Reviewerly/exHarmony huggingface dataset:
Deep Learning for Model-Based Multiobject Tracking

Multiobject tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications, such as autonomous driving, tracking animal behavior, defense systems, and others. The MOT task can be divided into two settings, model based or model free, depending on whether accurate and tractable models of the environment are available. Model-based MOT has Bayes-optimal closed-form solutions, which can achieve state-of-the-art (SOTA) performance. However, these methods require approximations in challenging scenarios to remain tractable, which impairs their performance. Deep learning (DL) methods offer a promising alternative, but existing DL models are almost exclusively designed for a model-free setting and are not easily translated to the model-based setting. This article proposes a DL-based tracker specifically tailored to the model-based MOT setting and provides a thorough comparison to SOTA alternatives. We show that our DL-based tracker is able to match performance to the benchmarks in simple tracking tasks while outperforming the alternatives as the tasks become more challenging. These findings provide strong evidence of the applicability of DL also to the model-based setting, which we hope will foster further research in this direction.
Comparative Analysis of Parametric Optimization Techniques: Fuzzy DSS for Medical Diagnostics

This paper addresses the issues of the research and comparative analysis of the advanced bio-inspired techniques for parametric optimization of fuzzy systems (FS). Several modifications of particle swarm optimization (PSO) and grey wolf optimization (GWO) multi-agent algorithms, adapted for optimization of FS parameters, are compared with the conventional search methods. The studies and comparative analysis are conducted on a specific example, namely, at parametric optimization of the fuzzy decision support system (DSS) of Takagi-Sugeno type for the medical diagnostics. The obtained simulation results confirm the high efficiency of the presented bio-inspired optimization techniques, taking into account the achieved performance of the DSS and the spent computing costs.
Optical Cryptography Using Reversible Logic Gate

This technology deals with scale back to the facility utilization of the digital circuit. This style is the most promising system that condenses of warmth and power consumption. Otherwise, energy reduction in digital system is mostly probable to zero. Nowadays, cryptography depends on exploitation of digital system. Our paper presents a resolution for cryptography by exploitation of optical digital system. In our proposition, it is indicated that the fundamental diagram of cryptography has four input terminals, and it is made of Fredkin and Toffoli gate. It is worked on primarily on MZI based reversible digital circuit.
Learning and Self-education of Complex Types of Objects

This paper presents information that, on the one hand, provides additional experimental, algorithmic and functional information, but on the other hand, reveals the essence of the problems of the upcoming research studies. In parallel, a mechanism is presented that allows the system to move to a new level of learning—self-learning. The developed self-learning method is a delayed learning mechanism, when the system learns to recognize new objects without a special command and when information characterizing a new object can enter the system as needed (depends on the system operator).
Deep Learning Based Real-Time Object Detection on Jetson Nano Embedded GPU

Object classification, detection, and tracking have been regarded as the most difficult problem domains for the last two decades. This area of research is important in a variety of sectors, including anomaly detection in medical imaging, driving in autonomous vehicles, biometrics, anomaly detection, and so on. The model accuracy has become very important parameter in computer vision. The rise of edge computing has opened the door to a plethora of exciting and complex applications in computer vision. An 'edge AI'-based implementation for object detection persuading from deep convolutional networks SSD Mobilenet, SSD Inception V3 using embedded GPU platform Jetson Nano is proposed. This research demonstrates that the NVIDIA Jetson is a low-power embedded computing device suited to accelerate deep learning applications. When compared to the other two models, the experimental findings reveal that the SSD Inception V3 model delivers the maximum accuracy.
The Problem of Parsing and Recognizing Individual Objects of Information on Base of Fuzzy Logic

This paper presents a method for solving the algorithm of the model of recognition of visual and audio information of the environment by constructing an algorithmic model that simulates the human abilities of the motor (unconscious or non-intellectual) behavior. The paper is devoted to the development of a machine intelligence system with a gradual increase in the complexity of the behavioral model of an artificial personality (AP) to experimentally study the issues of artificial intelligence.
Complementing Biometric Authentication System with Cognitive Skills

The current era is totally digitalized making all of us lead a digital life. There can be merely no imagination of how future would be without this ever blooming technology. As a fair side of technological advents, every common man has access to mobile phones, gadgets and has access to the Internet. On the dark side of technological boom lies the Dark web, privacy invasion, online frauds, piracy, hacking, online gambling etc. Protecting ourselves form the odd effects lies primarily in user authentication phase. The robust the Authentication phase is, the more cyber safe you are. Most of the current automated authentication schemes rely on biometrics. Biometrics has given a major breakthrough in robust user authentication. Essence of security and privacy lies in choosing a perfect combination of biometrics called as multi biometrics. Combining biometrics with cognitive skills makes the scheme more interactive and attack resilient.
Information Services Using Problem Based Learning to Prevent Online Game Addiction

This study aims to (1) analyze the level of understanding of the dangers of online game addiction of the experimental group students before (pretest) and after (posttest) given information services with a problem based learning model, (2) analyze the level of understanding of the dangers of online game addiction of control group students before ( pretest) and after (posttest) given information services, (3) testing the effectiveness of information services using a problem based learning model to prevent online game addiction. This study uses a quasi-experimental quantitative method of non-equivalent control group design. The research sample was class XI SMAN 13 Padang as many as 15 students in the experimental group and 15 students in the control group. The sample used was purposive sampling. Statistical analysis used is descriptive analysis and non-parametric statistics. The results showed that (1) there was an increase in the understanding of the dangers of online game addiction by the experimental group students before and after being given information services using a problem based learning model, (2) there was an increase in the understanding of the dangers of online game addiction by the control group students before and after being provided with information services, ( 3) there is a significant difference in the level of understanding of the dangers of online game addiction by the experimental group students who were provided with information services using a problem based learning model with the control group which was only provided with information services. Thus, information services using a problembased learning model are more effective in preventing students' online game addiction.
Confidentiality-preserving, blockchain-based, and data sharing: A survey

Data sharing has gained tremendous attention in the past few years. Information being the driving power of all strategic decision-making changes as organizations aim to improve their efficiency by sharing insights within departments and collaborating with partners. However, protecting the confidentiality of sensitive information is still one of the biggest challenges when sharing these valuable assets between differ partakers. Blockchain has been one of the technologies that are being explored to solve this problem. Blockchain technology had been renowned as a means of secure asset tracking, provide immutable transaction sharing and had been proven to limit the amount of trust collaborating parties needed to exchange sensitive data. In this paper, we hover the up-to-date, relevant techniques and propositions with regards to confidential data sharing using blockchain related approaches. We will provide a comprehensive comparison between different techniques based on the widely used frameworks and technical schemes summoned and cite the challenges blockchain based applications face in the realm of confidentiality preserving data sharing.
Face Recognition using Deep Learning

Identifying a person primarily relies on their facial features, which even distinguish identical twins. As a result, facial recognition and identification become crucial for distinguishing individuals. Biometric authentication technology, specifically facial recognition systems, are utilized to verify one’s identity. This technology has gained popularity in modern applications, such as phone unlock systems, criminal identification systems, and home security systems. Due to its reliance on a facial image rather than external factors like a card or key, this method is considered more secure. The process of recognizing a person involves two primary steps: face detection and face identification. This article delves into the concept of developing a face recognition system utilizing Python’s OpenCV library through deep learning. Due to its exceptional accuracy, deep learning is an ideal method for facial recognition. The proposed approach involves utilizing the Haar cascade techniques for face detection, followed by the following steps for face identification. To begin with, facial features are extracted through a combination of CNN methods and the linear binary pattern histogram (LBPH) algorithm. For attendance to be marked as “present,” the check-in and check-out times of the detected face must be legitimate. If not, the face will be displayed as “unknown.”
Authentication of Satellite-Based Augmentation Systems with Over-the-Air Rekeying Schemes

<h3>Abstract</h3> Here we delineate a complete satellite-based augmentation system (SBAS) authentication scheme, including over-the-air rekeying (OTAR), that uses the elliptic curve digital signature algorithm (ECDSA) and timed efficient stream loss-tolerant authentication (TESLA) without the quadrature (Q) channel. This scheme appends two new message types to the SBAS scheduler without over-burdening the message schedule. We have taken special care to ensure that our scheme (1) meets the appropriate security requirements needed to prevent and deter spoofing; (2) is compatible with existing cryptographic standards; (3) is flexible, expandable, and future-proof to different cryptographic and implementation schemes; and (4) is backward compatible with legacy receivers. The scheme accommodates a diverse set of features, including authenticating core-constellation ephemerides. We discuss the SBAS provider and receiver machine state and its startup, including its use by aircraft that traverse differing SBAS coverage areas. We tested our scheme with existing SBAS simulation and analysis tools and found that it had negligible effects on current SBAS availability and continuity requirements.
Kajian Kenyamanan Lingkungan Ruang Komputer Terhadap Pemenuhan Syarat Suhu Udara, Pencahayaan, Dan Akustik Dengan Metode Deskriptif Kualitatif

The Comfort against the influence of air temperature, humidity, indoor lighting, circulation and acoustics is part of the effort to improve the quality of the function of the room. If the room is comfortable, the activities that take place in it are optimal. This research is a qualitative descriptive method. The results in this study were obtained that the average temperature and humidity in South Tangerang City exceeded the comfort threshold of 22.8-25.8 oC, the emission value per square meter was 349.9 Lux, the noise level of the research object from the average measurement results. is 57 dB. It was concluded that the Lux number is very close to ideal, so it is stated that the room lighting in the research object meets the requirements, while the noise level requires a noise damper or barrier to be able to reduce noise so that the room is comfortable to use.Comfort against the influence of air temperature, humidity, indoor lighting, circulation and acoustics is part of the effort to improve the quality of the function of the room. If the room is comfortable, the activities that take place in it are optimal. This research is a qualitative descriptive method. The results in this study were obtained that the average temperature and humidity in South Tangerang City exceeded the comfort threshold of 22.8-25.8 oC, the emission value per square meter was 349.9 Lux, the noise level of the research object from the average measurement results. is 57 dB. It was concluded that the Lux number is very close to ideal, so it is stated that the room lighting in the research object meets the requirements, while the noise level requires a noise damper or barrier to be able to reduce noise so that the room is comfortable to use.
About new stage genres in the music of the second half XX century

New musical and theatrical genres that demonstrate post-non-classical musical thinking are considered. The most common are happening, performance and instrumental theater. Genres are considered differently by V. Petrov, M. Pereverzeva, Yu. Gnirenko and M. Katunyan, but, however, without disclosing their ontology and logical basis. The main distinguishing features of the new stage forms are the openness of the structure, the creative function of the performers and temporal unlimitedness, which indicates the rhizome and rhizome thinking of the composers. It is proposed to consider the new musical and theatrical realities as non-linear, in particular dissipative, based on a non-linear type of determinism – self-determinism. On its basis, a rational construct is established in the form of self-organization (performer), or performing will, which provide the musical material with the quality of integrity. Alternative musical theater is another form of new musical stage genres that have emerged in the post-non-classical era, or in the post-modern era. It is presented by alternative opera and alternative ballet, which establish the aesthetics of consonance, antimimesis and display (instead of experience), as well as the multivariant implementation of the musical and theatrical concept (concert, theater stage, etc.). The works of the American composer D. Cage, the Russian composer V. Martynov, the Belarusian composers V. Kuznetsov, L. Simakovich, A. Korotkina were selected as analytical material.
Mathematical Mastery in the Digital Age: Insights from Student Perspectives and Course Performance

This survey research aimed to identify the factors that influence student learning outcomes in the online Fundamental Mathematics I course at Del Institute of Technology during the odd semester of the 2022/2023 academic year.The study used a quantitative approach and a questionnaire as a data collection instrument.The participants were active students who took the course, and the data analysis used descriptive statistics and multinomial logistic regression.The findings revealed that internal factors, including interest and motivation, health, learning method, intelligence, and talent, significantly influenced student learning outcomes.Additionally, external factors such as campus and surroundings matter were also significant predictors of student achievement.The results of this study have important implications for educators and policymakers in designing effective online learning strategies that consider both internal and external factors to enhance student learning outcomes.
Feature Disentangling Autoencoder for Anomaly Detection of Reactor Core Temperature with Feature Increment Strategy

Anomaly detection for core temperature has great significance in maintaining the safety of nuclear power plants. However, traditional auto-encoder-based anomaly detection methods might extract the latent space features with redundancy, which may lead to missing and false alarms. To address this problem, the idea of feature disentangling is introduced under the auto-encoder framework in this paper. First, a feature disentangling auto-encoder (DAE) is proposed where a latent space disentangling loss is designed to disentangle the features. We further propose an incrementally feature disentangling auto-encoder (IDAE), which is the improved version of DAE. In the IDAE model, an incremental feature generation strategy is developed, which enables the model to evaluate the disentangling degree to adaptively determine the feature dimension. Furthermore, an iterative training framework is designed, which focuses on the parameter training of the newly incremented feature, overcoming the difficulty of model training. Finally, we illustrate the effectiveness and superiority of the proposed method on a real nuclear reactor core temperature dataset. IDAE achieves average false alarm rates of 4.745% and 6.315%, respectively, using two monitoring statistics, and achieves average missing alarm rates of 6.4% and 2.9%, respectively, using two monitoring statistics, outperforming the other methods.
How digital marketing and innovative performance contribute to hotel restaurant revenue growth: The mediating role of knowledge sharing

Innovative performance has been identified as a critical dynamic in various sectors, while little research has focused on the mediating role of knowledge sharing. The current study measures the role of knowledge sharing between digital marketing and innovative performance induced ultimately revenue growth in the hotel restaurants industry. A quantitative method was used with 134 questionnaires distributed to operational and marketing managers in Jordanian hotels. The Smart PLS was used, and the results illustrated a strong relationship between digital marketing and innovative performance, with mediating role of knowledge sharing on revenue growth. The study expressed the positive impact of employing digital marketing on the issue of knowledge sharing where the effect of the latter on innovative performance is optimistically positive. This further suggests eliminating any failure in the marketing strategies to avoid the effects on the organization’s profits; however, staying up to date with emerging digital revisions is crucial for the effectiveness of marketing activities.
THE MEDIA COMPETENCE OF A FUTURE SPECIALIST IS THE BASIS OF HIS COMPETITIVENESS IN THE LABOR MARKET

The article deals with the problem of training a media-competent specialist in the economic sphere. This problem can be solved in combination with media education which is currently attracting a lot of attention
Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3

Pins are essential connecting components in power transmission lines. Their extensive use yet leads to frequent defects. Given the small size of a pin and many similar components, the detection of such defects is not ideal, which is a technological problem in the identification and diagnosis of power defects. In response to the large size, complex background, and on-site requirements, such as real-time detection, of power transmission lines, this paper proposes a method to detect pin defects based on TPH-MobileNetv3 (Transformer prediction Head Mobilenetv3). This paper modifies and adds a self-attention layer to MobilNetV3-Small to improve the feature extraction capability of small targets after downsampling. A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. Compared with the traditional MobileNetV3, the detection accuracy of the algorithm in this paper has been raised by 24%, as shown in the detection results of measured data. Moreover, compared with the mainstream algorithms with the same detection accuracy, this algorithm not only reduces the model size and significantly enhances detection efficiency but also satisfies the requirement of edge image processing of power inspection.
FAIR data sharing: An international perspective on why medical researchers are lagging behind

FAIR data, that is, Findable, Accessible, Interoperable, and Reusable data, and Big Data intersect across issues related to data storage, access, and processing. The solution-oriented FAIR principles serve an integral role in improving Big Data; yet to date, the implementation of FAIR in multiple sectors has been fragmented. We conducted an exploratory analysis to identify incentives and barriers in creating FAIR data in the medical sector using digital concept mapping, a systematic mixed methods approach. Thirty-eight principal investigators (PIs) were recruited from North America, Europe, and Oceania. Our analysis revealed five clusters rated according to perceived relevance: ‘Efficiency and collaboration’ (rating 7.23), ‘Privacy and security’ (rating 7.18), ‘Data management standards’ (rating 7.16), ‘Organization of services’ (rating 6.98), and ‘Ownership’ (rating 6.28). All five clusters scored relatively high and within a narrow range (i.e., 6.28–7.69), implying that each cluster likely influences researchers’ decision-making processes. PIs harbor a positive view of FAIR data sharing, as exemplified by participants highly prioritizing ‘Efficiency and collaboration’. However, the other four clusters received only modestly lower ratings and largely contained barriers to FAIR data sharing. When viewed collectively, the benefits of efficiency and collaboration may not be sufficient in propelling FAIR data sharing. Arguably, until more of these reported barriers are addressed, widespread support of FAIR data will not translate into widespread practice. This research lays the preliminary foundation for conducting targeted large-scale research into FAIR data practices in the medical research community.