nils-herrmann/clean_scirepeval huggingface dataset:
Web Application Migration with Closure Reconstruction

Due to its high portability and simplicity, web application (app) based on HTML/JavaScript/CSS has been widely used for various smart-device platforms. To take advantage of its wide platform pool, a new idea called app migration has been proposed for the web platform. Web app migration is a framework to serialize a web app running on a device and restore it in another device to continue its execution. In JavaScript semantics, one of the language features that does not allow easy app migration is a closure. A JavaScript function can access variables defined in its outer function even if the execution of the outer function is terminated. It is allowed because the inner function is created as a closure such that it contains the outer function’s environment. This feature is widely used in web app development because it is the most common way to implement data encapsulation in web programming. Closures are not easy to serialize because environments can be shared by a number of closures and environments can be created in a nested way. In this paper, we propose a novel approach to fully serialize closures. We created mechanisms to extract information from a closure’s environment through the JavaScript engine and to serialize the information in a proper order so that the original relationship between closures and environments can be restored properly. We implemented our mechanism on the WebKit browser and successfully migrated Octane benchmarks and seven real web apps which heavily exploit closures. We also show that our mechanism works correctly even for some extreme, closure-heavy cases.
Progressive muscle relaxation reduces migraine frequency and normalizes amplitudes of contingent negative variation (CNV)

Central information processing, visible in evoked potentials like the contingent negative variation (CNV) is altered in migraine patients who exhibit higher CNV amplitudes and a reduced habituation. Both characteristics were shown to be normalized under different prophylactic migraine treatment options whereas Progressive Muscle Relaxation (PMR) has not yet been examined. We investigated the effect of PMR on clinical course and CNV in migraineurs in a quasi-randomized, controlled trial. Thirty-five migraine patients and 46 healthy controls were examined. Sixteen migraineurs and 21 healthy participants conducted a 6-week PMR-training with CNV-measures before and after as well as three months after PMR-training completion. The remaining participants served as controls. The clinical course was analyzed with two-way analyses of variance (ANOVA) with repeated measures. Pre-treatment CNV differences between migraine patients and healthy controls were examined with t-tests for independent measures. The course of the CNV-parameters was examined with three-way ANOVAs with repeated measures. After PMR-training, migraine patients showed a significant reduction of migraine frequency. Preliminary to the PMR-training, migraine patients exhibited higher amplitudes in the early component of the CNV (iCNV) and the overall CNV (oCNV) than healthy controls, but no differences regarding habituation. After completion of the PMR-training, migraineurs showed a normalization of the iCNV amplitude, but neither of the oCNV nor of the habituation coefficient. The results confirm clinical efficacy of PMR for migraine prophylaxis. The pre-treatment measure confirms altered cortical information processing in migraine patients. Regarding the changes in the iCNV after PMR-training, central nervous mechanisms of the PMR-effect are supposed which may be mediated by the serotonin metabolism.
Learning a robust Tonnetz-space transform for automatic chord recognition

Temporal pitch class profiles - commonly referred to as a chromagrams - are the de facto standard signal representation for content-based methods of musical harmonic analysis, despite exhibiting a set of practical difficulties. Here, we present a novel, data-driven approach to learning a robust function that projects audio data into Tonnetz-space, a geometric representation of equal-tempered pitch intervals grounded in music theory. We apply this representation to automatic chord recognition and show that our approach out-performs the classification accuracy of previous chroma representations, while providing a mid-level feature space that circumvents challenges inherent to chroma.
Integrating shape grammars into a generative system for Zhuang ethnic embroidery design exploration

The integration of shape grammars within a generative system is thought to have good potential to support design applications in the art and cultural domains. Based on a shape decomposing method, two-level generative shape grammars are developed within the application of Zhuang ethnic embroidery design. In our approach, free B-spline curves are used to derive initial design concepts together with a new interpolation algorithm. In order to cope with the potentially huge solution space, a novel aesthetic evaluation model and a design exploration algorithm are developed for designers to find their favorite design solutions based on their subjective assessments. A system is introduced to demonstrate this research with an example of Zhuang ethnic embroidery design.
Business Intelligence from Social Media: A Study from the VAST Box Office Challenge

With over 16 million tweets per hour, 600 new blog posts per minute, and 400 million active users on Facebook, businesses have begun searching for ways to turn real-time consumer-based posts into actionable intelligence. The goal is to extract information from this noisy, unstructured data and use it for trend analysis and prediction. Current practices support the idea that visual analytics (VA) can help enable the effective analysis of such data. However, empirical evidence demonstrating the effectiveness of a VA solution is still lacking. A proposed VA toolkit extracts data from Bitly and Twitter to predict movie revenue and ratings.  Office Challenge demonstrate the benefit of an interactive environment for predictive analysis, compared to a purely statistical modeling approach. The VA approach used by the toolkit is generalizable to other domains involving social media data, such as sales forecasting and advertisement analysis.
Why Knowledge Management Systems Fail ? Enablers and Constraints of Knowledge Management in Human Enterprises

Drawing upon lessons learned from the biggest failure of knowledge management in recent world history and the debacle of the 'new economy' enterprises, this chapter explains why knowledge management systems (KMS) fail and how risk of such failures may be minimized. The key thesis is that enablers of KMS designed for the 'knowledge factory' engineering paradigm often unravel and become constraints in adapting and evolving such systems for business environments characterized by high uncertainty and radical discontinuous change. Design of KMS should ensure that adaptation and innovation of business performance outcomes occurs in alignment with changing dynamics of the business environment. Simultaneously, conceiving multiple future trajectories of the information technology and human inputs embedded in the KMS can diminish the risk of rapid obsolescence of such systems. Envisioning business models not only in terms of knowledge harvesting processes for seeking optimization and efficiencies, but in combination with ongoing knowledge creation processes would ensure that organizations not only succeed in doing the thing right in the short term but also in doing the right thing in the long term. Embedding both these aspects in enterprise business models as simultaneous and parallel sets of knowledge processes instead of treating them in isolation would facilitate ongoing innovation of business value propositions and customer value propositions.
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration

Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.
BlinDar: An invisible eye for the blind people making life easy for the blind with Internet of Things (IoT)

Blindness is a condition in which an individual loses the ocular perception. Mobility and self-reliability for the visually impaired and blind people has always been a problem. In this paper a smart Electronic Traveling Aid (ETA) called BlinDar has been proposed. This smart guiding ETA ameliorates the life of blind as it is well equipped with Internet of Things (IoT) and is meant to aid the visually impaired and blind to walk without constraint in close as well as open environments. BlinDar is a highly efficient, reliable, fast responding, light weight, low power consuming and cost effective device for the blind. Ultrasonic sensors have been used to detect the obstacle and potholes within a range of 2m. GPS and ESP8266 Wi-Fi module has been used for sharing the location with the cloud. MQ2 gas sensor is used for detecting fire in path and a RF Tx/Rx module for finding the stick when it is misplaced. Arduino Mega2560 is the microcontroller used, which has 54 digital I/O pins which makes the interfacing of components easy.
Interpolation in Time Series : An Introductive Overview of Existing Methods , Their Performance Criteria and Uncertainty Assessment

A thorough review has been performed on interpolation methods to fill gaps in time-series, efficiency criteria, and uncertainty quantifications. On one hand, there are numerous available methods: interpolation, regression, autoregressive, machine learning methods, etc. On the other hand, there are many methods and criteria to estimate efficiencies of these methods, but uncertainties on the interpolated values are rarely calculated. Furthermore, while they are estimated according to standard methods, the prediction uncertainty is not taken into account: a discussion is thus presented on the uncertainty estimation of interpolated/extrapolated data. Finally, some suggestions for further research and a new method are proposed.
A unique trend of murder-suicide in the Jamnagar region of Gujarat, India (a retrospective study of 5 years).

Jamnagar region, Gujarat state, enjoys a relatively low incidence of homicide in India. In the 5 year period from 2000 to 2004, 8 mothers committed 13 murders involving 3 male and 10 female victims and in every case it was followed by suicide of the assailant mothers. During the study the annual incidence of murder-suicide was about 1.8 cases. All the assailants were mothers and the victims were their small children in the age group of 6 months to 7 years. Five incidents took place in rural areas and three in urban areas. It was prevalent only in low socio-economic families. Methods both for killing and suicide were either burning or drowning. All the mothers were legally married and living with the family. Family and family related matters were the main motives for killing. In one case there was history of depression of the mother due to her previous miscarriage. Alcohol consumption or drug abuse was not seen even in a single case. All cases fell in the altruistic category of filicide-suicide.
Geometric Structures for Three-Dimensional Shape Representation

Different geometric structures are investigated in the context of discrete surface representation. It is shown that minimal representations (i.e., polyhedra) can be provided by a surface-based method using nearest neighbors structures or by a volume-based method using the Delaunay triangulation. Both approaches are compared with respect to various criteria, such as space requirements, computation time, constraints on the distribution of the points, facilities for further calculations, and agreement with the actual shape of the object.
Intention-aware online POMDP planning for autonomous driving in a crowd

This paper presents an intention-aware online planning approach for autonomous driving amid many pedestrians. To drive near pedestrians safely, efficiently, and smoothly, autonomous vehicles must estimate unknown pedestrian intentions and hedge against the uncertainty in intention estimates in order to choose actions that are effective and robust. A key feature of our approach is to use the partially observable Markov decision process (POMDP) for systematic, robust decision making under uncertainty. Although there are concerns about the potentially high computational complexity of POMDP planning, experiments show that our POMDP-based planner runs in near real time, at 3 Hz, on a robot golf cart in a complex, dynamic environment. This indicates that POMDP planning is improving fast in computational efficiency and becoming increasingly practical as a tool for robot planning under uncertainty.
Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns

The knowledge discovery process is supported by data files information gathered from collected data sets, which often contain errors in the form of missing values. Data imputation is the activity aimed at estimating values for missing data items. This study focuses on the development of automated data imputation models, based on artificial neural networks for monotone patterns of missing values. The present work proposes a single imputation approach relying on a multilayer perceptron whose training is conducted with different learning rules, and a multiple imputation approach based on the combination of multilayer perceptron and k-nearest neighbours. Eighteen real and simulated databases were exposed to a perturbation experiment with random generation of monotone missing data pattern. An empirical test ean/mode model ultilayer perceptron egression model was accomplished on these data sets, including both approaches (single and multiple imputations), and three classical single imputation procedures – mean/mode imputation, regression and hot-deck – were also considered. Therefore, the experiments involved five imputation methods. The results, considering different performance measures, demonstrated that, in comparison with traditional tools, both proposals improve the automation level and data quality offering a satisfactory performance.
Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with “Big Data” analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, as well as the need for real-time processing of streaming data, pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from incomplete streaming data. For low-rank matrix data, a subspace estimator is proposed based on an exponentially weighted least-squares criterion regularized with the nuclear norm. After recasting the nonseparable nuclear norm into a form amenable to online optimization, real-time algorithms with complementary strengths are developed, and their convergence is established under simplifying technical assumptions. In a stationary setting, the asymptotic estimates obtained offer the well-documented performance guarantees of the batch nuclear-norm regularized estimator. Under the same unifying framework, a novel online (adaptive) algorithm is developed to obtain multi-way decompositions of low-rank tensors with missing entries and perform imputation as a byproduct. Simulated tests with both synthetic as well as real Internet and cardiac magnetic resonance imagery (MRI) data confirm the efficacy of the proposed algorithms, and their superior performance relative to state-of-the-art alternatives.
Wechsler Adult Intelligence Scale-IV Dyads for Estimating Global Intelligence.

All possible two-subtest combinations of the core Wechsler Adult Intelligence Scale-IV (WAIS-IV) subtests were evaluated as possible viable short forms for estimating full-scale IQ (FSIQ). Validity of the dyads was evaluated relative to FSIQ in a large clinical sample (N = 482) referred for neuropsychological assessment. Sample validity measures included correlations, mean discrepancies, and levels of agreement between dyad estimates and FSIQ scores. In addition, reliability and validity coefficients were derived from WAIS-IV standardization data. The Coding + Information dyad had the strongest combination of reliability and validity data. However, several other dyads yielded comparable psychometric performance, albeit with some variability in their particular strengths. We also observed heterogeneity between validity coefficients from the clinical and standardization-based estimates for several dyads. Thus, readers are encouraged to also consider the individual psychometric attributes, their clinical or research goals, and client or sample characteristics when selecting among the dyadic short forms.
A Survey of Intrusion Detection Techniques

Intrusion detection is an alternative to the situation of the security violation.Security mechanism of the network is necessary against the threat to the system. There are two types of intruders: external intruders, who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. This paper describes a brief overview of various intrusion detection techniques such as fuzzy logic, neural network, pattern recognition methods, genetic algorithms and related techniques is presented. Among the several soft computing paradigms, fuzzy rule-based classifiers, decision trees, support vector machines, linear genetic programming is model fast and efficient intrusion detection systems. KeywordsIntroduction, intrusion detection methods, misuse detection techniques, anomaly detection techniques, genetic algorithms.
Customer churn analysis : Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry

Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer’s status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer’s partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer’s status change, or both; therefore, a customer’s status change explains the relationship between churn determinants and the probability of churn. r 2006
An aspect representation for object manipulation based on convolutional neural networks

We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.
Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets

C product reviews have proliferated online, driven by the notion that consumers’ decision to purchase or not purchase a product is based on the positive or negative information about that product they obtain from fellow consumers. Using research on information processing as a foundation, we suggest that in the context of an online community, reviewer disclosure of identity-descriptive information is used by consumers to supplement or replace product information when making purchase decisions and evaluating the helpfulness of online reviews. Using a unique data set based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, we provide evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information. Online community members rate reviews containing identity-descriptive information more positively, and the prevalence of reviewer disclosure of identity information is associated with increases in subsequent online product sales. In addition, we show that shared geographical location increases the relationship between disclosure and product sales, thus highlighting the important role of geography in electronic commerce. Taken together, our results suggest that identity-relevant information about reviewers shapes community members’ judgment of products and reviews. Implications for research on the relationship between online word-of-mouth (WOM) and sales, peer recognition and reputation systems, and conformity to online community norms are discussed.