Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning in Robotics: Survey on Model Structures and Training Strategies

The ever-increasing complexity of robot applications induces the need for methods to approach problems with no (viable) analytical solution. Deep learning (DL) provides a set of tools to address this kind of problems. This survey presents a categorization of the major challenges in robotics that leverage DL technologies and introduces representative examples of successful solutions for the described problems. We also consider the question when and whether to use modular, monolithic models or end-to-end DL, in order to provide a guideline for the selection of the correct model structure and training strategy. By doing so, the current role and adaptability of different techniques at different hierarchical levels of a robot-application can be highlighted, thus providing a well-structured basis to assist future approaches.
Trust Management for IoT Security: Taxonomy and Future Research Directions

Current research in IoT security is still far from mature as it significantly differs from traditional networked systems, which have been studied comprehensively. This is because the IoT devices are limited by processing power, memory, and energy. Existing security models are not sufficient for handling security due to the scale of the IoT network and the resource-constrained nature of devices. Trust management (TM) has received significant interest in enhancing the security of the system by defining policies and provide users with specific access rights. In this review, recent advances in TM in IoT security are discussed. Based on the review, we propose the taxonomy of TM to achieve trust in an IoT ecosystem considering the requirements, the primary attacks, the trust management characteristics. Furthermore, a detailed discussion on open issues, challenges, and future research directions is provided on establishing trust in the IoT networks.
Consensus Problems on Clustered Networks

In this paper we address consensus in the context of networked agents whose interactions can be modelled by an undirected, signed, weighted, connected and clustered graph. We assume that individuals can be split into three clusters representing the decision classes on a given specific topic. Interactions between agents belonging to the same cluster are cooperative, meaning that the link connecting those agents has a non-negative weight, while interactions between agents belonging to different clusters are antagonistic and therefore a non-positive weight is associated to the link connecting them. We will show that under certain regularity assumptions it is possible to devise a simple modification of DeGroot’s algorithm that ensures that the opinions of agents who cooperate converge to consensus, i.e. the opinions of agents belonging to the same class converge to the same decision.
Service-Aware Two-Level Partitioning for Machine Learning-Based Network Intrusion Detection With High Performance and High Scalability

A network intrusion detection system (NIDS) is an important technology for cyber security. Recently, machine learning based NIDSs are being actively researched as various machine learning techniques are proposed. However, existing NIDSs have limitation in terms of generality because they have been designed based on specific characteristics obtained from analyzing some partial datasets. Moreover, in reality, the NIDS datasets have a significantly imbalanced ratio between normal and abnormal data. It causes the minority class problem, which needs to be addressed for developing robust and reliable NIDSs working in various environments. This paper proposes a novel technique using service-aware dataset partitioning, which provides high scalability to handle huge and rapidly growing network data flexibly, and helps the classifier to improve the classification performance in terms of accuracy and speed. We evaluated our approach with the Kyoto2016 dataset, which is a well-known dataset for highly imbalanced data, using various classification algorithms and parameters for achieving the best performance and compared it with existing state-of-the-art approaches. Our experimental results indicated that our approach can classify network traffics rapidly and accurately with huge imbalanced datasets. We conclude that it can relieve serious existing issues of imbalanced datasets for modern machine learning based NIDS solutions.
ECG-based Arrhythmia Detection using Average Energy and Zero-crossing Features with Support Vector Machine

Arrhythmia, a serious heart disease, is one of the main causes of sudden cardiac death around the globe. Cardiac arrhythmias can occur to anyone and at any age. The electrocardiogram (ECG) arrhythmia is a series of the abnormal electrical pulses recorded from heart by using electrocardiograph. In this study, we present a method for arrhythmia detection from the ECG signal based on the average energy and zero-crossing quantities that are extracted from the ECG records. The proposed system is composed of ECG signal preprocessing, feature extraction, and arrhythmia detection. The ECG signals used in this study were obtained from MIT-BIH arrythmia, MIT-BIH normal sinus rhythm and QT databases. To distinguish between the normal and abnormal (arrhythmia) ECG signal, we implement a support vector machine (SVM) as a classifier. K-fold cross validation technique is employed to evaluate and validate the performance of the proposed system. We measure the performance of the system via accuracy, sensitivity, specificity, and precision. The best detection results based on the average energy feature with 10-fold cross validation presents are 96.67 % average accuracy, 93.33% sensitivity, 100% specificity and 100% precision.
A Simulation Study of Strategic Consumer Purchases

Strategic consumer behavior has been observed and reported in various business environments. Previous studies revealed that ignoring consumers' strategic behavior may lead to negative impacts on retailers' business performance. Therefore, different strategies in terms of pricing, information release, and inventory are proposed for retailers to mitigate adverse impacts resulted from consumers' strategic waiting. However, effects of these strategies have not been examined. In this study, using simulation experiments, we investigate factors (product features, customer characteristics, and retailers' strategies) that impact consumers' strategic waiting and how corresponding sales revenue are affected. Our results indicate that with the presence of strategic consumers, the retailer’s benefits attained from price discrimination become less due to consumers’ strategic waiting. Meanwhile, the benefit from early discounts become more significant. Especially when the depreciation rate of products is slow or consumers' evaluation is highly heterogeneous, retailers can discount early to obtain more profits.
The Predictability of Driving in Typical Traffic Conditions

A reliable prediction of the vehicle velocity over the course of a defined route plays a vital role for the growing electric mobility and autonomous driving. The prediction of upcoming traffic, driving conditions and thus velocity trajectory helps to determine the range of electric vehicles with high accuracy and enables for energy-optimized driving of autonomous vehicles. In this paper, the requirements for a reliable prediction are discussed and an advanced approach of velocity estimation using various online APIs for data acquisition is presented. A prediction of a typical route profile is carried out regarding all available online information, vehicle and driver limitations and predictable influences of infrastructure and route topology. To validate the results, real driving patterns of the exact route are compared to the predictions. Hence, capabilities and limitations of the presented prediction method are shown.
Fast CU Partition Decision Algorithm for AVS3 Intra Coding

The third generation of Audio Video coding Standard (AVS3) is an emerging video coding standard that surpasses High Efficiency Video Coding (HEVC). AVS3 allows flexible block subdivision by applying Quad-Tree (QT), Binary-Tree (BT) plus Extend Quad-Tree (EQT) partition structure, while the increased flexibility comes at the cost of enormous coding complexity. In this paper, an ingenious early termination mechanism is proposed to skip unnecessary exhaustive searches of the whole tree branches. We carry out a series of restrictive measures based on Coding Unit (CU) size and iteration status to make the sophisticated EQT split mode concentrate more on small CUs with complex texture structure. Historical QTBT partition information is also adopted as an important factor to skip EQT split mode in advance. Meanwhile, a fast CU partition algorithm based on the gradient is proposed to skip horizontal or vertical BT/EQT partition of CUs with prominent texture structure in another direction, in which early termination can be directly conducted in homogenous areas at the same time. Extensive experiments demonstrate that the proposed method can save 43% encoding time with only 0.53% BDBR increase on average under All Intra(AI) configuration, which outperforms the preexisting fast algorithms.
Coherent Automotive Radar Networks: The Next Generation of Radar-Based Imaging and Mapping

Imaging radar is a key perception technology for automotive and industrial applications. A lot of progress has been made with high channel count systems, deploying, for example, 12 transmit and 16 receive channels with cascaded monolithic microwave integrated circuit solutions. Nevertheless, fully automated driving requires even higher angular resolution for drive-under/drive-over decisions and exact predictions of object trajectories in dense urban driving scenarios. Both problems can be solved by increasing the antenna size and building larger radars. However, there is a physical limit to what can be placed on the front of a car, and manufacturing very large arrays is quite difficult. Thus, coherent automotive radar networks are a way to achieve high spatial resolution and obtain the complete velocity vector of an object from a single measurement. This solution is commercially attractive, as the sensor can remain relatively small, and complexity can be moved from the physical hardware to algorithms and processing. Two different test setups, each comprised of two multiple-input multiple-output radar units in the 76–77 GHz band, are presented in this article. To obtain azimuth and additional elevation information, the setups use a 1D and a 2D antenna array, respectively. Processing-based coherent evaluation is employed to create an additional radar image with doubled azimuth resolution and improved signal-to-noise ratio and to enable the estimation of vectorial target velocities. These benefits are presented and compared with optical reference images in traffic scenarios.
Depression severity prediction from facial expression based on the DRR_DepressionNet network

Depression has become one of the serious mental health diseases in the world. The computer vision-based methods are expected to assist the clinical diagnosis of depression more efficiently and objectively, but the lack of clinical data and the low accuracy of recognition have hindered the broad application of automatic depression diagnosis. Given the shortcomings in predicting depression, this paper proposed a deep network based on the deep residual regression network to predict the severity of depression from facial expressions, named Deep Residual Regression Convolutional Neural Networks (DRR_DepressionNet). We firstly enhanced the original facial images to expanse the training data. Then we use these training data, which carry different feature information, to train a deep regression residual network (ResNet). Unlike the traditional ResNet network, we divided the network into three major modules, namely C_M block, Resblock, and GAP. We also replaced the cross-entropy loss function in the traditional structure by the Euclidean loss function as the basis for training the network. Finally, we applied the trained network to predict the Beck Depression Inventory (BDI) score of new subjects to reflect the severity of depression. The experiments were validated on AVEC2013 and AVEC2014 depression data, respectively. The experimental results showed that compared with the state-of-the-art performance, the proposed method could improve the RMSE and MAE by 2.4% and 0.3% respectively on the AVCE2013 data set, and improve the RMSE and MAE by 0.3% and 1.1% respectively on AVCE2014 data set.
IEDSS: Efficient Scheduling of Emergency Department Resources based on Fog Computing

Emergency is an essential mission of public hospitals, one of its main features is to meet requirements expected by the population, whatever their nature. This work proposes a fog-based architecture integrating intelligent algorithms; based on machine learning models, to improve the emergency department performance and the patient experience. The proposed architecture effectiveness is ensured via the fog infrastructure where interactions between the smart scheduling system; deployed on the cloud, and the doctors are maintained. To ensure the efficiency property, we adopt machine learning algorithms to assign and classify patients with regards to the urgency of their cases, their waiting time and physician's availability.
Business Model and Organization – Interdependencies for Customer-Centric Continuous Innovation in Subscription Business

Subscription business models are increasingly gaining attention in the manufacturing industry. By consequently focusing on solving the customers’ problems instead of focusing on products, machine manufacturers may establish a long-term relationship with their customers and gain a stronger competitive position. In order to be successful, the business model needs to include the ability for continuous innovation and thus steadily increase the customers’ benefits. At the same time, the introduction of a new business model comes along with a complex set of implications for the provider’s organization. Literature suggests that mutual dependencies between the provider’s business model, its organization and customers exist. Yet no management model so far is able to illustrate the complexity of these interdependencies in subscription models. The presented work intends to do so by including both the direct interdependencies between business models and organizations as well as indirect interdependencies which are caused by continuous innovation of offers and customer interactions. The model serves manufacturers of the machinery and equipment industry as an organizational guideline in managing subscription business models.
Quality of Performance Aware Data Transmission for Energy-Efficient Networked Control

The energy consumption of network interfaces in networked control systems (NCSs) must be reduced for sustainability. The sleep mechanisms of energy-efficient network interfaces for networked motor control systems, in which controller and motor devices are connected by communication networks, have attracted much interest in recent years. They reduce the energy consumption of network interfaces by making the transmitters installed on the controller and motor devices enter a sleep period when no data are to be transmitted. In a conventional send-on-delta (SoD)-based sleep control method, the network interfaces decide when to enter the sleep period based on the variations of the control input and response values. Although this method effectively reduces the communication rate for reducing energy consumption, it does not explicitly consider the quality of performance (QoP) or control performance such as tracking errors. In high-precision motion control systems, ensuring a certain QoP level is crucial. This study proposes a QoP-aware sleep control method for energy-efficient networked motor control systems to maintain the QoP at a certain level while operating the sleep mechanism. Here, the QoP is defined as the tracking error between the command and the response values. In the proposed method, network interfaces decide when to enter the sleep period based on the tracking error. Experimental results confirm that the QoP-aware sleep control method outperforms the SoD-based sleep control method in terms of the tracking errors (i.e., integral square error and steady-state error) at the expense of increased communication rates.
Theory of Nuclear Magnetic Shielding in Molecules. I. Long‐Range Dipolar Shielding of Protons

The general nuclear magnetic shielding theory of Ramsey is developed for the case of ``long‐range'' nuclear shieldings. Such shieldings arise from the magnetization induced in molecular electrons localized to groups one or more bond lengths removed from the shielded nucleus. Order‐of‐magnitude calculations indicate that these long‐range shielding fields can make significant contributions to proton chemical shifts in, for example, aromatic hydrocarbons and alkyl halides.
Resource Allocation Steps for an Efficient End-to-End Service Delivery in Network Function Virtualization: A Survey

Network Function Virtualization (NFV) is a new concept that has recently merged to cope with problems relative to high Capital and Operational expenditures (CAPEX and OPEX) generated by telecommunication hardware equipments. ETSI has proposed an architectural framework for the implementation of the concept. However, before coming to fruition, many challenges are to address. Those challenges include service management and orchestration, privacy and security, trust, and resource allocation. This paper focuses on resource allocation, by bringing an overview of what has been done in the domain and challenges that still need to be addressed. An experimental comparison of two approaches proposed in the coordinated chaining/mapping step of the resource allocation process is made and some orientations are suggested as a future field of research.
Dünnste Kohlenstoff-Folien

Bei der Reduktion von Graphitoxyd in sehr verdünnter alkalischer Suspension entsteht extrem feinlamellarer Kohlenstoff. Die Bestimmung der Dicke der dünnsten Lamellen aus dem Kontrast im Elektronenmikroskop ergab, daß sie nur aus einigen wenigen, z. T. wahrscheinlich nur aus einer einzigen Kohlenstoff-Sechseckschicht des Graphitgitters bestehen. Das Ergebnis wird gestützt durch röntgenographische Untersuchungen sowie durch Messungen der spezifischen Oberfläche.
Genetic Algorithm Applied to Discrete Optimization of Band-Pass Microwave Filter

Design of lumped-element filter is typically a multistep process that includes optimization of real-valued parameters, rounding of the optimized values to the nearest values of available components and further refinements. We propose a design approach based on an optimization of discrete-valued parameters of filters using genetic algorithm. The filter elements are allowed to have only commercially available values for SMD components. The optimized solution of a band-pass filter is verified by simulations using circuit solver, 3D electromagnetic models and measurements of the manufactured prototype.
Automotive Radar — From First Efforts to Future Systems

Although the beginning of research on automotive radar sensors goes back to the 1960s, automotive radar has remained one of the main drivers of innovation in millimeter wave technology over the past two decades. Today, millions of sensors are produced each year, which was made possible by inexpensive and mature millimeter wave technology. The technology maturity, in turn, enables research to be carried out on systems that are considerably more complex and powerful than was possible just a few years ago. The focus of research has thus shifted from purely hardware-oriented and device-level topics to sophisticated millimeter wave systems and RF signal processing topics. This opens up new research topics such as digital modulation schemes, radar networks, radar imaging, and machine learning. In this review paper, we sketch the path from the very beginning through the state of the art with sophisticated multiple-input multiple-output (MIMO) antenna arrays and mature assembly and interconnect concepts to today's key research topics of automotive radar.
A Multivocal Literature Review on Growing Social Engineering Based Cyber-Attacks/Threats During the COVID-19 Pandemic: Challenges and Prospective Solutions

The novel coronavirus (COVID-19) pandemic has caused a considerable and long-lasting social and economic impact on the world. Along with other potential challenges across different domains, it has brought numerous cybersecurity challenges that must be tackled timely to protect victims and critical infrastructure. Social engineering–based cyber-attacks/threats are one of the major methods for creating turmoil, especially by targeting critical infrastructure, such as hospitals and healthcare services. Social engineering–based cyber-attacks are based on the use of psychological and systematic techniques to manipulate the target. The objective of this research study is to explore the state-of-the-art and state-of-the-practice social engineering–based techniques, attack methods, and platforms used for conducting such cybersecurity attacks and threats. We undertake a systematically directed Multivocal Literature Review (MLR) related to the recent upsurge in social engineering–based cyber-attacks/threats since the emergence of the COVID-19 pandemic. A total of 52 primary studies were selected from both formal and grey literature based on the established quality assessment criteria. As an outcome of this research study; we discovered that the major social engineering–based techniques used during the COVID-19 pandemic are phishing, scamming, spamming, smishing, and vishing, in combination with the most used socio-technical method: fake emails, websites, and mobile apps used as weapon platforms for conducting successful cyber-attacks. Three types of malicious software were frequently used for system and resource exploitation are; ransomware, trojans, and bots. We also emphasized the economic impact of cyber-attacks performed on different organizations and critical infrastructure in which hospitals and healthcare were on the top targeted infrastructures during the COVID-19 pandemic. Lastly, we identified the open challenges, general recommendations, and prospective solutions for future work from the researcher and practitioner communities by using the latest technology, such as artificial intelligence, blockchain, and big data analytics.
Analytic Predictive of Hepatitis using The Regression Logic Algorithm

Hepatitis is an inflammation of the liver which is one of the diseases that affects the health of millions of people in the world of all ages. Predicting the outcome of this disease can be said to be quite challenging, where the main challenge for public health care services itself is due to a limited clinical diagnosis at an early stage. So by utilizing machine learning techniques on existing data, namely by concluding diagnostic rules to see trends in hepatitis patient data and see what factors are affecting patients with hepatitis, can make the diagnosis process more reliable to improve their health care. The approach that can be used to carry out this prediction process is a regression technique. The regression itself provides a relationship between the independent variable and the dependent variable. By using the hepatitis disease dataset from UCI Machine Learning, this study applies a logistic regression model that provides analysis results with an accuracy rate of 83.33%.
Modified Algorithm for Maximum Power Point Tracking in Photovoltaic Systems

The article describes a new way for tracking maximum power point (MPP) of a photovoltaic (PV) panel. The target of a new algorithm is to reduce the time of maximum power point tracking (MPPT) by using variable step size of the converter based on hill climbing (HC) tactics. The simulation results from MATLAB/Simulink are introduced to validate the efficiency of modified algorithm.