Reviewerly/exHarmony huggingface dataset:
Computer vision applications

Abstract This chapter provides a comprehensive coverage of applications of both directed and undirected PGMs to major computer vision tasks. It starts with a review of different computer vision tasks and classifying them into three categories: low level vision tasks, middle level vision tasks, and high level vision tasks. It then provides a detailed review of existing studies that involve PGMs for tasks at each level. Specifically, for low level vision tasks, this chapter discusses the applications of both MRFs, CRFs, and BNs for such tasks as image labeling, image segmentation, and image denoising. For the middle level vision tasks, this chapter covers the applications of both directed and undirected PGMs to such middle level vision tasks as object detection and recognition, object tracking, scene recognition, and 3D reconstruction. Finally, for high level vision tasks, this chapter discusses applications of different types of PGMs to various high level vision tasks, including facial expression recognition, human gesture recognition, human action and event recognition, and complex human activity recognition.
Study of the frequency range measurement method using train chirp signal

The paper proposes a method for measuring range in the track-while-scan mode. The dependence of the measurement error for different deviation frequencies is studied by the method of mathematical modeling in MATLAB .
Utilization of NON B3 Waste as Learning Media in Online Class during the Pandemic

Abstract Covid-19 pandemic had a significant impact on many aspects of life, including in the world of education. Pandemic conditions make the learning process adapt to new conditions, one of which is changing the form of face-to-face learning into online learning. The online learning process for teachers at Special School (SLB) faces challenges in delivering learning materials to students with special needs, especially in using appropriate learning media. This article discusses the training provided to special school teachers in Yogyakarta to make learning media utilizing non-B-3 waste as a support for the online learning process for children with special needs. The method used is a qualitative method with data collection techniques using interviews, observation, and performance evaluation. The results of this study indicate that the training in making learning media based on non-B-3 waste is significant in helping teachers solve online classroom learning problems for children with special needs. The reason for choosing non-B-3 waste for learning media are easy to get, and teachers can make media according to students abilities and learning objectives.
Proposal for a digital forensic investigation model in accordance with the legislation in Mexico

In this paper we collect and observes the existing digital forensic investigation models, which are essentially the application of information systems and communications engineering for forensic purposes. In addition, a review of the federal criminal situation in Mexico is presented (through the revision of the regulations in the Federal Criminal Code), because the Code indirectly describes the reality of what can be prosecuted and admitted as evidence, criminally speaking, with the application of digital forensic investigation models in Mexico. This is due to the significant deficiency in the proposal of digital forensic investigation models, in which there is not enough emphasis on the potential admissibility of the evidence gathered through the models, to give attention to the need to provide “evidence” to Institutions responsible for the impartation of justice, as if doing digital forensic investigation to be a technological issue and not as it really is: a socio-legal-technological issue. Therefore, considering the criminal reality in Mexico, locating the practices of existing models that make sense in accordance with the norm, an abbreviated model is proposed that really helps successful prosecutions.
Optimal graph edge weights driven nlms with multi-layer residual compensation

Abstract Non-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.
Transformation of Education Actor amidst Epidemiological Instability: Social and Philosophical Analysis

The study aims to shed light on the transformations that have happened to the actor of education, caused by transition of learning delivery modality to the sphere of distance interaction. The authors come to the conclusion that these changes are related to communicative, information-technological and cognitive aspects of educational process organisation. The work is novel in that it is the first to carry out a social and philosophical analysis of transformations of the actor of both secular and religious education from the standpoint of the student’s topological foundations. As a result, it is proved that the actor of education has undergone heterogeneous transformations related both to educational process and more fundamental psychological, anthropological and symbolic aspects of the student.
Optimized Dynamic Load Balancing in Cloud Environment Using B+ Tree

In cloud computing, resource utilization is the most important criterion to resolve task scheduling. Day by day users is increasing with their request for services as the popular feature pay-per-use in the cloud. Cloud servers are in demand as tasks of the user requests are more. Cloud must dynamically schedule the requested task to the best resources based on the load. While the allocation of resources the task should consider various parameters like time, cost, power, reliability, and availability. Many tasks scheduling algorithms not concentrated on allocation, reallocation, and deletion or removal of servers with respect to the load which optimizes the power utilization, reduce the waiting period of the task, and reduce its cost as a need to the IT-organization. The proposed method uses the B + Tree algorithm, to allocate or release the servers dynamically as the task arrives. The algorithm takes the input as execution time, transmission time, round trip time of tasks, and virtual machine resource capacity. The result shows that cloud providers, provide recourses as per the IT-industry financial constraints, which optimize the utilization of recourse using this proposed method.
Towards Automatic Route Description Unification in Spoken Dialog Systems

In telephone-based dialog navigation systems, scheduling and direction information are typically collected from routing APIs in text, and then delivered to users via speech. These systematic directions may be augmented with human descriptions to provide more accurate and personalized routes and cover broader user needs. However, manually collecting, transcribing, correcting, and rewriting human descriptions is time-consuming. Also its inconsistency with systematic directions can be confusing to users when delivered orally. This paper describes the construction of a pipeline to automate the route description unification process which also renders the resulting direction delivery more concise and consistent.
Concordance line sorting in <i>The Prime Machine</i>

Abstract Corpus data provide evidence of the patterning of language, and one way word usage can be analysed is through the study of concordance lines. While popular concordancers provide different sorting methods, they are typically only able to display lines in the order in which they occur in the corpus, randomly, or alphabetically by words in slots to the left or right of the word of interest. Less sophisticated users may find recognising patterns from these orderings quite challenging. This paper considers possible needs of language learners in terms of concordance ranking and introduces two methods which have been adopted and developed for The Prime Machine . The first method uses repeated patterns, measuring the number of matches made with other lines in the set. The second method incorporates collocation scores, providing examples with strong collocations from the entire corpus at the top of sampled concordance lines.
Comparison between Collaborative Filtering and Neural Collaborative Filtering in Music Recommendation System

Music is one of the most popular entertainments, and the music industry continues to increase over time.There are many types of genres in music, and everyone has their own choice of the type of music they want to listen to.The recommendation system is an important function in the application, especially when there are a large number of choices for a particular item.With a good recommendation system, users will be able to get help from the suggestions given and can improve the user experience of the application.By using collaborative filtering (CF) methods to recommend products related to personal preference history, this feature can be better provided.However, the CF method still lacks in integrating complex user data.Hybrid technology may be a solution to perfect the CF method.The combination of neural network and CF also called NCF is better than using CF alone.The focus of this research is a CF method combined with neural networks or neural collaborative filtering.In this study, we use 20,000 users, 6,000 songs, and 470,000 records of ratings then predict the score using CF and NCF approach.We aim to compare the recommendation systems using CF and NCF.The study shows that NCF is better in gathering certain playlists according to one's preferences, but it takes more time to build compared to user-based collaborative filtering.
Underground Coal Mine Target Tracking via Multi-Feature Joint Sparse Representation

Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video.In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation.First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted.Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient.Last, the particle with the lowest reconstruction error is deemed the tracking result.To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms.The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.
WITHDRAWN: Super resolution using generative adversarial networks

Abstract Resolution has continually been a problem since the term ‘picture’ or ‘image’ came into existence. There have been many researches on improvising the resolution of an image however none could create very fast sufficient strategies to get the correct effects that can manage both time and production of best outputs. So, in this, we are going to discover about super resolution which has been a hassle for many years with the usage of signal processing strategies. But now we are able to clear up this trouble using deep learning strategies. The method we endorse here can flip a low-resolution photo/video to high resolution ones, up to a pleasant and exceptional level. We aim to obtain these using GANs (Generative Adversarial Networks) at considerable upscaling factors.
PENGEMBANGAN MEDIA PEMBELAJARAN TEKS CERITA ULANG BERMUATAN TOKOH LOKAL PADA PEMBELAJARAN BAHASA INDONESIA

Abstract : This study aims to: (1) describe the feasibility of developing learning media for recount texts containing local figures, and (2) describe the results of the development of learning media for recount texts containing local figures in the form of integrated media that is websites. This research method is Research and Development (RD (2) expert validation of instructional media design with an average of 91.25% on very good criteria, (3) individual trials with an average of 75% with good criteria , (4) small group trials with an average of 82.5% with very good criteria, and (5) limited field group trials with an average of 93% with very good criteria. The final activity of developing instructional media is continued by observing student learning outcomes in rewriting the recount text containing local figures. The learning outcomes of the recount text writing test before using learning media were 73.85, while using learning media increased by 86.05. Difference of 12.2 from before to after using media. This proves that the learning media for recount texts containing local figures that are developed can improve student learning outcomes. Abstrak: Penelitian ini bertujuan: (1) mendeskripsikan kelayakan pengembangan media pembelajaran teks cerita ulang bermuatan tokoh lokal, dan (2) mendeskripsikan hasil pengembangan media pembelajaran teks cerita ulang bermuatan tokoh lokal berupa media yang dipadukan dalam bentuk website. Metode penelitian ini adalah Research and Development (RD (2) validasi ahli desain media pembelajaran dengan rata-rata 91.25% pada kriteria sangat baik, (4) uji coba perorangan dengan rata-rata 75% dengan kriteria baik, dan (5) uji coba kelompok lapangan terbatas dengan rata-rata 93% dengan kriteria sangat baik. Kegiatan akhir dari pengembangan media pembelajaran ini dilanjutkan dengan melihat hasil belajar siswa dalam menulis kembali teks cerita ulang bermuatan tokoh lokal. Hasil belajar tes menulis teks cerita ulang sebelum menggunakan media pembelajaran sebesar 73.85, sedangkan menggunakan media pembelajaran meningkat sebesar 86.05. Selisih 12.2 dari sebelum ke sesudah menggunakan media. Hal ini membuktikan bahwa media pembelajaran teks cerita ulang bermuatan tokoh lokal yang dikembangkan dapat meningkatkan hasil belajar siswa.
Implementation of Analytical Hierarchy Process (AHP) and Simple Addative Weighting (SAW) methods in the process of determining teacher certification participants

Abstract Teacher certification is a process of improving the quality and competency testing of educators regulated by the government through ministry of education. the education office, which it provides teacher certificates that have been declared to meet professional standards. It would not happen if the assessment is carried out using an effective method in selecting teacher certification participants. The purpose of this research is to make a decision support system by applying 2 methods, namely Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods.
Detection of Human Body Movement Patterns Using IMU and Barometer

With the 20th century's drastic increase in the human population, population aging is becoming a major cause for concern. With current organizations and service staffs struggling to adequately care for an aging population, there is an increasing need for the use of technologies that can assist with this care. Human motion detection technology, which detects and reports on movements of the human body, can offer solutions to these issues. In general, motion detection technologies are divided into two categories: visual detection and sensor network detection. This paper proposes the combined use of an inertial measurement unit (IMU) and a barometer to conduct sensor network detection. The IMU device includes a three-axis accelerometer and a three-axis gyroscope used for body coordinates' accelerations and angular velocities. The barometer is used to measure pressure and temperature at various points. In this paper, the k-nearest neighbor (KNN) and the support vector machine (SVM) act as a complementary filter algorithm to detect four different human body movement patterns: the standing-up pattern, the falling-down pattern, the running pattern, and the walking pattern. The proposed algorithm was tested in a hardware and software platform, and the results have shown a classification accuracy of over 90% for these movement patterns.
Research on Enhancing Generalization Ability of Question Answering System by Retelling Technology

Retelling generation technology has important applications in many aspects, such as: in question answering system, it can be used to improve the system’s ability to understand questions and optimize the system performance; With the continuous development and maturity of various underlying technologies of natural language processing, it is possible to study retelling, which has attracted more and more attention. In this paper, a method of generating retelling questions based on template matching is proposed, which can effectively preserve the structural and semantic features of questions. Introducing functional tags to highlight the structural features of questions; The introduction of dependency relationship improves the generalization performance of question template; By introducing candidate sorting, the accuracy of the generated results is greatly improved. As the retelling technique mainly operates on monolingual language, it is possible to use large-scale monolingual language data to generate retelling, thus alleviating the problem of insufficient bilingual language data to a certain extent.
Validation of virtual hug effect under presentation of body contact pressure and HyperMirror image of a hug

We initially proposed a virtual hug as an imaginary hug experience of two users over distance with a visual presentation of their synthesized hugging image. Since the image was synthesized to provide a mirror image of a hug, we call the visual presentation system “a HyperMirror.” We continued development of a virtual hug system and recently fabricated a jacket that presents body contact pressure caused by a hug in synchronous to the hugging action shown in the HyperMirror. Four pneumatic artificial muscles (PAMs) were sewed on the jacket to emulate body contact caused by a hug. The present paper reports the results of two experimental studies to clarify how a presentation of virtual hug image affects the sensitivity of touch pressure recognition. We first quantified the PAM pressure with which a person who wears the jacket feels comfortable, for 10 subjects enrolled in the present study. We then proceeded to the second experiment to verify a hypothesis that presentation of a hug image in HyperMirror increases the sensitivity of touch pressure recognition of a person and accordingly makes just a noticeable pressure difference small. It was shown that the result justified our thought.
Autoencoder-bank based design for adaptive channel-blind robust transmission

Abstract The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to $$10^{-4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:math> , our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme.
Immediate Conditional Insertion Sort (ICIS)

Searching and sorting are basic operations of the computer used in many applications. In this paper, we have modified the traditional insertion sort algorithm to give better performance in certain types of applications. It accepts the incoming data sequentially and analyzes it immediately to determine whether it will be part of the final result or be neglected. To determine the location of the incoming new value in the results array, the ICIS algorithm uses an idea similar to the binary search algorithm, but the comparison begins by comparing the new incoming value to the maximum value in the results array, not to the middle value as in the binary search algorithm. Data is stored in the results array sorted. The ICIS algorithm is an in-place sorting algorithm and its time complexity in average and worst cases is O (n log m), where m is less than n several times (m<n). The proposed algorithm takes into consideration the nature of data distribution and the purpose of the application which can save processing time and space.
Comparative Study of Machine Learning Algorithms for Performant Text Analysis in a Real World System

This work illustrates how the text analysis is done on XYZ GmbH’s (company’s name was changed due to privacy) ticketing engine “ManageEngine ServiceDesk Plus” (MSP) database using modern technology offered by Azure ML. Here, we will use Azure Machine Learning Studio to implemented Text Analysis techniques and machine learning algorithms on the data set obtained from MSP. This research paper will guide us to the process of data extraction, data processing, creation of a word cloud and keyword extraction. We will compare modern machine Learning algorithm like neural network, averaged perceptron and boosted decision tree available in Azure ML to train and score the model and do a prediction on a ticket’s probability of being assigned to a correct department.