nils-herrmann/clean_scirepeval huggingface dataset:
Multiphase System for Metal Disc Induction Heating: Modeling and RMS Current Control

This paper presents a multiphase induction system modeling for a metal disc heating and further industrial applications such as hot strip mill. An original architecture, with three concentric inductors supplied by three resonant current inverters, leads to a reduced element system, without any coupling transformers, phase loop, mobile screens, or mobile magnetic cores as it could be found in classical solutions. A simulation model is built, based on simplified equivalent models of electric and thermal phenomena. It takes into account the data extracted from Flux2D finite-element software, concerning the energy transfer between the inductor currents and the piece to be heated. It is implemented in a versatile software PSIM, initially dedicated to power electronics. An optimization procedure calculates the optimal supply currents in the inverters in order to obtain a desired power density profile in the work piece. This paper deals with the simulated and experimental results which are compared in open loop and closed loop. This paper ends with a current control method which sets rms inductor currents in continuous and digital conditions.
Opinion mining and analysis: A literature review

Sentiment analysis or opinion mining consist of many different fields like natural language processing, text mining, decision making and linguistics. It is a type of text analysis that classifies the text and makes decision by extracting and analyzing the text. Opinions can be categorized as positive and negative and measures the degree of positive or negative associated with that event (people, organization, social issues). So, it's basically people's opinion study, study of emotions and appraisals in the direction of any social issue, people or entity. Recently most of the researches have been done on the sentiment analysis of products and services. The analysis of events and issues, data is retrieved from social media like twitter etc.
Android malware attacks and countermeasures: Current and future directions

Smartphones are rising in popularity as well as becoming more sophisticated over recent years. This popularity coupled with the fact that smartphones contain a lot of private user data is causing a proportional rise in different malwares for the platform. In this paper we analyze and classify state-of-the-art malware techniques and their countermeasures. The paper also reports a novel method for malware development and novel attack techniques such as mobile botnets, usage pattern based attacks and repackaging attacks. The possible countermeasures are also proposed. Then a detailed analysis of one of the proposed novel malware methods is explained. Finally the paper concludes by summarizing the paper.
Human-Inspired Control of Bipedal Walking Robots

This paper presents a human-inspired control approach to bipedal robotic walking: utilizing human data and output functions that appear to be intrinsic to human walking in order to formally design controllers that provably result in stable robotic walking. Beginning with human walking data, outputs-or functions of the kinematics-are determined that result in a low-dimensional representation of human locomotion. These same outputs can be considered on a robot, and human-inspired control is used to drive the outputs of the robot to the outputs of the human. The main results of this paper are that, in the case of both under and full actuation, the parameters of this controller can be determined through a human-inspired optimization problem that provides the best fit of the human data while simultaneously provably guaranteeing stable robotic walking for which the initial condition can be computed in closed form. These formal results are demonstrated in simulation by considering two bipedal robots-an underactuated 2-D bipedal robot, AMBER, and fully actuated 3-D bipedal robot, NAO-for which stable robotic walking is automatically obtained using only human data. Moreover, in both cases, these simulated walking gaits are realized experimentally to obtain human-inspired bipedal walking on the actual robots.
Recursive Estimation of Motion, Structure, and Focal Length

We present a formulation for recursive recovery of motion, pointwise structure, and focal length from feature correspondences tracked through an image sequence. In addition to adding focal length to the state vector, several representational improvements are made over earlier structure from motion formulations, yielding a stable and accurate estimation framework which applies uniformly to both true perspective and orthographic projection. Results on synthetic and real imagery illustrate the performance of the estimator. Index Term-Structure from motion, camera model, camera calibration, recursive estimation, 3D representation, 3D modeling.
Supervised Keyphrase Extraction as Positive Unlabeled Learning

The problem of noisy and unbalanced training data for supervised keyphrase extraction results from the subjectivity of keyphrase assignment, which we quantify by crowdsourcing keyphrases for news and fashion magazine articles with many annotators per document. We show that annotators exhibit substantial disagreement, meaning that single annotator data could lead to very different training sets for supervised keyphrase extractors. Thus, annotations from single authors or readers lead to noisy training data and poor extraction performance of the resulting supervised extractor. We provide a simple but effective solution to still work with such data by reweighting the importance of unlabeled candidate phrases in a two stage Positive Unlabeled Learning setting. We show that performance of trained keyphrase extractors approximates a classifier trained on articles labeled by multiple annotators, leading to higher average F1scores and better rankings of keyphrases. We apply this strategy to a variety of test collections from different backgrounds and show improvements over strong baseline models.
Path and travel time inference from GPS probe vehicle data

We consider the problem of estimating real-time traffic conditions from sparse, noisy GPS probe vehicle data. We specifically address arterial roads, which are also known as the secondary road network (highways are considered the primary road network). We consider several estimation problems: historical traffic patterns, real-time traffic conditions, and forecasting future traffic conditions. We assume that the data available for these estimation problems is a small set of sparsely traced vehicle trajectories, which represents a small fraction of the total vehicle flow through the network. We present an expectation maximization algorithm that simultaneously learns the likely paths taken by probe vehicles as well as the travel time distributions through the network. A case study using data from San Francisco taxis is used to illustrate the performance of the algorithm.
Adaptive channel allocation spectrum etiquette for cognitive radio networks

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange
A SIFT-SVM method for detecting cars in UAV images

In the last years, the advent of unmanned aerial vehicles (UAVs) for civilian remote sensing purposes has generated a lot of interest because of the various new applications they can offer. One of them is represented by the automatic detection and counting of cars. In this paper, we propose a novel car detection method. It starts with a feature extraction process based on scalar invariant feature transform (SIFT) thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively, the process discriminates between keypoints assigned to cars and those associated with all remaining objects by means of a support vector machine (SVM) classifier. Experimental results have been conducted on a real UAV scene. They show how the proposed method allows providing interesting detection performances.
Opinion mining over twitterspace: Classifying tweets programmatically using the R approach

Today the channels for expressing opinions seem to increase daily. When these opinions are relevant to a company, they are important sources of business insight, whether they represent critical intelligence about a customer's defection risk, the impact of an influential reviewer on other people's purchase decisions, or early feedback on product releases, company news or competitors. Capturing and analyzing these opinions is a necessity for proactive product planning, marketing and customer service and it is also critical in maintaining brand integrity. The importance of harnessing opinion is growing as consumers use technologies such as Twitter to express their views directly to other consumers. Tracking the disparate sources of opinion is hard - but even harder is quickly and accurately extracting the meaning so companies can analyze and act. Tweets' Language is complicated and contextual, especially when people are expressing opinions and requires reliable sentiment analysis based on parsing many linguistic shades of gray. This article argues that using the R programming platform for analyzing tweets programmatically simplifies the task of sentiment analysis and opinion mining. An R programming technique has been used for testing different sentiment lexicons as well as different scoring schemes. Experiments on analyzing the tweets of users over six NHL hockey teams reveals the effectively of using the opinion lexicon and the Latent Dirichlet Allocation (LDA) scoring scheme.
NVMain 2.0: A User-Friendly Memory Simulator to Model (Non-)Volatile Memory Systems

In this letter, a flexible memory simulator - NVMain 2.0, is introduced to help the community for modeling not only commodity DRAMs but also emerging memory technologies, such as die-stacked DRAM caches, non-volatile memories (e.g., STT-RAM, PCRAM, and ReRAM) including multi-level cells (MLC), and hybrid non-volatile plus DRAM memory systems. Compared to existing memory simulators, NVMain 2.0 features a flexible user interface with compelling simulation speed and the capability of providing sub-array-level parallelism, fine-grained refresh, MLC and data encoder modeling, and distributed energy profiling.
Understanding and Supporting Fathers and Fatherhood on Social Media Sites

Fathers are taking on more childcare and household responsibilities than they used to and many non-profit and government organizations have pushed for changes in policies to support fathers. Despite this effort, little research has explored how fathers go online related to their roles as fathers. Drawing on an interview study with 37 fathers, we find that they use social media to document and archive fatherhood, learn how to be a father, and access social support. They also go online to support diverse family needs, such as single fathers' use of Reddit instead of Facebook, fathers raised by single mothers' search for role models online, and stay-at-home fathers' use of father blogs. However, fathers are constrained by privacy concerns and perceptions of judgment relating to sharing content online about their children. Drawing on theories of fatherhood, we present theoretical and design ideas for designing online spaces to better support fathers and fatherhood. We conclude with a call for a research agenda to support fathers online.
Link Prediction and Recommendation across Heterogeneous Social Networks

Link prediction and recommendation is a fundamental problem in social network analysis. The key challenge of link prediction comes from the sparsity of networks due to the strong disproportion of links that they have potential to form to links that do form. Most previous work tries to solve the problem in single network, few research focus on capturing the general principles of link formation across heterogeneous networks. In this work, we give a formal definition of link recommendation across heterogeneous networks. Then we propose a ranking factor graph model (RFG) for predicting links in social networks, which effectively improves the predictive performance. Motivated by the intuition that people make friends in different networks with similar principles, we find several social patterns that are general across heterogeneous networks. With the general social patterns, we develop a transfer-based RFG model that combines them with network structure information. This model provides us insight into fundamental principles that drive the link formation and network evolution. Finally, we verify the predictive performance of the presented transfer model on 12 pairs of transfer cases. Our experimental results demonstrate that the transfer of general social patterns indeed help the prediction of links.
IPM Machine Drive Design and Tests for an Integrated Starter–Alternator Application

This paper deals with an integrated starter-alternator (ISA) drive which exhibits a high torque for the engine start, a wide constant-power speed range for the engine speedup, and a high-speed generator mode operation for electric energy generation. Peculiarities of this ISA drive are thus its flux-weakening capability and the possibility to large torque overload at low speed. The focus on the design, analysis, and test of an interior permanent-magnet motor and drive for a prototype of ISA is given in this paper. In details, this paper reports on the design of stator and rotor geometries, the results of finite-element computations, the description of control system, and the experimental results of prototype tests.
A General Model for Multiple View Unsupervised Learning

Multiple view data, which have multiple representations from different feature spaces or graph spaces, arise in various data mining applications such as information retrieval, bioinformatics and social network analysis. Since different representations could have very different statistical properties, how to learn a consensus pattern from multiple representations is a challenging problem. In this paper, we propose a general model for multiple view unsupervised learning. The proposed model introduces the concept of mapping function to make the different patterns from different pattern spaces comparable and hence an optimal pattern can be learned from the multiple patterns of multiple representations. Under this model, we formulate two specific models for two important cases of unsupervised learning, clustering and spectral dimensionality reduction; we derive an iterating algorithm for multiple view clustering, and a simple algorithm providing a global optimum to multiple spectral dimensionality reduction. We also extend the proposed model and algorithms to evolutionary clustering and unsupervised learning with side information. Empirical evaluations on both synthetic and real data sets demonstrate the effectiveness of the proposed model and algorithms.
LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees

We present a method to generate a robot control strategy that maximizes the probability to accomplish a task. The task is given as a Linear Temporal Logic (LTL) formula over a set of properties that can be satisfied at the regions of a partitioned environment. We assume that the probabilities with which the properties are satisfied at the regions are known, and the robot can determine the truth value of a proposition only at the current region. Motivated by several results on partitioned-based abstractions, we assume that the motion is performed on a graph. To account for noisy sensors and actuators, we assume that a control action enables several transitions with known probabilities. We show that this problem can be reduced to the problem of generating a control policy for a Markov Decision Process (MDP) such that the probability of satisfying an LTL formula over its states is maximized. We provide a complete solution for the latter problem that builds on existing results from probabilistic model checking. We include an illustrative case study.
Mid-level Elements for Object Detection

Building on the success of recent discriminative midlevel elements, we propose a surprisingly simple approach for object detection which performs comparable to the current state-of-the-art approaches on PASCAL VOC comp-3 detection challenge (no external data). Through extensive experiments and ablation analysis, we show how our approach effectively improves upon the HOG-based pipelines by adding an intermediate mid-level representation for the task of object detection. This representation is easily interpretable and allows us to visualize what our object detector “sees”. We also discuss the insights our approach shares with CNN-based methods, such as sharing representation between categories helps.
Where, When and Why Brain Activation Differs for Bilinguals and Monolinguals during Picture Naming and Reading Aloud

Using functional magnetic resonance imaging, we found that when bilinguals named pictures or read words aloud, in their native or nonnative language, activation was higher relative to monolinguals in 5 left hemisphere regions: dorsal precentral gyrus, pars triangularis, pars opercularis, superior temporal gyrus, and planum temporale. We further demonstrate that these areas are sensitive to increasing demands on speech production in monolinguals. This suggests that the advantage of being bilingual comes at the expense of increased work in brain areas that support monolingual word processing. By comparing the effect of bilingualism across a range of tasks, we argue that activation is higher in bilinguals compared with monolinguals because word retrieval is more demanding; articulation of each word is less rehearsed; and speech output needs careful monitoring to avoid errors when competition for word selection occurs between, as well as within, language.
S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking

Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom studied in the context of structured learning despite their recent success on various classification and ranking tasks. In this paper, we propose S-MART, a tree-based structured learning framework based on multiple additive regression trees. S-MART is especially suitable for handling tasks with dense features, and can be used to learn many different structures under various loss functions. We apply S-MART to the task of tweet entity linking — a core component of tweet information extraction, which aims to identify and link name mentions to entities in a knowledge base. A novel inference algorithm is proposed to handle the special structure of the task. The experimental results show that S-MART significantly outperforms state-of-the-art tweet entity linking systems.
Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go

Monte Carlo Tree Search methods have led to huge progress in Computer Go. Still, program performance is uneven most current Go programs are much stronger in some aspects of the game, such as local fighting and positional evaluation, than in others. Well known weaknesses of many programs include the handling of several simultaneous fights, including the “two safe groups” problem, and dealing with coexistence in seki. Starting with a review of MCTS techniques, several conjectures regarding the behavior of MCTS-based Go programs in specific types of Go situations are made. Then, an extensive empirical study of ten leading Go programs investigates their performance of two specifically designed test sets containing “two safe group” and seki situations. The results give a good indication of the state of the art in computer Go as of 2012/2013. They show that while a few of the very top programs can apparently solve most of these evaluation problems in their playouts already, these problems are difficult to solve by global search. ∗shihchie@ualberta.ca †mmueller@ualberta.ca
Identifying Syntactic differences Between Two Programs

Programmers frequently face the need to identify the differences between two programs, usually two different versions of a program. Text-based tools such as the UNIX utility diff often produce unsatisfactory comparisons because they cannot accurately pinpoint the differences and because they sometimes produce irrelevant differences. Since programs have a rigid syntactic structure as described by the grammar of the programming language in which they are written, we develop a comparison algorithm that exploits knowledge of the grammar. The algorithm, which is based on a dynamic programming scheme, can point out the differences between two programs more accurately than previous text comparison tools. Finally, the two programs are pretty-printed ‘synchronously’ with the differences highlighted so that the differences are easily identified.
Link Prediction on Evolving Data Using Tensor Factorization

Within the last few years a lot of research has been done on large social and information networks. One of the principal challenges concerning complex networks is link prediction. Most link prediction algorithms are based on the underlying network structure in terms of traditional graph theory. In order to design efficient algorithms for large scale networks, researchers increasingly adapt methods from advanced matrix and tensor computations. This paper proposes a novel approach of link prediction for complex networks by means of multi-way tensors. In addition to structural data we furthermore consider temporal evolution of a network. Our approach applies the canonical Parafac decomposition to reduce tensor dimensionality and to retrieve latent trends. For the development and evaluation of our proposed link prediction algorithm we employed various popular datasets of online social networks like Facebook and Wikipedia. Our results show significant improvements for evolutionary networks in terms of prediction accuracy measured through mean average precision.