nils-herrmann/clean_scirepeval huggingface dataset:
Cloud4SNP: Distributed Analysis of SNP Microarray Data on the Cloud

Pharmacogenomics studies the impact of genetic variation of patients on drug responses and searches for correlations between gene expression or Single Nucleotide Polymorphisms (SNPs) of patient's genome and the toxicity or efficacy of a drug. SNPs data, produced by microarray platforms, need to be preprocessed and analyzed in order to find correlation between the presence/absence of SNPs and the toxicity or efficacy of a drug. Due to the large number of samples and the high resolution of instruments, the data to be analyzed can be very huge, requiring high performance computing. The paper presents the design and experimentation of Cloud4SNP, a novel Cloud-based bioinformatics tool for the parallel preprocessing and statistical analysis of pharmacogenomics SNP microarray data. Experimental evaluation shows good speed-up and scalability. Moreover, the availability on the Cloud platform allows to face in an elastic way the requirements of small as well as very large pharmacogenomics studies.
Lessons learned from the past on mental health care of refugee children in Serbia

102 unaccompanied asylum-seeking adolescents showed a high prevalence of infections (58.8 %; 20 % with parasites), iron deficiency anemia (17.6 %), and a very low prevalence of non-communicable diseases (<2.0 %) . The vast majority of exile children are traveling with their parents. Stress levels of parents have significantly increased which in turn negatively impacts children that are now significantly more traumatized. The number of unattended minors is rapidly rising, for instance in Germany the total number of unaccompanied minors in 2015 exceeded 60,000 . Unaccompanied minors are at higher risk to develop mental health problems. Unfortunately, a significant proportion of them may be at risk of trafficking, abuse or neglect. In the above study, mental illness was present in 13.7 % of adolescents and females were more frequently affected . Studies showed higher rates of depression, post-traumatic stress disorder and anxiety disorders among war refugees [4, 5]. A recent Sweden survey pointed out that neurotic disorders are more common in the unaccompanied refugees . A Greek study showed an increase in the incidents of deaths of unaccompanied minors during 2014–2015 compared to previous years 2011–2013; more than 70 children have drowned trying to rich a Greek island and majority of others are often soaking wet, freezing, suffering from hypothermia due to a winter conditions [7].
An End-to-end Approach for Handling Unknown Slot Values in Dialogue State Tracking

We highlight a practical yet rarely discussed problem in dialogue state tracking (DST), namely handling unknown slot values. Previous approaches generally assume predefined candidate lists and thus are not designed to output unknown values, especially when the spoken language understanding (SLU) module is absent as in many end-to-end (E2E) systems. We describe in this paper an E2E architecture based on the pointer network (PtrNet) that can effectively extract unknown slot values while still obtains state-of-the-art accuracy on the standard DSTC2 benchmark. We also provide extensive empirical evidence to show that tracking unknown values can be challenging and our approach can bring significant improvement with the help of an effective feature dropout technique.
Automated Self-Assembly of Large Maritime Structures by a Team of Robotic Boats

We present the methodology, algorithms, system design, and experiments addressing the self-assembly of large teams of autonomous robotic boats into floating platforms. Identical self-propelled robotic boats autonomously dock together and form connected structures with controllable variable stiffness. These structures can self-reconfigure into arbitrary shapes limited only by the number of rectangular elements assembled in brick-like patterns. An O(m2) complexity algorithm automatically generates assembly plans which maximize opportunities for parallelism while constructing operator-specified target configurations with m components. The system further features an O(n3) complexity algorithm for the concurrent assignment and planning of trajectories from n free robots to the growing structure. Such peer-to-peer assembly among modular robots compares favorably to a single active element assembling passive components in terms of both construction rate and potential robustness through redundancy. We describe hardware and software techniques to facilitate reliable docking of elements in the presence of estimation and actuation errors, and we consider how these local variable stiffness connections may be used to control the structural properties of the larger assembly. Assembly experiments validate these ideas in a fleet of 0.5 m long modular robotic boats with onboard thrusters, active connectors, and embedded computers.
Quantile Regression in Reproducing Kernel Hilbert Spaces

In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptotic theory for the KQR model.
Stacked Sparse Autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology

In this paper, a Stacked Sparse Autoencoder (SSAE) based framework is presented for nuclei classification on breast cancer histopathology. SSAE works very well in learning useful high-level feature for better representation of input raw data. To show the effectiveness of proposed framework, SSAE+Softmax is compared with conventional Softmax classifier, PCA+Softmax, and single layer Sparse Autoencoder (SAE)+Softmax in classifying the nuclei and non-nuclei patches extracted from breast cancer histopathology. The SSAE+Softmax for nuclei patch classification yields an accuracy of 83.7%, F1 score of 82%, and AUC of 0.8992, which outperform Softmax classifier, PCA+Softmax, and SAE+Softmax.
A Generalized Join Algorithm

Database query processing traditionally relies on three alternative join algorithms: index nested loops join exploits an index on its inner input, merge join exploits sorted inputs, and hash join exploits differences in the sizes of the join inputs. Cost-based query optimization chooses the most appropriate algorithm for each query and for each operation. Unfortunately, mistaken algorithm choices during compile-time query optimization are common yet expensive to investigate and to resolve. Our goal is to end mistaken choices among join algorithms by replacing the three traditional join algorithms with a single one. Like merge join, this new join algorithm exploits sorted inputs. Like hash join, it exploits different input sizes for unsorted inputs. In fact, for unsorted inputs, the cost functions for recursive hash join and for hybrid hash join have guided our search for the new join algorithm. In consequence, the new join algorithm can replace both merge join and hash join in a database management system. The in-memory components of the new join algorithm employ indexes. If the database contains indexes for one (or both) of the inputs, the new join can exploit persistent indexes instead of temporary in-memory indexes. Using database indexes to match input records, the new join algorithm can also replace index nested loops join. Results from an implementation of the core algorithm are reported.
Strategic Human Resource Management: Insights from the International Hotel Industry

In the strategic human resource management (SHRM) field three approaches have dominated, namely, the universal or best-practice, best-fit or contingency and resourcebased view (RBV). This study investigates evidence for the simultaneous or mixed adoption of these approaches by eight case study firms in the international hotel industry. Findings suggest there is considerable evidence of the combined use of the first two approaches but that the SHRM RBV approach was difficult to achieve by all companies. Overall, gaining differentiation through SHRM practices was found to be challenging due to specific industry forces. The study identifies that where companies derive some competitive advantage from their human resources and HRM practices they have closely aligned their managers’ expertise with their corporate market entry mode expertise and developed some distinctive, complex and integrated HRM interventions, which have a mutually reinforcing effect.
An update on Chinese herbal medicines as adjuvant treatment of anticancer therapeutics.

Numerous studies have indicated that in cancer treatment Chinese herbal medicines in combination with chemo-, radio-, or targeted-therapy can be used to enhance the efficacy of and diminish the side effects and complications caused by these therapies. Therefore, an understanding of Chinese herbal medicines is needed by physicians and other health care providers. This review provides an update on Chinese herbal medicines as adjuvant treatment of anticancer therapeutics. First, some Chinese herbal medicines (e.g. Astragalus, Ginseng, Scutellaria barbata, TJ-41, TJ-48, PHY906, Huachansu injection, and Kanglaite injection) that are commonly used for treating the cancer and/or reducing the toxicity induced by chemo-, radio-, or targeted-therapy are discussed. These Chinese herbal medicines have been shown to possess great advantages in terms of suppressing tumor progression, increasing the sensitivity of chemo-, radio-, or targeted-therapeutics, improving an organism's immune system function, and lessening the damage caused by these therapeutics. Second, some clinical trials using Chinese herbal medicines as adjuvant improving cancer treatment related side effects and complications are reviewed. Some Chinese herbal medicines have a significant effect on reducing cancer-related fatigue and pain, improving peripheral neuropathy and gastrointestinal side effects including diarrhea, nausea, and vomiting, decrease the incidence of bone marrow suppression, protecting anthracycline-induced cardiotoxicity and radiation-induced pneumonitis, and relieving EGFR-TKIs related acneiform eruptions and other side effects. This review of those medicines should contribute to an understanding of Chinese herbal medicines as adjuvant treatment for cancer and provide useful information for the development of more effective anti-cancer drugs. However, rigorously designed trials on potential Chinese herbal medicine must be further examined involving cancer treatment especially molecular targeted-therapy in the future.
SQL-IDS: a specification-based approach for SQL-injection detection

Vulnerabilities in web applications allow malicious users to obtain unrestricted access to private and confidential information. SQL injection attacks rank at the top of the list of threats directed at any database-driven application written for the Web. An attacker can take advantages of web application programming security flaws and pass unexpected malicious SQL statements through a web application for execution by the back-end database. This paper proposes a novel specification-based methodology for the detection of exploitations of SQL injection vulnerabilities. The new approach on the one hand utilizes specifications that define the intended syntactic structure of SQL queries that are produced and executed by the web application and on the other hand monitors the application for executing queries that are in violation of the specification.
 The three most important advantages of the new approach against existing analogous mechanisms are that, first, it prevents all forms of SQL injection attacks; second, its effectiveness is independent of any particular target system, application environment, or DBMS; and, third, there is no need to modify the source code of existing web applications to apply the new protection scheme to them.
 We developed a prototype SQL injection detection system (SQL-IDS) that implements the proposed algorithm. The system monitors Java-based applications and detects SQL injection attacks in real time. We report some preliminary experimental results over several SQL injection attacks that show that the proposed query-specific detection allows the system to perform focused analysis at negligible computational overhead without producing false positives or false negatives. Therefore, the new approach is very efficient in practice.
Design of a line-start synchronous reluctance motor

The design of line-start Synchronous Reluctance machines is proposed, based on the state of the art of motors designed for closed loop, vector control. The guidelines for overcoming the dilemma between synchronization capability and steady state performance are given, with examples of good and bad design choices. The proposed solutions compared via finite element analysis with a competitor induction motor in terms of efficiency and power factor. The finite element calculations are finally validated via experiments on a laboratory prototype.
Life cycle assessment of solid waste management options for Eskisehir, Turkey.

Life cycle assessment (LCA) methodology was used to determine the optimum municipal solid waste (MSW) management strategy for Eskisehir city. Eskisehir is one of the developing cities of Turkey where a total of approximately 750tons/day of waste is generated. An effective MSW management system is needed in this city since the generated MSW is dumped in an unregulated dumping site that has no liner, no biogas capture, etc. Therefore, five different scenarios were developed as alternatives to the current waste management system. Collection and transportation of waste, a material recovery facility (MRF), recycling, composting, incineration and landfilling processes were considered in these scenarios. SimaPro7 libraries were used to obtain background data for the life cycle inventory. One ton of municipal solid waste of Eskisehir was selected as the functional unit. The alternative scenarios were compared through the CML 2000 method and these comparisons were carried out from the abiotic depletion, global warming, human toxicity, acidification, eutrophication and photochemical ozone depletion points of view. According to the comparisons and sensitivity analysis, composting scenario, S3, is the more environmentally preferable alternative. In this study waste management alternatives were investigated only on an environmental point of view. For that reason, it might be supported with other decision-making tools that consider the economic and social effects of solid waste management.
Deep reinforcement learning based neuro-control for a two-dimensional magnetic positioning system

This paper presents a control scheme based on deep reinforcement learning for a two-dimensional positioning system with electromagnetic actuators. Two neuro-controllers are trained and used for controlling the X-Y position of an object. The neuro-controllers learning approach is based on the actor-critic architecture and the deep deterministic policy gradient (DDPG) algorithm using the Q-learning method. The performance of the control system is verified for different setpoints and working conditions.
Non-Orthogonal Access with Random Beamforming and Intra-Beam SIC for Cellular MIMO Downlink

We investigate non-orthogonal access with a successive interference canceller (SIC) in the cellular multiple-input multiple-output (MIMO) downlink for systems beyond LTE-Advanced. Taking into account the overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal multiuser multiplexing and the applicability of the SIC receiver in the MIMO downlink, we propose intra-beam superposition coding of a multiuser signal at the transmitter and the spatial filtering of inter-beam interference followed by the intra-beam SIC at the user terminal receiver. The intra-beam SIC cancels out the inter-user interference within a beam. Furthermore, the transmitter beamforming (precoding) matrix is controlled based on open loop-type random beamforming, which is very efficient in terms of the amount of feedback information from the user terminal. Simulation results show that the proposed non-orthogonal access scheme with random beamforming and the intra-beam SIC simultaneously achieves better sum and cell-edge user throughput compared to orthogonal access, which is assumed in LTE-Advanced.
Performance issues of hospital system using MySQL

To provide the better treatment in the hospitals, it is required to integrate all the hospitals of the country via internet. While visiting to hospital for treatment, a smart card with 10 digit unique patient-ID with his personal information is issued to the patient from the administrator of the hospital. As per unique patient-ID, all the doctor prescriptions, test reports, MRI, CT-scan images are stored in the database of the local server of the hospital and main centralized server of all the hospitals. Application of the smart card based on line hospital system has been developed as front end in .Net and back end in MySQL. In the development of application, all kinds of optimization have been covered at application level, database design level, memory utilization and optimization of queries for accessing the database. In this paper, some performance issues have been proposed for fast retrieval of patient data.
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN

Fine-grained image labels are desirable for many computer vision applications, such as visual search or mobile AI assistant. These applications rely on image classification models that can produce hundreds of thousands (e.g. 100K) of diversified fine-grained image labels on input images. However, training a network at this vocabulary scale is challenging, and suffers from intolerable large model size and slow training speed, which leads to unsatisfying classification performance. A straightforward solution would be training separate expert networks (specialists), with each specialist focusing on learning one specific vertical (e.g. cars, birds...). However, deploying dozens of expert networks in a practical system would significantly increase system complexity and inference latency, and consumes large amounts of computational resources. To address these challenges, we propose a Knowledge Concentration method, which effectively transfers the knowledge from dozens of specialists (multiple teacher networks) into one single model (one student network) to classify 100K object categories. There are three salient aspects in our method: (1) a multi-teacher single-student knowledge distillation framework; (2) a self-paced learning mechanism to allow the student to learn from different teachers at various paces; (3) structurally connected layers to expand the student network capacity with limited extra parameters. We validate our method on OpenImage and a newly collected dataset, Entity-Foto-Tree (EFT), with 100K categories, and show that the proposed model performs significantly better than the baseline generalist model.
A Parallel Implementation of k-Means in MATLAB

The aim of this work is the parallel implementation of k-means in MATLAB, in order to reduce the execution time. Specifically, a new function in MATLAB for serial k-means algorithm is developed, which meets all the requirements for the conversion to a function in MATLAB with parallel computations. Additionally, two different variants for the definition of initial values are presented. In the sequel, the parallel approach is presented. Finally, the performance tests for the computation times respect to the numbers of features and classes are illustrated. K-means algorithm, clustering, parallel computations, MATLAB.
Dynamic Resource Management for Efficient Utilization of Multitasking GPUs

As graphics processing units (GPUs) are broadly adopted, running multiple applications on a GPU at the same time is beginning to attract wide attention. Recent proposals on multitasking GPUs have focused on either spatial multitasking, which partitions GPU resource at a streaming multiprocessor (SM) granularity, or simultaneous multikernel (SMK), which runs multiple kernels on the same SM. However, multitasking performance varies heavily depending on the resource partitions within each scheme, and the application mixes. In this paper, we propose GPU Maestro that performs dynamic resource management for efficient utilization of multitasking GPUs. GPU Maestro can discover the best performing GPU resource partition exploiting both spatial multitasking and SMK. Furthermore, dynamism within a kernel and interference between the kernels are automatically considered because GPU Maestro finds the best performing partition through direct measurements. Evaluations show that GPU Maestro can improve average system throughput by 20.2% and 13.9% over the baseline spatial multitasking and SMK, respectively.
Privacy-by-design in big data analytics and social mining

Privacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.
Neurobiological mechanisms of the onset of puberty in primates.

An increase in pulsatile release of LHRH is essential for the onset of puberty. However, the mechanism controlling the pubertal increase in LHRH release is still unclear. In primates the LHRH neurosecretory system is already active during the neonatal period but subsequently enters a dormant state in the juvenile/prepubertal period. Neither gonadal steroid hormones nor the absence of facilitatory neuronal inputs to LHRH neurons is responsible for the low levels of LHRH release before the onset of puberty in primates. Recent studies suggest that during the prepubertal period an inhibitory neuronal system suppresses LHRH release and that during the subsequent maturation of the hypothalamus this prepubertal inhibition is removed, allowing the adult pattern of pulsatile LHRH release. In fact, y-aminobutyric acid (GABA) appears to be an inhibitory neurotransmitter responsible for restricting LHRH release before the onset of puberty in female rhesus monkeys. In addition, it appears that the reduction in tonic GABA inhibition allows an increase in the release of glutamate as well as other neurotransmitters, which contributes to the increase in pubertal LHRH release. In this review, developmental changes in several neurotransmitter systems controlling pulsatile LHRH release are extensively reviewed.