nils-herrmann/clean_scirepeval huggingface dataset:
Implementation of Sugeno FIS in model reference adaptive system adaptation scheme for speed sensorless control of PMSM

Model reference adaptive system (MRAS) is one of the popular methods to observe the speed and the angle information of the Permanent magnet synchronous motor (PMSM). This paper proposes a new adaptation scheme for MRAS to replace the conventional PI controller based on Popov Hyperstability theorem. In this project, the speed of PMSM is controlled by using field oriented control (FOC) method and the MRAS is used to observe the speed and the rotor position of the motor. The reference model for the MRAS is the PMSM itself while the adjustable model is the current model in the rotor reference frame. The Sugeno Fuzzy Logic Inference System (FIS) is implemented in the adaptation scheme in order to tune the error between the reference and the adjustable model. The new proposed method shows it is capable to track the motor speed efficiently at wide range of operating speeds.
Investigations on Knowledge Base Embedding for Relation Prediction and Extraction

We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.
Big Data Analytics for Air Quality Monitoring at a Logistics Shipping Base via Autonomous Wireless Sensor Network Technologies

The indoor air quality in industrial workplace buildings, e.g. air temperature, humidity and levels of carbon dioxide (CO2), play a critical role in the perceived levels of workers' comfort and in reported medical health. CO2 can act as an oxygen displacer, and in confined spaces humans can have, for example, reactions of dizziness, increased heart rate and blood pressure, headaches, and in more serious cases loss of consciousness. Specialized organizations can be brought in to monitor the work environment for limited periods. However, new low cost wireless sensor network (WSN) technologies offer potential for more continuous and autonomous assessment of industrial workplace air quality. Central to effective decision making is the data analytics approach and visualization of what is potentially, big data (BD) in monitoring the air quality in industrial workplaces. This paper presents a case study that monitors air quality that is collected with WSN technologies. We discuss the potential BD problems. The case trials are from two workshops that are part of a large on-shore logistics base a regional shipping industry in Norway. This small case study demonstrates a monitoring and visualization approach for facilitating BD in decision making for health and safety in the shipping industry. We also identify other potential applications of WSN technologies and visualization of BD in the workplace environments; for example, for monitoring of other substances for worker safety in high risk industries and for quality of goods in supply chain management.
Effective self-training author name disambiguation in scholarly digital libraries

Name ambiguity in the context of bibliographic citation records is a hard problem that affects the quality of services and content in digital libraries and similar systems. Supervised methods that exploit training examples in order to distinguish ambiguous author names are among the most effective solutions for the problem, but they require skilled human annotators in a laborious and continuous process of manually labeling citations in order to provide enough training examples. Thus, addressing the issues of (i) automatic acquisition of examples and (ii) highly effective disambiguation even when only few examples are available, are the need of the hour for such systems. In this paper, we propose a novel two-step disambiguation method, SAND (Self-training Associative Name Disambiguator), that deals with these two issues. The first step eliminates the need of any manual labeling effort by automatically acquiring examples using a clustering method that groups citation records based on the similarity among coauthor names. The second step uses a supervised disambiguation method that is able to detect unseen authors not included in any of the given training examples. Experiments conducted with standard public collections, using the minimum set of attributes present in a citation (i.e., author names, work title and publication venue), demonstrated that our proposed method outperforms representative unsupervised disambiguation methods that exploit similarities between citation records and is as effective as, and in some cases superior to, supervised ones, without manually labeling any training example.
Mixture of experts for classification of gender, ethnic origin, and pose of human faces

In this paper we describe the application of mixtures of experts on gender and ethnic classification of human faces, and pose classification, and show their feasibility on the FERET database of facial images. The FERET database allows us to demonstrate performance on hundreds or thousands of images. The mixture of experts is implemented using the "divide and conquer" modularity principle with respect to the granularity and/or the locality of information. The mixture of experts consists of ensembles of radial basis functions (RBFs). Inductive decision trees (DTs) and support vector machines (SVMs) implement the "gating network" components for deciding which of the experts should be used to determine the classification output and to restrict the support of the input space. Both the ensemble of RBF's (ERBF) and SVM use the RBF kernel ("expert") for gating the inputs. Our experimental results yield an average accuracy rate of 96% on gender classification and 92% on ethnic classification using the ERBF/DT approach from frontal face images, while the SVM yield 100% on pose classification.
Backing Rich Credentials with a Blockchain PKI ∗

This is the second of a series of papers describing the results of a project whose goal was to identify five remote identity proofing solutions that can be used as alternatives to knowledge-based verification. This paper describes the second solution, which makes use of a rich credential adapted for use on a blockchain and backed by a blockchain PKI. A rich credential, also used in Solution 1, allows the subject to identify him/herself to a remote verifier with which the subject has no prior relationship by presenting verification factors including possession of a private key, knowledge of a password, and possession of one or more biometric features, with selective disclosure of attributes and selective presentation of verification factors. In Solution 2 the issuer is a bank and the biometric verification factor is speaker recognition, which can be combined with face recognition to defeat voice morphing. The paper describes in detail the concept of a blockchain PKI, and shows that it has remarkable advantages over a traditional PKI, notably the fact that revocation checking is performed on the verifier’s local copy of the blockchain without requiring CRLs or OCSP.
Millimeter-wave large-scale phased-arrays for 5G systems

This talk will present our latest work on silicon RFICs for phased-array applications with emphasis on very large chips with built-in-self-test capabilities for 5G systems. SiGe is shown to be ideal for mm-wave applications due to its high temperature performance (automotive radars, base-stations, defense systems, etc.) and lower power consumption. These chips drastically reduce the cost of microwave and millimeter-wave phased arrays by combining many elements on the same chip, together with digital control and some cases, high-efficiency antennas. The phased-array chips also result in an easier packaging scheme using either a multi-layer PCB or wafer-level packages. We believe that this family of chips will be essential for millimeter-wave 5G communication systems.
A VLC media player plugin enabling dynamic adaptive streaming over HTTP

This paper describes the implementation of a VLC media player plugin enabling Dynamic Adaptive Streaming over HTTP (DASH). DASH is an emerging ISO/IEC MPEG and 3GPP standard for HTTP streaming. It aims to standardize formats enabling segmented progressive download by exploiting existing Internet infrastructure as such. Our implementation of these formats as described in this paper is based on the well-known VLC. Hence, it is fully integrated into the VLC structure and has been also submitted to the VLC development team for consideration in future releases of VLC. Therefore, it is licensed under the GNU Lesser General Public License (LGPL). The plugin provides a very flexible structure that could be easily extended with respect to different adaptation logics or profiles of the DASH standard. As a consequence, the plugin enables the integration of a variety of adaptation logics and comparison thereof, making it attractive for the research community.
Tracking Hands in Interaction with Objects: A Review

Markerless vision-based 3D hand motion tracking is a key and popular component for interaction studies in many domains such as virtual reality and natural human-computer interfaces. While this research field has been well studied in the last decades, most approaches have considered the human hand in isolation and not in action or in interaction with the environment or the other articulated human body parts. Employing contextual information about the surrounding environment (e.g. the shape, the texture, and the posture of the object in the hand) can remarkably constrain the tracking problem. The goal of this survey is to develop an up-to-date taxonomy of existing vision-based hand tracking methods with a new classification scheme: hand-object constraints. This taxonomy allows us to examine the strengths and weaknesses of the current state of the art and to highlight future trends in the domain.
Impact of an animal-assisted therapy programme on physiological and psychosocial variables of paediatric oncology patients

The objective of this study was to propose an intervention and safety protocol for performing animal-assisted therapy (AAT) and evaluating its efficacy in children under outpatient oncological treatment based on psychological, physiological, and quality of life indicators for the children and caregivers. The sample consisted of 24 children diagnosed with leukaemia and solid tumours (58% girls with a mean age of 8.0 years) who underwent an AAT programme consisting of three 30-min sessions in an open group. Two dogs (one Labrador retriever and one golden retriever) were used, and activities such as sensory stimulation, gait training, and socialization were conducted. The exclusion criteria were severe mental problems, inability to answer the questions included in the instruments used, allergy to animals, unavailability/lack of interest, isolation precaution, surgical wound, use of invasive devices, ostomy, no current blood count for evaluation, neutropaenia, infection, fever, diarrhoea, vomiting, respiratory symptoms at the beginning of the intervention or 1 week before the intervention, hospitalization or scheduled surgery, and non-completion of the AAT programme. The variables analysed using validated self or other evaluations were stress, pain, mood, anxiety, depression, quality of life, heart rate, and blood pressure. A quasi-experimental study design was used. We observed a decrease in pain (p = 0.046, d = -0.894), irritation (p = 0.041, d = -0.917), and stress (p = 0.005; d = -1.404) and a tendency towards improvement of depressive symptoms (p = 0.069; d = -0.801). Among the caregivers, an improvement was observed in anxiety (p = 0.007, d = -1.312), mental confusion (p = 0.006, d = -1.350), and tension (p = 0.006, d = -1.361). Therefore, the selection criteria and care protocols used for the AAT programme in the oncological context were adequate, and the programme was effective.
COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation

This paper presents a patch-based approach for crowd density estimation in public scenes. We formulate the problem of estimating density in a structured learning framework applied to random decision forests. Our approach learns the mapping between patch features and relative locations of all objects inside each patch, which contribute to generate the patch density map through Gaussian kernel density estimation. We build the forest in a coarse-to-fine manner with two split node layers, and further propose a crowdedness prior and an effective forest reduction method to improve the estimation accuracy and speed. Moreover, we introduce a semi-automatic training method to learn the estimator for a specific scene. We achieved state-of-the-art results on the public Mall dataset and UCSD dataset, and also proposed two potential applications in traffic counts and scene understanding with promising results.
Ontology and the Lexicon

A lexicon is a linguistic object and hence is not the same thing as an ontology, which is non-linguistic. Nonetheless, word senses are in many ways similar to ontological concepts and the relationships found between word senses resemble the relationships found between concepts. Although the arbitrary and semi-arbitrary distinctions made by natural languages limit the degree to which these similarities can be exploited, a lexicon can nonetheless serve in the development of an ontology, especially in a technical domain.
Do LoRa Low-Power Wide-Area Networks Scale?

New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and/or multiple sinks.
Analysis on Credit Card Fraud Detection Methods 1

Due to the theatrical increase of fraud which results in loss of dollars worldwide each year, several modern techniques in detecting fraud are persistently evolved and applied to many business fields. Fraud detection involves monitoring the activities of populations of users in order to estimate, perceive or avoid undesirable behavior. Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting. This paper presents a survey of current techniques used in credit card fraud detection and telecommunication fraud. The goal of this paper is to provide a comprehensive review of different techniques to detect fraud.
oddball: Spotting Anomalies in Weighted Graphs

Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the OddBall algorithm, to find such nodes. The contributions are the following: (a) we discover several new rules (power laws) in density, weights, ranks and eigenvalues that seem to govern the socalled “neighborhood sub-graphs” and we show how to use these rules for anomaly detection; (b) we carefully choose features, and design OddBall, so that it is scalable and it can work un-supervised (no user-defined constants) and (c) we report experiments on many real graphs with up to 1.6 million nodes, where OddBall indeed spots unusual nodes that agree with intuition.
A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of ‘mass’ that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover’s distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.
A compact super wideband monopole antenna

A novel compact Mickey-mouse shaped monopole antenna with super wideband (SWB) performance is presented. The proposed antenna consists of a radiating patch with a pair of ears and a top-corner rounded ground plane with a one-step staircase slot. In comparison to the conventional circular monopole structure, this configuration can achieve a much wider impedance bandwidth: in excess of 50:1 (from 2 to 100 GHz) for a return loss greater than 10 dB. In addition to the impedance, the radiation pattern and gain of the proposed antenna are also presented and discussed. This SWB prototype is implemented and the measured results are in good agreement with the simulated ones.
Online payment system using steganography and visual cryptography

A rapid growth in E-Commerce market is seen in recent time throughout the world. With ever increasing popularity of online shopping, Debit or Credit card fraud and personal information security are major concerns for customers, merchants and banks specifically in the case of CNP (Card Not Present). This paper presents a new approach for providing limited information only that is necessary for fund transfer during online shopping thereby safeguarding customer data and increasing customer confidence and preventing identity theft. The method uses combined application of steganography and visual cryptography for this purpose.
Secure LSB steganography for colored images using character-color mapping

Steganography is the science of embedding the secret messages inside other medium files in a way that hides the existence of the secret message at all. Steganography can be applied to text, audio, image, and video file types. In this study, we propose a new steganography approach for digital images in which the RGB coloring model was used. The efficiency of the proposed approach has been tested and evaluated. The experimental results show that the proposed approach produce high-quality stego images that resist against visual and statistical attacks.
From turtles to Tangible Programming Bricks: explorations in physical language design

This article provides a historical overview of educational computing research at MIT from the mid-1960s to the present day, focusing on physical interfaces. It discusses some of the results of this research: electronic toys that help children develop advanced modes of thinking through free-form play. In this historical context, the article then describes and discusses the author’s own research into tangible programming, culminating in the development of the Tangible Programming Bricks system—a platform for creating microworlds for children to explore computation and scientific thinking.
Illumination Invariant Face Recognition: A Survey

The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. In this paper an extensive and up-to-date survey of the existing techniques to address this problem is presented. This survey covers the passive techniques that attempt to solve the illumination problem by studying the visible light images in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.
A Kernel for Semi-Supervised Learning With MultiView Point Cloud Regularization

In semi-supervised learning (SSL), we learn a predictive model from a collection of labeled data and a typically much larger collection of unlabeled data. These lecture notes present a framework called multi-view point cloud regularization (MVPCR) , which unifies and generalizes several semi-supervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHS). Special cases of MVPCR include co-regularized least squares (CoRLS) [7, 3, 6], manifold regularization (MR) [1, 8, 4], and graph-based semisupervised learning. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.
Bangla license plate reader for metropolitan cities of Bangladesh using template matching

A simple algorithm for license plate reader for metropolitan cities in Bangladesh has been proposed in this paper. We have made use of a feature of Bangla script called “Matra” which joins the characters of a word together. Since the various words of a standard Bangladeshi license plate also have Matra, we have proposed a method where these words are segmented as single connected components and later recognized through template matching of words. There are also numbers and single letters in the number plate which are segmented and recognized as single characters. We have demonstrated that if we exploit the feature of Matra this way, the complexity of the algorithm is decreased, and the proposed system works smoothly for images taken under various conditions.
Performance Enhancement of Boost Converter Based on PID Controller Plus Linear-to-Nonlinear Translator

Here, A PID controller plus a novel linear-to-nonlinear translator is proposed and applied to the boost converter to improve the operating performance of the boost converter under large transient variations in load all over the quiescent dc input voltage range. The proposed translator is inserted between the PID controller and the main power stage such that the PID controller considers the behavior of the boost converter to be quasi-linear, thereby suppressing the effect of nonlinear behavior of the boost converter on the PID controller. Besides, variations in dc gain of the control-to-output transfer function with the translator are smaller than those without the translator, thus making system control easy to attain. Also, one-comparator counter-based sampling without any A-D converter is presented herein to realize the proposed control strategy. As mentioned above, the proposed control strategy can make the boost converter stably operated under large load transient responses all over the quiescent dc input voltage range.