Reviewerly/exHarmony huggingface dataset:
Re-ID-AR: Improved Person Re-identification in Video via Joint Weakly Supervised Action Recognition

We uniquely consider the task of joint person re-identification (Re-ID) and actionrecognition in video as a multi-task problem. In addition to the broader potential of jointRe-ID and action recognition within the context of automated multi-camera surveillance,we show that the consideration of action recognition in addition to Re-ID results in amodel that learns discriminative feature representations that both improve Re-ID performance and are capable of providing viable per-view (clip-wise) action recognition.Our approach uses a single 2D Convolutional Neural Network (CNN) architecture comprising a common ResNet50-IBN backbone CNN architecture, to extract frame-level features with subsequent temporal attention for clip level feature extraction, followed by twosub-branches:- the IDentification (sub-)Network (IDN) for person Re-ID and the ActionRecognition (sub-)Network for per-view action recognition. The IDN comprises a singlefully connected layer while the ARN comprises multiple attention blocks on a one-to-oneratio with the number of actions to be recognised. This is subsequently trained as a jointRe-ID and action recognition task using a combination of two task-specific, multi-lossterms via weakly labelled actions obtained over two leading benchmark Re-ID datasets(MARS, LPW). Our consideration of Re-ID and action recognition as a multi-task problem results in a multi-branch 2D CNN architecture that outperforms prior work in thefield (rank-1 (mAP) – MARS: 93.21%(87.23%), LPW: 79.60%) without any reliance3D convolutions or multi-stream networks architectures as found in other contemporarywork. Our work represents the first benchmark performance for such a joint Re-ID andaction recognition video understanding task, hitherto unapproached in the literature, andis accompanied by a new public dataset of supplementary action labels for the seminalMARS and LPW Re-ID datasets.
Cascaded Classifier for Pareto-Optimal Accuracy-Cost Trade-Off Using Off-the-Shelf ANNs

Machine-learning classifiers provide high quality of service in classification tasks. Research now targets cost reduction measured in terms of average processing time or energy per solution. Revisiting the concept of cascaded classifiers, we present a first of its kind analysis of optimal pass-on criteria between the classifier stages. Based on this analysis, we derive a methodology to maximize accuracy and efficiency of cascaded classifiers. On the one hand, our methodology allows cost reduction of 1.32x while preserving reference classifier's accuracy. On the other hand, it allows to scale cost over two orders while gracefully degrading accuracy. Thereby, the final classifier stage sets the top accuracy. Hence, the multi-stage realization can be employed to optimize any state-of-the-art classifier.
Assessing Barriers to Using Virtual Reality for Training the Navy

AbstractToday’s, with widespread advancement in technology, the use of modern technologies in education can cause more effectiveness regarding this important issue. One of these new technologies is the fact that has become widely used in developed countries for training various sectors, including the Navy, the Air Force, and the Army. In case of its goal, this research is an applied research, and is a descriptive-survey research. The study aimed to evaluate barriers to virtual reality use for training the Navy personnel in Iran. These barriers were extracted by reviewing the theoretical and experimental foundations related to the research topic. The DEMATEL method was used for ranking the barriers. The required data were collected using pairwise comparison questionnaires, which were completed by research experts. The results showed that the infrastructure, equipment cost, expert human resource, senior management support, and the difficulty of model construction are respectively the most important barriers to use virtual reality for navy training.
Sistem Laporan Keuangan Berbasis Aplikasi Website Pada Toko Maestro Jaya

Rapid technological advances greatly help daily activity, so many people start using technology in their business. Maestro Jaya shop is one of the MSMEs located in Batam City, the shop owner has problems in producing financial reports either monthly or annually. So in this article, the author implements a system that supports daily activities at the store such as making invoices, payments, and journaling to get financial reports. The author uses the PHP programming language and waterfall as the development method. The final result of this research is a website application that has been used by the shop owner for the shop's daily activities.
Punctuation Prediction in Vietnamese ASRs Using Transformer-Based Models

Punctuation prediction is the task of predicting and inserting punctuation like periods, commas, exclamation marks, etc. into the appropriate positions in transcribed texts in ASR systems. This helps to improve user readability and the performance of many downstream tasks. While most related studies have been performed for popular languages like English and Chinese, there is very little work done for low-resource languages. In order to stimulate the research on these languages, in this paper, we target to improve the quality of punctuation prediction for Vietnamese ASRs. Specifically, we propose a method based on recent advances on pre-trained language models (LMs) for general purposes such as BERT and ELECTRA. The benefit of using these models is that they can be effectively fine-tuned on this punctuation prediction task where only a small amount of training data is available. To further enhance the performance, a simple yet effective technique to provide more context information in predicting punctuation marks for the very left and right words in each segment is also proposed. The experimental results of the proposed model on public benchmark datasets are quite promising. Overall, the proposed architecture substantially enhanced the prediction performance by a large margin and yielded a new state-of-the-art result on these datasets. Specifically, we achieved the \(F_1\) scores of 71.49% and 80.38% on the Novel and Newspaper public datasets, respectively.
SEGMENTASI CITRA UNTUK MENGENALI TEKSTUR UIS KARO DENGAN METODE FILTER GABOR

With the development of image processing, humans are competing to produce an attractive image from existing simple images. In addition, according to the development of time, image processing is increasingly becoming a necessity for many people, especially in the arts, making films as a special effect on images. One of the image processing that is currently popular and widely used in the world of film or art is image. The process of the Gabor filter method is the process of creating an effect or adding a gray level, where an object will be slowly converted into a recognizable object.
Social and emotional learning as part of professional development in initial teacher education

Social and emotional learning (SEL) constitutes a powerful tool for professional teacher development. Recent research shows that in-service teachers are convinced that SEL is essential, they show a great readiness to implement SEL, and they profit from SEL trainings. To date, professional opportunities tend to be inadequate or insufficient in preparing teachers to incorporate SEL into their classrooms. Teachers need understanding and knowledge of social, emotional and cognitive processes, and research findings on SEL. There is a need to implement SEL approaches earlier and with regard to SEL principles in the teacher education programmes. This paper is based on a literature review and follows three main theses: A focus on SEL can foster professional teacher development. A focus on SEL can develop social and emotional competencies in student teacher and support the successful mastery of study. In the long run, the focus on SEL can qualify future teachers for the successful transfer of SEL principles, programmes and approaches in working with students in the classroom. The three theses raise the question: how can teacher educators, or more precisely universities, create supportive, caring and participative learning environments for future teachers? To answer this, strategies to integrate SEL into initial teacher education are presented.
Learning, Planning, and Acting with Models

Author(s): Kurutach, Thanard | Advisor(s): Abbeel, Pieter PA; Russell, Stuart SR | Abstract: While the classical approach to planning and control has enabled robots to achieve various challenging control tasks, it requires domain experts to specify transition dynamics as well as inferring hand-designed symbolic states from raw observations. Therefore, bringing such method into a diverse, unstructured environment is still a grand challenge. Recent successes in computer vision and natural language processing have shed light on how robot learning could be pivotal in tackling such complexity. However, there are many challenges in deploy learning-based systems such as (1) data efficiency -- how to minimize the amount of training data required, (2) generalization -- how to handle tasks that the robots are not explicitly trained on, and (3) long-horizon tasks -- how to simplify the optimization complexity when presented with temporal-extended tasks.In this thesis, we present learning-and-planning methods that utilize deep neural networks to model the environments in different forms in order to facilitate planning and acting. We validate the efficacy of our methods in data efficiency, generalization, and long-horizon tasks on simulated locomotion benchmarks, navigation tasks, and real robot manipulation tasks.
Allpying the Method of Criminalistc Computer Modeling in Crime Investigation

The authors of this article reviewed certain issues, application features and development perspectives of criminalistic computer modeling in crime investigation. The article gives a more precise definition to the term “criminalistic computer modeling” and the strategy of automized crime investigation, reviews the existing programmed hardware-software complexes and forensic equipment, which use criminalistic computer modeling. Moreover, it outlines the possibility of using certain AI elements for building automized strategies to investigate particular types of crimes. Nowadays, forensics as a science needs to develop new tactics, methods and recommendations, as well as forensic equipment and software, based on modern computer technologies and the method of forensic computer modeling, including the use of AI elements, to make crime investigation more effective. This becomes particularly important during the pre-trial investigation stage, closely connected to the development of new forensic equipment and methods. In this regard, the issues of using criminalistic computer modeling in crime investigation demand special attention. The results of this research lead the authors to believe that the possibility of applying criminalistic computer modeling opens new prospects for using it in forensics, including the criminalistic methodology, which will increase the effectiveness of crime investigation and optimize the work of the investigator.
Improving Image-Based Dialog by Reducing Modality Biases

Machines cannot outperform human intelligence yet; however, an image-based dialog can enable machines to perceive cues from different modalities and process information in a more human-like manner. The proposed solution is an AI-based agent that can have engaging conversations with humans by considering an image and answering questions about its visual content, taking into account both visual and textual context. Deep learning-based techniques like Recurrent Neural Networks (RNNs) with self-attention mechanisms have been employed. Responses generated by such models, in some cases, are more biased towards dialog history and are not very relevant to the actual question asked. The proposed work focuses on reducing the modality biases without compromising dialog history and improving the visual context through the use of dense captions to describe various entities in the image and generate relevant answers.
P-134 COVID-19: Analysis of Notifications According to Social Security Benefits and Accidents at Work in Brazil in 2020

<h3>Introduction</h3> On March 11, 2020, the World Health Organization declared the existence of a pandemic by SARS-CoV-2. On May 10, 2021, Brazil had reached the 410,000 mark, becoming the second country in number of deaths from COVID-19 and more than 15 million infected. COVID-19 generates withdrawal from work activities and consequent social security benefits. Knowing the profile of employees and benefits can contribute to a better overview of occupational approaches to COVID-19. <h3>Objective</h3> To analyze social security benefits generated by COVID-19 leave in Brazil. <h3>Methodology</h3> Descriptive analysis of data from the Observatory of Safety and Health at Work, in the period 2020, referring to social security benefits by COVID-19, with a comparative analysis of the year 2019. All social security gaps were considered. Used ICD - 10 = U07 and B34. Data analyzed: state of Brazil, occupation, economic activity, notification with 'report of work accident - CAT', age, gender, economic sector. <h3>Results</h3> 50 thousands were dismissed from social security benefits in 2020 according to CID U07 and B34. The state of Sao Paulo had the highest frequency with 36%. Men were 68%. The most prevalent economic sector was hospital (15%) and the occupation was nursing technicians (38%) The most frequent age group in men was 18–24 years and in women, it was 35 to 39 years. <h3>Conclusion</h3> Occupational epidemiology acting in the analysis of secondary, social security data on COVID-19 can contribute to the definition of public policies, expanding health promotion and prevention.
A Subgame Perfect Equilibrium Reinforcement Learning Approach to Time-inconsistent Problems

In this paper, we establish a subgame perfect equilibrium reinforcement learning (SPERL) framework for time-inconsistent (TIC) problems. In the context of RL, TIC problems are known to face two main challenges: the non-existence of natural recursive relationships between value functions at different time points and the violation of Bellman's principle of optimality that raises questions on the applicability of standard policy iteration algorithms for unprovable policy improvement theorems. We adapt an extended dynamic programming theory and propose a new class of algorithms, called backward policy iteration (BPI), that solves SPERL and addresses both challenges. To demonstrate the practical usage of BPI as a training framework, we adapt standard RL simulation methods and derive two BPI-based training algorithms. We examine our derived training frameworks on a mean-variance portfolio selection problem and evaluate some performance metrics including convergence and model identifiability.
A Study of the Application of Computer Translation Technology in the Process of English Teaching

Objectives: With the development of Internet and computer technology, auxiliary teaching has been widely used in English teaching. Methods: Based on this, this paper studied English teaching based on the computer corpus. First, we designed and built the corpus based on the outline of the current English Teaching in China. Results: Secondly, the diagnosis algorithm was designed according to the needs of auxiliary teaching. And the implementation steps were expounded. Finally, we applied the algorithm designed in this paper to the evaluation of English composition. Conclusion: Through the test of the algorithm, the difference range between the test data and the actual data was acceptable. The algorithm designed in this paper was effective, and it could be further promoted in English teaching.
Exam Answer Detection using Proposed Nested Loop Pixel Checking Based on Image Processing

Tracking object is one of the most discussed problems in digital images processing. The objectives of tracking objects include the counting, correction, classification and so on. This study discusses how tracking objects, in this case the multiple choice chosen by students, is carried out on the exam answer sheet using the proposed nested loop pixel checking method. The answer sheet used here is the answer sheet for the exam from SMK Khoiriyah Hasyim Tebuireng Jombang because each school has a different exam answer sheet format so that the coordinate parameters of each answer sheet between schools must be different. Before the nested loop pixel checking method, preprocessing was carried out first after taking the image, namely cropping the Region of Interest (ROI), conversion to grayscale, filtering, and edge detection using the adaptive Canny edge detection method. The process of nested loop pixel checking is done by iterating successively with steps per answer option, per number of answers, and per column (10 numbers). In addition, a special case is given to find out whether this proposed method works as desired. The results show that this method can track answers for both normal and special cases.
Silixa presenting at the UK CCUS and Hydrogen Decarbonisation Summit

Silixa is proud to be sponsoring the UK CCUS and Hydrogen Decarbonisation Summit on 4-5 February 2020 in Birmingham, UK. The event will address the ...
Perpustakaan sebagai tempat pemenuhan kebutuhan informasi mahasiswa dalam penyelesaian tugas akhir: Studi kasus di Fakultas Tarbiyah dan Keguruan UIN Sunan Ampel Surabaya

The focus of the problems in this study are: 1) What information needs must be met by students in completing their final assignments at the Tarbiyah and Teacher Training (FTK) Faculty of UIN Sunan Ampel Surabaya? and 2) What is the role of libraries in meeting the information needs of students who complete their final assignments at FTK UIN Sunan Ampel Surabaya? The results, it shows that Information needed by students of the FTK at UIN Sunan Ampel in completing their final project is all collections available in the library, both printed and digital. Print collections such as books, dictionaries, journals, research results are the most sought-after sources of information, and this can be obtained through the use of general collections, reservoirs and serials, specials, references, and corners. Other information needs are fulfilled from digital collections in the form of repositories that can be available through the website http://repository.uinsby.ac.id. The Sunan Ampel UIN library has a significant role in fulfilling the information needs of students who are in the process of completing their final assignments. There are at least two supporting indicators, namely the level of visitor visits which reached 141,438 students in a year or 11,786 people each month and book-borrowing transactions totaling 75,110 copies only in the period January to August 2019 or an average of 9,388 copies per month. Several reasons that make students prefer to visit the library compared to other places such as bookstores are due to the dampness and variety of collections, comfortable places, easy access to collection utilization, friendly staff, and the suitability of collections to scientific needs.
Formation of natural concepts at the stages of sensory knowledge

The State Standard of Basic Secondary Education states that the purpose of basic secondary education in Ukraine is the development of natural abilities, interests, talents of students, formation of competencies, etc. The competence potential of the natural education field and the basic knowledge of students in the basics of natural sciences are scientific facts, concepts, ideas, models, laws and methodologies of scientific knowledge – the language of science, empirical and theoretical research methods and more. This is a difficult task, and its solution is necessarily associated with the formation and development of concepts. The paper characterizes the preparatory stage for the formation of natural concepts. The method of actualization of basic knowledge and sensory experience of students and motivation of educational activity to the formation of concepts is revealed. The process of concept formation goes through two main stages of cognition – sensory and logical. The emphasis is placed on the method of formation of natural concepts at the stages of sensory cognition – sensation, perception, imagination.
Multi-view videogrammetry for the measurement of plate flexural vibration

This paper describes a simulation study of the measurement of the flexural deflection of a thin plate, excited by a transverse force at the fundamental frequency of the resonance modes, using an optical system consisting of multiple cameras. The estimate of the amplitude of the vibration is obtained starting from two or more sequences of images taken by the cameras, by means of triangulation. In this paper, we assume that the system is calibrated, and we focus on the error resulting from pixellisation. The simplified optical model is used to simulate how the accuracy of the measurements varies with respect to: the distance of the cameras from the structure; the opening angle between pairs of cameras; the resolution of the cameras and their number. The aim of the work is to evaluate the influence of the cameras arrangement on the measurement quality, and in particular if the use of a number greater than 2 cameras can allow a reduction of the measurement error, allowing a favorable compromise between spatial and temporal resolution of the cameras. As an example, the average error of the reconstruction of the first flexural deflection shape falls from 12% to 4%, when the measurement setup passes from 2 cameras to 12 cameras in the considered setup.
Attention based Image Captioning using Depth-wise Separable Convolution

Automatically generating descriptions for an image has been one of the trending topics in the field of Computer Vision. This is due to the fact that various real-life applications like self-driving cars, Google image search, etc. are dependent on it. The backbone of this work is the encoder-decoder architecture of deep learning. The basic image captioning model has CNN as an encoder and RNN as a decoder. Various deep CNNs like VGG-16 and VGG-19, ResNet, Inception have been explored but despite the comparatively better performance, Xception is not that familiar in this field. Again for the decoder, GRU is not been used much, despite being comparatively faster than LSTM. Keeping these things in mind, and being attracted by the accuracy of Xception and efficiency of GRU, we propose an architecture for image captioning task with Xception as encoder and GRU as decoder with an attention mechanism.