nils-herrmann/clean_scirepeval huggingface dataset:
Designing products with added emotional value ; development and application of an approach for research through design

In this paper a design approach is introduced for designing products with added emotional value. First, the approach is established, based on a theoretical framework and a non-verbal instrument to measure emotional responses. Second, the value of the design approach was assessed by applying it to the design of mobile telephones. Mobile telephones were found to be useful vehicles as people appear to have strong feelings about this product. A user study was conducted which resulted in the identification of two user groups (‘trend followers’ and ‘security seekers’) and for each group an emotional profile. Four telephones were designed which, for each group, either were intended to or were not intended to match the group’s profile. Finally, these designs were evaluated with the nonverbal instrument. The results indicate that the approach is appropriate for designing products with added emotional value. The advantages and disadvantages of the approach are discussed and further research directions indicated
A Wideband Dual-Band Magneto-Electric Dipole Antenna With Improved Feeding Structure

In this letter, a wideband dual-band magneto-electric dipole antenna with improved feeding structure is presented. A U-shaped electric dipole antenna is employed to generate the dual resonant frequencies. A folded shorted patch that works as a magnetic dipole antenna is assembled vertically to the ground plane. The antenna is excited by a novel feeding line with a polygonal structure that is designed to improve the impedance matching. Simulated and measured results show that the dual operation bands with bandwidths of 72% from 1.48 to 3.15 GHz and 21% from 4.67 to 5.78 GHz for |S11| <; -10 dB were achieved. Stable and symmetric unidirectional radiation patterns, low cross-polarization level, low back-radiation, and an antenna gain ranging from 6.5 to 9.1 dBi were obtained over the dual operating bands.
A New Fault Detection Scheme for Networked Control Systems Subject to Uncertain Time-Varying Delay

Fault detection of networked control systems (NCS) subject to uncertain time-varying delay is studied in this paper. For the convenience of residual generator design, influence caused by network-induced delay is first transformed into time-varying polytopic uncertainty, which greatly facilitates further manipulation. Then design of the optimal residual generator is formulated as a model matching problem, i.e., to design a residual generator best matching the optimal residual generator of NCS in the delay free case. This procedure is assisted by parameter-dependent Lyapunov function matrix based bounded real lemma, which has been proved to be much better than single constant Lyapunov function matrix based results. This approach not only can be used in conditions that the variation part of the delay is less than one sampling period, but also can be applied to scenarios where the variation part of the delay is larger than one sampling period. Simulation results are also given to illustrate effectiveness of the proposed method.
A Practical Network-Based Intrusion Detection and Prevention System

While Internet and network technology have been growing rapidly, cyber attack incidents also increase accordingly. The increasing occurrence of network attacks is an important problem to network services. In this paper, we present a network based Intrusion Detection and Prevention System DPS), which can efficiently detect many well-known attack types and can immediately prevent the network system from network attacks. Our approach is simple and efficient and can be used with several machine learning algorithms. We actually implement the IDPS using different machine learning algorithms and test in an online network environment. The experimental results show that our IDPS can distinguish normal network activities from main attack types (Probe and Denial of Service) with high accuracy of detection rate in a few seconds and automatically prevent the victim's computer network from the attacks. In addition, we apply a well-known machine learning technique called C4.5 Decision Tree in our approach to consider unknown or new network attack types. Surprisingly, the supervised Decision Tree technique can work very well, when experiencing with untrained or unknown network attack types.
Deep Learning for Photoacoustic Tomography from Sparse Data

The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data. keywords Photoacoustic tomography, sparse data, image reconstruction, deep learning, convolutional neural networks, inverse problems.
Never skip leg day: A novel wearable approach to monitoring gym leg exercises

We present a wearable textile sensor system for monitoring muscle activity, leveraging surface pressure changes between the skin and an elastic sport support band. The sensor is based on an 8×16 element fabric resistive pressure sensing matrix of 1cm spatial resolution, which can be read out with 50fps refresh rate. We evaluate the system by monitoring leg muscles during leg workouts in a gym out of the lab. The sensor covers the lower part of quadriceps of the user. The shape and movement of the two major muscles (vastus lateralis and medialis) are visible from the data during various exercises. The system registers the activity of the user for every second, including which machine he/she is using, walking, relaxing and adjusting the machines; it also counts the repetitions from each set and evaluate the force consistency which is related to the workout quality. 6 people participated in the experiment of overall 24 leg workout sessions. Each session includes cross-trainer warm-up and cool-down, 3 different leg machines, 4 sets on each machine. Plus relaxing, adjusting machines, and walking, we perform activity recognition and quality evaluation through 2-dimensional mapping and the time sequence of the average force. We have reached 81.7% average recognition accuracy on a 2s sliding window basis, 93.3% on an event basis, and 85.6% spotting F1-score. We further demonstrate how to evaluate the workout quality through counting, force pattern variation and consistency.
Broadband Circularly Polarized H-Shaped Patch Antenna Using Reactive Impedance Surface

A compact, single-feed, broadband circularly polarized patch antenna is proposed in this letter. The antenna comprises an H-shaped microstrip patch printed over a metamaterial-inspired reactive impedance surface (RIS). The RIS structure comprising a lattice of 4 × 4 periodic metallic square patches helps to increase the bandwidth of the antenna. The final optimized structure exhibits an impedance bandwidth of 44.5% (4.64–7.3 GHz) alongwith a 3 dB axial-ratio bandwidth of 27.5% (4.55–6 GHz). Moreover, the proposed antenna yields a good broadside gain of 7.2 dBi at 5.5 GHz. The radiation efficiency of the present structure is better than 77% for the entire band of operation.
Effectiveness of Front-Of-Pack Nutrition Labels in French Adults: Results from the NutriNet-Santé Cohort Study

To date, no consensus has emerged on the most appropriate front-of-pack (FOP) nutrition label to help consumers in making informed choices. We aimed to compare the effectiveness of the label formats currently in use: nutrient-specific, graded and simple summary systems, in a large sample of adults.



The FOP label effectiveness was assessed by measuring the label acceptability and understanding among 13,578 participants of the NutriNet-Santé cohort study, representative of the French adult population. Participants were exposed to five conditions, including four FOP labels: Guideline Daily Amounts (GDA), Multiple Traffic Lights (MTL), 5-Color Nutrition Label (5-CNL), Green Tick (Tick), and a "no label" condition. Acceptability was evaluated by several indicators: attractiveness, liking and perceived cognitive workload. Objective understanding was assessed by the percentage of correct answers when ranking three products according to their nutritional quality. Five different product categories were tested: prepared fish dishes, pizzas, dairy products, breakfast cereals, and appetizers. Differences among the label effectiveness were compared with chi-square tests.



The 5-CNL was viewed as the easiest label to identify and as the one requiring the lowest amount of effort and time to understand. GDA was considered as the least easy to identify and to understand, despite being the most attractive and liked label. All FOP labels were found to be effective in ranking products according to their nutritional quality compared with the "no label" situation, although they showed differing levels of effectiveness (p<0.0001). Globally, the 5-CNL performed best, followed by MTL, GDA and Tick labels.



The graded 5-CNL label was considered as easy to identify, simple and rapid to understand; it performed well when comparing the products' nutritional quality. Therefore, it is likely to present advantages in real shopping situations where choices are usually made quickly.
Locally Adaptive Metric Nearest-Neighbor Classification

Nearest neighbor classiication assumes locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with nite samples due to the curse of dimensionality. Severe bias can be introduced under these conditions when using the nearest neighbor rule. We propose a locally adaptive nearest neighbor classiication method to try to minimize bias. We use a Chi-squared distance analysis to compute a exible metric for producing neighborhoods that are highly adaptive to query locations. Neighborhoods are elongated along less relevant feature dimensions and constricted along most innuential ones. As a result, the class conditional probabilities tend to be smoother in the mod-iied neighborhoods, whereby better classiication performance can be achieved. The eecacy of our method is validated and compared against other techniques using a variety of simulated and real world data.
Image-driven simplification

We introduce the notion of image-driven simplification, a framework that uses images to decide which portions of a model to simplify. This is a departure from approaches that make polygonal simplification decisions based on geometry. As with many methods, we use the edge collapse operator to make incremental changes to a model. Unique to our approach, however, is the use at comparisons between images of the original model against those of a simplified model to determine the cost of an ease collapse. We use common graphics rendering hardware to accelerate the creation of the required images. As expected, this method produces models that are close to the original model according to image differences. Perhaps more surprising, however, is that the method yields models that have high geometric fidelity as well. Our approach also solves the quandary of how to weight the geometric distance versus appearance properties such as normals, color, and texture. All of these trade-offs are balanced by the image metric. Benefits of this approach include high fidelity silhouettes, extreme simplification of hidden portions of a model, attention to shading interpolation effects, and simplification that is sensitive to the content of a texture. In order to better preserve the appearance of textured models, we introduce a novel technique for assigning texture coordinates to the new vertices of the mesh. This method is based on a geometric heuristic that can be integrated with any edge collapse algorithm to produce high quality textured surfaces.
Gaming Social Capital: Exploring Civic Value in Multiplayer Video Games

Recent research suggests that social interactions in video games may lead to the development of community bonding and prosocial attitudes. Building on this line of research, a national survey of U.S. adults finds that gamers who develop ties with a community of fellow gamers possess gaming social capital, a new gaming-related community construct that is shown to be a positive antecedent in predicting both face-to-face social capital and civic participation.
Manifold Learning by Curved Cosine Mapping

In the field of pattern recognition, data analysis, and machine learning, data points are usually modeled as high-dimensional vectors. Due to the curse-of-dimensionality, it is non-trivial to efficiently process the orginal data directly. Given the unique properties of nonlinear dimensionality reduction techniques, nonlinear learning methods are widely adopted to reduce the dimension of data. However, existing nonlinear learning methods fail in many real applications because of the too-strict requirements (for real data) or the difficulty in parameters tuning. Therefore, in this paper, we investigate the manifold learning methods which belong to the family of nonlinear dimensionality reduction methods. Specifically, we proposed a new manifold learning principle for dimensionality reduction named Curved Cosine Mapping (CCM). Based on the law of cosines in Euclidean space, CCM applies a brand new mapping pattern to manifold learning. In CCM, the nonlinear geometric relationships are obtained by utlizing the law of cosines, and then quantified as the dimensionality-reduced features. Compared with the existing approaches, the model has weaker theoretical assumptions over the input data. Moreover, to further reduce the computation cost, an optimized version of CCM is developed. Finally, we conduct extensive experiments over both artificial and real-world datasets to demonstrate the performance of proposed techniques.
Twenty Years of Mixture of Experts

In this paper, we provide a comprehensive survey of the mixture of experts (ME). We discuss the fundamental models for regression and classification and also their training with the expectation-maximization algorithm. We follow the discussion with improvements to the ME model and focus particularly on the mixtures of Gaussian process experts. We provide a review of the literature for other training methods, such as the alternative localized ME training, and cover the variational learning of ME in detail. In addition, we describe the model selection literature which encompasses finding the optimum number of experts, as well as the depth of the tree. We present the advances in ME in the classification area and present some issues concerning the classification model. We list the statistical properties of ME, discuss how the model has been modified over the years, compare ME to some popular algorithms, and list several applications. We conclude our survey with future directions and provide a list of publicly available datasets and a list of publicly available software that implement ME. Finally, we provide examples for regression and classification. We believe that the study described in this paper will provide quick access to the relevant literature for researchers and practitioners who would like to improve or use ME, and that it will stimulate further studies in ME.
EFFECTS OF FEEDING FREQUENCY ON GROWTH PERFORMANCE AND SURVIVAL RATE OF ANGEL FISH, PTEROPHYLLUM SCALARE (PERCIFORMES: CICHLIDAE)

The freshwater angel fish (Pterophyllum scalare Schultze, 1823) is South American cichlid become very popular among aquarists. There is little information on their culture and aquarium husbandry. In this study growth performance and survival rate of angelfish subjected to different feeding frequencies were evaluated. Four groups of angel fish juveniles (0.87 ± 0.01 g; 3.98 ± 0.08 mm) were fed either four meals per day (F1), two meals per day (F2), one meal per day (F3) and every other day (F4) for 90 days. Final live weight and specific growth rate (SGR) values of group F1 and F2 were significantly higher than those of the other groups (P < 0.05). There was no significant difference (P > 0.05) in survival rate among the treatments. The best feed conversion ration (FCR) was obtained from four daily feeding (F1) (P < 0.05). Condition factor (CF) did not show a significant difference (P > 0.05) among experimental groups. In conclusion, the best results in growth performance were obtained by feeding four meals per day (F1) and two meals per day (F2), so they were recommended for angel fish feeding.
Evidence based practice profiles: Differences among allied health professions

Most previous studies of allied health professionals' evidence based practice (EBP) attitudes, knowledge and behaviours have been conducted with profession specific questionnaires of variable psychometric strength. This study compared the self-report EBP profiles of allied health professionals/trainees in an Australian university.



The Evidence-Based Practice Profile (EBP2) questionnaire assessed five domains (Relevance, Terminology, Practice, Confidence, Sympathy) in 918 subjects from five professional disciplines. One and 2-way factorial analysis of variance (ANOVA) and t-tests analysed differences based on prior exposure to EBP, stage of training, professional discipline, age and gender.



There were significant differences between stages of training (p < 0.001) for all domains and between EBP exposure groups for all but one domain (Sympathy). Professional discipline groups differed for Relevance, Terminology, Practice (p < 0.001) and Confidence (p = 0.006). Males scored higher for Confidence (p = 0.002) and females for Sympathy (p = 0.04), older subjects (> 24 years) scored higher for all domains (p < 0.05). Age and exposure affected all domains (p < 0.02). Differences in stages of training largely explained age-related differences in Confidence and Practice (p ≤ 0.001) and exposure-related differences in Confidence, Practice and Sympathy (p ≤ 0.023).



Across five allied health professions, self-report EBP characteristics varied with EBP exposure, across stages of training, with profession and with age.
The Use of Pit and Fissure Sealants—A Literature Review

This paper reviews the literature and discusses the latest updates on the use of pit and fissure sealants. It demonstrates the effectiveness of pit and fissure sealants in preventing caries and the management of early carious lesions. It compares the use of different sealant materials and their indications. It describes the application technique for sealants. It also reviews the cost-effectiveness of sealants as a preventive strategy. From this review and after the discussion of recently published studies on pit and fissure sealants, it is evident that sealants are effective in caries prevention and in preventing the progression of incipient lesions. It is therefore recommended that pit and fissure sealant be applied to high-caries-risk children for optimum cost-effectiveness. It is a highly sensitive technique that needs optimum isolation, cleaning of the tooth surface, etching, and the application of a thin bonding layer for maximum benefit. Recall and repair, when needed, are important to maximize the effectiveness of such sealant use.
Phycobiliproteins as a commodity: trends in applied research, patents and commercialization

Phycobiliproteins are a group of colored proteins commonly present in cyanobacteria and red algae possessing a spectrum of applications. They are extensively commercialized for fluorescent applications in clinical and immunological analysis. They are also used as a colorant, and their therapeutic value has also been categorically demonstrated. However, a comprehensive knowledge and technological base for augmenting their commercial utilities is lacking. Hence, this work is focused towards this objective by means of analyzing global patents and commercial activities with application oriented research. Strategic mining of patents was performed from global patent databases resulting in the identification of 297 patents on phycobiliproteins. The majority of the patents are from USA, Japan and Europe. Patents are grouped into fluorescent applications, general applications and production aspects of phycobiliproteins and the features of each group are discussed. Commercial and applied research activities are compared in parallel. It revealed that US patents are mostly related to fluorescent applications while Japanese are on the production, purification and application for therapeutic and diagnostic purposes. Fluorescent applications are well represented in research, patents and commercial sectors. Biomedical properties documented in research and patents are not ventured commercially. Several novel applications are reported only in patents. The paper further pinpoints the plethora of techniques used for cell breakage and for extraction and purification of phycobiliproteins. The analysis identifies the lacuna and suggests means for improvements in the application and production of phycobiliproteins.
Notable design patterns for domain-specific languages

The realisation of domain-speci®c languages (DSLs) diers in fundamental ways from that of traditional programming languages. We describe eight recurring patterns that we have identi®ed as being used for DSL design and implementation. Existing languages can be extended, restricted, partially used, or become hosts for DSLs. Simple DSLs can be implemented by lexical processing. In addition, DSLs can be used to create front-ends to existing systems or to express complicated data structures. Finally, DSLs can be combined using process pipelines. The patterns described form a pattern language that can be used as a building block for a systematic view of the software development process involving DSLs. Ó 2001
The importance of students' attention to program state: a case study of debugging behavior

To develop a model of students' debugging processes, I conducted a qualitative analysis of young students engaged in debugging computer programs they had written in the programming language Scratch. I present a microgenetic analysis that tracks how one student's attention to elements of computer program state shifted during his debugging process. I present evidence that this student had relevant domain knowledge and claim that his changing attention within the problem, and not his domain knowledge, mediated his debugging process. I hypothesize that a key competence in debugging is learning to identify what elements of program state are important to pay attention to and that this attention, and not only domain knowledge, mediates the debugging process. This hypothesis is consistent with a model of physics reasoning and learning from the Knowledge in Pieces theoretical framework and in this research I build upon education research outside of computer science. The case study analyzes the debugging process of a student entering the sixth grade, but I document an isomorphic case from a pair of college students to show that this pattern extends beyond this age.