nils-herrmann/clean_scirepeval huggingface dataset:
Automated Identification of Cryptographic Primitives in Binary Programs

Identifying that a given binary program implements a specific cryptographic algorithm and finding out more information about the cryptographic code is an important problem. Proprietary programs and especially malicious software (so called malware) often use cryptography and we want to learn more about the context, e.g., which algorithms and keys are used by the program. This helps an analyst to quickly understand what a given binary program does and eases analysis. In this paper, we present several methods to identify cryptographic primitives (e.g., entire algorithms or only keys) within a given binary program in an automated way. We perform fine-grained dynamic binary analysis and use the collected information as input for several heuristics that characterize specific, unique aspects of cryptographic code. Our evaluation shows that these methods improve the state-of-the-art approaches in this area and that we can successfully extract cryptographic keys from a given malware binary.
Input Voltage Sensorless Duty Compensation Control for a Three-Phase Boost PFC Converter

This paper proposes an input voltage sensorless control algorithm for three-phase active boost rectifiers. Using this approach, the input ac-phase voltages can be accurately estimated from the fluctuations of other measured state variables and preceding switching state information from converter dynamics. Furthermore, the proposed control strategy reduces the input current harmonics of an ac–dc three-phase boost power factor correction (PFC) converter by injecting an additional common-mode duty ratio term to the feedback controllers’ outputs. This additional duty compensation term cancels the unwanted input harmonics, caused by the floating potential between ac source neutral and dc link negative, without requiring any access to the neutral point. A 6-kW (continuous power)/10-kW (peak power) three-phase boost PFC prototype using SiC-based semiconductor switching devices is designed and developed to validate the proposed control algorithm. The experimental results show that an input power factor of 0.999 with a conversion efficiency of 98.3%, total harmonic distortion as low as 4%, and a tightly regulated dc-link voltage with 1% ripple can be achieved.
Aspects of Research through Design : A Literature Review

Research through design is, by nature, embedded in the design process. In other words, while its main concern is to inform a research question, it also must be concerned with the end product of the design. As such, designers/researchers become concerned with the same type of “wicked problems” the professional designers are, especially when using research through design. Moreover, they also add a new layer of complications that are inherent to research. Since the approach is quickly gaining in popularity, it is necessary to develop a base of knowledge about it. Thus, this paper’s aim is to present a literature review of texts about research through design to demystify this approach and provide a deeper understanding for future work in the field. The gathered views on the subject are then classified into one of six sets of aspects: ontological aspects, epistemological aspects, methodological aspects, limits and expected contributions.
A Security Threats Measurement Model for Reducing Cloud Computing Security Risk

Cloud computing is the trend of information resources sharing and also an important tool for enterprises or organizations to enhance competitiveness. Users have the flexibility to adjust request and configuration. However, network security and resource sharing issues have continued to threaten the operation of cloud computing, making cloud computing security encounter serious test. How to evaluate cloud computing security has become a topic worthy of further exploration. Combining system, management and technique three levels security factors, this paper proposes a Security Threats Measurement Model (STMM). Applying the STMM, security of cloud computing system environment can be effectively evaluated and cloud computing security defects and problems can be concretely identified. Based on the evaluation results, the user can choice the higher security service provider or request the service provider security improvement actions.
Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.
Analysis and Design of a Compact Dual-Band Directional Antenna

A new type of compact dual-band directional antenna is proposed for 2.4/5-GHz wireless access point and RFID reader applications. The dual-band antenna consists of a longer dipole for the lower band and a pair of shorter dipoles for the upper band. A simple coupling microstrip line is employed to excite the dipoles. Both the dual-band antenna and the feeding microstrip line are printed on the same substrate, leading to a fully planar structure. The length of the dipole for the lower band is shortened by a capacitive loading at each end of the dipole, offering a compact antenna configuration. The compact dual-band antenna achieves a desirable directional radiation pattern with an antenna gain of near 8 dBi for the lower band and 9-10 dBi for the upper band. An equivalent circuit analysis for the design of the dual-band directional antenna is presented.
Self-regulation and selective exposure: the impact of depleted self-regulation resources on confirmatory information processing.

In the present research, the authors investigated the impact of self-regulation resources on confirmatory information processing, that is, the tendency of individuals to systematically prefer standpoint-consistent information to standpoint-inconsistent information in information evaluation and search. In 4 studies with political and economic decision-making scenarios, it was consistently found that individuals with depleted self-regulation resources exhibited a stronger tendency for confirmatory information processing than did individuals with nondepleted self-regulation resources. Alternative explanations based on processes of ego threat, cognitive load, and mood were ruled out. Mediational analyses suggested that individuals with depleted self-regulation resources experienced increased levels of commitment to their own standpoint, which resulted in increased confirmatory information processing. In sum, the impact of ego depletion on confirmatory information search seems to be more motivational than cognitive in nature.
Autonomous Driving in the Real World: Experiences with Tesla Autopilot and Summon

As autonomous driving emerges, it is important to understand drivers' experiences with autonomous cars. We report the results of an online survey with Tesla owners using two autonomous driving features, Autopilot and Summon. We found that current users of these features have significant driving experience, high self-rated computer expertise and care about how automation works. Surprisingly, although automation failures are extremely common they were not perceived as risky. The most commonly occurring failures included the failure to detect lanes and uncomfortable speed changes of the vehicle. Additionally, a majority of the drivers emphasized the importance of being alert while driving with autonomous features and aware of the limitations of the current technology. Our main contribution is to provide a picture of attitudes and experiences towards semi-autonomous driving, revealing that some drivers adopting these features may not perceive autonomous driving as risky, even in an environment with regular automation failures.
Reinforcement Learning – Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

This paper presents a reinforcement learning (RL)–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming–based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.
Dimensional differences for evaluating the quality of footwear fit.

Very few standards exist for fitting products to people. Footwear is a noteworthy example. This study is an attempt to evaluate the quality of footwear fit using two-dimensional foot outlines. Twenty Hong Kong Chinese students participated in an experiment that involved three pairs of dress shoes and one pair of athletic shoes. The participants' feet were scanned using a commercial laser scanner, and each participant wore and rated the fit of each region of each shoe. The shoe lasts were also scanned and were used to match the foot scans with the last scans. The ANOVA showed significant (p < 0.05) differences among the four pairs of shoes for the overall, fore-foot and rear-foot fit ratings. There were no significant differences among shoes for mid-foot fit rating. These perceived differences were further analysed after matching the 2D outlines of both last and feet. The point-wise dimensional difference between foot and shoe outlines were computed and analysed after normalizing with foot perimeter. The dimensional difference (DD) plots along the foot perimeter showed that fore-foot fit was strongly correlated (R(2) > 0.8) with two of the minimums in the DD-plot while mid-foot fit was strongly correlated (R(2) > 0.9) with the dimensional difference around the arch region and a point on the lateral side of the foot. The DD-plots allow the designer to determine the critical locations that may affect footwear fit in addition to quantifying the nature of misfit so that design changes to shape and material may be possible.
An overview of MetaMap: historical perspective and recent advances

MetaMap is a widely available program providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text. This study reports on MetaMap's evolution over more than a decade, concentrating on those features arising out of the research needs of the biomedical informatics community both within and outside of the National Library of Medicine. Such features include the detection of author-defined acronyms/abbreviations, the ability to browse the Metathesaurus for concepts even tenuously related to input text, the detection of negation in situations in which the polarity of predications is important, word sense disambiguation (WSD), and various technical and algorithmic features. Near-term plans for MetaMap development include the incorporation of chemical name recognition and enhanced WSD.
Will Online Chat Help Alleviate Mood Loneliness?

The present study examines the relationship between social Internet use and loneliness and reviews the studies about this topic from both social psychology and computer-mediated communication literature, as a response to the call for interdisciplinary research from scholars in these two areas. Two hundred thirty-four people participated in both the survey testing trait loneliness and a 5-condition (face-to-face chatting, instant message chatting, watching video, writing assignments, and "do nothing") experiment. Participants reported increase of mood loneliness after chatting online. The level of mood loneliness after online chat was higher than that in face-to-face communication. For people with high trait loneliness, the mood loneliness increase in the computer-mediated communication condition was significantly higher than in the face-to-face communication condition. The author of the current study hopes to help clarify the mixed research findings in previous social Internet use literature about this topic and reminds communication researchers of the need to explore the constructs included in "psychological well-being" in terms of their nature, mechanism, causes, consequences, and furthermore, how they are related to communication.
Leveraging personal metadata for Desktop search: The Beagle++ system

Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this approach — the extensible Beagle toolbox. To prove the validity of our approach, we conducted a series of experiments. By comparing our results against the ones of a regular Desktop search solution — Beagle — we show an improved quality in search and overall performance.
Domain-Specific Knowledge Base Enrichment Using Wikipedia Tables

The knowledge base is a machine-readable set of knowledge. More and more multi-domain and large-scale knowledge bases have emerged in recent years, and they play an essential role in many information systems and semantic annotation tasks. However we do not have a perfect knowledge base yet and maybe we will never have a perfect one, because all the knowledge bases have limited coverage while new knowledge continues to emerge. Therefore populating and enriching the existing knowledge base become important tasks. Traditional knowledge base population task usually leverages the information embedded in the unstructured free text. Recently researchers found that massive structured tables on the Web are high-quality relational data and easier to be utilized than the unstructured text. Our goal of this paper is to enrich the knowledge base using Wikipedia tables. Here, knowledge means binary relations between entities and we focus on the relations in some specific domains. There are two basic types of information can be used in this task: the existing relation instances and the connection between types and relations. We firstly propose two basic probabilistic models based on two types of information respectively. Then we propose a light-weight aggregated model to combine the advantages of basic models. The experimental results show that our method is an effective approach to enriching the knowledge base with both high precision and recall.
Preliminary Design and Kinematic Analysis of a Mobility Platform with Two Actuated Spoke Wheels

IMPASS (Intelligent Mobility Platform with Active Spoke System) is a novel locomotion system concept that utilizes rimless wheels with individually actuated spokes to provide the ability to step over large obstacles like legs, adapt to uneven surfaces like tracks, yet retaining the speed and simplicity of wheels. Since it lacks the complexity of legs and has a large effective (wheel) diameter, this highly adaptive system can move over extreme terrain with ease while maintaining respectable travel speeds. This paper presents the concept, preliminary kinematic analyses and design of an IMPASS based robot with two actuated spoke wheels and an articulated tail. The actuated spoke wheel concept allows multiple modes of motion, which give it the ability to assume a stable stance using three contact points per wheel, walk with static stability with two contact points per wheel, or stride quickly using one contact point per wheel. Straight-line motion and considerations for turning are discussed for the oneand two-point contact schemes followed by the preliminary design and recommendations for future study. Index Terms – IMPASS, rimless wheel, actuated spoke wheel, mobility, locomotion.
Dielectric liquids for enhanced field force in macro scale direct drive electrostatic actuators and rotating machinery

Recently interest towards electrostatic actuators and machinery has increased for use in MEMS, mechatronics and renewable energy applications. Effective electromechanical power conversion using electrostatics requires dielectric liquids whose permittivity and breakdown field strength can facilitate adequate electric shear stress and pressure beyond the capabilities of media such as air or vacuum. This paper discusses design points for practical electrostatic force production and evaluates available off-the-shelf dielectric liquids in terms of electric pressure capability. Key dielectric properties including pressure, breakdown field strength, relative permittivity, and conductivity are measured. A custom test stand measures the attractive force between two electrodes mounted on load cells inside a tank filled with the liquid under test. Measurements demonstrate liquids capable of reaching several psi of electric pressure while correlating with analytical and finite element (FE) models, ultimately forming a basis for electrostatic actuator/machine design. The best liquid candidate, Vertrel XF, was poured into a proof of concept macro scale electrostatic machine demonstrating 0.9 N-m torque and 0.126 N-m/kg. Torque measurements verify the capability of Vertrel XF and the correlation between the electric pressure and shear stress in a rotating machine.
Internet of Things for Enterprise Systems of Modern Manufacturing

Design and operation of a manufacturing enterprise involve numerous types of decision-making at various levels and domains. A complex system has a large number of design variables and decision-making requires real-time data collected from machines, processes, and business environments. Enterprise systems (ESs) are used to support data acquisition, communication, and all decision-making activities. Therefore, information technology (IT) infrastructure for data acquisition and sharing affects the performance of an ES greatly. Our objective is to investigate the impact of emerging Internet of Things (IoT) on ESs in modern manufacturing. To achieve this objective, the evolution of manufacturing system paradigms is discussed to identify the requirements of decision support systems in dynamic and distributed environments; recent advances in IT are overviewed and associated with next-generation manufacturing paradigms; and the relation of IT infrastructure and ESs is explored to identify the technological gaps in adopting IoT as an IT infrastructure of ESs. The future research directions in this area are discussed.
Why does Deep Learning work? - A perspective from Group Theory

Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereby opening a new approach towards a theory of Deep learning. One factor behind the recent resurgence of the subject is a key algorithmic step called pretraining: first search for a good generative model for the input samples, and repeat the process one layer at a time. We show deeper implications of this simple principle, by establishing a connection with the interplay of orbits and stabilizers of group actions. Although the neural networks themselves may not form groups, we show the existence of shadow groups whose elements serve as close approximations. Over the shadow groups, the pretraining step, originally introduced as a mechanism to better initialize a network, becomes equivalent to a search for features with minimal orbits. Intuitively, these features are in a way the simplest. Which explains why a deep learning network learns simple features first. Next, we show how the same principle, when repeated in the deeper layers, can capture higher order representations, and why representation complexity increases as the layers get deeper.
From Regular Expressions to Deterministic Automata

The main theorem allows an elegant algorithm to be refined into an efficient one. The elegant algorithm for constructing a finite automaton from a regular expression is based on 'derivatives of' regular expressions; the efficient algorithm is based on 'marking of' regular expressions. Derivatives of regular expressions correspond to state transitions in finite automata. When a finite automaton makes a transition under input symbol a, a leading a is stripped from the remaining input. Correspondingly, if the input string is generated by a regular expression E, then the derivative of E by a generates the remaining input after a leading a is stripped. Brzozowski (1964) used derivatives to construct finite automata; the state for expression E has a transition under a to the state for the derivative of E by a. This approach extends to regular expressions with new operators, including intersection and complement; however, explicit computation of derivatives can be expensive. Marking of regular'expressions yields an expression with distinct input symbols. Following MeNaughton and Yamada (1960), we attach subscripts to each input symbol in an expression; (ab+b)*ba becomes (atb2+b3)*b4as. Conceptually, the efficient algorithm constructs an automaton for the marked expression. The marks on the transitions are then erased, resulting in a nondeterministic automaton for the original unmarked expression. This approach works for the usual operations of union, concatenation, and iteration; however, intersection and complement cannot be handled because marking and unmarking do not preserve the languages generated by regular expressions with these operators.