nils-herrmann/clean_scirepeval huggingface dataset:
The Digitization of Word-of-Mouth : Promise and Challenges of Online Reputation Systems 1

Online reputation mechanisms are emerging as a promising alternative to more traditional trust building mechanisms, such as branding and formal contracting, in settings where the latter may be ineffective or prohibitively expensive; a lot of electronic trading communities fall under these categories. Although a number of commercial websites already employ various forms of reputation mechanisms, rigorous research into their properties is still in its infancy. This fledgling field can benefit from past results in economics and game theory. Moreover, in order to translate the stylized results of game theory into concrete managerial guidance for implementing and participating in effective reputation mechanisms further advances are needed in a number of important areas: First, the design space of such mechanisms needs to be scoped and the effects of different design choices on performance need to be better understood. Second, the economic efficiency of various classes of reputation mechanisms needs to be quantified and compared to that of alternative mechanisms for building trust. Third, the robustness of those mechanisms against boundedly rational players, noisy ratings and strategic manipulation needs to be studied and improved. This paper surveys past results that have been derived in a variety of contexts, but which are relevant as a basis for building online reputation systems, presents two analytical models that illustrate the role of such systems in electronic markets and identifies opportunities for further MS/OR research in this fascinating area. 1 Preliminary draft of December 10, 2001 for comments and discussion. Please do not circulate.
A linear regression analysis of the effects of age related pupil dilation change in iris biometrics

Medical studies have shown that average pupil size decreases linearly throughout adult life. Therefore, on average, the longer the time between acquisition of two images of the same iris, the larger the difference in dilation between the two images. Several studies have shown that increased difference in dilation causes an increase in the false nonmatch rate for iris recognition. Thus, increased difference in pupil dilation is one natural mechanism contributing to an iris template aging effect. We present an experimental analysis of the change in genuine match scores in the context of dilation differences due to aging.
Low-Profile Three-Arm Folded Dipole Antenna for UHF Band RFID Tags Mountable on Metallic Objects

A low-profile radio frequency identification (RFID) tag mountable on metallic objects is presented, which employs a multiple-arm folded dipole antenna. The proposed antenna is printed on a single-layer circuit board with an overall thickness of 1.5 mm not requiring any via or shorting plate. The presented configuration allows to match an RFID chip impedance with an imaginary part around - 400 Ω . A slightly modified version of the tag that allows an easy tuning of the frequency response is illustrated as well as a more compact tag obtained by allowing the use of vias. Prototypes for the RFID UHF band allocated in Europe have been fabricated, and measurements of the impedance and read range are provided.
Entity Matching in Online Social Networks

In recent years, Online Social Networks (OSNs) have essentially become an integral part of our daily lives. There are hundreds of OSNs, each with its own focus and offers for particular services and functionalities. To take advantage of the full range of services and functionalities that OSNs offer, users often create several accounts on various OSNs using the same or different personal information. Retrieving all available data about an individual from several OSNs and merging it into one profile can be useful for many purposes. In this paper, we present a method for solving the Entity Resolution (ER), problem for matching user profiles across multiple OSNs. Our algorithm is able to match two user profiles from two different OSNs based on machine learning techniques, which uses features extracted from each one of the user profiles. Using supervised learning techniques and extracted features, we constructed different classifiers, which were then trained and used to rank the probability that two user profiles from two different OSNs belong to the same individual. These classifiers utilized 27 features of mainly three types: name based features (i.e., the Soundex value of two names), general user info based features (i.e., the cosine similarity between two user profiles), and social network topological based features (i.e., the number of mutual friends between two users' friends list). This experimental study uses real-life data collected from two popular OSNs, Facebook and Xing. The proposed algorithm was evaluated and its classification performance measured by AUC was 0.982 in identifying user profiles across two OSNs.
Applications of SAR Interferometry in Earth and Environmental Science Research

This paper provides a review of the progress in regard to the InSAR remote sensing technique and its applications in earth and environmental sciences, especially in the past decade. Basic principles, factors, limits, InSAR sensors, available software packages for the generation of InSAR interferograms were summarized to support future applications. Emphasis was placed on the applications of InSAR in seismology, volcanology, land subsidence/uplift, landslide, glaciology, hydrology, and forestry sciences. It ends with a discussion of future research directions.
Multimodal Affect Analysis for Product Feedback Assessment

Consumers often react expressively to products such as food samples, perfume, jewelry, sunglasses, and clothing accessories. This research discusses a multimodal affect recognition system developed to classify whether a consumer likes or dislikes a product tested at a counter or kiosk, by analyzing the consumer’s facial expression, body posture, hand gestures, and voice after testing the product. A depth-capable camera and microphone system Kinect for Windows – is utilized. An emotion identification engine has been developed to analyze the images and voice to determine affective state of the customer. The image is segmented using skin color and adaptive threshold. Face, body and hands are detected using the Haar cascade classifier. Canny edges are identified and the lip, body and hand contours are extracted using spatial filtering. Edge count and orientation around the mouth, cheeks, eyes, shoulders, fingers and the location of the edges are used as features. Classification is done by an emotion template mapping algorithm and training a classifier using support vector machines. The real-time performance, accuracy and feasibility for multimodal affect recognition in feedback assessment are evaluated.
Automated requirements engineering: Use case patterns-driven approach

Based on a novel multi-phases process, a new use case patterns catalogue is constructed. This catalogue is then utilised as a framework for a new use case patterns-driven approach for requirement engineering. The application of the proposed approach in a TestWarehouse environment showed promising results in saving up to 30% of the total software development project time, yet the resulted requirements models showed high, 85–95%, completeness percentage. Furthermore, the application of an automated version of the proposed approach saved an extra 43% of the time saved by its manual application. This had direct implications on improving requirements and design deliverables of agile software development processes. Nevertheless, users raised a number of concerns that have been considered to outline prospective phases of this research.
State of health monitoring of Li-ion batteries using dynamic resistance mapping and regression

The increase in speed and accuracy of computation methods and the advancement in sensor technologies has shifted the focus towards health monitoring systems of vehicles. Li-ion batteries which are used in electric vehicles, tend to get damaged and pose potential safety threats when ill-used. An adaptive control system called battery management system is needed to ensure the safety and life optimization of batteries in electric vehicles. This system takes care of both diagnosis and prognosis, giving the user an idea of current status as well as remaining lifetime of the battery. A novel algorithm based on dynamic resistance mapping is used to develop a data driven computational model for estimating the state of health of battery. A model with a correlation co-efficient of 99.45% is developed for state of health estimation to find the health of the battery during run time.
Language Modeling with Gated Convolutional Networks

The pre-dominant approach to language modeling to date is based on recurrent neural networks. In this paper we present a convolutional approach to language modeling. We introduce a novel gating mechanism that eases gradient propagation and which performs better than the LSTMstyle gating of Oord et al. (2016b) despite being simpler. We achieve a new state of the art on WikiText-103 as well as a new best single-GPU result on the Google Billion Word benchmark. In settings where latency is important, our model achieves an order of magnitude speed-up compared to a recurrent baseline since computation can be parallelized over time. To our knowledge, this is the first time a non-recurrent approach outperforms strong recurrent models on these tasks.
The Love/Hate Relationship with the C Preprocessor: An Interview Study (Artifact)

The C preprocessor has received strong criticism in academia, among others regarding separation of concerns, error proneness, and code obfuscation, but is widely used in practice. Many (mostly academic) alternatives to the preprocessor exist, but have not been adopted in practice. Since developers continue to use the preprocessor despite all criticism and research, we ask how practitioners perceive the C preprocessor. We performed interviews with 40 developers, used grounded theory to analyze the data, and cross-validated the results with data from a survey among 202 developers, repository mining, and results from previous studies. In particular, we investigated four research questions related to why the preprocessor is still widely used in practice, common problems, alternatives, and the impact of undisciplined annotations. Our study shows that developers are aware of the criticism the C preprocessor receives, but use it nonetheless, mainly for portability and variability. Many developers indicate that they regularly face preprocessorrelated problems and preprocessor-related bugs. The majority of our interviewees do not see any current C-native technologies that can entirely replace the C preprocessor. However, developers tend to mitigate problems with guidelines, even though those guidelines are not enforced consistently. We report the key insights gained from our study and discuss implications for practitioners and researchers on how to better use the C preprocessor to minimize its negative impact.
Content or Community? A Digital Business Strategy for Content Providers in the Social Age

The content industry has been undergoing a tremendous transformation in the last two decades. We focus in this paper on recent changes in the form of social computing. Although the content industry has implemented social computing to a large extent, it has done so from a techno-centric approach in which social features are viewed as complementary rather than integral to content. This approach does not capitalize on users’ social behavior in the website and does not answer the content industry’s need to elicit payment from consumers. We suggest that both of these objectives can be achieved by acknowledging the fusion between content and community, making the social experience central to the content website’s digital business strategy.
Privacy, confidentiality, and electronic medical records.

The enhanced availability of health information in an electronic format is strategic for industry-wide efforts to improve the quality and reduce the cost of health care, yet it brings a concomitant concern of greater risk for loss of privacy among health care participants. The authors review the conflicting goals of accessibility and security for electronic medical records and discuss nontechnical and technical aspects that constitute a reasonable security solution. It is argued that with guiding policy and current technology, an electronic medical record may offer better security than a traditional paper record.
The time-delayed inverted pendulum: implications for human balance control.

The inverted pendulum is frequently used as a starting point for discussions of how human balance is maintained during standing and locomotion. Here we examine three experimental paradigms of time-delayed balance control: (1) mechanical inverted time-delayed pendulum, (2) stick balancing at the fingertip, and (3) human postural sway during quiet standing. Measurements of the transfer function (mechanical stick balancing) and the two-point correlation function (Hurst exponent) for the movements of the fingertip (real stick balancing) and the fluctuations in the center of pressure (postural sway) demonstrate that the upright fixed point is unstable in all three paradigms. These observations imply that the balanced state represents a more complex and bounded time-dependent state than a fixed-point attractor. Although mathematical models indicate that a sufficient condition for instability is for the time delay to make a corrective movement, tau(n), be greater than a critical delay tau(c) that is proportional to the length of the pendulum, this condition is satisfied only in the case of human stick balancing at the fingertip. Thus it is suggested that a common cause of instability in all three paradigms stems from the difficulty of controlling both the angle of the inverted pendulum and the position of the controller simultaneously using time-delayed feedback. Considerations of the problematic nature of control in the presence of delay and random perturbations ("noise") suggest that neural control for the upright position likely resembles an adaptive-type controller in which the displacement angle is allowed to drift for small displacements with active corrections made only when theta exceeds a threshold. This mechanism draws attention to an overlooked type of passive control that arises from the interplay between retarded variables and noise.
CityMomentum: an online approach for crowd behavior prediction at a citywide level

Human movements are difficult to predict, especially, when we consider rare behaviors that deviate from normal daily routines. By tracing the behavior of a person over a long period, we can model their daily routines and predict periodical behaviors, whereas rare behaviors, such as participating in the New Year's Eve countdown, can hardly be predicted readily and thus they have usually been treated as outliers of the daily routines in most existing studies. However, for scenarios such as emergency management or intelligent traffic regulation, we are more interested in rare behaviors than daily routines. Using human mobility Big Data, the rare behavior of each individual in a social crowd is no longer rare and thus it may be predicted when we analyze the crowd behavior at a citywide level. Therefore in this study, instead of predicting movement based on daily routines, we make short-term predictions based on the recent movement observations. We propose a novel model called CityMomentum as a predicting-by-clustering framework for sampling future movement using a mixture of multiple random Markov chains, each of which is a Naive Movement Predictive model trained with the movements of the subjects that belong to each cluster. We apply our approach to a big mobile phone GPS log dataset and predict the short-term future movements, especially during the Comiket 80 and New Year's Eve celebration. We evaluate our prediction by a Earth Mover Distance (EMD) based metric, and show our approach accurately predicts the crowd behavior during the rare crowd events, which makes an early crowd event warning and regulation possible in the emergent situations.
Detecting abnormal events in video using Narrowed Motion Clusters

We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. After extracting motion features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of motion. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain only outliers and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normal motion. If the test sample does not fit well in any narrowed cluster, than it is labeled as abnormal. We also combine our approach based on motion features with a recent approach based on deep appearance features extracted with pre-trained convolutional neural networks (CNN). We combine our two-stage algorithm with the deep framework using a late fusion strategy, keeping the pipelines of the two approaches independent. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 32 frames per second on CPU.
From Node Embedding To Community Embedding

Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being “close” on the graph are close too in the low-dimensional space. Despite the success of embedding individual nodes for graph analytics, we notice that an important concept of embedding communities (i.e., groups of nodes) is missing. Embedding communities is useful, not only for supporting various community-level applications, but also to help preserve community structure in graph embedding. In fact, we see community embedding as providing a higher-order proximity to define the node closeness, whereas most of the popular graph embedding methods focus on first-order and/or second-order proximities. To learn the community embedding, we hinge upon the insight that community embedding and node embedding reinforce with each other. As a result, we propose ComEmbed, the first community embedding method, which jointly optimizes the community embedding and node embedding together. We evaluate ComEmbed on real-world data sets. We show it outperforms the state-of-the-art baselines in both tasks of node classification and community prediction.
A Study of Parts-Based Object Class Detection Using Complete Graphs

Object detection is one of the key components in modern computer vision systems. While the detection of a specific rigid object under changing viewpoints was considered hard just a few years ago, current research strives to detect and recognize classes of non-rigid, articulated objects. Hampered by the omnipresent confusing information due to clutter and occlusion, the focus has shifted from holistic approaches for object detection to representations of individual object parts linked by structural information, along with richer contextual descriptions of object configurations. Along this line of research, we present a practicable and expandable probabilistic framework for parts-based object class representation, enabling the detection of rigid and articulated object classes in arbitrary views. We investigate learning of this representation from labelled training images and infer globally optimal solutions to the contextual MAP-detection problem, using A *-search with a novel lower-bound as admissible heuristic. An assessment of the inference performance of Belief-Propagation and Tree-Reweighted Belief Propagation is obtained as a by-product. The generality of our approach is demonstrated on four different datasets utilizing domain dependent information cues.
An evidential approach to map-building for autonomous vehicles

In this work, we examine the problem of constructing and maintaining a map of an autonomous vehicle’s environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In this paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are “fused” into the map using the Dempster–Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.
Computer Game Mods, Modders, Modding, and the Mod Scene

Computer games have increasingly been the focus of user-led innovations in the form of game mods. This paper examines how different kinds of socio-technical affordances serve to organize the actions of the people who develop and share their game mods. The affordances examined include customization and tailoring mechanisms, software and content copyright licenses, game software infrastructure and development tools, career contingencies and organizational practices of mod teams, and social worlds intersecting the mod scene. Numerous examples will be used to ground this review and highlight how such affordances can organize, facilitate or constrain what can be done. Overall, this study helps to provide a deeper understanding of how a web of associated affordances collectively serve to govern what mods get made, how modding practices emerge and flourish, and how modders and the game industry serve each others' interests, though not always in equivocal terms.
DLHub: Model and Data Serving for Science

While the Machine Learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the “learning systems” needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications. DLHub addresses two significant shortcomings in current systems. First, its selfservice model repository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published models through a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications.