nils-herrmann/clean_scirepeval huggingface dataset:
Current location-based next POI recommendation

Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours. First we find the users with similar check-in sequences and depict their check-in sequences as a directed graph, then find the users current location. To recommend the new POI recommendation for next hour we refer to the directed graph we have created. Our algorithm considers both the temporal factor i.e., recommendation time, and the spatial(distance) at the same time. We conduct an experiment on random data collected from Foursquare and Gowalla. Experiment results show that our proposed model outperforms the collaborative-filtering based state-of-the-art recommender techniques.
A Layered Security Approach for Cloud Computing Infrastructure

This paper introduces a practical security model based on key security considerations by looking at a number of infrastructure aspects of Cloud Computing such as SaaS, Utility, Web, Platform and Managed Services, Service commerce platforms and Internet Integration which was introduced with a concise literature review. The purpose of this paper is to offer a macro level solution for identified common infrastructure security requirements. This model with a number of emerged patterns can be applied to infrastructure aspect of Cloud Computing as a proposed shared security approach in system development life cycle focusing on the plan-built-run scope.
Design, development, and evaluation of a mobile learning application for computing education

The study focused on the application of the design science research approach in the course of developing a mobile learning application, MobileEdu, for computing education in the Nigerian higher education context. MobileEdu facilitates the learning of computer science courses on mobile devices. The application supports ubiquitous, collaborative, and social aspects of learning among higher education students. Moreover, the application eases access to learning resources. The paper first describes analysis, design, and implementation activities related to the development of MobileEdu. Also, the paper deliberated on the characteristics and scope of the adherence of MobileEdu to the traits and ideas of design science research. To evaluate MobileEdu in a real-life learning setting, experiment was conducted with 142 third-year undergraduate students in a Nigerian university. Besides the learning achievement of the students using MobileEdu, the study examined the impact of MobileEdu on students’ attitudes toward studying in a system analysis and design course. Experimental data were collected from pre- and post quizzes, interviews, and a questionnaire administered to students. The results of the evaluation are encouraging and showed that the MobileEdu application has a potential to improve students’ learning achievements. In addition, the pedagogical experiences of students were mostly positive and students’ attitudes toward the system analysis and design course through MobileEdu was better than those of students who studied the course via traditional methods. Finally, the study offered suggestions for how to implement effectively a mobile learning-supported course in computing curriculum.
RECONFIGURABLE MICROSTRIP PATCH ANTENNA WITH SWITCHABLE POLARIZATION

A new reconfigurable microstrip patch antenna allowing switching between two circular polarizations is proposed. It consists of a square radiating patch and a 3 dB hybrid coupler. Using only a single-polar-double-throw (SPDT) switch, the polarization switching can be achieved. This design of the dc-bias network is extremely simple. From experimental results, the proposed antenna avoids the frequency offset phenomena which often happened to antennas with switchable polarization.
A High-Quality Denoising Dataset for Smartphone Cameras

The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While denoising for smartphone images is an active research area, the research community currently lacks a denoising image dataset representative of real noisy images from smartphone cameras with high-quality ground truth. We address this issue in this paper with the following contributions. We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras. Using this procedure, we have captured a dataset - the Smartphone Image Denoising Dataset (SIDD) - of ~30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras and generated their ground truth images. We used this dataset to benchmark a number of denoising algorithms. We show that CNN-based methods perform better when trained on our high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data.
A valid two-item food security questionnaire for screening HIV-1 infected patients in a clinical setting.

To validate a two-item food security questionnaire (FSQ) for use in a clinical setting to screen HIV-1 infected patients for food insecurity.



The present study was a questionnaire-based survey of forty-nine subjects attending an HIV clinic. Subjects completed a two-item questionnaire and a six-item validated FSQ contemporaneously.



A strong correlation was found between the two-item and six-item FSQ (rho = 0.895; 95 % CI 0.821, 0.940; P < 0.0001). Cronbach's alpha coefficient was found to be 0.94 and 0.90 for the two-item and six-item FSQ, respectively. The two-item FSQ yielded a sensitivity of 100 % (95 % CI 75, 100) and a specificity of 78 % (95 % CI 61, 90). The negative predictive value was found to be 100 % (95 % CI 88, 100).



The results of the present study suggest that the two-item FSQ is a valid, reliable and sensitive screening tool of food insecurity in people living with HIV in a clinical setting.
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both?

This work deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that data-driven approaches should not replace, but rather complement traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communications networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely-used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case-studies wherein the use of deep learning proves extremely useful for network design. For each case-study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement data-driven approaches. Finally, concluding remarks describe those that in our opinion are the major directions for future research in this field.
AUTOMATED IRRIGATION SYSTEM USING THERMOELECTRIC GENERATOR AS SOIL MOISTURE DETECTOR

This paper shows the design and fabrication of a Thermo-electric generator (TEG) and the implementation of an automated irrigation system using this TEG as a soil moisture detector. The TEG inserted in two heat exchangers is capable of finding the thermal difference between the air and the soil that establishes a relationship with the soil’s moisture condition. Being able to obtain the soil moisture level from the TEG’s output, a microcontroller is used to automate the irrigation system. The irrigation system adapts to the soil area’s condition it irrigates based from the moisture it detects via the TEG. The water consumption of the soil is controlled by the automated irrigation system based on the soil’s condition and therefore, promotes water conservation compared to that of the water consumption of manual irrigation system. It also optimizes plant growth in that it waters it to the correct moisture level at the right time.
Brain activation and sexual arousal in healthy, heterosexual males.

Despite the brain's central role in sexual function, little is known about relationships between brain activation and sexual response. In this study, we employed functional MRI (fMRI) to examine relationships between brain activation and sexual arousal in a group of young, healthy, heterosexual males. Each subject was exposed to two sequences of video material consisting of explicitly erotic (E), relaxing (R) and sports (S) segments in an unpredictable order. Data on penile turgidity was collected using a custom-built pneumatic pressure cuff. Both traditional block analyses using contrasts between sexually arousing and non-arousing video clips and a regression using penile turgidity as the covariate of interest were performed. In both types of analyses, contrast images were computed for each subject and these images were subsequently used in a random effects analysis. Strong activations specifically associated with penile turgidity were observed in the right subinsular region including the claustrum, left caudate and putamen, right middle occipital/ middle temporal gyri, bilateral cingulate gyrus and right sensorimotor and pre-motor regions. Smaller, but significant activation was observed in the right hypothalamus. Few significant activations were found in the block analyses. Implications of the findings are discussed. Our study demonstrates the feasibility of examining brain activation/sexual response relationships in an fMRI environment and reveals a number of brain structures whose activation is time-locked to sexual arousal.
Low cost planar coil structure for inductive sensors to measure absolute angular position

There are many applications that need to sense angular displacement/position, such as automotive electronic steering system, throttle and brake pedal position, motors, robotic arm, etc. Contactless sensors are becoming increasingly popular in these kinds of applications due to their robustness, reliability and longevity. The paper proposes a new contactless variable reluctance (VR) resolver that can be built on a PCB as coil traces. The coil layout consists of an excitation coil and two sense coils laid on a single plane of a PCB. A circular metallic disk with a sector aperture alters the induced voltage in the sense coils. The induced voltage in the sense coils is a sine and cosine function of the angular position of disk. The absolute angular position can be measured with high resolution based on sense coil voltages. The design is validated using an ASIC to process the sensor signals and compute the angular position. The linearity of the coil design has been validated through experimental results.
Iterative deep convolutional encoder-decoder network for medical image segmentation

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.
The ‘ ‘ Grammar Correction ’ ’ Debate in L 2 Writing : Where are we , and where do we go from here ? ( and what do we do in the meantime . . . ? )

The efficacy of teacher error/grammar correction in second language writing classes has been the subject of much controversy, including a published debate in an earlier volume of this journal [J. Second Language Writing 8 (1999) 1; J. Second Language Writing 8 (1999) 111]. In this paper, the state-of-the-art in error correction research in L2 writing is described (‘‘Where are we?’’), directions for future research are outlined (‘‘Where do we go from here?’’) and implications for current L2 composition pedagogy are suggested (‘‘What do we do in the meantime?’’). The primary thesis of the paper is that, despite the published debate and several decades of research activity in this area, we are virtually at Square One, as the existing research base is incomplete and inconsistent, and it would certainly be premature to formulate any conclusions about this topic. Thus, findings from previous research on this controversial yet ubiquitous pedagogical issue are recast as ‘‘predictions’’ about what future research might discover, rather than ‘‘conclusions’’ about what the previous research shows us.
Hyperbolic Attention Networks

We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models. Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact.
Face Morphing Detection: An Approach Based on Image Degradation Analysis

In 2014 a novel identity theft scheme targeting specific application scenarios in face biometrics was introduced. In this scheme, a so called face morph melts two or more face images of different persons into one image, which is visually similar to multiple real world persons. Based on this non authentic image, it is possible to apply for an image based identity document to be issued by a corresponding authority. Thus, multiple persons can use such a document to pass image based person verification scenarios with a single document containing an artificially weakened template. Currently there is no reliable existing security mechanism to detect this attack. This paper introduces a novel detection approach for face morphing forgeries based on a continuous image degradation. This is considered relevant because the degradation approach creates multiple artificial self-references and measures the “distance” from these references to the input. A small distance (significantly smaller than the one to be expected from a pristine image) could be considered as an anomaly here, indicating media manipulations (e.g. caused by morphing). Our degradation process is based on JPEG compression with different compression values. The evaluation results of our detection approach are classification accuracies of 90.1% under laboratory conditions and 84.3% under real world conditions.
Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images

This paper presents a novel classification-based optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. First, the green plane of each fundus image is resized and morphologically reconstructed using a circular structuring element. Bright regions are then extracted from the morphologically reconstructed image that lie in close vicinity of the major blood vessels. Next, the bright regions are classified as bright probable OD regions and non-OD regions using six region-based features and a Gaussian mixture model classifier. The classified bright probable OD region with maximum Vessel-Sum and Solidity is detected as the best candidate region for the OD. Other bright probable OD regions within 1-disc diameter from the centroid of the best candidate OD region are then detected as remaining candidate regions for the OD. A convex hull containing all the candidate OD regions is then estimated, and a best-fit ellipse across the convex hull becomes the segmented OD boundary. Finally, the centroid of major blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm has low computation time complexity and it is robust to variations in image illumination, imaging angles, and retinal abnormalities. This algorithm achieves 98.8%-100% OD segmentation success and OD segmentation overlap score in the range of 72%-84% on images from the six public datasets of DRIVE, DIARETDB1, DIARETDB0, CHASE_DB1, MESSIDOR, and STARE in less than 2.14 s per image. Thus, the proposed algorithm can be used for automated detection of retinal pathologies, such as glaucoma, diabetic retinopathy, and maculopathy.
Collective List-Only Entity Linking: A Graph-Based Approach

List-only entity linking (EL) is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional EL task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, the list-only EL can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in EL, which is yet currently neglected. In this paper, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of the list-only EL, but also opens up the bridge between the list-only EL and conventional EL solutions.
Psychopathy and the DSM-IV criteria for antisocial personality disorder.

The Axis II Work Group of the Task Force on DSM-IV has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. We describe an alternative to the approach taken in the rev. 3rd ed. of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R; American Psychiatric Association, 1987), namely, the revised Psychopathy Checklist. We also discuss the multisite APD field trials designed to evaluate and compare four criteria sets: the DSM-III-R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th ed. of the International Classification of Diseases (World Health Organization, 1990), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist.
A bidirectional battery charger for electric vehicles with V2G and V2H capability and active and reactive power control

A bidirectional battery charger using a three-phase voltage source inverter (VSI) is presented to be employed in plug-in electric vehicle applications enabling Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) functionality with simultaneous active and reactive power control. The charger is controlled by a grid supervising unit with voltage droop control, providing it with P (active) and Q (reactive) power requests, and is responsible of reporting back its available power reserves (apparent power). In V2G mode the grid voltage is observed and a current controller on the d-q coordinate system together with the three phase inverter can transfer energy in both directions. Also reactive power compensation, inductive or capacitive, is possible. Asymmetries on the grid load can also be compensated by slight modifications of the control regime. Further, in V2H mode, the charger system can be seen as the master grid-forming generator responsible to provide a stable voltage at the network frequency. A proper synchronization strategy is presented as well, to guarantee smooth reconnection with the power grid. The proposed architecture is modeled in Simulink and aims the realization in existing electric vehicle and smart grid prototypes of the Siemens AG.
Integration of alternative sources of energy as current sources

This work proposes an integration of alternative energy sources having the different generating modules connected as current sources. These sources were configured to inject current into a common DC bus. This feature avoids current circulation among sources due to the voltage differences, reducing the stress in the individual generators. A battery bank is a system part, used as an energy accumulator for periods when there is primary energy depletion. The bus voltage is controlled by modulation of a secondary load in parallel with the main load. A DC-DC Boost converter connects each primary source to a common bus, except for the storage system that uses a DC-DC Buck-Boost converter. Finally, it is presented a mathematical analysis through simulated results to show the sources behavior and overall performance when turned into current sources.
Development of a Wheelchair Skills Home Program for Older Adults Using a Participatory Action Design Approach

Restricted mobility is the most common impairment among older adults and a manual wheelchair is often prescribed to address these limitations. However, limited access to rehabilitation services results in older adults typically receiving little or no mobility training when they receive a wheelchair. As an alternative and novel approach, we developed a therapist-monitored wheelchair skills home training program delivered via a computer tablet. To optimize efficacy and adherence, principles of self-efficacy and adult learning theory were foundational in the program design. A participatory action design approach was used to engage older adult wheelchair users, care providers, and prescribing clinicians in an iterative design and development process. A series of prototypes were fabricated and revised, based on feedback from eight stakeholder focus groups, until a final version was ready for evaluation in a clinical trial. Stakeholder contributions affirmed and enhanced the foundational theoretical principles and provided validation of the final product for the target population.