nils-herrmann/clean_scirepeval huggingface dataset:
Teacher Technology Change: How Knowledge, Beliefs, and Culture Intersect

Recent national (CDW-G, 2006) and international (Voogt, 2008) reports paint a promising picture of classroom teachers' current efforts to use technology to support student learning. For example, among the 1000 teachers who responded to the Teachers Talk Tech survey, 790 teachers (79%) self-reported using computers " to teach students " (CDW-G). However, other research, resulting from both large (Project Tomorrow, 2008) and small-scale (Bauer & Kenton, 2005) efforts, suggests that we still haven't solved the " problem " of technology integration No doubt, teachers have increased their personal and professional uses of computers (van Braak, Tondeur, & Valcke, 2004). In response to the Teachers Talk Tech survey (CDW-G, 2006), 88% of the teachers reported using technology for administrative tasks, while 86% reported using technology for communication tasks. Similarly, 93% of the teachers who responded to the Speak Up 2007 survey (n = 23,756 / 25,544) reported using technology to communicate with colleagues or parents (Project Tomorrow, 2008). Alongside these increases in teachers' personal uses of the computer are increases in the reported uses of computers in the classroom (National Education Association, 2008; Project Tomorrow, 2008). Unfortunately, when we look closer at these data, reported uses still tend to be " low-level " (Russell, Bebell, O'Dwyer, & Kathleen, 2003), that is, those that support traditional, teacher-directed instruction (e.g., using PowerPoint to present a lesson, searching the Web for information resources) or that focus on the development of students' technical skills (Tondeur, van Braak, & Valcke, 2007b). Based on the results of the Speak Up 2007 national survey (Project Tomorrow, 2008), 51% of the teachers (n = 13027 / 25,544) reported that their primary uses of technology to " facilitate student learning " comprised 1) asking students to complete homework assignments using the computer (e.g., writing reports, finding information on the Internet) and 2) assigning practice work at the computer (e.g., using drill and practice software). This is verified, to some extent, by the large percentage of students (grades 6-12), taking the same survey, who reported using technology to 1) write assignments (74%), 2) conduct online research (72%), and check assignments or grades online (58%). Technology Integration for 21 st Century Learners It is no longer appropriate to suggest that these types of uses are adequate to meet the needs of the 21 st century learner. Using technology simply to support lecture-based instruction falls far short of recommended best practice
Three-Dimensional Container Loading : A Simulated Annealing Approach

High utilization of cargo volume is an essential factor in the success of modern enterprises in the market. Although mathematical models have been presented for container loading problems in the literature, there is still a lack of studies that consider practical constraints. In this paper, a Mixed Integer Linear Programming is developed for the problem of packing a subset of rectangular boxes inside a container such that the total value of the packed boxes is maximized while some realistic constraints, such as vertical stability, are considered. The packing is orthogonal, and the boxes can be freely rotated into any of the six orientations. Moreover, a sequence triple-based solution methodology is proposed, simulated annealing is used as modeling technique, and the situation where some boxes are preplaced in the container is investigated. These preplaced boxes represent potential obstacles. Numerical experiments are conducted for containers with and without obstacles. The results show that the simulated annealing approach is successful and can handle large number of packing instances.
Free-Form Flexible Lithium-Ion Microbattery

Wearable electronics need miniaturized, safe, and flexible power sources. Lithium-ion battery is a strong candidate as high performance flexible battery. The development of flexible materials for battery electrodes suffers from the limited material choices. In this paper, we present integration strategy to rationally design materials and processes to report flexible inorganic lithium-ion microbattery with no restrictions on the materials used. The battery shows an enhanced normalized capacity of 147 μAh/cm2 when bent.
Depth from focusing and defocusing

This paper studies the problem of obtaining depth information from focusing and defocusing, which have long been noticed as important sources of depth information for human and machine vision. In depth from focusing, we try to eliminate the local maxima problem which is the main source of inaccuracy in focusing; in depth from defocusing, a new computational model is proposed to achieve higher accuracy. The major contributions of this paper are: (1) In depth from focusing, instead of the popular Fibonacci search which is often trapped in local maxima, we propose the combination of Fibonacci search and curve tting, which leads to an unprecedentedly accurate result; (2) New model of the blurring e ect which takes the geometric blurring as well as the imaging blurring into consideration, and the calibration of the blurring model; (3) In spectrogram-based depth from defocusing, an iterative estimation method is proposed to decrease or eliminate the window e ect. This paper reports focus ranging with less than 1/1000 error and the defocus ranging with about 1/200 error. With this precision, depth from focus ranging is becoming competitive with stereo vision for reconstructing 3D depth information.
TasselNet: counting maize tassels in the wild via local counts regression network

Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting performance, with a mean absolute error of 6.6 and a mean squared error of 9.6 averaged over 8 test sequences. TasselNet can achieve robust in-field counting of maize tassels with a relatively high degree of accuracy. Our experimental evaluations also suggest several good practices for practitioners working on maize-tassel-like counting problems. It is worth noting that, though the counting errors have been greatly reduced by TasselNet, in-field counting of maize tassels remains an open and unsolved problem.
Enabling IoT Ecosystems through Platform Interoperability

Today, the Internet of Things (IoT) comprises vertically oriented platforms for things. Developers who want to use them need to negotiate access individually and adapt to the platform-specific API and information models. Having to perform these actions for each platform often outweighs the possible gains from adapting applications to multiple platforms. This fragmentation of the IoT and the missing interoperability result in high entry barriers for developers and prevent the emergence of broadly accepted IoT ecosystems. The BIG IoT (Bridging the Interoperability Gap of the IoT) project aims to ignite an IoT ecosystem as part of the European Platforms Initiative. As part of the project, researchers have devised an IoT ecosystem architecture. It employs five interoperability patterns that enable cross-platform interoperability and can help establish successful IoT ecosystems.
Crayon: saving power through shape and color approximation on next-generation displays

We present Crayon, a library and runtime system that reduces display power dissipation by acceptably approximating displayed images via shape and color transforms. Crayon can be inserted between an application and the display to optimize dynamically generated images before they appear on the screen. It can also be applied offline to optimize stored images before they are retrieved and displayed. Crayon exploits three fundamental properties: the acceptability of small changes in shape and color, the fact that the power dissipation of OLED displays and DLP pico-projectors is different for different colors, and the relatively small energy cost of computation in comparison to display energy usage.
 We implement and evaluate Crayon in three contexts: a hardware platform with detailed power measurement facilities and an OLED display, an Android tablet, and a set of cross-platform tools. Our results show that Crayon's color transforms can reduce display power dissipation by over 66% while producing images that remain visually acceptable to users. The measured whole-system power reduction is approximately 50%. We quantify the acceptability of Crayon's shape and color transforms with a user study involving over 400 participants and over 21,000 image evaluations.
Effectiveness and Users' Experience of Obfuscation as a Privacy-Enhancing Technology for Sharing Photos

Current collaborative photo privacy protection solutions can be categorized into two approaches: controlling the recipient, which restricts certain viewers' access to the photo, and controlling the content, which protects all or part of the photo from being viewed. Focusing on the latter approach, we introduce privacy-enhancing obfuscations for photos and conduct an online experiment with 271 participants to evaluate their effectiveness against human recognition and how they affect the viewing experience. Results indicate the two most common obfuscations, blurring and pixelating, are ineffective. On the other hand, inpainting, which removes an object or person entirely, and avatar, which replaces content with a graphical representation are effective. From a viewer experience perspective, blurring, pixelating, inpainting, and avatar are preferable. Based on these results, we suggest inpainting and avatar may be useful as privacy-enhancing technologies for photos, because they are both effective at increasing privacy for elements of a photo and provide a good viewer experience.
Two arcane areas in liposuction: The banana and the sensuous triangle

Two new approaches in suction lipectomy of the buttocks region are described: liposuction of the “banana” and liposuction of the “sensuous triangle.” The banana is the highest part of the posterior thigh just below the buttocks crease. It appears only in certain individuals and appears as a buldge under the buttocks crease. The preferred approach to liposuction of the banana is discussed as well as a theory as to its etiology. A common complication of liposuction of this area is ptosis of the buttocks crease. Methods of treatment of this complication are also discussed. The second topic addressed is liposuction of the sensuous triangle which is at the junction of the lateral buttocks, lateral thigh, and posterior thigh. The result of suctioning this area is a more athletic-appearing buttocks region.
Mining group stock portfolio by using grouping genetic algorithms

In this paper, a grouping genetic algorithm based approach is proposed for dividing stocks into groups and mining a set of stock portfolios, namely group stock portfolio. Each chromosome consists of three parts. Grouping and stock parts are used to indicate how to divide stocks into groups. Stock portfolio part is used to represent the purchased stocks and their purchased units. The fitness of each chromosome is evaluated by the group balance and the portfolio satisfaction. The group balance is utilized to make the groups represented by the chromosome have as similar number of stocks as possible. The portfolio satisfaction is used to evaluate the goodness of profits and satisfaction of investor's requests of all possible portfolio combinations that can generate from a chromosome. Experiments on a real data were also made to show the effectiveness of the proposed approach.
Efficacy of sodium hyaluronate and carboxymethylcellulose in treating mild to moderate dry eye disease.

We compared the efficacy and safety of sodium hyaluronate (SH) and carboxymethylcellulose (CMC) in treating mild to moderate dry eye.



Sixty-seven patients with mild to moderate dry eye were enrolled in this prospective, randomized, blinded study. They were treated 6 times a day with preservative-free unit dose formula eyedrops containing 0.1% SH or 0.5% CMC for 8 weeks. Corneal and conjunctival staining with fluorescein, tear film breakup time, subjective symptoms, and adverse reactions were assessed at baseline, 4 weeks, and 8 weeks after treatment initiation.



Thirty-two patients were randomly assigned to the SH group and 33 were randomly assigned to the CMC group. Both the SH and CMC groups showed statistically significant improvements in corneal and conjunctival staining sum scores, tear film breakup time, and dry eye symptom score at 4 and 8 weeks after treatment initiation. However, there were no statistically significant differences in any of the indices between the 2 treatment groups. There were no significant adverse reactions observed during follow-up.



The efficacies of SH and CMC were equivalent in treating mild to moderate dry eye. SH and CMC preservative-free artificial tear formulations appropriately manage dry eye sign and symptoms and show safety and efficacy when frequently administered in a unit dose formula.
Deep Continuous Clustering

Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoencoder is optimized as part of the clustering process. The resulting network produces clustered data. The presented approach does not rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear dimensionality reduction and clustering are formulated as optimization of a global continuous objective. We thus avoid discrete reconfigurations of the objective that characterize prior clustering algorithms. Experiments on datasets from multiple domains demonstrate that the presented algorithm outperforms state-of-theart clustering schemes, including recent methods that use deep networks. The code is available at http://github.com/shahsohil/DCC.
Enhanced semi-supervised learning for multimodal emotion recognition

Semi-Supervised Learning (SSL) techniques have found many applications where labeled data is scarce and/or expensive to obtain. However, SSL suffers from various inherent limitations that limit its performance in practical applications. A central problem is that the low performance that a classifier can deliver on challenging recognition tasks reduces the trustability of the automatically labeled data. Another related issue is the noise accumulation problem - instances that are misclassified by the system are still used to train it in future iterations. In this paper, we propose to address both issues in the context of emotion recognition. Initially, we exploit the complementarity between audio-visual features to improve the performance of the classifier during the supervised phase. Then, we iteratively re-evaluate the automatically labeled instances to correct possibly mislabeled data and this enhances the overall confidence of the system's predictions. Experimental results performed on the RECOLA database demonstrate that our methodology delivers a strong performance in the classification of high/low emotional arousal (UAR = 76.5%), and significantly outperforms traditional SSL methods by at least 5.0% (absolute gain).
The full spectrum of human naive T cells

Naive T cells have long been regarded as a developmentally synchronized and fairly homogeneous and quiescent cell population, the size of which depends on age, thymic output and prior infections. However, there is increasing evidence that naive T cells are heterogeneous in phenotype, function, dynamics and differentiation status. Current strategies to identify naive T cells should be adjusted to take this heterogeneity into account. Here, we provide an integrated, revised view of the naive T cell compartment and discuss its implications for healthy ageing, neonatal immunity and T cell reconstitution following haematopoietic stem cell transplantation. Evidence is increasing that naive T cells are heterogeneous in phenotype, function, dynamics and differentiation status. Here, van den Broek et al. provide a revised view of the naive T cell compartment and then discuss the implications for ageing, neonatal immunity and T cell reconstitution following haematopoietic stem cell transplantation.
End-user training methods: what we know, need to know

End-User Training (EUT) has enjoyed a rich tradition of research in Information Systems. However, with the growing pace of change in technology and the dynamic nature of business, organizations are spending an increasing amount of money on end-user training. Training methods are also changing with little research to support new approaches. Thus, extensive research is required in the future. To be credible, end-user training research should preserve and build upon the significant literature that exists, both in IS and Education. This paper provides a review of EUT literature focusing on training methods. It summarizes the findings, while pointing out key future research issues.
Stimulation of movement in a quiescent, hibernation-like form of Caenorhabditis elegans by dopamine signaling.

One of the characteristics of animals in hibernation is reduced behavioral activity. The Caenorhabditis elegans dauer state is a hibernation-like state of diapause that displays a dramatic reduction in spontaneous locomotion. A similar dauer-like quiescent state is produced in adults by relatively strong mutations in the insulin/IGF-1 receptor homolog daf-2. In this study, we show that mutations affecting the neurotransmitter dopamine, which regulates voluntary movement in many organisms, can stimulate movement in dauers and dauer-like quiescent adults. Surprisingly, the movement of quiescent animals is stimulated by conditions that reduce dopamine signaling and also by conditions predicted to increase dopamine signaling. Reducing dopamine signaling is likely to stimulate movement by activating a foraging response also seen in nondauers after withdrawal of food. In contrast, the stimulation of movement by increased dopamine is much more pronounced in quiescent daf-2(-) dauer and dauer-like adult animals than in nondauaer animals. This altered response to dopamine is primarily attributable to activity of the FOXO (forkhead box O) transcription factor DAF-16 in neurons. We suggest that dauers and dauer-like quiescent adults may have underlying changes in the dopamine system that enable them to respond differently to environmental stimulation.
Mobility performance evaluation of lunar rover and optimization of rocker-bogie suspension parameters

In order to provide a steady platform for scientific instruments, decrease the energy consumption, it is important to analyze and improve the mobility performance of lunar rovers. The mobility performance indexes are built based on four kinds of work conditions by summarizing the lunar surface terrain characteristics. The performance of overturning stability, load equalization of the wheels and trafficability are analyzed. The optimization mathematical model of the suspension parameters is established. Based on the sequence quadratic programming (SQP) algorithm, the optimal parameters of the suspension are obtained by taking the maximum energy consumption of motor and stationarity of mass center as the optimization objectives and overturning stability and trafficability parameters as constraints. The results indicate that the energy consumption of the motors, displacement and pitch angle of the mass center reduces significantly.
Ranking Causal Anomalies for System Fault Diagnosis via Temporal and Dynamical Analysis on Vanishing Correlations

Detecting system anomalies is an important problem in many fields such as security, fault management, and industrial optimization. Recently, invariant network has shown to be powerful in characterizing complex system behaviours. In the invariant network, a node represents a system component and an edge indicates a stable, significant interaction between two components. Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis. However, existing approaches to detect causal anomalies with the invariant network often use the percentage of vanishing correlations to rank possible casual components, which have several limitations: (1) fault propagation in the network is ignored, (2) the root casual anomalies may not always be the nodes with a high percentage of vanishing correlations, (3) temporal patterns of vanishing correlations are not exploited for robust detection, and (4) prior knowledge on anomalous nodes are not exploited for (semi-)supervised detection. To address these limitations, in this article we propose a network diffusion based framework to identify significant causal anomalies and rank them. Our approach can effectively model fault propagation over the entire invariant network and can perform joint inference on both the structural and the time-evolving broken invariance patterns. As a result, it can locate high-confidence anomalies that are truly responsible for the vanishing correlations and can compensate for unstructured measurement noise in the system. Moreover, when the prior knowledge on the anomalous status of some nodes are available at certain time points, our approach is able to leverage them to further enhance the anomaly inference accuracy. When the prior knowledge is noisy, our approach also automatically learns reliable information and reduces impacts from noises. By performing extensive experiments on synthetic datasets, bank information system datasets, and coal plant cyber-physical system datasets, we demonstrate the effectiveness of our approach.