Miracle-dz/UniFAR-FTUs huggingface dataset:
DRL-OR: Deep Reinforcement Learning-based Online Routing for Multi-type Service Requirements

Emerging applications raise critical QoS requirements for the Internet. The improvements of flow classification technologies, software defined networks (SDN), and programmable network devices make it possible to fast identify users’ requirements and control the routing for fine-grained traffic flows. Meanwhile, the problem of optimizing the forwarding paths for traffic flows with multiple QoS requirements in an online fashion is not addressed sufficiently. To address the problem, we propose DRL-OR, an online routing algorithm using multi-agent deep reinforcement learning. DRL-OR organizes the agents to generate routes in a hop-by-hop manner, which inherently has good scalability. It adopts a comprehensive reward function, an efficient learning algorithm, and a novel deep neural network structure to learn an appropriate routing policy for different types of flow requirements. To guarantee the reliability and accelerate the online learning process, we further introduce safe learning mechanism to DRL-OR. We implement DRL-OR under SDN architecture and conduct Mininet-based experiments by using real network topologies and traffic traces. The results validate that DRL-OR can well satisfy the requirements of latency-sensitive, throughput-sensitive, latency-throughput-sensitive, and latency-loss-sensitive flows at the same time, while exhibiting great adaptiveness and reliability under the scenarios of link failure, traffic change, and partial deployment.
Parameters Compressed Mechanism in Federated Learning for Edge Computing

With the continuous improvement of the performance of the IoT and mobile devices, the IoT technology brings convenience to people’s lives but leaks out personal privacy at the same time. Federated learning is a kind of machine learning technology that can protect data privacy. Different from current machine learning methods, Federated learning is usually facing non-Identically Independently Distributions (no-IID) data and keeps data in local places. Therefore, the effectiveness of existing machine learning methods in federated learning non-IID issues decreases greatly. At the same time, federated learning needs to interact with multiple edge learning nodes. With the increasing number of edge nodes, the cost of communication becomes higher and higher, and the parameter weight calculation of more independent nodes is also a problem. In this paper, we propose to balance the federated learning model by using Earth Mover’s Distance to calculate the weights of different node parameters. Our method can reduce the impact of data non-independent identically distributed problems on the model so that the federated learning model will not be biased to the distributed nodes in the data set. Besides, this paper also proposes a method to compress redundant communication between nodes and servers in the process of training. Experimental results show that this paper method improves the accuracy by about 4% compared with the traditional federal learning average algorithm, and the gradient communication is compressed to about 6% of the original communication times.
Miniature Tangible Cube: Concept and Design of Target-Object-Oriented User Interface for Dual-Arm Telemanipulation

In recent years, there has been a great deal of research on teleoperation of robots, and many end-effector-oriented control systems have been proposed, but these systems have difficulties in performing manipulation tasks with physical contacts between the target object, the robot, and the environment, such as dual-arm manipulation, because these systems can only express the end-effectors’ pose and force commands. In this study, we propose Target-object-oriented User Interface (TOOUI) that focuses on the target object rather than the end effectors of the robot, and develop a tangible device called Miniature Tangible Cube as a TOOUI. Through this device, the pilot can express the target orientation and contact state of the target object, and the robot can be teleoperated to do dual-arm manipulation tasks with geometric and physical constraints. For dual-arm manipulation tasks, we also implement a dual-arm telemanipulation system with the Miniature Tangible Cube using the motion planner based on state machine. In the experiments, we evaluated our system with the Miniature Tangible Cube by conducting dual-arm manipulation tasks with the dual-arm robot PR2 in the real world.
Brain Tumor Classification based on MR Images using GAN as a Pre-Trained Model

In the medical industry, misdiagnosis of disease is acknowledged as the most common and harmful medical errors as it can cost a human life. Radiologists require a lot of time to manually annotate and segment the images. Over the several years, deep learning has been playing a vital role in the field of computer vision. One of its key uses in the medical industry is to minimize misdiagnosis and the amount of time taken to annotate and segment the images. In this paper, a new deep learning approach for brain tumor classification on MRI Images is introduced. A deep neural network is pretrained as a discriminator in a generative adversarial network (GAN) on MR Images by using multi-scale gradient GAN (MSGGAN) with auxiliary classification to extract the features and to classify the images. In the discriminator, one of the fully connected blocks acts as an auxiliary classifier and the other fully connected block acts as an adversarial. The fully connected layers of the auxiliary block are fine-tuned to classify the type of tumor. The proposed approach is tested on two publicly available MRI datasets as a whole, consists of four types of brain tumors (glioma, meningioma, pituitary, and no tumor). Our proposed method achieved 98.57% accuracy which is better as compared to state of art methods. Also, our method appears to be a useful technique when the availability of medical images is limited.
The Impact of the Digital Transformation on Customer Experience in Palestine Banks

This research describes the effect of digital transformation on the relationship between the banks in Palestine and their customers and how this relation is influenced by digital transformation technologies. A survey was conducted among banks customer from different age intervals, in different work sectors, and with different educational levels that asked a set of questions that measured the usage of the digital banking technologies and interest among the participants. It also asked about customers' opinions about the digital banking technologies, in addition to that asked about the challenges and barriers related to the digital technology usage from the customer point of view. This research helped in building a better understanding of the customers' needs and preferences, awhich would help the banks in improving the quality of their services.
Atmospheric Chemistry Measurements at Whiteface Mountain, NY: Cloud Water Chemistry, Precipitation Chemistry, and Particulate Matter

ABSTRACTLong-term records of condensed-phase chemical data are presented from the Adirondack Mountain region of northern New York, USA. These data records are particularly valuable due to the combinations of aerosol, cloud, and precipitation measurements. Objectives of the research and this overview paper include the evaluation of emission reductions of regulated air pollutants and the observed effects on measured deposition, as well as the implications of changing pollutant concentration levels on human health and climate. Summer season cloud chemistry and year-round wet deposition and particulate matter data from two stations on Whiteface Mountain are presented to highlight some of the research and monitoring activities at this mountain location. Clear decreases in the anion concentrations and increases in pH over the past two decades have been observed in cloud and precipitation results. Large decreases in aerosol sulfate (> 80%) and aerosol optical black carbon (> 60%) have been observed for these species over the nearly 40 year summit observatory data record for these measurements, and decreases in PM2.5 mass, sulfate, nitrate, and ammonium have also been recorded over the shorter 15 year period of measurement at the Marble Mountain Lodge level. The studies cited here highlight some of the past successes of air pollution regulation under the Clean Air Act and Amendments and pave the way for future progress in reducing air pollution.
Real-Time Communication Model Based on OPC UA Wireless Network for Intelligent Production Line

With the continuous development of intelligent manufacturing technology, the intelligent production line for the Industrial Internet of Things occupies an important position in the field of industrial intelligence. OPC UA is a standard for communication data exchange between intelligent production line devices. OPC UA can establish a unified information model for production line devices and improve the connectivity of heterogeneous networks. However, in industrial wireless network application scenarios, there are various types of sensing information and large amounts of data to be transmitted. Each type of data has different requirements for real-time. The traditional OPC UA communication method is difficult to achieve real-time and reliable transmission in intelligent production lines and can’t meet the transmission requirements of time-sensitive data. In this paper, we propose a real-time communication model of the OPC UA wireless network for intelligent production lines (UAMPDS). IEEE 802.15. 4e TSCH is used as the wireless communication infrastructure, and OPC UA protocols are fused to this model. Meanwhile, we design a load-aware time-slot scheduling algorithm to dynamically allocate and schedule time slots according to the network topology and traffic load of each node. We also implement a dynamic preemptive resource scheduling strategy based on service differentiation to ensure real-time transmission of time-sensitive data. The experimental results show that this model achieves differentiated services based on data with different latency tolerance and effectively reduces the transmission latency of real-time service data under the constrained network resources. It can avoid the starvation phenomenon of low priority queues, and improves the overall quality of service of intelligent production line communication networks.
A 5G FR2 (n257/n258/n261) Transmitter Front-End with a Temperature-Invariant Integrated Power Detector for Closed-Loop EIRP Control

This paper presents a multiband millimeter-wave transmitter front-end with an integrated power detector for enabling closed-loop power control. The power amplifier delivers an output power of 12.5dBm, PAE of 10% and EVM less than -25dB with CP-OFDM/64QAM/400MHz. A transformer-based PA output matching-network is proposed that presents the PA with an optimal load over the 5G frequency bands from 24.5-29.5GHz, while suppressing the second harmonic power to meet the stringent 5G emission regulation. A novel integrated power-detector (PDET) which employs a current-mode SAR ADC to improve temperature robustness of power tracking with antenna VSWR variations.
DEDICAT 6G - Dynamic Coverage Extension and Distributed Intelligence for Human Centric Applications with Assured Security, Privacy and Trust: from 5G to 6G

5G networks offer unparalleled data rates and features. However these are still far from what a hyperconnected society and industry needs. Future wireless connectivity Beyond 5G (B5G)/6G will require a smart and green platform that is ultra-fast, highly adaptive, and dependable to support innovative, human-centric applications securely. This is the focus of the EU-funded DEDICAT 6G project, the vision and methodology of which is presented in this paper. DEDICAT 6G investigates enablers for dynamic distribution of intelligence to improve task execution time, energy efficiency, and ultimately, reduce end-to-end latency. The project also examines solutions for dynamic coverage extensions utilizing robots, connected vehicles and drones. The scope also comprises methods for security, privacy, and trust assurance including enablers for novel interaction between humans and digital systems exploiting innovative interfaces and devices, like smart glasses. DEDICAT 6G focuses on four representative 6G use cases: Smart Warehousing, Enhanced Experience, Public Safety and Smart Highway. The developed solutions will be demonstrated and tested in these use cases through experiments in laboratory environments, and larger field evaluations utilizing diverse assets and testing facilities. The aim is to derive results that will showcase substantial improvements in terms of intelligent network load balancing and resource allocation, extended coverage, enhanced security, privacy and trust and human-machine applications,
A Heterogeneous Lightweight Network for Plant Disease Classification

The economy of many countries worldwide depends on the agricultural sector. In agriculture, plant cultivation is often affected by diseases. The plant diseases can cause partial or complete damage to the leaves and fruit. This leads to significant economic loss in the agricultural industry. At the same time, plant diseases, if not detected in an early stage, have negative consequences on the quantity, quality and production of agricultural crops. Thus, the biggest challenge in agriculture is the accurate and early diagnosis and the prevention of plant diseases. In this study, we modified the structure of a popular classification network, the MobileNet V2 to improve the overall accuracy of it, by applying the transfer learning technique and fine-tuning. Experimental findings have shown that the Modified MobileNet V2 architecture achieves a high level of overall accuracy compared to other similar CNN lightweight architectures used in plant leaf disease detection in the recent literature.
Bidirectional Soft Switching Push–Pull Resonant Converter Over Wide Range of Battery Voltages

This article presents a bidirectional soft-switching push–pull resonant converter that is highly efficient over a wide range of battery voltages. It is derived by integrating a current-fed push–pull circuit and an active voltage doubler circuit with a bidirectional switch. The converter operates as a pulsewidth modulation (PWM) current-fed push–pull resonant-boost converter in the forward direction, but as a PWM half-bridge resonant converter in the backward direction. The most attractive feature of the proposed converter is that, by employing the bidirectional switch and corresponding switching modulation, it can achieve almost ZVS at turn-off instants under wide variations of battery voltages. Therefore, the proposed converter achieves high efficiency even during high-frequency operation. Moreover, no instantaneous reactive current flows through the circuit under wide variations of battery voltages and loads. Furthermore, the proposed bidirectional resonant converter has a high conversion ratio in both power flow directions without using a transformer that has high turns-ratio. The operating principles and characteristics of the proposed bidirectional resonant converter are presented in detail. Experimental results using a 1-kW prototype verified the feasibility and efficiency of the proposed converter.
Exploring Layered Container Structure for Cost Efficient Microservice Deployment

Container, as a light-weight virtualization technology with the advantages of continuous integration and easy deployment, has been widely adopted to support diverse microservices. At runtime, non-local container images need to be frequently pulled from remote registries to local servers, resulting in large pulling traffic and hence long startup time. A distinctive feature in container-based microservice, which has not been exploited, is that container images are in layered structure and some common base layers can be shared between co-located microservices. In this paper, we propose a layer sharing microservice deployment and image pulling strategy which explores the advantage of layer sharing to speedup microservice startup and lower image storage consumption. The problem is formulated into an Integer Linear Programming (ILP) form. An Accelerated Distributed Augmented Lagrangian (ADAL) based distributed algorithm executed cooperatively by registries and servers is proposed. Through extensive trace driven experiments, we validate the high efficiency of our ADAL based algorithm as it accelerates the microservice startup by 2.30 times in average and reduces the storage consumption by 55.33%.
Fundamentals of Semiconductors – Physics and Materials Properties

— quantum confinement of electrons and phonons. All chapters have their own references (from historical important papers to modern monographs and textbooks) and are enriched with tables of material parameters, figures and problems (105 in total). The book contains 246 two-color figures, 50 tables, a subject index and a very interesting appendix "Pioneers of Semiconductor Physics remember. ". Heavy emphasis on optical properties and photoelectron spectroscopy reflects the expertise of the authors. The book treats semiconductors as materials, no device applications are contained. It attempts to fill the gap between solid-state physics textbooks and research articles. The approach is physical and intuitive rather than formal. Theories are presented to explain experimental results. In the author's opinion it needs for understanding only undergraduate physics and mathematics course materials (with the exception of group theory, which is presented as a "crash course" in chapter 2.3), but in fact a deeper understanding of special topics requires knowledge in second quantization. Greens functions and Freynman diagrams (for calculating light scattering). The book will be of great value for graduate students and research students in the field of semiconductor physics, for lecturers of semiconductor physics and for engineers working in the field of research and development of electronic and especially of optoelectronic devices.
Authorship Identiﬁcation with Embeddings and Sentence Variations

In this paper, we aimed to solve the problem of predicting the author of a given piece of text. We created datasets mapping different-sized chunks of text to authors, and used them to train several models. These models include a basic ANN that takes in word count vectors, several RNN architectures that take in GloVe embeddings, and a sentence encoder that maps text into a latent space. All models produced impressive results on a variety of datasets, with the ANN performing the best. We conclude by analyzing potential reasons for this surprising result
Residual Learning: A New Paradigm to Improve Deep Learning-Based Segmentation of the Left Ventricle in Magnetic Resonance Imaging Cardiac Images

Background: Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate segmentation of the heart in captured images. Although various schemes have been tested for this segmentation so far, the latest proposed methods have used the concept of deep learning to estimate the range of the left ventricle in cardiac MRI images. While deep learning methods can lead to better results than their classical alternatives, but unfortunately, the gradient vanishing and exploding problems may hamper their efficiency for the accurate segmentation of the left ventricle in MRI heart images. Methods: In this article, a new concept called residual learning is utilized to improve the performance of deep learning schemes against gradient vanishing problems. For this purpose, the Residual Network of Residual Network (i.e., Residual of Residual) substructure is utilized inside the main deep learning architecture (e.g., Unet), which provides more significant detection indexes. Results and Conclusion: The proposed method's performances and its alternatives were evaluated on Sunnybrook Cardiac Data as a reliable dataset in the left ventricle segmentation. The results show that the detection parameters are improved at least by 5%, 3.5%, 8.1%, and 11.4% compared to its deep alternatives in terms of Jaccard, Dice, precision, and false-positive rate indexes, respectively. These improvements were made when the recall parameter was reduced to a negligible value (i.e., approximately 1%). Overall, the proposed method can be used as a suitable tool for more accurate detection of the left ventricle in MRI images.
Automatic Generation of Structured Radiology Reports for Volumetric Computed Tomography Images Using Question-Specific Deep Feature Extraction and Learning

Background: In today's modern medicine, the use of radiological imaging devices has spread at medical centers. Therefore, the need for accurate, reliable, and portable medical image analysis and understanding systems has been increasing constantly. Accompanying images with the required clinical information, in the form of structured reports, is very important, because images play a pivotal role in detect, planning, and diagnosis of different diseases. Report-writing can be exposure to error, tedious and labor-intensive for physicians and radiologists; to address these issues, there is a need for systems that generate medical image reports automatically and efficiently. Thus, automatic report generation systems are among the most desired applications. Methods: This research proposes an automatic structured-radiology report generation system that is based on deep learning methods. Extracting useful and descriptive image features to model the conceptual contents of the images is one of the main challenges in this regard. Considering the ability of deep neural networks (DNNs) in soliciting informative and effective features as well as lower resource requirements, tailored convolutional neural networks and MobileNets are employed as the main building blocks of the proposed system. To cope with challenges such as multi-slice medical images and diversity of questions asked in a radiology report, our system develops volume-level and question-specific deep features using DNNs. Results: We demonstrate the effectiveness of the proposed system on ImageCLEF2015 Liver computed tomography (CT) annotation task, for filling in a structured radiology report about liver CT. The results confirm the efficiency of the proposed approach, as compared to classic annotation methods. Conclusion: We have proposed a question-specific DNN-based system for filling in structured radiology reports about medical images.