Reviewerly/exHarmony huggingface dataset:
PIPArch: Programmable Image Processing Architecture Using Sliding Array

Image processing arises as a promising domain for manifold applications requiring for heavy computing power and memory bandwidth with higher image resolution. Graphics processing unit (GPU) is widely used in image processing algorithms but suffers from its powerful programmability that costs high hardware overhead. Moreover, GPU consumes much energy to access data from high-capacity register files, making it hard to implement on wearable devices. Enabling low power and efficient architecture with low hardware overhead remains challenging.In this paper, we propose a programmable image processing architecture (PIPArch) that explores the spatial locality in images to save energy while achieving high performance. We also design the instruction set architecture (ISA) to control the PIPArch. By supporting multiple parallel pipelines, we can keep the hardware utilization of PIPArch high. We evaluate the proposed PIPArch by developing the cycle-accurate simulator with some typical image processing algorithms. Compared to NVIDIA Tesla V100 GPU, PIPArch gains 23.63x speedup.
Multi-Agent System for Recommending Learning Objects in E-Learning Environments

This full paper of innovate-to-practice category presents a Multiagent System for recommending learning objects in Virtual Learning Environments (VLE), aiming to improve the customization of instructional guidance on educational content according to the student's profile. The methodology was initially research aimed at identifying the motivators of the students' performance and their weaknesses, adopting a personalized student model based on the level of knowledge, and providing predictive models to monitor the student's progress in the curriculum. This framework provides a distributed architecture, and consists of three layers: 1) Administrative layer; 2) Storage layer; 3) Pedagogical layer. For the recommendation of learning objects, a collaborative filter was used, which constitutes a successful technique in several recommendation applications, seeking similarities in users' habits to predict their future decisions.
The Specifics of Promoting Additional Educational Services in the Era of Digitalization

The research is aimed at identifying approaches to the promotion of educational resources of additional education. The article analyzes the specifics of approaches to the positioning of courses of additional vocational education of various centers of vocational education and advanced training in Moscow. The aim of the study is to identify modern technologies for promoting services in the conditions of increasing demand for online technologies due to the limitations caused by coronavirus infection.
A Research Framework of Service Efficiency of Chinese Government New Media during the Post-epidemic Period

As main body with the functions of information dissemination and multi-connection, the new media platform includes mainstream media, government accounts to social media. The setting of news topics in mainstream media and government new media has strengthened positive emotions. The rich emotional opinions of new media users and media content have been integrated into a new situational system, which has deepened the dissemination and influence of new media content. During the post-epidemic period, Chinese government new media should continue to think about how to further improve the service efficiency of new media. In this regard, a unified research framework has not yet been formed. Therefore, this paper fills this gap by proposing a research framework that integrates multi sources of media platforms (i.e., government new media) during the post-epidemic period. Based on the previous literature review and data collection, this research proposes that the subject content and user emotions of new media are two key factors affecting the efficiency of government new media services as well as the core tasks for the new media operation; then select some typical government new media accounts for case analysis by using the content analysis method and sentiment analysis method. Finally, the research focuses on putting forward practical measures to improve the operation of government new media, such as how to expand user participation through topic content editing and user comment observation, and how to encourage them to participate in the dissemination, reporting, verification and rumor refuting of major social events in the post-epidemic period.
Formation of Compositional Thinking Skills among Bachelors of Artistic-Pedagogical Specialities: Theoretical and Methodological Aspects

The paper justifies the reasonability of introducing the discipline “Introduction to Composition” into the educational process of first-year bachelors of the Institute of Culture and Arts of Karachay-Circassian State University named after U. D. Aliev. The article examines psychological and pedagogical conditions, forms and methods of compositional thinking development, analyses the content of a special course “Introduction to Composition”, presents pedagogical experiment results. The author justifies the necessity of introducing “Introduction to Composition” discipline into educational process, identifies methodical techniques promoting the formation of future drawing teachers’ compositional thinking, and herein lies scientific originality of the research. The findings allow concluding that studying the discipline “Introduction to Composition” helps to improve bachelors’ compositional thinking, enhances the acquisition of composition principles and expressive means, promotes composition skills development.
Selective Encryption of the Audio Extracted from the Video Streamed Over the Content Delivery Network

In today's scenario, large portion of the internet traffic over Content Delivery Network (CDN) consists of the multimedia data. The security of the video and audio becomes critical during its transmission over the CDN. The Video on Demand (VOD) and live video streaming applications require their video to be protected from unauthorized access by users in the network. To protect the videos transmitted over the network, encryption of the video data becomes inevitable. Full encryption of the video data takes considerable amount of time. Considerable research has been carried out in selectively encrypting the video in the frequency domain and the spatial domain. In this paper, a method has been proposed to selectively encrypt the audio extracted from the video data that is transmitted over CDN. Comparative analysis between the time taken for full encryption and decryption of the videos of different sizes and the proposed method is discussed.
A study on the application of knowledge distillation in ship type classification model development

In order to identify an object of interest in a given image with high accuracy, it is essential to select cumbersome deep learning models. However, these models are computationally expansive and cannot be deployed when limited environments. We experiment on a ship type classification model that supports fast inference while inheriting the performance of the teacher network through knowledge distillation and share the results. As can be seen from the experimental results, through knowledge distillation, we got a clue that we can learn a deep learning network that can provide real services. Through additional data learning and hyperparameter tuning, it can be deployed in the actual ship type classification system and utilized in various applications.
FBLE: A Feature-based Label Enhancement Method for Corporate Bond Credit Rating Prediction

For Corporate Bond Credit Rating (CPCR) prediction, instances in training datasets are assigned by one or more logical labels, indicating whether an instance belongs to a specific class. However, Label Distribution (LD), which reveals credit rating correlation information between labels, is usually ignored due to the difficulty of obtaining the label distributions directly. We present an easy-to-use method named FBLE to generate the label distributions from the logical labels in the training set via leveraging the relevance between labels and instances, being capable of improving the generalization of models without adding any extra parameters. Such a process of generating label distributions from logical labels is called Label Enhancement (LE), which reinforces the supervision information in the training sets. We constructed a CPCR dataset to show that models with FBLE could achieve state-of-the-art or competitive results. Electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet.
Developing the Social, Political, Economic, and Criminological Awareness of Cybersecurity Experts

The global shortage in skilled labor for cybersecurity and the risk it presents to international business can only be solved by a significant increase in the number of skilled personnel. However, as the nature of risks proliferate and bifurcate the training of such, personnel must incorporate a broader understanding of contemporary and future risks. That is, while technical training is highly important, it is contended that future cybersecurity experts need to be aware of social, political, economic, and criminological issues. Towards this end, this chapter considers a number of exemplary issues that are considered worthy of inclusion in the development of future cybersecurity workers. Accordingly, an overview is given of the issues of the “dark side of the net” that cause problems for global cybersecurity and international business risk. The issues are discussed so that from these a skill set can be articulated which will attend to (and mitigate against) potential threats.
Visual Odometry of a UAV with Episodic Correction of Coordinates

We consider the problem of visual odometry from a sequence of video frames, which are formed using a camera directed perpendicularly downward. We propose an adaptive visual odometry technology based on sequential determination of interframe shifts and episodic correction of current coordinate estimates. The frame shifts between two consecutive frames are determined by the correlation method with pixel precision. Then, those shifts are refined with subpixel precision using optical flow method. To improve reliability, the selection of the most consistent optical flow estimates is carried out. We present the results of experiments using publicly available test data.
A Survey Research on Feedback Practices in Agile Software Development Teams

The need for greater flexibility, autonomy and dynamism in software development has increased the adoption of agile methodologies. In order to support the recommended practices of these methodologies, maintaining good communication between team members and promoting a positive work environment that allows the individual growth of employees through autonomous learning and decision making have become key success factors. In this context, feedback is an important practice to provide each employee with an analysis of their performance, both personal and technical. The objective of this paper is to investigate the impact of feedback on motivational factors from the perspective of developers, testers and managers of agile development teams. We conducted a survey with 25 collaborators to assess motivational factors and possible points for improvement in the feedback process adopted by the teams. Among the obtained results, job satisfaction and productivity were positively influenced by the practice of feedback. In contrast, the periodicity of feedback and the absence of an evaluation among members were considered improvement points. Based on the survey results, we provided a set of recommendations to improve the quality of the feedback process in agile teams.
Certifications in Cybersecurity Workforce Development

The workforce demand for cybersecurity professionals has been substantial and fast growing. Qualified cybersecurity professionals with appropriate knowledge, skills, and abilities for various tasks and job roles are needed to perform the challenging work of defending the cyber space. The certified information systems security professional (CISSP) certification is a globally recognized premier cybersecurity credential and validation of qualifications. This case study analyzes the CISSP certification requirements, domains and objectives and attempts to map them to the cybersecurity industry competencies and the US national cybersecurity workforce framework (NCWF). This research is an extended study with full mapping of all CISSP domain areas to the knowledge, skills, and abilities in NCWF. The extended study aims to discover the in-depth value and role of reputable certifications such as CISSP in competency development for cybersecurity workforce. This article also discusses the value and implications of the CISSP certification on cybersecurity education and training.
Multi-step preferred elite-guided firefly algorithm

Firefly algorithm has large position oscillation and high computational complexity. To address these problems, a multi-step preferred elite-guided firefly algorithm (MPEFA) is proposed. Firstly, the algorithm uses multi-step learning to select historical elite individuals. The elites will guide the movement of fireflies. Secondly, if the elite stagnates, the perturbation factor is used to optimize it to promote algorithm convergence. Finally, if a common firefly stagnates, the even-order Chebyshev mapping and opposition-based learning will be used to reinitialize the individual position to improve the ability of algorithm to jump out of the local optimum. Theoretical analysis proves the global convergence of the MPEFA. The simulation is carried out on the CEC2020 standard test function. The experimental results show that MPEFA is superior to other comparison algorithms in the unimodal, mixed and composite classes of numerical functions with a large amount of calculation, and has higher accuracy.
Improvement of Elderly Speech Recognition Using Gammatone Filterbank Adaptation

Recently, the accuracy of speech recognition has been remarkably improved by the introduction of deep learning. However, the accuracy of speech recognition for old-old/oldest-old people is still insufficient and a spoken dialogue system for them has not been put into practical use. In this study, GtFDNN-HMM, which uses a gammatone filterbank for the feature extraction layer in the DNN, is used as an acoustic model, and speaker/elderly adaptation is performed to improve the accuracy of speech recognition for the old-old/oldest-old people. Only the parameters of the gammatone filterbank are applied for adaptation of GtFDNN, so speaker/elderly adaptation with a small amount of speech is possible, and it is suitable for speech recognition for old-old/oldest-old people, where it is difficult to collect a large amount of speech. From the result of experiments, the speech recognition performance was improved by using the elderly speech adaptation model.
Formalizing Affinization of a Projective Plane in Agda

We present a computer formalization of the problem known as affine reduction of a projective plane. The affine reduction (aka affinization) consists in the construction of an affine plane by removing a line of a projective plane. We work with a representation of von Plato axiom system of constructive geometry which allows the definition of affine and projective geometry as variants of a common structure called apartness geometry. The formalization is written in Agda, a functional programming language and proof-assistant based on the proposition-as-types paradigm. All mathematical definitions, propositions and proofs are constructed following the valid methods of constructive mathematics, and they are directly expressed in the language Agda. In addition to the description of a new formalization of an interesting mathematical problem, the paper can also contribute to introduce ideas about formalization of mathematics in type theory.
Security of Medical Images Using DWT and SVD Watermarking Technique

In today’s scenario, medical image security is a broad area of research using digital image processing. Medical image data has the main importance in e-health care systems these days. Also, the use of cloud computing in the health care sector needs medical image security to maintain the medical image safety, security, integrity, confidentiality, etc. So, for this purpose, encryption and techniques of watermarking have been utilized to secure the medical images. This paper gives a DWT- and SVD-based method to secure the medical images and provides a good PSNR value as illustrated in the result.
The Comparison of Nearest Neighbor Algorithm as Modeling in Conclusion of Interpretation of Bil Ma'tsur and Bil Ra'yi

Tafsir is one of many media utilized to convey the message of the Quran to readers. There are two types of tafsir, namely bil ma'tsur and bil ra'yi, which are grouped based on its respective sources known as shahih and dhaif. The main issue in practicality is differentiating between the two groups is not always easy. The technique most often used in machine learning for classification is the K-Nearest Neighbor (K-NN). On the other hand, K-NN has 3 variations of the algorithm, namely Fuzzy K-Nearest Neighbor (FK-NN), Modified K-Nearest Neighbor (MK-NN), and Improvement K-Nearest Neighbor (IK-NN). This study compares the four algorithms by modeling the best algorithm based on algorithm performance and algorithm complexity. From this research, it was found that MK-NN is a reliable algorithm with an accuracy of 98.07% and a processing time of 2.97 minutes in the deduction of bil ra&#x2019;yi and bil ma&#x2019;tsur with 486 documents in Surah Al-Baqoroh and Surah Ali Imron as samples. Furthermore, the best modeling of MK-NN is implemented into a programming language for the application of determining the type of interpretation, with the results of the User Acceptance Test (UAT) and user ease of approaching 99%.
Tennis Ball Collection Robot Based on MobileNet-SSD

With the development of artificial intelligence, the utilization of robots based on AI is widespread in our daily life, especially in the area of sports. In the aspect of tennis, collecting tennis balls on the ground after a fierce match or training would be tiresome work, so an automatic tennis ball picking robot becomes useful. Three main aspects should be considered in the research of the tennis ball collection robot: the recognition and localization of tennis balls, path planning for collecting every tennis ball, and the global positioning and navigation of the robot. Firstly, computer vision based on deep learning algorithms has excellent reliability, and the MobileNet-SSD model can be quantized and deployed on Raspberry Pi. Therefore, we choose the MobileNet-SSD model with a monocular camera catching pictures to recognize tennis balls. Secondly, perspective transformation is used to get the precise location of the target tennis ball. We propose a regional traversal algorithm to plan the path to collect as many tennis balls as possible. Thirdly, we utilize ultra-wide-band (UWB) supplemented by triangle centroid methods to locate the robot in a global position. After proper training, the tennis ball collection robot performs well and has excellent potential.
An Improved Local Binary Pattern Algorithm for Face Recognition Applications

Security and authentication have become an essential part of the society. Face biometrics are one of the biometric security systems by researchers over the past years due to their ease of acquisition. Human face recognition is usually executed in two stages, Face detection and Face recognition. The main obj ective of this technique is either to ensure that the rendered services are accessed only by a legitimate user or to create a safety warning system in a given environment. In this paper, a novel face recognition system is designed using Haar-Cascade algorithm and efficient Improved Local Binary Patterns Histograms (ILBPH) technique to identify a human face with an improved recognition rate. The results of the proposed algorithm show that it performs well in terms of processing time and training period. It also outperforms other existing algorithms with a recognition rate of about 96.4%. Therefore, the proposed method performs better in respect to temporal qualitative analysis.
Efficient Video Summarization Based on Semantic Segmentation Model

In recent years, the increasing demand for video summarization has led to a lot of research on video summarization, but there is still room for improvement in its accuracy. This paper proposes a video summarization that uses semantic segmentation model to improve its accuracy. This method employs semantic segmentation models such as U-Net and SegNet to estimate frame importance based on the similarity between video summarization and semantic segmentation. We train on the video summarization datasets, TvSum and SumMe, and compare the proposed method with the conventional methods, FCSN and vsLSTM. We compare the F-values as quantitative evaluation and the output videos as qualitative evaluation. The F-score shows that the proposed model is superior to existing methods by about 10%. Also, by comparing the generated moving images, the proposed model produces more qualitative output.
Proses digitalisasi naskah melalui media flipbook digital di Museum Bandar Cimanuk

ndramayu Regency has many ancient manuscripts that contain valuable information. Paper-based manuscripts are very easily damaged. Therefore, it is necessary to preserve ancient manuscripts. Switching to a digital flipbook is considered appropriate because it provides an interface that is similar to the original script. This study aims to describe the importance of preserving the cultural heritage of ancient manuscripts owned by the Bandar Cimanuk Indramayu Museum. Digitization can be an effort to preserve ancient manuscripts. The research used descriptive qualitative method. Data collection through observation, interviews, and documentation study. It is hoped that the research results in the form of flipbook development can be used as an effort to preserve ancient manuscripts that represent, protect, and exceed the physicality of ancient manuscripts. The conclusion of this study is that the ancient Legok Kolot manuscript is the identity and wealth of the Indramayu people, so it needs to be digitized so that the preservation of the script is maintained. The media transfer process at the Bandar Cimanuk Museum goes through three main stages, namely the pre-digitization process, the digitization process, and the post-digitization process. In its process, there are three important things that must be considered, such as source characteristics, product usability, and technological characteristics. The use of digital flipbooks as an effort to preserve ancient manuscripts is considered to protect, represent, and exceed the original ancient manuscripts