nils-herrmann/clean_scirepeval huggingface dataset:
Mechatronic Aeropendulum: Demonstration of Linear and Nonlinear Feedback Control Principles With MATLAB/Simulink Real-Time Windows Target

This paper presents a low-cost hands-on experiment for a classical undergraduate controls course for non-electrical engineering majors. The setup consists of a small dc electrical motor attached to one of the ends of a light rod. The motor drives a 2-in propeller and allows the rod to swing. Angular position is measured by a potentiometer attached to the pivot point. A custom-designed circuit board produces the controlled voltage input to the motor. The target board is powered and communicates with the PC through its USB port using a virtual RS-232 port. A simple MATLAB/Simulink module has been created to read the pendulum angle and send a command signal to the motor. The module is based on Real-time Windows Target software, which allows a sampling rate of up to 200 Hz. Students are able to design and test classical PID and phase lead-lag controllers, as well as modern controllers, including state-space controller design combined with feedback linearization. A semester-long series of assignments is described that can be carried out without the need for a specialized laboratory or teaching assistants. The project was tested in a classical control systems design class of senior-level mechanical engineering students. Student feedback and survey data on the effectiveness of the modules are also presented.
Full bridge phase-shifted soft switching high-frequency inverter with boost PFC function for induction heating system

This paper is mainly concerned with a high frequency soft-switching PWM inverter suitable for consumer induction heating system. Proposed system is composed of soft switching chopper based voltage boost PFC input stage and phase shifted PWM controlled full bridge ZCZVS high frequency inverter stage. Its fundamentals of operating performances are illustrated and evaluated on the experimental results. Its effectiveness is substantially proved on the basis of the experimental results from a practical point of view.
3D velocity model and ray tracing of antenna array GPR

Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.
A Novel Deep Learning Pipeline to Analyze Temporal Clinical Data

Analysis of clinical temporal data can be difficult due to natural properties that often characterize it. The large number of variables, missing values, and other characteristics lead to issues of sparsity and high dimensional complexity. We hypothesized that a pipeline application implementing relevant deep learning methods could sequentially address these difficulties, demonstrating their combined utility in a classification task. We implemented Word2Vec, t-distributed stochastic neighbor embedding, and a convolutional neural network in a pipeline application. To test the pipeline, we applied it to a simple, binary classification task to identify patient encounter care setting. In preliminary testing, the pipeline application achieved 92% accuracy. It also produced temporal data cubes indicative of clinical encounters in intensive care unit (ICU) and non-ICU care settings. A deep learning pipeline process combining multiple methods holds promise in improving analytical tasks of clinical temporal data.
AN ACCEPTANCE MODEL FOR USEFUL AND FUN INFORMATION SYSTEMS

Investigating the factors associated with user acceptance of new software systems has been an important research stream in the field of information systems for many years. The technology acceptance model has long been used to examine the acceptance of utilitarian systems. Recently, it has been used to examine recreational or pleasure-oriented systems. Many examples exist of software that, depending on the context of use, can be used for productive and pleasurable interaction. This paper examines the determinants of use of one such “dual” system. A survey of users of a dual system was conducted. Results show that perceived usefulness is more important in determining intention to use than perceived enjoyment, and that perceived ease of use has no direct impact on intention, but still has a strong indirect effect.
Adaptive MAC protocol for UAV communication networks using directional antennas

Unmanned aerial vehicle (UAV) has been used recently a lot in military applications as well as in civilian. It shows great advantages and importance in the search and rescue, real-time surveillance, reconnaissance operations, traffic monitoring, hazardous site inspection and range extension. Moreover, UAVs are suited for situations that are too dangerous for direct human monitoring. In general, UAVs have the potential to create an ad-hoc network and greatly reduce the hops from source to destination. However, the type of the mission and the capability of the UAV which are determined by its shape and construction causes a lot of networking issues. Most UAVs used in communication networks assumed the use of omni-directional antenna. In this paper, we considered a collection of UAVs that communicate through a wireless link as a mobile ad-hoc network (MANET) using directional antenna. This technique offers tremendous advantages over that of using omni-directional antenna, although it requires an adaptive medium access control (MAC) to adapt the new antenna system as well as the constraint imposed by the UAV. To be more specific, we introduced a new mechanism that is called target information table to work with our new scheme during the switch from omni-directional to directional antenna. We made a comparison between the two techniques using optimized network engineering tool (OPNET). Our results show good improvement for end-to-end delay as well as throughput.
Survey on 3D Hand Gesture Recognition

Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.
Captcha as graphical passwords-enhanced with video-based captcha for secure services

CAPTCHAs, also known as reverse Turing tests are real-time assessments that are commonly used by programs to tell humans and machines apart. This can be achieved by assigning and assessing or evaluating hard AI problems such that these problems could only be solved easily by human but not by machines. A new security approach based on hard AI problems and Captcha technology is known as Captcha as gRaphical Passwords (CaRP). This scheme can address many security problems such as dictionary attacks, online guessing attacks and shoulder-surfing attacks etc. In this paper, we present an enhanced security for the CaRP scheme i,e CaRP with motion-based Captcha. The motion can be done by using video. The movement of Captcha ensures high security over the normal CaRP scheme. Video-based captcha can provide a great challenge for humans in exploiting the remarkable perceptual abilities or skills of humans to unravel structure-from-motion. The scheme can resist attack caused due to moving object detection. Thus it can thwart many attacks, can enforce more security and it stipulates security along with usability to legitimate users in dealing with real time applications.
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder

Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of crosslingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised neural translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.
Neural networks underlying implicit and explicit moral evaluations in psychopathy

Psychopathy, characterized by symptoms of emotional detachment, reduced guilt and empathy and a callous disregard for the rights and welfare of others, is a strong risk factor for immoral behavior. Psychopathy is also marked by abnormal attention with downstream consequences on emotional processing. To examine the influence of task demands on moral evaluation in psychopathy, functional magnetic resonance imaging was used to measure neural response and functional connectivity in 88 incarcerated male subjects (28 with Psychopathy Checklist Revised (PCL-R) scores ⩾30) while they viewed dynamic visual stimuli depicting interpersonal harm and interpersonal assistance in two contexts, implicit and explicit. During the implicit task, high psychopathy was associated with reduced activity in the dorsolateral prefrontal cortex and caudate when viewing harmful compared with helpful social interactions. Functional connectivity seeded in the right amygdala and right temporoparietal junction revealed decreased coupling with the anterior cingulate cortex (ACC), anterior insula, striatum and ventromedial prefrontal cortex. In the explicit task, higher trait psychopathy predicted reduced signal change in ACC and amygdala, accompanied by decreased functional connectivity to temporal pole, insula and striatum, but increased connectivity with dorsal ACC. Psychopathy did not influence behavioral performance in either task, despite differences in neural activity and functional connectivity. These findings provide the first direct evidence that hemodynamic activity and neural coupling within the salience network are disrupted in psychopathy, and that the effects of psychopathy on moral evaluation are influenced by attentional demands.
Zero-voltage switching flyback-boost converter with voltage-doubler rectifier for high step-up applications

A zero-voltage switching (ZVS) flyback-boost (FB) converter with a voltage-doubler rectifier (VDR) has been proposed. By combining the common part between a flyback converter and a boost converter as a parallel-input/series-output (PISO) configuration, this proposed circuit can increase a step-up ratio and clamp the surge voltage of switches. The secondary VDR provides a further extended step-up ratio as well as its voltage stress to be clamped. An auxiliary switch instead of a boost diode enables all switches to be turned on under ZVS conditions. The zero-current turn-off of the secondary VDR alleviates its reverse-recovery losses. The operation principles, the theoretical analysis, and the design consideration are investigated. The experimental results from a 250W and 42V-to-400V prototype are shown to verify the proposed scheme.
Real-time 3D object pose estimation and tracking for natural landmark based visual servo

A real-time solution for estimating and tracking the 3D pose of a rigid object is presented for image-based visual servo with natural landmarks. The many state-of-the-art technologies that are available for recognizing the 3D pose of an object in a natural setting are not suitable for real-time servo due to their time lags. This paper demonstrates that a real-time solution of 3D pose estimation become feasible by combining a fast tracker such as KLT [7]  with a method of determining the 3D coordinates of tracking points on an object at the time of SIFT based tracking point initiation, assuming that a 3D geometric model with SIFT description of an object is known a-priori. Keeping track of tracking points with KLT, removing the tracking point outliers automatically, and reinitiating the tracking points using SIFT once deteriorated, the 3D pose of an object can be estimated and tracked in real-time. This method can be applied to both mono and stereo camera based 3D pose estimation and tracking. The former guarantees higher frame rates with about 1 ms of local pose estimation, while the latter assures of more precise pose results but with about 16 ms of local pose estimation. The experimental investigations have shown the effectiveness of the proposed approach with real-time performance.
An energy market for trading electricity in smart grid neighbourhoods

The smart grid vision relies on active interaction with all of its stakeholders. As consumers are acquiring energy generation capabilities, hence becoming prosumers (producers and consumers), a meaningful way to interact among them would be to trade over a marketplace. Market-driven interactions have been proposed as a promising potential interaction method due to the monetary incentives and other benefits involved for the participants . In the Internet era an on-line marketplace is an thriving concept as it overcomes potential accessibility issues, however it is not clear how they should be structured, operated, what their limits and benefits might be. The design, implementation, modus-operandi as well as the assessment of such an energy market place for smart grid neighbourhoods is presented.
Texture Based Palmprint Identification Using DCT Features

In the palmprint recognition application utilizing more information other than principle lines or minutiae is much helpful. In this paper we proposed Discrete Cosine Transform (DCT) based feature vector for palmprint representation and matching and compared with DFT and Wavelet transform. Here the central part of the palmprint image of size 128x128 is resized to the size of 64x64 and divided into four non overlapping sub-images. The transform is applied on each sub-image directly without any preprocessing. By dividing the transformed sub-image into nine blocks, standard deviation is calculated for each block and such in total 36 (9x4=36) standard deviations will form the feature vector. This feature vector is used in matching stage. Total 10 images per person are taken from standard database available. Training set is prepared with the help of k images where k varies from 1 to 8. Results are checked against remaining images image in identification mode. Results are represented in terms of Genuine acceptance rate(%). In identification mode 97.5% recognition rate is obtained. The work is preliminary but recognition rate is promising and without any pre-processing.
A new technique for aesthetic labia minora reduction.

A new technique has been developed to reduce the labia minora yet maintain the normal labial edge and color. Labia minora enlargement can be congenital or acquired by chronic irritation, exogenous androgenic hormones, and stretching with weights. This can cause inflammation, poor hygiene, interference with sexual intercourse, or intermittent urinary self-catheterization. Aesthetically, asymmetrical or enlarged labia minora causes self-consciousness sexually and when the subject wears tight pants. Previously labia minora reduction was performed by amputation of the protuberant segment and oversewing the edge. Now, rather than amputation, a wedge of protuberant labial tissue is excised and the labial edges are reapproximated. Four patients have undergone this aesthetic procedure with excellent results. No complications occurred. The new technique is relatively simple and can greatly enhance the patient's confidence.
On-road driver eye movement tracking using head-mounted devices

It is now evident from anecdotal evidence and preliminary research that distractions can hinder the task of operating a vehicle, and consequently reduce driver safety. However with increasing wireless connectivity and the portability of office devices, the vehicle of the future is visualized as an extension of the static work place - i.e. an office-on-the-move, with a phone, a fax machine and a computer all within the reach of the vehicle operator. For this research a Head mounted Eye-tracking Device (HED), is used for tracking the eye movements of a driver navigating a test route in an automobible while completing various driving tasks. Issues arising from data collection of eye movements during the completion of various driving tasks as well as the analysis of this data are discussed. Methods for collecting video and scan-path data, as well as difficulties and limitations are also reported.
A micro-video recommendation system based on big data

With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favorite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, this paper proposes a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Bid Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, this paper improves the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system.
Interactive Full Image Segmentation

We address the task of interactive full image annotation, where the goal is to produce accurate segmentations for all object and stuff regions in an image. To this end we propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions. This enables the annotator to focus on the largest errors made by the machine across the whole image, and to share corrections across nearby regions. Furthermore, we adapt Mask-RCNN [21] into a fast interactive segmentation framework and introduce a new instance-aware loss measured at the pixel-level in the full image canvas, which lets predictions for nearby regions properly compete. Finally, we compare to interactive single object segmentation on the on the COCO panoptic dataset [11, 25, 32]. We demonstrate that, at a budget of four extreme clicks and four corrective scribbles per region, our interactive full image segmentation approach leads to a 5% IoU gain, reaching 90% IoU.
WAIS-IV visual puzzles in a mixed clinical sample.

Little is known about which cognitive functions underlie the new Visual Puzzles subtest of the Wechsler Adult Intelligence Scale - Fourth Edition (WAIS-IV). The purpose of this study was to investigate relationships between Visual Puzzles and common neuropsychological measures in a mixed clinical sample. A total of 44 veterans (75% men) were administered the WAIS-IV as part of a neuropsychological evaluation. Average age was 47.4 years (SD = 11.8), and average education was 13.8 years (SD = 2.3). Correlations were conducted to examine relationships between Visual Puzzles, demographic variables, and neuropsychological measures. Hierarchical regression analysis was used to determine which measures contributed the most variance to Visual Puzzles. Visual Puzzles correlated significantly with measures of visuospatial reasoning, verbal learning and recall, mental flexibility, processing speed, and naming, which accounted for 50% of the variance in Visual Puzzles performance. The results indicate that Visual Puzzles is not a pure measure of visuoperceptual reasoning, at least in a mixed clinical sample, because memory, mental flexibility, processing speed, and language abilities also contribute to successful performance of the task. Thus it may be important to consider other aspects of cognitive functioning when interpreting Visual Puzzles performance.
A Survey of Physics-Based Attack Detection in Cyber-Physical Systems

Monitoring the “physics” of cyber-physical systems to detect attacks is a growing area of research. In its basic form, a security monitor creates time-series models of sensor readings for an industrial control system and identifies anomalies in these measurements to identify potentially false control commands or false sensor readings. In this article, we review previous work on physics-based anomaly detection based on a unified taxonomy that allows us to identify limitations and unexplored challenges and to propose new solutions.
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis

This paper describes the system developed at LIF for the SemEval-2016 evaluation campaign. The goal of Task 4.A was to identify sentiment polarity in tweets. The system extends the Convolutional Neural Networks (CNN) state of the art approach. We initialize the input representations with embeddings trained on different units: lexical, partof-speech, and sentiment embeddings. Neural networks for each input space are trained separately, and then the representations extracted from their hidden layers are concatenated as input of a fusion neural network. The system ranked 2nd at SemEval-2016 and obtained an average F1 of 63.0%.