nils-herrmann/clean_scirepeval huggingface dataset:
An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network

Detection of helmeted and non-helmeted motorcyclist is mandatory now-a-days in order to ensure the safety of riders on the road. However, due to many constraints such as poor video quality, occlusion, illumination, and other varying factors it becomes very difficult to detect them accurately. In this paper, we introduce an approach for automatic detection of helmeted and non-helmeted motorcyclist using convolutional neural network (CNN). During the past several years, the advancements in deep learning models have drastically improved the performance of object detection. One such model is YOLOv2  which combines both classification and object detection in a single architecture. Here, we use YOLOv2 at two different stages one after another in order to improve the helmet detection accuracy. At the first stage, YOLOv2 model is used to detect different objects in the test image. Since this model is trained on COCO dataset, it can detect all classes of the COCO dataset. In the proposed approach, we use detection of person class instead of motorcycle in order to increase the accuracy of helmet detection in the input image. The cropped images of detected persons are used as input to second YOLOv2 stage which was trained on our dataset of helmeted images. The non-helmeted images are processed further to extract license plate by using OpenALPR. In the proposed approach, we use two different datasets i.e., COCO and helmet datasets. We tested the potential of our approach on different helmeted and non-helmeted images. Experimental results show that the proposed method performs better when compared to other existing approaches with 94.70% helmet detection accuracy.
A compact power-efficient 3 V CMOS rail-to-rail input/output operational amplifier for VLSI cell libraries

AbstructThis paper presents a two-stage, compact, powerefficient 3 V CMOS operational amplifier with rail-to-rail input and output ranges. Because of its small die area of 0.04 mm', it is very suitable as a VLSI library cell. The floating class-AB control is shifted into the summing circuit, which results in a noise and offset of the amplifier which are comparable to that of a three stage amplifier. A floating current source biases the combined summing circuit and the class-AB control. This current source has the same structure as the class-AB control which provides a power-supply-independent quiescent current. Using the compact architecture, a 2.6 MHz amplifier with Miller compensation and a 6.4 MHz amplifier with cascoded-Miller compensation has been realized. The opamps have, respectively, a bandwidth-to-supplypower ratio of 4 MHdmW and 11 MHdmW for a capacitive load of 10 pF.
SmartPaste: Learning to Adapt Source Code

Deep Neural Networks have been shown to succeed at a range of natural language tasks such as machine translation and text summarization. While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this work, we introduce SmartPaste, a first task that requires to use such information. The task is a variant of the program repair problem that requires to adapt a given (pasted) snippet of code to surrounding, existing source code. As first solutions, we design a set of deep neural models that learn to represent the context of each variable location and variable usage in a data flow-sensitive way. Our evaluation suggests that our models can learn to solve the SmartPaste task in many cases, achieving 58.6% accuracy, while learning meaningful representation of variable usages.
Discourse Parsing with Attention-based Hierarchical Neural Networks

RST-style document-level discourse parsing remains a difficult task and efficient deep learning models on this task have rarely been presented. In this paper, we propose an attention-based hierarchical neural network model for discourse parsing. We also incorporate tensor-based transformation function to model complicated feature interactions. Experimental results show that our approach obtains comparable performance to the contemporary state-of-the-art systems with little manual feature engineering.
A digital receiver architecture for RFID readers

This paper presents a digital receiver architecture for an RFID reader. The main challenge in RFID reader design is the detection of the backscattered signals from the tags, which can be severely complicated due to the largely varying scale of possible receive powers. Furthermore noise, which power depends on the environment can degrade the detection performance. The detection of the signals of the tags is additionally impeded by the very strong self interference at the reader with the carrier it needs to send in order to supply the tags with energy. To fight these various disturbances, a new RFID receiver algorithm is proposed, that sets its decision threshold adaptively, depending on the strength of the input signal, the noise power at the receiver and the extent of the carrier interference. This is the first algorithm for signal detection in RFID, setting its threshold accordingly to the environmental conditions, and thus leading to near optimum performance. Details of the implementation of the digital receiver architecture on an FPGA are introduced. Bit error ratio measurements have been carried out to rate the receivers performance, which have never been shown before for RFID receivers. Presented measurement results substantiate the performance of the suggested algorithm.
A Sequence Model Approach to Relation Extraction in Portuguese

The task of Relation Extraction from texts is one of the main challenges in the area of Information Extraction, considering the required linguistic knowledge and the sophistication of the language processing techniques employed. This task aims at identifying and classifying semantic relations that occur between entities recognized in a given text. In this paper, we evaluated a Conditional Random Fields classifier for the extraction of any relation descriptor occurring between named entities (Organisation, Person and Place categories), as well as pre-defined relation types between these entities in Portuguese texts.
Perceptual dimensions of vibrotactile actuators

The objective of the present research was to determine how variations in the signals generated by different vibrotactile actuators are perceived and which features are judged as being distinctive. For this purpose, three different types of actuator were used to generate signals that varied in amplitude, waveform and frequency. Participants were required to judge the degree of similarity-dissimilarity between pairs of stimuli. Multi-dimensional scaling (MDS) techniques were then used as an exploratory data analysis technique to create a spatial map that depicted the relations among the various vibrotactile signals. The first dimension that emerged from the MDS represented a continuum associated with transitions in the amplitudes of the signals, with a smooth sine wave pattern contrasting with the more abrupt transitions in square waves. This may be considered a smooth-rough dimension. The second dimension extracted from the data was more difficult to characterize in that each of the two clusters along this dimension involved signals of varying waveform and frequency. Further work will aim at defining the perceptual qualities of this dimension.
Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks

Vasculature is known to be of key biological significance, especially in the study of cancer. As such, considerable effort has been focused on the automated measurement and analysis of vasculature in medical and pre-clinical images. In tumors in particular, the vascular networks may be extremely irregular and the appearance of the individual vessels may not conform to classical descriptions of vascular appearance. Typically, vessels are extracted by either a segmentation and thinning pipeline, or by direct tracking. Neither of these methods are well suited to microscopy images of tumor vasculature. In order to address this we propose a method to directly extract a medial representation of the vessels using Convolutional Neural Networks. We then show that these two-dimensional centerlines can be meaningfully extended into 3D in anisotropic and complex microscopy images using the recently popularized Convolutional Long Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this hybrid convolutional-recurrent architecture over both 2D and 3D convolutional comparators.
Optimized Continuous Collision Detection for Deformable Triangle Meshes

We present different approaches for accelerating the process of continuous collision detection for deformable triangle meshes. The main focus is upon the collision detection for simulated virtual clothing, especially for situations involving a high number of contact points between the triangle meshes, such as multi-layered garments. We show how the culling efficiency of bounding volume hierarchies may be increased by introducing additional bounding volumes for edges and vertices of the triangle mesh. We present optimized formulas for computing the time of collision for these primitives analytically, and describe an efficient iterative scheme that ensures that all collisions are treated in the correct chronological order.
[Hyaluronic acid rheology: Basics and clinical applications in facial rejuvenation].

Hyaluronic acid (HA) is the most widely used dermal filler to treat facial volume deficits and winkles specially for facial rejuvenation. Depending on various areas of the face, filler is exposed to two different forces (shear deformation and compression/stretching forces) resulting from intrinsec and external mechanical stress. The purpose of this technical note is to explain how rheology, which is the study of the flow and deformation of matter under strains, can be used in our clinical practice of facial volumization with fillers. Indeed, comprehension of the rheological properties of HA has become essential in selection of dermal filler targeted to the area of the face. Viscosity, elasticity and cohesivity are the main three properties to be taken into consideration in this selection. Aesthetic physicians and surgeons have to familiarize with those basics in order to select the HA with the right rheological properties to achieve a natural-looking and long-lasting outcome.
An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification

Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification ata mining unctional link artificial neural networks ulti-layer perception article swarm optimization mproved particle swarm optimization VM capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN = ISO − FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).
Lyapunov-based control for switched power converters

The fundamental properties, such as passivity or incremental passivity, of the network elements making up a switched power converter are examined. The nominal open-loop operation of a broad class of such converters is shown to be stable in the large via a Lyapunov argument. The obtained Lyapunov function is then shown to be useful for designing globally stabilizing controls that include adaptive schemes for handling uncertain nominal parameters. Numerical simulations illustrate the application of this control approach in DC-DC converters.<<ETX>>
Ranking-Oriented Collaborative Filtering: A Listwise Approach

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in both accuracy and efficiency in comparison with pairwise CF. In ListCF, each user is represented as a probability distribution of the permutations over rated items based on the Plackett-Luce model, and the similarity between users is measured based on the Kullback--Leibler divergence between their probability distributions over the set of commonly rated items. Given a target user and the most similar users, ListCF directly predicts a total order of items for each user based on similar users’ probability distributions over permutations of the items. Besides, we also reveal insightful connections among pointwise, pairwise, and listwise CF algorithms from the perspective of the matrix representations. In addition, to make our algorithm more scalable and adaptive, we present an incremental algorithm for ListCF, which allows incrementally updating the similarities between users when certain user submits a new rating or updates an existing rating. Extensive experiments on benchmark datasets in comparison with the state-of-the-art approaches demonstrate the promise of our approach.
Towards matching user mobility traces in large-scale datasets

The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources. With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories. Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people’s mobility traces, coming from a mobile network operator and transportation smart card usage. We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users. We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks. We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time. We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals.
Multi-entity Sentiment Scoring

We present a compositional framework for modelling entity-level sentiment (sub)contexts, and demonstrate how holistic multi-entity polarity scoring emerges as a by-product of compositional sentiment parsing. A data set of five annotators’ multi-entity judgements is presented, and a human ceiling is established for the challenging new task. The accuracy of an initial implementation, which includes both supervised learning and heuristic distance-based scoring methods, is 5.6∼6.8 points below the human ceiling amongst sentences and 8.1∼8.7 points amongst phrases.
Privacy Analysis of Android Apps: Implicit Flows and Quantitative Analysis

A static analysis is presented, based on the theory of abstract interpretation, for verifying privacy policy compliance by mobile applications. This includes instances where, for example, the application releases the user’s location or device ID without authorization. It properly extends previous work on datacentric semantics for verification of privacy policy compliance by mobile applications by (i) tracking implicit information flow, and (ii) performing a quantitative analysis of information leakage. This yields to a novel combination of qualitative and quantitative analyses of information flows in mobile applications.
CTR prediction for contextual advertising: learning-to-rank approach

Contextual advertising is a textual advertising displayed within the content of a generic web page. Predicting the probability that users will click on ads plays a crucial role in contextual advertising because it influences ranking, filtering, placement, and pricing of ads. In this paper, we introduce a click-through rate prediction algorithm based on the learning-to-rank approach. Focusing on the fact that some of the past click data are noisy and ads are ranked as lists, we build a ranking model by using partial click logs and then a regression model on it. We evaluated this approach offline on a data set based on logs from an ad network. Our method is observed to achieve better results than other baselines in our three metrics.
Wearable Ultrasonic Sensor Using Double-Layer PVDF Films for Monitoring Tissue Motion

Monitoring of the physical properties of the tissues provides valuable information for the clinical diagnosis and evaluation. However, one of the challenges of an ultrasonic method for continuous monitoring of a tissue motion using a conventional clinical ultrasonic image system could be motion artifacts due to the weight and size of its handheld ultrasonic probe employed. A wearable ultrasonic sensor, made of a polyvinylidene fluoride (PVDF) polymer piezoelectric film, may be able to reduce the motion artifacts due to its lightweight and flexible properties. However, the PVDF ultrasonic sensor has a relatively weak transmitting acoustic signal strength which causes poor signal-to-noise ratio of the ultrasonic signal acquired in pulse-echo measurements, particularly for the signal reflected from deeper tissue. This paper investigated an improvement of the ultrasonic performance of the WUS using double-layer PVDF films. The sensor was constructed using two 52-μm thick PVDF films. The developed double-layer WUS showed the 1.7 times greater ultrasonic signal amplitude compared to the WUS made of a single-layer PVDF having the equivalent PVDF film thickness. Thus, the developed double-layer PVDF WUS improved the depth of the ultrasonic penetration into the tissue. The developed WUS successfully demonstrated to monitor the contractions of biceps muscles in an upper arm. In addition, a cardiac tissue motion was clearly observed in the M-mode measurement corresponding with the cardiac cycles obtained from the ECG measurement.
Wearable driver drowsiness detection using electrooculography signal

Every year, more than 100,000 automobile crashes are caused by driver drowsiness. Various technologies have been developed to address this issue, including vehicle-based measurements, behavior change detection, and physiological signal analysis. Both vehicle-based measurements and behavior change detection require bulky components. They also identify the driver's drowsiness too late for effective accident prevention. The physiological signal changes in an early stage and can be used to detect the on-set of driver drowsiness. In this paper, the development of a wearable drowsiness detection system is introduced. This system measures the electrooculography (EOG) signal; transmits the signal to a smartphone wirelessly; and could alarm the driver based on a prediction algorithm that can estimate 0.5-second-ahead EOG signal behavior. This system is compact, comfortable, and cost effective. The 0.5-second-ahead estimation capability provides the critical time for a driver to correct the behavior, and ultimately saves lives.
A 60-GHz Dense Dielectric Patch Antenna Array

The dense dielectric (DD) patch antenna is investigated at the 60-GHz band. By employing the printed circuit board (PCB) technology, a 4 4 aperture-coupled DD patch antenna array is designed, fabricated and studied. An impedance bandwidth of 23.7% from 51.3 to 65.1 GHz for |S11| <; -10 dB, a gain up to 16.5 dBi with a 3-dB gain bandwidth of 32.5%, and symmetrical broadside radiation patterns with -15 dB cross-polarization are achieved.
The Critical Period Hypothesis Revisited

The Critical Period (CP) Hypothesis in essence contends that the ability to learn a language is limited to the years before puberty after which, most probably as a result of maturational processes in the brain, this ability disappears. Since Penfield & Roberts (1959), and especially since Lenneberg (1967), this has been one of the most hotly debated issues in psycholinguistics and, generally, in cognitive science. In an already imposing body of literature on CP there are a large variety of views on the nature of the phenomenon (e.g. whether it is a critical, a sensitive, or an optimal period), on its origin (e.g. whether it is caused by maturational or cognitive or some other factors), on its onset and completion times, etc. and, while the importance of such issues is acknowledged, they will not be addressed here. The principal goal of this paper is rather to consider some of the existing arguments against CP for second language (SL) acquisition in a framework which, following some recent proposals (e.g. Bley-Vroman 1989, Schachter 1996), assumes that first and second language acquisition are two fundamentally different processes.
Review of Farm Management Information Systems ( FMIS )

There have been considerable advancements in the field of MIS over the years, and it continues to grow and develop in response to the changing needs of the business and marketing environment. Professionals and academicians are contributing and serving the field of MIS through the dissemination of their knowledge and ideas in professional journals. Thus, changes and trends that likely have an impact on MIS concepts, processes, and implementation can be determined by reviewing the articles published in journals. Content of the articles published in journals can also give us an idea about the types of research and themes that are popular during a given period. To see the evolutionary change in the field of MIS, the present study examined the content of articles published in business and marketing journals.