nils-herrmann/clean_scirepeval huggingface dataset:
Dual-Band Circularly Polarized Stacked Annular-Ring Patch Antenna for GPS Application

A dual-band circularly polarized stacked annular-ring patch antenna is presented in this letter. This antenna operates at both the GPS L1 frequency of 1575 MHz and L2 frequency of 1227 MHz, whose frequency ratio is about 1.28. The proposed antenna is formed by two concentric annular-ring patches that are placed on opposite sides of a substrate. Wide axial-ratio bandwidths (larger than 2%), determined by 3-dB axial ratio, are achieved at both bands. The measured gains at 1227 and 1575 MHz are about 6 and 7 dBi, respectively, with the loss of substrate taken into consideration. Both simulated and measured results are presented. The method of varying frequency ratio is also discussed.
Spam Detection using Clustering, Random Forests, and Active Learning

This paper describes work in progress. Our research is focused on efficient construction of effective models for spam detection. Clustering messages allows for efficient labeling of a representative sample of messages for learning a spam detection model using a Random Forest for classification and active learning for refining the classification model. Results are illustrated for the 2007 TREC Public Spam Corpus. The area under the Receiver Operating Characteristic (ROC) curve is competitive with other solutions while requiring much fewer labeled training examples.
Exploring Educational and Cultural Adaptation through Social Networking Sites.

Social networking sites have seen tremendous growth and are widely used around the world. Nevertheless, the use of social networking sites in educational contexts is an under explored area. This paper uses a qualitative methodology, autoethnography, to investigate how social networking sites, specifically Facebook, can help first semester doctoral students adapt to a Ph.D. program and a new national culture. Each week during the semester, a Ph.D. Student Tip and a Cultural Tip was posted. Students would then post related discussions, observations, and/or questions. At the conclusion of the semester the doctoral students shared their thoughts about participating in the FacebookTM group through autobiographical narratives. A technique called open coding was used to analyze the narratives. Our findings suggest three emergent themes. Social networking sites can aid adaptation through: 1) facilitating knowledge exchange, 2) alleviating apprehension, and 3) enabling socialization and building community. The data showed that the FacebookTM discussions aided in various types of knowledge exchange, providing a conceptual map that facilitated greater adaptation. Next, the interactions appeared to help minimize trepidation related to embarking on a new program of study and adjusting to a new national culture. Third, the FacebookTM group was useful fostering socialization and community among the students. We make the following recommendations for professors when implementing SNS (Social Network Site) groups: 1) establish a FacebookTM group with a course-related identity, 2) be sensitive to security and privacy issues, 3) provide tips to “seed” various types of knowledge exchange, 4) encourage participation, and 5) assess and adjust. While our research is exploratory in nature, we believe it provides a foundation for future research in this important and rapidly expanding area of SNS usage.
Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining

The proliferation of textual data in business is overwhelming. Unstructured textual data is being constantly generated via call center logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, and so on. While the amount of textual data is increasing rapidly, businesses’ ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. This paper takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information to improve business operations and performance. Multiple business applications of case studies using real data that demonstrate applications of text analytics and sentiment mining using SAS® Text Miner and SAS® Sentiment Analysis Studio are presented. While SAS® products are used as tools for demonstration only, the topics and theories covered are generic (not tool specific).
Fast Parallel Sorting Algorithms on GPUs

This paper presents a comparative analysis of the three widely used parallel sorting algorithms: OddEven sort, Rank sort and Bitonic sort in terms of sorting rate, sorting time and speed-up on CPU and different GPU architectures. Alongside we have implemented novel parallel algorithm: min-max butterfly network, for finding minimum and maximum in large data sets. All algorithms have been implemented exploiting data parallelism model, for achieving high performance, as available on multi-core GPUs using the OpenCL specification. Our results depicts minimum speed-up19x of bitonic sort against oddeven sorting technique for small queue sizes on CPU and maximum of 2300x speed-up for very large queue sizes on Nvidia Quadro 6000 GPU architecture. Our implementation of full-butterfly network sorting results in relatively better performance than all of the three sorting techniques: bitonic, odd-even and rank sort. For min-max butterfly network, our findings report high speed-up of Nvidia quadro 6000 GPU for high data set size reaching 2 24 with much lower sorting time.
Eye tracking in the wild

An active contour tracker is presented which can be used for gaze-based interaction with off-the-shelf components. The underlying contour model is based on image statistics and avoids explicit feature detection. The tracker combines particle filtering with the EM algorithm. The method exhibits robustness to light changes and camera defocusing; consequently, the model is well suited for use in systems using off-the-shelf hardware, but may equally well be used in controlled environments, such as in IR-based settings. The method is even capable of handling sudden changes between IR and non-IR light conditions, without changing parameters. For the purpose of determining where the user is looking, calibration is usually needed. The number of calibration points used in different methods varies from a few to several thousands, depending on the prior knowledge used on the setup and equipment. We examine basic properties of gaze determination when the geometry of the camera, screen, and user is unknown. In particular we present a lower bound on the number of calibration points needed for gaze determination on planar objects, and we examine degenerate configurations. Based on this lower bound we apply a simple calibration procedure, to facilitate gaze estimation.
Survey and Performance Evaluation of DBSCAN Spatial Clustering Implementations for Big Data and High-Performance Computing Paradigms

Big data is often mined using clustering algorithms. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular spatial clustering algorithm. However, it is computationally expensive and thus for clustering big data, parallel processing is required. The two prevalent paradigms for parallel processing are High-Performance Computing (HPC) based on Message Passing Interface (MPI) or Open Multi-Processing (OpenMP) and the newer big data frameworks such as Apache Spark or Hadoop. This report surveys for these two different paradigms publicly available implementations that aim at parallelizing DBSCAN and compares their performance. As a result, it is found that the big data implementations are not yet mature and in particular for skewed data, the implementation’s decomposition of the input data into parallel tasks has a huge influence on the performance in terms of running time.
Highlight Detection in Movie Scenes Through Inter-users, Physiological Linkage

Automatic summarization techniques facilitate multimedia indexing and access by reducing the content of a given item to its essential parts. However, novel approaches for summarization should be developed since existing methods cannot offer a general and unobtrusive solution. Considering that the consumption of multimedia data is more and more social, we propose to use a physiological index of social interaction, namely, physiological linkage, to determine general highlights of videos. The proposed method detects highlights which are relevant to the majority of viewers without requiring them any conscious effort. Experimental testing has demonstrated the validity of the proposed system which obtained a classification accuracy of up to 78.2%. CHENES, Christophe, et al. Highlights Detection in Movie Scenes Through Inter-Users Physiological Linkage.  Social media retrieval. Springer, 2013.  10.1007/978-1-4471-4555-4_10
Impact of ambient odors on mall shoppers ’ emotions , cognition , and spending A test of competitive causal theories

The authors tested the effect of ambient scents in a shopping mall environment. Two competing models were used. The first model is derived from the environmental psychology research stream by Mehrabian and Russel (1974) and Donovan and Rossiter (1982) where atmospheric cues generate pleasure and arousal, and, in turn, an approach/avoidance behavior. The emotion–cognition model is supported by Zajonc and Markus (1984). The second model to be tested is based on Lazarus’ (1991) cognitive theory of emotions. In this latter model, shoppers’ perceptions of the retail environment and product quality mediate the effects of ambient scent cues on emotions and spending behaviors. Positive affect is enhanced from shoppers’ evaluations. Using structural equation modeling the authors conclude that the cognitive theory of emotions better explains the effect of ambient scent. Managerial implications are discussed. D 2003
An Assessment of NFC for Future Mobile Payment Systems

In this paper, we present an assessment of NFC (near field communication) for future mobile payment systems. NFC is expected to become a very trendy technology for mobile services, more specifically for mobile payments. The objective of our paper is to evaluate in a systematic manner the potential of NFC as an upcoming technology for mobile payments. In order to ensure the rigor of our research, we used a formal and structured approach based on multi-actor multi-criteria methods. Our research provides one of the first assessment of NFC and a realistic picture of the current Swiss situation as we involved numerous mobile payment experts. Our findings show that Swiss industry experts are quite enthusiastic about the future of NFC.
Faceshop: deep sketch-based face image editing

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.
A Shape-Preserving Approach to Image Resizing

We present a novel image resizing method which attempts to ensure that important local regions undergo a geometric similarity transformation, and at the same time, to preserve image edge structure. To accomplish this, we define handles to describe both local regions and image edges, and assign a weight for each handle based on an importance map for the source image. Inspired by conformal energy, which is widely used in geometry processing, we construct a novel quadratic distortion energy to measure the shape distortion for each handle. The resizing result is obtained by minimizing the weighted sum of the quadratic distortion energies of all handles. Compared to previous methods, our method allows distortion to be diffused better in all directions, and important image edges are well-preserved. The method is efficient, and offers a closed form solution.
PLC Forensics Based on Control Program Logic Change Detection

Supervisory Control and Data Acquisition (SCADA) system is an industrial control automated system. It is built with multiple Programmable Logic Controllers (PLCs). PLC is a special form of microprocessor-based controller with proprietary operating system. Due to the unique architecture of PLC, traditional digital forensic tools are difficult to be applied. In this paper, we propose a program called Control Program Logic Change Detector (CPLCD), which works with a set of Detection Rules (DRs) to detect and record undesired incidents on interfering normal operations of PLC. In order to prove the feasibility of our solution, we set up two experiments for detecting two common PLC attacks. Moreover, we illustrate how CPLCD and network analyzer Wireshark could work together for performing digital forensic investigation on PLC.
From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur

Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach, however, is that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.
Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future

Based on environmental, legal, social, and economic factors, reverse logistics and closed-loop supply chain issues have attracted attention among both academia and practitioners. This attention is evident by the vast number of publications in scientific journals which have been published in recent years. Hence, a comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework of the past, and to shed light on future directions. The aim of this paper is to review recently published papers in reverse logistic and closed-loop supply chain in scientific journals. A total of 382 papers published between January 2007 and March 2013 are selected and reviewed. The papers are then analyzed and categorized to construct a useful foundation of past research. Finally, gaps in the literature are identified to clarify and to suggest future research opportunities.
Fully Distributed Privacy Preserving Mini-batch Gradient Descent Learning

In fully distributed machine learning, privacy and security are important issues. These issues are often dealt with using secure multiparty computation (MPC). However, in our application domain, known MPC algorithms are not scalable or not robust enough. We propose a light-weight protocol to quickly and securely compute the sum of the inputs of a subset of participants assuming a semi-honest adversary. During the computation the participants learn no individual values. We apply this protocol to efficiently calculate the sum of gradients as part of a fully distributed mini-batch stochastic gradient descent algorithm. The protocol achieves scalability and robustness by exploiting the fact that in this application domain a “quick and dirty” sum computation is acceptable. In other words, speed and robustness takes precedence over precision. We analyze the protocol theoretically as well as experimentally based on churn statistics from a real smartphone trace. We derive a sufficient condition for preventing the leakage of an individual value, and we demonstrate the feasibility of the overhead of the protocol.
Verification, Validation, and Certification Challenges for Adaptive Flight-Critical Control System Software

This paper presents some of the unique verification, validation, and certification challenges that must be addressed during the development of adaptive system software for use in safety-critical aerospace applications. The paper first discusses the challenges imposed by the current regulatory guidelines for aviation software. Next, a number of individual technologies being researched by NASA and others are discussed that focus on various aspects of the software challenges. These technologies include the formal methods of model checking, compositional verification, static analysis, program synthesis, and runtime analysis. Then the paper presents some validation challenges for adaptive control, including proving convergence over long durations, guaranteeing controller stability, using new tools to compute statistical error bounds, identifying problems in fault-tolerant software, and testing in the presence of adaptation. These specific challenges are presented in the context of a software validation effort in testing the Integrated Flight Control System (IFCS) neural control software at the Dryden Flight Research Center. Lastly, the challenges to develop technologies to help prevent aircraft system failures, detect and identify failures that do occur, and provide enhanced guidance and control capability to prevent and recover from vehicle loss of control are briefly cited in connection with ongoing work at the NASA Langley Research Center.
QoS-Aware Web Service Recommendation by Collaborative Filtering

With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.
Beyond frequency: habit as mental construct.

Progress in habit theory can be made by distinguishing habit from frequency of occurrence, and using independent measures for these constructs. This proposition was investigated in three studies using a longitudinal, cross-sectional and experimental design on eating, mental habits and word processing, respectively. In Study 1, snacking habit and past snacking frequency independently predicted later snacking behaviour, while controlling for the theory of planned behaviour variables. Habit fully mediated the effect of past on later behaviour. In Study 2, habitual negative self-thinking and past frequency of negative self-thoughts independently predicted self-esteem and the presence of depressive and anxiety symptoms. In Study 3, habit varied as a function of experimentally manipulated task complexity, while behavioural frequency was held constant. Taken together, while repetition is necessary for habits to develop, these studies demonstrate that habit should not be equated with frequency of occurrence, but rather should be considered as a mental construct involving features of automaticity, such as lack of awareness, difficulty to control and mental efficiency.
‘TuskBot’: Design of the mobile stair climbing 2 by 2 wheels robot platform with novel passive structure ‘Tusk’

In this paper, we present ‘Tuskbot’, a wheel-based robot with a novel structure called ‘Tusk’, passive and protruded elements in the front part of the robot, which can create an angle-of-attack when it climbs stairs. The robot can easily overcome stairs with the help of Tusk, which does not require additional active mechanisms. We formulated a simplified mathematical model of the structure based on the geometrical relationship between the wheels and the stairs in each phase during the stair-climb. To test the model and Tusk structure, we calculated the length of each link and the angle of Tusk from the dimension of stair and radius of wheels, and built the robot accordingly. The results demonstrate the validity of the model and the structure.
The Body Extender: A Full-Body Exoskeleton for the Transport and Handling of Heavy Loads

This article introduces and describes an electrically powered full-body (FB) exoskeleton, called the body extender (BE), intended as a research platform for the study of the transport and handling of heavy loads up to 50 kg, with one hand at worst-load conditions (WLCs). The machine features a 22-degrees-of-freedom (DoF) quasi-anthropomorphic kinematic scheme and a modular hardware/software architecture that made it possible to manage the complexity of the system design. Besides providing a context and some general guidelines, which have driven the design of the BE, this article presents the hardware and software developments that have been achieved and implemented in the machine. The experimental results are shown that prove the functionalities of the BE in common operating conditions such as walking, squatting, and handling loads. The one-of-a-kind system demonstrates, in relevant laboratory settings, the feasibility of a complex, electrically powered full-body exoskeleton with such a target payload.