nils-herrmann/clean_scirepeval huggingface dataset:
How to Effectively Train IBM Watson: Classroom Experience

Watson is a question answering system that uses natural language processing, information retrieval, knowledge interpretation, automated reasoning and machine learning techniques. It can analyze millions of documents and answer most of the questions accurately with varying level of confidence. However, training IBM Watson may be tedious and may not be efficient if certain set of guidelines are not followed. In this paper, we discuss an effective strategy to train IBM Watson question answering system. We experienced this strategy during the classroom teaching of IBM Watson at Ryerson University in Big Data Analytics certification program. We have observed that if documents are well segmented, contain relevant titles and have consistent formatting, then the recall of the answers can be as high as 95%.
Native Language Identification Using Large, Longitudinal Data

Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research on NLI has focused on relatively small corpora. We apply NLI to the recently released EFCamDat corpus which is not only multiple times larger than previous L2 corpora but also provides longitudinal data at several proficiency levels. Our investigation using accurate machine learning with a wide range of linguistic features reveals interesting patterns in the longitudinal data which are useful for both further development of NLI and its application to research on L2 acquisition.
Learning sparse graphs under smoothness prior

In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we parameterize the graph topology using an edge sampling function. That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which provides an explicit handle to control the sparsity level of the graph. We solve the sparse graph learning problem given some training data in both the noiseless and noisy settings. Given the true smooth data, the posed sparse graph learning problem can be solved optimally and is based on simple rank ordering. Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.
QoS Verification for Minimization of Drive Tests in LTE Networks

Nowadays, operational quality and robustness of cellular networks are among the hottest topics wireless communications research. As a response to a growing need in reduction of expenses for mobile operators, 3rd Generation Partnership Project (3GPP) initiated work on Minimization of Drive Tests (MDT). There are several major areas of standardization related to MDT, such as coverage, capacity, mobility optimization and verification of end user quality . This paper presents results of the research devoted to Quality of Service (QoS) verification for MDT. The main idea is to jointly observe the user experienced QoS in terms of throughput, and corresponding radio conditions. Also the necessity to supplement the existing MDT metrics with the new reporting types is elaborated.
What You See is What I Need: Mobile Reporting Practices in Emergencies

Decisions of emergency response organisations (police, fire fighters, infrastructure providers, etc.) rely on accurate and timely information. Some necessary information is integrated into control centre’s IT (weather, availability of electricity, gauge information, etc.), but almost every decision needs to be based on very specific information of the current crisis situation. Due to the unpredictable nature of a crisis, gathering this kind of information requires much improvisation and articulation work which we aim to support. We present a study on how different emergency response organisations communicate with teams on-site to generate necessary information for the coordinating instances, and we described, implemented and evaluated an interaction concept as well as a prototype to support this communication by a semi-structured request-and-report system based on Android devices. We learned that (1) the accuracy of request and reports can be improved by using an appropriate metadata structure in addition to creating multimedia-based information content, (2) requirements of trusted and fast information need to be respected in support concepts although they may even be contradictory, and (3) the coordination strategy of the emergency response organisation also shapes the way this interaction needs to be designed.
Multifunctional Software-Defined Radar Sensor and Data Communication System

Orthogonal frequency-division multiplexing (OFDM) signal coding and system architecture were implemented to achieve radar and data communication functionalities. The resultant system is a software-defined unit, which can be used for range measurements, radar imaging, and data communications. Range reconstructions were performed for ranges up to 4 m using trihedral corner reflectors with approximately 203 m of radar cross section at the carrier frequency; range resolution of approximately 0.3 m was demonstrated. Synthetic aperture radar (SAR) image of a single corner reflector was obtained; SAR signal processing specific to OFDM signals is presented. Data communication tests were performed in radar setup, where the signal was reflected by the same target and decoded as communication data; bit error rate of was achieved at 57 Mb/s. The system shows good promise as a multifunctional software-defined sensor which can be used in radar sensor networks.
Guide for calculating and interpreting effect sizes and confidence intervals in intellectual and developmental disability research studies.

This paper includes a nontechnical description of methods for calculating effect sizes in intellectual and developmental disability studies. Different hypothetical studies are used to illustrate how null hypothesis significance testing (NHST) and effect size findings can result in quite different outcomes and therefore conflicting results. Whereas NHST uses probability levels (e.g., p < .05) to evaluate the results of studies, effect size analyses focus on the magnitude of differences between groups or contrasting conditions and the strength of the relationship among variables of interest to report and interpret study results. Two families of effect sizes are described (mean difference, correlation coefficients) that are likely to be applicable to most intellectual and developmental disability studies. Sources of information on effect size calculators are included to provide researchers ready-available data analysis procedures for computing effect sizes and confidence intervals for different types of research designs and studies.
A Novel Deep Learning-Based Method of Improving Coding Efficiency from the Decoder-End for HEVC

Improving the coding efficiency is the eternal theme in video coding field. The traditional way for this purpose is to reduce the redundancies inside videos by adding numerous coding options at the encoder side. However, no matter what we have done, it is still hard to guarantee the optimal coding efficiency. On the other hand, the decoded video can be treated as a certain compressive sampling of the original video. According to the compressive sensing theory, it might be possible to further enhance the quality of the decoded video by some restoration methods. Different from the traditional methods, without changing the encoding algorithm, this paper focuses on an approach to improve the video's quality at the decoder end, which equals to further boosting the coding efficiency. Furthermore, we propose a very deep convolutional neural network to automatically remove the artifacts and enhance the details of HEVC-compressed videos, by utilizing that underused information left in the bit-streams and external images. Benefit from the prowess and efficiency of the fully end-to-end feed forward architecture, our approach can be treated as a better decoder to efficiently obtain the decoded frames with higher quality. Extensive experiments indicate our approach can further improve the coding efficiency post the deblocking and SAO in current HEVC decoder, averagely 5.0%, 6.4%, 5.3%, 5.5% BD-rate reduction for all intra, lowdelay P, lowdelay B and random access configurations respectively. This method can aslo be extended to any video coding standards.
Virtual Augmented and Mixed Reality. Designing and Developing Augmented and Virtual Environments

Widespread acceptance of virtual reality has been partially handicapped by the inability of current systems to accommodate multiple viewpoints, thereby limiting their appeal for collaborative applications. We are exploring the ability to utilize passive, untracked participants in a powerwall environment. These participants see the same image as the active, immersive participant. This does present the passive user with a varying viewpoint that does not correspond to their current position. We demonstrate the impact this will have on the perceived image and show that human psychology is actually well adapted to compensating for what, on the surface, would seem to be a very drastic distortion. We present some initial guidelines for system design that minimize the negative impact of passive participation, allowing two or more collaborative participants. We then outline future experimentation to measure user compensation for these distorted viewpoints.
Capacity of a Mobile Multiple-Antenna Communication Link in Rayleigh Flat Fading

We analyze a mobile wireless link comprising M transmitter andN receiver antennas operating in a Rayleigh flat-fading environment. The propagation coef fici nts between every pair of transmitter and receiver antennas are statistically independent and un known; they remain constant for a coherence interval ofT symbol periods, after which they change to new independent v alues which they maintain for anotherT symbol periods, and so on. Computing the link capacity, associated with channel codin g over multiple fading intervals, requires an optimization over the joint density of T M complex transmitted signals. We prove that there is no point in making the number of transmitter antennas greater t han the length of the coherence interval: the capacity forM > T is equal to the capacity for M = T . Capacity is achieved when the T M transmitted signal matrix is equal to the product of two stat i ically independent matrices: a T T isotropically distributed unitary matrix times a certain T M random matrix that is diagonal, real, and nonnegative. This result enables us to determine capacity f or many interesting cases. We conclude that, for a fixed number of antennas, as the length of the coherence i nterval increases, the capacity approaches the capacity obtained as if the receiver knew the propagatio n coefficients. Index Terms —Multi-element antenna arrays, wireless communications, space-time modulation
Visually-aware Collaborative Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, user’s personal preference on food, and various contexts like what had been eaten in past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user’s choice on food, namely, 1) the user’s (and other users’) history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network-based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user’s preference at the ingredient level; and 3) learning user preference from the recipe’s visual image. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference on food.
A High-Efficiency Self-Oscillating Class-D Amplifier for Piezoelectric Speakers

The design tradeoffs of the class-D amplifier (CDA) for driving piezoelectric (PZ) speakers are presented, including efficiency, linearity, and electromagnetic interference. An implementation is proposed to achieve high efficiency in the CDA architecture for PZ speakers to extend battery life in mobile devices. A self-oscillating closed-loop architecture is used to obviate the need for a carrier signal generator to achieve low power consumption. The use of stacked-cascode CMOS transistors at the H-bridge output stage provides low-input capacitance to allow high-switching frequency to improve linearity with high efficiency. Moreover, the CDA monolithic implementation achieves 18 VPP output voltage swing in a low-voltage CMOS technology without requiring expensive high-voltage semiconductor devices. The prototype experimental results achieved a minimum THD + N of 0.025%, and a maximum efficiency of 96%. Compared to available CDA for PZ speakers, the proposed CDA achieved higher linearity, lower power consumption, and higher efficiency.
On Profiling Bots in Social Media

The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots had been focused largely on malicious bots, with the assumption that these bots should be removed. In this work, however, we find many bots that are benign, and propose a new, broader categorization of bots based on their behaviors. This includes broadcast, consumption, and spam bots. To facilitate comprehensive analyses of bots and how they compare to human accounts, we develop a systematic profiling framework that includes a rich set of features and classifier bank. We conduct extensive experiments to evaluate the performances of different classifiers under varying time windows, identify the key features of bots, and infer about bots in a larger Twitter population. Our analysis encompasses more than 159K bot and human (non-bot) accounts in Twitter. The results provide interesting insights on the behavioral traits of both benign and malicious bots.
An Adaptive Model Switching Approach for a Multisensor Tracking System used for Autonomous Driving in an Urban Environment

For most of the existing commercial driver assistance systems the use of a single environmental sensor and a tracking model tied to the characteristics of this sensor is sufficient. When using a multi-sensor fusion approach with heterogeneous sensors the information available for tracking depends on the sensors detecting the object. This paper describes an approach where multiple models are used for tracking moving objects. The best model for tracking is chosen based on the available sensor information. The architecture of the tracking system along with the tracking models and algorithm for model selection are presented. The design of the architecture and algorithms allows an extension of the system with new sensors and tracking models without changing existing software. The approach was implemented and successfully used in Tartan Racing’s autonomous vehicle for the Urban Grand Challenge. The advantages of the multisensor approach are explained and practical results of a representative scenario are presented.
Depth Map Super-Resolution by Deep Multi-Scale Guidance

We intend to show that the optimal filter size of backwards convolution (or deconvolution (deconv)) for upsampling is closely related to the upscaling factor s. For conciseness, we consider a single-scale network (SS-Net(ord)) trained in an ordinary domain for upsampling a LR depth map with an upscaling factor s = 4. Figure S1 shows an overview of SS-Net(ord). Specifically, the first and third layers perform convolution, whereas the second layer performs backwards strided convolution. Activation function PReLU is used in SS-Net(ord) except the last layer. We set the network parameters: n1 = 64, n2 = 32, n3 = 1 and f1 = f3 = 5. We evaluate the super-resolving performance of SS-Net(ord) by using different deconv filter sizes f2×f2. Figure S2 shows the convergence curves using f2 ∈ (3, 9, 11). It can be shown that upsampling accuracy increases with f2 until it reaches 2s+1 i.e. f2 = 9. In a compromise between computation efficiency and upsampling performance, we choose deconv filter size to (2s+ 1)× (2s+ 1).
On Improving User Response Times in Tableau

The rapid increase in data volumes and complexity of applied analytical tasks poses a big challenge for visualization solutions. It is important to keep the experience highly interactive, so that users stay engaged and can perform insightful data exploration. Query processing usually dominates the cost of visualization generation. Therefore, in order to achieve acceptable response times, one needs to utilize backend capabilities to the fullest and apply techniques, such as caching or prefetching. In this paper we discuss key data processing components in Tableau: the query processor, query caches, Tableau Data Engine [1, 2] and Data Server. Furthermore, we cover recent performance improvements related to the number and quality of remote queries, broader reuse of cached data, and application of inter and intra query parallelism.
Random Walks for Image Segmentation

A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs
Shiny-phyloseq: Web application for interactive microbiome analysis with provenance tracking

We have created a Shiny-based Web application, called Shiny-phyloseq, for dynamic interaction with microbiome data that runs on any modern Web browser and requires no programming, increasing the accessibility and decreasing the entrance requirement to using phyloseq and related R tools. Along with a data- and context-aware dynamic interface for exploring the effects of parameter and method choices, Shiny-phyloseq also records the complete user input and subsequent graphical results of a user's session, allowing the user to archive, share and reproduce the sequence of steps that created their result-without writing any new code themselves.



Shiny-phyloseq is implemented entirely in the R language. It can be hosted/launched by any system with R installed, including Windows, Mac OS and most Linux distributions. Information technology administrators can also host Shiny--phyloseq from a remote server, in which case users need only have a Web browser installed. Shiny-phyloseq is provided free of charge under a GPL-3 open-source license through GitHub at http://joey711.github.io/shiny-phyloseq/.
Kinematics Simulation and Analysis of 2-DOF Parallel Manipulator with Highly Redundant Actuation

In this paper, the mathematical model of the posture inverse kinematics is established. According to the structure of 2DOF parallel manipulator, the simulation model of mechanism is built using the Matlab/SimMechanics. The kinematics simulation of the parallel manipulator is obtained and confirmed correct. With the Virtual Reality Toolbox, the virtual reality of the parallel manipulator is carried out. During the simulation, the motion animate is obtained. It indicates that Matlab/Simulink can greatly reduce the designer’s work and provide a powerful and convenient tool for the simulation and analysis of parallel manipulators. Keywordsparallel manipulator; posture inverse kinematics; simulation; SimMechanics; virtual reality.
Nevus depigmentosus and nevus of Ito: pigmentary twin spotting.

Nevus depigmentosus presents with areas of hypopigmentation along Blaschko’s lines and may be associated with disorders of the central nervous system, musculoskeletal system, eyes, and teeth. Nevus of Ito is a dermal melanocytosis of the acromioclavicular and upper chest area. Although both nevus depigmentosus and nevus of Ito occur commonly, their coexistence in a manner representative of allelic twin spotting has not previously been reported.
DUAL-BAND PRINTED DIVERSITY ANTENNA FOR WIMAX / WLAN APPLICATIONS

A compact dual-band multiple-input-multiple-output (MIMO)/diversity antenna is proposed. This antenna is designed for 2.4/5.2/5.8GHz WLAN and 2.5/3.5/5.5 GHz WiMAX applications in portable mobile devices. It consists of two back-to-back monopole antennas connected with a T-shaped stub, where two rectangular slots are cut from the ground, which significantly reduces the mutual coupling between the two ports at the lower frequency band. The volume of this antenna is 40mm ∗ 30mm ∗ 1mm including the ground plane. Measured results show the isolation is better than −20 dB at the lower frequency band from 2.39 to 3.75GHz and −25 dB at the higher frequency band from 5.03 to 7 GHz, respectively. Moreover, acceptable radiation patterns, antenna gain, and envelope correlation coefficient are obtained. These characteristics indicate that the proposed antenna is suitable for some portable MIMO/diversity equipments.
Copper-induced stress and antioxidative responses in roots of Brassica juncea L.

Copper induced antioxidative reactions in the roots of Brassica juncea L. were investigated in both timeand concentration-dependent manners. The rapid uptake of Cu was observed immediately after the start of treatment. Application of Cu at 8 μM caused 50 percent reduction in biomass of Cu-treated roots as compared with control. Cu-induced root growth inhibition paralleled the level of root oxidative damage. Treatment with Cu at 8 μM induced a twofold increase in H 2 O 2 content during the first 4 d, but it declined to the basal level thereafter. We also observed a twofold increase in superoxide dismutase activities with 8 μM Cu during the first 2 d. The stimulation lasted for 4 d and then gradually declined. Activities of both ascorbate peroxidase and guaiacol peroxidase in roots were found to be low during the first 4 d after seedling exposure to 8 μM Cu, but significantly increased after that, suggesting that increased enzyme activities would be responsible for the removal of H 2 O 2 . Catalase activities were always suppressed under Cu stress. Treatment of seedlings with 8 μM Cu induced general decreases in both reduced ascorbate and dehydroascorbate. The reduced glutathione content decreased at early stages of Cu treatment. However, it was restored to the level of controls thereafter. In contrast, the oxidized glutathione contents showed a progressive increase during the time of Cu treatment. The total non-protein thiol content was shown to increase during the first several days, but it declined at later stages.