nils-herrmann/clean_scirepeval huggingface dataset:
ProRace: Practical Data Race Detection for Production Use

This paper presents ProRace, a dynamic data race detector practical for production runs. It is lightweight, but still offers high race detection capability. To track memory accesses, ProRace leverages instruction sampling using the performance monitoring unit (PMU) in commodity processors. Our PMU driver enables ProRace to sample more memory accesses at a lower cost compared to the state-of-the-art Linux driver. Moreover, ProRace uses PMU-provided execution contexts including register states and program path, and reconstructs unsampled memory accesses offline. This technique allows \ProRace to overcome inherent limitations of sampling and improve the detection coverage by performing data race detection on the trace with not only sampled but also reconstructed memory accesses. Experiments using racy production software including apache and mysql shows that, with a reasonable offline cost, ProRace incurs only 2.6% overhead at runtime with 27.5% detection probability with a sampling period of 10,000.
Adversarial Attack? Don't Panic

Deep learning is playing a more and more important role in our daily life and scientific research such as autonomous systems, intelligent life and data mining. However, numerous studies have showed that deep learning with superior performance on many tasks may suffer from subtle perturbations constructed by attacker purposely, called adversarial perturbations, which are imperceptible to human observers but completely effect deep neural network models. The emergence of adversarial attacks has led to questions about neural networks. Therefore, machine learning security and privacy are becoming an increasingly active research area. In this paper, we summarize the prevalent methods for the generating adversarial attacks according to three groups. We elaborated on their ideas and principles of generation. We further analyze the common limitations of these methods and implement statistical experiments of the last layer output on CleverHans to reveal that the detection of adversarial samples is not as difficult as it seems and can be achieved in some relatively simple manners.
Darwin the detective : Observable facial muscle contractions reveal emotional high-stakes lies ☆

Deception—a fundamental aspect of human communication—often is accompanied by the simulation of unfelt emotions or the concealment of genuine emotions to correspond to the false message. We investigated the consequences of extremely high-stakes emotional deception on the engagement of particular facial muscles, posited by Darwin [Darwin, C. (1872/2005). The expression of the emotions in man and animals. In J. D. Watson (Ed.), Darwin: The indelible stamp (pp. 1066–1257) Philadelphia: Running Press] to reveal the false face. The videotaped facial actions of a sample of individuals (N=52) emotionally pleading to the public for the return of a missing relative—half of whom eventually were convicted of murdering that person—were coded frame by frame (30 frames/s for a total of 23,622 frames). Findings support the view that emotional “leakage,” particularly via those facial muscles under less cortical control, is a byproduct of the overextended cognitive resources available to convey elaborate lies. Specifically, the “grief” muscles (corrugator supercilii, depressor anguli oris) were more often contracted in the faces of genuine than deceptive pleaders. Subtle contraction of the zygomatic major (masking smiles) and full contraction of the frontalis (failed attempts to appear sad) muscles were more commonly identified in the faces of deceptive pleaders. Thus, while interpersonal deception often is highly successful, signs of covert emotional states are communicated clearly to the informed observer.
Automating Generation of Low Precision Deep Learning Operators

State of the art deep learning models have made steady progress in the fields of computer vision and natural language processing, at the expense of growing model sizes and computational complexity. Deploying these models on low power and mobile devices poses a challenge due to their limited compute capabilities and strict energy budgets. One solution that has generated significant research interest is deploying highly quantized models that operate on low precision inputs and weights less than eight bits, trading off accuracy for performance. These models have a significantly reduced memory footprint (up to 32x reduction) and can replace multiply-accumulates with bitwise operations during compute intensive convolution and fully connected layers. Most deep learning frameworks rely on highly engineered linear algebra libraries such as ATLAS or Intel’s MKL to implement efficient deep learning operators. To date, none of the popular deep learning directly support low precision operators, partly due to a lack of optimized low precision libraries. In this paper we introduce a work flow to quickly generate high performance low precision deep learning operators for arbitrary precision that target multiple CPU architectures and include optimizations such as memory tiling and vectorization. We present an extensive case study on low power ARM Cortex-A53 CPU, and show how we can generate 1-bit, 2-bit convolutions with speedups up to 16x over an optimized 16-bit integer baseline and 2.3x better than handwritten implementations.
Demand forecasting with user-generated online information

Recently, there has been substantial research on augmenting aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies report increased accuracy, many exhibit design weaknesses including lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, which may change, as initially, consumers may search for pre-purchase information, but later for after-sales support. In this study, we first review the relevant literature and then attempt to support the key findings using two forecasting case studies. Our findings are in stark contrast to the literature, and we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better than when online information is included. Our research underlines the need for thorough forecast evaluation and argues that online platform data may be of limited use for supporting operational decisions.
Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations

This paper presents a monocular and purely vision based pedestrian trajectory tracking and prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, a lot of effort has been put into pedestrian detection over the last decade, and several pedestrian detection systems are indeed showing impressive results. Considerably less effort has been put into processing the detections further. We present a tracking system for pedestrians, which based on detection bounding boxes tracks pedestrians and is able to predict their positions in the near future. The tracking system is combined with a module which, based on the car's GPS position acquires a map and uses the road information in the map to know where the car can drive. Then the system warns the driver about pedestrians at risk, by combining the information about hazardous areas for pedestrians with a probabilistic position prediction for all observed pedestrians.
Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.



Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.



Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.



We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.
PID controller design for cruise control system using genetic algorithm

In this paper, the design of a Proportional-Integral-Derivative (PID) controller for the cruise control system has been proposed. The cruise control system, which is a highly nonlinear, has been linearized around the equilibrium point. The controller has been designed for the linearized model, by taking the dominant pole concept in the closed loop characteristic equation. The PID controller parameters, i.e. proportional, integral and derivative parameters have been tuned using Genetic Algorithm (GA). In this study, the performance of the controller has been compared with that of the conventional PID, state space and Fuzzy logic based controller. The simulation output reveals the superiority of the proposed controller in terms of maximum overshoot, peak time, rise time, settling time and steady state error. The sensitivity and complementary sensitivity analysis show the robust behaviour of the system with output disturbance and high-frequency noise rejection qualities. As a scope of further research, fractional order and 2-dof PID controller will be designed for this cruise control system and the performance will be compared with this design.
Classifying the findings in qualitative studies.

A key task in conducting research integration studies is determining what features to account for in the research reports eligible for inclusion. In the course of a methodological project, the authors found a remarkable uniformity in the way findings were produced and presented, no matter what the stated or implied frame of reference or method. They describe a typology of findings, which they developed to bypass the discrepancy between method claims and the actual use of methods, and efforts to ascertain its utility and reliability. The authors propose that the findings in journal reports of qualitative studies in the health domain can be classified on a continuum of data transformation as no finding, topical survey, thematic survey, conceptual/thematic description, or interpretive explanation.
CRITICAL DISCOURSE ANALYSIS

■ Abstract This paper provides a survey of critical discourse analysis (CDA), a recent school of discourse analysis that concerns itself with relations of power and inequality in language. CDA explicitly intends to incorporate social-theoretical insights into discourse analysis and advocates social commitment and interventionism in research. The main programmatic features and domains of enquiry of CDA are discussed, with emphasis on attempts toward theory formation by one of CDA’s most prominent scholars, Norman Fairclough. Another section reviews the genesis and disciplinary growth of CDA, mentions some of the recent critical reactions to it, and situates it within the wider picture of a new critical paradigm developing in a number of language-oriented (sub) disciplines. In this critical paradigm, topics such as ideology, inequality, and power figure prominently, and many scholars productively attempt to incorporate social-theoretical insights into the study of language.
Depression Prevalence and Exposure to Organophosphate Esters in Aircraft Maintenance Workers.

Previous studies found that aircraft maintenance workers may be exposed to organophosphates in hydraulic fluid and engine oil. Studies have also illustrated a link between long-term low-level organophosphate pesticide exposure and depression.



A questionnaire containing the Patient Health Questionnaire 8 depression screener was e-mailed to 52,080 aircraft maintenance workers (with N = 4801 complete responses) in a cross-sectional study to determine prevalence and severity of depression and descriptions of their occupational exposures.



There was no significant difference between reported depression prevalence and severity in similar exposure groups in which aircraft maintenance workers were exposed or may have been exposed to organophosphate esters compared to similar exposure groups in which they were not exposed. However, a dichotomous measure of the prevalence of depression was significantly associated with self-reported exposure levels from low (OR: 1.21) to moderate (OR: 1.68) to high exposure (OR: 2.70) and with each exposure route including contact (OR: 1.68), inhalation (OR: 2.52), and ingestion (OR: 2.55). A self-reported four-level measure of depression severity was also associated with a self-reported four-level measure of exposure.



Based on self-reported exposures and outcomes, an association is observed between organophosphate exposure and depression; however, we cannot assume that the associations we observed are causal because some workers may have been more likely to report exposure to organophosphate esters and also more likely to report depression. Future studies should consider using a larger sample size, better methods for characterizing crew chief exposures, and bioassays to measure dose rather than exposure. Hardos JE, Whitehead LW, Han I, Ott DK, Waller DK. Depression prevalence and exposure to organophosphate esters in aircraft maintenance workers. Aerosp Med Hum Perform. 2016; 87(8):712-717.
Towards Meaningful Engagement : A Framework for Design and

Gamification, an emerging idea for using game-design elements and principles to make everyday tasks more engaging, is permeating many different types of information systems. Excitement surrounding gamification results from its many potential organizational benefits. However, little research and design guidelines exist regarding gamified information systems. We therefore write this commentary to call upon information systems scholars to investigate the design and use of gamified information systems from a variety of disciplinary perspectives and theories, including behavioral economics, psychology, social psychology, information systems, etc. We first explicate the idea of gamified information systems, provide real-world examples of successful and unsuccessful systems, and based on a synthesis of the available literature, present a taxonomy of gamification design elements. We then develop a framework for research and design: its main theme is to create meaningful engagement for users, that is, gamified information systems should be designed to address the dual goals of instrumental and experiential outcomes. Using this framework, we develop a set of design principles and research questions, using a running case to illustrate some of our ideas. We conclude with a summary of opportunities for IS researchers to extend our knowledge of gamified information systems, and at the same time, advance
Fault-Tolerant Trajectory Tracking Control of a Quadrotor Helicopter Using Gain-Scheduled PID and Model Reference Adaptive Control

Based on two successfully and widely used control techniques in many industrial applications under normal (fault-free) operation conditions, the Gain-Scheduled Proportional-Integral-Derivative (GS-PID) control and Model Reference Adaptive Control (MRAC) strategies have been extended, implemented, and experimentally tested on a quadrotor helicopter Unmanned Aerial Vehicle (UAV) testbed available at Concordia University, for the purpose of investigation of these two typical and different control techniques as two useful Fault-Tolerant Control (FTC) approaches. Controllers are designed and implemented in order to track the desired trajectory of the helicopter in both normal and faulty scenarios of the flight. A Linear Quadratic Regulator (LQR) with integral action controller is also used to control the pitch and roll motion of the quadrotor helicopter. Square trajectory, together with specified autonomous and safe taking-off and landing path, is considered as the testing trajectory and the experimental flight testing results with both GS-PID and MRAC are presented and compared with tracking performance under partial loss of control power due to fault/damage in the propeller of the quadrotor UAV. The performance of both controllers showed to be good. Although GS-PID is easier for development and implementation, MRAC showed to be more robust to faults and noises, and is friendly to be applied to the quadrotor UAV.
The Influence of Diversification and Corporate Social Responsibility to Earnings Management with Audit Committee Effectiveness as a Moderating Variable

Author’s Details: (1) Titik Aryati (2) Eny Purwaningsih (1) (2) Faculty of Economics and Business Trisakti University titikar@yahoo.com Abstract: The purpose of this research is to determine the influence of diversification and corporate social responsibility on earnings management with audit committee effectiveness as moderating variable. Discretionary accrual used as a proxy for earnings management. Data for this research were obtained secondary data. There are 299 manufacturing companies listed in IDX on 2011-2015 used as sample. This research uses multiple regression technique as method of analysis.This research results show that the business diversification has a positive influence to earnings management. Meanwhile, geographic diversification and corporate social responsibility does not have any influence to earnings management. The results on moderating variable show that the expertise of audit committee can moderate the relationship between the business diversification and earnings management.
Information Security Risk Management: In Which Security Solutions Is It Worth Investing?

This material is brought to you by the Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in Communications of the Association for Information Systems by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact elibrary@aisnet.org. As companies are increasingly exposed to information security threats, decision makers are permanently forced to pay attention to security issues. Information security risk management provides an approach for measuring the security through risk assessment, risk mitigation, and risk evaluation. Although a variety of approaches have been proposed, decision makers lack well-founded techniques that (1) show them what they are getting for their investment, (2) show them if their investment is efficient, and (3) do not demand in-depth knowledge of the IT security domain. This article defines a methodology for management decision makers that effectively addresses these problems. This work involves the conception, design, and implementation of the methodology into a software solution. The results from two qualitative case studies show the advantages of this methodology in comparison to established methodologies.
Use of a Group on Facebook : Initial Findings and Perceptions

Social networking sites, especially Facebook, are an integral part of the lifestyle of contemporary youth. The facilities are increasingly being used by older persons as well. Usage is mainly for social purposes, but the groupand discussion facilities of Facebook hold potential for focused academic use. This paper describes and discusses a venture in which postgraduate distancelearning students joined an optional group for the purpose of discussions on academic, contentrelated topics, largely initiated by the students themselves. Learning and insight were enhanced by these discussions and the students, in their environment of distance learning, are benefiting by contact with fellow students.
Deep Fusion of Multiple Semantic Cues for Complex Event Recognition

We present a deep learning strategy to fuse multiple semantic cues for complex event recognition. In particular, we tackle the recognition task by answering how to jointly analyze human actions (who is doing what), objects (what), and scenes (where). First, each type of semantic features (e.g., human action trajectories) is fed into a corresponding multi-layer feature abstraction pathway, followed by a fusion layer connecting all the different pathways. Second, the correlations of how the semantic cues interacting with each other are learned in an unsupervised cross-modality autoencoder fashion. Finally, by fine-tuning a large-margin objective deployed on this deep architecture, we are able to answer the question on how the semantic cues of who, what, and where compose a complex event. As compared with the traditional feature fusion methods (e.g., various early or late strategies), our method jointly learns the essential higher level features that are most effective for fusion and recognition. We perform extensive experiments on two real-world complex event video benchmarks, MED'11 and CCV, and demonstrate that our method outperforms the best published results by 21% and 11%, respectively, on an event recognition task.
Leaf Recognition using Texture Features for Herbal Plant Identification

2017 Revised Nov 12, 2017  Dec 1, 2017 This research investigates the application of texture features for leaf recognition for herbal plant identification. Malaysia is rich with herbal plants but not many people can identify them and know about their uses. Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary. Leaf image is chosen for plant recognition since it is available and visible all the time. Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections. A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier. A new leaf dataset has been constructed from ten different herb plants. Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.