nils-herrmann/clean_scirepeval huggingface dataset:
Implementation Challenges for Information Security Awareness Initiatives in E-Government

With the widespread adoption of electronic government services, there has been a need to ensure a seamless flow of information across public sector organizations, while at the same time, maintaining confidentiality, integrity and availability. Governments have put in place various initiatives and programs including information security awareness to provide the needed understanding on how public sector employees can maintain security and privacy. Nonetheless, the implementation of such initiatives often faces a number of challenges that impede further take-up of e-government services. This paper aims to provide a better understanding of the challenges contributing towards the success of information security awareness initiatives implementation in the context of e-government. Political, organizational, social as well as technological challenges have been utilized in a conceptual framework to signify such challenges in e-government projects. An empirical case study conducted in a public sector organization in Greece was exploited in this research to reflect on these challenges. While, the results from this empirical study confirm the role of the identified challenges for the implementation of security awareness programs in e-government, it has been noticed that awareness programmers often pursue different targets of preserving security and privacy, which sometimes results in adding more complexity to the organization.
A 1.5 nW, 32.768 kHz XTAL Oscillator Operational From a 0.3 V Supply

This paper presents an ultra-low power crystal (XTAL) oscillator circuit for generating a 32.768 kHz clock source for real-time clock generation. An inverting amplifier operational from 0.3 V VDD oscillates the XTAL resonator and achieves a power consumption of 2.1 nW. A duty-cycling technique powers down the XTAL amplifier without losing the oscillation and reduces the power consumption to 1.5 nW. The proposed circuit is implemented in 130 nm CMOS with an area of 0.0625 mm2 and achieves a temperature stability of 1.85 ppm/°C.
CIS-UltraCal: An open-source ultrasound calibration toolkit

We present an open-source MATLAB toolkit for ultrasound calibration. It has a convenient graphical user interface which sits on top of an extensive API. Calibration using three different phantoms is explicitly supported: the cross-wire phantom, the single-wall phantom, and the Hopkins phantom. Image processing of the Hopkins phantom is automated by making use of techniques from binary morphology, radon transform and RANSAC. Numerous calibration and termination parameters are exposed. It is also modular, allowing one to apply the system to original phantoms by writing a minimum of new code.
Camera parameters auto-adjusting technique for robust robot vision

How to make vision system work robustly under dynamic light conditions is still a challenging research focus in computer/robot vision community. In this paper, a novel camera parameters auto-adjusting technique based on image entropy is proposed. Firstly image entropy is defined and its relationship with camera parameters is verified by experiments. Then how to optimize the camera parameters based on image entropy is proposed to make robot vision adaptive to the different light conditions. The algorithm is tested by using the omnidirectional vision in indoor RoboCup Middle Size League environment and the perspective camera in outdoor ordinary environment, and the results show that the method is effective and color constancy to some extent can be achieved.
Implementing the Chinese Wall Security Model in Workflow Management Systems

The Chinese wall security model (CWSM) was designed to provide access controls that mitigate conflict of interest in commercial organizations, and is especially important for large-scale interenterprise workflow applications. This paper describes how to implement the CWSM in a WfMS. We first demonstrate situations in which the role-based access control model is not sufficient for this, and we then propose a security policy language to solve this problem, also providing support for the intrinsic dynamic access control mechanism defined in the CWSM (i.e., the dynamic binding of subjects and elements in the company data set). This language can also specify several requirements of the dynamic security policy that arise when applying the CWSM in WfMSs. Finally we discuss how to implement a run-time system to implement CWSM policies specified by this language in a WfMS.
High-Frequency Behavior of Graphene-Based Interconnects—Part II: Impedance Analysis and Implications for Inductor Design

This paper provides the first detailed insights into the ultrahigh-frequency behavior of graphene ribbons (GRs) and analyzes their consequences in designing interconnects and low-loss on-chip inductors. In the companion paper (part I), an accurate impedance modeling methodology has been developed based on the Boltzmann equation with the magnetic vector potential Green's function approach incorporating the dependency of current on the nonlocal electric field. Based on the developed methodology, this paper for the first time embarks on the rigorous investigation of the intricate processes occurring at high frequencies in GRs, such as anomalous skin effect (ASE), high-frequency resistance and inductance saturation, intercoupled relation between edge specularity and ASE, and the influence of the linear dimensions on impedance. A comparative study of the high-frequency response of GRs with that of carbon nanotubes (CNTs) and Cu is made to highlight the potential of GR interconnects for high-frequency applications. Subsequently, the high-frequency performance of GR inductors is analyzed, and it is shown that they can achieve 32% and 50% improvements in maximum Q-factor compared to Cu and single-walled CNT inductors with 1/3 metallic fraction, respectively.
Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting

Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. Parametric and Nonparametric Techniques, Non-peak Traffic Forecasting
ARROW: GenerAting SignatuRes to Detect DRive-By DOWnloads

A drive-by download attack occurs when a user visits a webpage which attempts to automatically download malware without the user's consent. Attackers sometimes use a malware distribution network (MDN) to manage a large number of malicious webpages, exploits, and malware executables. In this paper, we provide a new method to determine these MDNs from the secondary URLs and redirect chains recorded by a high-interaction client honeypot. In addition, we propose a novel drive-by download detection method. Instead of depending on the malicious content used by previous methods, our algorithm first identifies and then leverages the URLs of the MDN's central servers, where a central server is a common server shared by a large percentage of the drive-by download attacks in the same MDN. A set of regular expression-based signatures are then generated based on the URLs of each central server. This method allows additional malicious webpages to be identified which launched but failed to execute a successful drive-by download attack. The new drive-by detection system named ARROW has been implemented, and we provide a large-scale evaluation on the output of a production drive-by detection system. The experimental results demonstrate the effectiveness of our method, where the detection coverage has been boosted by 96% with an extremely low false positive rate.
Two wheel self-balanced mobile robot identification based on experimental data

Many researches have been accomplished about modeling of two wheel robots which stabilize themselves autonomously and various models have been proposed to explain the dynamic model of them. This paper considers two phases of system identification process to achieve dynamic equilibrium of the robot. In the first phase, it investigates a part of system as multi input multi output (MIMO) system. Considering the current of motors as inputs, and the angular velocity of wheels as outputs. In the second phase, this study investigates another part of the system as multi input single output (MISO) system which its inputs are the angular velocity of the wheels (the output of the first phase) and output is the vertical angle of the robot axis. This study compares ARX, ARMAX, BJ and OE as candid as linear models and Wiener and Hammerstein as nonlinear models. The results shows that the Weiner nonlinear model with saturated output has the best performance for the first phase and the Wiener nonlinear model with Wavelet network is suggested for the second phase of identification process.
A Study on Consumers ’ Attitude towards Online Shopping in China

Online shopping provides a good example of the business revolution. In China, e-commerce is currently experiencing a period of rapid development; the large number of Internet users provides a good foundation for the expansion of the online shopping market. In this study, perceived usability, perceived security, perceived privacy, perceived after-sales service, perceived marketing mix, and perceived reputation were used for analysis. This research was conducted by using the primary data source, and the survey method was employed in the research. This research found that there were relationships between the perceived usability, perceived security, perceived privacy, perceived after-sales service, perceived marketing mix, perceived reputation and consumers’ attitude to adopting online shopping in China. However, only marketing mix and reputation were found to significantly influence consumers’ attitude to adopt online shopping. The findings help us in understanding consumers’ online purchase behaviour.
The C Programming Language

In this chapter we will learn how to • write simple computer programs using the C programming language; • perform basic mathematical calculations; • manage data stored in the computer memory and disk; • generate meaningful output on the screen or into a computer file. The C programming language was developed in the early 1970's by Ken Thomp-son and Dennis Ritchie at the Bell Telephone Laboratories. It was designed and implemented in parallel with the operating system Unix and was aimed mostly as a system implementation language . It, nevertheless, evolved into one of the most flexible and widely used computer programming languages today. In 1989, the American National Standards Institute (ANSI) adopted a document that standardized the C language. The particular version of the language that it describes is widely referred to as ANSI C or C89. (ISO) as C90 and was later expanded to the current standard, which is often referred to as C99. The C language consists of a small set of core commands and a large number of library functions that can be incorporated in a program, if necessary. One can find many excellent books that discuss the C language in detail, starting from the first book The C Programming Language by B. . This book describes ANSI C and remains today one of the easiest texts on the subject. In this chapter, we will cover the most basic of the core commands of the language, as well as those library functions that are useful in developing programs that involve scientific computations. 1.1 The first program It is a tradition in the culture of C programming that the first program compiled and executed by a new student of the language is one that prints on the screen the happy message " Hello World! "
Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability

Data mining now-a-days plays an important role in prediction of diseases in health care industry. The Health care industry utilizes data mining Techniques and finds out the information which is hidden in the data set. Many diagnoses have been done for predicting diseases. Without knowing the knowledge of profound medicine and clinical experience the treatment goes wrong. The time taken to recover from diseases depends on patients&apos; severity. For finding out the disease, number of test needs to be taken by patient. In most cases not all test become more effective. And at last it leads to the death of the patient. Many experiments have been conducted by comparing the performance of predictive data mining for reducing the number of test taken by the patient indirectly. This research paper is to present a survey on predicting the presence of life threatening diseases which causes to death and list out the various classification algorithms that has been used with number of attributes for prediction.
Reduction of Association Rules for Big Data Sets in Socially-Aware Computing

Reduction of the number of association rules in data mining is a very important issue in the field of socially-aware computing in which big data need to be manipulated. The existing schemes based on the frequency of occurrences are not effective for relatively large size dataset. In this paper we propose the tabular-algorithm that assigns a weight to each rule for the removal of unimportant rules and employs the Quine-Mccluskey method for rule reduction. Computer simulation reveals that the proposed scheme significantly improves support, credibility, rule reduction rate, and processing time compared to the representative existing schemes such as Apriori and FP-growth algorithm.
Development of a helical climbing modular snake robot

This research aims to study the grasping torque profile of the helical climbing robot on the cylindrical pole with constant radius. The modular snake robot is formed into a helical shape which can be described by various parameters such as the helical pitch angle, the radius and the pitch distance. The torque in each axis of rotation is affected by the helical pitch angle parameter. Five grasping configurations with different helical pitch angle were tested on the 18 degree of freedoms, 7 modules wheeled snake robot. The experimental result showed that the torque around yaw axis transferred to the pitch axis when the helical pitch angle was increasing. The profile of torque magnitude along the robot's body resulted in parabolic shape due to the unbalanced grasping force of the discrete points of contact between the robot and the pole.
GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Networks

The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. In order to extract invariant gait features, we proposed a method named as GaitGAN which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate invariant gait images that is side view images with normal clothing and without carrying bags. A unique advantage of this approach is that the view angle and other variations are not needed before generating invariant gait images. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN which has only one discriminator in that GaitGAN contains two discriminators. One is a fake/real discriminator which can make the generated gait images to be realistic. Another one is an identification discriminator which ensures that the the generated gait images contain human identification information. Experimental results show that GaitGAN can achieve state-of-the-art performance. To the best of our knowledge this is the first gait recognition method based on GAN with encouraging results. Nevertheless, we have identified several research directions to further improve GaitGAN.
Total sleep deprivation, chronic sleep restriction and sleep disruption.

Sleep loss may result from total sleep deprivation (such as a shift worker might experience), chronic sleep restriction (due to work, medical conditions or lifestyle) or sleep disruption (which is common in sleep disorders such as sleep apnea or restless legs syndrome). Total sleep deprivation has been widely researched, and its effects have been well described. Chronic sleep restriction and sleep disruption (also known as sleep fragmentation) have received less experimental attention. Recently, there has been increasing interest in sleep restriction and disruption as it has been recognized that they have a similar impact on cognitive functioning as a period of total sleep deprivation. Sleep loss causes impairments in cognitive performance and simulated driving and induces sleepiness, fatigue and mood changes. This review examines recent research on the effects of sleep deprivation, restriction and disruption on cognition and neurophysiologic functioning in healthy adults, and contrasts the similarities and differences between these three modalities of sleep loss.
Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback

Neurofeedback is a promising approach for non-invasive modulation of human brain activity with applications for treatment of mental disorders and enhancement of brain performance. Neurofeedback techniques are commonly based on either electroencephalography (EEG) or real-time functional magnetic resonance imaging (rtfMRI). Advances in simultaneous EEG-fMRI have made it possible to combine the two approaches. Here we report the first implementation of simultaneous multimodal rtfMRI and EEG neurofeedback (rtfMRI-EEG-nf). It is based on a novel system for real-time integration of simultaneous rtfMRI and EEG data streams. We applied the rtfMRI-EEG-nf to training of emotional self-regulation in healthy subjects performing a positive emotion induction task based on retrieval of happy autobiographical memories. The participants were able to simultaneously regulate their BOLD fMRI activation in the left amygdala and frontal EEG power asymmetry in the high-beta band using the rtfMRI-EEG-nf. Our proof-of-concept results demonstrate the feasibility of simultaneous self-regulation of both hemodynamic (rtfMRI) and electrophysiological (EEG) activities of the human brain. They suggest potential applications of rtfMRI-EEG-nf in the development of novel cognitive neuroscience research paradigms and enhanced cognitive therapeutic approaches for major neuropsychiatric disorders, particularly depression.
Dytan: a generic dynamic taint analysis framework

Dynamic taint analysis is gaining momentum. Techniques based on dynamic tainting have been successfully used in the context of application security, and now their use is also being explored in different areas, such as program understanding, software testing, and debugging. Unfortunately, most existing approaches for dynamic tainting are defined in an ad-hoc manner, which makes it difficult to extend them, experiment with them, and adapt them to new contexts. Moreover, most existing approaches are focused on data-flow based tainting only and do not consider tainting due to control flow, which limits their applicability outside the security domain. To address these limitations and foster experimentation with dynamic tainting techniques, we defined and developed a general framework for dynamic tainting that (1) is highly flexible and customizable, (2) allows for performing both data-flow and control-flow based tainting conservatively, and (3) does not rely on any customized run-time system. We also present DYTAN, an implementation of our framework that works on x86 executables, and a set of preliminary studies that show how DYTAN can be used to implement different tainting-based approaches with limited effort. In the studies, we also show that DYTAN can be used on real software, by using FIREFOX as one of our subjects, and illustrate how the specific characteristics of the tainting approach used can affect efficiency and accuracy of the taint analysis, which further justifies the use of our framework to experiment with different variants of an approach.
Designing Home Automation System ( HAS ) using Java ME for Mobile Phone

Home automation means to control home appliances and their ch a a teristics like on and off, speed, volume and dimming. Since last few years, ICT (Information Communication Technology) have been increasingly embed ded into surroundings. This inclination has been frequently referred to as pervasive or ubiquitous comput ing, where mobile devices are practiced widely. Home automation is taken up on th e basis of simplicity, protection and power consump tion effectiveness. This paper presents the design and i mplementation of an economical but yet flexible and secure home automation system based on Java and Bluetooth enabl ed mobile phone. The design is based on controlling an 8 bit Bluetooth interfaced microcontroller using User Interface (UI) of Java ME Application Program running on Bluetooth enabled Mobile Phone. The home appliances are connected to the on/off relay via microcontroller ports and controlled through two ways: one way is conventional domestic switch and second is through mobile phone. The communication between the mobile phone and the home automation system is thro ugh Bluetooth wireless technology. This system is d esigned to be economical and scalable allowing one to twenty four appliances to be controlled with minimum efforts. Password protection is being used to only allow authorized u sers to control the appliances at home.
Aggression in the Laboratory : Problems with the Validity of the Modified Taylor Competitive Reaction Time Test as a Measure of Aggression in Media Violence Studies

Many laboratory studies of aggression use a measure known as the modified Taylor Competitive Reaction Time Test (TCRTT), for which validation studies are lacking. Using sound blasts administered by the participant against a fictional human opponent, the TCRTT also allows for multiple methods of measuring aggression. The validity of the TCRTT was tested in 53 college student participants. Participants took a self-report measure of aggressiveness as well as neuropsychological measures of frontal lobe functioning predictive of aggression. Results were not supportive of the TCRTT’s validity and indicated concerns regarding the use of the TCRTT as a measure of aggression. Results suggest that labaratory studies of media violence using the TCRTT are of questionable validity.
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

This paper presents an open-source enforcement learning toolkit named CytonRL . The toolkit implements four recent advanced deep Q-learning algorithms from scratch using C++ and NVIDIA’s GPU-accelerated libraries. The code is simple and elegant, owing to an open-source general-purpose neural network library named CytonLib. Benchmark shows that the toolkit achieves competitive performances on the popular Atari game of Breakout.