Reviewerly/exHarmony huggingface dataset:
Spatio-temporal Weight of Active Region for Human Activity Recognition

Although activity recognition in the video has been widely studied with recent significant advances in deep learning approaches, it is still a challenging task on real-world datasets. Skeleton-based action recognition has gained popularity because of its ability to exploit sophisticated information about human behavior, but the most cost-effective depth sensor still has the limitation that it only captures indoor scenes. In this paper, we propose a framework for human activity recognition based on spatio-temporal weight of active regions by utilizing human a pose estimation algorithm on RGB video. In the proposed framework, the human pose-based joint motion features with body parts are extracted by adopting a publicly available pose estimation algorithm. Semantically important body parts that interact with other objects gain higher weights based on spatio-temporal activation. The local patches from actively interacting joints with weights and full body part image features are also combined in a single framework. Finally, the temporal dynamics are modeled by LSTM features over time. We validate the proposed method on two public datasets: the BIT-Interaction and UT-Interaction datasets, which are widely used for human interaction recognition performance evaluation. Our method showed the effectiveness by outperforming competing methods in quantitative comparisons.
Blurred Image Restoration Conditional Generative Adversarial Network Based on Attention Mechanism

In this paper, we propose a conditional generative adversarial network (CGAN) for restoring blurred image. The design of the generator derives from classic U-net network, but to increase the parameter capacity and improve its expression ability, we first modify the U-net by replacing some deep layers with stacked residual modules. Furthermore, we combine the channel and spatial attention modules and embed them into the generator to force it paying more attention to important channels and blurred local space. In the aspect of loss function design, we comprehensively incorporate the pixel loss, perception loss and adversarial loss to enhance the performance of the proposed CGAN. Finally, the GoPro dataset is used for training and evaluating the effectiveness of the network. The results show that the proposed CGAN can achieve restored image of very high quality which is comparable with some state of the art methods.
Comparative Study of Performance of Adaptive Kalman Filtering Applied to Vanadium and Rhodium SPNDs

Self powered neutron detectors with Vanadium/ Rhodium as emitters are widely used in large Heavy/Light Water nuclear reactors for monitoring the flux distribution inside the reactor core. However, these detectors are associated with response delay, which precludes their direct use for control and protection applications. Therefore, a method to improve the response time of these detectors would enable them for core monitoring and surveillance applications. In this paper, a residual based adaptation scheme is proposed for updating the measurement noise covariance matrix of linear Kalman filter in on-line fashion and the performance of adaptive Kalman filtering is evaluated in the problem of dynamic compensation of Vanadium and Rhodium SPNDs.
VR Human Body Treatment Game ‘BodyCureBot’ Using Hand Tracking

Virtual reality content is gaining popularity as a next-generation medium, however because the 180-degree contents previously used cannot be used in their current state, new VR content is required. Due to the current paucity of VR content, content production is happening in a range of fields, and discussions concerning viability continue. This article will detail the whole process of designing, manufacturing, and constructing a hand-tracking-based virtual reality game. It is built on the Unity game engine and was made using Autodesk’s 3ds MAX, Blender, C#, and JSON. Additionally, it is a real-time interactive game that the user manipulates with his or her hands. The game ‘BodyCureBot’ is designed in such a way that a user wearing a head-mounted display explores and cures the human body’s lungs, heart, and blood vessels. The player takes on the role of a nanorobot and inspects the diseased human body, treating the afflicted place as a game. The production of game content using hand tracking technology will deliver new experiences for game customers looking for variety. This paper proposes and applies various types of user interfaces that can be used in the VR environment. Additionally, educational benefits may be predicted when users are presented with medical knowledge and given the opportunity to understand it. ‘BodyCureBot,’ which was created to increase user immersion and deliver new material, wants to make realistic graphic content in the future to enable investigation of a broader range of human organs. The addition of multiplayer functionality via the identification of multiple gestures will result in increased content diversity as a consequence of user collaboration.
An Unsupervised Approach for Eos Lumping — a Comparative Study on Som-Ann Based Clustering and Swarm-Based K-Means Algorithm

The petroleum industry has always been attempting to find a good solution for compositional simulation to keep both CPU time and calculation errors at a low level. An approach to solving this problem is to find the optimal number of components to reach the highest accuracy with the lowest CPU time; briefly, a proper lumping scheme is needed. In this paper, the capital purpose is to find the most optimal lumping method. In order to lump the heavy-end components, the Self-organizing map (SOM) method is used. Also, four swarm-based metaheuristic optimization algorithms known as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Firefly algorithm (FA) and a hybrid algorithm (PSO-GA) are applied to optimize the K-means method. However, based on the flash calculation outputs, NFEs, and Cost function results, all the mentioned techniques obtain an accurate model. The SOM is the most optimum method due to its accurate results and short CPU time. Improved K-means method using PSO-GA hybrid is the second-best technique, the accuracy of its outputs is so close to the SOM accuracy, but SOM converges in a much shorter time. SOM technique is applied for lumping two other compositions, including black oil and gas condensate, and the results validate the previous observations of the volatile oil sample.
Adaptively Secure Computation for RAM Programs

In this work, we study the communication complexity of secure multiparty computation under minimal assumptions in the presence of an adversary that can adaptively corrupt all parties eventually. Specifically, we are interested in understanding the complexity when modeling the underlying function as a RAM program. Under minimal assumptions, the work of Canetti et al. (STOC 2017) and Benhamouda et al. (TCC 2018) give protocols whose communication complexity grows quadratically in the circuit size when the computation is expressed as a Boolean circuit. In this work, we obtain the first two-round two-party computation protocol, which is secure against adaptive adversaries who can adaptively corrupt all parties where the communication complexity is proportional to the square of the RAM complexity of the function up to polylogarithmic factors assuming the existence of non-committing encryption.
Improved Deep Neural Network for Real-Time Detection of Publicly Prohibited Items

An important approach that has been put in place for the purpose of ensuring that people are secure and safe in public places is the security check mounted at the entrance of most public places. On some occasions, it is trained personnel in this field while in some places they are machines which has helped in minimizing crime recorded in public areas significantly. Nevertheless, some places like hospitals, schools, and event centers may not have the luxury to install such devices and employ professionals, thereby exposing the people in such areas to safety threats raising security concern to the general public since safety may not be guaranteed in such places. Additionally, the way that baggage is packed, some prohibited items may be smuggled into public places unnoticed even if x-ray scanners or some persons are employed to carry out security checks at the door. It is with that in mind that we designed a real-time detection model on the basis of a Deep Neural Networks (DNN) that is able to detect publicly prohibited items. We manually annotated the dataset we used and utilized the benefits of DNN for the detection of the 9 classes of objects in our dataset. We as well used different input sizes for the training of the model and were able to compare the performance of the two different input sizes. The model with a better performance has an mAP of 76.75% as well as a speed of detection of 27.1 Frames per Second (FPS).
Application of Big Data Algorithms and AI Algorithms in the Construction of Corporate Governance Data Monitoring Platform

Affected by the new crown epidemic, the downward pressure on the economy has increased, coupled with the incomplete digestion of other risks, multiple factors superimposed, intertwined and interacted with each other, and corporate governance difficulties are prominent. If management, control and disposal are not in place, it will further hinder corporate development. This article discusses big data algorithms and AI algorithms, analyzes the big data scenarios application and AI algorithms in corporate governance data monitoring and analysis, and puts forward some suggestions for building a corporate governance data monitoring platform.
Elliptic Curves in Cryptography

The Diffie–Hellman protocol uses the group $$U(\mathbb Z_p)$$ to exchange keys. Other groups can be employed in a Diffie–Hellman-type protocol. For instance, we could use an elliptic curve group.
A Paper-Based Keyboard Using ArUco Codes: ArUco Keyboard

Object tracking in computer vision can be done either by using a marker-less or marker-based approach. Computer vision systems have been using Fiducial markers for pose estimation in different applications such as augmented reality [5] and robot navigation [4]. With the advancements in Augmented Reality (AR), new tools such as AugmentedReality uco (ArUco) [6] markers have been introduced to the literature. ArUco markers, are used to tackle the localization problem in AR, allowing camera pose estimation to be carried out by a binary matrix. Using a binary matrix not just simplifies the process but also increases the efficiency. As a part of our initiative to create a cost-efficient, 24/7 accessible, Virtual Reality (VR) based chemistry lab for underprivileged students, we wanted to create an alternative way of interacting with the virtual scene. In this study, we used ArUco markers to create a low-cost keyboard only using a piece of paper and an off-the-shelf webcam. We believe this method of keyboard will be more beneficial to the user as they can see the keys before they are typing in the corner of the screen instead of an insufficient on the screen VR keyboard or a regular keyboard where the user can’t see what they are typing with a VR headset. As potential extensions of the base system, we have also designed and evaluated a stereo camera and an IMU sensor based system with various sensor fusion techniques. In summary, the stereo camera reduces occlusion related problems, and the IMU sensor detects vibrations which in turn simplifies the KeyPress detection problem. It has been observed that use of any of these additional sensors improves the overall system performance.
Online Judge Systems and Case Teaching Used in Programming Courses

The key to programming courses, including programming languages, data structure, algorithm design and analysis, and so on, is to polish students’ programming skills solving problems. The current programming courses in universities are mainly classroom-teaching models, and students’ skills solving problems by programming can hardly be polished. Polishing students’ programming skills solving problems is implemented by teaching material construction and curriculum construction. In teaching material construction, the book series “Collegiate Programming Contests and Education” focus on polishing students’ programming skills solving problems in a systematic way, based on programming contests’ problems. Experiments, consisting of programming contest problems and their analysis and solutions, are basic units for the book series. The curriculum construction's guiding ideology is programming is a technology. In curriculum construction, first, the case teaching is widely used based on experiments. Students are put into a case of a problem description, apply knowledge that they have learned, think how to solve the problem, and propose the algorithm. And after students design the algorithm solving the problem, they need program and debug to pass all test cases within the time and memory limit. Second, informatization technologies, such as online judge systems, and virtual online contests based on online judge systems, are integrated into courses. Online judge systems are online systems to test programs whether is correct or not in programming practice. These systems can compile and execute programs, and test programs with the input for test data. Online judge systems are the platform on which students polish their programming skills. It has been proved by practice that these works can polish students’ programming skills solving problems efficiently.
Information Society and Informatization of the Educational Space: Global Trends and Prospects

The twenty-first century could be rightfully considered as an era of information accompanied by the global informatization of the modern society. The globalization of the information society in this research is interpreted as a macroscopic, multi-faceted and internally contradictory process of the growth of similarity in the world systems: economic, political (Rhodes, 1997), educational, social and legal. The current dependence of human civilization on its information component has made it much more vulnerable in this regard. In addition, such vulnerability is connected with the fact that our society today is a fundamentally open structure (Grossman, 1995). The most important result of the formation of information society was the emergence of the so-called global information space (Martin, 2014). The authors of this article seek to answer the following questions: how does the sphere of education develop in the context of global information society and what are the key trends in the process of informatization of the educational space. Information resources and structures have become a tool for achieving a strategic advantage. The authenticity, credibility, as well as an adequate reflection of information realities represent the key challenges for the communication society. It has become clear that to live in a more complex, interconnected and detailed world the mankind needs to be well-equipped with tools for understanding its own behavioral patterns and their impact on the new social dynamics.
Explainability of AI methods

The popularity of machine learning models and, in particular, deep learning approaches, has made possible the development of applications in a wide range of areas, such as recommendation systems, natural language processing and more, in general, in classification of text, images, speech, etc. However, deep models have the drawback of being highly non-human interpretable. Even though the existing literature does not uniformly agree on a common definition of the term explanation, few practical attempts have been made to build models that justify their actions. This chapter presents an overview of the problem.
Towards a Post-human Education for the Twenty-First Century

The present age has been described both as ‘post-human’ and ‘inhuman’. It is an age in which (i) man’s inhumanity to humanity is evident, in which (ii) humanity’s hubris over its knowledge has led to the be-spoiling of the planet, and in which (iii) the digital world, robots and the world of things are both supplanting humans and providing humans with undreamt possibilities. The very idea of ‘human education’ has, therefore, to be radically re-thought for a post-human age. There are several challenges here. Firstly, whereas ‘human education’ places the human at the centre of education (especially in the humanities), the idea of ‘post-human’ education dislodges the human. Secondly, the post-human has been deployed both as a descriptive category, identifying unfolding features of the contemporary age; and as a recommendatory category, offering suggestions as to ways forward. Thirdly, it has concerned itself with epistemological matters (how we are to understand the world) and with ontological matters (what is there in the world and what is the place of humanity in that world?). A post-human education must be sensitive to these matters and must have something to say about how they play out respectively across the sciences and across the humanities.
Revealing Reliable Signatures by Learning Top-Rank Pairs

Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In specific scenarios like confirming financial documents and legal instruments, ensuring the absolute reliability of signatures is of top priority. In this work, we proposed a new method to learn "top-rank pairs" for writer-independent offline signature verification tasks. By this scheme, it is possible to maximize the number of absolutely reliable signatures. More precisely, our method to learn top-rank pairs aims at pushing positive samples beyond negative samples, after pairing each of them with a genuine reference signature. In the experiment, BHSig-B and BHSig-H datasets are used for evaluation, on which the proposed model achieves overwhelming better pos@top (the ratio of absolute top positive samples to all of the positive samples) while showing encouraging performance on both Area Under the Curve (AUC) and accuracy.
Law, Political Stability, Tourism Management and Economic Development in Sub-Saharan Africa

This study complements the extant literature by assessing how the rule of law and political stability modulate tourism development dynamics (i.e. tourism receipts and tourism expenditure) to affect economic development in terms of gross domestic product (GDP) per capita. The study focuses on 47 countries in sub-Saharan Africa (SSA) with data for the period 2002 to 2018 and the empirical evidence is based on the Generalized Method of Moments. The study finds that: (i) the rule of law modulates both tourism receipts and tourism expenditure for overall positive effects on economic development and (ii) political stability modulates tourism receipts for an overall positive impact on economic development. Policy implications are discussed.
The Untold Impact of Learning Approaches on Software Fault-Proneness Predictions

Software fault-proneness prediction is an active research area, with many factors affecting prediction performance extensively studied. However, the impact of the learning approach (i.e., the specifics of the data used for training and the target variable being predicted) on the prediction performance has not been studied, except for one initial work. This paper explores the effects of two learning approaches, useAllPredictAll and usePrePredictPost, on the performance of software fault-proneness prediction, both within-release and across-releases. The empirical results are based on data extracted from 64 releases of twelve open-source projects. Results show that the learning approach has a substantial, and typically unacknowledged, impact on the classification performance.Â Specifically, using useAllPredictAll leads to significantly better performance than using usePrePredictPost learning approach, both within-release and across-releases. Furthermore, this paper uncovers that, for within-release predictions, this difference in classification performance is due to different levels of class imbalance in the two learning approaches. When class imbalance is addressed, the performance difference between the learning approaches is eliminated. Our findings imply that the learning approach should always be explicitly identified and its impact on software fault-proneness prediction considered. The paper concludes with a discussion of potential consequences of our results for both research and practice.
Information Systems Development and Technology Plan for NADAP Water Refilling Station

Poor transaction processes between staff and customers can have a significant influence on a company's or business's efficiency, and the majority of the time, the reason for this is because transactions are manually recorded and kept. Furthermore, if a corporation or business lacks information systems and IT infrastructures, it is possible that they will be unable to accomplish their long- and short-term objectives. This plan sought to find and propose information systems and IT infrastructures as a solution to the problems found by the researchers in the NADAP water refilling station. A survey was created containing basic questions about the background of the business, the business routines, business processes, and the business objectives to achieve. The results suggest that the company’s transaction processes and inventory management need to be changed for it is the reason of the business’s poor performance and efficiency.
Exploration of Pixel Digital Visual Design

Today is a digital age, new changes have taken place in the tools/performance/presentation/application of visual design, digital image is the development of modern design, both visual content production and information presentation rely on pixels. A pixel is the smallest unit in an image represented by a sequence of numbers. Pixels are also a necessary element in digital visual design, it plays an important role in the process of design, but it is often ignored. This paper is an exploration of pixel visual design method, through the attempt in design teaching, it summarizes the characteristics/forms and methods of pixels in digital vision design. Starting with the basic principle of pixels, combining the relationship between pixels and hardware/pixels and software, the composition of 2D pixels/3D pixels is summarized. Pixels are small but important, pixels largely determine the performance of design services and e-commerce. E-commerce in the digital age is more like a "visual war" of "pixel battle", pixel presentation will make visual design more diverse, and it has better scalability and applicability.