Reviewerly/exHarmony huggingface dataset:
FFAVOD: Feature Fusion Architecture for Video Object Detection

A significant amount of redundancy exists between consecutive frames of a video. Object detectors typically produce detections for one image at a time, without any capabilities for taking advantage of this redundancy. Meanwhile, many applications for object detection work with videos, including intelligent transportation systems, advanced driver assistance systems and video surveillance. Our work aims at taking advantage of the similarity between video frames to produce better detections. We propose FFAVOD, standing for feature fusion architecture for video object detection. We first introduce a novel video object detection architecture that allows a network to share feature maps between nearby frames. Second, we propose a feature fusion module that learns to merge feature maps to enhance them. We show that using the proposed architecture and the fusion module can improve the performance of three base object detectors on two object detection benchmarks containing sequences of moving road users. Additionally, to further increase performance, we propose an improvement to the SpotNet attention module. Using our architecture on the improved SpotNet detector, we obtain the state-of-the-art performance on the UA-DETRAC public benchmark as well as on the UAVDT dataset. Code is available at https://github.com/hu64/FFAVOD.
Effective Attention Modeling for Neural Relation Extraction

Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities can be located far from each other in a sentence. The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via co-reference. Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence. Also, the words in a sentence do not contribute equally in identifying the relation between the two entities. To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.
A Virtual Laboratory Ecosystem in Medical Education: Effectiveness of Simulations Made by Instructor

Theory: The technological revolution has profoundly changed our lives. Learning has also been affected by the significant advances in technology, leading human society to a place where distance learning would become inevitable. One of the many tools facilitating distance learning is virtual laboratories that use simulations for educational purposes and turn theoretical knowledge into practical skills. Virtual laboratories can break down the barriers of time and place for skill acquisition among learners. They offer access to laboratories or clinical equipment at a lower cost.&#x0D; Hypotheses: This study aimed to set up a digital ecosystem (VirtuLab) where instructors can create customized interactive simulators for medical students. We hypothesized that VirtuLab can motivate students and help them improve technical skills through steps.&#x0D; Methods: To compare the traditional teaching method (physical testing and working with real-life devices) and the virtual method (using a simulator), we initially created a graphic, virtual environment with the help of computer software and programming languages. We defined a simulated device that can help medical students (interns and externs) learn four different defibrillator applications (AED MODE, CARDIOVERSION, DC SHOCK, and PACEMAKER) virtually. After completing the virtual experiment, participants were invited to provide feedback for better performance. Then, using a questionnaire, users' opinions and scores were collected, and statistical comparisons were performed to determine the effectiveness of the virtual laboratory.&#x0D; Results: Our findings showed that the time spent on each step, the total number of clicks, and the number of mistakes (failed clicks) had decreased significantly from the initial steps to the last one. The feedback obtained from the learners showed that VirtuLab has made the educational content understandable and tangible for 93% of them. Also, 87% of participants expressed their satisfaction with the simulator and found it motivating.&#x0D; Conclusion: The times recorded by users during the steps indicated that with the help of a virtual simulator, learners' time and mistakes were significantly reduced through steps. With VirtuLab, it was possible to repeatedly learn and test different options without additional cost and improve practical skills in a series of steps. Besides, it was an appealing environment to up motivation, confidence, and effective learning in users.
Impact of Credit Rating on the Bond Market Based on TIC Model

Although China’s bond market is expanding quickly and on a bigger scale than the stock market, it still faces a number of issues. One issue that restricts its long-term development is the relatively small scale of credit bonds. For the growth of credit bonds, it is necessary to boost the credit rating market first. In this study, it is assumed that the higher the credit rating of the bonds, the lower the issue cost will be. To prove this hypothesis, an empirical analysis has been made herein on the influence of credit ratings on the bond floatation market.
The Effectiveness of DRTA-SQ3R Integration in Developing Preparatory School pupils'''' EFL Reading Comprehension Skills

The current study investigated the effectiveness of DRTA-SQ3R integration in developing preparatory school pupils'' EFL reading comprehension skills. The participants of the study comprised 60 pupils randomized from second-year preparatory pupils at Al Masaei preparatory school for girls in shibin El kom, Menoufia governorate during the second semester of the 2019 – 2020 scholastic year. They were divided into two groups; experimental (30) and control (30). DRTA-SQ3R integration was used in teaching the experimental group while the control group received regular instruction. A pre-posttest reading skills test was prepared by the researcher and administered before and after the treatment. The findings revealed that DRTA-SQ3R integration had a positive effect on developing EFL prep stage pupils’ reading comprehension skills. Hence, the results seemed to provide adequate evidence to support the hypotheses. It is recommended that teachers and curriculum designers pay attention to the importance of DRTA-SQ3R integration in EFL teaching and learning.
Distributed Anonymous Authentication with Accountability in Blockchain Systems

The conventional authentication mechanisms in blockchain systems rely on trusted service provider and creden-tial authority, which are undesirable for the distributed systems and facing risk of single point of failure. Moreover, a consortium blockchain system may suffer from both inside attacks and linking attacks caused by the authorized nodes, leading to the privacy leakage and high security risk. To circumvent these impediments, a distributed anonymous authentication scheme with accountability is proposed in this paper. First of all, a recom-mendation mechanism is developed, where multiple authorized nodes in the consortium blockchain system act as referrers to authenticate a public node and to generate an initial anonymous credential for the authenticated node to join in the system. Then, a reputation update strategy is designed for all nodes based on the evaluation of their behaviors, so that their authorizations will be dynamically updated to control the security risk of the system. To be more specific, the referrers should be responsible for their applicants, resulting in a supervised authentication scheme, named accountability. Finally, we develop a credential update strategy based on the mix-context for defending against linking attacks, such that the privacy of real identity of a node and its transactions will be protected. The theoretical and numerical results demonstrate that our solution significantly improves the authentication performance and protects privacy in blockchain systems.
MODELING BILINGUAL THESAURUS OF INVESTMENT TERMS

The paper is devoted to the principles of compiling a bilingual dictionary of investment terms. In the article, a preparatory stage of thesaurus modeling, including analysis of methodological base and identification of methods, is described. According to the parametric approach underlying the creation of a thesaurus, a number of key parameters of dictionary structures is identified.&#x0D; Background. The terminology system in the field of the investment activity on the equity market is constantly and intensively updated with new terms. The lack of such type of dictionaries, which are essential for linguists, translators and other professionals in linguistics as well as investors and traders, makes the compiling of the dictionary quite relevant.&#x0D; Purpose. Identification and description of principles and stages of compiling a bilingual dictionary of investment terms.&#x0D; Materials and methods. The materials for the study include financial market articles and reviews, fund platforms vocabulary as well as legal documents, governing investments on the stock market. Research methods include continuous sampling method, comparative method and stratification method.&#x0D; Results. The algorithm for practical stage of working with linguistic material on the example of modeling a dictionary entry of specified dictionary, which describes the term “Stock market index”, is presented.&#x0D; Practical implications. The practical significance of the research results lies in the possibility of using described principles and stage of compiling a thesaurus as well as a dictionary entry for compiling other dictionaries by linguists and translators.
Template-based Minor Embedding for Adiabatic Quantum Optimization

Quantum Annealing (QA) can be used to quickly obtain near-optimal solutions for Quadratic Unconstrained Binary Optimization (QUBO) problems. In QA hardware, each decision variable of a QUBO should be mapped to one or more adjacent qubits in such a way that pairs of variables defining a quadratic term in the objective function are mapped to some pair of adjacent qubits. However, qubits have limited connectivity in existing QA hardware. This has spurred work on preprocessing algorithms for embedding the graph representing problem variables with quadratic terms into the hardware graph representing qubits adjacencies, such as the Chimera graph in hardware produced by D-Wave Systems. In this paper, we use integer linear programming to search for an embedding of the problem graph into certain classes of minors of the Chimera graph, which we call template embeddings. One of these classes corresponds to complete bipartite graphs, for which we show the limitation of the existing approach based on minimum Odd Cycle Transversals (OCTs). One of the formulations presented is exact, and thus can be used to certify the absence of a minor embedding using that template. On an extensive test set consisting of random graphs from five different classes of varying size and sparsity, we can embed more graphs than a state-of-the-art OCT-based approach, our approach scales better with the hardware size, and the runtime is generally orders of magnitude smaller.
Should I take a walk? Estimating Energy Expenditure from Video Data

We explore the problem of automatically inferring the amount of kilocalories used by human during physical activity from his/her video observation. To study this underresearched task, we introduce Vid2Burn -- an omni-source benchmark for estimating caloric expenditure from video data featuring both, high- and low-intensity activities for which we derive energy expenditure annotations based on models established in medical literature. In practice, a training set would only cover a certain amount of activity types, and it is important to validate, if the model indeed captures the essence of energy expenditure, (e.g., how many and which muscles are involved and how intense they work) instead of memorizing fixed values of specific activity categories seen during training. Ideally, the models should look beyond such category-specific biases and regress the caloric cost in videos depicting activity categories not explicitly present during training. With this property in mind, Vid2Burn is accompanied with a cross-category benchmark, where the task is to regress caloric expenditure for types of physical activities not present during training. An extensive evaluation of state-of-the-art approaches for video recognition modified for the energy expenditure estimation task demonstrates the difficulty of this problem, especially for new activity types at test-time, marking a new research direction. Dataset and code are available at https://github.com/KPeng9510/Vid2Burn.
Design and Results of the Second International Competition on Computational Models of Argumentation

Argumentation is a major topic in the study of Artificial Intelligence. Since the first edition in 2015, advancements in solving (abstract) argumentation frameworks are assessed in competition events, similar to other closely related problem solving technologies. In this paper, we report about the design and results of the Second International Competition on Computational Models of Argumentation, which has been jointly organized by TU Dresden (Germany), TU Wien (Austria), and the University of Genova (Italy), in affiliation with the 2017 International Workshop on Theory and Applications of Formal Argumentation. This second edition maintains some of the design choices made in the first event, e.g. the I/O formats, the basic reasoning problems, and the organization into tasks and tracks. At the same time, it introduces significant novelties, e.g. three additional prominent semantics, and an instance selection stage for classifying instances according to their empirical hardness.
DESIGN OF FUZZY-PD CONTROLLER FOR HEATING SYSTEM TEMPERATURE CONTROL

The temperature control of medical and biological ovens required a high precision controller. A Fuzzy-PD (Fuzzy-Proportional Derivative) controller is designed in this research for controlling the temperature of the heating system. The controlled heating system is a small laboratory oven used for biological experiments. The controller is designed using MATLAB version 7.14 program. SIMULINK environment of MATLAB program is used in designing the controller. The output response of the complete heating system for unit step input is obtained. The inputs and output membership function (MF) and also the surface viewer for the Fuzzy-PD controller are presented in this research. The simulation results show the good performance of the designed controller and its suitability and adaptability for such sensitive application.
NDPD: an improved initial centroid method of partitional clustering for big data mining

Purpose The K-means (KM) clustering algorithm is extremely responsive to the selection of initial centroids since the initial centroid of clusters determines computational effectiveness, efficiency and local optima issues. Numerous initialization strategies are to overcome these problems through the random and deterministic selection of initial centroids. The random initialization strategy suffers from local optimization issues with the worst clustering performance, while the deterministic initialization strategy achieves high computational cost. Big data clustering aims to reduce computation costs and improve cluster efficiency. The objective of this study is to achieve a better initial centroid for big data clustering on business management data without using random and deterministic initialization that avoids local optima and improves clustering efficiency with effectiveness in terms of cluster quality, computation cost, data comparisons and iterations on a single machine. Design/methodology/approach This study presents the Normal Distribution Probability Density (NDPD) algorithm for big data clustering on a single machine to solve business management-related clustering issues. The NDPDKM algorithm resolves the KM clustering problem by probability density of each data point. The NDPDKM algorithm first identifies the most probable density data points by using the mean and standard deviation of the datasets through normal probability density. Thereafter, the NDPDKM determines K initial centroid by using sorting and linear systematic sampling heuristics. Findings The performance of the proposed algorithm is compared with KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms through Davies Bouldin score, Silhouette coefficient, SD Validity, S_Dbw Validity, Number of Iterations and CPU time validation indices on eight real business datasets. The experimental evaluation demonstrates that the NDPDKM algorithm reduces iterations, local optima, computing costs, and improves cluster performance, effectiveness, efficiency with stable convergence as compared to other algorithms. The NDPDKM algorithm minimizes the average computing time up to 34.83%, 90.28%, 71.83%, 92.67%, 69.53% and 76.03%, and reduces the average iterations up to 40.32%, 44.06%, 32.02%, 62.78%, 19.07% and 36.74% with reference to KM, KM++, Var-Part, Murat-KM, Mean-KM and Sort-KM algorithms. Originality/value The KM algorithm is the most widely used partitional clustering approach in data mining techniques that extract hidden knowledge, patterns and trends for decision-making strategies in business data. Business analytics is one of the applications of big data clustering where KM clustering is useful for the various subcategories of business analytics such as customer segmentation analysis, employee salary and performance analysis, document searching, delivery optimization, discount and offer analysis, chaplain management, manufacturing analysis, productivity analysis, specialized employee and investor searching and other decision-making strategies in business.
An Context-Aware Intelligent System to Automate the Conversion of 2D Audio to 3D Audio using Signal Processing and Machine Learning

As virtual reality technologies emerge, the ability to create immersive experiences visually drastically improved [1]. However, in order to accompany the visual immersion, audio must also become more immersive [2]. This is where 3D audio comes in. 3D audio allows for the simulation of sounds from specific directions, allowing a more realistic feeling [3]. At the present moment, there lacks sufficient tools for users to design immersive audio experiences that fully exploit the abilities of 3D audio. This paper proposes and implements the following systems [4]: 1. Automatic separation of stems from the incoming audio file, or letting the user upload the stems themselves 2. A simulated environment in which the separated stems will be automatically placed in 3. A user interface in order to manipulate the simulated positions of the separated stems. We applied our application to a few selected audio files in order to conduct a qualitative evaluation of our approach. The results show that our approach was able to successfully separate the stems and simulate a dimensional sound effect.
A Tracing-based Tennis Coaching and Smart Training Platform using Artificial Intelligence and Computer Vision

Athletes in technical sports often find it difficult to analyze their own technique while they’re playing [1]. Often, athletes look at the technique of professional players to identify problems they may have. Unfortunately, many types of techniques, such as forehand and backhand swings in tennis, are relatively similar between a beginner and a professional, making it more difficult for comparison. On the other hand, techniques that appear different between professionals and casual can also present different challenges. This is especially true for serves in tennis, where the speed of the swing, the motion of the player, and the angle of the camera recording the player all pose a challenge in analyzing differences between professional and learning tennis players [2]. In this paper, we used two machine learning approaches to compare the serves of two players. In addition, we also developed a website that utilizes these approaches to allow for convenient access and a better experience. We found that our algorithm is effective for comparing two serves of different speeds and synchronized the videos effectively.
MULTIDISCIPLINARY FORENSIC ANALYSIS ACTS AS AN EYEWITNESS TO LINK THE CRIME- CULPRIT- CRIME SCENE- DECEASED. - AN INTERESTING CASE STUDY

: Different types of evidence found on the crime scene can be classified into biological, chemical, physical, and digital. Biological evidence is a person's body fluids, hair, tissue, etc, chemical evidence is any material used in the crime such as poison, drugs, inflammable substances, chili powder, etc, objects such as weapons, soil, fibers, broken glass piece, etc constitute physical evidence and digital evidence includes CCTV footage, online purchase history of weapons, mobile phones, pen drive, etc. These evidences are analyzed by different disciplines of forensic science like Biology, DNA, Chemistry, Toxicology, Ballistics, and Cyber. In a gruesome murder that took place in broad daylight, the victim's car was surrounded by a few people at a traffic signal. Car's window glasses were broken and he was brutally murdered using sharp-edged weapons. Forensic Science Laboratory helped the investigating agency to collect evidences at the crime scene. Almost every discipline of forensic science was involved in the analysis of the evidences found (biological, chemical, physical and digital). Detection, assessment and interpretation of the evidences aided to fix the crime scene, the presence of the accused on the crime scene and his involvement in the crime. This multidisciplinary analysis freed the investigating agency from dependency on eyewitnesses. Thus, scientific evidence from various disciplines of forensic science contributed their part and completed the whole picture of murder.
A REVIEW ON APPLICATIONS OF BLOCKCHAIN IN BANKING SECTORS

In the banking and financial service domain, blockchain technology can make business process less complex while creating safe, trustworthy records of agreements and transactions. Public's huge support in Digital Ledger Technology &amp; blockchain's decentralized control can shift user trust from humans to machines. Decentralized Finance will grow more &amp; more so applying it to banking sectors might be very useful to get more people on board with this new technology. It also has the potential to solve traditional banking problems of lending money, trusting government &amp; bank authorities etc. The time needed to process various tasks such as tax filing , insurance , bank transactions of large amounts will be heavily reduced. Crypto is our future &amp; with the help of Smart Contracts , DAO , DeFietc we can flourish our banking sectors for the general public.