nils-herrmann/clean_scirepeval huggingface dataset:
A Conceptual Integrated Model for Measuring the Success of eLearning in Developing Countries: Literature Review

The increase in funding for e-learning is a good move that requires monitoring, as e-learning, like any other information systems, is a long-term investment with uncertainty for returns. This necessitates the need for e-learning evaluation to determine post adoption success. The evaluation of e-learning is a complex process, that requires comprehensive tools for measuring post adoption success. Subsequently, leading the study to investigate approaches utilised for performing post adoption e-learning success. Thereafter, proposing an adapted IS success integrated model for measuring post adoption e-learning success in developing country context, specifically, South Africa. A systematic literature review method was adopted to achieve an inductive study. The study objectives influenced the key words employed for searching relevant literature. They also drove the decision to follow thematic analysis method. Through a systematic review the study discovered that the Delone & Mclean IS success model is the most adopted and applied for measuring post adoption e-learning success. The model is also received as an effective tool to comprehensively gauge post adoption e-learning success. Additionally, the model is flexible and modifiable to suite different contexts. The study suggests further investigation and use of the adapted IS success model within HEIs to comprehensively assess post adoption e-learning success in South Africa. Future studies should be focused on conducting in-depth literature review on approaches utilised for assessing e-learning success post adoption. They should also do testing to determine the suitable process flow and constructs of an evaluation model for developing country contexts.
FTR-18: Collecting rumours on football transfer news

This paper describes ongoing work on the creation of a multilingual rumour dataset on football transfer news, FTR-18. Transfer rumours are continuously published by sports media. They can both harm the image of player or a club or increase the player’s market value. The proposed dataset includes transfer articles written in English, Spanish and Portuguese. It also comprises Twitter reactions related to the transfer rumours. FTR-18 is suited for rumour classification tasks and allows the research on the linguistic patterns used in
Trichilemmoma in continuity with pigmented basal cell carcinoma; with dermatoscopy and dermatopathology

A case of trichilemmoma in continuity with a pigmented basal cell carcinoma is presented with dermatoscopy and dermatopathology. The distinction between the two lesions was evident dermatoscopically and was confirmed dermatopathologically. While trichilemmoma has been reported in association with basal cell carcinoma and dermatoscopy images of four previous cases of trichilemmoma have been published, no previous dermatoscopy image has been published of trichilemmoma associated with basal cell carcinoma.
Convergence of MANET and WSN in IoT Urban Scenarios

Ubiquitous smart environments, equipped with low-cost and easy-deployable wireless sensor networks (WSNs) and widespread mobile ad hoc networks (MANETs), are opening brand new opportunities in wide-scale urban monitoring. Indeed, MANET and WSN convergence paves the way for the development of brand new Internet of Things (IoT) communication platforms with a high potential for a wide range of applications in different domains. Urban data collection, i.e., the harvesting of monitoring data sensed by a large number of collaborating sensors, is a challenging task because of many open technical issues, from typical WSN limitations (bandwidth, energy, delivery time, etc.) to the lack of widespread WSN data collection standards, needed for practical deployment in existing and upcoming IoT scenarios. In particular, effective collection is crucial for classes of smart city services that require a timely delivery of urgent data such as environmental monitoring, homeland security, and city surveillance. After surveying the existing WSN interoperability efforts for urban sensing, this paper proposes an original solution to integrate and opportunistically exploit MANET overlays, impromptu, and collaboratively formed over WSNs, to boost urban data harvesting in IoT. Overlays are used to dynamically differentiate and fasten the delivery of urgent sensed data over low-latency MANET paths by integrating with latest emergent standards/specifications for WSN data collection. The reported experimental results show the feasibility and effectiveness (e.g., limited coordination overhead) of the proposed solution.
FUNDAMENTALS OF FLUID FILM JOURNAL BEARING OPERATION AND MODELING by Minhui He

Widely used in turbomachinery, the fluid film journal bearing is critical to a machine’s overall reliability level. Their design complexity and application severity continue to increase making it challenging for the plant machinery engineer to evaluate their reliability. This tutorial provides practical knowledge on their basic operation and what physical effects should be included in modeling a bearing to help ensure its reliable operation in the field. All the important theoretical aspects of journal bearing modeling, such as film pressure, film and pad temperatures, thermal and mechanical deformations, and turbulent flow are reviewed. Through some examples, the tutorial explores how different effects influence key performance characteristics like minimum film thickness, Babbitt temperature as well as stiffness and damping coefficients. Due to their increasing popularity, the operation and analysis of advanced designs using directed lubrication principles, such as inlet grooves and starvation, are also examined with several examples including comparisons to manufacturers’ test data.
A New Buck–Boost Converter With Low-Voltage Stress and Reduced Conducting Components

This paper presents a new buck–boost converter. Unlike the single-switch buck–boost converter, the proposed converter has low-voltage stresses on semiconductors. Moreover, although both the conventional two-switch buck–boost (TSBB) and the proposed converters have the same number of passive and active components, and the proposed converter can reduce the conduction loss as a result of having fewer conducting components. Therefore, the proposed converter obtained a higher efficiency than the TSBB converter. A 48-V output voltage and 150-W output power prototype was fabricated to verify the effectiveness of the proposed converter.
Submodular Trajectory Optimization for Aerial 3D Scanning

Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model. Our method uses a coarse estimate of the scene geometry to plan camera trajectories that: (1) cover the scene as thoroughly as possible; (2) encourage observations of scene geometry from a diverse set of viewing angles; (3) avoid obstacles; and (4) respect a user-specified flight time budget. Our method relies on a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity. We leverage this property extensively to design a trajectory planning algorithm that reasons globally about the non-additive coverage reward obtained across a trajectory, jointly with the cost of traveling between views. We evaluate our method by using it to scan three large outdoor scenes, and we perform a quantitative evaluation using a photorealistic video game simulator.
Incorporating Reviewer and Product Information for Review Rating Prediction

Traditional sentiment analysis mainly considers binary classifications of reviews, but in many real-world sentiment classification problems, nonbinary review ratings are more useful. This is especially true when consumers wish to compare two products, both of which are not negative. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text features are modeled as a three-dimension tensor. Tensor factorization techniques can then be employed to reduce the data sparsity problems. We perform extensive experiments to demonstrate the effectiveness of our model, which has a significant improvement compared to state of the art methods, especially for reviews with unpopular products and inactive reviewers.
Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture

We propose an encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company.
 We begin by identifying the source-target transition pairs between items within each session. Then, the set of source items are passed through an encoder, whose learned representation is used by the decoder to estimate the sequence of target items. Therefore, as this process is performed pair-wise, we hypothesize that the model could capture the transition regularities in a more fine grained way. Additionally, our model incorporates an attention mechanism to explicitly learn the more expressive portions of the sequences in order to improve performance. Besides modeling the user sessions, we also extended the original architecture by means of attaching a second decoder that is jointly trained to predict the purchasing intent of user in each session. With this, we want to explore to what extent the model can capture inter session dependencies.
 We performed an empirical study comparing against several baselines on a large real world dataset, showing that our approach is competitive in both item and intent prediction.
Externally Leveraged Resonant Piezoelectric Actuator With Fast Response Time for Smart Devices

An externally leveraged circular resonant piezoelectric actuator with haptic natural frequency and fast response time was developed within the volume of 10 mm diameter and 3.4 mm thickness for application in mobile phones. An efficient displacement-amplifying mechanism was developed using a piezoelectric bimorph, a lever system, and a mass-spring system. The proposed displacement-amplifying mechanism utilizes both internally and externally leveraged structures. The former generates bending by means of bending deformation of the piezoelectric bimorph, and the latter transforms the bending to radial displacement of the lever system, which is transformed to a large axial displacement of the spring. The piezoelectric bimorph, lever system, and spring were designed to maximize static displacement and the mass-spring system was designed to have a haptic natural frequency. The static displacement, natural frequency, maximum output displacement, and response time of the resonant piezoelectric actuator were calculated by means of finite-element analyses. The proposed resonant piezoelectric actuator was prototyped and the simulated results were verified experimentally. The prototyped piezoelectric actuator generated the maximum output displacement of 290 μm at the haptic natural frequency of 242 Hz. Owing to the proposed efficient displacement-amplifying mechanism, the proposed resonant piezoelectric actuator had the fast response time of 14 ms, approximately one-fifth of a conventional resonant piezoelectric actuator of the same size.
Evaluating Machine Learning for Improving Power Grid Reliability

Ensuring reliability as the electrical grid morphs into the “smart grid” will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance – in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning) techniques. To help in meeting this challenge, we present the Neutral Online Visualization-aided Autonomic evaluation framework (NOVA) for evaluating machine learning algorithms for preventive maintenance on the electrical grid. NOVA has three stages provided through a unified user interface: evaluation of input data quality, evaluation of machine learning results, and evaluation of the reliability improvement of the power grid. A prototype version of NOVA has been deployed for the power grid in New York City, and it is able to evaluate machine learning systems effectively and efficiently. Appearing in the ICML 2011 Workshop on Machine Learning for Global Challenges, Bellevue, WA, USA, 2011.
Psychophysical Characterization and Testbed Validation of a Wearable Vibrotactile Glove for Telemanipulation

This paper describes and evaluates a high-fidelity, low-cost haptic interface for tele-operation. The interface is a wearable vibrotactile glove containing miniature voice coils that provides continuous, proportional force information to the user's finger-tips. In psychophysical experiments, correlated variations in the frequency and amplitude of the stimulators extended the user's perceptual response range compared to varying amplitude or frequency alone. In an adaptive, force-limited, pick-and-place manipulation task, the interface allowed users to control the grip forces more effectively than no feedback or binary feedback, which produced equivalent performance. A sorting experiment established that proportional tactile feedback enhances the user's ability to discriminate the relative properties of objects, such as weight. We conclude that correlated amplitude and frequency signals, simulating force in a remote environment, substantially improve teleoperation.
On-the-Fly Calibration of Low-Cost Gas Sensors

Air quality monitoring is extremely important as air pollution has a direct impact on human health. Low-cost gas sensors are used to effectively perceive the environment by mounting them on top of mobile vehicles, for example, using a public transport network. Thus, these sensors are part of a mobile network and perform from time to time measurements in each others vicinity. In this paper, we study three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly. Forward calibration, based on a traditional approach widely used in the literature, is used as performance benchmark for two novel algorithms: backward and instant calibration. We validate all three algorithms with real ozone pollution measurements carried out in an urban setting by comparing gas sensor output to high-quality measurements from analytical instruments. We find that both backward and instant calibration reduce the average measurement error by a factor of two compared to forward calibration. Furthermore, we unveil the arising difficulties if sensor calibration is not based on reliable reference measurements but on sensor readings of low-cost gas sensors which is inevitable in a mobile scenario with only a few reliable sensors. We propose a solution and evaluate its effect on the measurement accuracy in experiments and simulation.
Boosting Black Box Variational Inference

Approximating a probability density in a tractable manner is a central task in Bayesian statistics. Variational Inference (VI) is a popular technique that achieves tractability by choosing a relatively simple variational approximation. Borrowing ideas from the classic boosting framework, recent approaches attempt to boost VI by replacing the selection of a single density with an iteratively constructed mixture of densities. In order to guarantee convergence, previous works impose stringent assumptions that require significant effort for practitioners. Specifically, they require a custom implementation of the greedy step (called the LMO) for every probabilistic model with respect to an unnatural variational family of truncated distributions. Our work fixes these issues with novel theoretical and algorithmic insights. On the theoretical side, we show that boosting VI satisfies a relaxed smoothness assumption which is sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm. Furthermore, we rephrase the LMO problem and propose to maximize the Residual ELBO (RELBO) which replaces the standard ELBO optimization in VI. These theoretical enhancements allow for black box implementation of the boosting subroutine. Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.
Deep Logic Networks: Inserting and Extracting Knowledge From Deep Belief Networks

Developments in deep learning have seen the use of layerwise unsupervised learning combined with supervised learning for fine-tuning. With this layerwise approach, a deep network can be seen as a more modular system that lends itself well to learning representations. In this paper, we investigate whether such modularity can be useful to the insertion of background knowledge into deep networks, whether it can improve learning performance when it is available, and to the extraction of knowledge from trained deep networks, and whether it can offer a better understanding of the representations learned by such networks. To this end, we use a simple symbolic language—a set of logical rules that we call confidence rules—and show that it is suitable for the representation of quantitative reasoning in deep networks. We show by knowledge extraction that confidence rules can offer a low-cost representation for layerwise networks (or restricted Boltzmann machines). We also show that layerwise extraction can produce an improvement in the accuracy of deep belief networks. Furthermore, the proposed symbolic characterization of deep networks provides a novel method for the insertion of prior knowledge and training of deep networks. With the use of this method, a deep neural–symbolic system is proposed and evaluated, with the experimental results indicating that modularity through the use of confidence rules and knowledge insertion can be beneficial to network performance.
3Cat-2—An Experimental Nanosatellite for GNSS-R Earth Observation: Mission Concept and Analysis

Global navigation satellite system reflectometry is a multistatic radar using navigation signals as signals of opportunity. It provides wide-swath and improved spatiotemporal sampling over current space-borne missions. The lack of experimental datasets from space covering signals from multiple constellations (GPS, GLONASS, Galileo, and Beidou) at dual-band (L1 and L2) and dual-polarization (right- and left-hand circular polarization), over the ocean, land, and cryosphere remains a bottleneck to further develop these techniques. 3Cat-2 is a 6-unit (3 × 2 elementary blocks of 10 × 10 × 10 cm3) CubeSat mission designed and implemented at the Universitat Politècnica de Catalunya-BarcelonaTech to explore fundamental issues toward an improvement in the understanding of the bistatic scattering properties of different targets. Since geolocalization of the specific reflection points is determined by the geometry only, a moderate pointing accuracy is only required to correct the antenna pattern in scatterometry measurements. This paper describes the mission analysis and the current status of the assembly, integration, and verification activities of both the engineering model and the flight model performed at Universitat Politècnica de Catalunya NanoSatLab premises. 3Cat-2 launch is foreseen for the second quarter of 2016 into a Sun-Synchronous orbit of 510-km height.
A Fault Hypothesis for Integrated Architectures

Integrated architectures in the automotive and avionic domain promise improved resource utilization and enable a better tactic coordination of application subsystems compared to federated systems. In order to support safety-critical application subsystems, an integrated architecture needs to support fault-tolerant strategies that enable the continued operation of the system in the presence of failures. The basis for the implementation and validation of fault-tolerant strategies is a fault hypothesis that identifies the fault containment regions, specifies the failure modes and provides realistic failure rate assumptions. This paper describes a fault hypothesis for integrated architectures, which takes into account the collocation of multiple software components on shared node computers. We argue in favor of a differentiation of fault containment regions for hardware and software faults. In addition, the fault hypothesis describes the assumptions concerning the respective frequencies of transient and permanent failures in consideration of recent semiconductor trends
Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

c∈T ∑ u∈Sc log fu,c(X), where Sc – set of locations, which were identified as a class c ∈ C by the weak localization procedure. 2 Expansion principle • Expansion loss incorporates a prior knowledge about object sizes. • The characteristic size of any class c is controlled by a decay parameter dc. • We use decay d+ for all classes, which present in the image, and decay d− for all classes, which are absent. I = {i1, . . . , in} defines descending order for class scores: fi1,c(x) ≥ · · · ≥ fin,c(x) Gc(f(X);dc) = 1 Z(dc) n ∑
A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods

Multiple criteria decision-making (MCDM) research has developed rapidly and has become a main area of research for dealing with complex decision problems. The purpose of the paper is to explore the performance evaluation model. This paper develops an evaluation model based on the fuzzy analytic hierarchy process and the technique for order performance by similarity to ideal solution, fuzzy TOPSIS, to help the industrial practitioners for the performance evaluation in a fuzzy environment where the vagueness and subjectivity are handled with linguistic values parameterized by triangular fuzzy numbers. The proposed method enables decision analysts to better understand the complete evaluation process and provide a more accurate, effective, and systematic decision support tool.
Antioxidants: Molecules, medicines, and myths.

There is an industry-driven public obsession with antioxidants, which are equated to safe, health-giving molecules to be swallowed as mega-dose supplements or in fortified foods. Sometimes they are good for you, but sometimes they may not be, and pro-oxidants can be better for you in some circumstances. This article re-examines and challenges some basic assumptions in the nutritional antioxidant field.