nils-herrmann/clean_scirepeval huggingface dataset:
RECONCEPTUALISING GAMIFICATION : PLAY AND PEDAGOGY

Gamification is a complex and controversial concept. It has been both embraced as a marketing and education revolution, and dismissed as practice of exploitation. Contested within the debate around gamification has been the very concept of what a game is, what the core mechanics of games are, and whether gamification truly mobilises these core mechanics. This paper will challenge the foundation of this debate through reconceptualising gamification not as a simple set of techniques and mechanics, but as a pedagogic heritage, an alternative framework for training and shaping participant behaviour that has at its core the concepts of entertainment and engagement. In doing so it will recontextualise current practices of gamification into a longer and deeper history, and suggest potential pathways for more sophisticated gamification in the future.
Survey on Different Phases of DigitalForensics Investigation Models

Most forensics models focus on the investigative process and its different phases and are characterized by a rather informal and intuitive approach. When a formalized process been introduced, in 1984, a new and improved computer forensics investigation processes have been developed. A digital forensics investigation is a process that used science and technology to examine digital objects and tests theories, which can be entered into a court of law, to answer questions about events that occurred. There is various digital forensics investigation models which consist different phases. The interchanging one or any steps may lead the incomplete results hence wrong interpretation and conclusion. In this paper we reviewed a few investigation processes or models; discuss the phases and identifying common phases.
Microstrip series fed antenna array for millimeter wave automotive radar applications

A 2D tapered microstrip antenna array is analyzed and designed in the paper by adopting the method of Modified Transmission Line Model. The pattern of the E-plane is determined by 20 linear series fed rectangular microstrip patches with tapered width, and 16 linear arrays compose the 2D array fed by four-stage T-junction power divider network. The excitation coefficients in both E and H plane follow the Taylor distribution. The antenna array achieves a pencil beam of 29dB gain at W band, and its half power beamwidths(HPBW) for E plane and H plane are both about 5°. The Side Lobe Level (SLL) of the pattern at the center frequency is lower than -17dB and 19dB at E and H plane respectively.
Multi-Antenna Wireless Legitimate Surveillance Systems: Design and Performance Analysis

To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious communication link between a suspicious pair via proactive jamming. Assuming that the legitimate monitor can successfully overhear the suspicious information only when its achievable data rate is no smaller than that of the suspicious receiver, the key objective is to maximize the eavesdropping non-outage probability by joint design of the jamming power, receive and transmit beamformers at the legitimate monitor. Depending on the number of receive/transmit antennas implemented, i.e., single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output (MIMO), four different scenarios are investigated. For each scenario, the optimal jamming power is derived in a closed form and efficient algorithms are obtained for the optimal transmit/receive beamforming vectors. Moreover, low-complexity suboptimal beamforming schemes are proposed for the MIMO case. Our analytical findings demonstrate that by exploiting multiple antennas at the legitimate monitor, the eavesdropping non-outage probability can be significantly improved compared with the single-antenna case. In addition, the proposed suboptimal transmit zero-forcing scheme yields similar performance as the optimal scheme.
Overtaking Maneuvers in Simulated Highway Driving using Deep Reinforcement Learning

Most methods that attempt to tackle the problem of Autonomous Driving and overtaking usually try to either directly minimize an objective function or iteratively in a Reinforcement Learning like framework to generate motor actions given a set of inputs. We follow a similar trend but train the agent in a way similar to a curriculum learning approach where the agent is first given an easier problem to solve, followed by a harder problem. We use Deep Deterministic Policy Gradients to learn overtaking maneuvers for a car, in presence of multiple other cars, in a simulated highway scenario. The novelty of our approach lies in the training strategy used where we teach the agent to drive in a manner similar to the way humans learn to drive and the fact that our reward function uses only the raw sensor data at the current time step. This method, which resembles a curriculum learning approach is able to learn smooth maneuvers, largely collision free, wherein the agent overtakes all other cars, independent of the track and number of cars in the scene.
Analysis and modelling of a bidirectional push-pull converter with PWM plus phase-shift control

This paper presents analysis and modeling for a bidirectional push-pull dc-dc converter. In particular, attention is paid to leakage current reduction with PWM plus phase-shift control. The leakage current in different operating modes has been compared to identify the efficient modes for various operation conditions. The state space averaging method is adopted to derive the small signal and large signal models of the most efficient mode. A 30~70V/300V prototype was built. Both simulation results and experimental results prove the validity of the model.
Improving Speaker-Independent Lipreading with Domain-Adversarial Training

We present a Lipreading system, i.e. a speech recognition system using only visual features, which uses domain-adversarial training for speaker independence. Domain-adversarial training is integrated into the optimization of a lipreader based on a stack of feedforward and LSTM (Long Short-Term Memory) recurrent neural networks, yielding an end-to-end trainable system which only requires a very small number of frames of untranscribed target data to substantially improve the recognition accuracy on the target speaker. On pairs of different source and target speakers, we achieve a relative accuracy improvement of around 40% with only 15 to 20 seconds of untranscribed target speech data. On multi-speaker training setups, the accuracy improvements are smaller but still substantial.
Innovation in the pharmaceutical industry: New estimates of R&D costs.

The research and development costs of 106 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug and biologics development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per approved new compound is $1395 million (2013 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 10.5% yields a total pre-approval cost estimate of $2558 million (2013 dollars). When compared to the results of the previous study in this series, total capitalized costs were shown to have increased at an annual rate of 8.5% above general price inflation. Adding an estimate of post-approval R&D costs increases the cost estimate to $2870 million (2013 dollars).
18 kW three phase inverter system using hermetically sealed SiC phase-leg power modules

Power electronics play an important role in electricity utilization from generation to end customers. Thus, high-efficiency power electronics help to save energy and conserve energy resources. Research on silicon carbide (SiC) power electronics has shown their better efficiency compared to Si power electronics due to the significant reduction in both conduction and switching losses. Combined with their high-temperature capability, SiC power electronics are more reliable and compact. This paper focuses on the development of such a high efficiency, high temperature inverter based on SiC JFET and diode modules. It involves the work on high temperature packaging (>200 °C), inverter design and prototype development, device characterization, and inverter testing. A SiC inverter prototype with a power rating of 18 kW is developed and demonstrated. When tested at moderate load levels compared to the inverter rating, an efficiency of 98.2% is achieved by the initial prototype without optimization, which is higher than most Si inverters.
Front-of-pack nutrition labelling: Testing effectiveness of different nutrition labelling formats front-of-pack in four European countries

In two studies, the impact of eight front-of-pack nutrition labelling formats that differed in complexity was investigated across four European countries. In total 1630 men and women (18-55 yrs) were recruited from Internet panels in the United Kingdom, Germany, Italy and the Netherlands for study 1 and 776 in Italy and the United Kingdom for study 2. Participants evaluated several products (healthier and less healthy variants of the same product category) with a front-of-pack nutrition labelling format. The first study evaluated different labelling formats on consumer friendliness (comprehension, liking and credibility) and the second study measured the effect of the different labelling formats on decision-making (usage intention and process time). The results indicated minor differences in consumer friendliness and usage intention between simpler (such as Healthier Choice Tick, Smileys and Stars) and more complex front-of-pack nutrition labelling formats (such as Multiple Traffic Light, Wheel of Health and GDA scores). Endorsement by national and international health organisations strongly increased the labelling formats' credibility. Participants needed significantly less time to evaluate simpler front-of-pack labelling compared to the more complex labelling format. Thus simpler front-of-pack labelling formats seem more appropriate in a shopping environment where quick decisions are made.
Recognizing independent and joint activities among multiple residents in smart environments

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.
Evaluation of the correlation between blood pressure and pulse transit time

The arterial blood pressure is an essential physiological parameter for health monitoring. Most blood measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This method is uncomfortable to the user and may cause anxiety which in turns can affect the blood pressure (white coat syndrome). This paper investigates a cuff-less non-intrusive approach to measure arterial blood pressure that is suitable for continuous measurement. The approach is based on measuring the delay between the R-peak of the electrocardiogram (ECG) signal and the peaks of the finger photoplethysmograph (PPG) signal. The results of this paper show a high correlation between the blood pressure and the pulse transit time (PTT). Different polynomial regressions are applied for further estimation. The paper uses actual ECG, PPG and blood pressure measurements extracted from the MIMIC database that contains clinical signal data reflecting real measurements. The simulation results verify that the delay (PTT) between the R-peak of the ECG signal and the peaks of the finger PPG signal have a high correlation with arterial blood pressure and can be used as an indicator of the arterial blood pressure.
The Language of Place: Semantic Value from Geospatial Context

There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.
Player Typology in Theory and Practice

Player satisfaction modeling depends in part upon quantitative or qualitative typologies of playing preferences, although such approaches require scrutiny. Examination of psychometric typologies reveal that type theories have—except in rare cases—proven inadequate and have made way for alternative trait theories. This suggests any future player typology that will be sufficiently robust will need foundations in the form of a trait theory of playing preferences. This paper tracks the development of a sequence of player typologies developing from psychometric type theory roots towards an independently validated trait theory of play, albeit one yet to be fully developed. Statistical analysis of the results of one survey in this lineage is presented, along with a discussion of theoretical and practical ways in which the surveys and their implied typological instruments have evolved.
On time blood pressure prediction with the use of PPG signals

The article presents the compact system for predicting the blood pressure with the use of PPG (photoplethysmogram) curves. The algorithm for data measurement and analysis was proposed. The prototype system and initial experimental results were presented. In the system the filters were applied to the input signal for improving the peak detection algorithm. The measurement device allows the user to record the data and transmit to a remote location. In more compact form in future it can act as an Internet of Things wearable agent.
On the Spectral Bias of Neural Networks

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.
2D-3D Pose Consistency-based Conditional Random Fields for 3D Human Pose Estimation

This study considers the 3D human pose estimation problem in a single RGB image by proposing a conditional random field (CRF) model over 2D poses, in which the 3D pose is obtained as a byproduct of the inference process. The unary term of the proposed CRF model is defined based on a powerful heat-map regression network, which has been proposed for 2D human pose estimation. This study also presents a regression network for lifting the 2D pose to 3D pose and proposes the prior term based on the consistency between the estimated 3D pose and the 2D pose. To obtain the approximate solution of the proposed CRF model, the N-best strategy is adopted. The proposed inference algorithm can be viewed as sequential processes of bottom-up generation of 2D and 3D pose proposals from the input 2D image based on deep networks and top-down verification of such proposals by checking their consistencies. To evaluate the proposed method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental results show that the proposed method achieves the state-of-the-art 3D human pose estimation performance.
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling

Data collection for landslide susceptibility modeling is often an inhibitive activity. This is one reason why for quite some time landslides have been described and modelled on the basis of spatially distributed values of landslide-related attributes. This paper presents landslide susceptibility analysis in the Klang Valley area, Malaysia, using back-propagation artificial neural network model. A landslide inventory map with a total of 398 landslide locations was constructed using the data from various sources. Out of 398 landslide locations, 318 (80%) of the data taken before the year 2004 was used for training the neural network model and the remaining 80 (20%) locations (post-2004 events) were used for the accuracy assessment purpose. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Eleven landslide occurrence related factors were selected as: slope angle, slope aspect, curvature, altitude, distance to roads, distance to rivers, lithology, distance to faults, soil type, landcover and the normalized difference vegetation index value. For calculating the weight of the relative importance of each factor to the landslide occurrence, an artificial neural network method was developed. Each thematic layer’s weight was determined by the back-propagation training method and landslide susceptibility indices (LSI) were calculated using the trained back-propagation weights. To assess the factor effects, the weights were calculated three times, using all 11 factors in the first case, then recalculating after removal of those 4 factors that had the smallest weights, and thirdly after removal of the remaining 3 least influential factors. The effect of weights in landslide susceptibility was verified using the landslide location data. It is revealed that all factors have relatively positive effects on the landslide susceptibility maps in the study. The validation results showed sufficient agreement between the computed susceptibility maps and the existing data on landslide areas. The distribution of landslide susceptibility zones derived from ANN shows similar trends as those obtained by applying in GIS-based susceptibility procedures by the same authors (using the frequency ratio and logistic regression method) and indicates that ANN results are better than the earlier method. Among the three cases, the best accuracy (94%) was obtained in the case of the 7 factors weight, whereas 11 factors based weight showed the worst accuracy (91%).
Vocal communication of emotion: A review of research paradigms

The current state of research on emotion effects on voice and speech is reviewed and issues for future research efforts are discussed. In particular, it is suggested to use the Brunswikian lens model as a base for research on the vocal communication of emotion. This approach allows one to model the complete process, including both encoding (expression), transmission, and decoding (impression) of vocal emotion communication. Special emphasis is placed on the conceptualization and operationalization of the major elements of the model (i.e., the speaker’s emotional state, the listener’s attribution, and the mediating acoustic cues). In addition, the advantages and disadvantages of research paradigms for the induction or observation of emotional expression in voice and speech and the experimental manipulation of vocal cues are discussed, using pertinent examples drawn from past and present research.  Vocal communication of emotion: A review of research paradigms. Speech Communication, 2003, vol. 40, no. 1-2, p. 227-256 DOI : 10.1016/S0167-6393(02)00084-5
Bifurcation Analysis of a Mathematical Model for Malaria Transmission

We present an ordinary differential equation mathematical model for the spread of malaria in human and mosquito populations. Susceptible humans can be infected when they are bitten by an infectious mosquito. They then progress through the exposed, infectious, and recovered classes, before reentering the susceptible class. Susceptible mosquitoes can become infected when they bite infectious or recovered humans, and once infected they move through the exposed and infectious classes. Both species follow a logistic population model, with humans having immigration and disease-induced death. We define a reproductive number, R0, for the number of secondary cases that one infected individual will cause through the duration of the infectious period. We find that the disease-free equilibrium is locally asymptotically stable when R0 < 1 and unstable when R0 > 1. We prove the existence of at least one endemic equilibrium point for all R0 > 1. In the absence of disease-induced death, we prove that the transcritical bifurcation at R0 = 1 is supercritical (forward). Numerical simulations show that for larger values of the disease-induced death rate, a subcritical (backward) bifurcation is possible at R0 = 1.
Evolving to a New Dominant Logic for Marketing

Marketing inherited a model of exchange from economics, which had a dominant logic based on the exchange of “goods,” which usually are manufactured output. The dominant logic focused on tangible resources, embedded value, and transactions. Over the past several decades, new perspectives have emerged that have a revised logic focused on intangible resources, the cocreation of value, and relationships. The authors believe that the new perspectives are converging to form a new dominant logic for marketing, one in which service provision rather than goods is fundamental to economic exchange. The authors explore this evolving logic and the corresponding shift in perspective for marketing scholars, marketing practitioners, and marketing educators.