Reviewerly/exHarmony huggingface dataset:
Worst-Case Adversarial Perturbation and Effect of Feature Normalization on Max-Margin Multi-label Classifiers

Multi-label classification is a generalization of single-label classification, where an unseen sample is automatically assigned a subset of semantically relevant labels from a given vocabulary. In parallel, recent research has demonstrated the impact of adversarial examples, which are modifications of original samples and aim at fooling machine learning models. Unlike existing adversary generation techniques which are specific to single-label data and mostly assume the availability of training data and/or model to the attacker, in this paper, we propose a generalized adversary generation mechanism by generating worst-case perturbation. This perturbation, when added to the feature vector of the original sample, generates an adversarial sample without the need for the availability of either training data or model to the attacker. Next, for the first time as per our knowledge, we study and demonstrate the effect of feature normalization as a defense mechanism against adversarial attacks. Extensive experiments show the effectiveness of our adversarial attack and defense mechanisms using state-of-the-art max-margin multi-label classification algorithms on two benchmark datasets.
Adagard Accelerated Gradient with Weight Decay for Adversarial Attacks

Subtle noise added to the original images can affect the deep neural network model's accuracy, that is, the adversarial examples. Many tests have shown that adversarial examples have achieved a high success rate for many models, but their transferability needs to be improved for the models with adversarial training. Therefore, an adagard accelerated gradient with weight decay method is proposed to generate adversarial examples in this paper. To be more specific, in the iterative calculation of gradient, it is different from the momentum method to directly calculate the current gradient, we use adagard to calculate the gradient and use weight decay method to limit the weight, make the whole model weights are closer to zero, improve the generalization of models. The proposed method AWDI-FGSM could be more efficient in enhancing the transferability between different models, according to experiments on the ImageNet dataset.
Development of Automatic Transport System for Stacking Chairs

In schools and other educational institutions, there are many opportunities to carry classroom chairs according to purpose. Some chairs are on casters, but educational institutions typically use stacking chairs or folding chairs. Although effective in terms of storage, it must be lifted by hand during transportation, increasing the burden on workers. Automation of baggage transport in warehouses has improved significantly, but little attention has been given to chair transport automation. Self-propelled chairs were developed, but despite the need, automatic transport of chairs without casters were not noted. In this study, we constructed an automatic chair movement system using an omnidirectional mobile robot. Recognize the chair by image processing and bring the robot closer. After the robot dives under the chair, the lifting device attached to the omnidirectional robot is used to lift the chair and transport it to the front of the human.
Practice Exploration of Term Project Teaching Method for Electric Elements of Power Plant Course

On the basis of analyzing the deficiency of the traditional assignment, the term project driven teaching method is proposed for electric elements of power plant. The differences in the content and depth of term project can be tailored to students at different levels. It is good for effectively promoting students to complete their assignments independently, exercising their comprehensive application design ability, and improving the learning effect. Taking the course of electric elements of power plant as an example, the term project research content and specific implementation steps is designed. Through the implementation of the term project, the advantages of the term project driven teaching method and its influence on the subsequent courses and practice of the major are analyzed.
أثر العمل عن بعد كأحد عناصر جودة الحياة الوظيفية على أداء الموظف العام بناء على الرضا الوظيفي في المركز الوطني لحقوق الإنسان، الأردن = The Impact of Distance Work as One of the Quality Elements of Work Life on the Performance of the Public Employees Based on Jop Satisfaction at National Center for Human Rights, Jordan

العمل عن بعد | عناصر جودة الحياة الوظيفية | أداء الموظفين | الرضا الوظيفي | اتخاذ القرارات عن بعد | التواصل الفاعل بين المديرين والموظفين | Distance Work or Remote Work | Quality Elements of Work Life Performance of Employees | Jop Satisfaction | Distance Decision Making | Effective Communications between Managers and Employees
Syntactic Semantic Similarity Approach For Electronic Library Keyword Search

The Electronic Library provides collections in electronic form that users can access without place and time limits. Some benefits of using electronic libraries are neater book listings, faster book availability searches, computerized book lending, and return transactions because they involve electronic library applications and the computer equipment used. An important feature of the electronic library is the search for book titles which makes it easier for us to find the desired book. Usually, there are thousands of books stored in the electronic library database. The problem in the search feature is a mismatch between search words and search results. This study extends our previous research using syntactic semantic similarity methods. This study using a syntactic semantic similarity algorithm based on wordnet and Levenshtein distance called sinsem. For this research, our dataset from the previous study is added and updated. The precision and recall results show that the accuracy of the keyword search results in the electronic library using this method is increased.
3D Human Pose Estimation Based on Volumetric Joint Coordinates

Estimating three-dimensional human pose from color images of single person is a fundamental problem in many applications. However, the problems of inaccuracy and ill-posed poses have been not well solved. A novel deep learning based approach for estimating 3D human poses from images is proposed. First voxel representation is adopted and the joint coordinates are presented to represent the poses. Second, space integral regression is used to compute the output results of the convolutional network. Finally, the output is sent into the fully connected network for joint training. Proposed algorithm has been tested under two standard test protocol of human3.6m dataset. Experimental results show that it obtains higher accuracy than most of previous methods and achieves well generalization ability in MPI-INF-3DHP dataset.
Discrete modulation continuous-variable measurement-device-independent quantum key distribution scheme based on realistic detector compensation

Discrete modulation continuous variable measurement device independent quantum key distribution scheme has good compatibility with efficient error correction codes, which leads to high reconciliation efficiency even at low signal-to-noise ratio. Besides, the implementation of this protocol is simpler than that of Gaussian modulation scheme. However, the quantum efficiency of homodyne detector commonly used in the experiment is only 0.6, which will seriously affect the practical application performance of discrete modulation continuous variable measurement device independent quantum key distribution scheme. To solve this problem, we propose a discrete modulation continuous variable measurement device independent quantum key distribution scheme based on realistic detector compensation. In our scheme, for the outputs of two quantum channels, each adopts a phase sensitive amplifier to compensate for the corresponding realistic homodyne detector. The simulation results show that the phase sensitive amplifier can well compensate for the quantum efficiency of the realistic detector and effectively improve the performance of the discrete modulation continuous variable measurement device independent quantum key distribution scheme with realistic detector in terms of secret key rate and secure transmission distance. The proposed protocol provides an effective method for promoting the practical development of the discrete modulation continuous variable measurement device independent quantum key distribution scheme.
ENSURING THE READINESS OF PRIMARY SCHOOL TEACHERS TO WORK WITH CHILDREN WITH WRITING DISABILITIES

In the article author examines the general issues of writing disorders, gives a historical sketch of the appearance of dyslexia and dysgraphia, typical manifestations of these disorders in preschool and school age. The main idea is to create an integrated system aimed at educating primary school teachers and developing the necessary qualities for them to work with children with writing disorders.
Design and Build a Cooperative Management Information System in Savings and Loan Cooperatives

Cooperative is a business entity whose members are individuals or legal entities, cooperatives based on their activities based on the cooperative principle as well as a people's economic movement based on the principle of kinship. Ar-Rohman Savings and Loans Cooperative (KSP) is a cooperative engaged in the savings and loan sector, this cooperative has been operating since 2016 which is located in the village of the temple ban Sidoarjo. In general, Ar-Rohman Savings and Loans Cooperative has several problems. Among them are managers that still use books, take more time, and have more energy in managing them, and the absence of information technology in cooperative management. The method used in this research is the waterfall method and software as follows: Sublime Text as an editor, PHP programming language, Code Igniter framework, Apache web server and MySQL database. This research will produce a web-based cooperative management information system in the ar-rohman savings and loan cooperative which can assist in managing the Ar-Rohman Savings and Loan Cooperative efficiently, computerized, and as a cooperative archive storage facility.
AR IN VR: AUGMENTED REALITY CUES IN 360-DEGREE STEREOSCOPIC TELEPRESENCE FOR REMOTE COLLABORATION AND MAINTENANCE

Remote maintenance and collaboration applications are getting more interest with the development of mixed reality technologies. The combination of 360-degree telepresence and augmented reality (AR) cues have been shown to be an effective way of
Research and Application of Software Development Model in Computer Software Development

The research purpose of software development is to try to develop various software that can meet the needs of users with low cost and high reliability. At present, many new software development technologies and methods are still difficult to adapt to the main trends of software development in the new era in a short period of time, and how to make good use of software development models to better meet the needs of software development is still one of the urgent problems to be solved. From the perspective of software engineering, this paper deeply analyzes the advantages and existing problems of typical software development models, and analyzes the reasonable application of software development models in computer software development, which is for reference only.
Respon Mahasiswa dalam Pembelajaran Blended Learning pada Matakuliah Ilmu Gizi

Responding to developments in information and technology at this time universities are implementing blended learning in learning activities. The purpose of this study was to determine the response of students in Blended Learning learning in nutrition science courses. The research method used is descriptive qualitative through survey methods. The instrument used was a student response questionnaire to blended learning in nutrition science subjects. The subjects of this study were students of the PJKR class of 2016. Based on the results of the questionnaire survey as many as 6 question indicators, an average of 72.80% was obtained in the agreeing category and respondents who answered disagree were 27.19%. The conclusion in this study is that there is a positive response from students in learning using Blended Learning in Nutrition Science Subject
Identifikasi Ekspresi Idiomatik Menggunakan Distributional Semantic Based Approach dan Truth Discovery

Idiomatic expressions are phrases that consist of a sequence of two or more words that have a meaning that cannot be predicted from the meaning of the individual words that compose it. Idiomatic expressions exist in almost all languages ??but are difficult to extract because there is no algorithm that can precisely decipher the structure of idiomatic expressions, so most rule-based machine translation systems generally translate idiomatic expressions by translating word for word their constituents, but the translation results do not produce the true meaning of the idiomatic expression. Based on this problem, the author tries to do research on the identification of the use of idiomatic expressions in Indonesian sentences. First, the author conducts the sentence classification process using BERT to find out whether the sentence contains idiomatic expressions or not. Furthermore, idiomatic expressions are identified based on distributional semantic based approach and then validated automatically using the Truth Discovery method. From the research conducted, the identification of idiomatic expressions in Indonesian sentences using Distributional Semantic Based Approach and Truth Discovery obtained an accuracy of 0.82; precision 1.0; recall 0.64 and f1-score 0.78.
Environment recognition system using LiDAR in autonomous overhead traveling crane

This paper discusses the development of an environment recognition system in an autonomous overhead traveling crane. In the conventional studies, the trajectory planning methods with load sway suppression and obstacles avoidance have been proposed. The planning methods need the environment recognition with the obstacles location and size in the transport space. Therefore, the environment recognition system which enables to construct automatically the transport prohibited areas in crane transport space is proposed in this paper. In the proposed system, the transport space can be detected by a LiDAR, and the detected data are formed in the grid map. The obstacles are recognized by the morphology transform, the labeling process and k-means. The efficacy of the proposed system is verified by the experiments with the laboratory-type overhead traveling crane.
EXPERIENCE IN IMPLEMENTING A SYSTEM OF SOCIO-PSYCHOLOGICAL TESTING OF STUDENTS IN SECONDARY VOCATIONAL EDUCATION

The article presents the results of approbation of one of the solutions for digitalization of educational service activities in organizations of secondary vocational education using new evaluation tools of socio-psychological diagnosis. The article describes a potential tool for the effective formation of preventive and corrective measures in determining the main directions of educational work with students and for the promotion of students' adaptation to the conditions of education in the secondary vocational education institutions. The tool for creating a digital trace of the socio-psychological state of students in secondary vocational education institutions is proposed.
Comparative Analysis of Neural Architecture Search Methods for Classification of Cultural Heritage Sites

In the current era of Machine Learning, the performance of Neural Networks in object detection, image classification, and video analytics has improved with better design of architecture. It often requires engineers to spend substantial time and effort to design the network, which can be an error-prone method. This method does not exhaustively search the entire search space of possible neural network architecture and guarantees optimal accuracy from the designed model. Neural Architecture Search (NAS) automates this process to find the optimal network to outperform the hand-designed model. Though NAS methods have shown promising performance for image classification tasks, it is challenging to infer why they work well on standard data sets and perform poorly when transferring the same NAS method to custom/real-time data sets. This paper proposes a custom image data set based on Indian Heritage sites built using a crowdsourced framework to perform a comparative performance analysis of NAS methods for the image classification task. The data set consists of 20,000 color images of 1920*1080 pixels from 40 heritage sites, with 16,000 training and 4000 test images. The comparative study is performed on three primary NAS methods viz. Efficient Neural Architecture Search via parameter sharing (ENAS), Differentiable Architecture Search (DARTS), and Neural Architecture Search using Multi-Objective Genetic Algorithm (NSGA-Net). The DARTS showed 88.625% accuracy, ENAS showed 32.83% accuracy and NSGA-Net produced 69.92% accuracy on the custom data set.
Path Planning Method of Radar Unit Transfer

This paper studies how to select the optimal radar position combination and plan the transfer path. Aiming at the factors affecting the selection of radar position, this paper puts forward the indexes affecting the selection of radar position, such as radar detection range, radar monitoring time, radar shielding angle and radar overlapping coverage area. According to the indexes and the actual needs of position selection, the optimization objective function of position selection is obtained; Based on the selected radar position and the factors affecting radar transfer, a transfer model is proposed. By improving the ant colony algorithm and writing the algorithm flow, an optimal transfer path is calculated for each radar fleet. Compared with the traditional ant colony algorithm, the simulation results show that the improved ant colony algorithm has significantly improved the efficiency and quality of solution, and has a certain application value.
A Deep Learning Approach to Recognize Telugu Handwritten Numerals

Numerous languages are now recognized by numerical recognition systems due to the rise in international commerce and correspondence, especially in multilingual countries like India where many languages are spoken simultaneously. Every language has its own set of characters and numerals, so every language requires a model to recognize the characters and numerals written in the language. Many models have been proposed in the field of handwritten numeral recognition. But the research done in Indian languages is very limited because of the unavailability of datasets. Telugu is one of the Indian languages with one or two models proposed up to now for handwritten numeral recognition, but the datasets used for the development of those models are not robust. We created a dataset of Telugu numerals with 2250 samples and developed a CNN model that can recognize the handwritten Telugu numerals with an accuracy of around 97%. We also tried to recognize the multi-digit Telugu numerals with help of contour detection.
Random-Index Oblivious RAM

We study the notion of Random-index ORAM (RORAM), which is a weak form of ORAM where the Client is limited to asking for (and possibly modifying) random elements of the N-items memory, rather than specific ones. That is, whenever the client issues a request, it gets in return a pair $$(r,x_r)$$ where $$r\in _R[N]$$ is a random index and $$x_r$$ is the content of the r-th memory item. Then, the client can also modify the content to some new value $$x'_r$$ . We first argue that the limited functionality of RORAM still suffices for certain applications. These include various applications of sampling (or sub-sampling) and, in particular, the very-large-scale MPC application in the setting of Benhamouda et al. [2]. Clearly, RORAM can be implemented using any ORAM scheme (by the Client selecting the random r’s by itself), but the hope is that the limited functionality of RORAM can make it faster and easier to implement than ORAM. Indeed, our main contributions are several RORAM schemes (both of the hierarchical-type and the tree-type) of lighter complexity than that of ORAM.
Analysis of Data Quality and Data Privacy Factors on the Factors of Intention to Buy and Decision to Buy on e-Commerce during Pandemic COVID-19

The Covid-19 pandemic has changed the way customers shop. Previous research reports stated that during the Covid-19 pandemic there was an increase in transactions that occurred in e-Commerce and even a fairly large increase in income for the industry. On the other hand, it was found that there were often mistakes in shopping made by customers, resulting in product returns and so on. The implementation of data quality and data privacy is believed to have been carried out by e-Commerce. This study will explore how Data Quality and Data Privacy factors affect customer buying interest. This quantitative research uses the SmartPLS application to process the data and uses the SEM-PLS technique. This study used 477 e-commerce customers as respondents during the Covid-19 pandemic and from this research, interesting results were obtained for the development of e-commerce. The Complete Information factor was found to have a negative and significant influence on intention to buy, and other factors were also found that describe the behavior of customers who shop for e-commerce during the Covid-19 pandemic.
A Study of a Detachable Omni-directional Automotive Mechanism for Efficient Operation of Manual Wheelchairs

A detachable omnidirectional movement mechanism for improving the work efficiency of a manual wheelchair is shown. As a detachable method, the drive wheel of a manual wheelchair can be attached by simply riding on the mechanism and applying the brake, which makes it possible to easily attach and detach without the need for an assistant. In addition, in order to use the riding method, a spherical wheel that requires at least two wheels for omnidirectional movement including turning is adopted as the omnidirectional movement mechanism. So far, we have prototyped Units 2 and 3 in which the arrangement of the two spherical wheels is different from left to right and front and back. In this paper, we show the moving performance of two prototypes and verify them from the viewpoint of practical use.
Evolutionary-Based Co-optimization of DNN and Hardware Configurations on Edge GPU

The ever-increasing complexity of both Deep Neural Networks (DNN) and hardware accelerators has made the co-optimization of these domains extremely complex. Previous works typically focus on optimizing DNNs given a fixed hardware configuration or optimizing a specific hardware architecture given a fixed DNN model. Recently, the importance of the joint exploration of the two spaces draw more and more attention. Our work targets the co-optimization of DNN and hardware configurations on edge GPU accelerator. We investigate the importance of the joint exploration of DNN and edge GPU configurations. We propose an evolutionary-based co-optimization strategy for DNN by considering three metrics: DNN accuracy, execution latency, and power consumption. By combining the two search spaces, we have observed that we can explore more solutions and obtain a better tradeoff between DNN accuracy and hardware efficiency. Experimental results show that the co-optimization outperforms the optimization of DNN for fixed hardware configuration with up to 53% hardware efficiency gains for the same accuracy and latency.