nils-herrmann/clean_scirepeval huggingface dataset:
Factors and effects of information credibility

Website success hinges on how credible the consumers consider the information on the website. Unless consumers believe the website's information is credible, they are not likely to be willing to act on the advice and will not develop loyalty to the website. This paper reports on how individual differences and initial website impressions affect perceptions of information credibility of an unfamiliar advice website. Results confirm that several individual difference variables and initial impression variables (perceived reputation, perceived website quality, and willingness to explore the website) play an important role in developing information credibility of an unfamiliar website, with first impressions and individual differences playing equivalent roles. The study also confirms the import of information credibility by demonstrating it positively influences perceived usefulness, perceived site risk, willingness to act on website advice, and perceived consumer loyalty toward the website.
Classification of Cotton Leaf Spot Disease Using Support Vector Machine

In order to obtain more value added products, a product quality control is essentially required Many studies show that quality of agriculture products may be reduced from many causes. One of the most important factors of such quality plant diseases. Consequently, minimizing plant diseases allows substantially improving quality of the product Suitable diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian cotton regions. In this paper I express Technological Strategies uses mobile captured symptoms of Cotton Leaf Spot images and categorize the diseases using support vector machine. The classifier is being trained to achieve intelligent farming, including early detection of disease in the groves, selective fungicide application, etc. This proposed work is based on Segmentation techniques in which, the captured images are processed for enrichment first. Then texture and color Feature extraction techniques are used to extract features such as boundary, shape, color and texture for the disease spots to recognize diseases.
Blackboard vs. moodle: Comparing user experience of learning management systems

Learning management systems are becoming ubiquitous technology adopted at institutions of higher learning. Before these systems can be considered effective the user experience must be studied and analyzed to provide the optimum solution to meet pedagogical needs of both faculty and students. This study compared the user experience between the leading proprietary solution, Blackboard, and the leading open source solution, Moodle. We established a control group that only used the proprietary solution and two study groups, a faculty group and a student group that used the open source solution, but had previous experience with the proprietary solution. We used online surveys to compare the user experience of the basic functionality of each system such as communication tools, student-student interaction tools, student-instructor interaction tools. The study was conducted during the Fall 2006 semester at California State University Monterey Bay and included five upper division courses with the learning management systems used as an adjunct to a traditional face-to-face delivery modality.
Computational personality recognition in social media

A variety of approaches have been recently proposed to automatically infer users’ personality from their user generated content in social media. Approaches differ in terms of the machine learning algorithms and the feature sets used, type of utilized footprint, and the social media environment used to collect the data. In this paper, we perform a comparative analysis of state-of-the-art computational personality recognition methods on a varied set of social media ground truth data from Facebook, Twitter and YouTube. We answer three questions: (1) Should personality prediction be treated as a multi-label prediction task (i.e., all personality traits of a given user are predicted at once), or should each trait be identified separately? (2) Which predictive features work well across different on-line environments? and (3) What is the decay in accuracy when porting models trained in one social media environment to another?
An efficient MFCC extraction method in speech recognition

This paper introduces a new algorithm of extracting MFCC for speech recognition. The new algorithm reduces the computation power by 53% compared to the conventional algorithm. Simulation results indicate the new algorithm has a recognition accuracy of 92.93%. There is only a 1.5% reduction in recognition accuracy compared to the conventional MFCC extraction algorithm, which has an accuracy of 94.43%. However, the number of logic gates required to implement the new algorithm is about half of the MFCC algorithm, which makes the new algorithm very efficient for hardware implementation
Robustness of the Markov-chain model for cyber-attack detection

Cyber-attack detection is used to identify cyber-attacks while they are acting on a computer and network system to compromise the security (e.g., availability, integrity, and confidentiality) of the system. This paper presents a cyber-attack detection technique through anomaly-detection, and discusses the robustness of the modeling technique employed. In this technique, a Markov-chain model represents a profile of computer-event transitions in a normal/usual operating condition of a computer and network system (a norm profile). The Markov-chain model of the norm profile is generated from historic data of the system's normal activities. The observed activities of the system are analyzed to infer the probability that the Markov-chain model of the norm profile supports the observed activities. The lower probability the observed activities receive from the Markov-chain model of the norm profile, the more likely the observed activities are anomalies resulting from cyber-attacks, and vice versa. This paper presents the learning and inference algorithms of this anomaly-detection technique based on the Markov-chain model of a norm profile, and examines its performance using the audit data of UNIX-based host machines with the Solaris operating system. The robustness of the Markov-chain model for cyber-attack detection is presented through discussions & applications. To apply the Markov-chain technique and other stochastic process techniques to model the sequential ordering of events, the quality of activity-data plays an important role in the performance of intrusion detection. The Markov-chain technique is not robust to noise in the data (the mixture level of normal activities and intrusive activities). The Markov-chain technique produces desirable performance only at a low noise level. This study also shows that the performance of the Markov-chain techniques is not always robust to the window size: as the window size increases, the amount of noise in the window also generally increases. Overall, this study provides some support for the idea that the Markov-chain technique might not be as robust as the other intrusion-detection methods such as the chi-square distance test technique , although it can produce better performance than the chi-square distance test technique when the noise level of the data is low, such as the Mill & Pascal data in this study.
Bayesian Warped Gaussian Processes

Warped Gaussian processes (WGP)  model output observations in regression tasks as a parametric nonlinear transformation of a Gaussian process (GP). The use of this nonlinear transformation, which is included as part of the probabilistic model, was shown to enhance performance by providing a better prior model on several data sets. In order to learn its parameters, maximum likelihood was used. In this work we show that it is possible to use a non-parametric nonlinear transformation in WGP and variationally integrate it out. The resulting Bayesian WGP is then able to work in scenarios in which the maximum likelihood WGP failed: Low data regime, data with censored values, classification, etc. We demonstrate the superior performance of Bayesian warped GPs on several real data sets.
Photometric Stereo Using Internet Images

Photometric stereo using unorganized Internet images is very challenging, because the input images are captured under unknown general illuminations, with uncontrolled cameras. We propose to solve this difficult problem by a simple yet effective approach that makes use of a coarse shape prior. The shape prior is obtained from multi-view stereo and will be useful in twofold: resolving the shape-light ambiguity in uncalibrated photometric stereo and guiding the estimated normals to produce the high quality 3D surface. By assuming the surface albedo is not highly contrasted, we also propose a novel linear approximation of the nonlinear camera responses with our normal estimation algorithm. We evaluate our method using synthetic data and demonstrate the surface improvement on real data over multi-view stereo results.
Qualitative techniques for evaluating enterprise resource planning (ERP) user interfaces

Studies have recommended usability criteria for evaluating Enterprise Resource Planning (ERP) systems. However these criteria do not provide sufficient qualitative information regarding the behaviour of users when interacting with the user interface of these systems. A triangulation technique, including the use of time diaries, can be used in Human Computer Interaction (HCI) research for providing additional qualitative data that cannot be accurately collected by experimental or even observation means alone.
 Limited studies have been performed on the use of time diaries in a triangulation approach as an HCI research method for the evaluation of the usability of ERP systems. This paper reports on a case study where electronic time diaries were used in conjunction with other HCI research methods, namely, surveys and usability questionnaires, in order to evaluate the usability of an ERP system. The results of the study show that a triangulation technique including the use of time diaries is a rich and useful method that allows more flexibility for respondents and can be used to help understand user behaviour when interacting with ERP systems. A thematic analysis of the qualitative data collected from the time diaries validated the quantitative data and highlighted common problem areas encountered during typical tasks performed with the ERP system. An improved understanding of user behaviour enabled the redesign of the tasks performed during the ERP learning process and could provide guidance to ERP designers for improving the usability and ease of learning of ERP systems.
Pendra goes Dutch: lessons for the CE mark in Europe

The development of a truly non-invasive continuous glucose sensor is an elusive goal. We describe the rise and fall of the Pendra device. In 2000 the company Pendragon Medical introduced a truly non-invasive continuous glucose-monitoring device. This system was supposed to work through so-called impedance spectroscopy. Pendra was Conformité Européenne (CE) approved in May 2003. For a short time the Pendra was available on the Dutch direct-to-consumer market. A post-marketing reliability study was performed in six type 1 diabetes patients. Mean absolute difference between Pendra glucose values and values obtained through self-monitoring of blood glucose was 52%; the Pearson’s correlation coefficient was 35.1%; and a Clarke error grid showed 4.3% of the Pendra readings in the potentially dangerous zone E. We argue that the CE certification process for continuous glucose sensors should be made more transparent, and that a consensus on specific requirements for continuous glucose sensors is needed to prevent patient exposure to potentially dangerous situations.
A humanoid robot drawing human portraits

This paper presents the creation of a robot capable of drawing artistic portraits. The application is purely entertaining and based on existing tools for face detection and image reconstruction, as well as classical tools for trajectory planning of a 4 DOFs robot arm. The innovation of the application lies in the care we took to make the whole process as human-like as possible. The robot's motions and its drawings follow a style characteristic to humans. The portraits conserve the esthetics features of the original images. The whole process is interactive, using speech recognition and speech synthesis to conduct the scenario
Trust and Control in Autonomous Vehicle Interactions

Contacts : creechch@umich.edu, jskumaar@umich.edu, lprobert@umich.edu, tilbury@umich.edu, xijyang@umich.edu, anujkp@umich.edu, kate.tsui@tri.global Summary Our long term goal is to promote mutual trust based on the expectations of both a pedestrian and an autonomous vehicle (AV). Our first step to accomplishing this is to propose a user study that leverages virtual reality (VR) to examine the effects of an autonomous vehicle’s driving behavior and situational characteristics on a pedestrian’s trust.
An augmented reality training platform for assembly and maintenance skills

Training technicians to acquire newmaintenance and assembly skills is important for various industries. Becausemaintenance and assembly tasks can be very complex, training technicians to efficiently perform new skills is challenging. Training of this type can be supported by Augmented Reality, a powerful industrial training technology that directly links instructions on how to perform the service tasks to the machine parts that require processing. Because of the increasing complexity ofmaintenance tasks, it is not sufficient to train the technicians in task execution. Instead, techniciansmust be trained in the underlying skills—sensorimotor and cognitive—that are necessary for the efficient acquisition and performance of new maintenance operations. These facts illustrate the need for efficient training systems for maintenance and assembly skills that accelerate the technicians’ acquisition of new maintenance procedures. Furthermore, these systems should improve the adjustment of the training process for new training scenarios and enable the reuse of worthwhile existing training material. In this context, we have developed a novel concept and platform for multimodal Augmented Reality-based training of maintenance and assembly skills, which includes sub-skill training and the evaluation of the training system. Because procedural skills are considered as the most important skills for maintenance and assembly operations, we focus on these skills and the appropriate methods for improving them.
Collaborative Ontology Construction for Information Integration

Information integration is enabled by having a precisely defined common terminology. We call this combination of terminology and definitions an ontology. We have developed a set of tools and services to support the process of achieving consensus on such a common shared ontologies by geographically distributed groups. These tools make use of the world-wide web to enable wide access and provide users with the ability to publish, browse, create, and edit ontologies stored on an ontology server . Users can quickly assemble a new ontology from a library of modules. We discuss how our system was constructed, how it exploits existing protocols and browsing tools, and our experience supporting hundreds of users. We describe applications using our tools to achieve consensus on ontologies and to integrate information. The ontology server may be accessed through the URL http://www-ksl-svc.stanford.edu:5915/
Dense Event Ordering with a Multi-Pass Architecture

The past 10 years of event ordering research has focused on learning partial orderings over document events and time expressions. The most popular corpus, the TimeBank, contains a small subset of the possible ordering graph. Many evaluations follow suit by only testing certain pairs of events (e.g., only main verbs of neighboring sentences). This has led most research to focus on specific learners for partial labelings. This paper attempts to nudge the discussion from identifying some relations to all relations. We present new experiments on strongly connected event graphs that contain ∼10 times more relations per document than the TimeBank. We also describe a shift away from the single learner to a sieve-based architecture that naturally blends multiple learners into a precision-ranked cascade of sieves. Each sieve adds labels to the event graph one at a time, and earlier sieves inform later ones through transitive closure. This paper thus describes innovations in both approach and task. We experiment on the densest event graphs to date and show a 14% gain over state-of-the-art.
Privacy.tag: privacy concern expressed and respected

The ever increasing popularity of social networks and the ever easier photo taking and sharing experience have led to unprecedented concerns on privacy infringement. Inspired by the fact that the Robot Exclusion Protocol, which regulates web crawlers' behavior according a per-site deployed robots.txt, and cooperative practices of major search service providers, have contributed to a healthy web search industry, in this paper, we propose Privacy Expressing and Respecting Protocol (PERP) that consists of a Privacy.tag -- a physical tag that enables a user to explicitly and flexibly express their privacy deal, and Privacy Respecting Sharing Protocol (PRSP) -- a protocol that empowers the photo service provider to exert privacy protection following users' policy expressions, to mitigate the public's privacy concern, and ultimately create a healthy photo-sharing ecosystem in the long run. We further design an exemplar Privacy.Tag using customized yet compatible QR-code, and implement the Protocol and study the technical feasibility of our proposal. Our evaluation results confirm that PERP and PRSP are indeed feasible and incur negligible computation overhead.
SoundWrite: Text Input on Surfaces through Mobile Acoustic Sensing

Interacting with explosively growing mobile devices is becoming imperative. This paper presents SoundWrite, a mobile acoustic sensing system that enables text input into commercial off-the-shelf devices without any accessories. SoundWrite leverages the embedded microphone to capture subtle audio signals emitted from writing text on common found surfaces (eg., a wood table). It then extracts distinguishable features from both time and frequency information of received signals to recognize the text. We prototype SoundWrite on Smartphones as an Android application, and perform in-depth evaluation. The evaluation results validate the effectiveness and robustness of SoundWrite, and demonstrate that it could achieve an average recognition accuracy of above 90%.
Cooling Schedules for Optimal Annealing

A Monte Carlo c^timization technique called "simulated annealing" is a descent algorithm modified by random ascent moves in order to escape local minima which are not global minima. Tlie levd of randomization is determined by a control parameter T, called temperature, which tends to zero according to a deterministic "cooling schedule". We give a simple necessary and suffident conditicm on the cooling sdiedule for the algorithm state to converge in probability to the set of globally minimiim cost states. In the spedal case that the cooling schedule has parameuic form r({) » c/log(l + / ) , the condition for convergence is that c be greater than or equal to the depth, suitably defined, of the deepest local minimum which is not a global minimum state.
Classification of Abnormalities in Brain MRI Images Using PCA and SVM

The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
Autonomous driving in urban environments: approaches, lessons and challenges.

The development of autonomous vehicles for urban driving has seen rapid progress in the past 30 years. This paper provides a summary of the current state of the art in autonomous driving in urban environments, based primarily on the experiences of the authors in the 2007 DARPA Urban Challenge (DUC). The paper briefly summarizes the approaches that different teams used in the DUC, with the goal of describing some of the challenges that the teams faced in driving in urban environments. The paper also highlights the long-term research challenges that must be overcome in order to enable autonomous driving and points to opportunities for new technologies to be applied in improving vehicle safety, exploiting intelligent road infrastructure and enabling robotic vehicles operating in human environments.
BeThere: 3D mobile collaboration with spatial input

We present BeThere, a proof-of-concept system designed to explore 3D input for mobile collaborative interactions. With BeThere, we explore 3D gestures and spatial input which allow remote users to perform a variety of virtual interactions in a local user's physical environment. Our system is completely self-contained and uses depth sensors to track the location of a user's fingers as well as to capture the 3D shape of objects in front of the sensor. We illustrate the unique capabilities of our system through a series of interactions that allow users to control and manipulate 3D virtual content. We also provide qualitative feedback from a preliminary user study which confirmed that users can complete a shared collaborative task using our system.
A 70MS/s 69.3dB SNDR 38.2fJ/conversion-step time-based pipelined ADC

A Nyquist ADC with time-based pipelined architecture is proposed. The proposed hybrid pipeline stage, incorporating time-domain amplification based on a charge pump, enables power efficient analog to digital conversion. The proposed ADC also adopts a minimalist switched amplifier with 24dB open-loop dc gain in the first stage MDAC that is based on a new V-T operation, instead of a conventional high gain amplifier. The measured results of the prototype ADC implemented in a 0.13μm CMOS demonstrate peak SNDR of 69.3dB at 6.38mW power, with a near rail-to-rail 1MHz input of 2.4VP-P at 70MHz sampling frequency and 1.3V supply. This results in 38.2fJ/conversion-step FOM.
Super-resolution: a comprehensive survey

Super-resolution, the process of obtaining one or more high-resolution images from one or more low-resolution observations, has been a very attractive research topic over the last two decades. It has found practical applications in many real-world problems in different fields, from satellite and aerial imaging to medical image processing, to facial image analysis, text image analysis, sign and number plates reading, and biometrics recognition, to name a few. This has resulted in many research papers, each developing a new super-resolution algorithm for a specific purpose. The current comprehensive survey provides an overview of most of these published works by grouping them in a broad taxonomy. For each of the groups in the taxonomy, the basic concepts of the algorithms are first explained and then the paths through which each of these groups have evolved are given in detail, by mentioning the contributions of different authors to the basic concepts of each group. Furthermore, common issues in super-resolution algorithms, such as imaging models and registration algorithms, optimization of the cost functions employed, dealing with color information, improvement factors, assessment of super-resolution algorithms, and the most commonly employed databases are discussed.