nils-herrmann/clean_scirepeval huggingface dataset:
EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma.

EASL–EORTC Clinical Practice Guidelines (CPG) on the management of hepatocellular carcinoma (HCC) define the use of surveillance, diagnosis, and therapeutic strategies recommended for patients with this type of cancer. This is the first European joint effort by the European Association for the Study of the Liver (EASL) and the European Organization for Research and Treatment of Cancer (EORTC) to provide common guidelines for the management of hepatocellular carcinoma. These guidelines update the recommendations reported by the EASL panel of experts in HCC published in 2001 . Several clinical and scientific advances have occurred during the past decade and, thus, a modern version of the document is urgently needed. The purpose of this document is to assist physicians, patients, health-care providers, and health-policy makers from Europe and worldwide in the decision-making process according to evidencebased data. Users of these guidelines should be aware that the recommendations are intended to guide clinical practice in circumstances where all possible resources and therapies are available. Thus, they should adapt the recommendations to their local regulations and/or team capacities, infrastructure, and cost– benefit strategies. Finally, this document sets out some recommendations that should be instrumental in advancing the research and knowledge of this disease and ultimately contribute to improve patient care. The EASL–EORTC CPG on the management of hepatocellular carcinoma provide recommendations based on the level of evi-
6LoWPAN Stacks: A Survey

Wireless Sensor Network (WSN) is one of the key technologies of 21st century, while it is a very active and challenging research area. It seems that in the next coming year, thanks to 6LoWPAN, these wireless micro-sensors will be embedded in everywhere, because 6LoWPAN enables P2P connection between wireless nodes over IPv6. Nowadays different implementations of 6LoWPAN stacks are available so it is interesting to evaluate their performance in term of memory footprint and compliant with the RFC4919 and RFC4944. In this paper, we present a survey on the state-of-art of the current implementation of 6LoWPAN stacks such as uIP/Contiki, SICSlowpan, 6lowpancli, B6LoWPAN, BLIP, NanoStack and Jennic's stack. The key features of all these 6LoWPAN stacks will be established. Finally, we discuss the evolution of the current implementations of 6LoWPAN stacks.
Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification

Text classification can help users to effectively handle and exploit useful information hidden in large-scale documents. However, the sparsity of data and the semantic sensitivity to context often hinder the classification performance of short texts. In order to overcome the weakness, we propose a unified framework to expand short texts based on word embedding clustering and convolutional neural network (CNN). Empirically, the semantically related words are usually close to each other in embedding spaces. Thus, we first discover semantic cliques via fast clustering. Then, by using additive composition over word embeddings from context with variable window width, the representations of multi-scale semantic units1 in short texts are computed. In embedding spaces, the restricted nearest word embeddings (NWEs)2 of the semantic units are chosen to constitute expanded matrices, where the semantic cliques are used as supervision information. Finally, for a short text, the projected matrix 3 and expanded matrices are combined and fed into CNN in parallel. Experimental results on two open benchmarks validate the effectiveness of the proposed method.
Trend of High-Speed SAR ADC towards RF Sampling

One emerging trend of high-speed low-power ADC design is to leverage the successive approximation (SAR) topology. It has successfully advanced the power efficiency by orders of magnitude over the past decade. Given the nature of SAR algorithm, the conversion speed is intrinsically slow compared to other high-speed ADC architectures, and yet minimal static power is required due to the mostly digital implementation. This paper examines various speed enhancement techniques that enable SAR ADCs towards RF sampling, i.e. >GS/s sampling rate with >GHz input bandwidth, while maintaining low power and area consumption. It is expected to play a crucial role in the future energy-constrained wideband system.
Recommending new movies: even a few ratings are more valuable than metadata

The Netflix Prize (NP) competition gave much attention to collaborative filtering (CF) approaches. Matrix factorization (MF) based CF approaches assign low dimensional feature vectors to users and items. We link CF and content-based filtering (CBF) by finding a linear transformation that transforms user or item descriptions so that they are as close as possible to the feature vectors generated by MF for CF. We propose methods for explicit feedback that are able to handle 140,000 features when feature vectors are very sparse. With movie metadata collected for the NP movies we show that the prediction performance of the methods is comparable to that of CF, and can be used to predict user preferences on new movies. We also investigate the value of movie metadata compared to movie ratings in regards of predictive power. We compare our solely CBF approach with a simple baseline rating-based predictor. We show that even 10 ratings of a new movie are more valuable than its metadata for predicting user ratings.
Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves

Many errors in coreference resolution come from semantic mismatches due to inadequate world knowledge. Errors in named-entity linking (NEL), on the other hand, are often caused by superficial modeling of entity context. This paper demonstrates that these two tasks are complementary. We introduce NECO, a new model for named entity linking and coreference resolution, which solves both problems jointly, reducing the errors made on each. NECO extends the Stanford deterministic coreference system by automatically linking mentions to Wikipedia and introducing new NEL-informed mention-merging sieves. Linking improves mention-detection and enables new semantic attributes to be incorporated from Freebase, while coreference provides better context modeling by propagating named-entity links within mention clusters. Experiments show consistent improvements across a number of datasets and experimental conditions, including over 11% reduction in MUC coreference error and nearly 21% reduction in F1 NEL error on ACE 2004 newswire data.
An adaptive mechanism to achieve learning rate dynamically

Gradient descent is prevalent for large-scale optimization problems in machine learning; especially it nowadays plays a major role in computing and correcting the connection strength of neural networks in deep learning. However, many gradient-based optimization methods contain more sensitive hyper-parameters which require endless ways of configuring. In this paper, we present a novel adaptive mechanism called adaptive exponential decay rate (AEDR). AEDR uses an adaptive exponential decay rate rather than a fixed and preconfigured one, and it can allow us to eliminate one otherwise tuning sensitive hyper-parameters. AEDR also can be used to calculate exponential decay rate adaptively by employing the moving average of both gradients and squared gradients over time. The mechanism is then applied to Adadelta and Adam; it reduces the number of hyper-parameters of Adadelta and Adam to only a single one to be turned. We use neural network of long short-term memory and LeNet to demonstrate how learning rate adapts dynamically. We show promising results compared with other state-of-the-art methods on four data sets, the IMDB (movie reviews), SemEval-2016 (sentiment analysis in twitter) (IMDB), CIFAR-10 and Pascal VOC-2012.
International Association of Dental Traumatology guidelines for the management of traumatic dental injuries: 1. Fractures and luxations of permanent teeth.

Traumatic dental injuries (TDIs) of permanent teeth occur frequently in children and young adults. Crown fractures and luxations are the most commonly occurring of all dental injuries. Proper diagnosis, treatment planning and followup are important for improving a favorable outcome. Guidelines should assist dentists and patients in decision making and for providing the best care effectively and efficiently. The International Association of Dental Traumatology (IADT) has developed a consensus statement after a review of the dental literature and group discussions. Experienced researchers and clinicians from various specialties were included in the group. In cases where the data did not appear conclusive, recommendations were based on the consensus opinion of the IADT board members. The guidelines represent the best current evidence based on literature search and professional opinion. The primary goal of these guidelines is to delineate an approach for the immediate or urgent care of TDIs. In this first article, the IADT Guidelines for management of fractures and luxations of permanent teeth will be presented.
Game Design to Measure Reflexes and Attention Based on Biofeedback Multi-Sensor Interaction

This paper presents a multi-sensor system for implementing biofeedback as a human-computer interaction technique in a game involving driving cars in risky situations. The sensors used are: Eye Tracker, Kinect, pulsometer, respirometer, electromiography (EMG) and galvanic skin resistance (GSR). An algorithm has been designed which gives rise to an interaction logic with the game according to the set of physiological constants obtained from the sensors. The results reflect a 72.333 response to the System Usability Scale (SUS), a significant difference of p = 0.026 in GSR values in terms of the difference between the start and end of the game, and an r = 0.659 and p = 0.008 correlation while playing with the Kinect between the breathing level and the energy and joy factor. All the sensors used had an impact on the end results, whereby none of them should be disregarded in future lines of research, even though it would be interesting to obtain separate breathing values from that of the cardio.
Risks and Resilience of Collaborative Networks

Within any Supply Chain Risk Management (SCRM) approach, the concept “Risk” occupies a central interest. Numerous frameworks which differ by the provided definitions and relationships between supply chain risk dimensions and metrics are available. This article provides an outline of the most common SCRM methodologies, in order to suggest an “integrated conceptual model”. The objective of such an integrated model is not to describe yet another conceptual model of Risk, but rather to offer a concrete structure incorporating the characteristics of the supply chain in the risk management process. The proposed alignment allows a better understanding of the dynamic of risk management strategies. Firstly, the model was analyzed through its positioning and its contributions compared to existing tools and models in the literature. This comparison highlights the critical points overlooked in the past. Secondly, the model was applied on case studies of major supply chain crisis.
A Laser Obstacle Warning and Avoidance system for Manned and Unmanned Aircraft

In this paper we briefly review the research and flight test activities performed to develop and integrate the Laser Obstacle Avoidance and Monitoring (LOAM) system on helicopter platforms and focus on the recent research advances towards the development of a new scaled LOAM variant for small-to-medium size Unmanned Aircraft (UA) platforms. After a brief description of the system architecture and sensor characteristics, emphasis is given to the performance models and data processing algorithms developed for obstacle detection, classification and calculation of alternative flight paths, as well as to the flight test activities performed on various military platforms. A concluding section provides an overview of current LOAM research developments with a focus on non-cooperative UA Sense-and-Avoid (SAA) applications.
Assessment of personality dimensions in children and adolescents with bipolar disorder using the Junior Temperament and Character Inventory.

We compared temperament and character traits in children and adolescents with bipolar disorder (BP) and healthy control (HC) subjects.



Sixty nine subjects (38 BP and 31 HC), 8-17 years old, were assessed with the Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime. Temperament and character traits were measured with parent and child versions of the Junior Temperament and Character Inventory.



BP subjects scored higher on novelty seeking, harm avoidance, and fantasy subscales, and lower on reward dependence, persistence, self-directedness, and cooperativeness compared to HC (all p < 0.007), by child and parent reports. These findings were consistent in both children and adolescents. Higher parent-rated novelty seeking, lower self-directedness, and lower cooperativeness were associated with co-morbid attention-deficit/hyperactivity disorder (ADHD). Lower parent-rated reward dependence was associated with co-morbid conduct disorder, and higher child-rated persistence was associated with co-morbid anxiety.



These findings support previous reports of differences in temperament in BP children and adolescents and may assist in a greater understating of BP children and adolescents beyond mood symptomatology.
Design Patterns to Implement Safety and Fault Tolerance

This paper discusses an object orient approach based on design pattern and computational reflection concept to implement nonfunctional requirements of complex control system. Firstly we brief about software architecture design, followed by control-monitor safety pattern, Tri-Modular redundancy (TMR) pattern, reflective state pattern and fault tolerance redundancy patterns that are use for safety and fault management. Reflection state pattern is a refinement of the state design pattern based on reflection architectural pattern. With variation in reflective design pattern we can develop a well structured fault tolerant system. The main goal of this paper is to separate control and safety aspect from the application logic. It details its intent, motivation, participants, consequences and implementation of safety design pattern. General Terms Design pattern, Safety pattern, Fault tolerance.
Analysis of Association Rule Mining Algorithms to Generate Frequent Itemset

Association rule mining algorithm is used to extract relevant information from database and transmit into simple and easiest form. Association rule mining is used in large set of data. It is used for mining frequent item sets in the database or in data warehouse. It is also one type of data mining procedure. In this paper some of the association rule mining algorithms such as apriori, partition, FP-growth, genetic algorithm etc., can be analyzed for generating frequent itemset in an effective manner. These association rule mining algorithms may differ depend upon their performance and effective pattern generation. So, this paper may concentrate on some of the algorithms used to generate efficient frequent itemset using some of association rule mining algorithms.
The software value map - an exhaustive collection of value aspects for the development of software intensive products

In software intensive products such as cars or telecom systems, software has traditionally been associated with cost, and there has been no real perception of its value in relation to the entire product offering. However, because software is becoming a larger part of the main competitive advantage, driving innovation and product differentiation, hardware is becoming more standardized, thus the valuation of software is becoming critical. In existing literature, several value constructs and corresponding valuation/measurement solutions needed for making decisions about software product development are presented. However, the contributions are often isolated with respect to a certain perspective such as focusing on product’s internal or external quality aspects only. Consequently, a complete view of value constructs relevant from different perspectives required for making decisions about software product development is missing. This paper presents a consolidated view of the software value concept utilizing the major perspectives and introduces a software value map. The created value map was evaluated through an industry case study through the development of impact evaluation patterns, which were subsequently used by professionals in industry, and experiences gathered. During industry evaluation, practitioners found substantial benefits of having a consolidated, vastly improved, and extended value aspect’s view of software.
SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation

In this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked to produce Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the biomedical domain. In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain. A total of five sites participated in the parsing subtask, and four participated in the generation subtask. Along with a description of the task and the participants’ systems, we show various score ablations and some sample outputs.
A Novel Reconfigurable Metal Rim Integrated Open Slot Antenna for Octa-Band Smartphone Applications

In this paper, a novel reconfigurable open slot antenna has been proposed for LTE smartphone applications to cover a wide bandwidth of 698–960 and 1710–2690 MHz. The antenna is located at the bottom portion of the mobile phone and is integrated with metal rim, thereby occupying a small space and providing mechanical stability to the mobile phone. Varactor diode is used to cover the lower band frequencies, so as to achieve a good frequency coverage and antenna miniaturization. The operational principles of the antenna are studied and the final design is optimized, fabricated, and tested. It has achieved the desired impedance bandwidth and the total efficiency of minimum 50% in free space throughout the required bands. The antenna performance with mobile phone components and human hand is also been studied. Furthermore, the SAR in a human head is investigated and is found to be within allowable SAR limits. Finally a multiple-input multiple-output antenna configuration with high isolation is proposed; it has an identical reconfigurable open slot antenna integrated at the top edge of the mobile phone acting as the secondary antenna for 698–960 and 1710–2690 MHz. Thus the proposed antenna is an excellent candidate for LTE smartphones and mobile devices.
A Closed Loop Speed Control of a Switched Reluctance Motor with a New Converter Topology

Switched Reluctance Motor (SRM) has become a competitive selection for many applications of electric machine drive systems recently due to its relative simple construction and its robustness. . This paper describes the design of new converter, consisting of “halfbridge” IGBT modules and SCRs, for closed loop control of switched reluctance motor drives are proposed. The proposed converter topology is a variation of the conventional asymmetric bridge converter for switched reluctance motor drives. However, utilization of switch modules is enhanced considerably. The requirements of converters for switched reluctance motor drives and the operation of the proposed new converter are analyzed and discussed. In this paper a new converter topology for speed control of a switched reluctance motor (SRM) is proposed. The topology is verified through MATLAB simulation. Switched reluctance motor drives, Converter topology.
Feedback-Linearized Inverse Feedforward for Creep, Hysteresis, and Vibration Compensation in AFM Piezoactuators

In this brief, we study the design of a feedback and feedforward controller to compensate for creep, hysteresis, and vibration effects in an experimental piezoactuator system. First, we linearize the nonlinear dynamics of the piezoactuator by accounting for the hysteresis (as well as creep) using high-gain feedback control. Next, we model the linear vibrational dynamics and then invert the model to find a feedforward input to account vibration - this process is significantly easier than considering the complete nonlinear dynamics (which combines hysteresis and vibration effects). Afterwards, the feedforward input is augmented to the feedback-linearized system to achieve high-precision highspeed positioning. We apply the method to a piezoscanner used in an experimental atomic force microscope to demonstrate the method's effectiveness and we show significant reduction of both the maximum and root-mean-square tracking error. For example, high-gain feedback control compensates for hysteresis and creep effects, and in our case, it reduces the maximum error (compared to the uncompensated case) by over 90%. Then, at relatively high scan rates, the performance of the feedback controlled system can be improved by over 75% (i.e., reduction of maximum error) when the inversion-based feedforward input is integrated with the high-gain feedback controlled system.
Feedback-Control & Queueing Theory-Based Resource Management for Streaming Applications

Recent advances in sensor technologies and instrumentation have led to an extraordinary growth of data sources and streaming applications. A wide variety of devices, from smart phones to dedicated sensors, have the capability of collecting and streaming large amounts of data at unprecedented rates. A number of distinct streaming data models have been proposed. Typical applications for this include smart cites & built environments for instance, where sensor-based infrastructures continue to increase in scale and variety. Understanding how such streaming content can be processed within some time threshold remains a non-trivial and important research topic. We investigate how a cloud-based computational infrastructure can autonomically respond to such streaming content, offering Quality of Service guarantees. We propose an autonomic controller (based on feedback control and queueing theory) to elastically provision virtual machines to meet performance targets associated with a particular data stream. Evaluation is carried out using a federated Cloud-based infrastructure (implemented using CometCloud)-where the allocation of new resources can be based on: (i) differences between sites, i.e., types of resources supported (e.g., GPU versus CPU only), (ii) cost of execution; (iii) failure rate and likely resilience, etc. In particular, we demonstrate how Little's Law-a widely used result in queuing theory-can be adapted to support dynamic control in the context of such resource provisioning.