nils-herrmann/clean_scirepeval huggingface dataset:
Website fingerprinting as a cybercrime investigation model: Role and challenges

The mass-production of online websites is one of the most confrontations of detecting cybercriminal activities. Monitoring the construction or visitation of such websites is somewhat unsophisticated process if it relies on filtering the URL addresses alone, like Proxy. However, user online interactions can be concealed if passed through security protocols or anonymity networks, like Tor. Nowadays, Tor has been widely used to conceal website addresses, web page contents, user actions, and user anonymity. On the other hand, website traffic analysis and fingerprinting techniques endeavor to break such privacy by revealing user actions and anonymity. Basically, this is considered as a negative behavior. However, in this paper, we study how fingerprinting techniques can positively be adapted by Internet Service Providers to be used against Cybercrime activities. In other words, fingerprinting techniques can play a vital role for investigating and mitigating cybercrimes. We present several fingerprinting techniques and countermeasures from a cybercrime point of view. Then, we (1) illustrate how fingerprinting techniques can be applied as cybercrime investigation models, and (2) discuss the expected challenges of such application.
A structured approach to anomaly detection for in-vehicle networks

The complexity and connectivity of modern vehicles has constantly increased over the past years. Within the scope of this development the security risk for the in-vehicle network and its components has risen massively. Apart from threats for comfort and confidentiality, these attacks can also affect safety critical systems of the vehicle and therefore endanger the driver and other road users. In this paper the introduction of anomaly detection systems to the automotive in-vehicle network is discussed. Based on properties of typical vehicular networks, like the Controller Area Network (CAN), a set of anomaly detection sensors is introduced which allow the recognition of attacks during the operation of the vehicle without causing false positives. Moreover, important design and application criteria for a vehicular attack detection system are explained and discussed.
The Five Factor Model of personality and job performance in the European Community.

In 3 prior meta-analyses, the relationship between the Big Five factors of personality and job criteria was investigated. However, these meta-analyses showed different findings. Furthermore, these reviews included studies carried out only in the United States and Canada. This study reports meta-analytic research on the same topic but with studies conducted in the European Community, which were not included in the prior reviews. The results indicate that Conscientiousness and Emotional Stability are valid predictors across job criteria and occupational groups. The remaining factors are valid only for some criteria and for some occupational groups. Extraversion was a predictor for 2 occupations, and Openness and Agreeableness were valid predictors of training proficiency. These findings are consistent with M.R. Barrick and M.K. Mount (1991) and L.M. Hough, N.K. Eaton, M.D. Dunnette, J.D. Kamp, and R.A. McCloy (1990). Implications of the results for future research and the practice of personnel selection are suggested.
Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM

Sequence-to-sequence deep learning has recently emerged as a new paradigm in supervised learning for spoken language understanding. However, most of the previous studies explored this framework for building single domain models for each task, such as slot filling or domain classification, comparing deep learning based approaches with conventional ones like conditional random fields. This paper proposes a holistic multi-domain, multi-task (i.e. slot filling, domain and intent detection) modeling approach to estimate complete semantic frames for all user utterances addressed to a conversational system, demonstrating the distinctive power of deep learning methods, namely bi-directional recurrent neural network (RNN) with long-short term memory (LSTM) cells (RNN-LSTM) to handle such complexity. The contributions of the presented work are three-fold: (i) we propose an RNN-LSTM architecture for joint modeling of slot filling, intent determination, and domain classification; (ii) we build a joint multi-domain model enabling multi-task deep learning where the data from each domain reinforces each other; (iii) we investigate alternative architectures for modeling lexical context in spoken language understanding. In addition to the simplicity of the single model framework, experimental results show the power of such an approach on Microsoft Cortana real user data over alternative methods based on single domain/task deep learning.
Users' Awareness of Privacy on Online Social Networking Sites - Case Facebook

Online social networking offers a new, easy and inexpensive way to maintain already existing relationships and present oneself to others. However, the increasing number of actions in online services also gives a rise to privacy concerns and risks. In an attempt to understand the factors, especially privacy awareness, that influence users to disclose or protect information in online environment, we view privacy behavior from the perspectives of privacy protection and information disclosing. In our empirical study, we present results from a survey of 210 users of Facebook. Our results indicate, that most of our respondents, who seem to be active users of Facebook, disclose a considerable amount of private information. Contrary to their own belief, they are not too well aware of the visibility of their information to people they do not necessarily know. Furthermore, Facebook’s privacy policy and the terms of use were largely not known or understood by our respondents.
Doubly Fed Induction Generator Model-Based Sensor Fault Detection and Control Loop Reconfiguration

Fault tolerance is gaining interest as a means to increase the reliability and availability of distributed energy systems. In this paper, a voltage-oriented doubly fed induction generator, which is often used in wind turbines, is examined. Furthermore, current, voltage, and position sensor fault detection, isolation, and reconfiguration are presented. Machine operation is not interrupted. A bank of observers provides residuals for fault detection and replacement signals for the reconfiguration. Control is temporarily switched from closed loop into open-loop to decouple the drive from faulty sensor readings. During a short period of open-loop operation, the fault is isolated using parity equations. Replacement signals from observers are used to reconfigure the drive and reenter closed-loop control. There are no large transients in the current. Measurement results and stability analysis show good results.
Dynamic FAUST: Registering Human Bodies in Motion

While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data, that is 3D scans of moving non-rigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http://dfaust.is.tue.mpg.de.
Issues in Code-Switching: Competing Theories and Models

This paper provides a critical overview of the theoretical, analytical, and practical questions most prevalent in the study of the structural and the sociolinguistic dimensions of code-switching (CS). In doing so, it reviews a range of empirical studies from around the world. The paper first looks at the linguistic research on the structural features of CS focusing in particular on the code-switching versus borrowing distinction, and the syntactic constraints governing its operation. It then critically reviews sociological, anthropological, and linguistic perspectives dominating the sociolinguistic research on CS over the past three decades. Major empirical studies on the discourse functions of CS are discussed, noting the similarities and differences between socially motivated CS and style-shifting. Finally, directions for future research on CS are discussed, giving particular emphasis to the methodological issue of its applicability to the analysis of bilingual classroom interaction.
Management of a Remote Backup Copy for Disaster Recovery

A remote backup database system tracks the state of a primary system, taking over transaction processing when disaster hits the primary site. The primary and backup sites are physically isolated so that failures at one site are unlikely to propogate to the other. For correctness, the execution schedule at the backup must be equivalent to that at the primary. When the primary and backup sites contain a single processor, it is easy to achieve this property. However, this is harder to do when each site contains multiple processors and sites are connected via multiple communication lines. We present an efficient transaction processing mechanism for multiprocessor systems that guarantees this and other important properties. We also present a database initialization algorithm that copies the database to a backup site while transactions are being processed.
Mobile Cloud Computing Research - Issues, Challenges and Needs

The rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions. This drives a strong demand for mobile cloud applications and services for mobile device users. This brings out a great business and research opportunity in mobile cloud computing (MCC). This paper first discusses the market trend and related business driving forces and opportunities. Then it presents an overview of MCC in terms of its concepts, distinct features, research scope and motivations, as well as advantages and benefits. Moreover, it discusses its opportunities, issues and challenges. Furthermore, the paper highlights a research roadmap for MCC.
Perfect metamaterial absorber.

We present the design for an absorbing metamaterial (MM) with near unity absorbance A(omega). Our structure consists of two MM resonators that couple separately to electric and magnetic fields so as to absorb all incident radiation within a single unit cell layer. We fabricate, characterize, and analyze a MM absorber with a slightly lower predicted A(omega) of 96%. Unlike conventional absorbers, our MM consists solely of metallic elements. The substrate can therefore be optimized for other parameters of interest. We experimentally demonstrate a peak A(omega) greater than 88% at 11.5 GHz.
Unsupervised Feature Selection for Multi-View Clustering on Text-Image Web News Data

Unlabeled high-dimensional text-image web news data are produced every day, presenting new challenges to unsupervised feature selection on multi-view data. State-of-the-art multi-view unsupervised feature selection methods learn pseudo class labels by spectral analysis, which is sensitive to the choice of similarity metric for each view. For text-image data, the raw text itself contains more discriminative information than similarity graph which loses information during construction, and thus the text feature can be directly used for label learning, avoiding information loss as in spectral analysis. We propose a new multi-view unsupervised feature selection method in which image local learning regularized orthogonal nonnegative matrix factorization is used to learn pseudo labels and simultaneously robust joint $l_{2,1}$-norm minimization is performed to select discriminative features. Cross-view consensus on pseudo labels can be obtained as much as possible. We systematically evaluate the proposed method in multi-view text-image web news datasets. Our extensive experiments on web news datasets crawled from two major US media channels: CNN and FOXNews demonstrate the efficacy of the new method over state-of-the-art multi-view and single-view unsupervised feature selection methods.
Enhancing stance phase propulsion during level walking by combining fes with a powered exoskeleton for persons with paraplegia

This paper describes the design and implementation of a cooperative controller that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton to provide enhanced hip extension during the stance phase of walking in persons with paraplegia. The controller utilizes two sources of actuation: the electric motors of the powered exoskeleton and the user's machine (FSM), a set of FES. It consists of a finite-state machine (FSM), a set of proportional-derivative (PD) controllers for the exoskeleton and a cycle-to-cycle adaptive controller for muscle stimulation. Level ground walking is conducted on a single subject with complete T10 paraplegia. Results show a 34% reduction in electrical power requirements at the hip joints during the stance phase of the gait cycle with the cooperative controller compared to using electric motors alone.
Facial smile detection based on deep learning features

Smile detection from facial images is a specialized task in facial expression analysis with many potential applications such as smiling payment, patient monitoring and photo selection. The current methods on this study are to represent face with low-level features, followed by a strong classifier. However, these manual features cannot well discover information implied in facial images for smile detection. In this paper, we propose to extract high-level features by a well-designed deep convolutional networks (CNN). A key contribution of this work is that we use both recognition and verification signals as supervision to learn expression features, which is helpful to reduce same-expression variations and enlarge different-expression differences. Our method is end-to-end, without complex pre-processing often used in traditional methods. High-level features are taken from the last hidden layer neuron activations of deep CNN, and fed into a soft-max classifier to estimate. Experimental results show that our proposed method is very effective, which outperforms the state-of-the-art methods. On the GENKI smile detection dataset, our method reduces the error rate by 21% compared with the previous best method.
Development of Anti-Phishing Model for Classification of Phishing E-mail

Due to colossal financial losses in recent years, phishing has drawn attention of most of the individuals and organizations in the world of internet. Need for protection against phishing activities through fraudulent emails has increased remarkably. In this paper we propose a hybrid model to classify phishing emails using machine learning algorithms with the aspiration of developing an ensemble model for email classification with improved accuracy. We have used the content of emails and extracted 47 features from it. The processed emails are provided as input to various machine learning classifiers. Going through experiments, it is observed and inferred that Bayesian net classification model when ensemble with CART gives highest test accuracy of 99.32%.
A Horizontally Polarized Omnidirectional Printed Antenna for WLAN Applications

The design simulation, fabrication, and measurement of a 2.4-GHz horizontally polarized omnidirectional planar printed antenna for WLAN applications is presented. The antenna adopts the printed Alford-loop-type structure. The three-dimensional (3-D) EM simulator HFSS is used for design simulation. The designed antenna is fabricated on an FR-4 printed-circuit-board substrate. The measured input standing-wave-ratio (SWR) is less than three from 2.40 to 2.483 GHz. As desired, the horizontal-polarization H-plane pattern is quite omnidirectional and the E-plane pattern is also very close to that of an ideal dipole antenna. Also a comparison with the popular printed inverted-F antenna (PIFA) has been conducted, the measured H-plane pattern of the Alford-loop-structure antenna is better than that of the PIFA when the omnidirectional pattern is desired. Further more, the study of the antenna printed on a simulated PCMCIA card and that inserted inside a laptop PC are also conducted. The HFSS model of a laptop PC housing, consisting of the display, the screen, and the metallic box with the keyboard, is constructed. The effect of the laptop PC housing with different angle between the display and keyboard on the antenna is also investigated. It is found that there is about 15 dB attenuation of the gain pattern (horizontal-polarization field) in the opposite direction of the PCMCIA slot on the laptop PC. Hence, the effect of the large ground plane of the PCMCIA card and the attenuation effect of the laptop PC housing should be taken into consideration for the antenna design for WLAN applications. For the proposed antenna, in addition to be used alone for a horizontally polarized antenna, it can be also a part of a diversity antenna
Continuous Authentication for Voice Assistants

Voice has become an increasingly popular User Interaction (UI) channel, mainly contributing to the current trend of wearables, smart vehicles, and home automation systems. Voice assistants such as Alexa, Siri, and Google Now, have become our everyday fixtures, especially when/where touch interfaces are inconvenient or even dangerous to use, such as driving or exercising. The open nature of the voice channel makes voice assistants difficult to secure, and hence exposed to various threats as demonstrated by security researchers. To defend against these threats, we present VAuth, the first system that provides continuous authentication for voice assistants. VAuth is designed to fit in widely-adopted wearable devices, such as eyeglasses, earphones/buds and necklaces, where it collects the body-surface vibrations of the user and matches it with the speech signal received by the voice assistant's microphone. VAuth guarantees the voice assistant to execute only the commands that originate from the voice of the owner. We have evaluated VAuth with 18 users and 30 voice commands and find it to achieve 97% detection accuracy and less than 0.1% false positive rate, regardless of VAuth's position on the body and the user's language, accent or mobility. VAuth successfully thwarts various practical attacks, such as replay attacks, mangled voice attacks, or impersonation attacks. It also incurs low energy and latency overheads and is compatible with most voice assistants.
IGBT gate-drive with PCB Rogowski coil for improved short circuit detection and current turn-off capability

In this paper, a gate drive using gate boosting and double-stage turn off including voltage clamping as well as with detection of overcurrent and a too high di/dt during turn on is discussed in detail. Besides the gate drive, also the design of a PCB-Rogowski coil, which is used for measuring currents and for di/dt detection, is explained and different designs are compared. The presented coil has a bandwidth of more than 28MHz and a propagation delay of 11 ns.
Spatial Computing and Social Media in the Context of Disaster Management

The growing trend of using smartphones and other GPS-enabled devices has provided new opportunities for developing spatial computing applications and technologies in unanticipated and unprecedented ways. Some capabilities of today's smartphones highlight the potential of citizen sensors to enable the next generation of geoinformatics. One promising application area for this is social media and its application to disaster management. Dynamic, real-time incident information collected from onsite human responders about the extent of damage, the evolution of the event, the community's needs, and responders' ability to deal with the situation, combined with information from the larger emergency management community, could lead to more accurate and real-time situational awareness. This would enable informed decisions, better resource allocation and thus a better response and outcome to the total crisis. In this context, the US Department of Homeland Security's Science & Technology Directorate (DHS-S&T) has initiated the Social Media Alert and Response to Threats to Citizens" (SMART-C) program, which aims to develop citizen participatory sensing capabilities for decision support throughout the disaster life cycle via a multitude of devices and modalities. Here, the authors provide an overview of the envisioned SMART-C system's capabilities and discuss some of the interesting and unique challenges that arise due to the combination of spatial computing and social media within the context of disaster management.
Post-WIMP User Interfaces

characterized by four distinguishable interface styles, each lasting for many years and optimized to the hardware available at the time. In the first period, the early 1950s and 1960s, computers were used in batch mode with punched-card input and line-printer output; there were essentially no user interfaces because there were no interactive users (although some of us were privileged to be able to do console debugging using switches and lights as our “user interface”). The second period in the evolution of interfaces (early 1960s through early 1980s) was the era of timesharing on mainframes and minicomputers using mechanical or “glass” teletypes (alphanumeric displays), when for the first time users could interact with the computer by typing in commands with parameters. Note that this era persisted even through the age of personal microcomputers with such operating systems as DOS and Unix with their command line shells. During the 1970s, timesharing and manual command lines remained deeply entrenched, but at Xerox PARC the third age of user interfaces dawned. Raster graphics-based networked workstations and “pointand-click” WIMP GUIs (graphical user interfaces based on windows, icons, menus, and a pointing device, typically a mouse) are the legacy of Xerox PARC that we’re still using today. WIMP GUIs were popularized by the Macintosh in 1984 and later copied by Windows on the PC and Motif on Unix workstations. Applications today have much the same look and feel as the early desktop applications (except for the increased “realism” achieved through the use of drop shadows for buttons and other UI widgets); the main advance lies in the shift from monochrome displays to color and in a large set of software-engineering tools for building WIMP interfaces. I find it rather surprising that the third generation of WIMP user interfaces has been so dominant for more than two decades; they are apparently sufficiently good for conventional desktop tasks that the field is stuck comfortably in a rut. I argue in this essay that the status quo does not suffice—that the newer forms of computing and computing devices available today necessitate new thinking t h e h u m a n c o n n e c t i o n Andries van Dam