nils-herrmann/clean_scirepeval huggingface dataset:
IIoTEED: An Enhanced, Trusted Execution Environment for Industrial IoT Edge Devices

With the advent of the Internet of Things (IoT), security has emerged as a major design goal for smart connected devices. This explosion in connectivity created a larger attack surface area. Software-based approaches have been applied for security purposes; however, these methods must be extended with security-oriented technologies that promote hardware as the root of trust. The ARM TrustZone can enable trusted execution environments (TEEs), but existing solutions disregard real-time needs. Here, the authors demonstrate why TrustZone is becoming a reference technology for securing IoT edge devices, and how enhanced TEEs can help meet industrial IoT applications real-time requirements.
High-bandwidth and low-energy on-chip signaling with adaptive pre-emphasis in 90nm CMOS

Long on-chip wires pose well-known latency, bandwidth, and energy challenges to the designers of high-performance VLSI systems. Repeaters effectively mitigate wire RC effects but do little to improve their energy costs. Moreover, proliferating repeater farms add significant complexity to full-chip integration, motivating circuits to improve wire performance and energy while reducing the number of repeaters. Such methods include capacitive-mode signaling, which combines a capacitive driver with a capacitive load [1,2]; and current-mode signaling, which pairs a resistive driver with a resistive load [3,4]. While both can significantly improve wire performance, capacitive drivers offer added benefits of reduced voltage swing on the wire and intrinsic driver pre-emphasis. As wires scale, slow slew rates on highly resistive interconnects will still limit wire performance due to inter-symbol interference (ISI) . Further improvements can come from equalization circuits on receivers [2] and transmitters [4] that trade off power for bandwidth. In this paper, we extend these ideas to a capacitively driven pulse-mode wire using a transmit-side adaptive FIR filter and a clockless receiver, and show bandwidth densities of 2.2–4.4 Gb/s/µm over 90nm 5mm links, with corresponding energies of 0.24–0.34 pJ/bit on random data.
Data Mining Model-Decision Tree for Detecting Emotions Color

Affective computing is a newly trend the main goal is exploring the human emotion things. The human emotion is leaded into a key position of behavior clue, and hence it should be included within the sensible model when an intelligent system aims to simulate or forecast human responses. This research utilizes decision tree one of data mining model to classify the emotion. This research integrates and manipulates the Thayer's emotion mode and color theory into the decision tree model, C4.5 for an innovative emotion detecting system. This paper uses 320 data in four emotion groups to train and build the decision tree for verifying the accuracy in this system. The result reveals that C4.5 decision tree model can be effective classified the emotion by feedback color from human. For the further research, colors will not the only human behavior clues, even more than all the factors from human interaction.
Seismotectonics Considered Artificial Neural Network Earthquake Prediction in Northeast Seismic Region of China

It is well known that earthquakes are a regional event, strongly controlled by local geological structures and circumstances. Reducing the research area can reduce the influence of other irrelevant seismotectonics. A new sub regiondividing scheme, considering the seismotectonics influence, was applied for the artificial neural network (ANN) earthquake prediction model in the northeast seismic region of China (NSRC). The improved set of input parameters and prediction time duration are also discussed in this work. The new dividing scheme improved the prediction accuracy for different prediction time frames. Three different research regions were analyzed as an earthquake data source for the ANN model under different prediction time duration frames. The results show: (1) dividing the research region into smaller subregions can improve the prediction accuracies in NSRC, (2) larger research regions need shorter prediction durations to obtain better performance, (3) different areas have different sets of input parameters in NSRC, and (4) the dividing scheme, considering the seismotectonics frame of the region, yields better results.
Fritzing: a tool for advancing electronic prototyping for designers

Today a growing community of DIY-practitioners, artists and designers are using microcontroller-based toolkits to express their concepts for digital artifacts by building them. However, as these prototypes are generally constructed using solder-free technologies, they are often fragile and unreliable. This means a huge burden of care and upkeep for these inventions when they are either exhibited or sold.
 We present a software application called Fritzing which allows artists, designers and DIY-tinkerers to prepare their hardware inventions for production. Through an interface metaphor based on the typical workflow of the target group, Fritzing has proven its ability to provide useful support in the steps following the invention of an interactive artifact.
 Fritzing serves also as a tool for documenting these interactive artifacts. As sharing of knowledge has been a driving force within this new DIY-movement, there is a need for a consistent and readable form of documentation which Fritzing can provide.
 Fritzing has also proven to be a useful tool in teaching electronics to people without an engineering background.
Image Processing and Particle Analysis for Road Traffic Detection

This article presents a system developed using graphic programming on LabVIEW using image processing and particle analysis so as to indicate increase in vehicular density on particularly traffic-prone roads and hence alert necessary officials of the same instantly. This system primarily focusses on the vehicular density and sets specific thresholds in accordance with the time of the day to give almost accurate results. It also displays an application of computer vision for traffic flow management and road traffic analysis. The method mentioned provides the functionality of alert during times of road clogging and will hence; ensure immediate rectification of the same.
Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm

This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.
Autonomous pipeline inspection and maintenance robot with inch worm mobile mechanism

A new type of mobile robots with the inch worm mechanism is presented in this paper for inspecting pipelines from the outside of pipe surfaces under hostile environments. This robot, Mark 111, is made after the successful investigation of the prototypes, Mark I and 11, which can pass over obstacles on pipelines, such as flanges and T-joints and others. Newly developed robot, Mark 111, can move vertically along the pipeline and move to the adjacent pipeline for the inspection. The sensors, infra ray proximity sensor and ultra sonic sensors and others, are installed to detect these obstacles and can move autonomously controlled by the microprocessor. The control method of this robot can be carried out by the dual control mode proposed in this paper.
Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index

Visceral adipose tissue is associated with increased risk for cardiovascular disease risk factors and morbidity from cardiovascular diseases. Waist measurement and waist-to-height ratio (WHtR) have been used as proxy measures of visceral adipose tissue, mainly in adults. To validate body mass index (BMI), waist circumference and WHtR as predictors for the presence of cardiovascular disease risk factors in children of Greek-Cypriot origin. A total of 1037 boys and 950 girls with mean age 11.4±0.4 y were evaluated. Dependent variables for the study were total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholestrol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and systolic (SBP) and diastolic (DBP) blood pressure. When children were divided into two groups according to the 75th percentile for BMI, waist circumference and WHtR, all dependent variables had higher mean values in the highest percentile groups in WHtR groups and almost all variables in BMI and waist circumference groups. Adjusted odds ratios for predicting pathological values of cardiovascular disease risk factors were slightly higher for the highest WHtR group for predicting lipid and lipoprotein pathological values and for the highest BMI groups in predicting high blood pressure measurement. Using stepwise multiple regression analysis to explain the variance of the dependent variables, waist circumference was the most significant predictor for all variables both for boys and girls, whereas BMI had the lowest predictive value for the detection of cardiovascular disease risk factors. Waist circumference and WHtR are better predictors of cardiovascular disease risk factors in children than BMI. Further studies are necessary to determine the cutoff points for these indices for an accurate prediction of risk factors.
Architecture of an animation system for human characters

Virtual human characters are found in a broad range of applications, from movies, games and networked virtual environments to teleconferencing and tutoring applications. Such applications are available on a variety of platforms, from desktop and web to mobile devices. High-quality animation is an essential prerequisite for realistic and believable virtual characters. Though researchers and application developers have ample animation techniques for virtual characters at their disposal, implementation of these techniques into an existing application tends to be a daunting and time-consuming task. In this paper we present visage|SDK, a versatile framework for real-time character animation based on MPEG-4 FBA standard that offers a wide spectrum of features that includes animation playback, lip synchronization and facial motion tracking, while facilitating rapid production of art assets and easy integration with existing graphics engines.
Towards Computer-Assisted Flamenco Transcription: An Experimental Comparison of Automatic Transcription Algorithms as Applied to A Cappella Singing

This article deals with automatic transcription of flamenco music recordings—more specifically, a cappella singing. We first study the specifics of flamenco singing and propose a transcription system based on fundamental frequency and energy estimation, which incorporates an iterative strategy for note segmentation and labeling. The proposed approach is evaluated on a music collection of 72 performances, including a variety of singers and recording conditions, and the presence or absence of percussion, background voices, and noise. We obtain satisfying results for the different approaches tested, and our system outperforms a state-of-the-art approach designed for other singing styles. In this study, we discuss the difficulties found in transcribing flamenco singing and in evaluating the obtained transcriptions, we analyze the influence of the different steps of the algorithm, and we state the main limitations of our approach and discuss challenges for future studies.
Event Classification from Sensor Data using Spectral Analysis in Robotic Finishing Processes

Process monitoring using indirect methods leverages on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods helps to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40 kHz-51.2 kHz was calculated and the co-relation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welchs estimate method. A comparison between Welchs estimate method and statistical methods is also discussed. A clear co-relation was observed using Welchs estimate to classify the number of cyceles/passes.
Work-based learning in health care organisations experienced by nursing staff: A systematic review of qualitative studies.

The aim of this review is to systematically summarise qualitative evidence about work-based learning in health care organisations as experienced by nursing staff. Work-based learning is understood as informal learning that occurs inside the work community in the interaction between employees. Studies for this review were searched for in the CINAHL, PubMed, Scopus and ABI Inform ProQuest databases for the period 2000-2015. Nine original studies met the inclusion criteria. After the critical appraisal by two researchers, all nine studies were selected for the review. The findings of the original studies were aggregated, and four statements were prepared, to be utilised in clinical work and decision-making. The statements concerned the following issues: (1) the culture of the work community; (2) the physical structures, spaces and duties of the work unit; (3) management; and (4) interpersonal relations. Understanding the nurses' experiences of work-based learning and factors behind these experiences provides an opportunity to influence the challenges of learning in the demanding context of health care organisations.
Anomalies with Variable Partition Paging Algorithms

Five types of anomalous behaviour which may occur in paged virtual memory operating systems are defined. One type of anomaly, for example, concerns the fact that, with certain reference strings and paging algorithms, an increase in mean memory allocation may result in an increase in fault rate. Two paging algorithms, the page fault frequency and working set algorithms, are examined in terms of their anomaly potential, and reference string examples of various anomalies are presented. Two paging algorithm properties, the inclusion property and the generalized inclusion property, are discussed and the anomaly implications of these properties presented.
Using natural-language processing to produce weather forecasts

Improved numerical weather prediction simulations have led weather services to examine how and where human forecasters add value to forecast production. The Forecast Production Assistant (FPA) was developed with that in mind. The authors discuss the Forecast Generator (FOG), the first application developed on the FPA. FOG is a bilingual report generator that produces routine and special purpose forecast directly from the FPA's graphical weather predictions. Using rules and a natural-language generator, FOG converts weather maps into forecast text. The natural-language issues involved are relevant to anyone designing a similar system.<<ETX>>
ISO-TimeML Event Extraction in Persian Text

Recognizing TimeML events and identifying their attributes, are important tasks in natural language processing (NLP). Several NLP applications like question answering, information retrieval, summarization, and temporal information extraction need to have some knowledge about events of the input documents. Existing methods developed for this task are restricted to limited number of languages, and for many other languages including Persian, there has not been any effort yet. In this paper, we introduce two different approaches for automatic event recognition and classification in Persian. For this purpose, a corpus of events has been built based on a specific version of ISO-TimeML for Persian. We present the specification of this corpus together with the results of applying mentioned approaches to the corpus. Considering these methods are the first effort towards Persian event extraction, the results are comparable to that of successful methods in English.  نابز شزادرپ یاھدربراک زا یرايسب هناماس دننام یعيبط یاھ و یزاس هص2خ ،تاع2طا جارختسا ،خساپ و شسرپ یاھ ات دنراد زاين ینامز تاع2طا جارختسا هرابرد یشناد یاھداديور رد دوجوم نوتم یدورو شور .دنشاب هتشاد هک یياھ نيا دروم رد نونکات هدش داجيا هلئسم نابز دنچ هب دودحم ، صاخ نابز زا یرايسب رد و تسا اھ هلمج زا ،یسراف نابز یراک نونکات هدشن ماجنا هطبار نيا رد یسراف نابز رد اھداديور جارختسا یارب فلتخم شور ود ام ،هلاقم نيا رد .تسا یم هئارا .ميھد یارب هرکيپ ،راک نيا اب قباطم یا ISO-TimeML ، سن هتبلا هخ دش هتخاس ،نآ یسراف صاخ ی ام . ناشن ار ،نآ یور رب لصاح جياتن و هرکيپ نيا تاصخشم یم ميھد شور جياتن . هئارا یاھ هدش هلاقم نيا رد ناونع هب ، شور نيلوا هدايپ یاھ اب ،یسراف نابز یور رب هدش یزاس .تسا هسياقم لباق یسيلگنا نابز رد قفوم یاھ شور
Gene Transcriptional and Metabolic Profile Changes in Mimetic Aging Mice Induced by D-Galactose

D-galactose injection has been shown to induce many changes in mice that represent accelerated aging. This mouse model has been widely used for pharmacological studies of anti-aging agents. The underlying mechanism of D-galactose induced aging remains unclear, however, it appears to relate to glucose and 1ipid metabolic disorders. Currently, there has yet to be a study that focuses on investigating gene expression changes in D-galactose aging mice. In this study, integrated analysis of gas chromatography/mass spectrometry-based metabonomics and gene expression profiles was used to investigate the changes in transcriptional and metabolic profiles in mimetic aging mice injected with D-galactose. Our findings demonstrated that 48 mRNAs were differentially expressed between control and D-galactose mice, and 51 potential biomarkers were identified at the metabolic level. The effects of D-galactose on aging could be attributed to glucose and 1ipid metabolic disorders, oxidative damage, accumulation of advanced glycation end products (AGEs), reduction in abnormal substance elimination, cell apoptosis, and insulin resistance.
Multicenter study of nucleic acid amplification tests for detection of Chlamydia trachomatis and Neisseria gonorrhoeae in children being evaluated for sexual abuse.

Diagnosis of sexually transmitted infections in children suspected of sexual abuse is challenging due to the medico-legal implications of test results. Currently, the forensic standard for diagnosis of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) infections is culture. In adults, nucleic acid amplification tests (NAATs) are superior to culture for CT, but these tests have been insufficiently evaluated in pediatric populations for forensic purposes.



We evaluated the use of NAATs, using urine and genital swabs versus culture for diagnosis of CT and NG in children evaluated for sexual abuse in 4 US cities. Urine and a genital swab were collected for CT and NG NAATs along with routine cultures. NAAT positives were confirmed by PCR, using an alternate target.



Prevalence of infection among 485 female children were 2.7% for CT and 3.3% for NG by NAAT. The sensitivity of urine NAATs for CT and NG relative to vaginal culture was 100%. Eight participants with CT-positive and 4 with NG-positive NAATs had negative culture results (P = 0.018 for CT urine NAATs vs. culture). There were 24 of 485 (4.9%) female participants with a positive NAAT for CT or NG or both versus 16 of 485 (3.3%) with a positive culture for either, resulting in a 33% increase in children with a positive diagnosis.



These results suggest that NAATs on urine, with confirmation, are adequate for use as a new forensic standard for diagnosis of CT and NG in children suspected of sexual abuse. Urine NAATs offer a clear advantage over culture in sensitivity and are less invasive than swabs, reducing patient trauma and discomfort.
Pathogenesis of the epigastric hernia

Epigastric herniation is a rather common condition with a reported prevalence up to 10 %. Only a minority is symptomatic, presumably the reason for the scarce literature on this subject. Epigastric hernias have specific characteristics for which several anatomical theories have been developed. Whether these descriptions of pathological mechanisms still hold with regard to the characteristics of epigastric hernia is the subject of this review. A multi-database research was performed to reveal relevant literature by free text word and subject headings ‘epigastric hernia’, ‘linea alba’, ‘midline’ and ‘abdominal wall’. Reviewed were studies on anatomical theories describing the pathological mechanism of epigastric herniation, incidence, prevalence and female-to-male ratio and possible explanatory factors. Three different theories have been described of which two have not been confirmed by other studies. The attachment of the diaphragm causing extra tension in the epigastric region is the one still standing. Around 1.6–3.6 % of all abdominal hernias and 0.5–5 % of all operated abdominal hernias is an epigastric hernia. Epigastric hernias are 2–3 times more common in men, with a higher incidence in patients from 20 to 50 years. Some cadaver studies show an epigastric hernia rate of 0.5–10 %. These specific features of the epigastric hernias (the large asymptomatic proportion, male predominance, only above umbilical level) are discussed with regard to the general theories. The epigastric hernia is a very common condition, mostly asymptomatic. Together with general factors for hernia formation, the theory of extra tension in the epigastric region by the diaphragm is the most likely theory of epigastric hernia formation.
Music-genre classification system based on spectro-temporal features and feature selection

An automatic classification system of the music genres is proposed. Based on the timbre features such as mel-frequency cepstral coefficients, the spectro-temporal features are obtained to capture the temporal evolution and variation of the spectral characteristics of the music signal. Mean, variance, minimum, and maximum values of the timbre features are calculated. Modulation spectral flatness, crest, contrast, and valley are estimated for both original spectra and timbre-feature vectors. A support vector machine (SVM) is used as a classifier where an elaborated kernel function is defined. To reduce the computational complexity, an SVM ranker is applied for feature selection. Compared with the best algorithms submitted to the music information retrieval evaluation exchange (MIREX) contests, the proposed method provides higher accuracy at a lower feature dimension for the GTZAN and ISMIR2004 databases.