nils-herrmann/clean_scirepeval huggingface dataset:
Design of a low cost cavity backed wideband bow-tie antenna for ground penetrating radar systems

The ground penetrating radar (GPR) designs demand antennas with ultra wide bandwidths (UWB). Thus the conventional antennas such as dipoles cannot be used effectively. The GPR antennas are expensive. In this paper, the feasibility of building a low cost ultra-wide band bow-tie antenna for GPR using widely available low cost materials such as FR-4 PCB substrates, low cost TV baluns and scrap iron is explored. The objective was to develop the antenna to radiate in the range from 200 MHz to 600 MHz whilst restricting the antenna lobes to the direction of the ground. A cavity backed design is considered to obtain these requirements. The antenna was modeled using Ansoft HFSS (presently Ansys HFSS) software package which is widely used in current industrial domain. The simulations were carried out for different antenna configurations by changing its parameters according to linear programming techniques. Further, electromagnetic interferences (EMI) have been successfully reduced by integrating a metal cavity which acts as a reflector to the antenna. Parameters related to the cavity were tweaked in order to obtain the optimum frequency response. Considering the simulation results the most satisfactory antenna configuration was obtained and implemented.
Word Embedding-based Antonym Detection using Thesauri and Distributional Information

This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled text data. The evaluation results on the GRE antonym question task show that our model outperforms the state-of-the-art systems and it can answer the antonym questions in the F-score of 89%.
Supervised dictionary learning for music genre classification

This paper concerns the development of a music codebook for summarizing local feature descriptors computed over time. Comparing to a holistic representation, this text-like representation better captures the rich and time-varying information of music. We systematically compare a number of existing codebook generation techniques and also propose a new one that incorporates labeled data in the dictionary learning process. Several aspects of the encoding system such as local feature extraction and codeword encoding are also analyzed. Our result demonstrates the superiority of sparsity-enforced dictionary learning over conventional VQ-based or exemplar-based methods. With the new supervised dictionary learning algorithm and the optimal settings inferred from the performance study, we achieve state-of-the-art accuracy of music genre classification using just the log-power spectrogram as the local feature descriptor. The classification accuracies for benchmark datasets GTZAN and IS-MIR2004Genre are 84.7% and 90.8%, respectively.
Hippocampal Neurogenesis Regulates Forgetting During Adulthood and Infancy

Throughout life, new neurons are continuously added to the dentate gyrus. As this continuous addition remodels hippocampal circuits, computational models predict that neurogenesis leads to degradation or forgetting of established memories. Consistent with this, increasing neurogenesis after the formation of a memory was sufficient to induce forgetting in adult mice. By contrast, during infancy, when hippocampal neurogenesis levels are high and freshly generated memories tend to be rapidly forgotten (infantile amnesia), decreasing neurogenesis after memory formation mitigated forgetting. In precocial species, including guinea pigs and degus, most granule cells are generated prenatally. Consistent with reduced levels of postnatal hippocampal neurogenesis, infant guinea pigs and degus did not exhibit forgetting. However, increasing neurogenesis after memory formation induced infantile amnesia in these species.
Optimizing Players' Expected Enjoyment in Interactive Stories

In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player’s expected enjoyment without removing player agency. Our personalized drama manager models a player’s preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player’s expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players’ enjoyment ratings in an interactive storytelling testbed, compared to drama managers in pre-
Complementary approaches built as web service for arabic handwriting OCR systems via amazon elastic mapreduce (EMR) model

Arabic Optical Character Recognition (OCR) as Web Services represents a major challenge for handwritten document recognition. A variety of approaches, methods, algorithms and techniques have been proposed in order to build powerful Arabic OCR web services. Unfortunately, these methods could not succeed in achieving this mission in case of large large quantity Arabic handwritten documents. Intensive experiments and observations revealed that some of the existing approaches and techniques are complementary and can be combined to improve the recognition rate. Designing and implementing these recent sophisticated complementary approaches and techniques as web services are commonly complex; they require strong computing power to reach an acceptable recognition speed especially in case of large quantity documents. One of the possible solutions to overcome this problem is to benefit from distributed computing architectures such as cloud computing. This paper describes the design and implementation of Arabic Handwriting Recognition as a web service (AHRweb service) based on the complementary approach K-Nearest Neighbor (KNN) /Support Vector Machine (SVM) (K-NN/SVM) via Amazon Elastic MapReduce (EMR) model. The experiments were conducted on a cloud computing environment with a real large scale handwriting dataset from the Institute for Communications Technology (IFN)/ Ecole Nationale d’Ingénieur de Tunis (ENIT) IFN/ENIT database. The J-Sim (Java Simulator) was used as a tool to generate and analyze statistical results. Experimental results show that Amazon Elastic MapReduce (EMR) model constitutes a very promising framework for enhancing large AHRweb service performances.
An Improved Bank Credit Scoring Model: A Naïve Bayesian Approach

Credit scoring is a decision tool used by organizations to grant or reject credit requests from their customers. Series of artificial intelligent and traditional approaches have been used to building credit scoring model and credit risk evaluation. Despite being ranked amongst the top 10 algorithm in Data mining, Naïve Bayesian algorithm has not been extensively used in building credit score cards. Using demographic and material indicators as input variables, this paper investigate the ability of Bayesian classifier towards building credit scoring model in banking sector.
RFID-Based Students Attendance Management System

In recent years, there have been rise in the number of applications based on Radio Frequency Identification (RFID) systems and have been successfully applied to different areas as diverse as transportation, health-care, agriculture, and hospitality industry to name a few. RFID technology facilitates automatic wireless identification using electronic passive and active tags with suitable readers. In this paper, an attempt is made to solve recurrent lecture attendance monitoring problem in developing countries using RFID technology. The application of RFID to student attendance monitoring as developed and deployed in this study is capable of eliminating time wasted during manual collection of attendance and an opportunity for the educational administrators to capture face-to-face classroom statistics for allocation of appropriate attendance scores and for further managerial decisions.
All-metal Ku-band Luneburg lens antenna based on variable parallel plate spacing Fakir bed of nails

This paper presents an all-metal parallel plate waveguide (PPW) Luneburg lens designed using an array of subwavelength Fakir bed of nails unit-cells. Variation of the metal post height and PPW-spacing modulates the effective refractive index. The lens operates in the Ku-band of frequencies, with more than 40% bandwidth, in a quasi-TEM mode. Broadband and low loss, with high directivity and wide field-of-view, this antenna could be an excellent candidate as a lens-like beamformer for multiple beams satellite communication applications.
Eccentric training in chronic painful impingement syndrome of the shoulder: results of a pilot study

Treatment with painful eccentric muscle training has been demonstrated to give good clinical results in patients with chronic Achilles tendinosis. The pain mechanisms in chronic painful shoulder impingement syndrome have not been scientifically clarified, but the histological changes found in the supraspinatus tendon have similarities with the findings in Achilles tendinosis. In this pilot study, nine patients (five females and four males, mean age 54 years) with a long duration of shoulder pain (mean 41 months), diagnosed as having shoulder impingement syndrome and on the waiting list for surgical treatment (mean 13 months), were included. Patients with arthrosis in the acromio-clavicular joint or with large calcifications causing mechanical impingement during horizontal shoulder abduction were not included. We prospectively studied the effects of a specially designed painful eccentric training programme for the supraspintus and deltoideus muscles (3×15 reps, 2 times/day, 7 days/week, for 12 weeks). The patients evaluated the amount of shoulder pain during horizontal shoulder activity on a visual analogue scale (VAS), and satisfaction with treatment. Constant score was assessed. After 12 weeks of treatment, five patients were satisfied with treatment, their mean VAS had decreased (62–18, P<0.05), and their mean Constant score had increased (65–80, P<0.05). At 52-week follow-up, the same five patients were still satisfied (had withdrawn from the waiting list for surgery), and their mean VAS and Constant score were 31 and 81, respectively. Among the satisfied patients, two had a partial suprasinatus tendon rupture, and three had a Type 3 shaped acromion. In conclusion, the material in this study is small and the follow-up is short, but it seems that although there is a long duration of pain, together with bone and tendon abnormalities, painful eccentric supraspinatus and deltoideus training might be effective. The findings motivate further studies.
Investment and Financing Constraints : Evidence from the Funding of Corporate Pension Plans

I exploit sharply nonlinear funding rules for defined benefit pension plans in order to identify the dependence of corporate investment on internal financial resources in a large sample. Capital expenditures decline with mandatory contributions to defined benefit pension plans, even when controlling for correlations between the pension funding status itself and the firm’s unobserved investment opportunities. The effect is particularly evident among firms that face financing constraints based on observable variables such as credit ratings. Investment also displays strong negative correlations with the part of mandatory contributions resulting solely from unexpected asset market movements.  Graduate School of Business, University of Chicago. I thank James Poterba, Stew Myers, Jonathan Gruber, Dirk Jenter, Heitor Almeida, Daniel Bergstresser, Mihir Desai, Michael Greenstone, Robin Greenwood, David Scharfstein, Antoinette Schoar, Jeremy Stein, and Amir Sufi for helpful comments and discussions. I would also like to thank Rob Stambaugh (the editor) and the referees. This work benefited greatly from the thoughts of economics seminar participants at MIT, Harvard, Princeton, and the Kennedy School of Government, and of finance seminar participants at the University of Chicago, the University of Pennsylvania (Wharton), Harvard, Stanford, Columbia, NYU (Stern), Dartmouth (Tuck), Michigan (Ross), Boston College (Carroll), Northwestern (Kellogg), Duke (Fuqua), and the 2004 Western Finance Association meetings in Vancouver. I am grateful to the Center for Retirement Research at Boston College and the National Bureau of Economic Research for financial support.
A MODERN TAKE

We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly constant (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.
How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions]

^ IEEE ConsumEr ElECtronICs magazInE 19 By early 2016, Pabellon achieved another major milestone: a first touchfree control panel using solely the body’s electrolytes to control a matrix of magnetic pixels. Unlike conventional passive near-field communication technology that relies on receiver loop antennas for detection and identification, the magnetic pixel needs only our body salts to work. Each pixel represents a discrete sensing element. When arrayed or matrixed, the collection of pixels can be used to position index fingers approaching but not touching the surface. Pabellon’s powertransfer emitter turns into a touch-free pad. Unlike motion and other opticalsensing methods, the position of objects like fingers can be resolved by small changes in the magnetic near field. The ability to finely control the vast amount of things we contact on a daily basis may not require contact alone. The magnetic pixel has a very exciting future in the look-but-don’t-touch world that Pabellon envisions.
Microfinance and Poverty : Evidence Using Panel Data from Bangladesh

Microfinance supports mainly informal activities that often have a low return and low market demand. It may therefore be hypothesized that the aggregate poverty impact of microfinance is modest or even nonexistent. If true, the poverty impact of microfinance observed at the participant level represents either income redistribution or short-run income generation from the microfinance intervention. This article examines the effects of microfinance on poverty reduction at both the participant and the aggregate levels using panel data from Bangladesh. The results suggest that access to microfinance contributes to poverty reduction, especially for female participants, and to overall poverty reduction at the village level. Microfinance thus helps not only poor participants but also the local economy.
Estimation of land surface temperature over Delhi using Landsat-7 ETM +

Land surface temperature (LST) is important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The knowledge of surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environment interactions. In the study an attempt has been made to estimate surface temperature over Delhi area using Landsat-7 ETM+ satellite data. The variability of these retrieved LSTs has been investigated with respect to different land use / land cover (LU/LC) types determined from the Landsat visible and NIR channels. The classification uncertainties over different land use land cover were assessed and it has been observed that the classification uncertainties were found to be lowest using Minimum Noise Fraction (MNF) components. The emissivity per pixel is retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. Strong correlation is observed between surface temperature with Normalized Difference Vegetation Index (NDVI) over different LU/LC classes and the relationship is moderate with fractional vegetation cover (FVC). A regression relation between these parameters has also been estimated indicating that surface temperatures can be predicted if NDVI values are known. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.
The Effect of Using E-Learning Tools in Online and Campus-based Classrooms on Student Performance

Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual’s perception of how the adoption of a new technology will increase their efficiency, and the individual’s perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance.
Bangladeshi road sign recognition based on DtBs vector and artificial neural network

Road sign recognition from road image is a crucial area of intelligent transportation system which role is to raise awareness of drivers, pedestrians and automated driving system. In this regard, a framework has been proposed in this paper for recognizing Bangladeshi road sign (BRS). For detecting the road sign (RS), two natural properties of a BRS is utilized, they are — border color rim and shape of the RS. Secondly distance to borders (DtBs) vector is used for shape verification of BRS. For recognizing those candidate regions artificial neural network (ANN) has performed. Based on these ideas initially, YCbCr color model is used to eliminate the illumination sensitiveness and statistical threshold value is used for color segmentation. Next, labeling and filtering is used to extract the shapes of candidate region. DtBs vector is used to verify the region of interest (ROI) of BRS. Affine transformation is used for triangular and quadrangular region to avoid sheared road sign (RS) blob. Finally, ANN is used to recognize the BRS blob. Various road signs are utilized to test the proposed framework under various conditions and results are presented to verify its efficiency.
Compact, isolated and simple to implement gate driver using high frequency transformer

This paper presents a simple, ultra-compact and isolated gate driver system used to drive power switches. Using two legs of a CMOS inverter, a high frequency transformer and two zener diodes connected with the gate of power switch, this driver provides an optimal gate driver waveform with a high gate voltage to switch on the transistor, and a negative bias gate voltage during OFF state. In the paper, the proposed gate driver will be theoretically analyzed; simulation and experimental results for the system implemented to drive a high side MOSFET will be also showed and discussed.
Internet of Things (IoT) Applied to an Urban Garden

The constant increase of migration to the urban area has caused that the rural sector not have the manpower for agricultural production and is not able to supply food to the world's population, which has generated the need to expand the crops towards the cities. To encourage agricultural production in urban sectors, the use of technologies capable of automating and increasing production performance is proposed. This paper presents the develop of a system for the measurement, monitoring and automatic irrigation of an urban garden using Internet of Things (IoT) technology, it has sensors of CO2, humidity, luminosity, temperature, detection of plants and a hybrid application for its remote monitoring connected to a local area network. Also, the communication process used here is tested in order to provide a great connection service between the proposed system and the network.
The top-ten in journal impact factor manipulation

A considerable part of the scientific community is, at least to some degree, involved in the “impact factor game” Editors strive to increase their journals — impact factor (IF) in order to gain influence in the fields of basic and applied research and scientists seek to profit from the “added value” of publishing in top IF journals. In this article we point out the most common “tricks” of engineering and manipulating the IF undertaken by a portion of professionals of the scientific publishing industry. They attempt to increase the nominator or decrease the denominator of the IF equation by taking advantage of certain design flaws and disadvantages of the IF that permit a degree of artificial and arbitrary inflation. Some of these practices, if not scientifically unethical, are at least questionable and should be abandoned. Editors and publishers should strive for quality through fair and thoughtful selection of papers forwarded for peer review and editorial comments that enhance the quality and scientific accuracy of a manuscript.