nils-herrmann/clean_scirepeval huggingface dataset:
Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles

Future driver assistance systems will rely on accurate, reliable and continuous knowledge on the position of other road participants, including pedestrians, bicycles and other vehicles. The usual approach to tackle this requirement is to use on-board ranging sensors inside the vehicle. Radar, laser scanners or vision-based systems are able to detect objects in their line-of-sight. In contrast to these non-cooperative ranging sensors, cooperative approaches follow a strategy in which other road participants actively support the estimation of the relative position. The limitations of on-board ranging sensors regarding their detection range and angle of view and the facility of blockage can be approached by using a cooperative approach based on vehicle-to-vehicle communication. The fusion of both, cooperative and non-cooperative strategies, seems to offer the largest benefits regarding accuracy, availability and robustness. This survey offers the reader a comprehensive review on different techniques for vehicle relative positioning. The reader will learn the important performance indicators when it comes to relative positioning of vehicles, the different technologies that are both commercially available and currently under research, their expected performance and their intrinsic limitations. Moreover, the latest research in the area of vision-based systems for vehicle detection, as well as the latest work on GNSS-based vehicle localization and vehicular communication for relative positioning of vehicles, are reviewed. The survey also includes the research work on the fusion of cooperative and non-cooperative approaches to increase the reliability and the availability.
Loneliness, social network size, and immune response to influenza vaccination in college freshmen.

Antibody response to the influenza immunization was investigated in 83 1st-semester healthy university freshmen. Elevated levels of loneliness throughout the semester and small social networks were independently associated with poorer antibody response to 1 component of the vaccine. Those with both high levels of loneliness and a small social network had the lowest antibody response. Loneliness was also associated with greater psychological stress and negative affect, less positive affect, poorer sleep efficiency and quality, and elevations in circulating levels of cortisol. However, only the stress data were consistent with mediation of the loneliness-antibody response relation. None of these variables were associated with social network size, and hence none were potential mediators of the relation between network size and immunization response.
Smelling Screen: Development and Evaluation of an Olfactory Display System for Presenting a Virtual Odor Source

We propose a new olfactory display system that can generate an odor distribution on a two-dimensional display screen. The proposed system has four fans on the four corners of the screen. The airflows that are generated by these fans collide multiple times to create an airflow that is directed towards the user from a certain position on the screen. By introducing odor vapor into the airflows, the odor distribution is as if an odor source had been placed onto the screen. The generated odor distribution leads the user to perceive the odor as emanating from a specific region of the screen. The position of this virtual odor source can be shifted to an arbitrary position on the screen by adjusting the balance of the airflows from the four fans. Most users do not immediately notice the odor presentation mechanism of the proposed olfactory display system because the airflow and perceived odor come from the display screen rather than the fans. The airflow velocity can even be set below the threshold for airflow sensation, such that the odor alone is perceived by the user. We present experimental results that show the airflow field and odor distribution that are generated by the proposed system. We also report sensory test results to show how the generated odor distribution is perceived by the user and the issues that must be considered in odor presentation.
CMOS millimeter wave phase shifter based on tunable transmission lines

This paper presents a tunable transmission line (t-line) structure, featuring independent control of line inductance and capacitance. The t-line provides variable delay while maintaining relatively constant characteristic impedance using direct digital control through FET switches. As an application of this original structure, a 60 GHz RF-phase shifter for phased-array applications is implemented in a 32 nm SOI process attaining state-of-the-art performance. Measured data from two phase shifter variants at 60 GHz showed phase changes of 175° and 185°, S21 losses of 3.5-7.1 dB and 6.1-7.6 dB, RMS phase errors of 2° and 3.2°, and areas of 0.073 mm2 and 0.099 mm2 respectively.
Approximate Undirected Maximum Flows in O(mpolylog(n)) Time

We give the first O(mpolylog(n)) time algorithms for approximating maximum flows in undirected graphs and constructing polylog(n)-quality cut-approximating hierarchical tree decompositions. Our algorithm invokes existing algorithms for these two problems recursively while gradually incorporating size reductions. These size reductions are in turn obtained via ultra-sparsifiers, which are key tools in solvers for symmetric diagonally dominant (SDD) linear systems.
Adversarial Label Flips Attack on Support Vector Machines

To develop a robust classification algorithm in the adversarial setting, it is important to understand the adversary ’ strategy. We address the problem of label flips attack where an adversar y contaminates the training set through flipping labels. By analy zing the objective of the adversary, we formulate an optimization fr amework for finding the label flips that maximize the classification er ror. An algorithm for attacking support vector machines is derived . Experiments demonstrate that the accuracy of classifiers is signi fica tly degraded under the attack.
Cinematographic User Models for Automated Realtime Camera Control in Dynamic 3D Environments

Advances in 3D graphics technology have accelerated the con struction of dynamic 3D environments. Despite their promise for scientific and educational applications, much of this potential has gone unrealized because runtime c a era control software lacks user-sensitivity. Current environments rely on sequences of viewpoints that directly require the user’s control or are based primarily on actions and geom etry of the scene. Because of the complexity of rapidly changing environments, users typ ically cannot manipulate objects in environments while simultaneously issuing camera contr ol commands. To address these issues, we have developed UC AM , a realtime camera planner that employs cinematographic user models to render customized visualizations of dynamic 3D environments. After interviewing users to determine their preferred directorial sty e and pacing, UCAM examines the resulting cinematographic user model to plan camera sequen ces whose shot vantage points and cutting rates are tailored to the user in realtime. Evalu ations of UCAM in a dynamic 3D testbed are encouraging.
What makes good research in software engineering?

Physics, biology, and medicine have well-refined public explanations of their research processes. Even in simplified form, these provide guidance about what counts as “good research” both inside and outside the field. Software engineering has not yet explicitly identified and explained either our research processes or the ways we recognize excellent work. Science and engineering research fields can be characterized in terms of the kinds of questions they find worth investigating, the research methods they adopt, and the criteria by which they evaluate their results. I will present such a characterization for software engineering, showing the diversity of research strategies and the way they shift as ideas mature. Understanding these strategies should help software engineers design research plans and report the results clearly; it should also help explain the character of software engineering research to computer science at large and to other scientists.
SiC Trench MOSFET With Shielded Fin-Shaped Gate to Reduce Oxide Field and Switching Loss

A silicon carbide shielded fin-shaped gate metal-oxide-semiconductor field effect transistor (SF-MOS) is proposed in this letter, which utilizes a well-grounded p-region to shield the fin-shaped trench gate. Numerical simulations by Sentaurus TCAD are carried out to study the performance of SF-MOS, and comparisons with conventional trench MOSFET and the state-of-the-art double-trench MOSFET are presented. The maximum electric field in gate oxide of the SF-MOS is effectively lowered to below 3 MV/cm, which is a widely accepted criterion for long-term gate oxide reliability. Furthermore, with the shielding effects, the gate-to-drain charge of the SF-MOS is significantly reduced, leading to lower switching loss.
Combining News and Technical Indicators in Daily Stock Price Trends Prediction

Stock market prediction has always been one of the hottest topics in research, as well as a great challenge due to its complex and volatile nature. However, most of the existing methods neglect the impact from mass media that will greatly affect the behavior of investors. In this paper we present a system that combines the information from both related news releases and technical indicators to enhance the predictability of the daily stock price trends. The performance shows that this system can achieve higher accuracy and return than a single source system.
BlowFish: Dynamic Storage-Performance Tradeoff in Data Stores

We present BlowFish, a distributed data store that admits a smooth tradeoff between storage and performance for point queries. What makes BlowFish unique is its ability to navigate along this tradeoff curve efficiently at finegrained time scales with low computational overhead. Achieving a smooth and dynamic storage-performance tradeoff enables a wide range of applications. We apply BlowFish to several such applications from real-world production clusters: (i) as a data recovery mechanism during failures: in practice, BlowFish requires 5.4× lower bandwidth and 2.5× lower repair time compared to stateof-the-art erasure codes, while reducing the storage cost of replication from 3× to 1.9×; and (ii) data stores with spatially-skewed and time-varying workloads (e.g., due to object popularity and/or transient failures): we show that navigating the storage-performance tradeoff achieves higher system-wide utility (e.g., throughput) than selectively caching hot objects.
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization

A key challenge for timeline summarization is to generate a concise, yet complete storyline from large collections of news stories. Previous studies in extractive timeline generation are limited in two ways: first, most prior work focuses on fully-observable ranking models or clustering models with hand-designed features that may not generalize well. Second, most summarization corpora are text-only, which means that text is the sole source of information considered in timeline summarization, and thus, the rich visual content from news images is ignored. To solve these issues, we leverage the success of matrix factorization techniques from recommender systems, and cast the problem as a sentence recommendation task, using a representation learning approach. To augment text-only corpora, for each candidate sentence in a news article, we take advantage of top-ranked relevant images from the Web and model the image using a convolutional neural network architecture. Finally, we propose a scalable low-rank approximation approach for learning joint embeddings of news stories and images. In experiments, we compare our model to various competitive baselines, and demonstrate the stateof-the-art performance of the proposed textbased and multimodal approaches.
Video OCR: indexing digital news libraries by recognition of superimposed captions

The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multi-frame integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.
Information Security Management of Web Portals Based on Joomla CMS

Information is the key asset of all organizations and can exist in many forms. It can be printed or written on paper, stored electronically, transmitted by mail or by electronic means, shown in films, or spoken in conversation. In today's competitive business environment, such information is constantly under threat from many sources, which can be internal, external, accidental, or malicious. Joomla is a very popular Content Management System (CMS) used for web page maintenance. This highly versatile software has found itself in both large corporate web portals, and simple web pages such as blogs. Such popularity increases its vulnerability to potential attacks and therefore needs an appropriate security management. ISO (the International Organization for Standardization) and IEC (the International Electrotechnical Commission) created the series of standards aimed at providing a model for establishing, implementing, operating, monitoring, reviewing, maintaining and improving an Information Security Management System (ISMS). This paper shows how principles set in ISO/IEC 27000 series of standards can be used to improve security of Joomla based web portals.
SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories

Predicting the next location a user tends to visit is an important task for applications like location-based advertising, traffic planning, and tour recommendation. We consider the next location prediction problem for semantic trajectory data, wherein each GPS record is attached with a text message that describes the user's activity. In semantic trajectories, the confluence of spatiotemporal transitions and textual messages indicates user intents at a fine granularity and has great potential in improving location prediction accuracies. Nevertheless, existing methods designed for GPS trajectories fall short in capturing latent user intents for such semantics-enriched trajectory data. We propose a method named semantics-enriched recurrent model (SERM). SERM jointly learns the embeddings of multiple factors (user, location, time, keyword) and the transition parameters of a recurrent neural network in a unified framework. Therefore, it effectively captures semantics-aware spatiotemporal transition regularities to improve location prediction accuracies. Our experiments on two real-life semantic trajectory datasets show that SERM achieves significant improvements over state-of-the-art methods.
Direct Prediction of 3D Body Poses from Motion Compensated Sequences

We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Previous approaches typically compute candidate poses in individual frames and then link them in a post-processing step to resolve ambiguities. By contrast, we directly regress from a spatio-temporal volume of bounding boxes to a 3D pose in the central frame. We further show that, for this approach to achieve its full potential, it is essential to compensate for the motion in consecutive frames so that the subject remains centered. This then allows us to effectively overcome ambiguities and improve upon the state-of-the-art by a large margin on the Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation benchmarks.
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design in which the modeling and computing stages are studied in isolation. In this paper, we propose a learning algorithm, auto-context. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps created by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates until convergence. Auto-context integrates low-level and context information by fusing a large number of low-level appearance features with context and implicit shape information. The resulting discriminative algorithm is general and easy to implement. Under nearly the same parameter settings in training, we apply the algorithm to three challenging vision applications: foreground/background segregation, human body configuration estimation, and scene region labeling. Moreover, context also plays a very important role in medical/brain images where the anatomical structures are mostly constrained to relatively fixed positions. With only some slight changes resulting from using 3D instead of 2D features, the auto-context algorithm applied to brain MRI image segmentation is shown to outperform state-of-the-art algorithms specifically designed for this domain. Furthermore, the scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems.
Foundations for engineering biology

Engineered biological systems have been used to manipulate information, construct materials, process chemicals, produce energy, provide food, and help maintain or enhance human health and our environment. Unfortunately, our ability to quickly and reliably engineer biological systems that behave as expected remains quite limited. Foundational technologies that make routine the engineering of biology are needed. Vibrant, open research communities and strategic leadership are necessary to ensure that the development and application of biological technologies remains overwhelmingly constructive.
Smart objects as building blocks for the Internet of things

The combination of the Internet and emerging technologies such as nearfield communications, real-time localization, and embedded sensors lets us transform everyday objects into smart objects that can understand and react to their environment. Such objects are building blocks for the Internet of Things and enable novel computing applications. As a step toward design and architectural principles for smart objects, the authors introduce a hierarchy of architectures with increasing levels of real-world awareness and interactivity. In particular, they describe activity-, policy-, and process-aware smart objects and demonstrate how the respective architectural abstractions support increasingly complex application.
Investigation on the control strategy of soft wearable robotic hand with slack enabling tendon actuator

A soft wearable robot, which is an emerging type of wearable robot, can take advantage of tendon-driven mechanisms with a Bowden cable. These tendon-driven mechanisms benefits soft wearable robots because the actuator can be remotely placed and the transmission is very compact. However, it is difficult to compensate the friction along the Bowden cable which makes it hard to control. This study proposes the use of a position-based impedance controller, which is robust to the nonlinear dynamics of the system and provides compliant interaction between robot, human, and environment. Additionally, to eliminate disturbances from unexpected tension of the antagonistic wire arising from friction, this study proposes a new type of slack enabling tendon actuator. It can eliminate friction force along the antagonistic wire by actively pushing the wire while preventing derailment of the wire from the spool.
Legolizer: A Real-Time System for Modeling and Rendering LEGO Representations of Boundary Models

In this work we propose a method for converting triangular meshes into LEGO bricks through a voxel representation of boundary meshes. We present a novel voxelization approach that uses points sampled from a surface model to define which cubes (voxels) and their associated colors will compose the model. All steps of the algorithm were implemented on the GPU and real-time performance was achieved with satisfactory volumetric resolutions. Rendering results are illustrated using realistic graphics techniques such as screen space ambient occlusion and irradiance maps.
Effects of mindfulness-based stress reduction on emotional experience and expression: a randomized controlled trial.

Mindfulness-based stress reduction (MBSR) has been found to reduce psychological distress and improve psychological adjustment in medical, psychiatric, and nonclinical samples. We examined its effects on several processes, attitudes, and behavior patterns related to emotion regulation.



Fifty-six adults were randomly assigned to MBSR or to a waiting list (WL).



Compared with WL completers (n = 21), MBSR completers (n = 20) reported significantly greater increases in trait mindfulness and decreases in absent-mindedness, greater increases in self-compassion, and decreases in fear of emotions, suppression of anger, aggressive anger expression, worry, and difficulties regulating emotions. The WL group subsequently received MBSR, and the two groups combined showed significant changes on all of these variables from pre-MBSR to post-MBSR, and on all except the 2 anger variables from pre-test to 2-month follow-up, as well as significant reductions in rumination.



An 8-week mindfulness training program might increase mindful awareness in daily life and have beneficial impact on clinically relevant emotion regulation processes.
Broadband Waveguide-to-Microstrip Antipodal Finline Transition without Additional Resonance Preventer

This paper presents a design of waveguide- to-microstrip antipodal finline transition, which provides a broadband and low insertion loss performance needless of additional resonance frequency preventer in the model. The suppression of resonance frequency depends on the self-structure of the antipodal finline transition. Meanwhile several factors affecting transition performance are taken into account. The design was verified by experimental results at Ka-band, which has a coincidence with the results of simulation. The compact transition design could decrease the complexity of antipodal finline transition and effect of manufacture error.