Reviewerly/exHarmony huggingface dataset:
Flow-Packet Hybrid Traffic Classification for Class-Aware Network Routing

Network traffic classification using machine learning techniques has been widely studied. Most existing schemes classify entire traffic flows, but there are major limitations to their practicality. At a network router, the packets need to be processed with minimum delay, so the classifier cannot wait until the end of the flow to make a decision. Furthermore, a complicated machine learning algorithm can be too computationally expensive to implement inside the router. In this paper, we introduce flow-packet hybrid traffic classification (FPHTC), where the router makes a decision per packet based on a routing policy that is designed through transferring the learned knowledge from a flow-based classifier residing outside the router. We analyze the generalization bound of FPHTC and show its advantage over regular packet-based traffic classification. We present experimental results using a real-world traffic dataset to illustrate the classification performance of FPHTC. We show that it is robust toward traffic pattern changes and can be deployed with limited computational resource.
Mobile intelligent terminal privacy continuous protection system based on usage habits

Explicit authentication of mobile intelligent terminals is easy to be stolen or learned and cannot be continuously authenticated. We propose a continuous privacy protection system based on user habits. By using the touch screen and sensor data in smart mobile terminals, the legitimate users and illegal users of smart mobile terminals are distinguished. By installing applications on mobile intelligent terminals, the system obtains relevant data and uploads them to the cloud server. LSTM algorithm is used to determine behavior types, and CNN-LSTM algorithm is used to extract features and build user feature models. The model is then downloaded to local and updated regularly. If illegal users are detected during the use of smart mobile terminals, measures will be taken to protect the privacy of users. The system effectively realizes an implicit and continuous authentication mode, and effectively protects the privacy of users. Compared with explicit authentication, it has obvious advantages in security, accuracy, stability and total cost control.
Comparative Analysis of Stereo-Matching Technologies for Emphasizing Stereo-Vision Applications

Stereo vision is a growing research domain which seeks the attention of various researchers to attain deeper scene extraction. This work provides an extensive analysis towards the stereo-matching algorithms and stereo-vision to resolve the problems related to it. The analysis towards the stereo matching technologies is executed with benchmark standards with the focus on stereo vision methods. Thus the comparison of stereo matching algorithms can be done through the implementation of stereo vision application in a particular domain so that the results for the algorithms are comparatively analyzed. In most cases, the analysis and comparison are performed with statistical analysis and emphasize the benefits of various stereo algorithms. Some approaches give higher computational cost with expected outcomes over the lower time frame and provides competency towards parallel processing. The results obtained from the various stereo matching algorithms through the identified different parameters of bad pixels.
The Relationship Between Functional Empowerment and Creative Behavior of Workers During the COVID-19 Pandemic in the UAE

Employees’ functional Empowerment has always been a crucial topic for management sciences researchers. It involves several characteristics that help the workforce to manage their tasks through creative ideas and problem-solving behavior. However, the current pandemic has adversely affected the organizations leading the employees to take their functional Empowerment under consideration. The current study also examined the impact of functional Empowerment on the creative behavior of workers during the Covid19 pandemic in the United Arab Emirates. We used the research reviewed approach to validate the proposed argumentation. Thus, we found a set of deficiencies in the administrative and functional Empowerment of workers. Besides, functional Empowerment was strongly associated with improved creative behavior of the workforce individuals. Thus, we conclude that, if Emirati organizations focus more on creating, accelerating, and strengthening the functional Empowerment of the employees, they will exhibit a strong creative behavior to cope with Covid-19. Further, we discussed the study contributions and recommendations for future research accordingly.
Evaluation of a Mobile Augmented Reality Game Application as an Outdoor Learning Tool

There is a discussion on the potential of augmented reality (AR), mobile technologies to enhance learning. This article presents: 1) the EduPARK project's first cycle of design-based research for the development of a mobile AR game-like app that aims to promote learning in an urban park, and 2) an experience of students using it in loco. The focus is the students' perceptions regarding the usability and functionality of the app. Data collection involved focus groups, questionnaires and app usage information. Data was submitted to content analysis and descriptive statistics. Results revealed an excellent usability of the EduPARK app, with an average system usability scale of 85.6. Overall, students reported that the app was enjoyable, easy to use and promoted learning; however, improvements and more evaluation experiences are needed to better understand mobile AR game-like learning in urban parks.
Human-Aware Robot Navigation Based on Asymmetric Gaussian Model

Most human-aware navigation methods usually gather human features like: speed, gesture, and other human characteristics to establish a comfortable space. However, they do not consider the direction of human vision, which often results in the intrusion of human gaze space by robots. This will bring a strong sense of discomfort to humans. This paper proposes a navigation framework based on an asymmetric Gaussian model and pedestrian trajectory prediction. Firstly, the robot’s RGB-D camera and laser sensors are used to detect the pedestrian’s speed, gaze direction, and other personal information, and subsequently, multiple Gaussian functions are used to establish an asymmetric human comfort space. Then, the detected pedestrian speed is used to predict the trajectory of pedestrians, and the dynamic cost map is obtained through the hierarchical cost map mechanism so that the time information can be considered while planning the trajectory of the robot. Finally, A* algorithm based on lattice state search is used for trajectory planning to establish a trajectory that is consistent in the consideration of human comfort and robot dynamic constraints.
Self-Organizing Computational Efficiency in Quranic Grammar

The existing knowledge-based and data-driven systems for Arabic morphological analysis are all suffering three main computational drawbacks, viz. efficiency, domain, and abstraction. Although the knowledge-based systems employ heavy lexical databases, they generate highly ambiguous tags. And to cover a new domain their lexicon should be costly modified. They also do not provide the linguistic abstraction preferred especially in computational linguistics. Similarly, the systems developed following a data-driven approach ignore the linguistic tractability for Arabic morphology and are highly dependent on big sizes of domain-specific training data. The source of these drawbacks may be traced in the morphological approach they employ in their knowledge base or in their training data. This chapter introduces regex morpho-syntax for Arabic, a highly efficient formalism originating from the basic grammatical rules developed for diacritizing the Quran fourteen centuries ago. The developed formalism is implemented in the knowledge base of Mobin morpho-syntactic parser and tagger. The achieved F-score of 0.967 for the computational effectiveness of the system as well as its significant comparative efficiency measured in terms of Kolmogorov complexity highlights the inherent computational efficiency in Quranic grammar.
Bayesian Network Structure Learning Algorithm Based on Node Order Constraint

The K2 algorithm is one of the classical algorithms for Bayesian Network structure learning. However, the learning effect of K2 algorithm strongly depends on the maximum node in-degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> and the node order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho$</tex> . In order to solve this problem, this paper proposes a new Bayesian Network structure learning algorithm: MI-Kruskal-K2 algorithm. Firstly, the algorithm calculates the mutual information MI between variables, and uses the Kruskal algorithm in Graph Theory to construct the maximum spanning tree to obtain the maximum node in-degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> ; then, the maximum spanning tree was searched by Depth First Search to obtain the node order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho$</tex> ; finally, the K2 algorithm calls the node in-degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu$</tex> and the node order <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rho$</tex> to learn and obtain the optimal Bayesian Network structure. Experiments are carried out in a small Asia Network. Compared with the Greedy Search (GS) algorithm and Hill-Climbing (HC) algorithm, the MI-Kruskal-K2 algorithm performs better.
Image and video manipulation: The generation of deepfakes

The growth of fake news is nowadays a growing reality, leading to users feeling insecure when consuming information.Text-based news is perhaps the most manipulated, but the video is rapidly gaining ground.Technology allows content to be easily adulterated, leading users to become misinformed.In this study, we explain the different image, video, and audio manipulation techniques that are carried out with software and advanced audiovisual techniques.The most prominent methods are morphing and warping, together with machine learning techniques.Also, artificial intelligence is a critical element in the growth of fake video generation.This brings with it the need to identify the fake videos to verify the facts being told.It is also essential to understand social tolerance, especially in humour programmes, as some media outlets use these mechanisms.Moreover, it is analysed the importance of privacy policies that affect how the users' personal information is collected and their implications in their fundamental rights.Finally, it is concluded with the need for media and digital literacy campaigns in the education system to minimise this problem.
Web Scraper Application for Extracting Scientific Journals Data

Searching for certain subjects of articles that are disseminated throughout scientific journals would be a time-consuming task, as it would necessitate scouring many digital libraries or journal websites. This process can be performed efficiently by utilizing web scraping technology, in which a scraper is used to extract web page content into more organized and structured datasets. This paper proposes a customized web scraper called ”Research Scraper” that will extract content from scientific journal websites, allowing users to access all results from a single search interface. The proposed technique is simple to use and can help with the process of analyzing publications in a specific field. This paper presents and explains the development steps, system design, and technologies that will be used in the implementation phase.
ENHANCING STUDENTS INDEPENDENT LEARNING ACTIVITES IN A DIGITAL EDUCATIONAL ENVIROMENT

The article discusses the e-learning system as an actual form of digital and distance learning. In connection with the popularization of digital technologies, the education system has partially transformed and expanded its capabilities, making education accessible to everyone. Due to the unforeseen prevalence and the forced transition of learning to an electronic distance learning environment, there were still insufficiently tested pedagogical methods that stimulate students' interest in the online educational process. In this work, the authors analyze the methods and ways of enhancing the independent learning activity of students. The teacher's creative approach to the organization of the educational process in the online format serves as the basis for the development of independence and self-organization of students. Self-organization, in turn, is associated not only with educational activities, but also with the life and professional career of young people. For this purpose, in the process of educational activity, serious attention should be paid to self-organization not only within the educational process, but also in extracurricular activities.
A Secure And High Concurrency SM2 Cooperative Signature Algorithm For Mobile Network

Mobile devices have been widely used to deploy security-sensitive applications such as mobile payments, mobile offices etc. SM2 digital signature technology is critical in these applications to provide the protection including identity authentication, data integrity, action non-repudiation. Since mobile devices are prone to being stolen or lost, several server-aided SM2 cooperative signature schemes have been proposed for the mobile scenario. However, existing solutions could not well fit the high-concurrency scenario which needs lightweight computation and communication complexity, especially for the server sides. In this paper, we propose a SM2 cooperative signature algorithm (SM2-CSA) for the high-concurrency scenario, which involves only one-time client-server interaction and one elliptic curve addition operation on the server side in the signing procedure. Theoretical analysis and practical tests shows that SM2-CSA can provide better computation and communication efficiency compared with existing schemes without compromising the security.
Academic and Industrial Partnerships in the Research and Development of Hybrid Autonomous Systems: Challenges, Tools and Methods

Autonomous systems increasingly are integrated into larger, connected, and hybrid (Human-Machine) systems of systems, making them complex systems - which are hard to design and predicting emergent behaviour is difficult. These issues are faced increasingly across civil and military applications, both in the UK and NATO. A holistic approach is needed to fully quantify them. Working as a partnership between industry and academia has provided greater freedom to apply innovative technologies in the context of relevant use cases. This paper presents some tools and methods we have used in our research and development to support this approach and address the challenges of deploying autonomous systems in the future. We discuss the use of simulations and how they can support every step of the process, from academic experiments to digital twins; where the right level of fidelity is needed at different times to give maximum benefit. The use of a common simulation platform to align control design exploration with human factors research is discussed, enabling questions of human-machine teaming and trust. We highlight how foundational research on: architecture and modelling, network topology, decision making processes and human interactions impact on the overall development of a system. Included are our lessons identified from this partnership.
Efficient Framework for Sentiment and Pattern Analysis on Movie Data

Nowadays, many social networks are available such as LinkedIn, Twitter, Facebook, etc. These types of networks are based on an opinion by the people. Opinion mining predicts the result based on opinion. Opinion mining is also called Sentiment Analysis. There are various opinion mining applications like Machine Learning (ML), Artificial Intelligence (AI), Data Mining, Web Mining, Text Mining, etc. It can also be analyzed sentiment on movies data. It can predict various types of intrinsic information using movies data. Here we proposed an efficient approach for sentiment and pattern analysis on movie data. It is based on people’s ratings on movies, and it can predict the behavior of movie users.
The Wind Generator’ Power Effective Forecast Method Based on Modified One-Dimensional Convolutional Neural Network and Metaheuristics

The method for predicting of wind generator’ power based on a modified one-dimensional convolutional neural network was proposed in the article. The model of a modified one-dimensional convolutional neural network was created; it allows to extract the most significant features and increase the forecast accuracy due to the automatic calculation of the convolution, pooling and dense (fully connected) layers’ number and sizes. The method for neural network model parametric identification based on local search was developed; according to the absence of recurrent connections it allows to use a batch learning mode for the learning rate’ increase. The method for the neural network model parametric identification was created; according to the swarm of cats’ adaptive optimization and using of simulated annealing it makes possible to make the search global at the first iterations, and to make the search local at the last iterations; it increases of the forecast accuracy too. The method for predicting the wind generator’ power based on a modified one-dimensional convolutional neural network and metaheuristics increases the forecast efficiency and can be used in various intelligent systems for analyzing the characteristics of technical objects with high dynamics.
Genetic Algorithm-Based Deep Learning Models: A Design Perspective

Deep learning models are immensely used for numerous problems in different domains and have proven to be superior over feature-based machine learning techniques. However, the success of any deep learning model is dependent on several factors like the tuning of appropriate different hyper-parameters, neural network architectures, optimizers, etc. To learn neural network weights, gradient-based optimizers such as stochastic gradient descent, min-batch gradient descent and the Adam optimizer are widely used. However, the architecture of neural networks and hyper-parameters has to be tuned manually for better performance of the model. Evolutionary deep learning models are gaining attention in recent years to overcome the manual tuning of hyper-parameters and the network architecture. This paper presents a design perspective of recently developed Genetic Algorithm-driven evolutionary deep learning models along with the inherent challenges.
Storing Student Records Using Blockchain and IPFS

Securing and issuing academic transcripts is one of the most mind-numbing tasks in the modern world as there are multiple disciplines and manually maintaining the accuracy of the data is tough. Moreover, the data is stored in an untrusted centralized cloud storage system, this type of model possesses a number of issues like high operational cost, data availability, and data security. The current research paper tries to exploit the decentralization and the smart contract feature of the ethereum blockchain. A model where basic student data is stored in a smart contract and large files are stored in IPFS protocol.
A Knowledge-Based Recommender Framework to Recommend Destination Country for Human Migrants (KBRF)

Human migration is a challenging process in Ethiopia, where special illegal human migrants cross different border countries without any basic information or knowledge. Due to this, there is the difficulty of getting a vast amount of information to know the destination countries. They go without any information, which also wastes more time, cost, violates their human rights, and endangers their lives. This study used a KBRF to assess the user input and compare them with the stored cases. For this, 75,920 cases were collected from MoLSA and central statistical agencies with and attributes selected for the query of users as well as for their solution. The best solutions are displayed for a user after the similarity calculation of the query and the stored cases. Then the users can decide on which similar cases satisfy their needs. KBRF has an average performance, recall, and precision score of 92% (4.60), 0.91 (91%), and 0.63 (63%), respectively, from domain experts and system performance.
Summary of Fault Diagnosis Technology in Smart Grid

China's power grid has a complex structure and a wide range of power supply areas. Problems such as fault identification, difficulty in locating, and long fault recovery time are prone to occur after failure. Smart grids need to be combined with actual situation. In response to the needs of the intelligent development of power grids, the current smart methods of power grid fault diagnosis are reviewed, including expert system, neural network algorithm, Bayesian network, Petri net, and comprehensive analysis of the application scenarios, advantages and disadvantages of various methods, and finally discussed the development prospects of fault diagnosis technology under the requirements of intelligent power grid.
Machine Propositional Model Research Based on Natural Language Processing

Machine propositions are the research frontier of natural language processing technology, and the core of the technology path is the result of the development of reading comprehension and question answering systems. Machine propositions can help save a lot of manpower and time, especially in the case of the continuous development of the Covid-19, and help the teaching acceptance and learning evaluation of online learning. At present, the simultaneous pursuit of autonomous propositions and precision requirements in the academic field of machine propositions is the research focus and difficulty.
Pseudorandom Number Generators

Randomness is a key element in cryptography, as cryptosystems and protocols are based on arbitrary numbers that cannot be predicted. Numbers with such characteristics are known as randomly generated numbers (or random numbers), and the technique of their generation lies in a statistical context. Random numbers are mainly used in domains in which results that cannot be predicted are expected, for example, statistical sampling, simulations, or even gambling.
Wavefront Optical Spacing of Freeform Surfaces and its Measurement Using Cgh Interferometry

Wavefront optical spacing (WOS, or spacing d for short) is an important parameter of optical surfaces. It determines the on-axis curvature deviation and the best-fit sphere radius of the surface under test. Thus, affects the optical distance, effective focal length, and tolerance design in optical system. The analytical form of WOS can be utilized in critical tolerance balancing, and its test results can help in optical system redesign. Aiming at computer-generated hologram (CGH) interferometric test, this paper proposes the WOS concept, deduces the coupling relation between the surface error and spacing d , especially in freeform surface conditions. A cat-eye CGH interferometric method is presented to test the spacing error within several micron precision. Simulation and error budget show that this method is capable to measure the WOS of freeform surfaces. Experiment results indicate the metrology accuracy is 10.2μm (absolute accuracy), 4.5μm (repeatability), and 2.1ppm (relative accuracy) respectively.