nils-herrmann/clean_scirepeval huggingface dataset:
Online Self-Tracking Groups to Increase Fruit and Vegetable Intake: A Small-Scale Study on Mechanisms of Group Effect on Behavior Change

Web-based interventions with a self-tracking component have been found to be effective in promoting adults' fruit and vegetable consumption. However, these interventions primarily focus on individual- rather than group-based self-tracking. The rise of social media technologies enables sharing and comparing self-tracking records in a group context. Therefore, we developed an online group-based self-tracking program to promote fruit and vegetable consumption.



This study aims to examine (1) the effectiveness of online group-based self-tracking on fruit and vegetable consumption and (2) characteristics of online self-tracking groups that make the group more effective in promoting fruit and vegetable consumption in early young adults.



During a 4-week Web-based experiment, 111 college students self-tracked their fruit and vegetable consumption either individually (ie, the control group) or in an online group characterized by a 2 (demographic similarity: demographically similar vs demographically diverse) × 2 (social modeling: incremental change vs ideal change) experimental design. Each online group consisted of one focal participant and three confederates as group members or peers, who had their demographics and fruit and vegetable consumption manipulated to create the four intervention groups. Self-reported fruit and vegetable consumption were assessed using the Food Frequency Questionnaire at baseline and after the 4-week experiment.



Participants who self-tracked their fruit and vegetable consumption collectively with other group members consumed more fruits and vegetables than participants who self-tracked individually (P=.01). The results did not show significant main effects of demographic similarity (P=.32) or types of social modeling (P=.48) in making self-tracking groups more effective in promoting fruit and vegetable consumption. However, additional analyses revealed the main effect of performance discrepancy (ie, difference in fruit and vegetable consumption between a focal participant and his/her group members during the experiment), such that participants who had a low performance discrepancy from other group members had greater fruit and vegetable consumption than participants who had a high performance discrepancy from other group members (P=.002). A mediation test showed that low performance discrepancy led to greater downward contrast (b=-0.78, 95% CI -2.44 to -0.15), which in turn led to greater fruit and vegetable consumption.



Online self-tracking groups were more effective than self-tracking alone in promoting fruit and vegetable consumption for early young adults. Low performance discrepancy from other group members lead to downward contrast, which in turn increased participants' fruit and vegetable consumption over time. The study highlighted social comparison processes in online groups that allow for sharing personal health information. Lastly, given the small scale of this study, nonsignificant results with small effect sizes might be subject to bias.
CS Unplugged and Middle-School Students' Views, Attitudes, and Intentions Regarding CS

Many students hold incorrect ideas and negative attitudes about computer science (CS). In order to address these difficulties, a series of learning activities called Computer Science Unplugged was developed by Tim Bell and his colleagues. These activities expose young people to central concepts in CS in an entertaining way without requiring a computer. The CS Unplugged activities have become more and more popular among CS educators and several activities are recommended in the ACM K-12 curriculum for elementary schools. CS Unplugged is used worldwide and has been translated into many languages.
 We examined the effect of the CS Unplugged activities on middle-school students’ ideas about CS and their desire to consider and study it in high school. The results indicate that following the activities the ideas of the students on what CS is about were partially improved, but their desire to study CS lessened.
 In order to provide possible explanations to these results, we analyzed the CS Unplugged activities to determine to what extent the objectives of CS Unplugged were addressed in the activities. In addition, we checked whether the activities were designed according to constructivist principles and whether they were explicitly linked to central concepts in CS. We found that only some of the objectives were addressed in the activities, that the activities do not engage with the students’ prior knowledge and that most of the activities are not explicitly linked to central concepts in CS. We offer suggestions for modifying the CS Unplugged activities so that they will be more likely to achieve their objectives.
foo . castr : visualising the future AI workforce

*Correspondence: mmolinas@ic.ac.uk 1Data Science Institute, Imperial College London, London, UK Full list of author information is available at the end of the article Abstract Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.
Designing an UHF RFID Reader Antenna

In this paper, a circularly polarized (CP) Octagonal shape microstrip patch antenna fed by coaxial feed is designed and analyzed for RFID (Radio Frequency Identification) reader applications. The physical parameters of the structure as well as its partial ground plane are analyzed and optimized using Zealand IE3D simulation software. Return loss (S11), voltage standing wave ratio (VSWR), directivity and gain are carried out. The results show that the proposed antenna has good impedance and radiation characteristics over the required bandwidth, 860-960 MHz (UHF RFID band). The return loss of the optimized Octagonal-shaped microstrip patch antenna is below 10dB over the UHF frequency band. The proposed antenna is very promising for various modern communication applications.
An Implicit Time-Stepping Scheme for Rigid Body Dynamics with Coulomb Friction

In this paper a new time-stepping method for simulating systems of rigid bodies is given. Unlike methods which take an instantaneous point of view, our method is based on impulse-momentum equations, and so does not need to explicitly resolve impulsive forces. On the other hand, our method is distinct from previous impulsive methods in that it does not require explicit collision checking and it can handle simultaneous impacts. Numerical results are given for one planar and one threedimensional example, which demonstrate the practicality of the method, and its convergence as the step size becomes small.
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

One of the most challenging problems in modern neuroimaging is detailed characterization of neurodegeneration. Quantifying spatial and longitudinal atrophy patterns is an important component of this process. These spatiotemporal signals will aid in discriminating between related diseases, such as frontotemporal dementia (FTD) and Alzheimer's disease (AD), which manifest themselves in the same at-risk population. Here, we develop a novel symmetric image normalization method (SyN) for maximizing the cross-correlation within the space of diffeomorphic maps and provide the Euler-Lagrange equations necessary for this optimization. We then turn to a careful evaluation of our method. Our evaluation uses gold standard, human cortical segmentation to contrast SyN's performance with a related elastic method and with the standard ITK implementation of Thirion's Demons algorithm. The new method compares favorably with both approaches, in particular when the distance between the template brain and the target brain is large. We then report the correlation of volumes gained by algorithmic cortical labelings of FTD and control subjects with those gained by the manual rater. This comparison shows that, of the three methods tested, SyN's volume measurements are the most strongly correlated with volume measurements gained by expert labeling. This study indicates that SyN, with cross-correlation, is a reliable method for normalizing and making anatomical measurements in volumetric MRI of patients and at-risk elderly individuals.
Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.
Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning

Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter optimization method that can find optimal hyperparameters for a given sequence using an action-prediction network leveraged on Continuous Deep Q-Learning. Since the common state-spaces for object tracking tasks are significantly more complex than the ones in traditional control problems, existing Continuous Deep Q-Learning algorithms cannot be directly applied. To overcome this challenge, we introduce an efficient heuristic to accelerate the convergence behavior. We evaluate our method on several tracking benchmarks and demonstrate its superior performance1.
Calcium hydroxylapatite associated soft tissue necrosis: a case report and treatment guideline.

We present an uncommon case of nasal alar and facial necrosis following calcium hydroxylapatite filler injection performed elsewhere without direct physician supervision. The patient developed severe full-thickness necrosis of cheek and nasal alar skin 24 h after injections into the melolabial folds. Management prior to referral included oral antibiotics, prednisone taper, and referral to a dermatologist (day 3) who prescribed valacyclovir for a presumptive herpes zoster reactivation induced by the injection. Referral to our institution was made on day 11, and after herpetic outbreak was ruled out by a negative Tzanck smear, debridement with aggressive local wound care was initiated. After re-epithelialization and the fashioning of a custom intranasal stent to prevent vestibular stenosis, pulsed dye laser therapy was performed for wound modification. The patient healed with an acceptable cosmetic outcome. This report underscores the importance of facial vasculature anatomy, injection techniques, and identification of adverse events when using fillers. A current treatment paradigm for such events is also presented.
Exploring the Effects of Word Roots for Arabic Sentiment Analysis

The inherent morphological complexity of languages such as Arabic entails the exploration of language traits that could be valuable to the task of detecting and classifying sentiment within text. This paper investigates the relevance of using the roots of words as input features into a sentiment analysis system under two distinct domains, in order to tailor the task more suitably to morphologically-rich languages such as Arabic. Different wordrooting solutions are employed in conjunction with a basic sentiment classifier, in order to demonstrate the potential of mapping Arabic words to basic roots for a language-specific development to the sentiment analysis task, showing a noteworthy improvement to baseline performance.
Review of Flexible Temperature Sensing Networks for Wearable Physiological Monitoring.

Physiological temperature varies temporally and spatially. Accurate and real-time detection of localized temperature changes in biological tissues regardless of large deformation is crucial to understand thermal principle of homeostasis, to assess sophisticated health conditions, and further to offer possibilities of building a smart healthcare and medical system. Additionally, continuous temperature mapping in flexible and stretchable formats opens up many other potential areas, such as artificially electronic skins and reflection of emotional changes. This review exploits a comprehensive investigation onto recent advances in flexible temperature sensors, stretchable sensor networks, and platforms constructed in soft and compliant formats for wearable physiological monitoring. The most recent examples of flexible temperature sensors are first discussed regarding to their materials, structures, electrical and mechanical properties; temperature sensing network technologies in new materials and structural designs are then presented based on platforms comprised of multiple physical sensors and stretchable electronics. Finally, wearable applications of the sensing network are described, such as detection of human activities, monitoring of health conditions, and emotion-related bodily sensations. Conclusions are made with emphasis on critical issues and new trends in the field of wearable temperature sensor network technologies.
Evaluation of Gabor-Wavelet-Based Facial Action Unit Recognition in Image Sequences of Increasing Complexity

Previous work suggests that Gabor-wavelet-based methods can achieve high sensitivity and specificity for emotionspecified expressions (e.g., happy, sad) and single action units (AUs) of the Facial Action Coding System (FACS). This paper evaluates a Gabor-wavelet-based method to recognize AUs in image sequences of increasing complexity. A recognition rate of 83% is obtained for three single AUs when image sequences contain homogeneous subjects and are without observable head motion. The accuracy of AU recognition decreases to 32% when the number of AUs increases to nine and the image sequences consist of AU combinations, head motion, and non-homogeneous subjects. For comparison, an average recognition rate of 87.6% is achieved for the geometry-feature-based method. The best recognition is a rate of 92.7% obtained by combining Gabor wavelets and geometry features.
Beyond-Regular Typestate

We present an extension for regular typestates, called BeyondRegular Typestate(BR-Typestate), which is expressive enough to model non-regular properties of programs and protocols over data. We model the BR-Typestate system over a dependently typed, state based, imperative core language, and we prove its soundness and tractability. We have implemented a prototype typechecker for the language, and we show how several important, real world non-regular properties of programs and protocols can be verified.
Nuclear CDKs Drive Smad Transcriptional Activation and Turnover in BMP and TGF-β Pathways

TGF-beta and BMP receptor kinases activate Smad transcription factors by C-terminal phosphorylation. We have identified a subsequent agonist-induced phosphorylation that plays a central dual role in Smad transcriptional activation and turnover. As receptor-activated Smads form transcriptional complexes, they are phosphorylated at an interdomain linker region by CDK8 and CDK9, which are components of transcriptional mediator and elongation complexes. These phosphorylations promote Smad transcriptional action, which in the case of Smad1 is mediated by the recruitment of YAP to the phosphorylated linker sites. An effector of the highly conserved Hippo organ size control pathway, YAP supports Smad1-dependent transcription and is required for BMP suppression of neural differentiation of mouse embryonic stem cells. The phosphorylated linker is ultimately recognized by specific ubiquitin ligases, leading to proteasome-mediated turnover of activated Smad proteins. Thus, nuclear CDK8/9 drive a cycle of Smad utilization and disposal that is an integral part of canonical BMP and TGF-beta pathways.
Rendering synthetic objects into legacy photographs

We propose a method to realistically insert synthetic objects into existing photographs without requiring access to the scene or any additional scene measurements. With a single image and a small amount of annotation, our method creates a physical model of the scene that is suitable for realistically rendering synthetic objects with diffuse, specular, and even glowing materials while accounting for lighting interactions between the objects and the scene. We demonstrate in a user study that synthetic images produced by our method are confusable with real scenes, even for people who believe they are good at telling the difference. Further, our study shows that our method is competitive with other insertion methods while requiring less scene information. We also collected new illumination and reflectance datasets; renderings produced by our system compare well to ground truth. Our system has applications in the movie and gaming industry, as well as home decorating and user content creation, among others.
Food-Bridging: A New Network Construction to Unveil the Principles of Cooking

In this manuscript, we propose, analyze, and discuss a possible new principle behind traditional cuisine: the Food-bridging hypothesis and its comparison with the food-pairing hypothesis using the same dataset and graphical models employed in the food-pairing study by Ahn et al. (2011). The Food-bridging hypothesis assumes that if two ingredients do not share a strong molecular or empirical affinity, they may become affine through a chain of pairwise affinities. That is, in a graphical model as employed by Ahn et al., a chain represents a path that joints the two ingredients, the shortest path represents the strongest pairwise chain of affinities between the two ingredients. Food-pairing and Food-bridging are different hypotheses that may describe possible mechanisms behind the recipes of traditional cuisines. Food-pairing intensifies flavor by mixing ingredients in a recipe with similar chemical compounds, and food-bridging smoothes contrast between ingredients. Both food-pairing and food-bridging are observed in traditional cuisines, as shown in this work. We observed four classes of cuisines according to food-pairing and food-bridging: (1) East Asian cuisines, at one extreme, tend to avoid food-pairing as well as food-bridging; and (4) Latin American cuisines, at the other extreme, follow both principles. For the two middle classes: (2) Southeastern Asian cuisines, avoid foodpairing and follow food-bridging; and (3) Western cuisines, follow food-pairing and avoid food-bridging.
Awireless passive radar system for real-time through-wall movement detection

In this paper, a reconfigurable real-time passive wireless detection system is described. The system is based on software-defined radio (SDR) architecture. The signal processing method and processing flow that enable through-wall target detection are introduced. The high-speed noise and interference mitigation methods implemented in the system for through-wall target detection are also described. A series of experimental results are presented for both large and small human body movements in through-wall scenarios. It is shown that the high-resolution Doppler event history implemented in the system enables the system to recognize and distinguish a range of body movements. The results demonstrate that this real-time SDR-based wireless detection system is a low-cost solution for human movement and recognition, with a range of applications.
Motion Detection and Face Recognition for CCTV Surveillance System

Closed Circuit Television (CCTV) is currently used in daily basis for a wide variety of purposes. The development of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognition in CCTV video is used as the basis of decision making to produce automatic, effective and efficient integrated system. This CCTV video process provides three outputs, motion detection information, face detection information and face identification information. Accumulative Differences Images (ADI) method is used for motion detection, and Haar Classifiers Cascade method is used for face detection. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). Then, these features are trained by Counter-Propagation Network (CPN). Offline tests are performed on 45 CCTV video. The result shows 92.655% success rate on motion detection,76% success rate on face detection, and 60% success rate on face detection. It shows that this faces detection and identification through CCTV video have not been able to obtain optimal results. The motion detection process is ideal to be applied in real-time conditions. Yet if it’s combined with face recognition process, it causes a significant time delay.  ADI, Haar Cascade Classifiers, SURF, PCA, CPN  ISSN (print): 1978-1520, ISSN (online): 2460-7258