nils-herrmann/clean_scirepeval huggingface dataset:
AMAP: automatically mining abbreviation expansions in programs to enhance software maintenance tools

When writing software, developers often employ abbreviations in identifier names. In fact, some abbreviations may never occur with the expanded word, or occur more often in the code. However, most existing program comprehension and search tools do little to address the problem of abbreviations, and therefore may miss meaningful pieces of code or relationships between software artifacts. In this paper, we present an automated approach to mining abbreviation expansions from source code to enhance software maintenance tools that utilize natural language information. Our scoped approach uses contextual information at the method, program, and general software level to automatically select the most appropriate expansion for a given abbreviation. We evaluated our approach on a set of 250 potential abbreviations and found that our scoped approach provides a 57% improvement in accuracy over the current state of the art.
ASN: A Dynamic Barrier-Based Approach to Confirmation of Deadlocks from Warnings for Large-Scale Multithreaded Programs

Many large-scale multithreaded programs incur deadlock bugs. Existing deadlock warning detection techniques only report warning scenarios, which may or may not be real deadlocks. Each warning should be further verified on whether it may manifest into a real deadlock. For this purpose, a number of active randomized testing schedulers have been developed to trigger them, and yet pervious experiments show that their deadlock confirmation probability can be low. This paper presents ASN, a novel barrier-based randomized scheduler that triggers real deadlocks with high probabilities. We exploit the insights that in a confirmation run, the threads involved in a real deadlock should properly acquire one or more sets of locks prior to deadlocking. ASN automatically identifies three interesting sets of such positions. It guides the threads participating in a given warning to stay at these position sets in turn. When all the threads are staying at the last position set, ASN checks whether any deadlock that matches with the given warning has been triggered. We have evaluated ASN on 15 deadlock bugs in a suite of real-world multithreaded programs. The results show that ASN either confirms more deadlocks from the benchmark suite or triggers the same deadlocks with significantly higher probabilities than existing schedulers.
Simplifying surfaces with color and texture using quadric error metrics

There are a variety of application areas in which there is a ne ed for simplifying complex polygonal surface models. These mo dels often have material properties such as colors, textures, an d surface normals. Our surface simplification algorithm, based on ite rative edge contraction and quadric error metrics, can rapidly pro duce high quality approximations of such models. We present a nat ural extension of our original error metric that can account for a wide range of vertex attributes. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—surface and object representat io s
Design and characterization of a compact rotary Series Elastic Actuator for knee assistance during overground walking

In wearable robotics applications, actuators are required to satisfy strict constraints in terms of safety and controllability. The introduction of intrinsic compliance can help to meet both these requirements. However, the high torque and power necessary for robotic systems for gait assistance requires the use of custom elements, able to guarantee high performances with a compact and lightweight design. This paper presents a rotary Series Elastic Actuator (SEA), suitable to be used in an active orthosis for knee assistance during overground walking. The system includes a commercial flat brushless DC motor, a Harmonic Drive gear and a custom-designed torsion spring. Spring design has been optimized by means of an iterative FEM simulations-based process and can be directly connected to the output shaft, thus guaranteeing high torque fidelity. With a total weight of 1.8 kg, it is possible to directly include the actuator in the frame of a wearable orthosis for knee flexion/extension assistance. The presented design allows to obtain a large-force bandwidth of 5 Hz and to regulate output impedance in a range compatible to locomotion assistance of elderly subjects with an age-related decay of motor performances.
Influence Maximization Across Partially Aligned Heterogenous Social Networks

The influence maximization problem aims at finding a subset of seed users who can maximize the spread of influence in online social networks (OSNs). Existing works mostly focus on one single homogenous network. However, in the real world, OSNs (1) are usually heterogeneous, via which users can influence each others in multiple channels; and (2) share common users, via whom information could propagate across networks. In this paper, for the first time we study the influence maximization problem in multiple partially aligned heterogenous OSNs. A new model, multi-aligned multi-relational network influence maximizer (M&M), is proposed to address this problem. M&M extracts multi-aligned multirelational networks (MMNs) from aligned heterogeneous OSNs based on a set of inter and intra network social meta paths. Besides, M&M extends traditional linear threshold (LT) model to depict the information diffusion across MMNs. In addition, M&M, which selects seed users greedily, is proved to achieve a (1 − 1 e )-approximation of the optimal solution. Extensive experiments conducted on two real-world partially aligned heterogeneous OSNs demonstrate its effectiveness.
Predicting collective sentiment dynamics from time-series social media

More and more people express their opinions on social media such as Facebook and Twitter. Predictive analysis on social media time-series allows the stake-holders to leverage this immediate, accessible and vast reachable communication channel to react and proact against the public opinion. In particular, understanding and predicting the sentiment change of the public opinions will allow business and government agencies to react against negative sentiment and design strategies such as dispelling rumors and post balanced messages to revert the public opinion. In this paper, we present a strategy of building statistical models from the social media dynamics to predict collective sentiment dynamics. We model the collective sentiment change without delving into micro analysis of individual tweets or users and their corresponding low level network structures. Experiments on large-scale Twitter data show that the model can achieve above 85% accuracy on directional sentiment prediction.
CUSTOMER LIFETIME VALUE: MARKETING MODELS AND APPLICATIONS

the topic (a) has been dedicated to extolling its use as a decisionmaking criterion; (b) has presented isolated numerical examples of its calculation/determination; and (c) has considered it as part of the general discussions of profitability and discussed its role in customer acquisition decisions and customer acquisition/retention trade-offs. There has been a dearth of general modeling of the topic. This paper presents a series of mathematical models for determination of customer lifetime value. The choice of the models is based on a systematic theoretical taxonomy and on assumptions grounded in customer behavior. In NADA I. NASR is a doctoral student in Marketing at the School addition, selected managerial applications of these general models of of Management, Boston University. customer lifetime value are offered.
Electronic business adoption by European firms: a cross-country assessment of the facilitators and inhibitors

15 May 2002  31  18 July 2003  In this study, we developed a conceptual model for studying the adoption of electronic business (e-business or EB) at the firm level, incorporating six adoption facilitators and inhibitors, based on the technology–organization– environment theoretical framework. Survey data from 3100 businesses and 7500 consumers in eight European countries were used to test the proposed adoption model. We conducted confirmatory factor analysis to assess the reliability and validity of constructs. To examine whether adoption patterns differ across different e-business environments, we divided the full sample into high EB-intensity and low EB-intensity countries. After controlling for variations of industry and country effects, the fitted logit models demonstrated four findings: (1) Technology competence, firm scope and size, consumer readiness, and competitive pressure are significant adoption drivers, while lack of trading partner readiness is a significant adoption inhibitor. (2) As EB-intensity increases, two environmental factors – consumer readiness and lack of trading partner readiness – become less important, while competitive pressure remains significant. (3) In high EB-intensity countries, e-business is no longer a phenomenon dominated by large firms; as more and more firms engage in e-business, network effect works to the advantage of small firms. (4) Firms are more cautious in adopting e-business in high EB-intensity countries – it seems to suggest that the more informed firms are less aggressive in adopting e-business, a somehow surprising result. Explanations and implications are offered.  (2003) 12, 251–268. doi:10.1057/ palgrave.ejis.3000475
A 20 Mb/s Phase Modulator Based on a 3.6 GHz Digital PLL With −36 dB EVM at 5 mW Power

This paper presents a low-power high-bit-rate phase modulator based on a digital PLL with single-bit TDC and two-point injection scheme. At high bit rates, this scheme requires a controlled oscillator with wide tuning range and becomes critically sensitive to the delay spread between the two injection paths, considerably degrading the achievable error-vector magnitude and causing significant spectral regrowth. A multi-capacitor-bank oscillator topology with an automatic background regulation of the gains of the banks and a digital adaptive filter for the delay-spread correction are introduced. The phase modulator fabricated in a 65-nm CMOS process synthesizes carriers in the 2.9-to-4.0-GHz range from a 40-MHz crystal reference and it is able to produce a phase change up to ±π with 10-bit resolution in a single reference cycle. Measured EVM at 3.6 GHz is -36 dB for a 10-Mb/s GMSK and a 20-Mb/s QPSK modulation. Power dissipation is 5 mW from a 1.2-V voltage supply, leading to a total energy consumption of 0.25 nJ/bit.
Grid-free out-of-core voxelization to sparse voxel octrees on GPU

In this paper, we present the first grid-free, out-of-core GPU voxelization method. Our method combines efficient parallel triangle voxelization on GPU with out-of-core technologies in order to allow the processing of scenes with large triangle counts at a high resolution. We directly generate the voxelized data in a sparse voxel octree (SVO) representation, without any intermediate grid structure ("grid-free"). We apply triangle preprocessing and avoid atomic operations, thus leading to an optimized balanced GPU workload and efficient parallel triangle processing. Compared to existing out-of-core CPU approaches, we manage a proper handling of voxel attributes, i.e. all triangle attributes contributing to a voxel are accessible when calculating the voxel attribute.
 We test and compare our approach to state-of-the-art methods and demonstrate its viability in terms of speed, input triangle count, resolution and output quality.
A Natural Language Database Interface Based on a Probabilistic Context Free Grammar

This paper presents a natural language interface to relational database. It introduces some classical NLDBI products and their applications and proposes the architecture of a new NLDBI system including its probabilistic context free grammar, the inside and outside probabilities which can be used to construct the parse tree, an algorithm to calculate the probabilities, and the usage of dependency structures and verb subcategorization in analyzing the parse tree. Some experiment results are given to conclude the paper.
Cash Forecasting: An Application of Artificial Neural Networks in Finance

Artificial Neural Networks are universal and highly flexible function approximators first used in the fields of cognitive science and engineering. In recent years, Neural Networks have become increasingly popular in finance for tasks such as pattern recognition, classification and time series forecasting. The ability to predict cash requirement within reasonable accuracy of actual demand provides target for supply optimization well in time. Every financial institution (large or small) faces the same daily challenge. While it would be devastating to run out of cash, it is important to keep cash at the right levels to meet customer demand. In such case, it becomes very necessary to have a forecasting system in order to get a clear picture of demand well in advance. This paper presents two neural network models for cash forecasting for a bank branch. One is daily model – taking the parameter values for a day as input to forecast cash requirement for the next day and the other is weekly model, which takes the withdrawal affecting input patterns of a week to predict cash requirement for the next week. The system performs better than other cash forecasting systems. This system can be scaled for all branches of a bank in an area by incorporating historical data from these branches.
Efficient Graphlet Counting for Large Networks

From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of graphlets to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting graphlets of size k={3,4}-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed graphlet counting algorithms leverages a number of proven combinatorial arguments for different graphlets. For each edge, we count a few graphlets, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of 300+ networks from a variety of domains, our graphlet counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of graphlets on much larger networks and newer applications as we show in the experiments. To the best of our knowledge, this paper provides the largest graphlet computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.
Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification

In many languages, sparse availability of resources causes numerous challenges for textual analysis tasks. Text classification is one of such standard tasks that is hindered due to limited availability of label information in lowresource languages. Transferring knowledge (i.e. label information) from high-resource to low-resource languages might improve text classification as compared to the other approaches like machine translation. We introduce BRAVE (Bilingual paRAgraph VEctors), a model to learn bilingual distributed representations (i.e. embeddings) of words without word alignments either from sentencealigned parallel or label-aligned non-parallel document corpora to support cross-language text classification. Empirical analysis shows that classification models trained with our bilingual embeddings outperforms other stateof-the-art systems on three different crosslanguage text classification tasks.
Coarse-to-Fine Attention Models for Document Summarization

Sequence-to-sequence models with attention have been successful for a variety of NLP problems, but their speed does not scale well for tasks with long source sequences such as document summarization. We propose a novel coarse-to-fine attention model that hierarchically reads a document, using coarse attention to select top-level chunks of text and fine attention to read the words of the chosen chunks. While the computation for training standard attention models scales linearly with source sequence length, our method scales with the number of top-level chunks and can handle much longer sequences. Empirically, we find that while coarse-tofine attention models lag behind state-ofthe-art baselines, our method achieves the desired behavior of sparsely attending to subsets of the document for generation.
The 2007 MIREX Audio Mood Classification Task: Lessons Learned

Recent music information retrieval (MIR) research pays increasing attention to music classification based on moods expressed by music pieces. The first Audio Mood Classification (AMC) evaluation task was held in the 2007 running of the Music Information Retrieval Evaluation eXchange (MIREX). This paper describes important issues in setting up the task, including dataset construction and ground-truth labeling, and analyzes human assessments on the audio dataset, as well as system performances from various angles. Interesting findings include system performance differences with regard to mood clusters and the levels of agreement amongst human judgments regarding mood labeling. Based on these analyses, we summarize experiences learned from the first community scale evaluation of the AMC task and propose recommendations for future AMC and similar evaluation tasks.
Modeling of IGBT Resistive and Inductive Turn-On Behavior

Although insulated-gate bipolar-transistor (IGBT) turn-on losses can be comparable to turn-off losses, IGBT turn-on has not been as thoroughly studied in the literature. In the present work IGBT turn on under resistive and inductive load conditions is studied in detail through experiments, finite element simulations, and circuit simulations using physics-based semiconductor models. Under resistive load conditions, it is critical to accurately model the conductivity-modulation phenomenon. Under clamped inductive load conditions at turn-on there is strong interaction between the IGBT and the freewheeling diode undergoing reverse recovery. Physics-based IGBT and diode models are used that have been proved accurate in the simulation of IGBT turn-off.
A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics

This paper presents a database containing ‘ground truth’ segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region prop-
Novel low power full adder cells in 180nm CMOS technology

This paper proposes four low power adder cells using different XOR and XNOR gate architectures. Two sets of circuit designs are presented. One implements full adders with 3 transistors (3-T) XOR and XNOR gates. The other applies Gate-Diffusion-Input (GDI) technique to full adders. Simulations are performed by using Hspice based on 180nm CMOS technology. In comparison with Static Energy Recovery Full (SERF) adder cell module, the proposed four full adder cells demonstrate their advantages, including lower power consumption, smaller area, and higher speed.
Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors.

To make sound economic decisions, the brain needs to compute several different value-related signals. These include goal values that measure the predicted reward that results from the outcome generated by each of the actions under consideration, decision values that measure the net value of taking the different actions, and prediction errors that measure deviations from individuals' previous reward expectations. We used functional magnetic resonance imaging and a novel decision-making paradigm to dissociate the neural basis of these three computations. Our results show that they are supported by different neural substrates: goal values are correlated with activity in the medial orbitofrontal cortex, decision values are correlated with activity in the central orbitofrontal cortex, and prediction errors are correlated with activity in the ventral striatum.
Iolus: A Framework for Scalable Secure Multicasting

As multicast applications are deployed for mainstream use, the need to secure multicast communications will become critical. Multicast, however, does not fit the point-to-point model of most network security protocols which were designed with unicast communications in mind. As we will show, securing multicast (or group) communications is fundamentally different from securing unicast (or paired) communications. In turn, these differences can result in scalability problems for many typical applications.In this paper, we examine and model the differences between unicast and multicast security and then propose Iolus: a novel framework for scalable secure multicasting. Protocols based on Iolus can be used to achieve a variety of security objectives and may be used either to directly secure multicast communications or to provide a separate group key management service to other "security-aware" applications. We describe the architecture and operation of Iolus in detail and also describe our experience with a protocol based on the Iolus framework.
HAND TALK ASSISTIVE SYSTEM

About nine billion people in the world are deaf and dumb. The communication between a deaf and hearing person poses to be a serious problem compared to communication between blind and normal visual people. This creates a very little room for them with communication being a fundamental aspect of human life. The blind people can talk freely by means of normal language whereas the deaf-dumb have their own manual-visual language known as sign language. Sign language is a non-verbal form of intercourse which is found amongst deaf communities in world. The languages do not have a common origin and hence difficult to interpret. The project aims to facilitate people by means of a glove based communication interpreter system. The glove is internally equipped with five flex sensors. For each specific gesture, the flex sensor produces a proportional change in resistance. The output from the sensor is analog values it is converted to digital. The processing of these hand gestures is in Arduino Duemilanove Board which is an advance version of the microcontroller. It compares the input signal with predefined voltage levels stored in memory. According to that required output displays on the LCD in the form of text & sound is produced which is stored is memory with the help of speaker. In such a way it is easy for deaf and dumb to communicate with normal people. This system can also be use for the woman security since we are sending a message to authority with the help of smart phone.