nils-herrmann/clean_scirepeval huggingface dataset:
MTC value network for smart city ecosystems

Looking for new markets and revenue streams, the future Smart Cities comprise a good opportunity for traditional actors of the telecommunication industry. This opportunity requires a new mindset among these actors that corresponds to re-positioning in the Smart City value chain. This means that, in order to play a role that can not be overlooked, Telecom actors should perform rather different blocks of the Smart City value chain compared to their traditional activity blocks in Mobile Telephony value chain. The Fifth Generation of mobile telecommunications technology (5G), by some actors, is then considered as the major ICT enabler for this new paradigm. This paper intends to highlight the role of Machine Type Communications (MTC) for enabling Smart Cities. In order to do so, we introduce the building blocks of Smart City followed by four use cases from Intelligent Transport Systems and Digital Built Environment. We use these cases as the proof of concept for defining the generic MTC activities in the context of Smart City. Eventually the paper introduces the MTC value network in the context of Smart City, based on the resources associated with the activities.
Development of a multidimensional assessment tool for uraemic pruritus: Uraemic Pruritus in Dialysis Patients (UP-Dial).

Dialysis patients with uraemic pruritus (UP) have significantly impaired quality of life. To assess the therapeutic effect of UP treatments, a well-validated comprehensive and multidimensional instrument needed to be established.



To develop and validate a multidimensional scale assessing UP in patients on dialysis: the Uraemic Pruritus in Dialysis Patients (UP-Dial).



The development and validation of the UP-Dial instrument were conducted in four phases: (i) item generation, (ii) development of a pilot questionnaire, (iii) refinement of the questionnaire with patient recruitment and (iv) psychometric validation. Participants completed the UP-Dial, the visual analogue scale (VAS) of UP, the Dermatology Life Quality Index (DLQI), the Kidney Disease Quality of Life-36 (KDQOL-36), the Pittsburgh Sleep Quality Index (PSQI) and the Beck Depression Inventory (BDI) between 15 May 2012 and 30 November 2015.



The 27-item pilot UP-Dial was generated, with 168 participants completing the pilot scale. After factor analysis was performed, the final 14-item UP-Dial encompassed three domains: signs and symptoms, psychosocial, and sleep. Face and content validity were satisfied through the item generation process and expert review. Psychometric analysis demonstrated that the UP-Dial had good convergent and discriminant validity. The UP-Dial was significantly correlated [Spearman rank coefficient, 95% confidence interval (CI)] with the VAS-UP (0·76, 0·69-0·83), DLQI (0·78, 0·71-0·85), KDQOL-36 (-0·86, -0·91 to -0·81), PSQI (0·85, 0·80-0·89) and BDI (0·70, 0·61-0·79). The UP-Dial revealed excellent internal consistency (Cronbach's α 0·90, 95% CI 0·87-0·92) and reproducibility (intraclass correlation 0·95, 95% CI 0·90-0·98).



The UP-Dial is valid and reliable for assessing UP among patients on dialysis. Future research should focus on the cross-cultural adaptation and translation of the scale to other languages.
CMOS 2.45GHz RF power amplifier for RFID reader

A high linearity CMOS RF (radio frequency) power amplifier, which has the advantage of being low-cost and easily integrated on chip has become the technology of choice for RFID readers. This work demonstrates a 3.3V single voltage self-biased 2.4GHz--2.5GHz high linearity RF power amplifier for RFID applications.
An evaluation of streaming digital video resources in on- and off-campus engineering management education

A recent television documentary on the Columbia space shuttle disaster was converted to streaming digital video format for educational use by onand off-campus students in an engineering management study unit examining issues in professional engineering ethics. An evaluation was conducted to assess the effectiveness of this new resource. Use of the video was optional, and about half of the class reported using the video, though usage was 90.0 percent for off-campus students. Most on-campus students accessed the video on-line, while all off-campus students accessed the video via CD-ROM. Offcampus students rated the educational value of the video higher than on-campus students, and were more likely to indicate that the video helped them understand the issues being studied. Most students were able to view the videos without any technical playback problems.  Corresponding author.  2818; fax: + 61 3 5227 2167 E-mail address: spalm@deakin.edu.au (S. Palmer).
Design of a novel six-axis force/torque sensor based on strain gauges by finite element method

This paper presents a novel six-axis force/torque sensor based on strain gauges. Firstly, the six-axis force/torque sensor's design criterion is systematically expounded by calibration matrix, condition number, coupling error, sensor accuracy, strain gauge sensitivity, sensor sensitivity and sensor stiffness. Secondly, a novel sensing element is designed and simulated by finite element method. Then, the performance of the sensor is obtained by analysis. In the end, the sensor was manufactured.
Spasticity management in multiple sclerosis.

Spasticity is a prevalent and potentially disabling symptom common in individuals with multiple sclerosis. Adequate evaluation and management of spasticity requires a careful assessment of the patient's history to determine functional impact of spasticity and potential exacerbating factors, and physical examination to determine the extent of the condition and culpable muscles. A host of options for spasticity management are available: therapeutic exercise, physical modalities, complementary/alternative medicine interventions, oral medications, chemodenervation, and implantation of an intrathecal baclofen pump. Choice of treatment hinges on a combination of the extent of symptoms, patient preference, and availability of services.
Probabilistic reasoning with a Bayesian DNA device based on strand displacement

We present a computing model based on the DNA strand displacement technique, which performs Bayesian inference. The model will take single-stranded DNA as input data, that represents the presence or absence of a specific molecular signal (evidence). The program logic encodes the prior probability of a disease and the conditional probability of a signal given the disease affecting a set of different DNA complexes and their ratios. When the input and program molecules interact, they release a different pair of single-stranded DNA species whose ratio represents the application of Bayes’ law: the conditional probability of the disease given the signal. The models presented in this paper can have the potential to enable the application of probabilistic reasoning in genetic diagnosis in vitro.
Citation Handling: Processing Citation Texts in Scientific Documents

Thesis directed by: Professor Bonnie Dorr Dr. David Zajic Department of Computer Science Citation sentences (sentences that cite other papers) play a key role in the summarization of scientific articles. However, a citation-based summarization system that depends on generic natural language processing components, such as parsers or sentence compressors, will perform poorly if those components cannot handle citations correctly. In this thesis, I examine the effect of citation handling on parsing, sentence compression, and multi-document summarization. There are two types of citations that occur in citation sentences: constituent citations and parenthetical citations. I propose an automatic citation classifier based on training data created through Mechanical Turk tasks. I demonstrate that the use of type-specific citation handling as pre-processing improves the performance of a state-of-the-art generic parser, both for quality of the parse trees and running time. Extrinsic evaluations demonstrate that improving the performance of a parser on citation sentences in turn improves the performance of a sentence compressor, Trimmer Zajic et al. (2007), and a multidocument summarization system, MASCS, according to several summarization measures.
Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation

Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-to-fine cascade framework that first operates a candidate generation system at sensitivities ~ 100% of but at high FP levels. By leveraging existing CADe systems, coordinates of regions or volumes of interest (ROI or VOI) are generated and function as input for a second tier, which is our focus in this study. In this second stage, we generate 2D (two-dimensional) or 2.5D views via sampling through scale transformations, random translations and rotations. These random views are used to train deep convolutional neural network (ConvNet) classifiers. In testing, the ConvNets assign class (e.g., lesion, pathology) probabilities for a new set of random views that are then averaged to compute a final per-candidate classification probability. This second tier behaves as a highly selective process to reject difficult false positives while preserving high sensitivities. The methods are evaluated on three data sets: 59 patients for sclerotic metastasis detection, 176 patients for lymph node detection, and 1,186 patients for colonic polyp detection. Experimental results show the ability of ConvNets to generalize well to different medical imaging CADe applications and scale elegantly to various data sets. Our proposed methods improve performance markedly in all cases. Sensitivities improved from 57% to 70%, 43% to 77%, and 58% to 75% at 3 FPs per patient for sclerotic metastases, lymph nodes and colonic polyps, respectively.
Robust position control of electro-hydraulic actuator systems using the adaptive back-stepping control scheme

In general, the position control of electro-hydraulic actuator (EHA) systems is difficult because of system uncertainties such as Coulomb friction, viscous friction, and pump leakage coefficient. Even if the exact values of the friction and pump leakage coefficient may be obtained through experiment, the identification procedure is very complicated and requires much effort. In addition, the identified values may not guarantee the reliability of systems because of the variation of the operating condition. Therefore, in this paper, an adaptive backstepping control (ABSC) scheme is proposed to overcome the problem of system uncertainties effectively and to improve the tracking performance of EHA systems. In order to implement the proposed control scheme, the system uncertainties in EHA systems are considered as only one term. In addition, in order to obtain the virtual controls for stabilizing the closed-loop system, the update rule for the system uncertainty term is induced by the Lyapunov control function (LCF). To verify the performance and robustness of the proposed control system, computer simulation of the proposed control system is executed first and the proposed control scheme is implemented for an EHA system by experiment. From the computer simulation and experimental results, it was found that the ABSC system produces the desired tracking performance and has robustness to the system uncertainties of EHA systems.
The Rey 15-item memory test for malingering: a meta-analysis.

The Rey 15-Item Memory Test (RMT) is frequently used to detect malingering. Many objections to the test have been raised. Nevertheless, the test is still widely used.



To provide a meta-analysis of the available studies using the RMT and provide an overall assessment of the sensitivity and specificity of the test, based on the cumulative data.



The results show that, excluding patients with mental retardation, the RMT has a low sensitivity but an excellent specificity.



These results provide the basis for the ongoing use of the test, given that it is acceptable to miss some cases of malingering with such a screening test, but one does not want to have many false positives.
Risk factors related to the presence and severity of hot flushes in mid-aged Ecuadorian women.

Several studies drawn from the Ecuadorian population have previously reported that more than half of mid-aged women present hot flushes, which can impair their quality of life. However up-to-date risk factors for their presence and severity have not been assessed.



To assess hot flush frequency and intensity and related risk factors among middle-aged Ecuadorian women.



In this cross-sectional study, 1154 healthy women aged 40-59 years, visiting healthcare centers of eight main cities of Ecuador with more than 100,000 inhabitants, were assessed with the first item of the Menopause Rating Scale (MRS) and a questionnaire containing female and partner socio-demographic data.



Mean age of the entire sample was 48.8+/-5.6 years (median 48), a 48.7% had 12 or less years of schooling, 52.8% were postmenopausal, 43.6% lived at high altitude, 56.8% were married and 10% were on hormonal therapy (HT). Hot flushes accounted for 56% (n=646) of the whole sample, of which 29.1% and 9.1% were respectively graded as severe and very severe. Logistic regression determined that female sedentarism (OR: 2.42, CI 95% [1.63-3.59]), accessing a free healthcare system (OR: 1.96, CI 95% [1.30-2.96]), living at high altitude (OR: 1.82, CI 95% [1.14-2.90]) and having a partner abusing alcohol (OR: 1.92, CI 95% [1.09-3.35]) were significant risk factors related to the presence of hot flushes. The regression model also determined that among women with hot flushes (n=646), sedentarism (OR: 1.73, CI 95% [1.14-2.62]) and having a partner with erectile dysfunction (OR: 2.57, CI 95% [1.44-4.59]) were significant risk factors related to severe/very severe hot flushes whereas married status (OR: 0.53, CI 95% [0.32-0.86]), living at high altitude (OR: 0.46, CI 95% [0.26-0.78]) and partner healthiness (OR: 0.59, CI 95% [0.36-0.95]) were not.



To the best of our knowledge this is the first and largest study assessing hot flushes in a mid-aged Ecuadorian population. We found that the presence and severity were not significantly related to age and hormonal status yet to other individual female/male characteristics and the demography of the studied population.
Zero-Shot Learning for Natural Language Understanding Using Domain-Independent Sequential Structure and Question Types

Natural language understanding (NLU) is an important module of spoken dialogue systems. One of the difficulties when it comes to adapting NLU to new domains is the high cost of constructing new training data for each domain. To reduce this cost, we propose a zero-shot learning of NLU that takes into account the sequential structures of sentences together with general question types across different domains. Experimental results show that our methods achieve higher accuracy than baseline methods in two completely different domains (insurance and sightseeing).
Effects of intensive glucose lowering in type 2 diabetes.

Epidemiologic studies have shown a relationship between glycated hemoglobin levels and cardiovascular events in patients with type 2 diabetes. We investigated whether intensive therapy to target normal glycated hemoglobin levels would reduce cardiovascular events in patients with type 2 diabetes who had either established cardiovascular disease or additional cardiovascular risk factors.



In this randomized study, 10,251 patients (mean age, 62.2 years) with a median glycated hemoglobin level of 8.1% were assigned to receive intensive therapy (targeting a glycated hemoglobin level below 6.0%) or standard therapy (targeting a level from 7.0 to 7.9%). Of these patients, 38% were women, and 35% had had a previous cardiovascular event. The primary outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, or death from cardiovascular causes. The finding of higher mortality in the intensive-therapy group led to a discontinuation of intensive therapy after a mean of 3.5 years of follow-up.



At 1 year, stable median glycated hemoglobin levels of 6.4% and 7.5% were achieved in the intensive-therapy group and the standard-therapy group, respectively. During follow-up, the primary outcome occurred in 352 patients in the intensive-therapy group, as compared with 371 in the standard-therapy group (hazard ratio, 0.90; 95% confidence interval [CI], 0.78 to 1.04; P=0.16). At the same time, 257 patients in the intensive-therapy group died, as compared with 203 patients in the standard-therapy group (hazard ratio, 1.22; 95% CI, 1.01 to 1.46; P=0.04). Hypoglycemia requiring assistance and weight gain of more than 10 kg were more frequent in the intensive-therapy group (P<0.001).



As compared with standard therapy, the use of intensive therapy to target normal glycated hemoglobin levels for 3.5 years increased mortality and did not significantly reduce major cardiovascular events. These findings identify a previously unrecognized harm of intensive glucose lowering in high-risk patients with type 2 diabetes.
FLICA: A Framework for Leader Identification in Coordinated Activity

Leadership is an important aspect of social organization that affects the processes of group formation, coordination, and decision-making in human societies, as well as in the social system of many other animal species. The ability to identify leaders based on their behavior and the subsequent reactions of others opens opportunities to explore how group decisions are made. Understanding who exerts influence provides key insights into the structure of social organizations. In this paper, we propose a simple yet powerful leadership inference framework extracting group coordination periods and determining leadership based on the activity of individuals within a group. We are able to not only identify a leader or leaders but also classify the type of leadership model that is consistent with observed patterns of group decision-making. The framework performs well in differentiating a variety of leadership models (e.g. dictatorship, linear hierarchy, or local influence). We propose five simple features that can be used to categorize characteristics of each leadership model, and thus make model classification possible. The proposed approach automatically (1) identifies periods of coordinated group activity, (2) determines the identities of leaders, and (3) classifies the likely mechanism by which the group coordination occurred. We demonstrate our framework on both simulated and real-world data: GPS tracks of a baboon troop and video-tracking of fish schools, as well as stock market closing price data of the NASDAQ index. The results of our leadership model are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our approach is easily generalizable to any coordinated activity data from interacting entities.
Recent Advances and Challenges towards Sustainable Polyhydroxyalkanoate (PHA) Production

Sustainable biofuels, biomaterials, and fine chemicals production is a critical matter that research teams around the globe are focusing on nowadays. Polyhydroxyalkanoates represent one of the biomaterials of the future due to their physicochemical properties, biodegradability, and biocompatibility. Designing efficient and economic bioprocesses, combined with the respective social and environmental benefits, has brought together scientists from different backgrounds highlighting the multidisciplinary character of such a venture. In the current review, challenges and opportunities regarding polyhydroxyalkanoate production are presented and discussed, covering key steps of their overall production process by applying pure and mixed culture biotechnology, from raw bioprocess development to downstream processing.
Detecting, Tracing, and Monitoring Architectural Tactics in Code

Software architectures are often constructed through a series of design decisions. In particular, architectural tactics are selected to satisfy specific quality concerns such as reliability, performance, and security. However, the knowledge of these tactical decisions is often lost, resulting in a gradual degradation of architectural quality as developers modify the code without fully understanding the underlying architectural decisions. In this paper we present a machine learning approach for discovering and visualizing architectural tactics in code, mapping these code segments to tactic traceability patterns, and monitoring sensitive areas of the code for modification events in order to provide users with up-to-date information about underlying architectural concerns. Our approach utilizes a customized classifier which is trained using code extracted from fifty performance-centric and safety-critical open source software systems. Its performance is compared against seven off-the-shelf classifiers. In a controlled experiment all classifiers performed well; however our tactic detector outperformed the other classifiers when used within the larger context of the Hadoop Distributed File System. We further demonstrate the viability of our approach for using the automatically detected tactics to generate viable and informative messages in a simulation of maintenance events mined from Hadoop's change management system.
InfoPods: Zigbee-based remote information monitoring devices for smart-homes

Recent developments in ad-hoc wireless networks, single-chip embedded systems and the wide-spread availability of Internet in homes has made it possible to remotely monitor status of external data sources and home appliances using small, independent wireless devices. This paper presents an open-architecture and an implementation of such a system called the IPnfopods System. This architecture is based on a Zigbee-based controller. The architecture allows multiple family members to simultaneously monitor their home appliances as well as external Internet resources using cheap, stand-alone hand-held mobile wireless devices. The proposed architecture can be easily integrated with existing smart-home systems. The architecture is implemented and demonstrated in the context of an existing Java-based smarthome architecture.
Blockchain Based Secured Identity Authentication and Expeditious Revocation Framework for Vehicular Networks

Authentication and revocation of users in Vehicular Adhoc Networks (VANETS) are two vital security aspects. It is extremely important to perform these actions promptly and efficiently. The past works addressing these issues lack in mitigating the reliance on the centralized trusted authority and therefore do not provide distributed and decentralized security. This paper proposes a blockchain based authentication and revocation framework for vehicular networks, which not only reduces the computation and communication overhead by mitigating dependency on a trusted authority for identity verification, but also speedily updates the status of revocated vehicles in the shared blockchain ledger. In the proposed framework, vehicles obtain their Pseudo IDs from the Certificate Authority (CA), which are stored along with their certificate in the immutable authentication blockchain and the pointer corresponding to the entry in blockchain, enables the Road Side Units (RSUs) to verify the identity of a vehicle on road. The efficiency and performance of the framework has been validated using the Omnet++ simulation environment.