nils-herrmann/clean_scirepeval huggingface dataset:
Virtual reality and hand tracking system as a medical tool to evaluate patients with parkinson's

In this paper, we take advantage of the free hand interaction technology as a medical tool, either in rehabilitation centers or at home, that allows the evaluation of patients with Parkinson's. We have created a virtual reality scene to engage the patient to feel in an activity that can be found in daily life, and use the Leap Motion controller tracking to evaluate and classify the tremor in the hands. A sample of 33 patients diagnosed with Parkinson's disease (PD) participated in the study. Three tests were performed per patient, the first two to evaluate the amplitude of the postural tremor in each hand, and the third to measure the time to complete a specific task. Analysis shows that our tool can be used effectively to classify the stage of Parkinson's disease.
Intelligent stock data prediction using predictive data mining techniques

Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.
Literature mining, ontologies and information visualization for drug repurposing

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.
Transparent Microstrip Patch Antennas With Multilayer and Metal-Mesh Films

This letter presents transparent microstrip patch antennas made of two types of transparent conductive films, which are multilayer film (MLF; IZTO/Ag/IZTO) and metal-mesh film (MMF; Cu). The sheet resistance levels of the MLF and MMF are 2.52 and 0.18 Ω/□. The transparencies of the MLF and MMF are over 80% and 60% at a 550-nm wavelength, respectively. The design and dimensions of the antennas follow the conventional simple microstrip patch antenna. A transparent acryl substrate is used for the proposed transparent antennas. The conductive parts of the proposed antennas are also made of copper sheets (CS; case 1) for the performance comparison to two types of transparent antennas made of MLF (case 2) and MMF (case 3). The resonance frequency band of the antennas is 2.4–2.5 GHz for Wi-Fi service. The case-1, case-2, and case-3 antennas have peak gains of 4.75, −4.23, and 2.63 dB and have radiation efficiencies of 66.32%, 7.76%, and 42.69% at the center frequency of the Wi-Fi service band (2.45 GHz), respectively.
Ordinal Constrained Binary Code Learning for Nearest Neighbor Search

Recent years have witnessed extensive attention in binary code learning, a.k.a. hashing, for nearest neighbor search problems. It has been seen that high-dimensional data points can be quantized into binary codes to give an efficient similarity approximation via Hamming distance. Among existing schemes, ranking-based hashing is recent promising that targets at preserving ordinal relations of ranking in the Hamming space to minimize retrieval loss. However, the size of the ranking tuples, which shows the ordinal relations, is quadratic or cubic to the size of training samples. By given a large-scale training data set, it is very expensive to embed such ranking tuples in binary code learning. Besides, it remains a dificulty to build ranking tuples efficiently for most ranking-preserving hashing, which are deployed over an ordinal graph-based setting. To handle these problems, we propose a novel ranking-preserving hashing method, dubbed Ordinal Constraint Hashing (OCH), which efficiently learns the optimal hashing functions with a graph-based approximation to embed the ordinal relations. The core idea is to reduce the size of ordinal graph with ordinal constraint projection, which preserves the ordinal relations through a small data set (such as clusters or random samples). In particular, to learn such hash functions effectively, we further relax the discrete constraints and design a specific stochastic gradient decent algorithm for optimization. Experimental results on three large-scale visual search benchmark datasets, i.e. LabelMe, Tiny100K and GIST1M, show that the proposed OCH method can achieve superior performance over the state-ofthe-arts approaches.
How gender-stereotypical are selfies? A content analysis and comparison with magazine adverts

Selfies (self-portrait photographs often taken with a camera phone) are popularly used for selfpresentation in social media like Facebook and Instagram. These modern user-generated self-portraits have the potential to draw a more versatile picture of the genders instead of reproducing traditional gender stereotypes often presented in mainstream media and advertising. To investigate the degree of gender stereotyping in selfies, a random sample of 500 selfies uploaded on Instagram (50% representing females, 50% males) was drawn and subjected to quantitative content analysis. The degree of gender stereotyping in the selfies was measured using Goffman's (1979) and Kang's (1997) gender display categories (e.g. feminine touch, lying posture, withdrawing gaze, sparse clothing) plus three social media-related categories (kissing pout, muscle presentation, faceless portrayal). Additionally, gender stereotyping in selfies was directly compared to the degree of gender stereotyping in magazine adverts measured in the same way (D€ oring & P€ oschl, 2006). Results reveal that male and female Instagram users' selfies not only reflect traditional gender stereotypes, but are even more stereotypical than magazine adverts.
Novel miniaturized CPW fed MIMO antenna arrays for UWB applications

This paper presents a novel miniaturized CPW fed UWB antenna. The compactness of the antenna is achieved by using modifications in ground plane of the CPW feed structure and the radiator design. The volume of this proposed antenna is 18 × 16 × 1.6 mm3. This antenna is used to make two novel miniaturized 2 × 2 MIMO antenna arrays of dimension 32 × 18 × 1.6 mm3 and 34 × 18 × 1.6 mm3 for UWB applications. These MIMO antenna arrays with modified ground structure are made by lateral and orthogonal positioning of the proposed UWB antenna with itself. The proposed UWB antenna and other two MIMO antennas cover entire UWB range 3.1–10.6 GHz. The proposed MIMO antennas are uniplanar having satisfactory performance in terms of return loss, isolation, envelope correlation coefficient (ECC) and diversity gain.)
Experimental study on population-based incremental learning algorithms for dynamic optimization problems

Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of PopulationBased Incremental Learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBIL’s adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.
Modified timeboxing process model for proper utilization of resources

Now a day's speed and time are the two major factors in software industry, so in this paper we propose a modified timeboxing model for proper utilization of resources and reduction of slack time. As a result a good quality software is produced in less time period. In case of modified timeboxing process model, we divide each stages of timebox of timeboxing process model to different sub-stages and execute them concurrently to reduce the slack (free) time. In the proposed model each iteration is done in a timebox of fixed duration having some specific functionality.
Enabling the coexistence of LTE and Wi-Fi in unlicensed bands

The expansion of wireless broadband access network deployments is resulting in increased scarcity of available radio spectrum. It is very likely that in the near future, cellular technologies and wireless local area networks will need to coexist in the same unlicensed bands. However, the two most prominent technologies, LTE and Wi-Fi, were designed to work in different bands and not to coexist in a shared band. In this article, we discuss the issues that arise from the concurrent operation of LTE and Wi-Fi in the same unlicensed bands from the point of view of radio resource management. We show that Wi-Fi is severely impacted by LTE transmissions; hence, the coexistence of LTE and Wi-Fi needs to be carefully investigated. We discuss some possible coexistence mechanisms and future research directions that may lead to successful joint deployment of LTE and Wi-Fi in the same unlicensed band.
Animating fluid sediment mixture in particle-laden flows

In this paper, we present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows. We develop a Multigrid Preconditioned fluid solver for the Locally Averaged Navier Stokes equation. This is discretized purely on a semi-staggered standard MPM grid. Sedimentation is modeled with the Drucker-Prager elastoplasticity flow rule, enhanced by a novel particle density estimation method for converting particles between representations of either continuum or discrete points. Fluid and sediment are two-way coupled through a momentum exchange force that can be easily resolved with two MPM background grids. We present various results to demonstrate the efficacy of our method.
From Hashing to CNNs: Training Binary Weight Networks via Hashing

Deep convolutional neural networks (CNNs) have shown appealing performance on various computer vision tasks in recent years. This motivates people to deploy CNNs to realworld applications. However, most of state-of-art CNNs require large memory and computational resources, which hinders the deployment on mobile devices. Recent studies show that low-bit weight representation can reduce much storage and memory demand, and also can achieve efficient network inference. To achieve this goal, we propose a novel approach named BWNH to train Binary Weight Networks via Hashing. In this paper, we first reveal the strong connection between inner-product preserving hashing and binary weight networks, and show that training binary weight networks can be intrinsically regarded as a hashing problem. Based on this perspective, we propose an alternating optimization method to learn the hash codes instead of directly learning binary weights. Extensive experiments on CIFAR10, CIFAR100 and ImageNet demonstrate that our proposed BWNH outperforms current state-of-art by a large margin.
Higher mode SIW excitation technology and its array application

An excitation technology of TE40 mode for substrate integrated waveguide (SIW) and its application in designing a slot antenna array is proposed. In order to excite the TE40 mode, a microstrip power divider and two coupling slots driven by microstrip lines in the longitudinal direction of SIW are designed to convert the electromagnetic field pattern from slot lines to SIW. The advantages of the TE40 mode are not only reducing process complexity but also simplifying the feeding network so as to reduce the production bottlenecks at high frequency. A 16-element slot array antenna fed by the TE40 mode SIW is designed based on the standing wave antenna array principle. Experimental results show that a 4×4 antenna array fed by TE40 mode achieves a maximum radiation gain of 15.2 dBi and 85% radiation efficiency at 10.4GHz and an impedance bandwidth ranging from 9.95 to 10.6GHz with |S11| below −10 dB. The achieved results demonstrate the superiority of the higher mode excitation technology and it is reasonable to deduce that other higher modes can be achieved based on the proposed technology.
Indoor Localization Algorithm based on Fingerprint Using a Single Fifth Generation Wi-Fi Access Point

This paper proposes an indoor positioning system (IPS) based on WLAN using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses fingerprint and classification models based on KNN (K-nearest neighbor) and Bayes rule. The fingerprint is formed by beam RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.
TwitterReporter: Breaking News Detection and Visualization through the Geo-Tagged Twitter Network

The Twitter social network provides a constant stream of concise data, useful within both geospatial and temporal domains. Previous studies have attempted to mine and find recent news topics within the data set. Although the efforts were a step in the right direction, we believe more is needed to accomplish a useful and interesting task: using live Twitter data to automatically identify breaking news events in near real-time. In this paper, we present methods to collect data, identify breaking news topics, and display results in a geo-temporal visualization. Our goal is to provide a solution that discovers breaking news faster than traditional reporting mediums.
Unsupervised detection of surface defects: A two-step approach

In this paper, we focus on the problem of finding anomalies in surface images. Despite enormous research efforts and advances, it still remains a big challenge to be solved. This paper proposes a unified approach for defect detection. Our proposed method consists of two phases: (1) global estimation and (2) local refinement. First, we roughly estimate defects by applying a spectral-based approach in a global manner. We then locally refine the estimated region based on the distributions of pixel intensities derived from defect and defect-free regions. Experimental results show that the proposed method outperforms the previous defect detection methods and gives robust results even in noisy surface defect images.
Dynamic decoupling control method for PMSM drive with cross-coupling inductances

This paper proposes a decoupling control method for high-performance drive of permanent magnet synchronous motors (PMSMs). The conventional decoupling schemes proposed for removing coupling effects on d-q current regulation have the limited performance because the cross-coupling inductances caused by asymmetric structure and saturation are not considered. Those inductances may degrade the decoupling performance and even make the current control unstable by inducing the additional coupling voltages. Therefore, in this paper, a new voltage model including the cross-coupling inductances is developed and the compensation voltages for decoupling based on the model are calculated. The performance of the proposed decoupling method is verified by experimental results.
Fractional Order ControlA Tutorial

Many real dynamic systems are better characterized using a non-integer order dynamic model based on fractional calculus or, differentiation or integration of noninteger order. Traditional calculus is based on integer order differentiation and integration. The concept of fractional calculus has tremendous potential to change the way we see, model, and control the nature around us. Denying fractional derivatives is like saying that zero, fractional, or irrational numbers do not exist. In this paper, we offer a tutorial on fractional calculus in controls. Basic definitions of fractional calculus, fractional order dynamic systems and controls are presented first. Then, fractional order PID controllers are introduced which may make fractional order controllers ubiquitous in industry. Additionally, several typical known fractional order controllers are introduced and commented. Numerical methods for simulating fractional order systems are given in detail so that a beginner can get started quickly. Discretization techniques for fractional order operators are introduced in some details too. Both digital and analog realization methods of fractional order operators are introduced. Finally, remarks on future research efforts in fractional order control are given.
Analytics-as-a-Service (AaaS) Tool for Unstructured Data Mining

Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the "Big Data" epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ the NoSQL data storages to accommodate the high-dimensional data. In this paper, we introduce an AaaS tool that aims at accomplishing terms and topics extraction and organization from unstructured data sources such as NoSQL databases and textual contents (e.g., websites). The primary accomplishment in this paper is the detail justification of the architectural design of our proposed framework. This includes the proposed algorithms (e.g., concurrency search, linear search, etc.) and the performance of macro tasks such as filtering, tagging, and so on.
Evidence for the Flipped Classroom in STEM

The flipped classroom (or flipping)  is a new educational technique which seeks to invert the traditional model of in-classroom lectures and out-of-class homework. Instead, it advocates in-class interactive group learning, and out-of-class instruction via videos or podcasts. Intuitively, this approach moves classroom time away from mere dissemination of information, and allows teachers to focus on solving student problems and enabling learning. This connects with the increasing evidence that problem-based learning is an effective teaching technique (see e.g. the meta-analysis in ). The intuition for flipping seems compelling, reflected in attention in the non-academic media (e.g. [11, 18]). However, flipping is not trivial to apply. Applying it means restructuring the entire course to focus on group learning, while also recording supporting instructional material. Given the up-front costs, it is important to know whether flipping is truly effective before applying it in practice. In this study, I investigate the evidence that flipping is effective in improving student learning outcomes in STEM subjects, and I also examine some of the pitfalls and opportunities flipping presents. My aim is to understand better how these ideas can be applied to teaching in my home department, Computer Science.
A Simple Approach to Intrinsic Correspondence Learning on Unstructured 3D Meshes

The question of representation of 3D geometry is of vital importance when it comes to leveraging the recent advances in the field of machine learning for geometry processing tasks. For common unstructured surface meshes state-of-the-art methods rely on patch-based or mapping-based techniques that introduce resampling operations in order to encode neighborhood information in a structured and regular manner. We investigate whether such resampling can be avoided, and propose a simple and direct encoding approach. It does not only increase processing efficiency due to its simplicity – its direct nature also avoids any loss in data fidelity. To evaluate the proposed method, we perform a number of experiments in the challenging domain of intrinsic, non-rigid shape correspondence estimation. In comparisons to current methods we observe that our approach is able to achieve highly competitive results.
A NEW GENERALIZATION OF FIBONACCI SEQUENCE AND EXTENDED BINET’S FORMULA

Consider the Fibonacci sequence {Fn}n=0 with initial conditions F0 = 0, F1 = 1 and recurrence relation Fn = Fn−1 + Fn−2 (n ≥ 2). The Fibonacci sequence has been generalized in many ways, some by preserving the initial conditions, and others by preserving the recurrence relation. In this article, we study a new generalization {qn}, with initial conditions q0 = 0 and q1 = 1, which is generated by the recurrence relation qn = aqn−1 + qn−2 (when n is even) or qn = bqn−1 + qn−2 (when n is odd), where a and b are nonzero real numbers. Some well-known sequences are special cases of this generalization. The Fibonacci sequence is a special case of {qn} with a = b = 1. Pell’s sequence is {qn} with a = b = 2 while k-Fibonacci sequence has a = b = k. We produce an extended Binet’s formula for {qn} and, thereby, identities such as Cassini’s, Catalan’s, d’Ocagne’s, etc.