Miracle-dz/UniFAR-FTUs huggingface dataset:
New Directions and Challenges within Identity and Access Management

Identity and access management is a core building block for the majority of web services. Cloud-based services, social webs, mobile apps, but also IoT-related services rely on identity management to provide a seamless and secure user experience. Transmitting and sharing sensitive information with other organizations always poses a security and privacy risk to all participating entities. One solution to tackle this problem is the principle of federated identity management (FIM). FIM is used to authenticate and authorize users across multiple organizations and platforms in order to obtain access to resources and services. The benefits of FIM are, for example, consistent data, reduced amount of sensitive information needed to be shared, as well as less passwords for the user to remember. Both predominant standards, Secure Assertion Markup Language (SAML) 2.0 and Open Authentication (OAuth) 2.0 with the authentication layer OpenID Connect, are in wide-spread practical use for at least a decade. However, these protocols were developed with different requirements in mind than nowadays present. This led to several extensions to tackle real-world problems, making it cumbersome to comply with every flavor. Also, Request for Comments (RFC) 8252 suggests that a native app opens a system browser for user authentication; consequently, new protocols are currently developed. For example, within Internet Engineering Task Force (IETF), Kantara Initiative, and OpenID Foundation, which (should) have three main goals in common: • Reducing the complexity in contrast to SAML 2.0 and OAuth 2.0. • Decreasing the amount of extensions and varieties found in the wild. Both help developers to comply with the standards and, consequently, increase the security. • Including edge and future use cases, making the protocols even more useful. This article gives insights into current developments and possible future paths.
Intelligent Waste Sorting Platform Based on Taurus & Pegasus

Garbage sorting is an action for everyone to participate, but the existing garbage sorting mainly relies on manual discrimination. In this paper, an intelligent garbage sorting platform is designed using Taurus & Pegasus kit, which records the video stream of garbage using the sensor side of Taurus vision development board. The generated video files are then copied to an SD card for dataset creation, data cleaning, and data annotation. ResNetl8 network model is built using Pytorch framework for training (get pytorch), model transformation (model-caffe), quantization and simulation. Do end-to-end modeling and visualization. Network models are combined with HiSilicon NNIE deep learning acceleration engine to implement fast target classification algorithms. Finally, the model is deployed to the Taurus side for image recognition using an SD card. Residents place garbage in front of the camera, and the Taurus side recognizes and displays the garbage classification inference results and probabilities on the screen. At the same time, the result is sent to Pegasus via serial communication. Pegasus receives the classification result and drives the corresponding steering engine to open the garbage can lid and give voice prompt. When no object or background picture is captured in the camera screen, the trash can lid will be closed.
Pattern Optimization of Beam-Reconfigurable Antenna Fed by Varactor-based Phase Shifters

Pattern optimization is proposed for a beam-steerable dielectric resonator antenna (DRA) array fed by a two-stage feeding network based on varactor-based phase shifter pairs. Based on the calibration on the feeding network and the actual measured radiation patterns, unexpected distortion of patterns such as inaccurate beam direction caused by material and fabrication tolerance can be corrected. According to the observed distortions, correcting phase differences on the phase shifter pair can be identified. To verify this process, two scenarios of accurate beam direction correction are illustrated in this paper. The optimized patterns obtained in measurement show that the proposed pattern optimization is efficient and practical for the beam-steerable array fed by varactor-based phase shifters.
HODL: The Hold of Reddit Over the Stock Market

WallStreetBets (WSB) became the center of attention among all the social media outlets following the Gamestop short squeeze incident in 2021. Naturally, this raises scientific queries about how much WSB has since sustained its influence on the stock market. With Reddit and other social media networks, prior efforts have concentrated on evaluating textual sentiment impacts using lexical or conventional machine-learning approaches. However, the natural language processing landscape has evolved remarkably with the introduction of large-scale language models, and it remains to be seen how much these models can help scrutinize Reddit’s impact on the market participant. This research offers a BERT-based sentiment scoring approach tailored specifically for the text used in Reddit’s wallstreetbets community. We anticipate that this will encourage future studies in financial literature to use pre-trained language models for text analysis.
A Self-managed Marketplace for Sharing IoT Sensors

Internet of Things (IoT) applications can significantly reduce the cost and timeframe of providing their benefits by taking advantage of the billions of existing IoT sensors and other IoT devices that have been deployed by others in IoT. This paper proposes a self-managed Sensor Sharing Marketplace (SenShaMart) that allows IoT applications to discover, integrate, and pay for the use of sensors that are managed by different parties. In addition to being a self-managed IoT marketplace, SenShaMart is fully autonomic and cannot be controlled by any specific party. IoT applications and IoT sensor providers interact via SenShaMart provided services for semantic description of IoT sensors and their data, semantic query processing, automatic integration of sensors and their data, and IoT sensor payment transactions that control the access to the flow of sensor data according to the sensor payment terms. Self-management and full autonomy are achieved via a specialized SenShaMart blockchain that keeps and manages all data needed by the SenShaMart services that encapsulate it. The paper presents the SenShaMart architecture and service organization, its specialized blockchain, as well as its current performance characteristics and future research.
Gate Driver IC with Fully Integrated Overcurrent Protection Function by Measuring Gate-to-Emitter Voltage During IGBT Conduction

To achieve low-cost overcurrent protection for IGBTs without using external components such as high-voltage diodes, a gate driver IC with a fully integrated overcurrent protection function by measuring gate-to-emitter voltage ($V_{\text{GE}}$) during IGBT conduction is proposed. In the proposed gate driver IC, while the IGBTs are ON, constant gate charge is periodically discharged and charged, and when $V_{\text{GE}}$ dropped by each discharge is less than the reference voltage, it is detected as the overcurrent and the IGBTs are immediately turned off to protect from the overcurrent. In a single-pulse test of an inductive load at 300 V for an IGBT with a pulse rating of 200 A, the proposed gate driver IC fabricated with 180-nm BCD process successfully protected the overcurrent of 370 A with the protection delay of 810 ns.
Impact of Digital Transformation in Sourcing & Tender Management Processes on Employee Job Satisfaction - A Study on Malaysian Multinational Electricity Company

This Digital Transformation is a methodology of leveraging emerging digital technologies & platforms to re-imagine and re-engineer AS-IS business/operational processes, organizational culture, and user experiences to meet evolving global business requirements. Hence, reimagining the business in the digital age with technological innovation & adoption is the essence of digital transformation. Job Satisfaction is measured as the extent to which employees prefer their job or specific aspects of jobs, like the profile of work or quality of the job and has a direct relationship with employee Work-life Balance, Work Performance Management, Job Rotation/Distribution & Skill Management.Key objective of this research is focused on the Analysis of the Impact of Digital Transformation & Business Process Reengineering in the areas of E2E Sourcing/Procurement Management & Tender Life Cycle Management eco-system on the Employee Job Satisfaction Index. This study is intended to deal with existing issues, to analyze process gaps & to assess potential improvement in the team’s job satisfaction of Sourcing & Category Managers of the Procurement Division with the help of Digital Technologies - Business Process Management, Robotic Process Automation, Natural Language Processing, specifically in the context of a Malaysian Multinational Electricity & Utility Company (to be referred as Case-Organization). The intended research finding analysis should help senior management of the procurement division to focus their attention to digitalization & automation activities from internal human resource & leadership perspectives to leverage if digital transformation brings any value in effective work allocation, better job rotation, improved work-life balance, enhanced work quality and efficient performance management etc.
Open-switch Fault Diagnosis of Inverters in Linear Induction Machine Drives Based on Normalized Wavelet Packet Entropy

The linear induction machine (LIM) drive is the power source of the medium-low speed Maglev trains, in which the inverter plays the role of regulating the speed of the LIM. Thus, the reliable operation of the inverter is importance. In this paper, a fault diagnosis method for the open-switch faults in the inverter is introduced. It utilizes the phase currents only and requires no additional sensors or hardware. The amplitudes of the currents are normalized to strengthen the performance under different working conditions. Meanwhile, the several kinds of wavelet packet entropies are investigated regarding the diagnosis ability. Finally, an open-switch fault diagnosis method is proposed, and its effectiveness is verified by the simulation results.
Classification of Skin Cancer With Using Color-ILQP and MEETG

Skin cancer is one of the most common forms of cancer in the world that has grown dramatically over the past decades. Malignant melanoma is the deadliest type of skin cancer. Melanocytic nevi are benign whereas melanoma is malignant. Most skin cancers are treatable in the early stages. So, rapid diagnosis and the importance of early stage can be very important to cure it and increasing day by day. Today, artificial intelligence can represent an important role in medical image diagnosis. The aim of this paper is to an auto-diagnosis system can be deployed to help dermatologists in identifying melanoma that may facilitate early detection of melanoma, and hence substantially reduce the mortality chance of this dangerous malignancy. We used image processing tools to diagnose melanoma skin cancer. In this paper, the advantage of improved local quinary pattern (ILQP) is used as texture feature extraction method and used mixture of ELM-based experts with a trainable gating network (MEETG) for skin cancer classification. Our proposed method achieved the classification accuracy on f and d datasets, 97.05% and 86.61% respectively.
Auto-Provisioning of UAV Industry Cloud Platform for Intelligent Multi UAVs Simulation

Most recently, generic cloud platform model is difficult to satisfy the inherent, uncertain and streamline requirements of the specific domains. Industry Cloud Platform (ICP) turn the generic cloud platform into a professional platform designed to fit the specifics of the user's industry. ICP integrates the traditional cloud services with the specialized field to create greater value for the organizations. In the proposed ICP, requirements of the Software In The Loop (SITL) simulation environments provided as Container as a Service (CaaS) for the Urban Area Vehicle (UAV) with autoscaling and auto provisioning features.
Development of Authentic-Based Multimedia Learning in African Higher Education Institutions: Prospect and Problems

Multimedia-based learning has been employed as an informative tool in teaching spaces since the Second World War, and its employment in higher education is still a trend in this day and age. Improvements in information and technology have a significant impact on the teaching methods used in schools. However, the concept of authentic-based multimedia is still emerging. It makes teaching curriculum more real and relevant for student education needs to create inventive students, deep learners with excellent problem-solving abilities. Previous reports showed that engaging students in an authentic- based multimedia learning methodologies to innovate in technology-enabled 21st-century classrooms made students expressed a favorable attitude toward learning in this setting and the genuine activities in the modules with huge academic improvement. However, African institutions are still lagging due to policies’ non-implementation to facility deficiencies. There are reports on efforts of some African tertiary institutions to create this deep teaching and learning atmosphere, while some are yet to understand this concept, less implementing it, besides the problems peculiar to African institutions. Educators still lack the confidence to design proper learning environments that engage and educate students. There is more to utilizing information technology than merely uploading teaching materials online. This paper gives an overview of the level of inclusion of authentic learning strategies into a multimedia learning environment that influences student learning. Decisive efforts must be geared towards providing effective and adequate facilities bridging infrastructural deficiencies and training teachers in both education and technological skills, as well as access to a technology specialist who can assist with technical assistance for multimedia operations to keep students engaged in the learning environment.
Exploring Gender Differences in Emotion Recognition with Electroencephalography

Gender has been demonstrated to have a significant impact on emotion response. However, most previous researches on the effects of gender on emotion have largely focused on the fields of psychology and neuroscience. This paper employs electroencephalography signals to investigate gender differences in emotional expressivity, emotional experience and emotion recognition from the perspective of affective computing. Considering the advantages of being concise, comprehensible and emotionally impactful, the short videos are chosen to elicit positive, neutral and negative emotional categories. The analysis results on emotional responses show that the gender differences do exist in evoking emotion during watching short videos. Additionally, we use differential entropy as features and conduct the cross-subject emotion recognition experiment employing support vector machine. The experimental results show that the same gender training strategy outperforms the different gender training one, which implies that there should be gender in-group advantage in electroencephalogram patterns when processing emotions. These findings provide valuable information for a deeper understanding of the role of gender differences in emotion processing and emphasize the importance of integrating neuroscience and computational approaches in emotion recognition tasks.
Solving The Model of Two Dimensional RDB System by Modified Vibrational Iteration Method with Genetic Algorithm

A modification of the variational iteration method with a genetic algorithm is presented for the numerical results of the nonlinear partial differential equations. Therefore, a new correction function through an auxiliary parameter (p) for making sure the convergence of the standard method and improved results by using genetic techniques will be introduced. The standard variational iteration method is applied first before improving it. This method was studied to solve numerically the system of two-dimensional reaction-diffusion brusselatorequations. Its effectiveness and high-order precision have been demonstrated through numerical experiments. The algorithm converges readily, yields correct solutions and better accuracy in comparison with other previous methods have been noticed. Moreover, with greater accuracy, the approach can be used for a variety of linear and nonlinear differential equations.
D3T: Double Deep Q-Network Decision Transformer for Service Function Chain Placement

Network Function Virtualization (NFV) has become a promising technology, which is used to replace the complex hardware implementation of various Network Functions. Virtual Network Functions (VNFs) are placed on the servers to realize these functions of a network, for example 5G network. Such an implementation reduces the expenditure and latency significantly. However, it also comes with its own set of challenges. One of the prominent challenges of NFV is the placement problem. The requests are in the form of a Service Function Chain (SFC), which is a pre-defined sequence of VNFs. When an SFC request comes in, resources must be allocated to the VNF instances to satisfy the requirements. In this paper, we propose an effective algorithm D3T (Double Deep Q-Network Decision Transformer) to optimize the SFC placement. The algorithm is designed using a Decision Transformer (DT) that is assisted by a Double Deep Q-Network (DDQN). We employ a DDQN model as the baseline algorithm to generate offline training data. The trajectory data in the Experience Reply Memory of DDQN will be processed into sequences and modeled by a transformer. The algorithm’s objective function considers end-to-end delay and rejection ratio as the objectives. Specifically, D3T combines the transformer with DRL to give an optimal solution. The results presented demonstrate the effectiveness of the proposed solution.
Research on Positive Emotion Recognition Based on EEG Signals

Human logical decision-making, perception, learning, and many functions, which influence people's decision-making on things and perception of external things, are all significantly influenced by emotions. An essential component of emotional computing is emotional cognition. By examining the psychological and psychological traits of the client, it evaluates the psychological condition of the service object. Currently, most of the research on emotions based on electroencephalogram (EEG) signals focuses on classifying positive, neutral, and negative emotions, or studying negative emotions, with less attention paid to specifically identifying positive emotions. This experiment proposed an experimental design that utilized virtual reality technology as an inducing method to stimulate positive emotions, and identified and evaluated the emotions of happiness, desire, and healing. This experiment collected datasets on three positive emotions of happiness, desire, and healing, and collected the Positive Affect and Negative Affect Scale (PANAS) and self-assessment (SAM) form of the participants. Divided the experiment into two groups, one was the experimental group wearing VR, and the other was the experimental group not wearing VR. The immersion feeling scale was used to study the effect of VR on stimulating emotions. Through the design of emotion induction experiments, the EEG signals of experiencing happiness, desire, and healing were collected. The EEG signals were input into a CNN network for feature extraction, both in the form of images and time series. The Resnet18 network was used for image-based emotion recognition with an accuracy of 93%. The time-series data was processed using an LSTM network for emotion recognition with an accuracy of 94.9%.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning

Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key, longstanding challenges for AI. In this paper we consider how these challenges can be addressed within the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We extend the usual notion of action in this framework to include options —closed-loop policies for taking action over a period of time. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as muscle twitches and joint torques. Overall, we show that options enable temporally abstract knowledge and action to be included in the reinforcement learning framework in a natural and general way. In particular, we show that options may be used interchangeably with primitive actions in planning methods such as dynamic programming and in learning methods such as Q-learning. Formally, a set of options deﬁned over an MDP constitutes a semi-Markov decision process (SMDP), and the theory of SMDPs provides the foundation for the theory of options. However, the most interesting issues concern the interplay between the underlying MDP and the SMDP and are thus beyond SMDP theory. We present results for three such cases: (1) we show that the results of planning with options can be used during execution to interrupt options and thereby perform even better than planned, (2) we introduce new intra-option methods that are able to learn about an option from fragments of its execution, and (3) we propose a notion of subgoal that can be used to improve the options themselves. All of these results have precursors in the existing literature; the contribution of this paper is to establish them in a simpler and more general setting with fewer changes to the existing reinforcement learning framework. In particular, we show that these results can be obtained without committing to (or ruling out) any particular approach to state
Innite games with nite knowledge gaps

Innite games where several players seek to coordinate under imperfect infor- mation are believed to be intractable, unless the information is hierarchically ordered among the players. We identify a class of games for which joint winning strategies can be con- structed eectively without restricting the direction of information ow. In- stead, our condition requires that the players attain common knowledge about the actual state of the game over and over again along every play. We show that it is decidable whether a given game satises the condition, and prove tight complexity bounds for the strategy synthesis problem under parity winning conditions.
Texturing of Soy Yoghurt Alternatives: Pectin Microgel Particles Serve as Inactive Fillers and Weaken the Soy Protein Gel Structure

Soy-based yoghurt alternatives were highly requested by consumers over the last few years. However, their texture does not always fulfil consumers’ demands as such yoghurt alternatives are often perceived as too firm or too soft, sandy, or fibrous. In order to improve the texture, fibres, for example, in the form of microgel particles (MGP), can be added to the soy matrix. MGP are expected to interact with soy proteins, creating different microstructures and, thus, different gel properties after fermentation. In this study, pectin-based MGP were added in different sizes and concentrations, and the soy gel properties after fermentation were characterised. It was found that the addition of 1 wt. % MGP influenced neither the flow behaviour nor the tribological/lubrication properties of the soy matrix, regardless of the MGP size. However, at higher MGP concentrations (3 and 5 wt.%), the viscosity and yield stress were reduced, the gel strength and cross-linking density decreased, and the water-holding capacity was reduced. At 5 wt. %, strong and visible phase separation occurred. Thus, it can be concluded that apple pectin-based MGP serve as inactive fillers in fermented soy protein matrices. They can, therefore, be used to weaken the gel matrix purposely to create novel microstructures.