nils-herrmann/clean_scirepeval huggingface dataset:
IOT Gateway: BridgingWireless Sensor Networks into Internet of Things

With the development of sensor, wireless mobile communication, embedded system and cloud computing, the technologies of Internet of Things have been widely used in logistics, Smart Meter, public security, intelligent building and so on. Because of its huge market prospects, Internet of Things has been paid close attention by several governments all over the world, which is regarded as the third wave of information technology after Internet and mobile communication network. Bridging between wireless sensor networks with traditional communication networks or Internet, IOT Gateway plays an important role in IOT applications, which facilitates the seamless integration of wireless sensor networks and mobile communication networks or Internet, and the management and control with wireless sensor networks. In this paper, we proposed an IOT Gateway system based on Zigbee and GPRS protocols according to the typical IOT application scenarios and requirements from telecom operators, presented the data transmission between wireless sensor networks and mobile communication networks, protocol conversion of different sensor network protocols, and control functionalities for sensor networks, and finally gave an implementation of prototyping system and system validation.
Simplified minimal gated unit variations for recurrent neural networks

Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit which further simplify that design by reducing the number of parameters in the forget-gate dynamic equation. These three model variants, referred to simply as MGU1, MGU2, and MGU3, were tested on sequences generated from the MNIST dataset and the real sequences from the Reuters Newswire Topics (RNT) dataset. Here, we report on the RNT results. The new models have shown similar accuracy to the MGU model while using fewer parameters and thus lower training expense. One model variant, namely MGU2, performed better than MGU on the datasets considered, and thus may be used as an alternate to MGU or GRU in recurrent neural networks.
A New Switching Strategy for Pulse Width Modulation (PWM) Power Converters

This paper presents a new switching strategy for pulse width modulation (PWM) power converters. Since the proposed strategy uses independent on/off switching action of the upper or lower arm according to the polarity of the current, the dead time is not needed except instant of current polarity change. Therefore, it is not necessary to compensate the dead time effect and the possibility of arm short is strongly eliminated. The current control of PWM power converters can easily adopt the proposed switching strategy by using the polarity information of the reference current instead of the real current, thus eliminating the problems that commonly arise from real current detection. In order to confirm the usefulness of the proposed switching strategy, experimental tests were done using a single-phase inverter with passive loads, a three-phase inverter for induction motor drives, a three-phase ac/dc PWM converter, a three-phase active power filter, and a class-D amplifier, the results of which are presented in this paper
Word Sense Discovery Based on Sense Descriptor Dissimilarity

In machine translation, information on word ambiguities is usually provided by the lexicographers who construct the lexicon. In this paper we propose an automatic method for word sense induction, i.e. for the discovery of a set of sense descriptors to a given ambiguous word. The approach is based on the statistics of the distributional similarity between the words in a corpus. Our algorithm works as follows: The 20 strongest first-order associations to the ambiguous word are considered as sense descriptor candidates. All pairs of these candidates are ranked according to the following two criteria: First, the two words in a pair should be as dissimilar as possible. Second, although being dissimilar their co-occurrence vectors should add up to the co-occurrence vector of the ambiguous word scaled by two. Both conditions together have the effect that preference is given to pairs whose co-occurring words are complementary. For best results, our implementation uses singular value decomposition, entropy-based weights, and second-order similarity metrics.
Why marketplace experimentation is harder than it seems: the role of test-control interference

Classical statistical inference of experimental data assumes that the treatment affects the test group but not the control group. This assumption will typically be violated when experimenting in marketplaces because of general equilibrium effects: changing test demand affects the supply available to the control group. We illustrate this with an email marketing campaign performed by eBay. Ignoring test-control interference leads to estimates of the campaign's effectiveness which are too large by a factor of around two. We present the simple economics of this bias in a supply and demand framework, showing that the bias is larger in magnitude where there is more inelastic supply, and is positive if demand is elastic.
Communication-Avoiding Parallel Strassen: Implementation and performance

Matrix multiplication is a fundamental kernel of many high performance and scientific computing applications. Most parallel implementations use classical O(n3) matrix multiplication, even though there exist algorithms with lower arithmetic complexity. We recently presented a new Communication-Avoiding Parallel Strassen algorithm (CAPS), based on Strassen's fast matrix multiplication, that minimizes communication (SPAA '12). It communicates asymptotically less than all classical and all previous Strassen-based algorithms, and it attains theoretical lower bounds.
 In this paper we show that CAPS is also faster in practice. We benchmark and compare its performance to previous algorithms on Hopper (Cray XE6), Intrepid (IBM BG/P), and Franklin (Cray XT4). We demonstrate significant speedups over previous algorithms both for large matrices and for small matrices on large numbers of processors. We model and analyze the performance of CAPS and predict its performance on future exascale platforms.
Is Naïve Bayes a Good Classifier for Document Classification ?

Document classification is a growing interest in the research of text mining. Correctly identifying the documents into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naïve Bayes is potentially good at serving as a document classification model due to its simplicity. The aim of this paper is to highlight the performance of employing Naïve Bayes in document classification. Results show that Naïve Bayes is the best classifiers against several common classifiers (such as decision tree, neural network, and support vector machines) in term of accuracy and computational efficiency.
Hybrid Phase-Shift-Controlled Three-Level and LLC DC–DC Converter With Active Connection at the Secondary Side

This paper proposes a hybrid phase-shift-controlled three-level (TL) and LLC dc-dc converter. The TL dc-dc converter and LLC dc-dc converter have their own transformers. Compared with conventional half-bridge TL dc-dc converters, the proposed one has no additional switch at the primary side of the transformer, where the TL converter shares the lagging switches with the LLC converter. At the secondary side of the transformers, the TL and LLC converters are connected by an active switch. With the aid of the LLC converter, the zero voltage switching (ZVS) of the lagging switches can be achieved easily even under light load conditions. Wide ZVS range for all the switches can be ensured. Both the circulating current at the primary side and the output filter inductance are reduced. Furthermore, the efficiency of the converter is improved dramatically. The features of the proposed converter are analyzed, and the design guidelines are given in the paper. Finally, the performance of the converter is verified by a 1-kW experimental prototype.
Real-Time Performance of a Self-Powered Environmental IoT Sensor Network System

Wireless sensor networks (WSNs) play an increasingly important role in monitoring applications in many areas. With the emergence of the Internet-of-Things (IoT), many more lowpower sensors will need to be deployed in various environments to collect and monitor data about environmental factors in real time. Providing power supply to these sensor nodes becomes a critical challenge for realizations of IoT applications as sensor nodes are normally battery-powered and have a limited lifetime. This paper proposes a wireless sensor network that is powered by solar energy harvesting. The sensor network monitors the environmental data with low-power sensor electronics and forms a network using multiple XBee wireless modules. A detailed performance analysis of the network system under solar energy harvesting has been presented. The sensor network system and the proposed energy-harvesting techniques are configured to achieve a continuous energy source for the sensor network. The proposed energy-harvesting system has been successfully designed to enable an energy solution in order to keep sensor nodes active and reliable for a whole day. The paper also outlines some of our experiences in real-time implementation of a sensor network system with energy harvesting.
Predicting Hedge Fund Fraud

Recent hedge fund scandals have fueled a debate over appropriate oversight of the industry and question the ability of regulators to protect investors by preventing fraud. This paper examines whether the presence of suspicious patterns in fund returns raises the probability of subsequent fraud. We find that the patterns exist in funds prosecuted by the SEC at the same frequency as in other funds, suggesting that performance data is not useful for predicting fraud. The sensitivity of capital flow to performance, however, is significantly stronger for the former group, indicating that some investors have the ability to distinguish between the two sets of funds.
The power of simulation: Imagining one's own and other's behavior

A large number of cognitive neuroscience studies point to the similarities in the neural circuits activated during the generation, imagination, as well as observation of one's own and other's behavior. Such findings support the shared representations account of social cognition, which is suggested to provide the basic mechanism for social interaction. Mental simulation may also be a representational tool to understand the self and others. However, successfully navigating these shared representations--both within oneself and between individuals--constitutes an essential functional property of any autonomous agent. It will be argued that self-awareness and agency, mediated by the temporoparietal (TPJ) area and the prefrontal cortex, are critical aspects of the social mind. Thus, differences as well as similarities between self and other representations at the neural level may be related to the degrees of self-awareness and agency. Overall, these data support the view that social cognition draws on both domain-general mechanisms and domain-specific embodied representations.
Teaching a Smarter Learner

We introduce a formal model of teaching in which the teacher is tailored to a particular learner, yet the teaching protocol is designed so that no collusion is possible. Not surprisingly, such a model remedies the non-intuitive aspects of other models in which the teacher must successfully teach any consistent learner. We prove that any class that can be exactly identiied by a determin-istic polynomial-time algorithm with access to a very rich set of example-based queries is teachable by a computationally unbounded teacher and a polynomial-time learner. In addition, we present other general results relating this model of teaching to various previous results. We also consider the problem of designing teacher/learner pairs in which both the teacher and learner are polynomial-time algorithms and describe teacher/learner pairs for the classes of 1-decision lists and Horn sentences.
End-to-end Data Integrity for File Systems: A ZFS Case Study

We present a study of the effects of disk and memory corruption on file system data integrity. Our analysis focuses on Sun’s ZFS, a modern commercial offering with numerous reliability mechanisms. Through careful and thorough fault injection, we show that ZFS is robust to a wide range of disk faults. We further demonstrate that ZFS is less resilient to memory corruption, which can lead to corrupt data being returned to applications or system crashes. Our analysis reveals the importance of considering both memory and disk in the construction of truly robust file and storage systems.
CAp 2017 challenge: Twitter Named Entity Recognition

The paper describes the CAp 2017 challenge. The challenge concerns the problem of Named Entity Recognition (NER) for tweets written in French. We first present the data preparation steps we followed for constructing the dataset released in the framework of the challenge. We begin by demonstrating why NER for tweets is a challenging problem especially when the number of entities increases. We detail the annotation process and the necessary decisions we made. We provide statistics on the inter-annotator agreement, and we conclude the data description part with examples and statistics for the data. We, then, describe the participation in the challenge, where 8 teams participated, with a focus on the methods employed by the challenge participants and the scores achieved in terms of F1 measure. Importantly, the constructed dataset comprising ∼6,000 tweets annotated for 13 types of entities, which to the best of our knowledge is the first such dataset in French, is publicly available at http://cap2017.imag.fr/competition.html .
Probabilistic Model-Based Clustering of Multivariate and Sequential Data

Probabilistic model-based clustering, based on nite mixtures of multivariate models, is a useful framework for clustering data in a statistical context. This general framework can be directly extended to clustering of sequential data, based on nite mixtures of sequential models. In this paper we consider the problem of tting mixture models where both multivariate and sequential observations are present. A general EM algorithm is discussed and experimental results demonstrated on simulated data. The problem is motivated by the practical problem of clustering individuals into groups based on both their static characteristics and their dynamic behavior.
On integral control in backstepping: analysis of different techniques

Including integral action in a nonlinear backstepping design is the topic of this paper. Two methods for adding integral feedback are proposed and analyzed. These are compared to the more traditional methods: 1) adaptive backstepping, and 2) plant augmentation that adds an extra relative degree and thus gives one extra step of backstepping. A test plant is used to compare the different control laws. Based on the theoretical analysis and the simulations, some interesting conclusions are made for each integral control strategy.
Iterative Ranking from Pair-wise Comparisons

The question of aggregating pairwise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR’s TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining ranking, finding ‘scores’ for each object (e.g. player’s rating) is of interest to understanding the intensity of the preferences. In this paper, we propose a novel iterative rank aggregation algorithm for discovering scores for objects from pairwise comparisons. The algorithm has a natural random walk interpretation over the graph of objects with edges present between two objects if they are compared; the scores turn out to be the stationary probability of this random walk. The algorithm is model independent. To establish the efficacy of our method, however, we consider the popular Bradley-Terry-Luce (BTL) model in which each object has an associated score which determines the probabilistic outcomes of pairwise comparisons between objects. We bound the finite sample error rates between the scores assumed by the BTL model and those estimated by our algorithm. This, in essence, leads to order-optimal dependence on the number of samples required to learn the scores well by our algorithm. Indeed, the experimental evaluation shows that our (model independent) algorithm performs as well as the Maximum Likelihood Estimator of the BTL model and outperforms a recently proposed algorithm by Ammar and Shah .
Dialog in the open world: platform and applications

We review key challenges of developing spoken dialog systems that can engage in interactions with one or multiple participants in relatively unconstrained environments. We outline a set of core competencies for open-world dialog, and describe three prototype systems. The systems are built on a common underlying conversational framework which integrates an array of predictive models and component technologies, including speech recognition, head and pose tracking, probabilistic models for scene analysis, multiparty engagement and turn taking, and inferences about user goals and activities. We discuss the current models and showcase their function by means of a sample recorded interaction, and we review results from an observational study of open-world, multiparty dialog in the wild.
Designing a hybrid AI system as a forex trading decision support tool

In this study, a hybrid artificial intelligent (AI) system integrating neural network and expert system is proposed to support foreign exchange (forex) trading decisions. In this system, a neural network is used to predict the forex price in terms of quantitative data, while an expert system is used to handle qualitative factor and to provide forex trading decision suggestions for traders incorporating experts' knowledge and the neural network's results. The effectiveness of the proposed hybrid AI system is illustrated by simulation experiments
Twitter sentiment classification using machine learning techniques for stock markets

Sentiment classification of Twitter data has been successfully applied in finding predictions in a variety of domains. However, using sentiment classification to predict stock market variables is still challenging and ongoing research. The main objective of this study is to compare the overall accuracy of two machine learning techniques (logistic regression and neural network) with respect to providing a positive, negative and neutral sentiment for stock-related tweets. Both classifiers are compared using Bigram term frequency (TF) and Unigram term frequency - inverse document term frequency (TF-IDF) weighting schemes. Classifiers are trained using a dataset that contains 42,000 automatically annotated tweets. The training dataset forms positive, negative and neutral tweets covering four technology-related stocks (Twitter, Google, Facebook, and Tesla) collected using Twitter Search API. Classifiers give the same results in terms of overall accuracy (58%). However, empirical experiments show that using Unigram TF-IDF outperforms TF.
MATLAB MODELS FOR PNEUMATIC ARTIFICIAL MUSCLES

Pneumatic artificial muscles (PAMs) are becoming more commonly used as actuators in modern robotics. The most made and common type of these artificial muscles in use is the McKibben artificial muscle that was developed in 1950’s. This paper presents the geometric model of PAM and different Matlab models for pneumatic artificial muscles. The aim of our models is to relate the pressure and length of the pneumatic artificial muscles to the force it exerts along its entire exists.
An ECC-Based Blind Signature Scheme

Cryptography is increasingly applied to the E-commerce world, especially to the untraceable payment system and the electronic voting system. Protocols for these systems strongly require the anonymous digital signature property, and thus a blind signature strategy is the answer to it. Chaum stated that every blind signature protocol should hold two fundamental properties, blindness and intractableness. All blind signature schemes proposed previously almost are based on the integer factorization problems, discrete logarithm problems, or the quadratic residues, which are shown by Lee et al. that none of the schemes is able to meet the two fundamental properties above. Therefore, an ECC-based blind signature scheme that possesses both the above properties is proposed in this paper.
Highway Vehicle Counting in Compressed Domain

This paper presents a highway vehicle counting method in compressed domain, aiming at achieving acceptable estimation performance approaching the pixel-domain methods. Such a task essentially is challenging because the available information (e.g. motion vector) to describe vehicles in videos is quite limited and inaccurate, and the vehicle count in realistic traffic scenes always varies greatly. To tackle this issue, we first develop a batch of low-level features, which can be extracted from the encoding metadata of videos, to mitigate the informational insufficiency of compressed videos. Then we propose a Hierarchical Classification based Regression (HCR) model to estimate the vehicle count from features. HCR hierarchically divides the traffic scenes into different cases according to vehicle density, such that the broad-variation characteristics of traffic scenes can be better approximated. Finally, we evaluated the proposed method on the real highway surveillance videos. The results show that our method is very competitive to the pixel-domain methods, which can reach similar performance along with its lower complexity.
Acoustic Data Transmission Based on Modulated Complex Lapped Transform

Acoustic data transmission is a technique to embed the data in a sound wave imperceptibly and to detect it at the receiver. This letter proposes a novel acoustic data transmission system designed based on the modulated complex lapped transform (MCLT). In the proposed system, data is embedded in an audio file by modifying the phases of the original MCLT coefficients. The data can be transmitted by playing the embedded audio and extracting it from the received audio. By embedding the data in the MCLT domain, the perceived quality of the resulting audio could be kept almost similar as the original audio. The system can transmit data at several hundreds of bits per second (bps), which is sufficient to deliver some useful short messages.