nils-herrmann/clean_scirepeval huggingface dataset:
Pedestrian dead reckoning system with phone location awareness algorithm

In this paper we present a PDR (Pedestrian Dead Reckoning) system with a phone location awareness algorithm. PDR is a device which provides position information of the pedestrian. In general, the step length is estimated using a linear combination of the walking frequency and the acceleration variance for the mobile phone. It means that the step length estimation accuracy is affected by coefficients of the walking frequency and the acceleration variance which are called step length estimation parameters. Developed PDR is assumed that it is embedded in the mobile phone. Thus, parameters can be different from each phone location such as hand with swing motion, hand without any motion and pants pocket. It means that different parameters can degrade the accuracy of the step length estimation. Step length estimation result can be improved when appropriate parameters which are determined by phone location awareness algorithm are used. In this paper, the phone location awareness algorithm for PDR is proposed.
OpenIGTLink: an open network protocol for image-guided therapy environment.

With increasing research on system integration for image-guided therapy (IGT), there has been a strong demand for standardized communication among devices and software to share data such as target positions, images and device status.



We propose a new, open, simple and extensible network communication protocol for IGT, named OpenIGTLink, to transfer transform, image and status messages. We conducted performance tests and use-case evaluations in five clinical and engineering scenarios.



The protocol was able to transfer position data with submillisecond latency up to 1024 fps and images with latency of <10 ms at 32 fps. The use-case tests demonstrated that the protocol is feasible for integrating devices and software.



The protocol proved capable of handling data required in the IGT setting with sufficient time resolution and latency. The protocol not only improves the interoperability of devices and software but also promotes transitions of research prototypes to clinical applications.
Deep learning based FACS Action Unit occurrence and intensity estimation

Ground truth annotation of the occurrence and intensity of FACS Action Unit (AU) activation requires great amount of attention. The efforts towards achieving a common platform for AU evaluation have been addressed in the FG 2015 Facial Expression Recognition and Analysis challenge (FERA 2015). Participants are invited to estimate AU occurrence and intensity on a common benchmark dataset. Conventional approaches towards achieving automated methods are to train multiclass classifiers or to use regression models. In this paper, we propose a novel application of a deep convolutional neural network (CNN) to recognize AUs as part of FERA 2015 challenge. The 7 layer network is composed of 3 convolutional layers and a max-pooling layer. The final fully connected layers provide the classification output. For the selected tasks of the challenge, we have trained two different networks for the two different datasets, where one focuses on the AU occurrences and the other on both occurrences and intensities of the AUs. The occurrence and intensity of AU activation are estimated using specific neuron activations of the output layer. This way, we are able to create a single network architecture that could simultaneously be trained to produce binary and continuous classification output.
IoT based intelligent billboard using data mining

The paper presents the design and implementation of an intelligent advertising system using Raspberry Pi B+ v1.2 development board, RFID and display panels integrated over a network. This system can be used in multi retail stores, malls and shopping complexes. The proposed system works on performing data mining and trend analysis over sales, inventory data which is collected in real time by using POS system implemented using RFID tags attached to products. The display panels(billboards) will be placed at strategic locations. The proposed outcome of the project aims to provide better offers and deals to customers without targeting them individually, rather using a segmented approach.
Adapting reflexes controlling the human posture

Doubt about the role of stretch reflexes in movement and posture control has remained in part because the questions of reflex “usefulness” and the postural “set” have not been adequately considered in the design of experimental paradigms. The intent of this study was to discover the stabilizing role of stretch reflexes acting upon the ankle musculature while human subjects performed stance tasks requiring several different postural “sets”. Task specific differences of reflex function were investigated by experiments in which the role of stretch reflexes to stabilize sway during stance could be altered to be useful, of no use, or inappropriate. Because the system has available a number of alternate inputs to posture (e.g., vestibular and visual), stretch reflex responses were in themselves not necessary to prevent a loss of balance. Nevertheless, 5 out of 12 subjects in this study used long-latency (120 msec) stretch reflexes to help reduce postural sway. Following an unexpected change in the usefulness of stretch reflexes, the 5 subjects progressively altered reflex gain during the succeeding 3–5 trials. Adaptive changes in gain were always in the sense to reduce sway, and therefore could be attenuating or facilitating the reflex response. Comparing subjects using the reflex with those not doing so, stretch reflex control resulted in less swaying when the task conditions were unchanging. However, the 5 subjects using reflex controls oftentimes swayed more during the first 3–5 trials after a change, when inappropriate responses were elicited. Four patients with clinically diagnosed cerebellar deficits were studied briefly. Among the stance tasks, their performance was similar to normal in some and significantly poorer in others. Their most significant deficit appeared to be the inability to adapt long-latency reflex gain following changes in the stance task. The study concludes with a discussion of the role of stretch reflexes within a hierarchy of controls ranging from muscle stiffness up to centrally initiated responses.
A systemic and cognitive approach for IoT security

The Internet of Things (IoT) will enable objects to become active participants of everyday activities. Introducing objects into the control processes of complex systems makes IoT security very difficult to address. Indeed, the Internet of Things is a complex paradigm in which people interact with the technological ecosystem based on smart objects through complex processes. The interactions of these four IoT components, person, intelligent object, technological ecosystem, and process, highlight a systemic and cognitive dimension within security of the IoT. The interaction of people with the technological ecosystem requires the protection of their privacy. Similarly, their interaction with control processes requires the guarantee of their safety. Processes must ensure their reliability and realize the objectives for which they are designed. We believe that the move towards a greater autonomy for objects will bring the security of technologies and processes and the privacy of individuals into sharper focus. Furthermore, in parallel with the increasing autonomy of objects to perceive and act on the environment, IoT security should move towards a greater autonomy in perceiving threats and reacting to attacks, based on a cognitive and systemic approach. In this work, we will analyze the role of each of the mentioned actors in IoT security and their relationships, in order to highlight the research challenges and present our approach to these issues based on a holistic vision of IoT security.
Hopsworks: Improving User Experience and Development on Hadoop with Scalable, Strongly Consistent Metadata

Hadoop is a popular system for storing, managing,and processing large volumes of data, but it has bare-bonesinternal support for metadata, as metadata is a bottleneck andless means more scalability. The result is a scalable platform withrudimentary access control that is neither user- nor developer-friendly. Also, metadata services that are built on Hadoop, suchas SQL-on-Hadoop, access control, data provenance, and datagovernance are necessarily implemented as eventually consistentservices, resulting in increased development effort and morebrittle software. In this paper, we present a new project-based multi-tenancymodel for Hadoop, built on a new distribution of Hadoopthat provides a distributed database backend for the HadoopDistributed Filesystem's (HDFS) metadata layer. We extendHadoop's metadata model to introduce projects, datasets, andproject-users as new core concepts that enable a user-friendly, UI-driven Hadoop experience. As our metadata service is backed bya transactional database, developers can easily extend metadataby adding new tables and ensure the strong consistency ofextended metadata using both transactions and foreign keys.
3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection

The goal of this paper is to perform 3D object detection in the context of autonomous driving. Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo imagery. We formulate the problem as minimizing an energy function that encodes object size priors, placement of objects on the ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. We then exploit a CNN on top of these proposals to perform object detection. In particular, we employ a convolutional neural net (CNN) that exploits context and depth information to jointly regress to 3D bounding box coordinates and object pose. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. When combined with the CNN, our approach outperforms all existing results in object detection and orientation estimation tasks for all three KITTI object classes. Furthermore, we experiment also with the setting where LIDAR information is available, and show that using both LIDAR and stereo leads to the best result.
Search-Based Procedural Content Generation

Recently, a small number of papers have appeared in which the authors implement stochastic search algorithms, such as evolutionary computation, to generate game content, such as levels, rules and weapons. We propose a taxonomy of such approaches, centring on what sort of content is generated, how the content is represented, and how the quality of the content is evaluated. The relation between search-based and other types of procedural content generation is described, as are some of the main research challenges in this new field. The paper ends with some successful examples of this approach.
Implementation of detection and tracking mechanism for small UAS

Unmanned Aircraft Systems (UAS) are being used commonly for video surveillance, providing valuable video data and reducing the risks associated with human operators. Thanks to its benefits, the UAS traffic is nearly doubling every year. However, the risks associated with the UAS are also growing. According to the FAA, the volume of air traffic will grow steadily, doubling in the next 20 years. Paired with the exponential growth of the UAS traffic, the risk of collision is also growing as well as privacy concerns. An effective UAS detection and/or tracking method is critically needed for air traffic safety. This research is aimed at developing a system that can identify/detect a UAS, which will subsequently enable counter measures against UAS. The proposed system will identify a UAS through various methods including image processing and mechanical tracking. Once a UAS is detected, a countermeasure can be employed along with the tracking system. In this research, we describe the design, algorithms, and implementation details of the system as well as some performance aspects. The proposed system will help keep the malicious or harmful UAS away from the restricted or residential areas.
KNN-CF Approach: Incorporating Certainty Factor to kNN Classification

 KNN classification finds k nearest neighbors of a query in training data and then predicts the class of the query as the most frequent one occurring in the neighbors. This is a typical method based on the majority rule. Although majority-rule based methods have widely and successfully been used in real applications, they can be unsuitable to the learning setting of skewed class distribution. This paper incorporates certainty factor (CF) measure to kNN classification, called kNN-CF classification, so as to deal with the above issue. This CF-measure based strategy can be applied on the top of a kNN classification algorithm (or a hot-deck method) to meet the need of imbalanced learning. This leads to that an existing kNN classification algorithm can easily be extended to the setting of skewed class distribution. Some experiments are conducted for evaluating the efficiency, and demonstrate that the kNN-CF classification outperforms standard kNN classification in accuracy.
A Longitudinal Study of the Use of a Test-Driven Development Practice in Industry

Test-Driven Development (TDD) is an agile practice that is widely accepted and advocated by most agile methods and methodologists. In this paper, we report on a longitudinal case study of an IBM team who has sustained use of TDD for five years and over ten releases of a Java-implemented product. The team worked from a design and wrote tests incrementally before or while they wrote code and, in the process, developed a significant asset of automated tests. The IBM team realized sustained quality improvement relative to a pre-TDD project and consistently had defect density below industry standards. As a result, our data indicate that the TDD practice can aid in the production of high quality products. This quality improvement would compensate for the moderate perceived productivity losses. Additionally, the use of TDD may decrease the degree to which code complexity increases as software ages.
Discovery of user behavior patterns from geo-tagged micro-blogs

The explosive growth of micro-blogging sites such as Twitter has enabled folks to share their personal up-to-dates. Compared to conventional blog sites, through the short length of messages, micro-blogging sites help users easily express their experiences, thoughts and feelings and share them instantly and globally. In addition, mobile devices based micro-blogging applications are ensuring the usefulness in a variety of our daily activities without spatial or temporal restriction. Especially, the most significant characteristics chiefly possible in such mobile micro-blogging is on the fact that the cutting-edge smartphones can utilize location sensing information that make it clear to analyze where the published messages are made almost in real time. In the respect of the diversity and the quantity of crowds writing the micro-blogs, we are sure that the micro-blogging sites can be a very important social media platform where a lot of valuable knowledge such as geographic social phenomena can be extracted. In this paper, we endeavor to find geographic social patterns from user movement histories made by mass mobile micro-bloggers. We particularly propose fundamental models based on aggregation and dispersion about movements of micro-bloggers in geographic regions. We also performed experiments to discover geographic characteristics from the micro-blog data actually gathered from Twitter.
Intrinsic dimension estimation: Advances and open problems

Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionality. They have the aim of projecting the original data set of dimensionality N, without information loss, onto a lower M-dimensional submanifold. Since the value of M is unknown, techniques that allow knowing in advance the value of M, called intrinsic dimension (ID), are quite useful. The aim of the paper is to review state-of-the-art of the methods of ID estimation, underlining the recent advances and the open problems.
Cling: A Memory Allocator to Mitigate Dangling Pointers

Use-after-free vulnerabilities exploiting so-called dangling pointers to deallocated objects are just as dangerous as buffer overflows: they may enable arbitrary code execution. Unfortunately, state-of-the-art defenses against use-after-free vulnerabilities require compiler support, pervasive source code modifications, or incur high performance overheads. This paper presents and evaluates Cling, a memory allocator designed to thwart these attacks at runtime. Cling utilizes more address space, a plentiful resource on modern machines, to prevent typeunsafe address space reuse among objects of different types. It infers type information about allocated objects at runtime by inspecting the call stack of memory allocation routines. Cling disrupts a large class of attacks against use-after-free vulnerabilities, notably including those hijacking the C++ virtual function dispatch mechanism, with low CPU and physical memory overhead even for allocation intensive applications.
Analysis and Design of Thin Circular Polarizers Based on Meander Lines

A circular polarizer is a single layer or multi-layer structure that converts linearly polarized waves into circularly polarized ones and vice versa. In this communication, a simple method based on transmission line circuit theory is proposed to model and design circular polarizers. This technique is more flexible than those previously presented in the way that it permits to design polarizers with the desired spacing between layers, while obtaining surfaces that may be easier to fabricate and less sensitive to fabrication errors. As an illustrating example, a modified version of the meander-line polarizer being twice as thin as its conventional counterpart is designed. Then, both polarizers are fabricated and measured. Results are shown and compared for normal and oblique incidence angles in the planes φ = 0° and φ = 90°.
Cross-Domain Collaborative Filtering over Time

Collaborative filtering (CF) techniques recommend items to users based on their historical ratings. In real-world scenarios, user interests may drift over time since they are affected by moods, contexts, and pop culture trends. This leads to the fact that a user’s historical ratings comprise many aspects of user interests spanning a long time period. However, at a certain time slice, one user’s interest may only focus on one or a couple of aspects. Thus, CF techniques based on the entire historical ratings may recommend inappropriate items. In this paper, we consider modeling user-interest drift over time based on the assumption that each user has multiple counterparts over temporal domains and successive counterparts are closely related. We adopt the cross-domain CF framework to share the static group-level rating matrix across temporal domains, and let user-interest distribution over item groups drift slightly between successive temporal domains. The derived method is based on a Bayesian latent factor model which can be inferred using Gibbs sampling. Our experimental results show that our method can achieve state-of-the-art recommendation performance as well as explicitly track and visualize user-interest drift over time.
HealthyTogether: exploring social incentives for mobile fitness applications

A crucial element in many mobile fitness applications is gamification that makes physical activities fun. While many methods focus on competition and individual users' interaction with the game, the aspect of social interaction and how users play games together in a group remains an open subject. To investigate these issues, we developed a mobile game, HealthyTogether, to understand how users interact in different group gamification settings: competition, cooperation, or hybrid. We describe the results of a user study involving 18 dyads (N=36) over a period of two weeks. Results show that users significantly enhanced physical activities using HealthyTogether compared with when they exercised alone by up to 15%. Among the group settings, cooperation (21% increase) and hybrid (18% increase) outperformed competition (8% increase). Additionally, users sent significantly more messages in cooperation setting than hybrid and competition. Furthermore, physical activities are positively correlated with the number of messages they exchanged. Based on the findings, we derive design implications for practitioners.
Curiosity , interest and engagement in technology-pervasive learning environments : a new research agenda

This paper identifies the need for developing new ways to study curiosity in the context of today’s pervasive technologies and unprecedented information access. Curiosity is defined in this paper in a way which incorporates the concomitant constructs of interest and engagement. A theoretical model for curiosity, interest and engagement in new media technology-pervasive learning environments is advanced, taking into consideration personal, situational and contextual factors as influencing variables. While the path associated with curiosity, interest, and engagement during learning and research has remained essentially the same, how individuals tackle research and information-seeking tasks and factors which sustain such efforts have changed. Learning modalities for promoting this theoretical model are discussed leading to a series of recommendations for future research. This article offers a multi-lens perspective on curiosity and suggests a multi-method research agenda for validating such a perspective.
A new social communication intervention for children with autism: pilot randomised controlled treatment study suggesting effectiveness.

Psychosocial treatments are the mainstay of management of autism in the UK but there is a notable lack of a systematic evidence base for their effectiveness. Randomised controlled trial (RCT) studies in this area have been rare but are essential because of the developmental heterogeneity of the disorder. We aimed to test a new theoretically based social communication intervention targeting parental communication in a randomised design against routine care alone.



The intervention was given in addition to existing care and involved regular monthly therapist contact for 6 months with a further 6 months of 2-monthly consolidation sessions. It aimed to educate parents and train them in adapted communication tailored to their child's individual competencies. Twenty-eight children with autism were randomised between this treatment and routine care alone, stratified for age and baseline severity. Outcome was measured at 12 months from commencement of intervention, using standardised instruments.



All cases studied met full Autism Diagnostic Interview (ADI) criteria for classical autism. Treatment and controls had similar routine care during the study period and there were no study dropouts after treatment had started. The active treatment group showed significant improvement compared with controls on the primary outcome measure--Autism Diagnostic Observation Schedule (ADOS) total score, particularly in reciprocal social interaction--and on secondary measures of expressive language, communicative initiation and parent-child interaction. Suggestive but non-significant results were found in Vineland Adaptive Behaviour Scales (Communication Sub-domain) and ADOS stereotyped and restricted behaviour domain.



A Randomised Treatment Trial design of this kind in classical autism is feasible and acceptable to patients. This pilot study suggests significant additional treatment benefits following a targeted (but relatively non-intensive) dyadic social communication treatment, when compared with routine care. The study needs replication on larger and independent samples. It should encourage further RCT designs in this area.