Reviewerly/exHarmony huggingface dataset:
Learning-Based JND-Directed HDR Video Preprocessing for Perceptually Lossless Compression With HEVC

The final consumer of videos is mostly human. Therefore, if videos can be compressed by fully utilizing the perception characteristics of human visual systems (HVS), the bitrates of the compressed videos can be significantly reduced with subjective visual quality degradation as little as possible. Based on this, we newly propose a learning-based Just Noticeable Distortion (JND)-directed preprocessing scheme for perceptual video compression, especially for 10-bit High Dynamic Range (HDR) videos, which is called the HDR-JNDNet. Our HDR-JNDNet effectively suppresses the perceptual redundancy of 10-bit HDR video signals so that the compression efficiency can be significantly enhanced for the HEVC main10 profile encoder. To our best knowledge, our work is the first approach to training a CNN-based model to directly generate the JND-directed suppressed frames of 10-bit HDR video with the negligible perceptual quality difference between the decoded frames for the original HDR video input with and without the preprocessing by our HDR-JNDNet. Via intensive experiments, when the HDR-JNDNet is applied as preprocessing for the HDR video input before compression, it allows to remarkably save the required bitrates up to the maximum (average) 40.66% (18.37%) for 4K-UHD/HDR test videos, with little subjective video quality degradation without increasing the computational complexity.
Complexity of Nonempty Existence Problems in Incomplete Argumentation Frameworks

Abstract argumentation frameworks (AFs) are a prevailing model for the formal representation of argumentations in AI research. The generalized model of incomplete AFs extends the basic model by allowing for the representation of unquantified uncertainty about the existence of elements in an argumentation. For this extended model of AFs, we formally define a natural generalization of the nonempty existence problem, which asks whether there exists a nonempty set of arguments satisfying the conditions specified by a given semantics. Focusing on the fundamental semantics for incomplete AFs and considering a possible and a necessary variant of the nonempty existence problem, this yields a family of related problems, and we provide a full analysis of their computational complexity.
More Trees or Larger Trees: Parallelizing Monte Carlo Tree Search

Monte Carlo tree search (MCTS) is being effectively used in many domains, but acquiring good results from building larger trees takes time that can in many cases be impractical. In this article, we show that parallelizing the tree building process using multiple independent trees (root parallelization) can improve results when limited time is available, and compare these results to other parallelization techniques and to results obtained from running for an extended time. We obtained our results using MCTS in the domain of computer Go, which has the most mature implementations. Compared to previous studies, our results are more precise and statistically significant.
Research on influence mechanism of running clothing fatigue based on BP neural network

Dress fatigue can affect the efficiency of sports, especially for running, the dress fatigue has a greater impact on it. Moreover, at present, there are few studies on dress fatigue. Based on this, this study is based on BP neural network, and uses surface electromyography theory and muscle fatigue measurement method to perform fatigue measurement. The fatigue threshold analysis is mainly carried out by the experimental method, and the prediction model of the wearing fatigue threshold based on BP neural network is constructed based on the actual demand. Moreover, this paper verifies the reliability of threshold distribution by experimental analysis combined with model analysis. In addition, the study sets the organizational structure and clothing pressure as verification indicators to analyze the performance of the model. The research results show that the model constructed in this study can effectively analyze the mechanism of fatigue impact of running dress, and this paper can provide reference for the study of dress fatigue.
Design of SW Framework for Trustworthy AI-Data Commons

AI/Data commons, through a data utilization value chain by ensuring data sovereignty and protecting sensitive data, support the establishment of an open collaborative ecosystem based on PCI(Participation-Collaboration-Incentives). And it can solve a variety of user-defined customized AI problems. In this paper, the high-level functional architecture for trustworthy AI/Data commons were designed.
Construction of first-class university course based on artificial intelligence and neural network algorithm

Curriculum is the basis of vocational training, its development level and teaching efficiency determine the realization of vocational training objectives, as well as the quality and level of major vocational academic training. Therefore, the development of curriculum is an important issue. And affect the school’s teaching capacity building. The analysis of the latest developments in the main courses shows that there are some deviations or irrationalities in the curriculum in some colleges and universities, and the general problems of understanding the latest courses, such as lack of solid foundation in curriculum setting, unclear direction of objectives, unclear reform ideas, inadequate and systematic construction measures, lack of attention to the quality of education. This paper explains the rules for the establishment of first-level courses, clarifies the ideas and priorities of architecture, and explores strategies for building university-level courses using knowledge of artificial intelligence and neural network algorithms in order to gain experience from them.
Proposed Smart University Model as a Sustainable Living Lab for University Digital Transformation

Recently, there is an emerging and rapidly growing research on smart universities due to the impact of advanced technologies such as artificial intelligence, IoT technologies and big data to enhance performance of teaching activities and university management systems. The motivation of this paper is to study university digital transformation and analyze smart university framework. The study begins with a description of trends in initiating various concepts, challenges as well as opportunities on development of smart universities. The second contribution focuses on expressing a systematic approach of designing the smart university concept, that separates its application domains and covers its technological system. A smart university model is then proposed which includes five domains: IoT ecosystem, smart infrastructure, smart applications and services, smart teaching and learning, data-driven smart analysis. Lastly a proposal of establishing a smart university as a living laboratory will be discussed. Several pilot projects which have been implemented in the UD-DUT testbed lab will be further analyzed, such as employee attendance management system, smart office energy management, campus public LoRA network. In addition, possible challenges and future works regarding this novel smart university framework will be explored.
A multi-branch ResNet with discriminative features for detection of replay speech signals

Nowadays, the security of ASV systems is increasingly gaining attention. As one of the common spoofing methods, replay attacks are easy to implement but difficult to detect. Many researchers focus on designing various features to detect the distortion of replay attack attempts. Constant-Q cepstral coefficients (CQCC), based on the magnitude of the constant-Q transform (CQT), is one of the striking features in the field of replay detection. However, it ignores phase information, which may also be distorted in the replay processes. In this work, we propose a CQT-based modified group delay feature (CQTMGD) which can capture the phase information of CQT. Furthermore, a multi-branch residual convolution network, ResNeWt, is proposed to distinguish replay attacks from bonafide attempts. We evaluated our proposal in the ASVspoof 2019 physical access dataset. Results show that CQTMGD outperformed the traditional MGD feature, and the fusion with other magnitude-based and phase-based features achieved a further improvement. Our best fusion system achieved 0.0096 min-tDCF and 0.39% EER on the evaluation set and it outperformed all the other state-of-the-art methods in the ASVspoof 2019 physical access challenge.
Examining the Role of Power-Control Theory and Self-Control to Account for Computer Hacking

Criminological inquiry consistently identifies a gender difference in offending rates, which are also evident among certain forms of cybercrime. The gender difference in cybercrime offending is particularly large within computer hacking, though few have specifically addressed this issue through applications of criminological theory. The current study attempted to account for the gender disparity in hacking through a test of power-control theory, which considers the role of class and family structure. This analysis also incorporated an extension of power-control theory through the influence of low self-control. Using data from the Second International Self-Report of Delinquency study (ISRD-2), logistic regression analyses were estimated, producing partial support for both theories to account for hacking. Implications for theory and research were explored in detail.
Head-AR: Human Activity Recognition with Head-Mounted IMU Using Weighted Ensemble Learning

This paper describes the machine learning (ML) method Head-AR, which achieved the highest performance in a competition with 11 other algorithms and won the Emteq Activity Recognition challenge. The goal of the challenge was to recognize eight activities of daily life from a device mounted on the head, which provided data from a 3-axis IMU: accelerometer, gyroscope, and magnetometer. The challenge dataset was collected by four subjects, of which one subject was used as a test for the challenge evaluation. The method processes the stream of sensors data and recognizes one of the eight activities every two seconds. The method is based on weighted ensemble learning, which combines three models: (i) a dynamic time warping classification model, which analyzes raw accelerometer data; (ii) a classification model that uses expert features; (iii) and a classification model that uses features selected by a feature selection algorithm. To compute the final output, the predictions of the three models are combined using a novel weighing scheme. The method achieved an F1-score of 61.25% on the competition's evaluation.
Deep Learning Methods for Human Behavior Recognition

In this paper, we investigate the problem of human behavior recognition by using the state-of-the-art deep learning methods. In order to achieve sufficient recognition accuracy, both spatial and temporal information was acquired to implement the recognition in this project. We propose a novel YOLOv4 + LSTM network, which yields promising results for real-time recognition. For the purpose of comparisons, we implement Selective Kernel Network (SKNet) with attention mechanism. The key contributions of this paper are: (1) YOLOv4 + LSTM network is implemented to achieve 97.87% accuracy based on our own dataset by using spatiotemporal information from pre-recorded video footages. (2) The SKNet with attention model that earns the best accuracy of human behaviour recognition at the rate up to 98.7% based on multiple public datasets.
Generative Adversarial Network with Residual Dense Generator for Remote Sensing Image Super Resolution

Improving image resolution, especially spatial resolution, has been one of the most important concerns on remote sensing research communities. An efficient solution for improving spatial resolution is by using algorithm, known as super-resolution (SR). The super-resolution technique that received special attention recently is super-resolution based on deep learning. In this paper, we propose deep learning approach based on generative adversarial network (GAN) for remote sensing images super resolution. We used residual dense network (RDN) as generator network. Generally, deep learning with residual dense network (RDN) gives high performance on classical (objective) evaluation metrics meanwhile generative adversarial network (GAN) based approach shows a high perceptual quality. Experiment results show that combination of residual dense network generator with generative adversarial network training is found to be effective. Our proposed method outperforms the baseline method in terms of objective and perceptual quality evaluation metrics.
APL: Adaptive Preloading of Short Video with Lyapunov Optimization

Short video applications, like TikTok, have attracted many users across the world. It can feed short videos based on users' preferences and allow users to slide the boring content anywhere and anytime. To reduce the loading time and keep playback smoothness, most of the short video apps will preload the recommended short videos in advance. However, these apps preload short videos in fixed size and fixed order, which can lead to huge playback stall and huge bandwidth waste. To deal with these problems, we present an Adaptive Preloading mechanism for short videos based on Lyapunov Optimization, also called APL, to achieve near-optimal playback experience, i.e., maximizing playback smoothness and minimizing bandwidth waste considering users' sliding behaviors. Specifically, we make three technical contributions: (1) We design a novel short video streaming framework which can dynamically preload the recommended short videos before the current video is downloaded completely. (2) We formulate the preloading problem into a playback experience optimization problem to maximize the playback smoothness and minimize the bandwidth waste. (3) We transform the playback experience optimization problem during the whole viewing process into a single-step greedy algorithm based on the Lyapunov optimization theory to make the online decisions during playback. Through extensive experiments based on the real datasets that generously provided by TikTok, we demonstrate that APL can reduce the stall ratio by 81%/12% and bandwidth waste by 11%/31% compared with no-preloading/fixed-preloading mechanism.
Cryptocurrency Grade of Green; IOTA Energy Consumption Modeling and Measurement

Over the last decade, cryptocurrencies have made a significant progress as Bitcoin. Nowadays, one of the challenges in the cryptocurrencies community is the high energy consumption of Bitcoin. Not only Bitcoin but also some other cryptocurrencies deal with this problem. This work-in-progress paper defines macro standardization for energy consumption of each transaction based on VISA and MasterCard energy consumption, IOTA energy consumption modeling, and measuring of IOTA current rate control mechanism (Proof-of-Work).
LWEDM: Learning with error based secure mobile digital rights management system

Abstract Mobile multimedia security is essential to ensure a secure communication in industries, businesses, and organizations. Mobile multimedia is a new concept for an enterprise, where organizations need to be secure. In the last decade, a number of anonymous authentication protocols have been designed based on factorization and discrete logarithm assumptions. Shor's algorithm solves a number‐theoretic assumption in polynomial time using postquantum computers that creates a trouble. However, learning with errors is an advanced hard assumption that cannot be solved by any known existing quantum algorithm. This inspires one to design an anonymous postquantum authentication protocol for the mobile digital rights management system. The protocol has been proved secure in a random oracle model under learning with errors assumption. Moreover, the article contributes an informal security discussion, an implementation using automated validation of internet security protocols and applications. The performance analysis ensures its efficiency even in the postquantum era.
A comparative analysis of tools for developing location based games

The extended usage of mobile digital devices has nowadays become commonplace. Location-based games make the most of the mobile technologies by being played solely on mobile devices. The ever-growing interest for this type of applications goes beyond the mere goal of player entertainment, even reaching to the advocacy of civilization, research and education. In this article, three of the most famous and modern authoring tools for location-based games, Taleblazer, ARIS and Unity with Mapbox, are studied in-depth. The objective is to produce a comparative analysis of the aforementioned tools. Game developers may refer to this analysis to opt for the appropriate tool based on their needs, goals and technical expertise. In order to better understand the tool functionality and thus construct a reliable comparison framework, three game prototypes have been implemented. The results have shown that all studied tools provide the mechanics to develop location-based interactive experiences. Nevertheless, significant differences exist in their functionality, in their coding requirements, and in the extent of their respective learning curves. In conclusion, TaleBlazer seems to favor educational role-based games while ARIS favors hybrid story-based games. Unity with Mapbox, in turn, favors the development of recreational action-based games.
Analyzing the Impact of Refactoring on Bad Smells

Refactoring aims to remove bad smells and increase software maintainability by improving the software structure without changing its behavior. However, some studies show that refactoring tools may introduce new bad smells into the source code, but to the best of our knowledge, we have not been able to find a complete catalog that states the bad smells introduced from refactoring. To bridge this gap, this paper goal is to evaluate the impacts of refactoring on the detection of bad smells in open-source Java systems. Hence, we want to know if and when the automated refactoring removes or introduces bad smells.
Scalability of Streaming on Migrating Threads

Applications where streams of data are passed through large data structures are becoming of increasing importance. Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. This paper discusses the implementation of the Firehose streaming benchmark on a novel parallel architecture with greatly enhanced multi-threading characteristics that avoids the conventional inefficiencies. Results are promising, with both far better scaling and increased performance over previously reported implementations, on a prototype platform with consid-erably less intrinsic hardware computational resources.
Virtual reality: The genesis of the concept and trends of use in education

The development of modern Russian education in the context of its digitalization actualizes issues related to the presentation and use of existing educational resources in a new digitalized form, as well as to the search for new resources and models, methods, forms and means of teaching that contribute to the effectiveness and efficiency of the educational process in school and university, but in the new conditions of the formation of a digital society. At the same time, the issues of virtualization of the educational process are of particular importance due to the use of virtual reality tools and technologies in teaching various subjects. In this context, the genesis of the concept of “virtual reality” plays a special role in the search for answers to these questions, since in the search for something new it is very important to rely on an understanding of how the concept itself developed, and not only from the standpoint of philosophy and general methodological aspects, but also from technological, in particular, from the standpoint of the development of virtualization tools and technologies based on the achievements of physics, informatics and other sciences. Of particular importance for pedagogy in this context is the description of the experience of using “virtual reality” and tools of virtualization of the educational process at different levels of education, since it is genesis and empiricism that allow in the future to determine new trends in virtualization of the educational process, and to deal with the negative consequences of virtualization in the context development, for example, cyberspace, with the help of achievements and special methods of psychological and pedagogical science.
Preprocessing Using Correlation Based Features Selection on Naive Bayes Classification

Abstract The birth of a healthy baby is generally around 38-42 weeks of pregnancy. However, there are many babies born at an inadequate age of birth and the age of birth that is past its time. This study aims to predict the age of the birth of a patient. The method used is a classification with the Naïve Bayes algorithm with input variable (X), the factors experienced by pregnant women in the form of 8 variables X and Y variable in the form of Birth Age. Problems that arise are too many attributes that affect the results of accuracy. To overcome this, preprocessing is used with the Correlation Based Features Selection (CBFS) method. CBFS chose the X variables which had the highest correlation with the Y variable (Birth Age) but had the least correlation between the X variables. From the CBFS that had been done, produced 4 X variables, namely: blood pressure, number of babies, congenital diseases before pregnancy, and problems during pregnancy. The results of the test showed an increase in Precision, recall, and accuracy in the Naïve Bayes classification when implemented CBFS. The highest value of accuracy after preprocessing is 67% with an increase of 2 percent compared to before preprocessing.
Leukemia Detection Mechanism through Microscopic Image and ML Techniques

It is reported that since 2016 there are over sixty thousand diagnosed cases of Leukemia in the United States of America alone. It also suggests that Leukemia is the most common type of cancer seen in the age of twenty. Although the study is based on a Western country, it is equally alarming for an Asian country like Bangladesh where healthcare system is not up to the standard. Researches show that the Chronic Lymphocytic Leukemia has about 83% five-year long survival rates. This paper focuses on Acute Lymphocytic Leukemia (ALL) as this is the most common type of Leukemia in Bangladesh. It is common knowledge among oncologists, that cancer is much easier to treat if it is detected in the early stages. Thus the treatment needs to begin as early as possible. We propose a hands-on approach in detecting the irregular blood components (e.g., Neutrophils, Eosinophils, Basophils, Lymphocytes and Monocytes) that are typically found in a cancer patient. In this work, we first identify 14 attributes to prepare the dataset and determine 4 major attributes that play a significant role in determining a Leukemia patient. We have also collected 256 primary data from Leukemia patient. The data is then processed using microscope to obtain images and fetch into Faster-RCNN machine learning algorithm to predict the odds of cancer cells forming. Here we have applied two loss functions to both the RPN (Region Convolutional Neural Network) model and the classifier model to detect the similar blood object. After identifying the object, we have calculated the corresponding object and based on the count of the corresponding object we finally detect Leukemia. The mean average precision observed are 0.10, 0.16 and 0, where the epochs are 40, 60 and 120, respectively.