Miracle-dz/UniFAR-FTUs huggingface dataset:
Rolling Labs - Teaching Vehicle Electronics from the Beginning

This paper describes the pedagogical approach to teach vehicle electronics at the University of Applied Science Aachen. As vehicle electronics is getting more and more complex, in particular with regard to Advanced Driver Assistance Systems and autonomous driving, a dedicated concept is needed to integrate the require-ments of vehicle electronics into the studies of electrical engineering. The con-cept of rolling labs is established during the last years for the studies of electrical engineering. The rolling labs cover different levels of complexity and integration of the electronics and components used in modern applications and hence under-graduate students can start working with interesting and close to reality systems rather early. The rolling labs enable several different possibilities of teaching and learning, either for standard labs, for team works in terms of problem-based learning (PBL) or for individual works like bachelor or master thesis. In addi-tion, the systems can be used as research platforms and for demonstration and marketing purpose to introduce the challenging topic of vehicle electronics to other people.
A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems

Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with other agents through autonomous exploration of the environment. However, learning a complex task from scratch is impractical due to the huge sample complexity of RL algorithms. For this reason, reusing knowledge that can come from previous experience or other agents is indispensable to scale up multiagent RL algorithms. This survey provides a unifying view of the literature on knowledge reuse in multiagent RL. We define a taxonomy of solutions for the general knowledge reuse problem, providing a comprehensive discussion of recent progress on knowledge reuse in Multiagent Systems (MAS) and of techniques for knowledge reuse across agents (that may be actuating in a shared environment or not). We aim at encouraging the community to work towards reusing all the knowledge sources available in a MAS. For that, we provide an in-depth discussion of current lines of research and open questions.
A Novel Lane Change Decision-Making Model of Autonomous Vehicle Based on Support Vector Machine

Autonomous driving is a crucial issue of the automobile industry, and research on lane change is its significant part. Previous works on the autonomous vehicle lane change mainly focused on lane change path planning and path tracking, but autonomous vehicle lane change decision making is rarely mentioned. Therefore, this paper establishes an autonomous lane change decision-making model based on benefit, safety, and tolerance by analyzing the factors of the autonomous vehicle lane change. Then, because of the multi-parameter and non-linearity of the autonomous lane change decision-making process, a support vector machine (SVM) algorithm with the Bayesian parameters optimization is adopted to solve this problem. Finally, we compare a lane change model based on rules with the proposed SVM model in the test set, and results illustrate that the SVM model performs better than the rule-based lane change model. Moreover, the real car experiment is carried out to verify the effectiveness of the decision model.
A Survey on Security Concerns in Internet of Things

Internet of Things is the kinship using Internet of computing equipments ingrained in everyday gadgets which facilitate them to deliver and retrieve data. But the use of such gadgets was not encouraged to much greater extent due to many security concerns arising with them. Most of the malicious attacks happening on IoT devices are carried out by botnets. Now different devices require different security measures. If different types of security options are available, we need to decide which solutions are best for a particular IoT device or application. It is important to understand the requirements for the threat prevention of a particular device. Individual appliances or a whole enterprise need a system which can detect and respond to different threats, malware or hacking of the system. In this paper, we are producing a review on various security outbreaks that can take place on different layers that constitute IoT. Distinct mechanisms are examined that present clarifications to these security concerns along with their disadvantages. Also we have discussed about honeypots which are a much better security scheme that can prevent the attacks from occurring so that there is no need to detect them. Here we have also explained setting up a honeypot for resource constrained IoT devices using a simple illustration. Future work is also discussed such that strength and safety of IoT should enhance and their various devices get more protection against many familiar attacks.
Generalized Dirichlet-Process-Means for Robust and Maximum Distortion Criteria

DP-means clustering was obtained as an extension of K-means clustering. While it is implemented with a simple and efficient algorithm, it can estimate the number of clusters simultaneously. However, DP-means is specifically designed for the average distortion criterion. Therefore, it is vulnerable to outliers in data, and can cause large maximum distortion in clusters. This study introduces a new parameter to the objective function of DP-means to provide an extension of DP-means, which bridges robust estimation of cluster centers and minimization of the maximum distortion criterion.
A low power, high sensitivity SiGe HBT static frequency divider up to 90 GHz for millimeter-wave application

A layout and connection optimization for static frequency divider is presented. The layout optimization provides a new circle topology transistors placement and reasonable connection structure, which reduces the parasitic effectively and enables self-oscillation frequency enhancement. Besides, bandwidth enhancement techniques based on a center-tap capacitor in input balun design and inductive peaking in latch design are adopted to improve further high frequency performance with low power consumption. As a proof of concept, design of a divide-by-2 static frequency divider in 0.13 μm SiGe BiCMOS technology is reported. With single-ended input clock signal, the divider is measured to be operated from 40 to 90 GHz. Phase noise measurements of a 90 GHz input clock signal indicate ideal behavior with no measurable noise contribution from the divider. The divider followed by a buffer that can deliver more than −10 dBm output power, which is sufficient to drive succeeding stage. To the author's knowledge, the divider exhibits a competitive power dissipation and the highest FOM among silicon based frequency dividers that operating higher than 70 GHz.
Lithium-Ion Battery RUL Indirect Prediction Based on GAAA-ELM

Since direct prediction of remaining useful life (RUL) for lithium-ion battery is difficult, the indirect RUL prediction method based on Genetic Algorithm Ant Algorithm-Extreme Learning Machine (GAAA-ELM) model is proposed in this paper. Equal-voltage drop discharge time is selected as indirect life characteristic parameter of lithium-ion battery. The first-order partial correlation coefficient method was used to verify the correlation between the indirect life parameter and direct life parameter. Build the relational model of equal-voltage drop discharge time and actual capacity and the prediction model of the equal-voltage drop discharge time are built based on GAAA-ELM. GAAA is applied to optimize the weight and threshold of ELM. Lithium-ion battery RUL is predicted by the proposed method based on NASA lithium-ion battery data sets. By comparing with BP prediction method, ELM prediction method and GA-ELM prediction method, it is shown that the proposed method can effectively predict lithium-ion battery RUL and get better prediction accuracy.
Botnet Detection Techniques: A Review

Botnets are emerging as a severe threat to the internet and consequently led to rising concerns about the security of network embedded devices. Botnets are networks formed as a string of various machines infected with malwares that are controlled by an antagonist and coordinated together to maintain a distributed platform for performing certain illicit activities on a large scale such as launching DDOS, Spamming etc. In recent years, botnets have been the subject of raising interest and great scientific efforts have been devoted to provide effective and efficient detection of botnets. This paper comprises of a comprehensive overview of existing botnet detection methods and their comparative analysis by outlining their certain characteristics which will help to analyze the current state of work in this field.
Intrinsic linking with linking numbers of specified divisibility

Let n , q and r be positive integers, and let K N n be the n -skeleton of an ( N − 1) -simplex. We show that for N sufficiently large every embedding of K N n in ℝ 2 n + 1 contains a link consisting of r disjoint n -spheres, such that every pairwise linking number is a nonzero multiple of q . This result is new in the classical case n = 1 (graphs embedded in ℝ 3 ) as well as the higher dimensional cases n ≥ 2 ; and since it implies the existence of an r -component link with all pairwise linking numbers at least q in absolute value, it also extends a result of Flapan et al. from n = 1 to higher dimensions. Additionally, for r = 2 we obtain an improved upper bound on the number of vertices required to force a two-component link with linking number a nonzero multiple of q . Our new bound has growth O ( n q 2 ) , in contrast to the previous bound of growth O (√( n )4 n q n + 2 ) .
Assessment and enforcement of wireless sensor network-based SCADA systems security. (Évaluation et mise en oeuvre de la sécurité dans les systèmes SCADA à base de réseaux de capteurs sans fil)

La securite des systemes de controle industriel est une preoccupation majeure. En effet, ces systemes gerent des installations qui jouent un role economique important. En outre, attaquer ces systemes peut non seulement entrainer des pertes economiques, mais aussi menacer des vies humaines. Par consequent, et comme ces systemes dependent des donnees collectees, il devient evident qu’en plus des exigences de temps reel, il est important de securiser les canaux de communication entre ces capteurs et les controleurs principaux. Ces problemes sont plus difficiles a resoudre dans les reseaux de capteurs sans fil (WSN). Cette these a pour but d’aborder les questions de securite des WSN. Tout d’abord, nous effectuons une etude de securite approfondie du protocole WirelessHART. Ce dernier est le protocole leader pour les reseaux de capteurs sans fil industriels (WISN). Nous evaluons ses forces et soulignons ses faiblesses et ses limites. En particulier, nous decrivons deux vulnerabilites de securite dangereuses dans son schema de communication et proposons des ameliorations afin d’y remedier. Ensuite, nous presentons wIDS, un systeme de detection d’intrusion (IDS) multicouches qui se base sur les specifications, specialement developpe pour les reseaux de capteurs sans fil industriels. L’IDS propose verifie la conformite de chaque action effectuee par un noeud sans fil sur la base d’un modele formel du comportement normal attendu.
Deep Multi-view Sparse Subspace Clustering

Most multi-view subspace clustering algorithms construct the affinity matrix with shallow features extracted from each view separately. The integration of multi-view features are left for extended spectral clustering algorithm. The lack of deep feature extraction and interaction across different views prevents the effective exploration of information complementary for multi-view datasets. To address this problem, this paper proposes a novel deep multi-view sparse subspace clustering (DMVSSC) model which consists of convolutional auto-encoders (CAEs) and CCA-based self-expressive module. The proposed model can not only extract deep features of each view data with few parameters but also integrate multi-view features based on CCA. Furthermore, a two-stage joint optimization strategy is proposed for tuning the whole model. Experiments on four benchmark data sets show that our proposed model significantly outperforms the state-of-the-art multi-view subspace clustering algorithms.
Backtesting VaR and expectiles with realized scores

Several statistical functionals such as quantiles and expectiles arise naturally as the minimizers of the expected value of a scoring function, a property that is called elicitability (see Gneiting in J Am Stat Assoc 106:746–762, 2011 and the references therein). The existence of such scoring functions gives a natural way to compare the accuracy of different forecasting models, and to test comparative hypotheses by means of the Diebold–Mariano test as suggested in a recent work. In this paper we suggest a procedure to test the accuracy of a quantile or expectile forecasting model in an absolute sense, as in the original Basel I backtesting procedure of value-at-risk. To this aim, we study the asymptotic and finite-sample distributions of empirical scores for normal and uniform i.i. d. samples. We compare on simulated data the empirical power of our procedure with alternative procedures based on empirical identification functions (i.e. in the case of VaR the number of violations) and we find an higher power in detecting at least misspecification in the mean. We conclude with a real data example where both backtesting procedures are applied to AR(1)–Garch(1,1) models fitted to SP500 logreturns for VaR and expectiles’ forecasts.
End-to-end Multiplayer Violence Detection based on Deep 3D CNN

Numerous behavior recognition researches have focused on UCF-101 video dataset, such as sports, cooking and other simple routines. Yet these studies are less useful in real-life surveillance scenarios. Violence detection in crowded scenes (such as shopping malls, banks, and stadiums) is significantly important but little research has been done. Based on this situation, this paper proposes a multiplayer violence detection method based on deep three-dimensional convolutional neural network (3D CNN), which extracts the spatiotemporal feature information of multiplayer violence. Our method directly detects violence in an input video by end-to-end. The experimental results show that the accuracy of our method is higher than the methods of artificially extracting features in violence detection.
Do We Adopt the Intentional Stance Toward Humanoid Robots?

In daily social interactions, we need to be able to navigate efficiently through our social environment. According to Dennett (1971), explaining and predicting others’ behavior with reference to mental states (adopting the intentional stance) allows efficient social interaction. Today we also routinely interact with artificial agents: from Apple’s Siri to GPS navigation systems. In the near future, we might start casually interacting with robots. This paper addresses the question of whether adopting the intentional stance can also occur with respect to artificial agents. We propose a new tool to explore if people adopt the intentional stance toward an artificial agent (humanoid robot). The tool consists in a questionnaire that probes participants’ stance by requiring them to choose the likelihood of an explanation (mentalistic vs. mechanistic) of a behavior of a robot iCub depicted in a naturalistic scenario (a sequence of photographs). The results of the first study conducted with this questionnaire showed that although the explanations were somewhat biased toward the mechanistic stance, a substantial number of mentalistic explanations were also given. This suggests that it is possible to induce adoption of the intentional stance toward artificial agents, at least in some contexts.
Opportunities to Target the Life Cycle of Epstein-Barr Virus (EBV) in EBV-Associated Lymphoproliferative Disorders

Many lymphoproliferative disorders (LPDs) are considered “EBV associated” based on detection of the virus in tumor tissue. EBV drives proliferation of LPDs via expression of the viral latent genes and many pre-clinical and clinical studies have shown EBV-associated LPDs can be treated by exploiting the viral life cycle. After a brief review of EBV virology and the natural life cycle within a host we will discuss the importance of the viral gene programs expressed during specific viral phases, as well as within immunocompetent vs. immunocompromised hosts and corresponding EBV-associated LPDs. We will then review established and emerging treatment approaches for EBV-associated LPDs based on EBV gene expression programs. Patients with EBV-associated LPDs can have a poor performance status, multiple comorbidities, and/or are immunocompromised from organ transplantation, autoimmune disease, or other congenital or acquired immunodeficiency making them poor candidates to receive intensive cytotoxic chemotherapy. With the emergence of EBV-directed therapy there is hope that we can devise more effective therapies that confer milder toxicity.
GABAA Receptor Subunit α3 in Network Dynamics in the Medial Entorhinal Cortex

Layer II of the medial entorhinal cortex (MEC LII) contains the largest number of spatially modulated grid cells and is one of the first regions in the brain to express Alzheimer’s disease (AD)-related pathology. The most common principal cell type in MEC LII, reelin-expressing stellate cells, are grid cell candidates. Recently we found evidence that γ-aminobutyric acid (GABA)A receptor subunits show a specific distribution in MEC LII, in which GABAA α3 is selectively associated with reelin-positive neurons, with limited association with the other principal cell type, calbindin (CB)-positive pyramidal neurons. Furthermore, the expression of α3 subunit decreases in mice between P15 and P25, which coincides with the emergence of stable grid cell activity. It has been shown that the α3 subunit undergoes specific developmental changes and that it may exert pro-inflammatory actions if improperly regulated. In this review article, we evaluate the changing kinetics of α3-GABAA receptors (GABAARs). during development in relation to α3-subunit expression pattern in MEC LII and conclude that α3 could be closely related to the stabilization of grid cell activity and theta oscillations. We further conclude that dysregulated α3 may be a driving factor in early AD pathology.
Visual Entrainment at 10 Hz Causes Periodic Modulation of the Flash Lag Illusion

It has long been debated whether visual processing is, at least partially, a discrete process. Although vision appears to be a continuous stream of sensory information, sophisticated experiments reveal periodic modulations of perception and behavior. Previous work has demonstrated that the phase of endogenous neural oscillations in the 10 Hz range predicts the “lag” of the flash lag effect, a temporal visual illusion in which a static object is perceived to be lagging in time behind a moving object. Consequently, it has been proposed that the flash lag illusion could be a manifestation of a periodic, discrete sampling mechanism in the visual system. In this experiment we set out to causally test this hypothesis by entraining the visual system to a periodic 10 Hz stimulus and probing the flash lag effect (FLE) at different time points during entrainment. We hypothesized that the perceived FLE would be modulated over time, at the same frequency as the entrainer (10 Hz). A frequency analysis of the average FLE time-course indeed reveals a significant peak at 10 Hz as well as a strong phase consistency between subjects (N=26). Our findings provide evidence for a causal relationship between alpha oscillations and fluctuations in temporal perception.
The Effectiveness of Facilitated Discussions in REsolving Conflict at the New Bedford Harbor Suspended Site

This study examined a case of conflict between Federal decision-makers and local citizens regarding the selection of a clean-up plan for a coastal area contaminated by hazardous wastes. A prolonged dispute over remediation of the New Bedford Harbor Superfund Site resulted in a series of facilitated discussions intended to resolve the conflict. A descriptive survey was used to explore the effect of the discussions upon the risk perceptions of citizens and managers, thereby examining the effectiveness of the facilitated discussions in resolving conflict at the Site. The study found that the facilitated discussions did not cause risk perceptions to converge, . and concluded that the discussions averted, rather than resolved, conflict between citizens and managers at the New Bedford Harbor Superfund Site.
Homer1a Attenuates Endoplasmic Reticulum Stress-Induced Mitochondrial Stress After Ischemic Reperfusion Injury by Inhibiting the PERK Pathway

Homer1a is the short form of a scaffold protein that plays a protective role in many forms of stress. However, the role of Homer1a in cerebral ischemia/reperfusion (I/R) injury and its potential mechanism is still unknown. In this study, we found that Homer1a was upregulated by oxygen and glucose deprivation (OGD) and that overexpression of Homer1a alleviated OGD-induced lactate dehydrogenase (LDH) release and cell death in cultured cortical neurons. After OGD treatment, the overexpression of Homer1a preserved mitochondrial function, as evidenced by less cytochrome c release, less reactive oxygen species (ROS) production, less ATP and mitochondrial membrane potential (MMP) loss, less caspase-9 activation, and inhibition of endoplasmic reticulum (ER) stress confirmed by the decreased expression of phosphate-PKR-like ER Kinase (p-PERK)/PERK and phosphate- inositol-requiring enzyme 1 (p-IRE1)/IRE1 and immunofluorescence (IF) staining. In addition, mitochondrial protection of Homer1a was blocked by the ER stress activator Tunicamycin (TM) with a re-escalated ROS level, increasing ATP and MMP loss. Furthermore, Homer1a overexpression-induced mitochondrial stress attenuation was significantly reversed by activating the PERK pathway with TM and p-IRE1 inhibitor 3,5-dibromosalicylaldehyde (DBSA), as evidenced by increased cytochrome c release, increased ATP loss and a higher ROS level. However, activating the IRE1 pathway with TM and p-PERK inhibitor GSK2656157 showed little change in cytochrome c release and exhibited a moderate upgrade of ATP loss and ROS production in neurons. In summary, these findings demonstrated that Homer1a protects against OGD-induced injury by preserving mitochondrial function through inhibiting the PERK pathway. Our finding may reveal a promising target of protecting neurons from cerebral I/R injury.
Propofol Alleviates DNA Damage Induced by Oxygen Glucose Deprivation and Reperfusion via FoxO1 Nuclear Translocation in H9c2 Cells

Ischemia/reperfusion (I/R) injury induces irreversible oxidative stress damage to the cardiac myocytes. Many studies have revealed that propofol alleviates the important organelle-mediated injury from oxidative stress in vitro. However, it remains unclear whether propofol prevents I/R-induced DNA damage in cardiomyocytes. In our study, we established an oxygen glucose deprivation/reoxygenation (OGD/R) model in H9c2 cells and found that propofol decreased reactive oxygen species (ROS) levels and suppressed cell apoptosis induced by OGD/R in H9c2 cells. In addition, propofol significantly reduced the molecular marker of DNA damage and inhibited double-strand breaks of DNA damage induced by OGD/R in H9c2 cells in a dose-dependent manner. Furthermore, we investigated the molecular mechanisms and demonstrated that propofol inhibited forkhead box O 1 (FoxO1) phosphorylation and increased FoxO1 nuclear translocation through inhibition of protein kinase B (Akt) and adenosine 5’-monophosphate-activated protein kinase (AMPK) pathways. The protective effects of propofol against oxidative stress-induced DNA damage were reversed by silencing FoxO1. Taken together, our results suggest that oxidative stress aggravates DNA damage and apoptosis in H9C2 cells, which can be reversed by propofol via FoxO1 nuclear translocation.