Reviewerly/exHarmony huggingface dataset:
Mask Detection Using Convolutional Neural Network

COVID-19 virus has hit Indonesia since early March 2020. One of the government’s efforts to prevent the spread of COVID-19 is to do physical distancing to require people to wear masks when doing activities outside the home. One way to overcome this problem is by detecting mask users to be more obedient and obedient to the rules, then the identification process is carried out for mask users and those who do not use masks. The process is carried out using the Convolutional Neural Network method. CNN is known to be superior and does not require pre-processing so it saves more time. In terms of algorithmic competence, CNN is considered capable of carrying out the data detection process well. Of the 1376 datasets used, 30 epochs, accuracy = 0.988, recall = 0.990, precision = 0.987, and F1 = 0.988 with the required detection time for each image between 4 to 5 seconds.
Performance Improvement by Controlling VM Migration between Edge Nodes in a Multi-Stage Information Processing System

We propose two VM migration methods that improve the total sum of program correct by dynamically moving VMs to appropriate edge node. We assume a multi-hierarchy information processing system that the cloud data center and the edge node VM (Virtual Machine) perform processing at the same time, the data center completes all processing, and the VM has the required response time and returns a result that the processing has progressed (called accuracy). The first method is “VM sweeping method” that allocates more CPU time to VMs that perform high accuracy processing. The second methods is “VM sweeping method” that allocates more CPU time to VMs that perform high accuracy processing that improves per unit CPU time (AIR), and “VM number averaging method” that attempts to move VMs so that the assignment status of the CPU time in all edge nodes is equal. The accuracy improvement effect of the proposed VM migration method was evaluated by a simulator. Evaluations changed the AIR from the Early-blooming type where the accuracy greatly increases in the first half to the Late-blooming type where the accuracy greatly increases in the second half, and comparing it with the case where the VM is not moved. It was clarified that a big effect can be expected in the VM sweeping method, and the VM number averaging method in the Earlyblooming type program.
Application of Accelerometer for Vehicle Parameterization

Accelerometer and magnetic sensor data fusion shows promising results for vehicle classification applications in a smart city. In this paper, the following algorithms for accelerometer data usage are proposed: extraction of axle times and locations, speed calculation and magnetic signature correction. Examples are shown to suggest the usage of the algorithms: classification to 4 vehicle types including trailer detection, speed estimation method, which is more accurate than a traditional one and correction of magnetic signatures for various purposes.
Similarity between ‘N1+把+N2+V1+在/到+place’ and ‘N1+把+N2+[V1+R (result complement)]’ and the difference in degree of ‘transitivity’

This paper intends to study the difference in the degree of ‘transitivity’ in the two phrase types through a comparative study of X type and Y type of the ‘把’ construction. First, I would like to discuss the difference between the subjectivization of the ‘Transitivity’ action chain events that appear in the billiard ball chain movement diagrams of the ‘passivity event’ and ‘Transitive event’ events of the X type and Y type. Second, by comparing the degree of ‘Transitivity’ of ‘Agency’, ‘Affectedness’, ‘Kinesis’ of X type and Ytype, it was explained which sentence type showed higher degree of ‘Transitivity’
A Study on the Possibility of Establishing Contract Department of Early Employment and Exploration on the Direction of Education and Training in Tourism Major

The purpose of this study is to explore the possibility of opening an early employment contract department in tourism major and to propose an education and training program design direction considering securing the effectiveness in terms of education and training. The Ministry of Education's early employment contract department development project is an educational model for youth job creation that can simultaneously achieve the competitiveness of small and medium-sized companies by securing excellent talents with high school graduates' desire to enter school and early employment. First, in order to explore the possibility of opening an early employment contract department for tourism majors, we looked at employment-related indexes such as mismatches considering business purposes, and related indexes and demand survey results such as employment status in the tourism industry. Based on the results of the possibility of opening, all matters to be considered when designing education and training programs were discussed, and NCS-KQF-Labour Job Level-Tourism Job Level was linked and reviewed to set the difficulty (level) of the contents of the education and training program. Based on this, the direction and guide to the composition of education and training programs for the development of trainees' competencies were proposed, focusing on NCS classification and competency units (L2 to L4).
Autoregressive GAN for Semantic Unconditional Head Motion Generation

In this work, we address the task of unconditional head motion generation to animate still human faces in a low-dimensional semantic space from a single reference pose. Different from traditional audio-conditioned talking head generation that seldom puts emphasis on realistic head motions, we devise a GAN-based architecture that learns to synthesize rich head motion sequences over long duration while maintaining low error accumulation levels.In particular, the autoregressive generation of incremental outputs ensures smooth trajectories, while a multi-scale discriminator on input pairs drives generation toward better handling of high- and low-frequency signals and less mode collapse.We experimentally demonstrate the relevance of the proposed method and show its superiority compared to models that attained state-of-the-art performances on similar tasks.
Block-Based Forgery Detection with Binary Gradient Model

Significant advances in audio technology and enhanced functionality of editing tools have made it very easy to forge audio content. Many studies have been proposed in recent years to detect copy-paste, which is one of the most common types of forgery. In the copy-move fraud, the duplication of audio segments, the content is copied in a recording and moved to another place within the same recording. It is not always difficult to detect this. But things can get tricky due to the availability of sophisticated tools that can easily hide tampering without leaving any traces. Considering the importance of using audio signals in daily life, the necessity of an efficient audio verification system for accurate and reliable tamper detection is understood. The proposed method uses Mel spectrogram images obtained from the audio file and then converts the Mel spectrogram image to gray level images and divides it into overlapping sub-blocks and extracts features for each block via the Binary Gradient Model. Feature vectors are sorted lexicographically to group similar vectors together. The similarity between the blocks gives a clue about forgery. According to the given experimental results, we can say that the proposed scheme has better detection results even for tough attacks.
Translated Skip Connections - Expanding the Receptive Fields of Fully Convolutional Neural Networks

The effective receptive field of a fully convolutional neural network is an important consideration when designing an architecture, as it defines the portion of the input visible to each convolutional kernel. We propose a neural network module, extending traditional skip connections, called the translated skip connection. Translated skip connections geometrically increase the receptive field of an architecture with negligible impact on both the size of the parameter space and computational complexity. By embedding translated skip connections into a benchmark architecture, we demonstrate that our module matches or outperforms four other approaches to expanding the effective receptive fields of fully convolutional neural networks. We confirm this result across five contemporary image segmentation datasets from disparate domains, including the detection of COVID-19 infection, segmentation of aerial imagery, common object segmentation, and segmentation for self-driving cars.
Model of interaction of higher educational institutions with the communications industry in the professional student training

In modern conditions, a system of interaction between higher educational institutions and enterprises of various profiles has developed. It is considered as a necessary condition for achieving high quality professional training of bachelors and masters for various sectors of the economy, culture, and education. The communications industry has grown rapidly over the past three decades. In the last period, the highlights and trends are related to digital communication. Internet communications dominate the advertising services market. It is important that this speed of development is accompanied by trends in professional education. In various articles on the topic of interaction between higher educational institutions and enterprises, researchers offer models of cooperation between higher educational institutions and production. We are interested in following these trends in the cooperation between higher educational institutions and the communications industry.
Recognition of Struck Out Words Using a Deep Learning Approach

Handwritten document contains a lot of unreadable texts or elements which have no meaning such as struck out words or characters. If such words are fed into a handwriting recognition system, there can be a drop in the accuracy of the system, and it may result into predicting false words. In this paper, we propose an approach to detect these struck out words. With the help of CNN model, we train the model to recognize and differentiate the normal words from the struck out words. For this purpose, some common types of struck out strokes were handled. In order to obtain a handwritten text free of these words, this method may identify strike-through text, locate the word/character, and erase these words. The model was trained on a set of English words and characters that we generated, and it was then put to the test on a range of texts that contained words that had been struck out. The experimental results demonstrate the accuracy of the proposed approach, where the models achieved accuracy levels of 100%.
Comparative Analysis of Intra- and Inter-Prediction Compression Techniques for Endoscopic Videos

The cloud-based healthcare system has opened up new possibilities in the medical profession. By monitoring the whole digestive tract, endoscopic technology has improved the diagnosis of gastrointestinal illnesses and diseases. Image compression enhances the frame rate, which helps the diagnostic procedure. The aims of presenting this paper are to put light on brief review of image compression into two categories intra- and inter-prediction for endoscopy video compression, which is efficient and simple. Our research work shows an acceptable compression performance; the initial phase of work is an implementation of intra-prediction, and the second phase of work is inter-prediction technique, which shows a mark able performance in term of PSNR value, bit rate and compression ratio.
AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System for COVID-19 Prevention

The outbreak of COVID-19 had brought a great challenge for the World Health Organization (WHO) in preventing the spreading of SARS-CoV-2. The Ministry of Health (MOH) of Malaysia introduced the MySejahtera mobile application for health monitoring and contact tracing. Wearing a face mask in public areas had been made compulsory by the Government. The overhead cost incurred in hiring the extra manpower to ensure all the visitors wear a face mask, check-in through MySejahtera and the status in MySejahtera is healthy before entering a premise. A low-cost solution is urgently needed to reduce the heavy overhead cost. An AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System (AFMHS) is proposed to perform real-time detection for the face mask and MySejahtera Check-In tickets by using artificial intelligence. MobileNetV2 was used for the detection and recognition of face and face masks. YOLOv3 was used for the detection of the region of interest for the MySejahtera Check-In ticket to locate the health and vaccination status of the visitor. Optical character recognition (OCR) is a technique that is used to detect the text captured in an image and encode the recognized text. OCR is implemented to recognize the text extracted from the ticket. Tesseract is used as the OCR engine in AFMHS. Raspberry-Pi-4B (Raspberry Pi Generation 4 Model B) with 4 GB RAM is used as the processing unit of AFMHS. The total cost of the AFMHS is only USD220. Extensive experimental tests were carried out to evaluate the performance of AFMHS. The optimum operation setup conditions are proposed to achieve 100% accuracy. The optimum operating distance for the face mask detector and MySejahtera Check-In ticket detector are 1.5m and 15cm respectively.
Anniversaries of the Ministry of Internal Affairs of Russia in the pedagogical process of the Academy of Management of the Ministry of Internal Affairs of Russia

The article examines the elements of the corporate culture of the internal affairs bodies of the Russian Federation: professional values, traditions, rituals, professional holidays and memorable dates; professional symbols, as well as pedagogical possibilities of socio-cultural activities aimed at organizing the celebration of the most important events in history, anniversaries and memorable dates of the Ministry of Internal Affairs of Russia and the Academy of Management of the Ministry of Internal Affairs of Russia. The author offers a toolkit for entering facts and events into a holistic pedagogical process, traces the influence exerted on the training, upbringing and development of the personality of an employee of the internal affairs bodies, reveals the main content of this activity, and considers the prospects for its development. The scheme of implementation of elements of corporate culture in the Academy of Management of the Ministry of Internal Affairs of Russia is presented through: a holistic pedagogical process, moral and psychological support of activities, mentoring, activities of public organizations and formations, historical and museum work. The results of the sociological survey "Anniversaries and memorable dates of the Ministry of Internal Affairs of Russia of the past, present and future" are presented. Special attention is paid to the positive experience of the Academy of Management of the Ministry of Internal Affairs of Russia in organizing scientific, educational and socio-cultural activities related to the preparation and celebration of anniversaries and memorable dates in the history of the Ministry of Internal Affairs of Russia.
Aesthetic Measure of Architectural Photography utilizing Computer Vision: Parts-from-Wholes

The existing methods for solution space navigation require numerical values to score solutions. The authors introduce a method of quantitative aesthetic evaluation utilizing Computer Vision (CV) as a criterion to navigate solution spaces. Therefore, aesthetics can complement structural, environmental, and other quantitative criteria. The work stands in the extended history of quantifying the visual aesthetic experience. Some precedents are: Birkhoff [1933] and Max Bense [1965] built an approach with experiments to empirically support a measure, whereas Birkin [2010], Ostwald, and Vaughan [2016] devised the first computational methods working on vector drawings. Our research automates and accelerates aesthetic quantification by utilizing CV to extract computable datasets from images. We are especially keen on architectural images as a shorthand to assign an aesthetic value to design, aiming to navigate the solution space in architecture. This work devises a method for rearranging parts in architectural images focusing on formal aspects, in opposition to semantic segmentation where objects unrelated to architectural design (cars, persons, sky…) are quantified to score images [Verma and Jana and Ramamritham 2018]. It uses Maximally Stable Extremal Regions (MSER) [Matas 2004] to recognize architectural parts because it is superior to similar methods such as SimpleBlobDetector in this task. Our method disassembles the parts in a diagram of scaled parts (Fig. 2) to analyze them in isolation, and a diagram of connectivity graph (Fig. 3), to evaluate relationships. These diagrams are examined to compare photos of buildings, cars, and trees to assess the applicability of such a method to a range of objects. Parts and connections are thus quantified, and these values are inputted in a refined version of Birkhoff’s formula to calculate an aesthetic score for each image for navigating the solution space. Finally, it tests the method to draw comparisons between the discrete and continuous paradigms (Fig. 1) in the contemporary discourse of architecture, comparing Zaha Hadid Architects` Heydar Aliyev Centre and Gilles Retsin´s Diamonds House to argue that there is a difference between the aesthetic effects of continuous and discrete designs, besides their distinction in tectonic logic. The method proved to be an efficient procedure for comparatively quantifying the aesthetic judgment of architectural images, enabling designers to incorporate aesthetics as a complementary criterion for solution space navigation in computational design. The method of computational aesthetic measure for solution space navigation and its calibrations via crowdsourced evaluation of images is further detailed in a paper by the authors being published at the 2022 eCAADe conference.
Mix Reality, Data and Experiences

The workshop will explore the workflow of preparing and transferring data from CAD packages to the Unity Game engine. A 3d model of a house will be prepared for the mix reality experiences, going through the process of optimising the model geometry, adding textures and optimising the output required for mobile devices. In the game engine, we will prepare a diorama model that can be shared either as an augmented reality experience or as a virtual reality experience. We will be exploring how to add time base effects and create a user interface for the mix reality experiences.
Analysis of Perceptions of Undergraduate Participants in Extracurricular Programs in the Untact Distance Education Environment

The purpose of this study is to examine undergraduate students’ perceptions of extracurricular programs that have been operated in the form of untact distance education, and to seek the direction of operating extracurricular programs the form of untact distance education. To this end, the study stated three research problems and reached the following conclusions and implications: First, special lecture-type programs that simply provide information were suitable for operating extracurricular programs in the form of untact distance education. Second, the students counted decreased concentration as the biggest difficulty when participating in the programs in the untact distance education. Third, when operating extracurricular programs in the form of untact distance education, it is necessary to prepare to include teaching strategies including the quality of content and interaction, which are program factors.
Bayesianism in Cognitive Science

The fourth edition of this popular text has been significantly rewritten to make it more accessible to students and easier for instructors to use. It remains distinctive in presenting a unified narrative of cognitive science as a field of inquiry in its own right. Thematically organized, Cognitive Science underscores the problems and solutions of cognitive science rather than more narrowly examining individually the subjects that contribute to it - psychology, neuroscience, linguistics, and so on. The generous use of examples, illustrations, and applications demonstrates how theory and experiment can be applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been updated and enhanced with a new chapter on emotions and the emerging field of affective science. An extensive online set of resources is available to aid both instructors and students.
A Bartlett-type correction for likelihood ratio tests with application to testing equality of Gaussian graphical models

This work defines a new correction for the likelihood ratio test for a two-sample problem within the multivariate normal context.This correction applies to decomposable graphical models, where testing equality of distributions can be decomposed into lower dimensional problems.
Hardware-Level Design: Neural Engines and Tensor Accelerators

Model compression is a potential strategy for reducing computing overhead and increasing processing speed in ML systems, which are generally constrained by limited computational resources. Research into how to perform machine learning tasks on mobile and edge devices has been a hot topic in recent years. In order to maximize training acceleration, model compression methods can be integrated into the design of specialized hardware. New-generation mobile phones and computational primitives might leverage FPGA chips that allow 8-bit or fewer float-point operations to implement the data quantization approach. It is also necessary to have the appropriate hardware to use neural network architecture and training algorithm optimization properly. This chapter inspects this problem from multiple aspects to demonstrate the relationship between real-world on-device learning requirements and hardware implementation. Researchers might use this conversation to develop learning frameworks. The introduction of model compression into specialized hardware design is thus still an open issue that requires more investigation.
M2GCF: A multi-mixing strategy for graph neural network based collaborative filtering

Graph Neural Networks (GNNs) have been successfully used to learn user and item representations for Collaborative Filtering (CF) based recommendations (GNN-CF). Besides the main recommendation task in a GNN-CF model, contrastive learning is taken as an auxiliary task to learn better representations. Both the main task and the auxiliary task face the noise problem and the distilling hard negative problem. However, existing GNN-CF models only focus on one of them and ignore the other. Aiming to solve the two problems in a unified framework, we propose a Multi-Mixing strategy for GNN-based CF (M2GCF). In the main task, M2GCF perturbs embeddings of users, items and negative items with sample-noise by a mixing strategy. In the auxiliary task, M2GCF utilizes a contrastive learning mechanism with a two-step mixing strategy to construct hard negatives. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed model. Further experimental analysis shows that M2GCF is robust against interaction noise and is accurate for long-tail item recommendations.