nils-herrmann/clean_scirepeval huggingface dataset:
Recognition of Multiple-Food Images by Detecting Candidate Regions

In this paper, we propose a two-step method to recognize multiple-food images by detecting candidate regions with several methods and classifying them with various kinds of features. In the first step, we detect several candidate regions by fusing outputs of several region detectors including Felzenszwalb's deformable part model (DPM) , a circle detector and the JSEG region segmentation. In the second step, we apply a feature-fusion-based food recognition method for bounding boxes of the candidate regions with various kinds of visual features including bag-of-features of SIFT and CSIFT with spatial pyramid (SP-BoF), histogram of oriented gradient (HoG), and Gabor texture features. In the experiments, we estimated ten food candidates for multiple-food images in the descending order of the confidence scores. As results, we have achieved the 55.8% classification rate, which improved the baseline result in case of using only DPM by 14.3 points, for a multiple-food image data set. This demonstrates that the proposed two-step method is effective for recognition of multiple-food images.
Autonomous robotic monitoring of underground cable systems

Maintenance of distributed infrastructures requires periodic measurement of many physical variables at numerous locations. This task can potentially be accomplished with autonomous robotic mobile platforms. The challenges in realizing this vision include electromechanical design of the robot itself, integration of sensors able to estimate the physical properties of the infrastructure, and autonomous operation. This paper describes the electromechanical and sensing system design of the autonomous robot for the inspection of electric power cables. The multiprocessor distributed control architecture developed in this project allows real-time operation of multiple sensors and a possibility to switch between an autonomous mode of operation and a remote-controlled one. The diagnostic sensor array includes thermal, visual, dielectric, and acoustic sensors for the measurement of cable status. Laboratory tests demonstrate the ability of integrated sensors to measure parameters of interest with the resolution required by the application. Field tests in the underground cable system demonstrate the ability of the designed platform to travel along the cable, navigate typical obstacles, and communicate with the host computer
From e-ladder to e-diamond - re-conceptualising models for public e-services

There is rapid growth in the development and launching of new public e-services to citizens. In doing this, government agencies base their work on national and international programs for e-government development. Many such programs include a stage model for public e-services. Many such models comprise stages of 1) information, 2) interaction, 3) transaction and 4) interaction. The wide spread use of such stage models (e-ladders) give rise to several questions. Are the categories of a stage model well chosen? Do e-services evolve through such a series of stages? Is there a real advancement between the different stages? Should one always strive for higher stages? Are higher stages inherently better than lower stages? Is a stage model a proper yardstick for evaluation and benchmarking? The paper pursues a critical examination of such stage models (called e-ladder). A conceptual analysis of stage models is performed based on a socio-pragmatic foundation. Empirical examples are given that show weaknesses in the assumptions and conceptualisations of stage models. An alternative model the e-diamond is presented consisting of three polarities (informative vs performative; standardized vs individualized; separate vs coordinated).
The performance of touch screen soft buttons

The introduction of a new generation of attractive touch screen-based devices raises many basic usability questions whose answers may influence future design and market direction. With a set of current mobile devices, we conducted three experiments focusing on one of the most basic interaction actions on touch screens: the operation of soft buttons. Issues investigated in this set of experiments include: a comparison of soft button and hard button performance; the impact of audio and vibrato-tactile feedback; the impact of different types of touch sensors on use, behavior, and performance; a quantitative comparison of finger and stylus operation; and an assessment of the impact of soft button sizes below the traditional 22 mm recommendation as well as below finger width.
Mining people's trips from large scale geo-tagged photos

Photo sharing is one of the most popular Web services. Photo sharing sites provide functions to add tags and geo-tags to photos to make photo organization easy. Considering that people take photos to record something that attracts them, geo-tagged photos are a rich data source that reflects people's memorable events associated with locations. In this paper, we focus on geo-tagged photos and propose a method to detect people's frequent trip patterns, i.e., typical sequences of visited cities and durations of stay as well as descriptive tags that characterize the trip patterns. Our method first segments photo collections into trips and categorizes them based on their trip themes, such as visiting landmarks or communing with nature. Our method mines frequent trip patterns for each trip theme category. We crawled 5.7 million geo-tagged photos and performed photo trip pattern mining. The experimental result shows that our method outperforms other baseline methods and can correctly segment photo collections into photo trips with an accuracy of 78%. For trip categorization, our method can categorize about 80% of trips using tags and titles of photos and visited cities as features. Finally, we illustrate interesting examples of trip patterns detected from our dataset and show an application with which users can search frequent trip patterns by querying a destination, visit duration, and trip theme on the trip.
Theory and Design of Octave Tunable Filters With Lumped Tuning Elements

This paper presents octave-tunable resonators and filters with surface mounted lumped tuning elements. Detailed theoretical analysis and modeling in terms of tuning range and unloaded quality factor (Qu) are presented in agreement with simulated and measured results. Based on the models, a systematic design method to maximize tuning ratio and optimize Qu of the resonator is suggested. A resonator tuning from 0.5 to 1.1 GHz with Qu ranging from 90 to 214 is demonstrated using solid-state varactors. A two-pole filter with a tuning range of 0.5-1.1 GHz with a constant 3-dB fractional bandwidth (FBW) of 4±0.1% and insertion loss of 1.67 dB at 1.1 GHz is demonstrated along with a three-pole filter with a tuning range of 0.58-1.22 GHz with a constant 3-dB FBW of 4±0.2% and insertion loss of 2.05 dB at 1.22 GHz. The measured input third-order intermodulation is better than 17 dBm over the frequency range for the two-pole filter.
Predicting Cross-Site Scripting (XSS) security vulnerabilities in web applications

Recently, machine-learning based vulnerability prediction models are gaining popularity in web security space, as these models provide a simple and efficient way to handle web application security issues. Existing state-of-art Cross-Site Scripting (XSS) vulnerability prediction approaches do not consider the context of the user-input in output-statement, which is very important to identify context-sensitive security vulnerabilities. In this paper, we propose a novel feature extraction algorithm to extract basic and context features from the source code of web applications. Our approach uses these features to build various machine-learning models for predicting context-sensitive Cross-Site Scripting (XSS) security vulnerabilities. Experimental results show that the proposed features based prediction models can discriminate vulnerable code from non-vulnerable code at a very low false rate.
Real-Time Street Parking Availability Estimation

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a traveler's mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phone's Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.
A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this article, we present a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection, and human pose estimation.
Planetary rovers' wheel-soil interaction mechanics: new challenges and applications for wheeled mobile robots

With the increasing challenges facing planetary exploration missions and the resultant increase in the performance requirements for planetary rovers, terramechanics (wheel–soil interaction mechanics) is playing an important role in the development of these rovers. As an extension of the conventional terramechanics theory for terrestrial vehicles, the terramechanics theory for planetary rovers, which is becoming a new research hotspot, is unique and puts forward many new challenging problems. This paper first discusses the significance of the study of wheel–soil interaction mechanics of planetary rovers and summarizes the differences between planetary rovers and terrestrial vehicles and the problems arising thereof. The application of terramechanics to the development of planetary rovers can be divided into two phases (the R&D phase and exploration phase for rovers) corresponding to the high-fidelity and simplified terramechanics models. This paper also describes the current research status by providing an introduction to classical terramechanics and the experimental, theoretical, and numerical researches on terramechanics for planetary rovers. The application status of the terramechanics for planetary rovers is analyzed from the aspects of rover design, performance evaluation, planetary soil parameter identification, dynamics simulation, mobility control, and path planning. Finally, the key issues for future research are discussed. The current planetary rovers are actually advanced wheeled mobile robots L. Ding · Z. Deng · H. Gao (B) Harbin Institute of Technology, Harbin, China e-mail: gaohaibo@hit.edu.cn; liangding@hit.edu.cn K. Nagatani · K. Yoshida Department of Aerospace Engineering, Tohoku University, Sendai 980-8579, Japan (WMRs), developed employing cutting-edge technologies from different fields. The terramechanics for planetary rovers is expected to present new challenges and applications for WMRs, making it possible to develop WMRs using the concepts of mechanics and dynamics.
Effective Slot Filling Based on Shallow Distant Supervision Methods

Spoken Language Systems at Saarland University (LSV) participated this year with 5 runs at the TAC KBP English slot filling track. Effective algorithms for all parts of the pipeline, from document retrieval to relation prediction and response post-processing, are bundled in a modular end-to-end relation extraction system called RelationFactory. The main run solely focuses on shallow techniques and achieved significant improvements over LSV’s last year’s system, while using the same training data and patterns. Improvements mainly have been obtained by a feature representation focusing on surface skip n-grams and improved scoring for extracted distant supervision patterns. Important factors for effective extraction are the training and tuning scheme for distant supervision classifiers, and the query expansion by a translation model based on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the submitted main run of the LSV RelationFactory system achieved the top-ranked F1-score of 37.3%.
Formalizing the Pragmatics of Metaphor Understanding

While the ubiquity and importance of nonliteral language are clear, people’s ability to use and understand it remains a mystery. Metaphor in particular has been studied extensively across many disciplines in cognitive science. One approach focuses on the pragmatic principles that listeners utilize to infer meaning from metaphorical utterances. While this approach has generated a number of insights about how people understand metaphor, to our knowledge there is no formal model showing that effects in metaphor understanding can arise from basic principles of communication. Building upon recent advances in formal models of pragmatics, we describe a computational model that uses pragmatic reasoning to interpret metaphorical utterances. We conduct behavioral experiments to evaluate the model’s performance and show that our model produces metaphorical interpretations that closely fit behavioral data. We discuss implications of the model for metaphor understanding, principles of communication, and formal models of language understanding.
Sparse Occlusion Detection with Optical Flow

We tackle the problem of detecting occluded regions in a video stream. Under assumptions of Lambertian reflection and static illumination, the task can be posed as a variational optimization problem, and its solution approximated using convex minimization. We describe efficient numerical schemes that reach the global optimum of the relaxed cost functional, for any number of independently moving objects, and any number of occlusion layers. We test the proposed algorithm on benchmark datasets, expanded to enable evaluation of occlusion detection performance, in addition to optical flow.
A Library of Parameterizable Floating-Point Cores for FPGAs and Their Application to Scientific Computing

Advances in field programmable gate arrays (FPGAs), which are the platform of choice for reconfigurable computing, have made it possible to use FPGAs in increasingly ma ny areas of computing, including complex scientific applicati ons. These applications demand high performance and high-preci s on, floating-point arithmetic. Until now, most of the research has not focussed on compliance with IEEE standard 754, focusing ins tead upon custom formats and bitwidths. In this paper, we present double-precision floating-point cores that are parameteri zed by their degree of pipelining and the features of IEEE standard754 that they implement. We then analyze the effects of supporti ng the standard when these cores are used in an FPGA-based accelerator for Lennard-Jones force and potential calculations that are part of molecular dynamics (MD) simulations.
High-Frequency High-Efficiency $CLL$ Resonant Converters With Synchronous Rectifiers

This paper proposes a CLL resonant dc-dc converter as an option for offline applications. This topology can achieve zero-voltage switching from zero load to a full load and zero-current switching for output rectifiers and makes the implementation of a secondary rectifier easy. This paper also presents a novel methodology for designing CLL resonant converters based on efficiency and holdup time requirements. An optimal transformer structure is proposed, which uses a current-type synchronous rectifier (SR) drive scheme. An 800-kHz 250-W CLL resonant converter prototype is built to verify the proposed circuit, design method, transformer structure, and SR drive scheme.
Stochastic reachability based motion planning for multiple moving obstacle avoidance

One of the many challenges in designing autonomy for operation in uncertain and dynamic environments is the planning of collision-free paths. Roadmap-based motion planning is a popular technique for identifying collision-free paths, since it approximates the often infeasible space of all possible motions with a networked structure of valid configurations. We use stochastic reachable sets to identify regions of low collision probability, and to create roadmaps which incorporate likelihood of collision. We complete a small number of stochastic reachability calculations with individual obstacles a priori. This information is then associated with the weight, or preference for traversal, given to a transition in the roadmap structure. Our method is novel, and scales well with the number of obstacles, maintaining a relatively high probability of reaching the goal in a finite time horizon without collision, as compared to other methods. We demonstrate our method on systems with up to 50 dynamic obstacles.
Query expansion using associated queries

Hundreds of millions of users each day use web search engines to meet their information needs. Advances in web search effectiveness are therefore perhaps the most significant public outcomes of IR research. Query expansion is one such method for improving the effectiveness of ranked retrieval by adding additional terms to a query. In previous approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We propose a new method of obtaining expansion terms, based on selecting terms from past user queries that are associated with documents in the collection. Our scheme is effective for query expansion for web retrieval: our results show relative improvements over unexpanded full text retrieval of 26%--29%, and 18%--20% over an optimised, conventional expansion approach.
A Strategy Development Process for Enterprise Content Management

Today, many organizations maintain a variety of systems and databases in a complex ad-hoc architecture that does not seem to fulfill the needs for company-wide unstructured information management in business processes, business functions, and the extended enterprise. We describe a framework to implement Enterprise Content Management (ECM) in order to address this problem. ECM refers to the technologies, tools, and methods used to capture, manage, store, preserve, and deliver content (e.g. documents, graphics, drawings, web pages) across an enterprise. The framework helps to select content objects that can be brought under ECM to create business value and guide the IT investments needed to realize ECM. The framework was tested in a large high tech organization.
Big Data Quality: A Quality Dimensions Evaluation

Data is the most valuable asset companies are proud of. When its quality degrades, the consequences are unpredictable, can lead to complete wrong insights. In Big Data context, evaluating the data quality is challenging, must be done prior to any Big data analytics by providing some data quality confidence. Given the huge data size, its fast generation, it requires mechanisms, strategies to evaluate, assess data quality in a fast, efficient way. However, checking the Quality of Big Data is a very costly process if it is applied on the entire data. In this paper, we propose an efficient data quality evaluation scheme by applying sampling strategies on Big data sets. The Sampling will reduce the data size to a representative population samples for fast quality evaluation. The evaluation targeted some data quality dimensions like completeness, consistency. The experimentations have been conducted on Sleep disorder's data set by applying Big data bootstrap sampling techniques. The results showed that the mean quality score of samples is representative for the original data, illustrate the importance of sampling to reduce computing costs when Big data quality evaluation is concerned. We applied the Quality results generated as quality proposals on the original data to increase its quality.
Theory and Evaluation of Human Robot Interactions

Human-robot interaction (HRI) for mobile robots is still in its infancy. Most user interactions with robots have been limited to teleoperation capabilities where the most common interface provided to the user has been the video feed from the robotic platform and some way of directing the path of the robot. For mobile robots with semi-autonomous capabilities, the user is also provided with a means of setting way points. More importantly, most HRI capabilities have been developed by robotics experts for use by robotics experts. As robots increase in capabilities and are able to perform more tasks in an autonomous manner we need to think about the interactions that humans will have with robots and what software architecture and user interface designs can accommodate the human in-the-loop. We also need to design systems that can be used by domain experts but not robotics experts. This paper outlines a theory of human-robot interaction and proposes the interactions and information needed by both humans and robots for the different levels of interaction, including an evaluation methodology based on situational awareness.
Bird's-Eye View Vision System for Vehicle Surrounding Monitoring

Blind spots usually lead to difficulties for drivers to maneuver their vehicles in complicated environments, such as garages, parking spaces, or narrow alleys. This paper presents a vision system which can assist drivers by providing the panoramic image of vehicle surroundings in a bird’s-eye view. In the proposed system, there are six fisheye cameras mounted around a vehicle so that their views cover the whole surrounding area. Parameters of these fisheye cameras were calibrated beforehand so that the captured images can be dewarped into perspective views for integration. Instead of error-prone stereo matching, overlapping regions of adjacent views are stitched together by aligning along a seam with dynamic programming method followed by propagating the deformation field of alignment with Wendland functions. In this way the six fisheye images can be integrated into a single, panoramic, and seamless one from a look-down viewpoint. Our experiments clearly demonstrate the effectiveness of the proposed image-stitching method for providing the bird’s eye view vision for vehicle surrounding monitoring.