nils-herrmann/clean_scirepeval huggingface dataset:
Experiments with SVM to classify opinions in different domains

0957-4174/$ see front matter 2011  A doi:10.1016/j.eswa.2011.05.070 ⇑ Corresponding author.  212898; fax: E-mail address: msaleh@ujaen.es (M. Rushdi Saleh 1 http://www.amazon.com. 2 http://www.epinions.com. 3 http://www.imdb.com. Recently, opinion mining is receiving more attention due to the abundance of forums, blogs, e-commerce web sites, news reports and additional web sources where people tend to express their opinions. Opinion mining is the task of identifying whether the opinion expressed in a document is positive or negative about a given topic. In this paper we explore this new research area applying Support Vector Machines (SVM) for testing different domains of data sets and using several weighting schemes. We have accomplished experiments with different features on three corpora. Two of them have already been used in several works. The last one has been built from Amazon.com specifically for this paper in order to prove the feasibility of the SVM for different domains.
Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data

Data mining is the process of discovering meaningful new correlation, patterns and trends by sifting through large amounts of data, using pattern recognition technologies as well as statistical and mathematical techniques. Cluster analysis is often used as one of the major data analysis technique widely applied for many practical applications in emerging areas of data mining. Two of the most delegated, partition based clustering algorithms namely k-Means and Fuzzy C-Means are analyzed in this research work. These algorithms are implemented by means of practical approach to analyze its performance, based on their computational time. The telecommunication data is the source data for this analysis. The connection oriented broad band data is used to find the performance of the chosen algorithms. The distance (Euclidian distance) between the server locations and their connections are rearranged after processing the data. The computational complexity (execution time) of each algorithm is analyzed and the results are compared with one another. By comparing the result of this practical approach, it was found that the results obtained are more accurate, easy to understand and above all the time taken to process the data was substantially high in Fuzzy C-Means algorithm than the k-Means.
Author Profiling using Stylometric and Structural Feature Groupings

In this paper we present an approach for the task of author profiling. We propose a coherent grouping of features combined with appropriate preprocessing steps for each group. The groups we used were stylometric and structural, featuring among others, trigrams and counts of twitter specific characteristics. We address gender and age prediction as a classification task and personality prediction as a regression problem using Support Vector Machines and Support Vector Machine Regression respectively on documents created by joining each user’s tweets.
A Robust Optimization Perspective on Stochastic Programming

In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. Using a linear decision rule, we also propose a tractable approximation approach for solving a class of multistage chance-constrained stochastic linear optimization problems. An attractive feature of the framework is that we convert the original model into a second-order cone program, which is computationally tractable both in theory and in practice. We demonstrate the framework through an application of a project management problem with uncertain activity completion time.
TrustDump: Reliable Memory Acquisition on Smartphones

With the wide usage of smartphones in our daily life, new malware is emerging to compromise the mobile OS and steal the sensitive data from the mobile applications. Anti-malware tools should be continuously updated via static and dynamic malware analysis to detect and prevent the newest malware. Dynamic malware analysis depends on a reliable memory acquisition of the OS and the applications running on the smartphones. In this paper, we develop a TrustZone-based memory acquisition mechanism called TrustDump that is capable of reliably obtaining the RAM memory and CPU registers of the mobile OS even if the OS has crashed or has been compromised. The mobile OS is running in the TrustZone’s normal domain, and the memory acquisition tool is running in the TrustZone’s secure domain, which has the access privilege to the memory in the normal domain. Instead of using a hypervisor to ensure an isolation between the OS and the memory acquisition tool, we rely on ARM TrustZone to achieve a hardware-assisted isolation with a small trusted computing base (TCB) of about 450 lines of code. We build a TrustDump prototype on Freescale i.MX53 QSB. It can reliably acquire and transmit the kernel memory to a remote machine and calculate a hash value of the Linux kernel in 1.56ms.
Recurrent Topic-Transition GAN for Visual Paragraph Generation

A natural image usually conveys rich semantic content and can be viewed from different angles. Existing image description methods are largely restricted by small sets of biased visual paragraph annotations, and fail to cover rich underlying semantics. In this paper, we investigate a semi-supervised paragraph generative framework that is able to synthesize diverse and semantically coherent paragraph descriptions by reasoning over local semantic regions and exploiting linguistic knowledge. The proposed Recurrent Topic-Transition Generative Adversarial Network (RTT-GAN) builds an adversarial framework between a structured paragraph generator and multi-level paragraph discriminators. The paragraph generator generates sentences recurrently by incorporating region-based visual and language attention mechanisms at each step. The quality of generated paragraph sentences is assessed by multi-level adversarial discriminators from two aspects, namely, plausibility at sentence level and topic-transition coherence at paragraph level. The joint adversarial training of RTT-GAN drives the model to generate realistic paragraphs with smooth logical transition between sentence topics. Extensive quantitative experiments on image and video paragraph datasets demonstrate the effectiveness of our RTT-GAN in both supervised and semi-supervised settings. Qualitative results on telling diverse stories for an image verify the interpretability of RTT-GAN.
Tutorial on advanced design patterns in event processing

We introduce a reference architecture for event processing, as defined by the EPTS reference architecture (RA) working group. An event processing reference architecture allows users to quickly create event processing solutions that adhere to known stakeholder requirements and architectural qualities. The focus in this paper is on the EPTS reference architecture description of the functional view which is supported by a mapping of its functions into design patterns as means to derive and prove these architectural descriptions to be usable solutions for recurring best practice implementations in common CEP languages.
Mining social media with social theories: a survey

The increasing popularity of social media encourages more and more users to participate in various online activities and produces data in an unprecedented rate. Social media data is big, linked, noisy, highly unstructured and in- complete, and differs from data in traditional data mining, which cultivates a new research field - social media mining. Social theories from social sciences are helpful to explain social phenomena. The scale and properties of social media data are very different from these of data social sciences use to develop social theories. As a new type of social data, social media data has a fundamental question - can we apply social theories to social media data? Recent advances in computer science provide necessary computational tools and techniques for us to verify social theories on large-scale social media data. Social theories have been applied to mining social media. In this article, we review some key social theories in mining social media, their verification approaches, interesting findings, and state-of-the-art algorithms. We also discuss some future directions in this active area of mining social media with social theories.
ROCR: An Energy-Efficient Dynamic Wall-Climbing Robot

We present a novel bioinspired dynamic climbing robot, with a recursive name: ROCR is an oscillating climbing robot. ROCR, pronounced “Rocker,” is a pendular, two-link, serial-chain robot that utilizes alternating handholds and an actuated tail to propel itself upward in a climbing style based on observation of human climbers and brachiating gibbons. ROCR's bioinspired pendular climbing strategy is simple and efficient. In fact, to our knowledge, ROCR is also the first climbing robot that is designed for efficiency. ROCR is a lightweight, flexible, and self-contained robot. This robot is intended for autonomous surveillance and inspection on sheer vertical surfaces. Potential locomotion gait strategies were investigated in simulation using Working Model 2D, and were evaluated on a basis of climbing rate, energy efficiency, and whether stable open-loop climbing was achieved. We identified that the most effective climbing resulted from sinusoidal tail motions. The addition of a body stabilizer reduced the robot's out-of-plane motion at higher frequencies and promoted more reliable gripper attachment. Experimental measurements of the robot showed climbing efficiencies of over 20% and a specific resistance of 5.0, while consuming 27 J/m at a maximum climbing speed of 15.7 cm/s (0.34 body lengths/s) - setting a first benchmark for efficiency of climbing robots. Future work will include further design optimization, integration of more complex gripping mechanisms, and investigating more complex control strategies.
Real-Time High-Resolution Sparse Voxelization with Application to Image-Based Modeling

We present a system for real-time, high-resolution, sparse voxelization of an image-based surface model. Our approach consists of a coarse-to-fine voxel representation and a collection of parallel processing steps. Voxels are stored as a list of unsigned integer triples. An oracle kernel decides, for each voxel in parallel, whether to keep or cull its voxel from the list based on an image consistency criterion of its projection across cameras. After a prefix sum scan, kept voxels are subdivided and the process repeats until projected voxels are pixel size. These voxels are drawn to a render target and shaded as a weighted combination of their projections into a set of calibrated RGB images. We apply this technique to the problem of smooth visual hull reconstruction of human subjects based on a set of live image streams. We demonstrate that human upper body shapes can be reconstructed to giga voxel resolution at greater than 30 fps on modern graphics hardware.
Object classification in radar using ensemble methods

To enable autonomous driving, a semantic knowledge of the environment is unavoidable. We therefore introduce a multiclass classifier to determine the classes of an object relying solely on radar data. This is a challenging problem as objects of the same category have often a diverse appearance in radar data. As classification methods a random forest classifier and a deep convolutional neural network are evaluated. To get good results despite the limited training data available, we introduce a hybrid approach using an ensemble consisting of the two classifiers. Further we show that the accuracy can be improved significantly by allowing a lower detection rate.
Robust human action recognition via long short-term memory

The long short-term memory (LSTM) neural network utilizes specialized modulation mechanisms to store information for extended periods of time. It is thus potentially well-suited for complex visual processing, where the current video frame must be considered in the context of past frames. Recent studies have indeed shown that LSTM can effectively recognize and classify human actions (e.g., running, hand waving) in video data; however, these results were achieved under somewhat restricted settings. In this effort, we seek to demonstrate that LSTM's performance remains robust even as experimental conditions deteriorate. Specifically, we show that classification accuracy exhibits graceful degradation when the LSTM network is faced with (a) lower quantities of available training data, (b) tighter deadlines for decision making (i.e., shorter available input data sequences) and (c) poorer video quality (resulting from noise, dropped frames or reduced resolution). We also clearly demonstrate the benefits of memory for video processing, particularly, under high noise or frame drop rates. Our study is thus an initial step towards demonstrating LSTM's potential for robust action recognition in real-world scenarios.
Achieving "Massive MIMO" Spectral Efficiency with a Not-so-Large Number of Antennas

Time-Division Duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas from a finite number of orthogonal uplink pilot signals, by exploiting channel reciprocity. Based on this observation, a recently proposed "Massive MIMO" scheme was shown to achieve unprecedented spectral efficiency in realistic conditions of distance-dependent pathloss and channel coherence time and bandwidth. The main focus and contribution of this paper is an improved Network-MIMO TDD architecture achieving spectral efficiencies comparable with "Massive MIMO", with one order of magnitude fewer antennas per active user per cell (roughly, from 500 to 50 antennas). The proposed architecture is based on a family of Network-MIMO schemes defined by small clusters of cooperating base stations, zero-forcing multiuser MIMO precoding with suitable inter-cluster interference mitigation constraints, uplink pilot signals allocation and frequency reuse across cells. The key idea consists of partitioning the users into equivalence classes, optimizing the Network-MIMO scheme for each equivalence class, and letting a scheduler allocate the channel time-frequency dimensions to the different classes in order to maximize a suitable network utility function that captures a desired notion of fairness. This results in a mixed-mode Network-MIMO architecture, where different schemes, each of which is optimized for the served user equivalence class, are multiplexed in time-frequency. In order to carry out the performance analysis and the optimization of the proposed architecture in a systematic and computationally efficient way, we consider the large-system regime where the number of users, the number of antennas, and the channel coherence block length go to infinity with fixed ratios.
Color-based road detection and its evaluation on the KITTI road benchmark

Road detection is one of the key issues of scene understanding for Advanced Driving Assistance Systems (ADAS). Recent approaches has addressed this issue through the use of different kinds of sensors, features and algorithms. KITTI-ROAD benchmark has provided an open-access dataset and standard evaluation mean for road area detection. In this paper, we propose an improved road detection algorithm that provides a pixel-level confidence map. The proposed approach is inspired from our former work based on road feature extraction using illuminant intrinsic image and plane extraction from v-disparity map segmentation. In the former research, detection results of road area are represented by binary map. The novelty of this improved algorithm is to introduce likelihood theory to build a confidence map of road detection. Such a strategy copes better with ambiguous environments, compared to a simple binary map. Evaluations and comparisons of both, binary map and confidence map, have been done using the KITTI-ROAD benchmark.
Bayesian measures of model complexity and fit

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Developing Predictive Models Using Electronic Medical Records: Challenges and Pitfalls

While Electronic Medical Records (EMR) contain detailed records of the patient-clinician encounter - vital signs, laboratory tests, symptoms, caregivers' notes, interventions prescribed and outcomes - developing predictive models from this data is not straightforward. These data contain systematic biases that violate assumptions made by off-the-shelf machine learning algorithms, commonly used in the literature to train predictive models. In this paper, we discuss key issues and subtle pitfalls specific to building predictive models from EMR. We highlight the importance of carefully considering both the special characteristics of EMR as well as the intended clinical use of the predictive model and show that failure to do so could lead to developing models that are less useful in practice. Finally, we describe approaches for training and evaluating models on EMR using early prediction of septic shock as our example application.
Automarking: using computational linguistics to score short‚ free−text responses

Many of UCLES' academic examinations make extensive use of questions that require candidates to write one or two sentences. For example, questions often ask candidates to state, to suggest, to describe, or to explain. These questions are a highly regarded and integral part of the examinations, and are also used extensively by teachers. A system that could partially or wholly automate valid marking of short, free text answers would therefore be valuable, but until  The UCLES Group provides assessment services worldwide through three main business units. • Cambridge-ESOL (English for speakers of other languages) provides examinations in English as a foreign language and qualifications for language teachers throughout the world. • CIE (Cambridge International Examinations) provides international school examinations and international vocational awards. • OCR (Oxford, Cambridge and RSA Examinations) provides general and vocational qualifications to schools, colleges, employers, and training providers in the UK. For more information please visit http://www.ucles.org.uk
Analysis of the root-cepstrum for acoustic modeling and fast decoding in speech recognition

Root-cepstral analysis has been proposed previously for speech recognition in car environments . In this paper, we focus on an alternative aspect of Root-cepstrum as it applies to discriminative acoustic modeling and fast speech recognizer decoding. We compare Root-cepstrum to Mel-Frequency cepstrum Coefficients (MFCC) in terms of their noise immunity during modeling and decoding speed. Our experiments use the SPINE [5] corpus which is composed of clean and noisy data with a 5K vocabulary size. Experiments were performed that allow pair-wise comparisons of acoustic models across different feature sets and acoustic units. We observed that for 84% of the phonemes, the average distance to all other acoustic units is increased in the Root-cepstrum domain compared to MFCC resulting in a sharp acoustic model set. Therefore, the ambiguity in the Root-cepstrum space is reduced. Large vocabulary noisy speech recognition experiments showed a 27.5% reduction in real–time processing factor (RTF) compared to MFCC features while improving overall recognition accuracy.
An Efficient Density Based Incremental Clustering Algorithm in Data Warehousing Environment

Data Warehouses are a good source of data for downstream data mining applications. New data arrives in data warehouses during the periodic refresh cycles. Appending of data on existing data requires that all patterns discovered earlier using various data mining algorithms are updated with each refresh. In this paper, we present an incremental density based clustering algorithm. Incremental DBSCAN is an existing incremental algorithm in which data can be added/deleted to/from existing clusters, one point at a time. Our algorithm is capable of adding points in bulk to existing set of clusters. In this new algorithm, the data points to be added are first clustered using the DBSCAN algorithm and then these new clusters are merged with existing clusters, to come up with the modified set of clusters. That is, we add the clusters incrementally rather than adding points incrementally. It is found that the proposed incremental clustering algorithm produces the same clusters as obtained by Incremental DBSCAN. We have used R*-trees as the data structure to hold the multidimensional data that we need to cluster. One of the major advantages of the proposed approach is that it allows us to see the clustering patterns of the new data along with the existing clustering patterns. Moreover, we can see the merged clusters as well. The proposed algorithm is capable of considerable savings, in terms of region queries performed, as compared to incremental DBSCAN. Results are presented to support the claim.
C-language based PV array simulation technique considering effects of partial shading

The performance of photovoltaic (PV) array is strongly dependent on the operating conditions especially the solar irradiation. The shading on PV array by the passing clouds or neighboring buildings causes not only power losses, but also non-linearity of V-I characteristics of PV array. Under partially shaded conditions, the characteristics have more non-linearity with multiple local maxima. In spite of several researchers have studied the PV characteristics under the partial shading condition, it is difficult to simulate the partial shading condition on the simulation tool for power electronics. The C-language based PV array simulation strategy proposed in here is to simulate the effects of partial shading condition for developing more advanced maximum power point tracking algorithms or power conversion systems. Proposed modeling strategy is easily connected to simulation tools. In this paper, the modeling of photovoltaic array is based on the numerical analysis, and the verification is performed using PSIM.
On Graph-Based Name Disambiguation

Name ambiguity stems from the fact that many people or objects share identical names in the real world. Such name ambiguity decreases the performance of document retrieval, Web search, information integration, and may cause confusion in other applications. Due to the same name spellings and lack of information, it is a nontrivial task to distinguish them accurately. In this article, we focus on investigating the problem in digital libraries to distinguish publications written by authors with identical names. We present an effective framework named GHOST (abbreviation for GrapHical framewOrk for name diSambiguaTion), to solve the problem systematically. We devise a novel similarity metric, and utilize only one type of attribute (i.e., coauthorship) in GHOST. Given the similarity matrix, intermediate results are grouped into clusters with a recently introduced powerful clustering algorithm called Affinity Propagation. In addition, as a complementary technique, user feedback can be used to enhance the performance. We evaluated the framework on the real DBLP and PubMed datasets, and the experimental results show that GHOST can achieve both high precision and recall.