nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture

We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As opposed to the neural tensor layer that has been adopted recently, the holographic composition provides the benefits of scalable and rich representational learning approach without incurring huge parameter costs. Overall, we present Holographic Dual LSTM (HD-LSTM), a unified architecture for both deep sentence modeling and semantic matching. Essentially, our model is trained end-to-end whereby the parameters of the LSTM are optimized in a way that best explains the correlation between question and answer representations. In addition, our proposed deep learning architecture requires no extensive feature engineering. Via extensive experiments, we show that HD-LSTM outperforms many other neural architectures on two popular benchmark QA datasets. Empirical studies confirm the effectiveness of holographic composition over the neural tensor layer.
A diffusion mechanism for social advertising over microblogs

a r t i c l e i n f o Social media have increasingly become popular platforms for information dissemination. Recently, companies have attempted to take advantage of social advertising to deliver their advertisements to appropriate customers. The success of message propagation in social media depends greatly on the content relevance and the closeness of social relationships. In this paper, considering the factors of user preference, network influence , and propagation capability, we propose a diffusion mechanism to deliver advertising information over microblogging media. Our experimental results show that the proposed model could provide advertisers with suitable targets for diffusing advertisements continuously and thus efficiently enhance advertising effectiveness. In recent years, social media, such as Facebook, Twitter and Plurk, have flourished and raised much attention. Social media provide users with an excellent platform to share and receive information and give marketers a great opportunity to diffuse information through numerous populations. An overwhelming majority of mar-keters are using social media to market their businesses, and a significant 81% of these marketers indicate that their efforts in social media have generated effective exposure for their businesses [59]. With effective vehicles for understanding customer behavior and new hybrid elements of the promotion mix, social media allow enterprises to make timely contact with the end-consumer at relatively low cost and higher levels of efficiency [52]. Since the World Wide Web (Web) is now the primary message delivering medium between advertisers and consumers, it is a critical issue to find the best way to utilize on-line media for advertising purposes [18,29]. The effectiveness of advertisement distribution highly relies on well understanding the preference information of the targeted users. However, some implicit personal information of users, particularly the new users, may not be always obtainable to the marketers [23]. As users know more about their friends than marketers, the relations between the users become a natural medium and filter for message diffusion. Moreover, most people are willing to share their information with friends and are likely to be affected by the opinions of their friends [35,45]. Social advertising is a kind of recommendation system, of sharing information between friends. It takes advantage of the relation of users to conduct an advertising campaign. In 2010, eMarketer reported that 90% of consumers rely on recommendations from people they trust. In the same time, IDG Amplify indicated that the efficiency of social advertising is greater than the traditional …
BRVO: Predicting pedestrian trajectories using velocity-space reasoning

We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot’s environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of Ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.
Chapter 19 Semantic Information Integration for Smart Grid Applications *

The Los Angeles Smart Grid Project aims to use informatics techniques to bring about a quantum leap in the way demand response load optimization is performed in utilities. Semantic information integration, from sources as diverse as Internet-connected smart meters and social networks, is a linchpin to support the advanced analytics and mining algorithms required for this. In association with it, semantic complex event processing system will allow consumer and utility managers to easily specify and enact energy policies continuously. We present the information systems architecture for the project that is under development, and discuss research issues that emerge from having to design a system that supports 1.4 million customers and a rich ecosystem of Smart Grid applications from users, third party vendors, the utility and regulators.
Extracting Opinion Targets in a Single and Cross-Domain Setting with Conditional Random Fields

In this paper, we focus on the opinion target extraction as part of the opinion mining task. We model the problem as an information extraction task, which we address based on Conditional Random Fields (CRF). As a baseline we employ the supervised algorithm by Zhuang et al. (2006), which represents the state-of-the-art on the employed data. We evaluate the algorithms comprehensively on datasets from four different domains annotated with individual opinion target instances on a sentence level. Furthermore, we investigate the performance of our CRF-based approach and the baseline in a singleand cross-domain opinion target extraction setting. Our CRF-based approach improves the performance by 0.077, 0.126, 0.071 and 0.178 regarding F-Measure in the single-domain extraction in the four domains. In the crossdomain setting our approach improves the performance by 0.409, 0.242, 0.294 and 0.343 regarding F-Measure over the baseline.
Instructional Design Models for Well-Structured and Ill-Structured Problem-Solving Learning Outcomes

Although problem solving is regarded by most educators as among the most important learning outcomes, few instructional design prescriptions are available for designing problem-solving instruction and engaging learners. This paper distinguishes between well-structured problems and ill-structured problems. Well-structured problems are constrained problems with convergent solutions that engage the application of a limited number of rules and principles within welldefined parameters. Ill-structured problems possess multiple solutions, solution paths, fewer parameters which are less manipulable, and contain uncertainty about which concepts, rules, and principles are necessary for the solution or how they are organized and which solution is best. For both types of problems, this paper presents models for how learners solve them and models for designing instruction to support problem-solving skill development. The model for solving wellstructured problems is based on information processing theories of learning, while the model for solving ill-structured problems relies on an emerging theory of ill-structured problem solving and on constructivist and situated cognition approaches to learning.
A Scalable and Privacy-Aware IoT Service for Live Video Analytics

We present OpenFace, our new open-source face recognition system that approaches state-of-the-art accuracy. Integrating OpenFace with inter-frame tracking, we build RTFace, a mechanism for denaturing video streams that selectively blurs faces according to specified policies at full frame rates. This enables privacy management for live video analytics while providing a secure approach for handling retrospective policy exceptions. Finally, we present a scalable, privacy-aware architecture for large camera networks using RTFace.
Against the moral Turing test: accountable design and the moral reasoning of autonomous systems

This paper argues against the moral Turing test (MTT) as a framework for evaluating the moral performance of autonomous systems. Though the term has been carefully introduced, considered, and cautioned about in previous discussions (Allen et al.  2000; ), it has lingered on as a touchstone for developing computational approaches to moral reasoning (Gerdes and Øhrstrøm  2015). While these efforts have not led to the detailed development of an MTT, they nonetheless retain the idea to discuss what kinds of action and reasoning should be demanded of autonomous systems. We explore the flawed basis of an MTT in imitation, even one based on scenarios of morally accountable actions. MTT-based evaluations are vulnerable to deception, inadequate reasoning, and inferior moral performance vis a vis a system’s capabilities. We propose verification—which demands the design of transparent, accountable processes of reasoning that reliably prefigure the performance of autonomous systems—serves as a superior framework for both designer and system alike. As autonomous social robots in particular take on an increasing range of critical roles within society, we conclude that verification offers an essential, albeit challenging, moral measure of their design and performance.
Feature representation and discrimination based on Gaussian mixture model probability densities - Practices and algorithms

This study promotes the use of statistical methods in specifi c classification tasks since statistical methods have certain advantages which advocate th eir use in pattern recognition. One central problem in statistical methods is estimation of class conditional probability density functions based on examples in a training set. In thi s study maximum likelihood estimation methods for Gaussian mixture models are reviewed a nd discussed from a practical point of view. In addition, good practices for utilizing pro bability densities in feature classification and selection are discussed for Bayesian, and mor e i portantly, for non-Bayesian tasks. As a result, the use of confidence information in the cl assification is proposed and a method for confidence estimation is presented. The proposit ion are tested experimentally.
100% accuracy in automatic face recognition.

Accurate face recognition is critical for many security applications. Current automatic face-recognition systems are defeated by natural changes in lighting and pose, which often affect face images more profoundly than changes in identity. The only system that can reliably cope with such variability is a human observer who is familiar with the faces concerned. We modeled human familiarity by using image averaging to derive stable face representations from naturally varying photographs. This simple procedure increased the accuracy of an industry standard face-recognition algorithm from 54% to 100%, bringing the robust performance of a familiar human to an automated system.
Strategies for automatic segmentation of audio data

In many applications,like indexing of broadcast news or surveillanceapplications,theinput dataconsistsof a continuous,unsegmentedaudio stream. Speechrecognitiontechnology , however, usuallyrequiressegmentsof relatively shortlengthasinput. For suchapplications,effective methodsto segmentcontinuousaudio streamsinto homogeneousegmentsarerequired. In this paper , threedifferentsegmentingstrategies(model-based, metric-basedandenergy-based)arecomparedon thesamebroadcast news test data. It is shown that model-basedand metric-basedtechniquesoutperformthe simpler energy-basedalgorithms. While model-basedsegmentersachieve very high level of segmentboundaryprecision,the metric-basedsegmenterperformes better in terms of segment boundaryrecall (RCL). To combinethe advantagesof both strategies, a new hybrid algorithm is introduced. For this, the resultsof a preliminary metric-basedsegmentationare usedto constructthe modelsfor the final model-basedsegmenterrun. The new hybrid approachis shown to outperformtheothersegmentingstrategies.
Towards increased road safety: Real-time decision making for driverless city vehicles

This work elaborates on the topic of decision making for driverless city vehicles, particularly focusing on the aspects on how to develop a reliable approach which meets the requirements of safe city traffic. Decision making in this context refers to the problem of identifying the most appropriate driving maneuver to be performed in a given traffic situation. The overall decision making problem is decomposed into two consecutive stages. The first stage is safety-crucial, representing the decision regarding the set of feasible driving maneuvers. The second stage represents the decision regarding the most appropriate driving maneuver from the set of feasible ones. The developed decision making approach has been implemented in C++ and initially tested in a 3D simulation environment and, thereafter, in real-world experiments. The real-world experiments also included the integration of wireless communication between vehicles.
Recognizing Lower Face Action Units for Facial Expression Analysis

Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions (e.g. happiness and anger). Such prototypic expressions, however, occur infrequently. Human emotions and intentions are communicated more often by changes in one or two discrete facial features. In this paper, we develop an automatic system to analyze subtle changes in facial expressions based on both permanent (e.g. mouth, eye, and brow) and transient (e.g. furrows and wrinkles) facial features in a nearly frontal image sequence. Multi-state facial component models are proposed for tracking and modeling different facial features. Based on these multi-state models, and without artificial enhancement, we detect and track the facial features, including mouth, eyes, brow, cheeks, and their related wrinkles and facial furrows. Moreover we recover detailed parametric descriptions of the facial features. With these features as the inputs, 11 individual action units or action unit combinations are recognized by a neural network algorithm. A recognition rate of 96.7% is obtained. The recognition results indicate that our system can identify action units regardless of whether they occurred singly or in combinations.
Hamilton Paths in Grid Graphs

A grid graph is a node-induced finite subgraph of the infinite grid. It is rectangular if its set of nodes is the product of two intervals. Given a rectangular grid graph and two of its nodes, we give necessary and sufficient conditions for the graph to have a Hamilton path between these two nodes. In contrast, the Hamilton path (and circuit) problem for general grid graphs is shown to be NP-complete. This provides a new, relatively simple, proof of the result that the Euclidean traveling salesman problem is NP-complete.
MAC-RANSAC : a robust algorithm for the recognition of multiple objects

This paper addresses the problem of recognizing multiple rigid objects that are common to two images. We propose a generic algorithm that allows to simultaneously decide if one or several objects are common to the two images and to estimate the corresponding geometric transformations. The considered transformations include similarities, homographies and epipolar geometry. We first propose a generalization of an a contrario formulation of the RANSAC algorithm proposed in . We then introduce an algorithm for the detection of multiple transformations between images and show its efficiency on various experiments.
Core stability exercises on and off a Swiss ball.

To assess lumbopelvic muscle activity during different core stability exercises on and off a Swiss ball.



Prospective comparison study.



Research laboratory.



Eight healthy volunteers from a university population.



Subjects performed 4 exercises on and off a Swiss ball: inclined press-up, upper body roll-out, single-leg hold, and quadruped exercise.



Surface electromyography from selected lumbopelvic muscles, normalized to maximum voluntary isometric contraction, and median frequency analysis of electromyography power spectrum. Visual analog scale for perception of task difficulty.



There was a significant increase in the activation of the rectus abdominus with performance of the single-leg hold and at the top of the press-up on the Swiss ball. This led to changes in the relation between the activation levels of the lumbopelvic muscles measured.



Although there was evidence to suggest that the Swiss ball provides a training stimulus for the rectus abdominus, the relevance of this change to core stability training requires further research because the focus of stabilization training is on minimizing rectus abdominus activity. Further support has also been provided about the quality of the quadruped exercise for core stability.
Cognitive Radio Design Challenges and Techniques

Cognitive radios are expected to communicate across two or three frequency decades by continually sensing the spectrum and identifying available channels. This paper describes the issues related to the design of wideband signal paths and the decades-wide synthesis of carrier frequencies. A new CMOS low-noise amplifier topology for the range of 50 MHz to 10 GHz is introduced that achieves a noise figure of 2.9 to 5.7 dB with a power dissipation of 22 mW. Several multi-decade carrier generation techniques are proposed and a CMOS prototype is presented that exhibits a phase noise of -94 to -120 dBc/Hz at 1-MHz offset while consuming 31 mW.
Patient perceptions of physician empathy, satisfaction with physician, interpersonal trust, and compliance

This study was designed to investigate psychometric properties of the Jefferson Scale of Patient Perceptions of Physician Empathy (JSPPPE), and to examine correlations between its scores and measures of overall satisfaction with physicians, personal trust, and indicators of patient compliance.  Research participants included 535 out-patients (between 18-75 years old, 66% female). A survey was mailed to participants which included the JSPPPE (5-item), a scale for measuring overall satisfaction with the primary care physician (10-item), and demographic questions. Patients were also asked about compliance with their physician’s recommendation for preventive tests (colonoscopy, mammogram, and PSA for age and gender appropriate patients).  Factor analysis of the JSPPPE resulted in one prominent component. Corrected item-total score correlations ranged from .88 to .94. Correlation between scores of the JSPPPE and scores on the patient satisfaction scale was 0.93. Scores of the JSPPPE were highly correlated with measures of physician-patient trust (r >.73). Higher scores of the JSPPPE were significantly associated with physicians’ recommendations for preventive tests (colonoscopy, mammogram, and PSA) and with compliance rates which were > .80). Cronbach’s coefficient alpha for the JSPPPE ranged from .97 to .99 for the total sample and for patients in different gender and age groups.  Empirical evidence supported the psychometrics of the JSPPPE, and confirmed significant links with patients’ satisfaction with their physicians, interpersonal trust, and compliance with physicians’ recommendations. Availability of this psychometrically sound instrument will facilitate empirical research on empathy in patient care in different countries.
Employees' Information Security Awareness and Behavior: A Literature Review

Today's organizations are highly dependent on information management and processes. Information security is one of the top issues for researchers and practitioners. In literature, there is consent that employees are the weakest link in IS security. A variety of researchers discuss explanations for employees' security related awareness and behavior. This paper presents a theory-based literature review of the extant approaches used within employees' information security awareness and behavior research over the past decade. In total, 113 publications were identified and analyzed. The information security research community covers 54 different theories. Focusing on the four main behavioral theories, a state-of-the-art overview of employees' security awareness and behavior research over the past decade is given. From there, gaps in existing research are uncovered and implications and recommendations for future research are discussed. The literature review might also be useful for practitioners that need information about behavioral factors that are critical to the success of a organization's security awareness.
Towards identifying software project clusters with regard to defect prediction

This paper describes an analysis that was conducted on newly collected repository with 92 versions of 38 proprietary, open-source and academic projects. A preliminary study perfomed before showed the need for a further in-depth analysis in order to identify project clusters.
  The goal of this research is to perform clustering on software projects in order to identify groups of software projects with similar characteristic from the defect prediction point of view. One defect prediction model should work well for all projects that belong to such group. The existence of those groups was investigated with statistical tests and by comparing the mean value of prediction efficiency.
 Method: Hierarchical and k-means clustering, as well as Kohonen's neural network was used to find groups of similar projects. The obtained clusters were investigated with the discriminant analysis. For each of the identified group a statistical analysis has been conducted in order to distinguish whether this group really exists. Two defect prediction models were created for each of the identified groups. The first one was based on the projects that belong to a given group, and the second one - on all the projects. Then, both models were applied to all versions of projects from the investigated group. If the predictions from the model based on projects that belong to the identified group are significantly better than the all-projects model (the mean values were compared and statistical tests were used), we conclude that the group really exists.
  Six different clusters were identified and the existence of two of them was statistically proven: 1) cluster proprietary B -- T=19, p=0.035, r=0.40; 2) cluster proprietary/open - t(17)=3.18, p=0.05, r=0.59. The obtained effect sizes (r) represent large effects according to Cohen's benchmark, which is a substantial finding.
  The two identified clusters were described and compared with results obtained by other researchers. The results of this work makes next step towards defining formal methods of reuse defect prediction models by identifying groups of projects within which the same defect prediction model may be used. Furthermore, a method of clustering was suggested and applied.