nils-herrmann/clean_scirepeval huggingface dataset:
Internet gaming disorder in children and adolescents: a systematic review.

Internet gaming disorder (IGD) is a serious disorder leading to and maintaining pertinent personal and social impairment. IGD has to be considered in view of heterogeneous and incomplete concepts. We therefore reviewed the scientific literature on IGD to provide an overview focusing on definitions, symptoms, prevalence, and aetiology.



We systematically reviewed the databases ERIC, PsyARTICLES, PsycINFO, PSYNDEX, and PubMed for the period January 1991 to August 2016, and additionally identified secondary references.



The proposed definition in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition provides a good starting point for diagnosing IGD but entails some disadvantages. Developing IGD requires several interacting internal factors such as deficient self, mood and reward regulation, problems of decision-making, and external factors such as deficient family background and social skills. In addition, specific game-related factors may promote IGD. Summarizing aetiological knowledge, we suggest an integrated model of IGD elucidating the interplay of internal and external factors.



So far, the concept of IGD and the pathways leading to it are not entirely clear. In particular, long-term follow-up studies are missing. IGD should be understood as an endangering disorder with a complex psychosocial background.


 THIS PAPER ADDS
In representative samples of children and adolescents, on average, 2% are affected by Internet gaming disorder (IGD). The mean prevalences (overall, clinical samples included) reach 5.5%. Definitions are heterogeneous and the relationship with substance-related addictions is inconsistent. Many aetiological factors are related to the development and maintenance of IGD. This review presents an integrated model of IGD, delineating the interplay of these factors.
STROKENET: A NEURAL PAINTING ENVIRONMENT

We’ve seen tremendous success of image generating models these years. Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the environment and the agent is required to allow trials. However, the environment is usually non-differentiable, leading to slow convergence and massive computation. In this paper we try to address the discrete nature of software environment with an intermediate, differentiable simulation. We present StrokeNet, a novel model where the agent is trained upon a wellcrafted neural approximation of the painting environment. With this approach, our agent was able to learn to write characters such as MNIST digits faster than reinforcement learning approaches in an unsupervised manner. Our primary contribution is the neural simulation of a real-world environment. Furthermore, the agent trained with the emulated environment is able to directly transfer its skills to real-world software.
An analytical review of vasculobiliary injury in laparoscopic and open cholecystectomy.

Biliary injuries are frequently accompanied by vascular injuries, which may worsen the bile duct injury and cause liver ischemia. We performed an analytical review with the aim of defining vasculobiliary injury and setting out the important issues in this area.



A literature search of relevant terms was performed using OvidSP. Bibliographies of papers were also searched to obtain older literature.


Vasculobiliary injury was defined as: an injury to both a bile duct and a hepatic artery and/or portal vein; the bile duct injury may be caused by operative trauma, be ischaemic in origin or both, and may or may not be accompanied by various degrees of hepatic ischaemia. Right hepatic artery (RHA) vasculobiliary injury (VBI) is the most common variant. Injury to the RHA likely extends the biliary injury to a higher level than the gross observed mechanical injury. VBI results in slow hepatic infarction in about 10% of patients. Repair of the artery is rarely possible and the overall benefit unclear. Injuries involving the portal vein or common or proper hepatic arteries are much less common, but have more serious effects including rapid infarction of the liver.



Routine arteriography is recommended in patients with a biliary injury if early repair is contemplated. Consideration should be given to delaying repair of a biliary injury in patients with occlusion of the RHA. Patients with injuries to the portal vein or proper or common hepatic should be emergently referred to tertiary care centers.
Integrated Genomic Characterization of Papillary Thyroid Carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D, and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors, and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.
Generating Textual Entailment Using Residual LSTMs

Generating textual entailment (GTE) is a recently proposed task to study how to infer a sentence from a given premise. Current sequence-to-sequence GTE models are prone to produce invalid sentences when facing with complex enough premises. Moreover, the lack of appropriate evaluation criteria hinders researches on GTE. In this paper, we conjecture that the unpowerful encoder is the major bottleneck in generating more meaningful sequences, and improve this by employing the residual LSTM network. With the extended model, we obtain state-of-the-art results. Furthermore, we propose a novel metric for GTE, namely EBR (Evaluated By Recognizing textual entailment), which could evaluate different GTE approaches in an objective and fair way without human effort while also considering the diversity of inferences. In the end, we point out the limitation of adapting a general sequence-to-sequence framework under GTE settings, with some proposals for future research, hoping to generate more public discussion.
Primary Classic Kaposi's Sarcoma of the Penis in an HIV-Negative Patient

Kaposi's sarcoma (KS) is a multifocal hemorrhagic sarcoma that occurs primarily on the extremities. KS limited to the penis is rare and a well-recognized manifestation of acquired immune deficiency syndrome (AIDS). However, KS confined to the penis is extraordinary in human immunodeficiency virus (HIV)-negative patients. We present the case of a 68-year-old man with a dark reddish ulcerated nodule on the penile skin, which was reported as a nodular stage of KS. We detected no evidence of immunosuppression or AIDS or systemic involvements in further evaluations. In his past medical history, the patient had undergone three transurethral resections of bladder tumors due to urothelial cell carcinoma since 2000 and total gastrectomy, splenectomy, and adjuvant fluorouracil/cisplatin chemotherapy for 7 months due to advanced gastric carcinoma in 2005. The patient was circumcised and has had no recurrence for 2 years.
Active Learning for Level Set Estimation

Many information gathering problems require determining the set of points, for which an unknown function takes value above or below some given threshold level. As a concrete example, in the context of environmental monitoring of Lake Zurich we would like to estimate the regions of the lake where the concentration of chlorophyll or algae is greater than some critical value, which would serve as an indicator of algal bloom phenomena. A critical factor in such applications is the high cost in terms of time, baery power, etc. that is associated with each measurement, therefore it is important to be careful about selecting “informative” locations to sample, in order to reduce the total sampling effort required. We formalize the task of level set estimation as a classification problem with sequential measurements, where the unknown function is modeled as a sample from a Gaussian process (GP). We propose LSE, an active learning algorithm that guides both sampling and classification based on GP-derived confidence bounds, and provide theoretical guarantees about its sample complexity. Furthermore, we extend LSE and its theory to two more natural seings: (1) where the threshold level is implicitly defined as a percentage of the (unknown) maximum of the target function and (2) where samples are selected in batches. Based on the laer extension we also propose a simple path planning algorithm. We evaluate the effectiveness of our proposed methods on two problems of practical interest, namely the aforementioned autonomous monitoring of algal populations in Lake Zurich and geolocating network latency.
System requirements engineering in complex situations

The purpose of this paper is to present an alternative systems thinking–based perspective and approach to the requirements elicitation process in complex situations. Three broad challenges associated with the requirements engineering elicitation in complex situations are explored, including the (1) role of the system observer, (2) nature of system requirements in complex situations, and (3) influence of the system environment. Authors have asserted that the expectation of unambiguous, consistent, complete, understandable, verifiable, traceable, and modifiable requirements is not consistent with complex situations. In contrast, complex situations are an emerging design reality for requirements engineering processes, marked by high levels of ambiguity, uncertainty, and emergence. This paper develops the argument that dealing with requirements for complex situations requires a change in paradigm. The elicitation of requirements for simple and technically driven systems is appropriately accomplished by proven methods. In contrast, the elicitation of requirements in complex situations (e.g., integrated multiple critical infrastructures, system-of-systems, etc.) requires more holistic thinking and can be enhanced by grounding in systems theory.
Emulation of obsolete hardware in open source virtualization software

Nuclear Power plants are built to last 40 years. From the beginning of their lifetime up to now, dozens of specific, but not critical, applications have been developed. Their cost does not only reside in the development but mainly in the qualification and validation processes. Most of the time, once these applications have been commissioned and have fulfilled their functions, no functional or performance reason require them to be modified later on. They should stay unchanged during the whole lifetime of the plant. Meanwhile the hardware has changed many times; the average lifespan of a standard PC is 1 to 2 years. In order to cope with these frequent changes, we have come out with a new solution: emulation of obsolete hardware through virtualization.
SLIC Superpixels ?

Superpixels are becoming increasingly popular for use in computer vision applications. However, there are few algorithms that output a desired number of regular, compact superpixels with a low computational overhead. We introduce a novel algorithm that clusters pixels in the combined five-dimensional color and image plane space to efficiently generate compact, nearly uniform superpixels. The simplicity of our approach makes it extremely easy to use – a lone parameter specifies the number of superpixels – and the efficiency of the algorithm makes it very practical. Experiments show that our approach produces superpixels at a lower computational cost while achieving a segmentation quality equal to or greater than four state-of-the-art methods, as measured by boundary recall and under-segmentation error. We also demonstrate the benefits of our superpixel approach in contrast to existing methods for two tasks in which superpixels have already been shown to increase performance over pixel-based methods.
Support Vector Machine with Inverse Fringe as Feature for MNIST Dataset

In this paper we proposed SVM algorithm for MNIST dataset with fringe and its complementary version, inverse fringe as feature for SVM. MNIST data-set is consists of 60000 examples of training set and 10000 examples of test set. In our experiments we started with using fringe distance map as feature and found that the accuracy of system on trained data is 99.99% and on test data it is 97.14%, using inverse fringe distance map as feature and found that the accuracy of system on trained data is 99.92% and on test data is 97.72% and using combination of above two feature as feature and found that the accuracy of system on trained data is 100 and on test data is 97.55%.
Impact of microbial transformation of food on health - from fermented foods to fermentation in the gastro-intestinal tract.

Fermentation of food components by microbes occurs both during certain food production processes and in the gastro-intestinal tract. In these processes specific compounds are produced that originate from either biotransformation reactions or biosynthesis, and that can affect the health of the consumer. In this review, we summarize recent advances highlighting the potential to improve the nutritional status of a fermented food by rational choice of food-fermenting microbes. The vast numbers of microbes residing in the human gut, the gut microbiota, also give rise to a broad array of health-active molecules. Diet and functional foods are important modulators of the gut microbiota activity that can be applied to improve host health. A truly multidisciplinary approach is required to increase our understanding of the molecular mechanisms underlying health beneficial effects that arise from the interaction of diet, microbes and the human body.
Reflections on different governance styles in regulating science: a contribution to ‘Responsible Research and Innovation’

In European science and technology policy, various styles have been developed and institutionalised to govern the ethical challenges of science and technology innovations. In this paper, we give an account of the most dominant styles of the past 30 years, particularly in Europe, seeking to show their specific merits and problems. We focus on three styles of governance: a technocratic style, an applied ethics style, and a public participation style. We discuss their merits and deficits, and use this analysis to assess the potential of the recently established governance approach of 'Responsible Research and Innovation' (RRI). Based on this analysis, we reflect on the current shaping of RRI in terms of 'doing governance'.
Chapter 11 QUEUEING ANALYSIS IN HEALTHCARE

Many organizations, such as banks, airlines, telecommunications companies, and police departments, routinely use queueing models to help determine capacity levels needed to respond to experienced demands in a timely fashion. Though queueing analysis has been used in hospitals and other healthcare settings, its use in this sector is not widespread. Yet, given the pervasiveness of delays in healthcare and the fact that many healthcare facilities are trying to meet increasing demands with tightly constrained resources, queueing models can be very useful in developing more effective policies for bed allocation and staffing, and in identifying other opportunities for improving service. Queueing analysis is also a key tool in estimating capacity requirements for possible future scenarios, including demand surges due to new diseases or acts of terrorism. This chapter describes basic queueing models as well as some simple modifications and extensions that are particularly useful in the healthcare setting, and give examples of their use. The critical issue of data requirements is also be discussed as well as model choice, modelbuilding and the interpretation and use of results.
High-Power Waveguide Low-Pass Filter With All-Higher-Order Mode Suppression Over a Wide-Band for Ka-Band Satellite Applications

In this letter, the design of a compact high-power waveguide low-pass filter with low insertion loss, all-higher order mode suppression, and stopband rejection up to the third harmonic, intended for Ka-band satellite applications, is presented. The method is based on step-shaped bandstop elements separated by very short (ideally of zero length) waveguide sections easily manufactured by low-cost computer-controlled milling. Matching is achieved by short input/output networks based on stubs whose heights are optimized following classical approaches. The novel filter presents a reduction in insertion loss and a remarkable increase in the high-power handling capability when compared to the classical waffle-iron filter and alternative solutions previously proposed, while the out-of-band frequency behavior remains unaltered.
Experimental manipulation of the Trier Social Stress Test-Modified (TSST-M) to vary arousal across development

Reliable laboratory protocols manipulating the intensity of biobehavioral arousal for children are uncommon, and those available have minimal converging evidence of their efficacy in manipulating arousal across multiple biological systems. This report presents two studies of the efficacy of the modified Trier Social Stress Test (TSST-M). In Study 1, sixty-three 7-15-year olds, and 19 young adults (18-25 yrs) completed the TSST-M. Comparable reactivity across age groups was observed for salivary cortisol, salivary alpha-amylase (sAA), pre-ejection period (PEP) and respiratory sinus arrhythmia (RSA), whereas self-reported stress was higher in adults compared to children. In Study 2, eighty-four 7-8-year olds and eighty-six 12-15-year olds were randomly assigned to a standard or low-stress TSST-M condition. Cortisol and self-reported stress responses were higher in the standard compared to the low-stress condition. In contrast, sAA and PEP were higher in the low-stress condition and RSA responses were comparable between the two TSST-M conditions. In addition, age group differences emerged in Study 2, though never in conjunction with the TSST-M manipulation. To test, refine, and advance theory about the implications of stress for child development, laboratory tasks that manipulate and enable assessment of biobehavioral arousal in children are needed.
Plant Secondary Metabolites in the Sustainable Diamondback Moth ( Plutella xylostella l . ) Management *

Diamondback moth, Plutella xylostella (L.) (Lepidoptera: Plutellidae) is one of the most important insect pests causing severe damage to crucifers worldwide. Chemical methods have failed to control this pest as this has developed resistance to almost all synthetic insecticides available. Hence, alternative sources such as plants can serve as the rich source of secondary bioactive molecules with insecticidal properties. For the past two decades, considerable efforts have been directed toward screening plants to determine their biological activity against DBM. Additionally, local abundant plant sources can be easily utilised to prepare pesticidal formulations from the local flora. The strategy to maximise control by insecticides from plant origin and reduce selection pressure by applying insecticides only when necessary, and by rotating insecticide use between chemical groups judiciously. The growth inhibiting activity of a phytochemical may be essential for sustainable pest management strategies. While the design of novel crop protection chemicals possessing superior properties is of prime importance. These plant sources can be structurally modified to design new safer insecticidal molecules with novel mode of action for sustainable pest management in future by using modern technologies.
SpotTune: Transfer Learning through Adaptive Fine-tuning

Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision. The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pretrained on the source task using data from the target task. In this paper, we propose an adaptive fine-tuning approach, called SpotTune, which finds the optimal fine-tuning strategy per instance for the target data. In SpotTune, given an image from the target task, a policy network is used to make routing decisions on whether to pass the image through the fine-tuned layers or the pre-trained layers. We conduct extensive experiments to demonstrate the effectiveness of the proposed approach. Our method outperforms the traditional fine-tuning approach on 12 out of 14 standard datasets. We also compare SpotTune with other stateof-the-art fine-tuning strategies, showing superior performance. On the Visual Decathlon datasets, our method achieves the highest score across the board without bells and whistles.
An efficient method for evaluating alerts of Intrusion Detection Systems

With thousands of alerts identified by IDSs every day, the process of distinguishing which alerts are important (i.e., true positives) and which are is irrelevant (i.e., false positives) is become more complicated. The security administrator must analyze each single alert either a true of false alert. This paper proposes an alert prioritization model, which is based on risk assessment. The model uses indicators, such as priority, reliability, asset value, as decision factors to calculate alert's risk. The objective is to determine the impact of certain alerts generated by IDS on the security status of an information system, also improve the detection of intrusions using snort by classifying the most critical alerts by their levels of risk, thus, only the alerts that presents a real threat will be displayed to the security administrator, so, we reduce the number of false positives, also we minimize the analysis time of the alerts. The model was evaluated using KDD Cup 99 Dataset as test environment and a pattern matching algorithm.
Articulatory-based conversion of foreign accents with deep neural networks

We present an articulatory-based method for real-time accent conversion using deep neural networks (DNN). The approach consists of two steps. First, we train a DNN articulatory synthesizer for the non-native speaker that estimates acoustics from contextualized articulatory gestures. Then we drive the DNN with articulatory gestures from a reference native speaker –mapped to the nonnative articulatory space via a Procrustes transform. We evaluate the accent-conversion performance of the DNN through a series of listening tests of intelligibility, voice identity and nonnative accentedness. Compared to a baseline method based on Gaussian mixture models, the DNN accent conversions were found to be 31% more intelligible, and were perceived more native-like in 68% of the cases. The DNN also succeeded in preserving the voice identity of the nonnative speaker.