nils-herrmann/clean_scirepeval huggingface dataset:
The Simple Economics of Open Source

There has been a recent surge of interest in open source software development, which involves developers at many different locations and organizations sharing code to develop and refine programs. To an economist, the behavior of individual programmers and commercial companies engaged in open source projects is initially startling. This paper makes a preliminary exploration of the economics of open source software. We highlight the extent to which labor economics, especially the literature on “career concerns,” can explain many of these projects’ features. Aspects of the future of open source development process, however, remain somewhat difficult to predict with “offthe-shelf” economic models. Josh Lerner Jean Triole Harvard Business School Institut D'Economie Indutrielle (IDEI) Morgan Hall, Room 395 Manufacture des Tabacs MF529 Boston, MA 02163, 21 Allées de Brienne and NBER 31000 Toulouse Cedex FRANCE jlerner@hbs.edu
Query Expansion by Mining User Logs

Queries to search engines on the Web are usually short. They do not provide sufficient evidence for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.
Person Search with Natural Language Description

Searching persons in large-scale image databases with the query of natural language description has important applications in video surveillance. Existing methods mainly focused on searching persons with image-based or attribute-based queries, which have major limitations for a practical usage. In this paper, we study the problem of person search with natural language description. Given the textual description of a person, the algorithm of the person search is required to rank all the samples in the person database then retrieve the most relevant sample corresponding to the queried description. Since there is no person dataset or benchmark with textual description available, we collect a large-scale person description dataset with detailed natural language annotations and person samples from various sources, termed as CUHK Person Description Dataset (CUHK-PEDES). A wide range of possible models and baselines have been evaluated and compared on the person search benchmark. An Recurrent Neural Network with Gated Neural Attention mechanism (GNA-RNN) is proposed to establish the state-of-the art performance on person search.
Automatic Text Categorization by Unsupervised Learning

The goal of text categorization is to classify documents into a certain number of predefined categories. The previous works in this area have used a large number of labeled training doculnents for supervised learning. One problem is that it is difficult to create the labeled training documents. While it is easy to collect the unlabeled documents, it is not so easy to manually categorize them for creating traiuing documents. In this paper, we propose an unsupervised learning method to overcome these difficulties. The proposed lnethod divides the documents into sentences, and categorizes each sentence using keyword lists of each category and sentence simihuity measure. And then, it uses the categorized sentences for refining. The proposed method shows a similar degree of performance, compared with the traditional supervised learning inethods. Therefore, this method can be used in areas where low-cost text categorization is needed. It also can be used for creating training documents.
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation

In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A functional view is taken to represent localized information on graphs, so that annotations such as part segment or keypoint are nothing but 0-1 indicator vertex functions. Compared with images that are 2D grids, shape graphs are irregular and non-isomorphic data structures. To enable the prediction of vertex functions on them by convolutional neural networks, we resort to spectral CNN method that enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strives to overcome two key challenges: how to share coefficients and conduct multi-scale analysis in different parts of the graph for a single shape, and how to share information across related but different shapes that may be represented by very different graphs. Towards these goals, we introduce a spectral parametrization of dilated convolutional kernels and a spectral transformer network. Experimentally we tested SyncSpecCNN on various tasks, including 3D shape part segmentation and keypoint prediction. State-of-the-art performance has been achieved on all benchmark datasets.
Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model

In this paper, we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the conditional mixed Markov model, is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth.
Vision-based Human Gender Recognition: A Survey

Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.
TheAdvisor: a webservice for academic recommendation

The academic community has published millions of research papers to date, and the number of new papers has been increasing with time. To discover new research, researchers typically rely on manual methods such as keyword-based search, reading proceedings of conferences, browsing publication lists of known experts, or checking the references of the papers they are interested. Existing tools for the literature search are suitable for a first-level bibliographic search. However, they do not allow complex second-level searches. In this paper, we present a web service called TheAdvisor (http://theadvisor.osu.edu) which helps the users to build a strong bibliography by extending the document set obtained after a first-level search. The service makes use of the citation graph for recommendation. It also features diversification, relevance feedback, graphical visualization, venue and reviewer recommendation. In this work, we explain the design criteria and rationale we employed to make the TheAdvisor a useful and scalable web service along with a thorough experimental evaluation.
Sokoban : Reversed Solving

This article describes a new method for attempting to solve Sokoban puzzles by means of an efficient algorithm, a task which has proven to be extremely difficult because of both the huge search tree depth and the large branching factor. We present a way of solving Sokoban puzzles that, using several heuristics, starts from the final state of a puzzle, and from there works its way back to the initial state. This method makes the timeconsuming checking for a large portion of the undesired deadlocks unnecessary, giving some interesting results.
Characterizing logging practices in open-source software

Software logging is a conventional programming practice. As its efficacy is often important for users and developers to understand what have happened in production run, yet software logging is often done in an arbitrarily manner. So far, there have been little study for understanding logging practices in real world software. This paper makes the first attempt (to the best of our knowledge) to provide quantitative characteristic study of the current log messages within four pieces of large open-source software. First, we quantitatively show that software logging is pervasive. By examining developers’ own modifications to logging code in revision history, we find that they often do not make the log messages right in their first attempts, and thus need to spend significant amount of efforts to modify the log messages as after-thoughts. Our study further provides several interesting findings on where developers spend most of their efforts in modifying the log messages, which can give insights for programmers, tool developers, and language and compiler designers to improve the current logging practice. To demonstrate the benefit of our study, we built a simple checker based on one of our findings and effectively detected 138 new problematic logging code from studied software (24 of them are already confirmed and fixed by developers).
Discovering value in academic social networks: A case study in ResearchGate

The research presented in this paper is about detecting collaborative networks inside the structure of a research social network. As case study we consider ResearchGate and SEE University academic staff. First we describe the methodology used to crawl and create an academic-academic network depending from their fields of interest. We then calculate and discuss four social network analysis centrality measures (closeness, betweenness, degree, and PageRank) for entities in this network. In addition to these metrics, we have also investigated grouping of individuals, based on automatic clustering depending from their reciprocal relationships.
Electricity Futures : Inventories and Price Relationships at Nord Pool

Electricity is a non-storable commodity for consumers, while hydropower producers may store future electricity as water in their reservoirs. Consequently, there is an asymmetry between producers’ and consumers’ possibilities of spot-futures arbitrage. Furthermore, marginal warehousing costs in hydro based electricity production are zero as long as water reservoirs are not full, jumping to the prevailing spot price in the case that dams are filled up and water is running over the edge without being utilised. In this explorative study, we analyse price relationships at the world’s largest multinational market place for electricity (Nord Pool). We find tha the futures price at Nord Pool periodically has been outside its (theoretical) arbitrage limits. Furthermore, the futures price and the basis have been biased and poor predictors of subsequent spot price levels and changes, respectively. Forecast errors have been systematic, and the futures price does not seem to incorporate available information. The findings indicate non-rational pricing behaviour. Alternatively, the results may represent circumstantial evidence of market power on the producer side.
Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views

Materialized views (MVs), stored pre-computed results, are widely used to facilitate fast queries on large datasets. When new records arrive at a high rate, it is infeasible to continuously update (maintain) MVs and a common solution is to defer maintenance by batching updates together. Between batches the MVs become increasingly stale with incorrect, missing, and superfluous rows leading to increasingly inaccurate query results. We propose Stale View Cleaning (SVC) which addresses this problem from a data cleaning perspective. In SVC, we efficiently clean a sample of rows from a stale MV, and use the clean sample to estimate aggregate query results. While approximate, the estimated query results reflect the most recent data. As sampling can be sensitive to long-tailed distributions, we further explore an outlier indexing technique to give increased accuracy when the data distributions are skewed. SVC complements existing deferred maintenance approaches by giving accurate and bounded query answers between maintenance. We evaluate our method on a generated dataset from the TPC-D benchmark and a real video distribution application. Experiments confirm our theoretical results: (1) cleaning an MV sample is more efficient than full view maintenance, (2) the estimated results are more accurate than using the stale MV, and (3) SVC is applicable for a wide variety of MVs.
Comparison of Induction and PM Synchronous Motor Drives for EV Application Including Design Examples

Three different motor drives for electric traction are compared, in terms of output power and efficiency at the same stack dimensions and inverter size. Induction motor (IM), surface-mounted permanent-magnet (PM) (SPM), and interior PM (IPM) synchronous motor drives are investigated, with reference to a common vehicle specification. The IM is penalized by the cage loss, but it is less expensive and inherently safe in case of inverter unwilled turnoff due to natural de-excitation. The SPM motor has a simple construction and shorter end connections, but it is penalized by eddy-current loss at high speed, has a very limited transient overload power, and has a high uncontrolled generator voltage. The IPM motor shows the better performance compromise, but it might be more complicated to be manufactured. Analytical relationships are first introduced and then validated on three example designs and finite element calculated, accounting for core saturation, harmonic losses, the effects of skewing, and operating temperature. The merits and limitations of the three solutions are quantified comprehensively and summarized by the calculation of the energy consumption over the standard New European Driving Cycle.
Understanding compliance with internet use policy from the perspective of rational choice theory

a r t i c l e i n f o Keywords: Internet abuses Internet use policy Rational choice Cost benefit analysis Current studies on compliance with security policies have largely ignored the impact of the perceived benefits of deviant behavior, personal norms, and organizational context. Drawing on the literature in criminology, this paper applies rational choice theory to examine how employees' intention to comply with Internet use policy is driven by cost–benefit assessments, personal norms and organizational context factors. The results indicate that employees' compliance intention is the result of competing influences of perceived benefits, formal sanctions, and security risks. Furthermore, the effect of sanction severity is found to be moderated by personal norms. In recent decades, companies have increasingly leveraged the Internet to enhance their internal and external connectivity. Accompanying the well-known benefits of the use of Internet technology in the workplace are new threats such as increased security risks and improper use. Internet abuses by employees at the workplace are a real concern to nearly all companies and include various non-work-related Internet activities such as checking personal e-mails, browsing non-work-related Web sites, chatting online, gaming, investing, shopping, or even getting involved in cybercrimes " The International Data Corp. estimated that 30% to 40% of employee Internet use isn't work related " . Eighty-seven percent of employees have used the Internet for personal purposes while at work and, of this proportion, 53% browse the Internet for personal purposes on a daily basis [44]. It is estimated that companies lose $54 billion annually as a result of lost productivity from Web surfing . These non-work-related Internet activities not only reduce the productivity of employees but also open the door to a wide spectrum of security breaches such as viruses and spywares. According to Sophos, in 2007, the Web became the primary route for the spread of malicious codes [42]. Sophos discovered one new infected webpage every 14 s, or 6000 a day in 2007. It has become critically important for companies to safeguard their Internet communications and regulate the Internet usage of their employees. An Internet use policy (IUP), an important type of security policy, is adopted by most companies to reduce Internet use for non-work-related purposes [51]. For example, Websense, Inc., an Internet access management company, surveyed 224 companies in 2001 and found that 83% of them have adopted Internet use policies [51]. Despite the wide deployment of …
Harmonic tunnelling: tracking non-stationary noises during speech

This paper presents a novel noise robust front-end algorithm and evaluates its performance on the Aurora 2 database. Most algorithms aimed at improving the performance of recognisers in background noise make an estimate of the noise spectrum that is then used to obtain an improved estimate of the spectrum of the underlying speech. In the case of stationary noises it is sufficient to take an average noise spectrum from the period before the speech utterance and/or to use a speech/non-speech detector to update this estimate using the noise sampled from any gaps in an utterance. For nonstationary noises where the noise spectrum changes faster than the duration of a typical utterance (e.g. within 0.5s) then there can be substantial differences between the estimated and actual noise spectrum for a particular frame, leading to poor performance. The algorithm presented here provides an improved estimate of the noise that can be tracked throughout the duration of the speech utterance, by making use of the harmonic structure of the voiced speech spectrum. This running estimate of the noise is obtained by sampling the noise spectrum in the gaps (or “tunnels”) between the harmonic spectral peaks. Compared to the ETSI standard MFCC front-end , the proposed algorithm delivers an average improvement in performance of 43.93% on the Aurora 2 database [2]. 1. Front-end Algorithm Overview A summary of the algorithm is shown in figure 1 and the details of the processing performed by each block described in the section 2.  Block diagram of front-end algorithm The spectrum of the signal is first obtained by taking an FFT. The peaks in this spectrum are then determined from spectral derivatives. Each of these candidate peaks are analysed to categorise them as a peak coming from either a voiced speech harmonic or noise. The noise spectrum at a peak categorised as speech is estimated by interpolation from the adjacent noise spectra in the surrounding “tunnels”. These frame based noise measurements contribute to the running average of the noise spectrum in the Mel domain. Whilst this noise estimate could be used in many alternative algorithms, in this implementation it is used for an SNR dependent spectral subtraction. The remaining processing blocks are spectral normalisation performed in the Mel domain, normalising with the long term average of the spectrum and also by the frame energy. Finally a cube root compression is performed followed by cosine transform to produce 12 cepstral coefficients and a log energy measure. 2. Front-end Algorithm Details
A Framework and Algorithm for Energy Efficient Container Consolidation in Cloud Data Centers

One of the major challenges that cloud providers face is minimizing power consumption of their data centers. To this point, majority of current research focuses on energy efficient management of resources in the Infrastructure as a Service model and through virtual machine consolidation. However, containers are increasingly gaining popularity and going to be major deployment model in cloud environment and specifically in Platform as a Service. This paper focuses on improving the energy efficiency of servers for this new deployment model by proposing a framework that consolidates containers on virtual machines. We first formally present the container consolidation problem and then we compare a number of algorithms and evaluate their performance against metrics such as energy consumption, Service Level Agreement violations, average container migrations rate, and average number of created virtual machines. Our proposed framework and algorithms can be utilized in a private cloud to minimize energy consumption, or alternatively in a public cloud to minimize the total number of hours the virtual machines leased.
Efficient and self-tuning incremental query expansion for top-k query processing

We present a novel approach for efficient and self-tuning query expansion that is embedded into a top-k query processor with candidate pruning. Traditional query expansion methods select expansion terms whose thematic similarity to the original query terms is above some specified threshold, thus generating a disjunctive query with much higher dimensionality. This poses three major problems: 1) the need for hand-tuning the expansion threshold, 2) the potential topic dilution with overly aggressive expansion, and 3) the drastically increased execution cost of a high-dimensional query. The method developed in this paper addresses all three problems by dynamically and incrementally merging the inverted lists for the potential expansion terms with the lists for the original query terms. A priority queue is used for maintaining result candidates, the pruning of candidates is based on Fagin's family of top-k algorithms, and optionally probabilistic estimators of candidate scores can be used for additional pruning. Experiments on the TREC collections for the 2004 Robust and Terabyte tracks demonstrate the increased efficiency, effectiveness, and scalability of our approach.
Design of power-efficient approximate multipliers for approximate artificial neural networks

Artificial neural networks (NN) have shown a significant promise in difficult tasks like image classification or speech recognition. Even well-optimized hardware implementations of digital NNs show significant power consumption. It is mainly due to non-uniform pipeline structures and inherent redundancy of numerous arithmetic operations that have to be performed to produce each single output vector. This paper provides a methodology for the design of well-optimized power-efficient NNs with a uniform structure suitable for hardware implementation. An error resilience analysis was performed in order to determine key constraints for the design of approximate multipliers that are employed in the resulting structure of NN. By means of a search based approximation method, approximate multipliers showing desired tradeoffs between the accuracy and implementation cost were created. Resulting approximate NNs, containing the approximate multipliers, were evaluated using standard benchmarks (MNIST dataset) and a real-world classification problem of Street-View House Numbers. Significant improvement in power efficiency was obtained in both cases with respect to regular NNs. In some cases, 91% power reduction of multiplication led to classification accuracy degradation of less than 2.80%. Moreover, the paper showed the capability of the back propagation learning algorithm to adapt with NNs containing the approximate multipliers.