nils-herrmann/clean_scirepeval huggingface dataset:
Dose-related neurocognitive effects of marijuana use.

Although about 7 million people in the US population use marijuana at least weekly, there is a paucity of scientific data on persistent neurocognitive effects of marijuana use.



To determine if neurocognitive deficits persist in 28-day abstinent heavy marijuana users and if these deficits are dose-related to the number of marijuana joints smoked per week.



A battery of neurocognitive tests was given to 28-day abstinent heavy marijuana abusers.



As joints smoked per week increased, performance decreased on tests measuring memory, executive functioning, psychomotor speed, and manual dexterity. When dividing the group into light, middle, and heavy user groups, the heavy group performed significantly below the light group on 5 of 35 measures and the size of the effect ranged from 3.00 to 4.20 SD units. Duration of use had little effect on neurocognitive performance.



Very heavy use of marijuana is associated with persistent decrements in neurocognitive performance even after 28 days of abstinence. It is unclear if these decrements will resolve with continued abstinence or become progressively worse with continued heavy marijuana use.
ViziQuer: A Tool to Explore and Query SPARQL Endpoints

The presented tool uses a novel approach to explore and query a SPARQL endpoint. The tool is simple to use as a user needs only to enter an address of a SPARQL endpoint of one’s interest. The tool will extract and visualize graphically the data schema of the endpoint. The user will be able to overview the data schema and use it to construct a SPARQL query according to the data schema. The tool can be downloaded from http://viziquer.lumii.lv. There is also additional information and help on how to use it in practice.
Penoscrotal plication as a uniform approach to reconstruction of penile curvature.

To present our 4-year experience of using a minimally invasive technique, penoscrotal plication (PSP), as a uniform treatment for men with debilitating penile curvature resulting from Peyronie's disease.



In 48 men (median age 58.7 years) with penile curvature the penis was reconstructed by imbricating the tunica albuginea opposite the curvature with multiple nonabsorbable sutures. All patients, regardless of the degree or direction of curvature, were approached through a small penoscrotal incision made without degloving the penis. Detailed measurements of penile shaft angle and stretched penile length were recorded and analysed before and after reconstruction, and the numbers of sutures required for correction were documented.



Nearly all patients had dorsal and/or lateral deformities that were easily corrected via a ventral penoscrotal incision. The median (range) degree of correction was 28 (18-55) degrees and number of sutures used was 6 (4-17). Stretched penile length measurements before and after plication showed no significant difference. A single PSP procedure was successful in 45/48 (93%) patients; two were dissatisfied with the correction, one having repeat plication and the other a penile prosthesis; one other required a suture release for pain.



PSP is safe and effective and should be considered even for cases with severe or biplanar curvature.
Characterization of double-layer capacitors (DLCs) for power electronics applications

The double-layer capacitor (DLC) for power applications is a new device. A simple resistive capacitive equivalent circuit is insufficient to characterize its terminal behaviour. Based on physical reasoning, an equivalent circuit is proposed to provide the power electronics engineer with a model for the terminal behaviour of the DLC. The equivalent model consists of three Re branches, one of them with a voltage dependent capacitor. A method to identify the circuit parameters is presented. Measurements of carbon-based DLCs for power applications are presented, analysed, and the equivalent circuit response is compared with experimental results.
A web-based bayesian intelligent tutoring system for computer programming

In this paper, we present a Web-based intelligent tutoring system, called BITS. The decision making process conducted in our intelligent system is guided by a Bayesian network approach to support students in learning computer programming. Our system takes full advantage of Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. We discuss how to employ Bayesian networks as an inference engine to guide the students’ learning processes . In addition, we describe the architecture of BITS and the role of each module in the system. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student nav igate through the online course materials, recommend learning goals, and generate appropriate reading sequences.
Social Norms and Community Enforcement

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Learning Non-Lambertian Object Intrinsics Across ShapeNet Categories

We focus on the non-Lambertian object-level intrinsic problem of recovering diffuse albedo, shading, and specular highlights from a single image of an object. Based on existing 3D models in the ShapeNet database, a large-scale object intrinsics database is rendered with HDR environment maps. Millions of synthetic images of objects and their corresponding albedo, shading, and specular ground-truth images are used to train an encoder-decoder CNN, which can decompose an image into the product of albedo and shading components along with an additive specular component. Our CNN delivers accurate and sharp results in this classical inverse problem of computer vision. Evaluated on our realistically synthetic dataset, our method consistently outperforms the state-of-the-art by a large margin. We train and test our CNN across different object categories. Perhaps surprising especially from the CNN classification perspective, our intrinsics CNN generalizes very well across categories. Our analysis shows that feature learning at the encoder stage is more crucial for developing a universal representation across categories. We apply our model to real images and videos from Internet, and observe robust and realistic intrinsics results. Quality non-Lambertian intrinsics could open up many interesting applications such as realistic product search based on material properties and image-based albedo/specular editing.
Adaptive Scale Selection for Multiscale Segmentation of Satellite Images

With dramatically increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. Multiscale image segmentation is a prerequisite step that splits an image into hierarchical homogeneous segmented objects for OBIA. However, scale selection remains a challenge in multiscale segmentation. In this study, we presented an adaptive approach for defining and estimating the optimal scale in the multiscale segmentation process. Central to our method is the combined use of image features from segmented objects and prior knowledge from historical thematic maps in a top-down segmentation procedure. Specifically, the whole image was first split into segmented objects, with the largest scale in a presupposition segmentation scale sequence. Second, based on segmented object features and prior knowledge in the local region of thematic maps, we calculated complexity values for each segmented object. Third, if the complexity values of an object were large enough, this object would be further split into multiple segmented objects with a smaller scale in the scale sequence. Then, in the similar manner, complex segmented objects were split into the simplest objects iteratively. Finally, the final segmentation result was obtained and evaluated. We have applied this method on a GF-1 multispectral satellite image and a ZY-3 multispectral satellite image to produce multiscale segmentation maps and further classification maps, compared with the state-of-the-art and the traditional mean shift algorithm. The experimental results illustrate that the proposed method is practically helpful and efficient to produce the appropriate segmented image objects with optimal scales.
Altered salience network connectivity predicts macronutrient intake after sleep deprivation

Although insufficient sleep is a well-recognized risk factor for overeating and weight gain, the neural mechanisms underlying increased caloric (particularly fat) intake after sleep deprivation remain unclear. Here we used resting-state functional magnetic resonance imaging and examined brain connectivity changes associated with macronutrient intake after one night of total sleep deprivation (TSD). Compared to the day following baseline sleep, healthy adults consumed a greater percentage of calories from fat and a lower percentage of calories from carbohydrates during the day following TSD. Subjects also exhibited increased brain connectivity in the salience network from the dorsal anterior cingulate cortex (dACC) to bilateral putamen and bilateral anterior insula (aINS) after TSD. Moreover, dACC-putamen and dACC-aINS connectivity correlated with increased fat and decreased carbohydrate intake during the day following TSD, but not during the day following baseline sleep. These findings provide a potential neural mechanism by which sleep loss leads to increased fat intake.
Use of trajectory and spatiotemporal features for retrieval of videos with a prominent moving foreground object

This paper presents generalized spatiotemporal analysis and lookup tool (GESTALT), an unsupervised framework for content-based video retrieval. GESTALT takes a query video and retrieves “similar” videos from the database. Motion and dynamics of appearance (shape) patterns of a prominent moving foreground object are considered as the key components of the video content and captured using corresponding feature descriptors. GESTALT automatically segments the moving foreground object from the given query video shot and estimates the motion trajectory. A graph-based framework is used to explicitly capture the structural and kinematics property of the motion trajectory, while an improved version of an existing spatiotemporal feature descriptor is proposed to model the change in object shape and movement over time. A combined match cost is computed as a convex combination of the two match scores, using these two feature descriptors, which is used to rankorder the retrieved video shots. Effectiveness of GESTALT is shown using extensive experimentation, and comparative study with recent techniques exhibits its superiority.
Modeling Credit Risk for SMEs : Evidence from the US Market

Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model’s ability to lower bank capital requirements considering the new Basel Capital Accord’s rules for SMEs.
Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images

Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their interpretation is time consuming and subjective, even for trained dermatologists. The current computer-aided diagnosis (CAD) systems are mainly noninteractive in nature and their prediction represents just a cue for the dermatologist, as the final decision regarding the likelihood of the presence of a melanoma is left exclusively to him/her. Recently, developing CAD schemes that use image retrieval approach to search for the clinically relevant and visually similar lesions has been attracting research interest. Although preliminary studies have suggested that using retrieval might improve dermatologists' performance and/or increase their confidence in the decision making, this technology is still in the early development stage with lack of benchmark evaluation in ground-truth datasets to compare retrieval effectiveness. A CAD system based on both classification and retrieval would be more effective and robust. This work is focusing on by addressing the various issues related to the development of such an integrated and interactive CAD system by performing automatic lesion segmentation with an adaptive thresholding and region growing approach, extracting invariant features from lesions and classifying and retrieving those using Extreme Learning Machines (ELM) and a similarity fusion approach. Finally, various methods are evaluated with promising results in a benchmark dataset of International Skin Imaging Collaboration (ISIC).
Programs, life cycles, and laws of software evolution

By classifying programs according to their relationship to the environment in which they are executed, the paper identifies the sources of evolutionary pressure on computer applications and programs and shows why this results in a process of never ending maintenance activity. The resultant life cycle processes are then briefly discussed. The paper then introduces laws of Program Evolution that have been formulated following quantitative studies of the evolution of a number of different systems. Finally an example is provided of the application of Evolution Dynamics models to program release planning.
Introducing the Robot Security Framework (RSF), a standardized methodology to perform security assessments in robotics

Robots have gained relevance in society, increasingly performing critical tasks. Nonetheless, robot security is being underestimated. Robotics security is a complex landscape, which often requires a cross-disciplinar perspective to which classical security lags behind. To address this issue, we present the Robot Security Framework (RSF), a methodology to perform systematic security assessments in robots. We propose, adapt and develop specific terminology and provide guidelines to enable a holistic security assessment following four main layers (Physical, Network, Firmware and Application). We argue that modern robotics should regard as equally relevant internal and external communication security. Finally, we advocate against ”security by obscurity”. We conclude that the field of security in robotics deserves further research efforts.
Visual-Quality-Driven Learning for Underwater Vision Enhancement

The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the visual quality of underwater images preserving their edges and also performed well considering the UCIQE metric.
Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features for High-Dimensional Classification

Feature selection and discretisation have shown their effectiveness for data preprocessing especially for high-dimensional data with many irrelevant features. While feature selection selects only relevant features, feature discretisation finds a discrete representation of data that contains enough information but ignoring some minor fluctuation. These techniques are usually applied in two stages, discretisation and then selection since many feature selection methods work only on discrete features. Most commonly used discretisation methods are univariate in which each feature is discretised independently; therefore, the feature selection stage may not work efficiently since information showing feature interaction is not considered in the discretisation process. In this study, we propose a new method called PSO-DFS using bare-bone particle swarm optimisation (BBPSO) for discretisation and feature selection in a single stage. The results on ten high-dimensional datasets show that PSO-DFS obtains a substantial dimensionality reduction for all datasets. The classification performance is significantly improved or at least maintained on nine out of ten datasets by using the transformed “small” data obtained from PSO-DFS. Compared to applying the two-stage approach which uses PSO for feature selection on the discretised data, PSO-DFS achieves better performance on six datasets, and similar performance on three datasets with a much smaller number of features selected.
Absorptive Frequency-Selective Transmission Structure With Square-Loop Hybrid Resonator

A novel design of an absorptive frequency-selective transmission structure (AFST) is proposed. This structure is based on the design of a frequency-dependent lossy layer with square-loop hybrid resonator (SLHR). The parallel resonance provided by the hybrid resonator is utilized to bypass the lossy path and improve the insertion loss. Meanwhile, the series resonance of the hybrid resonator is used for expanding the upper absorption bandwidth. Furthermore, the absorption for out-of-band frequencies is achieved by using four metallic strips with lumped resistors, which are connected with the SLHR. The quantity of lumped elements required in a unit cell can be reduced by at least 50% compared to previous structures. The design guidelines are explained with the aid of an equivalent circuit model. Both simulation and experiment results are presented to demonstrate the performance of our AFST. It is shown that an insertion loss of 0.29 dB at 6.1 GHz and a 112.4% 10 dB reflection reduction bandwidth are obtained under the normal incidence.
Distance estimation with 2 . 5 D anchors and its application to robot navigation

Estimating the distance of a target object from a single image is a challenging task since a large variation in the object appearance makes the regression of the distance difficult. In this paper, to tackle such the challenge, we propose 2.5D anchors which provide the candidate of distances based on a perspective camera model. This candidate is expected to relax the difficulty of the regression model since only the residual from the candidate distance needs to be taken into account. We show the effectiveness of the regression with our proposed anchors, by comparing with ordinary regression methods and state-of-the-art 3D object detection methods, through Pascal 3D+ TV monitor and KITTI car experiments. In addition, we also show an example of practical uses of our proposed method in a real-time system, robot navigation, by integrating with ROS-based simultaneous localization and mapping.
Omnidirectional conformal antenna array based on E-shaped patches

An antenna consisting of 4 element subarrays of strongly coupled E-shaped patches placed conformally on a metal cylinder is proposed for use in wireless local area networks (WLAN). The use of this special array configuration in the cylindrical case allows reaching a 8.3 % bandwidth and an omnidirectional radiation pattern in the horizontal plane with only 2 subarrays. CST Microwave Studio is used for validation purposes before manufacturing. To the knowledge of the authors, it is the first time that this recently introduced concept of strongly coupled E-shaped patches is used in a conformal antenna.
Alive human body detection system using an autonomous mobile rescue robot

In this paper, a new approach for detecting alive humans in destructed environments using an autonomous robot is proposed. Human detection in an unmanned area can be done only by an automated system. Alive human body detection system proposed a monitoring system using ultrasonic sensors and camera to record, transmit and analyze conditions of human body. The task of identify human being in rescue operations is difficult for the robotic agent but it is simple for the human agent. In order to detect a human body, an autonomous robot must be equipped with a specific set of sensors that provide information about the presence of a person in the environment around. This work describes an autonomous robot for rescue operations. The proposed system uses an ultrasonic sensor in order to detect the existence of living humans and a low-cost camera in order to acquire a video of the scene as needed. Additional, other sensors include temperature, fire and metal detector works as bomb sensor to detect the presence of bomb in Warfield and in rescue operations. Having detected a sign of a living human, the ultrasonic sensor Triggers the camera to show live scene. The video is then displayed on the screen. This approach requires a relatively small number of data to be acquired and processed during the rescue operation. This way, the real-time cost of processing and data transmission is considerably reduced. This system has the potential to achieve high performance in detecting alive humans in devastated environments relatively quickly and cost-effectively. The detection depending on a number of factors such as the body position and the light intensity of the scene. Results show that the system provides an efficient way to track human motion. The aim of this article is to present our experience with various sensors designed and developed.
Colour image segmentation using the self-organizing map and adaptive resonance theory

We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability–plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SOM. In order to explore the generation of a growing feature map using ART and to motivate the main contribution, we first present a preliminary experimental model, SOMART, based on Fuzzy ART. Then we propose the new model, SmART, that utilizes a novel lateral control of plasticity to resolve the stability–plasticity problem. SmART has been experimentally found to perform well in RGB colour space, and is believed to be more coherent than Fuzzy ART. q 2005