nils-herrmann/clean_scirepeval huggingface dataset:
A Framework for Flexible Access Control in Digital Library Systems

Traditional access control models are often found to be inadequate for digital libraries. This is because the user population for digital libraries is very dynamic and not completely known in advance. In addition, the objects stored in a digital library are characterized by fine-grained behavioral interfaces and highly-contextualized access restrictions that require a user’s access privileges to be updated dynamically. These motivate us to propose a trust-based authorization model for digital libraries. Access privileges can be associated with both objects and content classes. Trust levels associated with these specify the minimum acceptable level of trust needed of a user to allow access to the objects. We use a vector trust model to calculate the system’s trust about a user. The model uses a number of different types of information about a user, for example, prior usage history, credentials, recommendations etc., to calculate the trust level in a dynamic manner and thus achieve a fine-grained access control.
Indoor Mobile Coverage Problem Using UAVs

Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks are not operational due to natural disasters. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. Prior studies on UAV-based wireless coverage typically consider an air-to-ground path loss model, which assumes that the users are outdoor and located on a 2-D plane. In this paper, we propose using UAVs to provide wireless coverage for indoor users inside a high-rise building. First, we present realistic outdoor–indoor path loss models and describe the tradeoff introduced by these models. Then, we study the problem of efficient placement of a single UAV, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is generally difficult to solve. To that end, we consider three cases of practical interest and provide efficient solutions to the formulated problem under these cases. Then, we study the problem of minimizing the number of UAVs required to provide wireless coverage to the high-rise building and prove that this problem is NP-complete. Due to the intractability of the problem, we use clustering to minimize the number of UAVs required to cover the indoor users. In our proposed algorithm, we check if the maximum transmit power of a UAV is sufficient to cover each cluster. If not, the number of clusters is incremented by one, and the problem is solved again. In the uniform split method, we split the building into  $k$ regular structures and utilize  $k$ UAVs to provide wireless coverage for indoor users regardless of user distribution. We demonstrate through simulations that the method that splits the building into regular structures requires 80% more number of UAVs relative to our proposed algorithm.
Directional People Counter Based on Head Tracking

This paper presents an application for counting people through a single fixed camera. This system performs the count distinction between input and output of people moving through the supervised area. The counter requires two steps: detection and tracking. The detection is based on finding people's heads through preprocessed image correlation with several circular patterns. Tracking is made through the application of a Kalman filter to determine the trajectory of the candidates. Finally, the system updates the counters based on the direction of the trajectories. Different tests using a set of real video sequences taken from different indoor areas give results ranging between 87% and 98% accuracies depending on the volume of flow of people crossing the counting zone. Problematic situations, such as occlusions, people grouped in different ways, scene luminance changes, etc., were used to validate the performance of the system.
On the Resistance of Neural Nets to Label Noise

We investigate the behavior of convolutional neural networks (CNN) in the presence of label noise. We show empirically that CNN prediction for a given test sample depends on the labels of the training samples in its local neighborhood. This is similar to the way that the K-nearest neighbors (K-NN) classifier works. With this understanding, we derive an analytical expression for the expected accuracy of a KNN, and hence a CNN, classifier for any level of noise. In particular, we show that K-NN, and CNN, are resistant to label noise that is randomly spread across the training set, but are very sensitive to label noise that is concentrated. Experiments on real datasets validate our analytical expression by showing that they match the empirical results for varying degrees of label noise.
Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting pathological brains in MRI scanning.  The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO.  The 10 repetition of k-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed fourteen state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. The proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053s for online prediction.  The proposed WE + HBP-FNN method achieves nearly perfect detection pathological brains in MRI scanning.
VMSP: Efficient Vertical Mining of Maximal Sequential Patterns

Sequential pattern mining is a popular data mining task with wide applications. However, it may present too many sequential patterns to users, which makes it difficult for users to comprehend the results. As a solution, it was proposed to mine maximal sequential patterns, a compact representation of the set of sequential patterns, which is often several orders of magnitude smaller than the set of all sequential patterns. However, the task of mining maximal patterns remains computationally expensive. To address this problem, we introduce a vertical mining algorithm named VMSP (Vertical mining of Maximal Sequential Patterns). It is to our knowledge the first vertical mining algorithm for mining maximal sequential patterns. An experimental study on five real datasets shows that VMSP is up to two orders of magnitude faster than the current state-of-the-art algorithm.
Voltage source inverters for grid connected photovoltaic systems

This paper describes a novel concept of an inverter for grid connected photovoltaic arrays. It is shown that the use of a three level voltage source inverter without transformer is a reasonable solution for the input to grid in the lower power range (< 5kW). The structure of the photovoltaic power system is presented. Each component of the system will be discussed in detail. The demands of photovoltaic inverters and special design procedures are presented. Simulation results demonstrate the suitability of the suggested solution for a grid connected photovoltaic system which is currently under development.
Real-Time IRC Threat Detection Framework

Most of the social media platforms generate a massive amount of raw data that is slow-paced. On the other hand, Internet Relay Chat (IRC) protocol, which has been extensively used by hacker community to discuss and share their knowledge, facilitates fast-paced and real-time text communications. Previous studies of malicious IRC behavior analysis were mostly either offline or batch processing. This results in a long response time for data collection, pre-processing, and threat detection. However, since the threats can use the latest vulnerabilities to exploit systems (e.g. zero-day attack) and which can spread fast using IRC channels. Current IRC channel monitoring techniques cannot provide the required fast detection and alerting. In this paper, we present an alternative approach to overcome this limitation by providing real-time and autonomic threat detection in IRC channels. We demonstrate the capabilities of our approach using as an example the shadow brokers' leak exploit (the exploit leveraged by WannaCry ransomware attack) that was captured and detected by our framework.
Vitrectomy in ocular trauma. Factors influencing final visual outcome.

The results of vitrectomy in 240 consecutive cases of ocular trauma were reviewed. Of these cases, 71.2% were war injuries. Intraocular foreign bodies were present in 155 eyes, of which 74.8% were metallic and 61.9% ferromagnetic. Multivariate analysis identified the prognostic factors predictive of poor visual outcome, which included: (1) presence of an afferent pupillary defect; (2) double perforating injuries; and (3) presence of intraocular foreign bodies. Association of vitreous hemorrhage with intraocular foreign bodies was predictive of a poor prognosis. Eyes with foreign bodies retained in the anterior segment and vitreous had a better prognosis than those with foreign bodies embedded in the retina. Timing of vitrectomy and type of trauma had no significant effect on the final visual results. Prophylactic scleral buckling reduced the incidence of retinal detachment after surgery. Injuries confined to the cornea had a better prognosis than scleral injuries.
Fatal ulcerative enteritis of the small intestine in a patient with ulcerative colitis treated with vedolizumab.

Vedolizumab (VDZ) inhibits α4β7 integrins and is used to target intestinal immune responses in patients with inflammatory bowel disease, which is considered to be relatively safe. Here we report on a fatal complication following VDZ administration. A 64-year-old female patient with ulcerative colitis (UC) refractory to tumor necrosis factor inhibitors was treated with VDZ. One week after the second VDZ infusion, she was admitted to hospital with severe diarrhea and systemic inflammatory response syndrome (SIRS). Blood stream infections were ruled out, and endoscopy revealed extensive ulcerations of the small intestine covered with pseudomembranes, reminiscent of invasive candidiasis or mesenteric ischemia. Histology confirmed subtotal destruction of small intestinal epithelia and colonization with Candida. Moreover, small mesenteric vessels were occluded by hyaline thrombi, likely as a result of SIRS, while perfusion of large mesenteric vessels was not compromised. Beta-D-glucan concentrations were highly elevated, and antimycotic therapy was initiated for suspected invasive candidiasis but did not result in any clinical benefit. Given the non-responsiveness to anti-infective therapies, an autoimmune phenomenon was suspected and immunosuppressive therapy was escalated. However, the patient eventually died from multi-organ failure. This case should raise the awareness for rare but severe complications related to immunosuppressive therapy, particularly in high risk patients.
Orientalism in Euro-American and Indian psychology: historical representations of "natives" in Colonial and postcolonial contexts.

The author examines the historical role of Euro-American psychology in constructing Orientalist representations of the natives who were colonized by the European colonial powers. In particular, the author demonstrates how the power to represent the non-Western "Other" has always resided, and still continues to reside, primarily with psychologists working in Europe and America. It is argued that the theoretical frameworks that are used to represent non-Westerners in contemporary times continue to emerge from Euro-American psychology. Finally, the author discusses how non-Western psychologists internalized these Orientalist images and how such a move has led to a virtual abandonment of pursuing "native" forms of indigenous psychologies in Third World psychology departments.
Capparis Spinosa L. promotes anti-inflammatory response in vitro through the control of cytokine gene expression in human peripheral blood mononuclear cells

Capparis Spinosa L. is an aromatic plant growing wild in dry regions around the Mediterranean basin. Capparis Spinosa was shown to possess several properties such as antioxidant, antifungal, and anti-hepatotoxic actions. In this work, we aimed to evaluate immunomodulatory properties of Capparis Spinosa leaf extracts in vitro on human peripheral blood mononuclear cells (PBMCs) from healthy individuals. Using MTT assay, we identified a range of Capparis Spinosa doses, which were not toxic. Unexpectedly, we found out that Capparis Spinosa aqueous fraction exhibited an increase in cell metabolic activity, even though similar doses did not affect cell proliferation as shown by CFSE. Interestingly, Capparis Spinosa aqueous fraction appeared to induce an overall anti-inflammatory response through significant inhibition of IL-17 and induction of IL-4 gene expression when PBMCs were treated with the non toxic doses of 100 and/or 500 μg/ml. Phytoscreening analysis of the used Capparis Spinosa preparations showed that these contain tannins; sterols, alkaloids; polyphenols and flavonoids. Surprisingly, quantification assays showed that our Capparis Spinosa preparation contains low amounts of polyphenols relative to Capparis Spinosa used in other studies. This Capparis Spinosa also appeared to act as a weaker scavenging free radical agent as evidenced by DPPH radical scavenging test. Finally, polyphenolic compounds including catechin, caffeic acid, syringic acid, rutin and ferulic acid were identified by HPLC, in the Capparis spinosa preparation. Altogether, these findings suggest that our Capparis Spinosa preparation contains interesting compounds, which could be used to suppress IL-17 and to enhance IL-4 gene expression in certain inflammatory situations. Other studies are underway in order to identify the compound(s) underlying this effect.
Distributed Data Streams

A majority of today’s data is constantly evolving and fundam entally distributed in nature. Data for almost any large-sc ale data-management task is continuously collected over a wide area, and at a much greater rate than ever before. Compared to t aditional, centralized stream processing, querying such la rge-scale, evolving data collections poses new challenges , due mainly to the physical distribution of the streaming data and the co mmunication constraints of the underlying network. Distri buted stream processing algorithms should guarantee efficiency n ot o ly in terms ofspaceand processing time(as conventional streaming techniques), but also in terms of the communication loadimposed on the network infrastructure.
Detection-assisted interference parameter estimation and interference cancellation for LTE-Advanced system

Inter-cell interference is the main obstacle for increasing the network capacity of Long-Term Evolution Advanced (LTE-A) system. Interference Cancellation (IC) is a promising way to improve spectral efficiency. 3rd Generation Partnership Project (3GPP) raises a new research project — Network-Assisted Interference Cancellation and Suppression (NAICS) in LTE Rel-12. Advanced receivers used in NAICS include maximum likelihood (ML) receiver and symbol-level IC (SLIC) receiver. Those receivers need some interference parameters, such as rank indicator (RI), precoding matrix indicator (PMI) and modulation level (MOD). This paper presents a new IC receiver based on detection. We get the clean interfering signal assisted by detection and use it in SLIC. The clean interfering signal makes the estimation of interfering transmitted signal more accurate. So interference cancellation would be more significant. We also improve the method of interference parameter estimation that avoids estimating power boosting and precoding matrix simultaneously. The simulation results show that the performance of proposed SLIC is better than traditional SLIC and close to ML.
SHAGE: a framework for self-managed robot software

Behavioral, situational and environmental changes in complex software, such as robot software, cannot be completely captured in software design. To handle this dynamism, self-managed software enables its services dynamically adapted to various situations by reconfiguring its software architecture during run-time. We have developed a practical framework, called SHAGE (Self-Healing, Adaptive, and Growing SoftwarE), to support self-managed software for intelligent service robots. The SHAGE framework is composed of six main elements: a situation monitor to identify internal and external conditions of a software system, ontology-based models to describe architecture and components, brokers to find appropriate architectural reconfiguration patterns and components for a situation, a reconfigurator to actually change the architecture based on the selected reconfiguration pattern and components, a decision maker/learner to find the optimal solution of reconfiguring software architecture for a situation, and repositories to effectively manage and share architectural reconfiguration patterns, components, and problem solving strategies. We conducted an experiment of applying the framework to an infotainment robot. The result of the experiment shows the practicality and usefulness of the framework for the intelligent service robots.
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network’s performance and that even modest implementation efforts produce state-of-the-art results. ∗apostolia.tsirikoglou@liu.se †magnus@7dlabs.com ‡jonas.unger@liu.se
Comparison of Qblade and CFD results for small-scaled horizontal axis wind turbine analysis

The software QBlade under General Public License is used for analysis and design of wind turbines. QBlade uses the Blade Element Momentum (BEM) method for the simulation of wind turbines and it is integrated with the XFOIL airfoil design and analysis. It is possible to predict wind turbine performance with it. Nowadays, Computational Fluid Dynamics (CFD) is used for optimization and design of turbine application. In this study, Horizontal wind turbine with a rotor diameter of 2 m, was designed and objected to performance analysis by QBlade and Ansys-Fluent. The graphic of the power coefficient vs. tip speed ratio (TSR) was obtained for each result. When the results are compared, the good agreement has been seen.
Architecture of the human regulatory network derived from ENCODE data

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.
DYNAMICS OF HYPERLIPIDEMIA AND PERIPHERAL BLOOD FLOW IN PATIENTS WITH DIABETES MELLITUS AFTER THE COURSE OF COMBINED LASER THERAPY IN AMBULATORY -

The study demonstrates that hypercholesterinemia in patients with coronary heart disease (CHD) is associated with functional depression of microcirculation, increase in total peripheral vascular resistance, reduction in the functional efficiency of heart and decrease in activity tolerance. After receiving a course of low-intensity infrared laser radiation treatment the patients displayed positive changes in blood lipid spectrum, which was associated with improvement in microcirculation, decrease in afterload, increase in economization of heart functioning and activity tolerance. The obtained results demonstrate that the hypolipidemic effect of laser radiation is a substantial factor in the regression of CHD manifestations.
Genetic diversity of Plasmodium falciparum infections in mild and severe malaria of children from Kampala, Uganda

Diversity in parasite virulence is one of the factors that contribute to the clinical outcome of malaria infections. The association between the severity of Plasmodium falciparum malaria and the number of distinct parasite populations infecting the host (multiplicity of infection) or polymorphism within any of the specific antigen genes was investigated. The study included 164 children presenting with mild and severe malaria from central Uganda where malaria is meso-endemic. The polymorphic regions of the circumsporozoite protein (csp), merozoite surface proteins 1 and 2 (msp1 and msp2), and glutamate-rich protein (glurp) were genotyped by polymerase chain reaction methods and fragment analysis by gel electrophoresis. In a subset of samples fragment analysis was also performed by fluorescent PCR genotyping followed by capillary electrophoresis. The multiplicity of infection (MOI), determined as the highest number of alleles detected within any of the four genetic loci, was significantly higher in severe than in mild malaria cases (mean 3.7 and 3.0, respectively, P = 0.002). No particular genotype or allelic family of msp1 or msp2 was associated with severity of malaria, and nor did the genotyping method reveal any significant difference in MOI when only assessed by msp2 genotyping. Severity of malaria was not linked to the predominance of any particular msp1 or msp2 allelic types, independent of methods used for genotyping. Monitoring the dynamics of multiple clone infections in relation to disease outcome, host immune status and genetic factors will provide more insight into parasite virulence mechanisms.