology still has improvement spaces in complex process, third-party specialized tools required, specific the compatibility of code and running environment. In this paper, we propose an automatic smart contract generation method, which is domain-oriented and configuration-based. It is designed and implemented with the application scenarios of government service. The process of configuration, public state database definition, code generation and formal verification are included. In the Hyperledger Fabric environment, the applicability of the generated smart contract code is verified. Furthermore, its quality and security are formally verified with the help of third-party testing tools. The experimental results show that the quality and security of the generated smart contract code meet the expect standards. The automatic smart contract generation will “elegantly” be applied on the work of anti-disclosure, privacy protection, and prophecy processing in government service. To effectively enable develop “programmable government”.
The Implication of Teachers’ Politeness Strategy In Teaching on Higher Institution

This research aims to explain the types of politeness strategies used by teachers and students in class interactions, how these strategies are manifested in actions in class interactions and pedagogical functions of politeness strategies. The subjects in this study are teachers and students who used politeness strategies as many as 52 students and one teacher in the learning and teaching process. The research is a qualitative research. Data collected through observation and interviews. The results showed that is used by students to talk to the teacher when the meaning of speech is conveyed implicitly. There are four pedagogic functions in politeness strategies, namely giving the distance between teacher and students, atmosphere in class, reducing student tension, and making social interaction between teacher and students.
Unveiling photon statistics with a 100-pixel photon-number-resolving detector

Single-photon detectors are ubiquitous in quantum information science and quantum sensing. They are key enabling technologies for numerous scientific discoveries and fundamental tests of quantum optics. Photon-number-revolving detectors are the ultimate measurement tool of light. However, few detectors to date can provide high-fidelity photon number resolution at few-photon levels. Here, we demonstrate an on-chip detector that can resolve up to 100 photons by spatiotemporally multiplexing an array of superconducting nanowires along a single waveguide. The unparalleled photon number resolution paired with the high-speed response exclusively allows us to unveil the quantum photon statistics of a true thermal light source for the first time, which is realized by direct measurement of high-order correlation function g^(N) with N up to 15, observation of photon-subtraction-induced photon number enhancement, and quantum-limited state discrimination against a coherent light source. Our detector provides a viable route towards various important applications, including photonic quantum computation and quantum metrology.
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector

The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade. However, ASV systems can be easily neutralized by spoofing attacks. Therefore, the Spoofing-Aware Speaker Verification (SASV) challenge is designed and held to promote development of systems that can perform ASV considering spoofing attacks by integrating ASV and spoofing countermeasure (CM) systems. In this paper, we propose two back-end systems: multi-layer perceptron score fusion model (MSFM) and integrated embedding projector (IEP). The MSFM, score fusion back-end system, derived SASV score utilizing ASV and CM scores and embeddings. On the other hand,IEP combines ASV and CM embeddings into SASV embedding and calculates final SASV score based on the cosine similarity. We effectively integrated ASV and CM systems through proposed MSFM and IEP and achieved the SASV equal error rates 0.56%, 1.32% on the official evaluation trials of the SASV 2022 challenge.
Abstract interpretation, Hoare logic, and incorrectness logic for quantum programs

Abstract interpretation, Hoare logic, and incorrectness (or reverse Hoare) logic are powerful techniques for static analysis of computer programs. All of them have been successfully extended to the quantum setting, but largely developed in parallel. In this paper, we examine the relationship between these techniques in the context of verifying quantum while-programs, where the abstract domain and the set of assertions for quantum states are well-structured. In particular, we show that any complete quantum abstract interpretation induces a quantum Hoare logic and a quantum incorrectness logic, both of which are sound and relatively complete. Unlike the logics proposed in the literature, the induced logic systems are in a forward manner, making them more useful in certain applications. Conversely, any sound and relatively complete quantum Hoare logic or quantum incorrectness logic induces a complete quantum abstract interpretation. As an application, we are able to show the non-existence of any sound and relatively complete quantum Hoare logic or incorrectness logic if tuples of local subspaces are taken as assertions.
Reduction Principle of Ajtai

Abstract In 1996, the famous scholar Ajtai proposed the reduction principle from the worst case to the average case at the 28th Summer Symposium of the American Computer Society (ACM), named the Ajtai reduction principle [see Ajtai (1996), Ajtai (1999) and Ajtai and Dwork (1997)]. Subsequently, Ajtai and Dwork presented the first lattice-based cryptosystem, which is called the Ajtai-Dwork cryptosystem in the academic circles. The proof of this cryptosystem resisting Shor’s quantum computing is to apply Ajtai reduction principle to transform searching for collision points of the Hash function into the SIS problem, and Ajtai reduction principle proves that the difficulty of solving the SIS problem is polynomially equivalent to the shortest vector problem on lattice. The main purpose of this chapter is to prove the Ajtai reduction principle.
Multi-level Consistency Learning for Semi-supervised Domain Adaptation

Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain. In this paper, we propose a Multi-level Consistency Learning (MCL) framework for SSDA. Specifically, our MCL regularizes the consistency of different views of target domain samples at three levels: (i) at inter-domain level, we robustly and accurately align the source and target domains using a prototype-based optimal transport method that utilizes the pros and cons of different views of target samples; (ii) at intra-domain level, we facilitate the learning of both discriminative and compact target feature representations by proposing a novel class-wise contrastive clustering loss; (iii) at sample level, we follow standard practice and improve the prediction accuracy by conducting a consistency-based self-training. Empirically, we verified the effectiveness of our MCL framework on three popular SSDA benchmarks, i.e., VisDA2017, DomainNet, and Office-Home datasets, and the experimental results demonstrate that our MCL framework achieves the state-of-the-art performance.
Gender Bias in Password Managers

For the first time, we report gender bias in people's choice and use of password managers, through a semi-structured interview ($n=18$) and a questionnaire-based survey ($n=200$, conducted `in the wild'). Not only do women and men prefer different types of password managers, but software features that women and men frequently use also differ. These differences are statistically significant. The factors that women and men consider the most important or influential in choosing their password managers differ, too. Choice of convenience and brand are on the top of the women's consideration, whereas security and the number of features top the list for men. This difference is statistically significant.
Critical Studies of Technology, Psychology, and Education on Online Games

nline games are developing rapidly and have an impact on children's behaviour. This study aims to explain the phenomenon of online game addiction from the perspective of psychology, technology, and education. The method is narratively qualitative—data obtained from online focus group discussions. The results of this study explain that online games today tend towards addictive behaviour. This situation is supported by the use of current technology in programming that allows the provision of varied features to increase the game's attractiveness. This addictive behaviour affects other behaviours that can affect the development of attitudes. This behaviour change affects learning performance. The teacher should adjust learning strategies in the digital era to adapt to children's new behaviours today related to online games.
Service Desk in Perspective of Information Technology Infrastructure Library 3rd-Version

ICT services are an essential element in modern organizations. Understanding the workings of service systems with specific frameworks helps manage services well. One of the services in this management is the Service Desk or Helpdesk. This study aims to explain the Service Desk using the ITIL V.3 framework. The method used is descriptive qualitative using relevant references. The results of this study indicate that ITIL V.3 can be used to describe the management of the Service Desk accurately.
Development of a Technique for Classifying Photovoltaic Panels Using Sentinel-1 and Machine Learning

With the increasing interest in effective renewable alternative energy sources resulting from the Paris Agreement on Climate Change in 2015, photovoltaic (PV) power generation is attracting attention as a practical measure. In this study, we develop procedures for efficiently monitoring PV panels in a large area and increasing their classification accuracy to enable efficient management of PV panels, an important component of renewable energy generation. To accomplish this, first, the persistent scatterer characteristics (e.g., polarization, imaging module, and topography) of PV panels in SAR images were utilized. Then, we developed a technique for classifying panels over a certain size using the polarization and pulse-scattering characteristics of Sentinel-1. Next, by stacking Sentinel-1 ground range Doppler (GRD) images and comparing them with the surroundings of the same area, the morphological features of PV panels were derived and built as learning data for machine learning. Then, a more precise classification of PV panels was performed by applying these learning data in AI algorithms. When SAR-based AI training data for the same PV panels were used in the YOLOv3 and YOLOv5 algorithms, both algorithms showed high accuracy of over 90%, but there were differences in precision and recall. These findings will enable more efficient monitoring of PV panels, the use of which is expected to increase in the future. In addition, they can serve as a proactive response tool to address environmental problems such as PV panel waste and panels washed away during natural disasters.
FIX&amp;FOXY’s poetik, metode og organisering

FIX&amp;FOXY's poetik, metode og organisering. Interview with Biering by Christoffersen, Krøgholt and Winkelhorn. Biering tells about the principles and the theatre view that have been guiding for FIX&amp;FOXY’s creative and theatrical strategies throughout the theatre’s work.
Efektivitas permainan monopoli untuk meningkatkan pengetahuan tentang budaya Sulawesi Selatan pada siswa di Runiah School

Monopoly game is a game specially modified to be used as a learning medium. The use of games as learning media is used to make students interested and happy to learn. The purpose of implementing this monopoly game is to introduce the culture in South Sulawesi to students at Runiah School. The cultural monopoly game was played by 4 fifth-grader at Runiah School. The results obtained from the implementation of the cultural monopoly game show that the cultural monopoly game helps significantly increase students' understanding of culture in South Sulawesi.
Analysis of the Fulfilment of Sailing Safety Equipment on Traditional Ships on Lake Towuti, South Sulawesi, Indonesia

The motorboat on Lake Towuti is the main transportation for the people in the village of Tokalimbo, Bantilang, Lengkobale, and Loeha to Timampu Harbour as the center of destination main. The purpose of this study is to analyze the fulfillment of safety equipment and to find out the user community's perception of the current safety condition and what is expected. Based on the Importance Performance Analysis, it was found that four out of ten indicators are in Quadrant IV, which means that these aspects/attributes are very needed to get attention to improve its performance, namely the existence of a life jacket, firefighters, life rafts, and information on the whereabouts of equipment facilities safety. Of the four indicators, the existence of a life jacket is essential and mandatory for every sailor to wear a life jacket to minimize the risk of death from sinking. It is necessary to build awareness of the importance of using life jackets for crew and ship passengers and strict supervision from port authorities.
Analysis Of The Road Markings Reflectivity Service Life On Highways To Improve Safety

Road markings can function for two years after installation. The maintenance and replacement program for highway markings aims to meet the established performance standards. Good visibility of the markings will help reduce the chances of traffic accidents. The problems are found in faded road markings along the segment during the service period. The research was conducted to investigate the change in reflectivity over time and to find a pattern of decreasing reflectivity in the service life during the design life. The research was conducted experimentally by making test objects. The reflectivity test at the existing location using a reflectometer is carried out periodically every two months. The results showed a pattern of decreasing the reflectivity of road markings during the 2-month service period by 2%, four months by 3%, six months by 6%, and eight months by 16%, and the pattern of declining marker reflectivity stops between 10 and 12 months. The maximum decrease occurred during the service period of 8 months, 16%. The age of markings on the highway cannot last for a service period of 2 years under actual conditions in the field. At the end of year 1, the average value was 242 mcd/m2/lux; this value is below the minimum required reflectivity of 250 mcd/m2/lux.
Optimization of Firefighting Posts in Tabanan District

Tabanan is one of the districts in Bali which has ten sub-districts. The size of site in Tabanan will affect the handling of fire incidents in each area in Tabanan district. This handling relates to the response time of fire brigade, who must be able to arrive at the location after 15 minutes from received fire report. Travel time from existing fire station to other remote districts will take more than 15 minutes. This is considered to result in delays in handling fire disasters—the impact of adding new fire fighting posts in other sub-districts outside Tabanan District. However, setting up a fire station in every sub-district in Tabanan district will impact the number of costs that need to be incurred for constructing fire station. Therefore, to overcome these problems, a location optimization method is required to provide aspects of effectiveness of planning the establishment of a new fire station. The set covering problem can overcome this problem, providing the optimal number of fire stations by considering the distance travelled. This method will give results in the form of the optimal number of fire stations so can cover all areas with a response time of 15 minutes. This study uses two-speed data obtained from interviews with Tabanan districts Fire Department and speed data obtained from google maps. This study also uses the support of the QGIS application to determine the distance between sub-districts and uses the centroid point as the midpoint utilized to draw the line between the sub-districts.
Business Practices of Beneficiaries of Women Development Fund in Iringa Municipal Council, Iringa Region, Tanzania

The study was conducted in 2021 to examine the business practices of the beneficiaries of the Women Development Fund (WDF) in Iringa District Council, Tanzania. The study adopted a cross-sectional research design where checklists and semi-structured questionnaires were used to gather information from a sample size of 100 respondents who were beneficiaries of WDF. The respondents involved in the study were randomly selected from the study population. Descriptive analysis was used to analyze quantitative information using Statistical Package for Social Sciences (SPSS. 20.0) computer software. Thematic analysis was used to analyze qualitative data collected from the key-informants. The study findings showed that beneficiaries of WDF in the study area are operating their business with low start-up capital, inadequate technical and managerial skills, lack of business premises, operating unregistered business activities, low savings, and poor record keeping of their business activities. The study recommends to the Local Government Authority (LGA) to provide a friendly business environment for beneficiaries of WDF to undertake business activities by providing technical and managerial skills that will enable them to have the ability to manage their undertakings successfully.