nils-herrmann/clean_scirepeval huggingface dataset:
Yoga for Adult Women with Chronic PTSD: A Long-Term Follow-Up Study.

Yoga-the integrative practice of physical postures and movement, breath exercises, and mindfulness-may serve as a useful adjunctive component of trauma-focused treatment to build skills in tolerating and modulating physiologic and affective states that have become dysregulated by trauma exposure. A previous randomized controlled study was carried out among 60 women with chronic, treatment-resistant post-traumatic stress disorder (PTSD) and associated mental health problems stemming from prolonged or multiple trauma exposures. After 10 sessions of yoga, participants exhibited statistically significant decreases in PTSD symptom severity and greater likelihood of loss of PTSD diagnosis, significant decreases in engagement in negative tension reduction activities (e.g., self-injury), and greater reductions in dissociative and depressive symptoms when compared with the control (a seminar in women's health). The current study is a long-term follow-up assessment of participants who completed this randomized controlled trial.



Participants from the randomized controlled trial were invited to participate in long-term follow-up assessments approximately 1.5 years after study completion to assess whether the initial intervention and/or yoga practice after treatment was associated with additional changes. Forty-nine women completed the long-term follow-up interviews. Hierarchical regression analysis was used to examine whether treatment group status in the original study and frequency of yoga practice after the study predicted greater changes in symptoms and PTSD diagnosis.



Group assignment in the original randomized study was not a significant predictor of longer-term outcomes. However, frequency of continuing yoga practice significantly predicted greater decreases in PTSD symptom severity and depression symptom severity, as well as a greater likelihood of a loss of PTSD diagnosis.



Yoga appears to be a useful treatment modality; the greatest long-term benefits are derived from more frequent yoga practice.
Benchmarking Internet of things devices

The use of commercial off-the-shelf components for implementing Internet of Things devices has become a common practice amongst researchers and solution providers. IOT solutions, based on the Raspberry Pi, BeagleBone and BeagleBone Black, offer cost effective, versatile and uncomplicated platforms for rapid application development. The devices are treated as black box devices and little work has been done to quantify the performance of these devices when the system architecture, software components or communication channels are varied. This paper introduces micro- and macro-benchmarking methods for these devices; quantifying the performance of each device for the varying hardware architectures. Micro-benchmarking was performed using lmbench - a cross platform benchmarking framework for UNIX devices. The macro-benchmarking was implemented using a custom developed CoAP benchmarking utility created using the libCoAP library. The results showed that the selection of the platform processor is a key design requirement and has the most potential to optimise CoAP server performance. The latency associated with the communication channels was found to be a dominating factor for round-trip times associated with CoAP requests.
Task constraints in visual working memory

This paper examines the nature of visual representations that direct ongoing performance in sensorimotor tasks. Performance of such natural tasks requires relating visual information from different gaze positions. To explore this we used the technique of making task relevant display changes during saccadic eye movements. Subjects copied a pattern of colored blocks on a computer monitor, using the mouse to drag the blocks across the screen. Eye position was monitored using a dual-purkinje eye tracker, and the color of blocks in the pattern was changed at different points in task performance. When the target of the saccade changed color during the saccade, the duration of fixations on the model pattern increased, depending on the point in the task that the change was made. Thus different fixations on the same visual stimulus served a different purpose. The results also indicated that the visual information that is retained across successive fixations depends on moment by moment task demands. This is consistent with previous suggestions that visual representations are limited and task dependent. Changes in blocks in addition to the saccade target led to greater increases in fixation duration. This indicated that some global aspect of the pattern was retained across different fixations. Fixation durations revealed effects of the display changes that were not revealed in perceptual report. This can be understood by distinguishing between processes that operate at different levels of description and different time scales. Our conscious experience of the world may reflect events over a longer time scale than those underlying the substructure of the perceptuo-motor machinery.
Test Case Prioritization: An Empirical Study

Testcaseprioritization techniquesscheduletestcasesfor executionin anorderthatattemptsto maximizesomeobjective function. A varietyof objectivefunctionsare applicable; onesuch functioninvolvesrateof fault detection— a measur eofhowquickly faultsaredetectedwithin thetesting process.An improvedrateof fault detectionduring regression testingcanprovide fasterfeedback on a systemunder regressiontestand let debuggers begin their work earlier thanmightotherwisebepossible. In thispaper, wedescribe several techniquesfor prioritizing testcasesandreportour empiricalresultsmeasuringtheeffectivenessof thesetechniquesfor improvingrateof fault detection.Theresultsprovide insightsinto the tradeofs amongvarious techniques for testcaseprioritization.
Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration

We present a novel CNN-based image editing method that allows the user to change the semantic information of an image over a user-specified region. Our method makes this possible by combining the idea of manifold projection with spatial conditional batch normalization (sCBN), a version of conditional batch normalization with userspecifiable spatial weight maps. With sCBN and manifold projection, our method lets the user perform (1) spatial class translation that changes the class of an object over an arbitrary region of user’s choice, and (2) semantic transplantation that transplants semantic information contained in an arbitrary region of the reference image to an arbitrary region in the target image. These two transformations can be used simultaneously, and can realize a complex composite image-editing task like “change the nose of a beagle to that of a bulldog, and open her mouth”. The user can also use our method with intuitive copy-paste-style manipulations. We demonstrate the power of our method on various images. Code will be available at https://github. com/pfnet-research/neural-collage.
Tactile sensing for mechatronics * a state of the art survey

In this paper we examine the state of the art in tactile sensing for mechatronics[ We de_ne a tactile sensor as a device or system that can measure a given property of an object or contact event through physical contact between the sensor and the object[ We consider any property that can be measured through contact\ including the shape of an object\ texture\ temperature\ hardness\ moisture content\ etc[ A comprehensive search of the literature revealed that there was a signi_cant increase in publications on tactile sensing from 0880 onwards[ Considerable e}ort in the 0879s was spent investigating transduction techniques and developing new sensors\ whilst emphasis in more recent research has focused on experiments using tactile sensors to perform a variety of tasks[ This paper reports on progress in tactile sensing in the following areas] cutaneous sensors\ sensing _ngers\ soft materials\ industrial robot grippers\ multi_ngered hands\ probes and whiskers\ analysis of sensing devices\ haptic perception\ processing sensory data and new application areas[ We conclude that the predominant choice of transduction method is piezoelectric\ with arrays using resistive or capacitive sensing[ We found that increased emphasis on understanding tactile sensing and perception issues has opened up potential for new application areas[ The predicted growth in applications in industrial automation has not eventuated[ New applications for tactile sensing including surgery\ rehabilitation and service robotics\ and food processing automation show considerable potential and are now receiving signi_cant levels of research attention[ Þ 0887 Elsevier Science Ltd
Carpenter: finding closed patterns in long biological datasets

The growth of bioinformatics has resulted in datasets with new characteristics. These datasets typically contain a large number of columns and a small number of rows. For example, many gene expression datasets may contain 10,000-100,000 columns but only 100-1000 rows.Such datasets pose a great challenge for existing (closed) frequent pattern discovery algorithms, since they have an exponential dependence on the average row length. In this paper, we describe a new algorithm called CARPENTER that is specially designed to handle datasets having a large number of attributes and relatively small number of rows. Several experiments on real bioinformatics datasets show that CARPENTER is orders of magnitude better than previous closed pattern mining algorithms like CLOSET and CHARM.
Planar Slot Antenna Backed by Substrate Integrated Waveguide Cavity

A novel design method of low profile cavity backed planar slot antenna has been described in this paper. The whole antenna including backed cavity and feeding element is completely constructed at a single substrate by using substrate integrated waveguide technique and grounded coplanar waveguide. An example with 1.7% bandwidth has been presented, which has 5.4 dBi gain, 16.1 dB front-to-back ratio and -19 dB maximum cross polarized radiation level with its total thickness less than lambda0/50. The proposed antenna keeps good radiation performance of conventional cavity backed antenna and has advantages of conventional planar antenna including low profile, light weight, easy fabrication with low cost and convenient integration with planar circuit.
LaneQuest: An accurate and energy-efficient lane detection system

Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.
Representative Discovery of Structure Cues for Weakly-Supervised Image Segmentation

Weakly-supervised image segmentation is a challenging problem with multidisciplinary applications in multimedia content analysis and beyond. It aims to segment an image by leveraging its image-level semantics (i.e., tags). This paper presents a weakly-supervised image segmentation algorithm that learns the distribution of spatially structural superpixel sets from image-level labels. More specifically, we first extract graphlets from a given image, which are small-sized graphs consisting of superpixels and encapsulating their spatial structure. Then, an efficient manifold embedding algorithm is proposed to transfer labels from training images into graphlets. It is further observed that there are numerous redundant graphlets that are not discriminative to semantic categories, which are abandoned by a graphlet selection scheme as they make no contribution to the subsequent segmentation. Thereafter, we use a Gaussian mixture model (GMM) to learn the distribution of the selected post-embedding graphlets (i.e., vectors output from the graphlet embedding). Finally, we propose an image segmentation algorithm, termed representative graphlet cut, which leverages the learned GMM prior to measure the structure homogeneity of a test image. Experimental results show that the proposed approach outperforms state-of-the-art weakly-supervised image segmentation methods, on five popular segmentation data sets. Besides, our approach performs competitively to the fully-supervised segmentation models.
Designing for the deluge: understanding & supporting the distributed, collaborative work of crisis volunteers

Social media are a potentially valuable source of situational awareness information during crisis events. Consistently, "digital volunteers" and others are coming together to filter and process this data into usable resources, often coordinating their work within distributed online groups. However, current tools and practices are frequently unable to keep up with the speed and volume of incoming data during large events. Through contextual interviews with emergency response professionals and digital volunteers, this research examines the ad hoc, collaborative practices that have emerged to help process this data and outlines strategies for supporting and leveraging these efforts in future designs. We argue for solutions that align with current group values, work practices, volunteer motivations, and organizational structures, but also allow these groups to increase the scale and efficiency of their operations.
DAG-aware AIG rewriting: a fresh look at combinational logic synthesis

This paper presents a technique for preprocessing combinational logic before technology mapping. The technique is based on the representation of combinational logic using And-Inverter Graphs (AIGs), a networks of two-input ANDs and inverters. The optimization works by alternating DAG-aware AIG rewriting, which reduces area by sharing common logic without increasing delay, and algebraic AIG balancing, which minimizes delay without increasing area. The new technology-independent flow is implemented in a public-domain tool ABC. Experiments on large industrial benchmarks show that the proposed methodology scales to very large designs and is several orders of magnitude faster than SIS and MVSIS while offering comparable or better quality when measured by the quality of the network after mapping.
The construction of autobiographical memories in the self-memory system.

The authors describe a model of autobiographical memory in which memories are transitory mental constructions within a self-memory system (SMS). The SMS contains an autobiographical knowledge base and current goals of the working self. Within the SMS, control processes modulate access to the knowledge base by successively shaping cues used to activate autobiographical memory knowledge structures and, in this way, form specific memories. The relation of the knowledge base to active goals is reciprocal, and the knowledge base "grounds" the goals of the working self. It is shown how this model can be used to draw together a wide range of diverse data from cognitive, social, developmental, personality, clinical, and neuropsychological autobiographical memory research.
Fashion-focused creative commons social dataset

In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on image's tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.
Automatic Assessment of Japanese Text Readability Based on a Textbook Corpus

This paper describes a method of readability measurement of Japanese texts based on a newly compiled textbook corpus. The textbook corpus consists of 1,478 sample passages extracted from 127 textbooks of elementary school, junior high school, high school, and university; it is divided into thirteen grade levels and the total size is about a million characters. For a given text passage, the readability measurement method determines the grade level to which the passage is the most similar by using character-unigram models, which are constructed from the textbook corpus. Because this method does not require sentence-boundary analysis and word-boundary analysis, it is applicable to texts that include incomplete sentences and non-regular text fragments. The performance of this method, which is measured by the correlation coefficient, is considerably high (R > 0.9); in case that the length of a text passage is limited in 25 characters, the correlation coefficient is still high (R = 0.83).
Hover , Transition , and Level Flight Control Design for a Single-Propeller Indoor Airplane

This paper presents vehicle models and test flight results for an autonomous fixed-wing airplane that is designed to take-off, hover, transition to and from level-flight modes, and perch on a vertical landing platform in a highly space constrained environment. By enabling a fixed-wing UAV to achieve these feats, the speed and range of a fixed-wing aircraft in level flight are complimented by hover capabilities that were typically limited to rotorcraft. Flight and perch landing results are presented. This capability significantly eases support and maintenance of the vehicle. All of the flights presented in this paper are performed using the MIT Real-time Autonomous Vehicle indoor test ENvironment (RAVEN).
Community-based features for identifying spammers in Online Social Networks

The popularity of Online Social Networks (OSNs) is often faced with challenges of dealing with undesirable users and their malicious activities in the social networks. The most common form of malicious activity over OSNs is spamming wherein a bot (fake user) disseminates content, malware/viruses, etc. to the legitimate users of the social networks. The common motives behind such activity include phishing, scams, viral marketing and so on which the recipients do not indent to receive. It is thus a highly desirable task to devise techniques and methods for identifying spammers (spamming accounts) in OSNs. With an aim of exploiting social network characteristics of community formation by legitimate users, this paper presents a community-based framework to identify spammers in OSNs. The framework uses community-based features of OSN users to learn classification models for identification of spamming accounts. The preliminary experiments on a real-world dataset with simulated spammers reveal that proposed approach is promising and that using community-based node features of OSN users can improve the performance of classifying spammers and legitimate users.
Combining body pose, gaze, and gesture to determine intention to interact in vision-based interfaces

Vision-based interfaces, such as those made popular by the Microsoft Kinect, suffer from the Midas Touch problem: every user motion can be interpreted as an interaction. In response, we developed an algorithm that combines facial features, body pose and motion to approximate a user's intention to interact with the system. We show how this can be used to determine when to pay attention to a user's actions and when to ignore them. To demonstrate the value of our approach, we present results from a 30-person lab study conducted to compare four engagement algorithms in single and multi-user scenarios. We found that combining intention to interact with a 'raise an open hand in front of you' gesture yielded the best results. The latter approach offers a 12% improvement in accuracy and a 20% reduction in time to engage over a baseline 'wave to engage' gesture currently used on the Xbox 360.
Insertion-based Decoding with automatically Inferred Generation Order

Conventional neural autoregressive decoding commonly assumes a left-to-right generation order. In this work, we propose a novel decoding algorithm – INDIGO – which supports flexible generation in an arbitrary order with the help of insertion operations. We use Transformer, a state-of-the-art sequence generation model, to efficiently implement the proposed approach, enabling it to be trained with either a pre-defined generation order or an adaptive order searched based on the model’s own preference. Experiments on three real-world tasks, including machine translation, word order recovery and code generation, demonstrate that our algorithm can generate sequences in an arbitrary order, while achieving competitive or even better performance compared to the conventional left-to-right generation. Case studies show that INDIGO adopts adaptive generation orders based on input information.