nils-herrmann/clean_scirepeval huggingface dataset:
Leg stiffness and stride frequency in human running.

When humans and other mammals run, the body's complex system of muscle, tendon and ligament springs behaves like a single linear spring ('leg spring'). A simple spring-mass model, consisting of a single linear leg spring and a mass equivalent to the animal's mass, has been shown to describe the mechanics of running remarkably well. Force platform measurements from running animals, including humans, have shown that the stiffness of the leg spring remains nearly the same at all speeds and that the spring-mass system is adjusted for higher speeds by increasing the angle swept by the leg spring. The goal of the present study is to determine the relative importance of changes to the leg spring stiffness and the angle swept by the leg spring when humans alter their stride frequency at a given running speed. Human subjects ran on treadmill-mounted force platform at 2.5ms-1 while using a range of stride frequencies from 26% below to 36% above the preferred stride frequency. Force platform measurements revealed that the stiffness of the leg spring increased by 2.3-fold from 7.0 to 16.3 kNm-1 between the lowest and highest stride frequencies. The angle swept by the leg spring decreased at higher stride frequencies, partially offsetting the effect of the increased leg spring stiffness on the mechanical behavior of the spring-mass system. We conclude that the most important adjustment to the body's spring system to accommodate higher stride frequencies is that leg spring becomes stiffer.
Secure E-voting with blind signature

With a rapid growth in computer networks, many people can access the network through the Internet and therefore an electronic voting can be a viable alternative for conducting an election. Electronic voting system must attempt to achieve at least the same level of security as ordinary elections. We have developed an electronic voting system, E-Voting for a general election. E-Voting system employs cryptographic techniques to overcome the security issues in the election process. In this system, voter’s privacy is guaranteed by using a blind signature for confidentiality and voter’s digital signature for voter’s authentication. E-Voting is implemented by employing Java socket technology and BouncyCastle cryptography provider. The provider, which is an open source library, is used to provide the secure communication channel. The voter’s private key for digital signature is protected by using password-based encryption with SHA and Twofish-CBC algorithm so that only valid voter can use it.
Deep Cuboid Detection: Beyond 2D Bounding Boxes

We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches which fit a 3D model from low-level cues like corners, edges, and vanishing points, we propose an end-to-end deep learning system to detect cuboids across many semantic categories (e.g., ovens, shipping boxes, and furniture). We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid’s corners, effectively producing a 3D interpretation of box-like objects. We refine keypoints by pooling convolutional features iteratively, improving the baseline method significantly. Our deep learning cuboid detector is trained in an end-to-end fashion and is suitable for real-time applications in augmented reality (AR) and robotics.
LabVIEW: A software system for data acquisition, data analysis, and instrument control

Computer-based data acquisition systems play an important role in clinical monitoring and in the development of new monitoring tools. LabVIEW (National Instruments, Austin, TX) is a data acquisition and programming environment that allows flexible acquisition and processing of analog and digital data. The main feature that distinguishes LabVIEW from other data acquisition programs is its highly modular graphical programming language, “G,” and a large library of mathematical and statistical functions. The advantage of graphical programming is that the code is flexible, reusable, and self-documenting. Subroutines can be saved in a library and reused without modification in other programs. This dramatically reduces development time and enables researchers to develop or modify their own programs. LabVIEW uses a large amount of processing power and computer memory, thus requiring a powerful computer. A large-screen monitor is desirable when developing larger applications. LabVIEW is excellently suited by testing new monitoring paradigms, analysis algorithms, or user interfaces. The typical LabVIEW user is the researcher who wants to develop a new monitoring technique, a set of new (derived) variables by integrating signals from several existing patient monitors, closed-loop control of a physiological variable, or a physiological simulator.
Investigation of wide-band microstrip slot antenna

This paper presents the simulation and experimental investigations of a printed microstrip slot antenna. It is a quarter wavelength monopole slot cut in the finite ground plane edge, and fed electromagnetically by a microstrip transmission line. It provides a wide impedance bandwidth adjustable by variation of its parameters, such as the relative permittivity and thickness of the substrate, width, and location of the slot in the ground plane, and feed and ground plane dimensions. The ground plane is small, 50 mm/spl times/80 mm, and is about the size of a typical PC wireless card. At the center frequency of 3.00 GHz, its width of 50 mm is about /spl lambda//2 and influences the slot impedance and bandwidth significantly. An impedance bandwidth (S/sub 11/=-10 dB) of up to about 60% is achieved by individually optimizing its parameters. The simulation results are confirmed experimentally. A dual complementary slot antenna configuration is also investigated for the polarization diversity.
Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance

In this paper, we present a novel probabilistic compact representation of the on-road environment, i.e., the dynamic probabilistic drivability map (DPDM), and demonstrate its utility for predictive lane change and merge (LCM) driver assistance during highway and urban driving. The DPDM is a flexible representation and readily accepts data from a variety of sensor modalities to represent the on-road environment as a spatially coded data structure, encapsulating spatial, dynamic, and legal information. Using the DPDM, we develop a general predictive system for LCMs. We formulate the LCM assistance system to solve for the minimum-cost solution to merge or change lanes, which is solved efficiently using dynamic programming over the DPDM. Based on the DPDM, the LCM system recommends the required acceleration and timing to safely merge or change lanes with minimum cost. System performance has been extensively validated using real-world on-road data, including urban driving, on-ramp merges, and both dense and free-flow highway conditions.
MADA + TOKAN : A Toolkit for Arabic Tokenization , Diacritization , Morphological Disambiguation , POS Tagging , Stemming and Lemmatization

We describe the MADA+TOKAN toolkit, a versatile and freely available system that can derive extensive morphological and contextual information from raw Arabic text, and then use this information for a multitude of crucial NLP tasks. Applications include high-accuracy part-of-speech tagging, diacritization, lemmatization, disambiguation, stemming, and glossing. MADA operates by examining a list of all possible analyses for each word, and then selecting the analysis that matches the current context best by means of support vector machine models classifying for 19 distinct, weighted morphological features. The selected analyses carry complete diacritic, lexemic, glossary and morphological information; thus all disambiguation decisions are made in one step. TOKAN takes the information provided by MADA to generate tokenized output in a wide variety of customizable formats. MADA, TOKAN and their support utilities are highly configurable, allowing users to extract and manipulate the exact information that they require. In this paper we describe the features and capabilities of MADA+TOKAN, detail recent improvements, and provide examples of the toolkit’s use.
Phoneypot: Data-driven Understanding of Telephony Threats

Cyber criminals are increasingly using robocalling, voice phishing and caller ID spoofing to craft attacks that are being used to scam unsuspecting users who have traditionally trusted the telephone. It is necessary to better understand telephony threats to effectively combat them. Although there exist crowd sourced complaint datasets about telephony abuse, such complaints are often filed after a user receives multiple calls over a period of time, and sometimes they lack important information. We believe honeypot technologies can be used to augment telephony abuse intelligence and improve its quality. However, a telephony honeypot presents several new challenges that do not arise in other traditional honeypot settings. We present Phoneypot, a first large scale telephony honeypot, that allowed us to explore ways to address these challenges. By presenting a concrete implementation of Phoneypot using a cloud infrastructure and 39,696 phone numbers (phoneytokens), we provide evidence of the benefits of telephony honeypots. Phoneypot received 1.3 million calls from 250K unique sources over a period of seven weeks. We detected several debt collectors and telemarketers calling patterns and an instance of a telephony denial-of-service attack. This provides us with new insights into telephony abuse and attack patterns.
Security maturity in NoSQL databases - are they secure enough to haul the modern IT applications?

NoSQL phenomenon has taken the database and IT application world by storm. Growth and penetration of NoSQL applications, driven by Silicon Valley giants like Facebook, Twitter, Yahoo, Google and LinkedIn, has created an unprecedented database revolution, to inspire smaller companies to join the NoSQL bandwagon. While expansion and growth of these databases are adding glory and success to many corporate IT departments, it is very pertinent to explore the security aspects of these new era databases. Confidentiality, integrity and availability (CIA) are the very foundation of data protection and privacy. In this paper a sincere attempt is made to survey, analyze and assess the maturity of NoSQL databases through the lens of CIA triad. While the concept of CIA has its origins in relational databases, it is very important to understand, survey and delineate the security capabilities of this new generation databases, in terms of CIA fulfillment.
Trust Aware Recommender Systems : A Survey on Implicit Trust Generation Techniques

Development of Web 2.0 enabled users to share information online, which results into an exponential growth of world wide web data. This leads to the so-called information overload problem. Recommender Systems (RS) are intelligent systems, helping on-line users to overcome information overload by providing customized recommendations on various items. In real world, people are willing to take advice and recommendation from their trustworthy friends only. Trust plays a key role in the decision-making process of a person. Incorporation of trust information in RS, results in a new class of recommender systems called trust aware recommender systems (TARS). This paper presents a survey on various implicit trust generation techniques in context of TARS. We have analyzed eight different implicit trust metrics, with respect to various properties of trust proposed by researchers in regard to TARS. implicit trust; trust aware recommender system; trust metrics.
Model driven software development: An overview

The vision of the MDSD is an era of software engineering where modelling completely replaces programming i.e. the systems are entirely generated from high-level models, each one specifying a different view of the same system. The MDSD can be seen as the new generation of visual programming languages which provides methods and tools to streamline the process of software engineering. Productivity of the development process is significantly improved by the MDSD approach and it also increases the quality of the resulting software system. The MDSD is particularly suited for those software applications which require highly specialized technical knowledge due to the involvement of complex technologies and the large number of complex and unmanageable standards. In this paper, an overview of the MDSD is presented; the working styles and the main concepts are illustrated in detail.
Off-line signature verification and forgery detection using fuzzy modeling

Automatic signature verification is a well-established and an active area of research with numerous applications such as bank check verification, ATM access, etc. This paper proposes a novel approach to the problem of automatic off-line signature verification and forgery detection. The proposed approach is based on fuzzy modeling that employs the Takagi-Sugeno (TS) model. Signature verification and forgery detection are carried out using angle features extracted from box approach. Each feature corresponds to a fuzzy set. The features are fuzzified by an exponential membership function involved in the TS model, which is modified to include structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect moods. The membership functions constitute weights in the TS model. The optimization of the output of the TS model with respect to the structural parameters yields the solution for the parameters. We have also derived two TS models by considering a rule for each input feature in the first formulation (Multiple rules) and by considering a single rule for all input features in the second formulation. In this work, we have found that TS model with multiple rules is better than TS model with single rule for detecting three types of forgeries; random, skilled and unskilled from a large database of sample signatures in addition to verifying genuine signatures. We have also devised three approaches, viz., an innovative approach and two intuitive approaches using the TS model with multiple rules for improved performance.
An interleaved high step-up DC-DC converter with reduced voltage stress across semiconductors

The high voltage gain converter is widely employed in renewable energy conversions such as photovoltaic and fuel cell power generation systems. An interleaved high step-up DC-DC converter with coupled inductor and voltage multiplier cell is proposed in this paper. The voltage multiplier cell is composed of two diodes, two capacitors and two coupled inductors. During the switch-on interval in each phase, the corresponding capacitors are charged in parallel by the coupled inductors. Similarly, during the switch-off interval in the same phase, the corresponding capacitors are discharged in series to pump their energy to the load. In this way the proposed converter can achieve high voltage conversion ratio. Other features of the proposed converter are low voltage stress across the main switches due to clamp capacitors, lower voltage stress across diodes compared to the conventional converters, low input current ripple due to interleaved structure, and alleviation of the diodes reverse recovery problems. Switches with lower RDS-ON can be used that decreases conduction losses. The principle operation of the proposed converter is given by detailed mathematical analysis. Performance of the proposed converter is validated by simulation results in PSCAD-EMTDC.
Speed and balancing control for unicycle robot

Unicycle mobile robot is wheeled mobile robot that can stand and move around using one wheel. It has attached a lot of researchers to conduct studies about the system, particularly in the design of the system mechanisms and the control strategies. Unlike two wheel balancing mobile robot which mechanically stable on one side, unicycle mobile robot requires additional mechanisms to keep balancing robot on all sides. By assuming that both roll dynamics and pitch dynamics are decoupled, so the balancing mechanisms can be designed separately. The reaction wheel is used for obtaining balancing on the roll angle by rotating the disc to generate momentum. While the wheeled robot is used for obtaining balancing on the pitch angle by rotating wheel to move forward or backward. A PID controller is used as balancing control which will control the rotation motor on the reaction disc and wheel based on the pitch and roll feedback from the sensor. By adding the speed controller to the pitch control, the system will compensate automatically for perfectly center of gravity on the robot. Finally, the unicycle robot will be able to balance on pitch angle and roll angle. Based on simulation result validates that robot can balance using PID controller, while based on balancing pitch experiment result, robot can achieve balancing with maximum inclination about ±23 degree on pitch angle and ±3.5 degree on roll angle with steady state error 0.1 degree.
Recurrent Neural Networks for driver activity anticipation via sensory-fusion architecture

Anticipating the future actions of a human is a widely studied problem in robotics that requires spatio-temporal reasoning. In this work we propose a deep learning approach for anticipation in sensory-rich robotics applications. We introduce a sensory-fusion architecture which jointly learns to anticipate and fuse information from multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We train our architecture in a sequence-to-sequence prediction manner, and it explicitly learns to predict the future given only a partial temporal context. We further introduce a novel loss layer for anticipation which prevents over-fitting and encourages early anticipation. We use our architecture to anticipate driving maneuvers several seconds before they happen on a natural driving data set of 1180 miles. The context for maneuver anticipation comes from multiple sensors installed on the vehicle. Our approach shows significant improvement over the state-of-the-art in maneuver anticipation by increasing the precision from 77.4% to 90.5% and recall from 71.2% to 87.4%.
Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.
Improved velocity estimation for low-speed and transient regimes using low-resolution encoders

This paper presents a new estimation method, entitled the asynchronous sampling pulse method (ASPM), for determining velocity in systems with low but variable speeds, and/or with low-resolution encoders. The ASPM forces the estimator procedure to synchronize with the actual encoder pulse input, thus eliminating encoder positioning error independent of target velocity variations. This method is based on using an auxiliary sampling period to measure the time interval between the moment of encoder input and the control sampling instant, and its precision is shown to be dependent only on this auxiliary sampling period. Thus, the ASPM improves over existing methods in which estimator performance is dependent on both target acceleration and encoder resolution. Simulation and experimental results are used to verify the method.
Depressive Moods of Users Portrayed in Twitter

Potential benefits of using online social network data for clinical studies on depression are tremendous. In this paper, we present a preliminary result on building a research framework that utilizes real-time moods of users captured in the Twitter social network and explore the use of language in describing depressive moods. First, we analyzed a random sample of tweets posted by the general Twitter population during a two-month period to explore how depression is talked about in Twitter. A large number of tweets contained detailed information about depressed feelings, status, as well as treatment history. Going forward, we conducted a study on 69 participants to determine whether the use of sentiment words of depressed users differed from a typical user. We found that the use of words related to negative emotions and anger significantly increased among Twitter users with major depressive symptoms compared to those otherwise. However, no difference was found in the use of words related to positive emotions between the two groups. Our work provides several evidences that online social networks provide meaningful data for capturing depressive moods of users.
Choosing nodes in parametric curve interpolation

Perhaps a few words might be inserted here to avoid In parametric curve interpolation, the choice of the any possible confusion. In the usual function interpolation interpolating nodes makes a great deal of difference in the resulting curve. Uniform parametrization is generally setting, the problem is of the form P~ = (x, y~) where the x~ are increasing, and one seeks a real-valued unsatisfactory. It is often suggested that a good choice polynomial y = y(x) so that y(x~)= y~. This is identical of nodes is the cumulative chord length parametrization. to the vector-valued polynomial Examples presented here, however, show that this is not so. Heuristic reasoning based on a physical analogy leads P(x) = (x, y(x)) to a third parametrization, (the "centripetal model'), which almost invariably results in better shapes than with x as the parameter, except with the important either the chord length or the uniform parametrization. distinction that here the interpolating conditions As with the previous two methods, this method is "global'and is 'invariant" under similarity transformations, y(x~) = y~ are (It turns out that, in some sense, the method has been anticipated in a paper by Hosaka and Kimura.) P(x~) = P~, 0 <~ i <~ n