Miracle-dz/UniFAR-FTUs huggingface dataset:
VKE: a Visual Analytics Tool for CyberSecurity Data

Several changes have occurred throughout the past few decades regarding how organizations and individuals generate, process, and share information. The emergence of new devices has enhanced the abilities of individuals and organizations to create unique and different types of data, which are shared in real time at high speeds. Thus, it is becoming more common for data with diverse and complex formats to be collected and transmitted from numerous sources. The term Big Data is frequently used to refer to the volume, variety, velocity, and complexity [15] of data created daily. The main goal of Big Data related research and development is to manage and transform available real-time and historical data to inform decisions. The relevance of big data cuts across all sectors, including cybersecurity; indeed, it has become one of the most important considerations for organizations looking to protect themselves from cyber attacks. Therefore, organizations have made significant investments in security infrastructure and monitoring systems to protect their data and technological assets; however, due to the characteristics of big data, this is a challenging endeavor. In this context, Visual Analytics (VA) deserves special attention, as it provides advantages and opportunities for making sense of big data and informing decision making. In this paper, we describe a process to offer guidance on the design of VA tools and explain the characteristics of a tool called VKE. We reflect on its utility and potential applications.
TIME SCALE MODIFICATION OF AUDIO USING NON-NEGATIVE MATRIX FACTORIZATION

This paper introduces an algorithm for time-scale modification of audio signals based on using non-negative matrix factorization. The activation signals attributed to the detected components are used for identifying sound events. The segmentation of these events is used for detecting and preserving transients. In addition, the algorithm introduces the possibility of preserving the envelopes of overlapping sound events while globally modifying the duration of an audio clip.
Effect of CBEX-Dr-containing Drink on Physical Fatigue in Healthy Volunteers

Objectives CBEX-Dr is a chicken breast extract containing imidazole dipeptides developed by Nippon Meat Packers, Inc. We investigated the effect of CBEX-Dr-containing drink on physical fatigue in healthy volunteers. Methods In a present double-blind, placebo-controlled, crossover trial, 20 healthy volun- teers were randomized to oral CBEX-Dr or placebo administration for 29 days. No statistical differences in age, gender or body mass index(BMI)were shown between the two groups. Subjects took CBEX-Dr(imidazole dipeptides 400 mg)or placebo once a day for 29 days. As a fatigue-inducing physical task, subjects performed workload trials on a cycle ergometer at fixed workloads for 4 hours and then rested for 4 hours. We evaluated physical performance by 10-second high power test and subjective sensation of fatigue by visual analogue scale. Results Oral CBEX-Dr administration inhibited the decrease in physical performance and attenuated fatigue sensation by physical load. Conclusions CBEX-Dr is effective for the attenuation of fatigue and CBEX-Dr-containing drink is one of the promising candidates for anti-fatigue food. (Jpn Pharmacol Ther 2008;36:199-212)
On the MHD Casson Axisymmetric Marangoni Forced Convective Flow of Nanofluids

The proposed investigation concerns the impact of inclined magnetohydrodynamics (MHD) in a Casson axisymmetric Marangoni forced convective flow of nanofluids. Axisymmetric Marangoni convective flow has been driven by concentration and temperature gradients due to an infinite disk. Brownian motion appears due to concentration of the nanosize metallic particles in a typical base fluid. Thermophoretic attribute and heat source are considered. The analysis of flow pattern is perceived in the presence of certain distinct fluid parameters. Using appropriate transformations, the system of Partial Differential Equations (PDEs) is reduced into non-linear Ordinary Differential Equations (ODEs). Numerical solution of this problem is achieved invoking Runge–Kutta fourth-order algorithm. To observe the effect of inclined MHD in axisymmetric Marangoni convective flow, some suitable boundary conditions are incorporated. To figure out the impact of heat/mass phenomena on flow behavior, different physical and flow parameters are addressed for velocity, concentration and temperature profiles with the aid of tables and graphs. The results indicate that Casson fluid parameter and angle of inclination of MHD are reducing factors for fluid movement; however, stronger Marangoni effect is sufficient to improve the velocity profile.
The History and Cycles of Squatting in Berlin (1969–2016)

Our investigation on squatting in West and East Berlin from 1969 to 2016 reveals that approximately 650 entities—from houses, factories and villas to parks, unbuilt land, or the former death strip of the border between the two German states—have been squatted within a political framework and with political intentions (azozomox 2014b). Squatting is related to the general political circumstances and to the strength of the political movements. The Berlin squatter movements emerged and developed in a specific historical context, with particular structural conditions and were part of different cycles of protest (Tarrow 1998).
The Chia Network Blockchain

This document outlines the basic design principles of the consensus layer (the blockchain) of the Chia network. It is inspired by and similar to the Bitcoin blockchain, which achieves consensus when a majority of the computing power dedicated towards securing it is controlled by honest parties. In Chia the resource is not computing power, but disk space. To achieve this, the proofs of work used in Bitcoin are replaced by proofs of space. To get a mining dynamic like in the Bitcoin blockchain, Chia alternates proofs of space with verifiable delay functions. We provide an initial security analysis of the Chia backbone, showing that as long as at least ≈ 61.5% of the space is controlled by honest parties Chia satisfies basic blockchain security properties.
Laser-Guide Star Point-Spread Function Reconstruction for Extremely Large Telescopes

This paper discusses a simulation-based framework for lase guide star (LGS) point spread function reconstruction (PSFR) for the Thirty Meter Telescope’s 1st-light multi-conjugate adaptive optics system NFIRAOS. The approach is based on processing first the NFIRAOS on -axis LGS measurements to compute a tip /tiltremoved (TTR) LGS structure function (SF) using Véran’s me thod. This reconstructed LGS SF can then be used to estimate the PSF in science directions as discussed in [Gi lles et al., 2011] in this same conference.
Out-of-Band Authentication Using 2-Factor Image Matching

This paper presents a unique approach to online multi-factor authentication called 2-Factor Image Matching (2FIM). While existing solutions focus on only one side of the security vs. usability equation, our approach seamlessly blends the two. It delivers a robust and intuitive out-of-band authentication that is as simple as matching two images. However, this simple manual image match by a user is backed by strong cryptographic protocols. The paper describes the constraints and challenges of out-of-band authentication and channel binding, and how our approach addresses them. We illustrate the application flows for authentication and device registration use cases, and explain the underlying security and cryptography of such a solution. Finally, we compare it with commonly used solutions. We also show how push notifications and other spam messages can be avoided to improve existing industry specifications, such as MobileConnect from GSMA.
Statement pursuant to § 23 paragraph 7 of APB

Deep reinforcement learning achieved outstanding results in the last years, since it has emerged from reinforcement learning combined with the capabilities of deep neural networks. This thesis is about Boosted Deep Q-Network (BDQN), a novel deep reinforcement learning algorithm that employs gradient boosting to estimate the action-value function in reinforcement learning problems. The aim with this approach is to meet some of the major challenges still present in the field. On the one hand, they consist of the necessary model complexity for high-dimensional problems and moreover, of the requirement to make efficiently use of the environment’s data. BDQN was evaluated empirically first on two standard control problems, and then on one more complex environment with a high-dimensional observation space. In comparison to Deep Q-Network (DQN), the algorithm performed competitively on the standard tasks using a smaller model complexity, but had serious problems learning a reasonable policy in the complex environment. Zusammenfassung Deep-Reinforcement-Learning hat in den letzten Jahren hervorragende Ergebnisse erzielt, seit es aus ReinforcementLearning in Verbindung mit den Fähigkeiten tiefer neuraler Netze entstanden ist. In dieser Thesis geht es um Boosted Deep Q-Network (BDQN), einen neuartigen Deep-Reinforcement-Learning Algorithmus, welcher gradient boosting verwendet, um die Action-Value Funktion in Reinforcement-Learning Problemen anzunähern. Ziel dieses Ansatzes ist es, einige der großen Herausforderungen zu meistern, die auf diesem Gebiet noch bestehen. Diese bestehen zum einen aus der benötigten Modellkomplexität für hochdimensionale Probleme, und zum anderen aus der Anforderung, Daten aus der Umgebung möglichst effizient zu nutzen. BDQN wurde empirisch zunächst auf zwei Standard-Kontrollproblemen und dann in einer komplexeren Umgebung mit hochdimensionalem Beobachtungsraum ausgewertet. Im Vergleich zu Deep Q-Network (DQN) hat der Algorithmus mit geringerer Modellkomplexität kompetitiv die Standardaufgaben gelöst, hatte aber starke Probleme beim Erlernen einer vernünftigen Strategie in der komplexen Umgebung.
Nitinol-Constrained Balloon Dilatation to Minimize Vessel Trauma and Dissections in Peripheral Arterial Interventions

Balloon angioplasty of severely calcified peripheral arterial lesions often results in vessel trauma and dissection. We present a case of right superficial femoral artery calcified stenosis treated using rotational atherectomy with aspiration thrombectomy complicated by a small non-flow-limiting dissection of the treated segment. Subsequent nitinol-constrained balloon angioplasty significantly improved the angiographic result with intravascular ultrasound-confirmed resolution of dissection and improved luminal gain. VASCULAR DISEASE MANAGEMENT 2013:10(10):E220-E222 Jonathan Nachtigall, DO, Jon C. George, MD From the Division of Interventional Cardiology & Endovascular Medicine Deborah Heart and Lung Center, Browns Mills, New Jersey. CASE REPORT A 61-year-old female with history of coronary artery disease (CAD) and extensive peripheral arterial disease (PAD) presented to the endovascular clinic for significant lower extremity pain with ambulation. On history, she had Rutherford Class III claudication symptoms in the right lower extremity without rest pain. Her PAD history included stenting of the left internal carotid artery, right brachiocephalic artery, abdominal aorta, right common iliac artery, left superficial femoral artery (SFA), and cutting balloon angioplasty to the left tibioperoneal trunk. Her atherosclerotic medication regimen included aspirin, clopidogrel, and atorvastatin. Clinical examination revealed no signs of ulceration, skin breakdown, or edema. Ankle Brachial Index was abnormal at 0.65 in the right lower extremity, and arterial Doppler ultrasound displayed elevated velocities of 252 cm per second in the mid to distal SFA segment. Based on these findings, an angiogram of the right lower extremity was recommended. Access was obtained in the left common femoral artery (CFA) and a 7 Fr Pinnacle Destination sheath (Terumo) was positioned across the aortic bifurcation in the right CFA. Selective angiogram of the right lower extremity confirmed mid to distal 80% calcified stenosis of the right SFA (Figure 1), mild popliteal artery disease, and 2-vessel run-off into the right foot. A Grand Slam Wire (Abbott Vascular) was advanced across the right SFA lesion into the distal peroneal artery after anticoagulation with intravenous heparin. Intravascular ultrasound (IVUS) imaging (Volcano) was performed to determine the burden and character of the plaque, which confirmed a severe calcific and eccentric stenosis in the mid SFA (Figure 2). Rotational Figure 1. Selective angiogram of the right lower extremity demonstrating mid to distal 80% calcified stenosis of the right superficial femoral artery (arrow). Co py rig ht HM P Co mm un ica tio ns
Joint Learning of Human and Robots

Object disentangling and motion planning are interesting fields in robotics. They require e.g. a robot arm to be aware of obstacles in the way. Furthermore, the robot arm has to know how to grasp an object properly. Typically, it is hard for humans to provide proper demonstrations for untrivial disentangling tasks, when an imitation learning approach is chosen. Therefore, the use of reinforcement learning algorithms is required in order to infer an optimal execution trajectory. This bachelor thesis mainly focuses on implementing and testing the algorithm called Relevance Weighted Policy Optimization (RWPO) which is described in section 5 of the paper "Assisting Movement Training and Execution with Visual and Haptic Feedback" by Ewerton et al. [1]. RWPO is a reinforcement learning algorithm. In this thesis, different aspects of the algorithm are discussed and some of them are adjusted. Among the discussed details are the punishment of too long or too jerky trajectories and learning in up to 7 dimensions for multiple via points. This work shows two application scenarios of RWPO. In the first scenario, RWPO is used to teach a robot arm how to disentangle objects in the robot simulation environment gazebo. The second experiment is similar to the piano movers problem. It is conducted in the simulation environment rviz and involves using the Haption Virtuose 6D device. In this experiment, a bar is teleoperated from a starting point to an end point in task space through a small window in a wall.
Projection-based Inference for High-dimensional Linear Models

We develop a new method to estimate the projection direction in the debiased Lasso estimator. The basic idea is to decompose the overall bias into two terms corresponding to strong and weak signals respectively. We propose to estimate the projection direction by balancing the squared biases associated with the strong and weak signals as well as the variance of the projection-based estimator. Standard quadratic programming solver can efficiently solve the resulting optimization problem. In theory, we show that the unknown set of strong signals can be consistently estimated and the projection-based estimator enjoys the asymptotic normality under suitable assumptions. A slight modification of our procedure leads to an estimator with a potentially smaller order of bias comparing to the original debiased Lasso. We further generalize our method to conduct inference for a sparse linear combination of the regression coefficients. Numerical studies demonstrate the advantage of the proposed approach concerning coverage accuracy over some existing alternatives.
Comparison of Occupant Protection in Electric vs . Internal Combustion Vehicles

The study compares occupant protection in hybrid, hybrid electric, plug-in hybrid electric, and allelectric vehicles (referred to collectively as EVs) to the protection offered by equivalent vehicle models powered by an internal combustion engine (ICE). Two series of moving car-to-moving car frontal offset tests were conducted, each with the THOR or Hybrid III 50th percentile male anthropomorphic test device (ATD) in the driver's seat and the Hybrid III 5th percentile female in the right front passenger seat. The first series consisted of 17 tests, each involving an EV impacting the equivalent ICE model at the same impact velocity. The second series consisted of five (5) pairs of tests, in which each EV and the equivalent ICE model impacted the same baseline striking vehicle. Statistically significant differences of EV/ICE pairs were found primarily in the upper body responses of the ATDs. Where differences were statistically significant, ATD measurements in ICE vehicles were slightly greater than those in EV counterparts. Occupant protection in EV models was found to be as good as or better than protection in ICE model equivalents.
Fast Handovers with a Robot Character: Small Sensorimotor Delays Improve Perceived Qualities

We present a system for fast and robust handovers with a robot character, together with a user study investigating the effect of robot speed and reaction time on perceived interaction quality. The system can match and exceed human speeds and confirms that users prefer human-level timing.The system has the appearance of a robot character, with a bear-like head and a soft anthropomorphic hand and uses Bézier curves to achieve smooth minimum-jerk motions. Fast timing is enabled by low latency motion capture and real-time trajectory generation: the robot initially moves towards an expected handover location and the trajectory is updated on-the-fly to converge smoothly to the actual handover location. A hybrid automaton provides robustness to failure and unexpected human actions.In a 3x3 user study, we vary the speed of the robot and add variable sensorimotor delays. We evaluate the social perception of the robot using the Robot Social Attribute Scale (RoSAS). Inclusion of a small delay, mimicking the delay of the human sensorimotor system, leads to an improvement in perceived qualities over both no delay and long delay conditions. Specifically, with no delay the robot is perceived as more discomforting, and with a long delay it is perceived as less warm.
Contributions of Phonology and Orthography to Spelling in Children with Dyslexia

Learning to spell is dependent on a child’s ability to simultaneously process phonological (i.e., related to sounds) and orthographic (i.e., related to letters) information. The contributions of phonological and orthographic processes in children with dyslexia have been explored more in reading than in spelling. Evidence from reading research indicates that children with dyslexia tend to rely on relatively preserved orthographic knowledge to compensate for their phonological weaknesses. More studies are needed in spelling, as existing evidence is not clear as to whether phonological and orthographic processes make joint or separate contributions. The present study used phonological and orthographic neighbors (i.e., words differing by a single sound or letter) to examine processing abilities in children with dyslexia and children with typical reading skills. A total of 57 children with dyslexia (grade 4), age-matched typically developing children (grade 4), and reading-level-matched typically developing children (grades 1 and 2) were recruited from elementary schools in Kansas. Participants were asked to spell and read nonwords that varied in the number of phonological and orthographic neighbors (i.e., dense/large neighborhoods vs. sparse/small neighborhoods). Our results revealed that nonwords with many phonological neighbors facilitated spelling and reading performances, whereas nonwords with many orthographic neighbors did not. Performances were similar between children with dyslexia and typical readers. Our findings do not support the idea of orthographic compensation in children with dyslexia and instead, they suggest that children rely more on their phonological knowledge than their orthographic knowledge. We discuss how our findings inform theoretical models of spelling and reading, and how methodological characteristics may explain discrepancies between our study and previous studies.
Toward IoT system project : BRICS Mosaic model and system engineering management

One of the targets of Internet of Things (IoT) systems is to provide access to any service, to any user, anytime, anywhere, regardless the access network technology or the type of user device. The development of these systems demand the evaluation of several dimensions that are present in an IoT solution, including the integrated management of the engineering effort throughout system life cycle. As our knowledge about IoT systems grows and evolves, so has our understanding about the need of a management process to conduct the system life cycle. The diversity of dimensions present in IoT systems demands a systemic management process to promote the system vision as a whole. This work presents the BRICS Mosaic Model and the Feasibility Barriers Factors to evaluate IoT solutions. It is a model that quantifies, through the developers’ experience and analysis of application scenarios, a numerical relationship that allows identifying barriers to IoT solution development. This model is integrated with systems engineering management concepts, both to reduce a failure point, the managerial error, and to framework and guidance all engineering activities within the IoT system life cycle, from lust-to-dust. Keywords—Internet of things, systems management, systems modeling, socio, socio-technical systems.
Effect of Self-Attribution Bias on Investment in the Rwandan Stock Market

The main objective of this study was to establish the effect of self-attribution bias on investment in the Rwanda Stock Exchange. The study used cross-sectional descriptive survey research design to ascertain and establish the effect of behavioural biases on investment in the Rwanda stock exchange. The target population comprised of 13,543 individual, group investors at the Rwanda Stock Exchange. Random sampling was used where the targeted population was individual investors to finally yield a sample size of 374 respondents. A questionnaire was used to collect the primary data. A pilot test was undertaken by carrying out a small scale trial run of the research instrument. Data analysis involved the use of descriptive and inferential statistics. A Linear regression model was used to predict the probability of different possibility outcomes of dependent variables, helping to predict the probability of an investor to invest in RSE. The results confirmed that there was a significant positive linear relationship between selfattribution biasand Investment in Rwanda stock market.The study also concluded that most investors suffered from self-attribution bias in investment in stock markets.The study recommends that investors should be keen to identify such bias to increase their rationality in stock trading.
Partnership Characteristics within Large Software Ecosystems

Ecosystem participant clusters are a powerful tool for large software ecosystems to manage clusters within their ecosystems. Two examples of ecosystem participant clusters are partnership models and membership models. An ecosystem participant cluster consists of a set of commitments between cluster owner and participant, this paper discusses the identifying characteristics of those commitments. A taxonomy is created to describe the structure of an ecosystem participant cluster. This taxonomy includes the role fulfilled by the participant within this commitment, also including the dimension of the role as well as resulting benefits, requirements and costs. Furthermore, the taxonomy and three case studies form the basis for a classification of ecosystem participant clusters, using a set of characteristics to identify the main differences between clusters used within the industry. These differences include different forms of model governance, entry barriers and goals for the three respective clusters.