nils-herrmann/clean_scirepeval huggingface dataset:
Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

This paper introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon, and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, MPQA Subjectivity Lexicon, General Inquirer, NRC Word-Sentiment Association Lexicon and SO-CAL lexicon. The effectiveness of the sentiment lexicons for sentiment categorization at the document-level and sentence-level was evaluated using an Amazon product review dataset and a news headlines dataset. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorization, obtaining accuracy rates of 75% to 77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words, for both document-level and sentencelevel sentiment categorization. The WKWSCI lexicon obtained the best accuracy of 69% on the news headlines sentiment categorization task, and the sentiment strength values obtained a Pearson correlation of 0.57 with human assigned sentiment values. It is recommended that the Hu & Liu lexicon be used for product review texts, and the WKWSCI lexicon for non-review texts.
La Classification non Supervisée (Clustering) de Documents Textuels par les Automates Cellulaires

Dans cet article nous présentons un automate cellulaire (Class_AC) pour résoudre un problème de text mining en l’occurrence la classification non supervisée (Clustering). Avant de procéder à l’expérimentation par l’automate cellulaire, nous avons vectorisés nos données en procédant à l’indexation des documents textuels provenant de la base de donnée REUTERS 21578 par l’approche Wordnet. L’automate que nous proposons dans cet article est une grille de cellules de structure plane avec un voisinage découlant de cette structure (planaire). Trois fonctions de transitions ont servi à faire varier l’automate ayant quatre états pour chaque cellule. Les résultats obtenus montrent que la machine virtuelle à calcul parallèle (Class_AC) regroupe efficacement des documents similaires à un seuil prés.
Online Product Quantization

Approximate nearest neighbor (ANN) search has achieved great success in many tasks. However, existing popular methods for ANN search, such as hashing and quantization methods, are designed for static databases only. They cannot handle well the database with data distribution evolving dynamically, due to the high computational effort for retraining the model based on the new database. In this paper, we address the problem by developing an online product quantization (online PQ) model and incrementally updating the quantization codebook that accommodates to the incoming streaming data. Moreover, to further alleviate the issue of large scale computation for the online PQ update, we design two budget constraints for the model to update partial PQ codebook instead of all. We derive a loss bound which guarantees the performance of our online PQ model. Furthermore, we develop an online PQ model over a sliding window with both data insertion and deletion supported, to reflect the real-time behavior of the data. The experiments demonstrate that our online PQ model is both time-efficient and effective for ANN search in dynamic large scale databases compared with baseline methods and the idea of partial PQ codebook update further reduces the update cost.
Clustering techniques: The user's dilemma

-Numerous papers on clustering techniques and their applications in engineering, medical, and biological areas have appeared in pattern recognition literature during the past decade. This paper attempts to set some guidelines for a potential user of a clustering technique. We examine eight clustering programs which are representative of the various available techniques and compare their performances from several points of view. A formal comparative analysis is also performed with a portion of Munson's handprinted character data set. We believe that an understanding of the intrinsic characteristics of a clustering technique is essential to the intelligent application of the technique. Further, the output of a clustering program, along with whatever information a user may have about the data set, should be used together to form hypotheses about the structure of the data set. Clustering technique Patterns Features Squared error Distance measures Dendrogram Similarity matrix Hierarchical clustering Minimum spanning tree Admissibility criteria
Training restricted Boltzmann machines: An introduction

Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. They have attracted much attention as building blocks for the multi-layer learning systems called deep belief networks, and variants and extensions of RBMs have found application in a wide range of pattern recognition tasks. This tutorial introduces RBMs from the viewpoint of Markov random fields, starting with the required concepts of undirected graphical models. Different learning algorithms for RBMs, including contrastive divergence learning and parallel tempering, are discussed. As sampling from RBMs, and therefore also most of their learning algorithms, are based on Markov chain Monte Carlo (MCMC) methods, an introduction to Markov chains and MCMC techniques is provided. Experiments demonstrate relevant aspects of RBM training.
Detecting and Tracking The Real-time Hot Topics: A Study on Computational Neuroscience

In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the “usage count” data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like “fmri”, “eeg”, “erp”, etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the “heat” of hot topics timely and dynamically.
Segmentation Based Urdu Nastalique OCR

Urdu Language is written in Nastalique writing style, which is highly cursive, context sensitive and is difficult to process as only the last character in its ligature resides on the baseline. This paper focuses on the development of OCR using Hidden Markov Model and rule based post-processor. The recognizer gets the main body (without diacritics) as input and recognizes the corresponding ligature. Accuracy of the system is 92.73% for printed and then scanned document images at 36 font size.
A novel and secure smart parking management system (SPMS) based on integration of WSN, RFID, and IoT

Nowadays, finding a secure parking lot in modern cities deemed as very hard and time-consuming task. Leaving negative implications on traffic congestion, air pollutions, climate changes, etc. are also creating difficult situations to find the secure parking lot on required time. Thus, Smart Parking System (SPS) deemed inevitable option to solve those issues and build a comprehensive smart transportation system. Toward this end, this paper aims to design a secure and smart parking monitoring, controlling and management solutions based on the integration of Wireless Sensor Network (WSN), Radio Frequency Identification (RFID), Adhoc Network, and Internet of Things (IoT). Considering cyber security issues in IoT environment, we adopt a lightweight cryptographic algorithm that meets IoT device requirements in term of computational cost and energy consumption. Fog computing has been adopted to process and manipulate sensitive data within the edge of the network and accelerate response time for any emergency circumstance. The proposed model provides real-time information for detecting parking lots and reservation, e-payment solutions to mitigate traffic congestion, parking management optimization and enhance user experience while preserving user privacy and security.
A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition

Absb-act—In speech analysis, the voiced-unvoiced decision is usually performed in conjunction with pitch analysis. The linking of voiced-unvoiced (V-UV) decision to pitch analysis not only results in unnecessary complexity, but makes it difficult to classify short speech segments which are less than a few pitch periods in duration. In this paper, we describe a pattern recognition approach for deciding whether a given segment of a speech signal should be classified as voiced speech, un-voiced speech, or silence, based on measurements made on the signal. In this method, five different measurements axe made on the speech segment to be classified. The measured parameters are the zero-crossing rate, the speech energy, the correlation between adjacent speech samples, the first predictor coefficient from a 12-pole linear predictive coding (LPC) analysis, and the energy in the prediction error. The speech segment is assigned to a particular class based on a minimum-distance rule obtained under the assumption that the measured parameters are distributed according to the multidimensional Gaussian probability density function. The means and covariances for the Gaussian distribution are determined from manually classified speech data included in a training set. The method has been found to provide reliable classification with speech segments as short as 10 ms and has been used for both speech analysis-synthesis and recognition applications. A simple nonlinear smoothing algorithm is described to provide a smooth 3-level contour of an utterance for use in speech recognition applications. Quantitative results and several examples illustrating the performance of the methOd are included in the paper.
Structuring E-Commerce Inventory

Large e-commerce enterprises feature millions of items entered daily by a large variety of sellers. While some sellers provide rich, structured descriptions of their items, a vast majority of them provide unstructured natural language descriptions. In the paper we present a 2 steps method for structuring items into descriptive properties. The first step consists in unsupervised property discovery and extraction. The second step involves supervised property synonym discovery using a maximum entropy based clustering algorithm. We evaluate our method on a year worth of ecommerce data and show that it achieves excellent precision with good recall.
Hybrid CMOS/memristor circuits

This is a brief review of recent work on the prospective hybrid CMOS/memristor circuits. Such hybrids combine the flexibility, reliability and high functionality of the CMOS subsystem with very high density of nanoscale thin film resistance switching devices operating on different physical principles. Simulation and initial experimental results demonstrate that performance of CMOS/memristor circuits for several important applications is well beyond scaling limits of conventional VLSI paradigm.
Dynablock: Dynamic 3D Printing for Instant and Reconstructable Shape Formation

This paper introduces Dynamic 3D Printing, a fast and reconstructable shape formation system. Dynamic 3D Printing can assemble an arbitrary three-dimensional shape from a large number of small physical elements. Also, it can disassemble the shape back to elements and reconstruct a new shape. Dynamic 3D Printing combines the capabilities of 3D printers and shape displays: Like conventional 3D printing, it can generate arbitrary and graspable three-dimensional shapes, while allowing shapes to be rapidly formed and reformed as in a shape display. To demonstrate the idea, we describe the design and implementation of Dynablock, a working prototype of a dynamic 3D printer. Dynablock can form a three-dimensional shape in seconds by assembling 3,000 9 mm blocks, leveraging a 24 x 16 pin-based shape display as a parallel assembler. Dynamic 3D printing is a step toward achieving our long-term vision in which 3D printing becomes an interactive medium, rather than the means for fabrication that it is today. In this paper, we explore possibilities for this vision by illustrating application scenarios that are difficult to achieve with conventional 3D printing or shape display systems.
Mechanical Design and Dynamic Modeling of a Two-Wheeled Inverted Pendulum Mobile Robot

The differential drive two-wheeled inverted pendulum mobile robot has advantage of high maneuverability on slopes and in narrow spaces. It can be used to carry human beings as well as other goods. This paper mainly discusses mechanical design and dynamic modeling of this type of mobile robot platform. The first prototype is 12 kg of weight with dimensions of 42x36x15 cm. A multi modal control system is developed which includes the human transport control system and goods transport control system mode. An embedded control system is implemented to control two motors for both modes. In human transport mode the master controller receives the data from the data conditioning module and generates the reference for PID controllers whereas in goods transport mode the master controller receives command from the teleoperator. The effectiveness of the developed mobile robot is confirmed by experiments such as climbing slope, pivot around and surpassing small obstacles.
A Customizable Matrix Multiplication Framework for the Intel HARPv2 Xeon+FPGA Platform: A Deep Learning Case Study

General Matrix to Matrix multiplication (GEMM) is the cornerstone for a wide gamut of applications in high performance computing (HPC), scientific computing (SC) and more recently, deep learning. In this work, we present a customizable matrix multiplication framework for the Intel HARPv2 CPU+FPGA platform that includes support for both traditional single precision floating point and reduced precision workloads. Our framework supports arbitrary size GEMMs and consists of two parts: (1) a simple application programming interface (API) for easy configuration and integration into existing software and (2) a highly customizable hardware template. The API provides both compile and runtime options for controlling key aspects of the hardware template including dynamic precision switching; interleaving and block size control; and fused deep learning specific operations. The framework currently supports single precision floating point (FP32), 16, 8, 4 and 2 bit Integer and Fixed Point (INT16, INT8, INT4, INT2) and more exotic data types for deep learning workloads: INT16xTernary, INT8xTernary, BinaryxBinary.
 We compare our implementation to the latest NVIDIA Pascal GPU and evaluate the performance benefits provided by optimizations built into the hardware template. Using three neural networks (AlexNet, VGGNet and ResNet) we illustrate that reduced precision representations such as binary achieve the best performance, and that the HARPv2 enables fine-grained partitioning of computations over both the Xeon and FPGA. We observe up to 50x improvement in execution time compared to single precision floating point, and that runtime configuration options can improve the efficiency of certain layers in AlexNet up to 4x, achieving an overall 1.3x improvement over the entire network.
Language and the Newborn Brain: Does Prenatal Language Experience Shape the Neonate Neural Response to Speech?

Previous research has shown that by the time of birth, the neonate brain responds specially to the native language when compared to acoustically similar non-language stimuli. In the current study, we use near-infrared spectroscopy to ask how prenatal language experience might shape the brain response to language in newborn infants. To do so, we examine the neural response of neonates when listening to familiar versus unfamiliar language, as well as to non language stimuli. Twenty monolingual English-exposed neonates aged 0-3 days were tested. Each infant heard low-pass filtered sentences of forward English (familiar language), forward Tagalog (unfamiliar language), and backward English and Tagalog (non-language). During exposure, neural activation was measured across 12 channels on each hemisphere. Our results indicate a bilateral effect of language familiarity on neonates' brain response to language. Differential brain activation was seen when neonates listened to forward Tagalog (unfamiliar language) as compared to other types of language stimuli. We interpret these results as evidence that the prenatal experience with the native language gained in utero influences how the newborn brain responds to language across brain regions sensitive to speech processing.
Detection and Classification of Apple Fruit Diseases Using Complete Local Binary Patterns

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of apple fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.
A single layer dual linearly polarized microstrip patch antenna array for automotive applications in the 77 GHz band student submission

A dual linearly polarized antenna array for automotive applications in the 77 GHz band is presented. The array is composed of 10 single microstrip patch radiators excited by a sophisticated series circuit feed network. The realized amplitude taper and the carefully adjusted excitation phases lead to a low sidelobe level (SLL) and boresight radiation in broadside direction. The ports of the antenna array are precisely matched to 50 Q over the complete frequency band from 76 to 79 GHz. With an extremely low cross-talk of below — 35 dB, purely linearly polarized waves are radiated in both channels. The radiation behavior is measured and shows a strong main beam in broadside direction, low sidelobes, and low cross polarized components.
Paraphrasing: Generating Parallel Programs Using Refactoring

Refactoring is the process of changing the structure of a program without changing its behaviour. Refactoring has so far only really been deployed effectively for sequential programs. However, with the increased availability of multicore (and, soon, manycore) systems, refactoring can play an important role in helping both expert and non-expert parallel programmers structure and implement their parallel programs. This paper describes the design of a new refactoring tool that is aimed at increasing the programmability of parallel systems. To motivate our design, we refactor a number of examples in C, C++ and Erlang into good parallel implementations, using a set of formal pattern rewrite rules.
A Smart Cloud Robotic System Based on Cloud Computing Services

In this paper, we present a smart service robotic system based on cloud computing services. The design and implementation of infrastructure, computation components and communication components are introduced. The proposed system can alleviate the complex computation and storage load of robots to cloud and provide various services to the robots. The computation components can dynamically allocate resources to the robots. The communication components allow easy access of the robots and provide flexible resource management. Furthermore, we modeled the task-scheduling problem and proposed a max-heaps algorithm. The simulation results demonstrate that the proposed algorithm minimized the overall task costs.
A Fair Protocol for Data Trading Based on Bitcoin Transactions

On-line commercial transactions involve an inherent mistrust between participant parties since, sometimes, no previous relation exists between them. Such mistrust may be a deadlock point in a trade transaction where the buyer does not want to perform the payment until the seller sends the good and the seller does not want to do so until the buyer pays for the purchase. In this paper we present a fair protocol for data trading where the commercial deal, in terms of delivering the data and performing the payment, is atomic since the seller cannot redeem the payment unless the buyer obtains the data and the buyer cannot obtain the data without performing the payment. The protocol is based on Bitcoin scripting language and the fairness of the protocol can be probabilistically enforced.
Map-reduce based parallel support vector machine for risk analysis

Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier in machine learning field. Hence a new technique comes into picture which performs parallel algorithm in a efficient way to work data having large scale called as PSVM. In this paper we are going to discuss a PSVM model for risk analysis which is based on map-reduce, and can easily handle a huge amount of data in a distributed manner.
A T-Coil-Enhanced 8.5 Gb/s High-Swing SST Transmitter in 65 nm Bulk CMOS With $≪ -$16 dB Return Loss Over 10 GHz Bandwidth

A source-series-terminated (SST) transmitter in a 65 nm bulk CMOS technology is presented. The circuit exhibits an eye height greater than 1.0 V for data rates of up to 8.5 Gb/s. A thin-oxide pre-driver stage running at 1.0 V drives 22 parallel connected thick-oxide SST output stages operated at 1.5 V that feature a 5-bit 2-tap FIR filter whose adaptation is independent of the impedance tuning. To achieve a return loss of <-16 dB up to 10 GHz a 40 mum times 40 mum T-coil complements the transmitter output. This half-bit-rate clock SST transmitter has a duty-cycle restoration capability of 5x, and the common-mode voltage noise is below 10 mV rms for high-, mid- and low-level terminations. The chip consumes 96 mW at 8.5 Gb/s and occupies 180 mum times 360 mum. In addition to the transmitter design, guidelines for the T-coil design are presented.