nils-herrmann/clean_scirepeval huggingface dataset:
A Study of an Earthworm Type Inspection Robot Movable in Long Pipes

This paper describes development of a three somites earthworm type mobile inspection robot which is able to move in long pipes. We have many small diameter pipes which are gas or water pipes for individual or corporate houses and boilers or hot water pipes for industries. They are settled in the ground horizontally or in the narrow spaces vertically and some of them are covered by casings or hard heat insulating matters. It is very difficult to inspect from out of the pipes. If we can insert a mobile inspection robot into the pipes from the suitable position, we are easy to inspect the pipes. Authors fabricated a three somites earthworm type mobile inspection robot in which we use three rubber bellows as pneumatic actuators. Sixteen rubber friction rings are used to get friction force between pneumatic actuators and the pipes. The electromagnetic valves and air-feeding tubes for the pneumatic actuators are carried on trolleys in order to decrease friction force between the pipes and them. The fabricated three somites earthworm type mobile inspection robot can move in a pipe which is 78 mm in inner diameter and vertical pipe carrying a load that is equivalent to the air-feeding tube of 32 m length. Consequently, the inspection robot was confirmed to move in the long pipes.
Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning

Convolutional neural networks (CNN) have achieved state of the art performance on both classification and segmentation tasks. Applying CNNs to microscopy images is challenging due to the lack of datasets labeled at the single cell level. We extend the application of CNNs to microscopy image classification and segmentation using multiple instance learning (MIL). We present the adaptive Noisy-AND MIL pooling function, a new MIL operator that is robust to outliers. Combining CNNs with MIL enables training CNNs using full resolution microscopy images with global labels. We base our approach on the similarity between the aggregation function used in MIL and pooling layers used in CNNs. We show that training MIL CNNs end-to-end outperforms several previous methods on both mammalian and yeast microscopy images without requiring any segmentation steps.
Labor Market Forecasting by Using Data Mining

Data mining approach was used in this paper to predict labor market needs, by implementing Naïve Bayes Classifiers, Decision Trees, and Decision Rules techniques. Naïve Bayes technique implemented by creating tables of training; the sets of these tables were generated by using four factors that affect continuity in their jobs. The training tables used to predict the classification of other (unclassified) instances, and tabulate the results of conditional and prior probabilities to test unknown instance for classification. The information obtained can classify unknown instances for employment in the labor market. In Decision Tree technique, a model was constructed from a dataset in the form of a tree, created by a process known as splitting on the value of attributes. The Decision Rules, which was constructed from Decision Trees of rules gave the best results, therefore we recommended using this method in predicting labor market.
Efficient and Secure Service-Oriented Authentication Supporting Network Slicing for 5G-Enabled IoT

5G network is considered as a key enabler in meeting continuously increasing demands for the future Internet of Things (IoT) services, including high data rate, numerous devices connection, and low service latency. To satisfy these demands, network slicing and fog computing have been envisioned as the promising solutions in service-oriented 5G architecture. However, security paradigms enabling authentication and confidentiality of 5G communications for IoT services remain elusive, but indispensable. In this paper, we propose an efficient and secure service-oriented authentication framework supporting network slicing and fog computing for 5G-enabled IoT services. Specifically, users can efficiently establish connections with 5G core network and anonymously access IoT services under their delegation through proper network slices of 5G infrastructure selected by fog nodes based on the slice/service types of accessing services. The privacy-preserving slice selection mechanism is introduced to preserve both configured slice types and accessing service types of users. In addition, session keys are negotiated among users, local fogs and IoT servers to guarantee secure access of service data in fog cache and remote servers with low latency. We evaluate the performance of the proposed framework through simulations to demonstrate its efficiency and feasibility under 5G infrastructure.
Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning.

Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm  ×  800 µm from 100 µm  ×  100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79  ±  0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC  =  0.72  ±  0.18 and r  =  0.85. For the independent test set, DCNN achieved DC  =  0.76  ±  0.09 and r  =  0.94, while feature-based learning achieved DC  =  0.62  ±  0.21 and r  =  0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.
Suicide pact by drowning with bound wrists: a case of medico-legal importance.

Suicide pacts are uncommon and mainly committed by male-female pairs in a consortial relationship. The victims frequently choose methods such as hanging, poisoning, using a firearm, etc; however, a case of a suicide pact by drowning is rare in forensic literature. We report a case where a male and a female, both young adults, in a relationship of adopted "brother of convenience" were found drowned in a river. The victims were bound together at their wrists which helped with our conclusion this was a suicide pact. The medico-legal importance of wrist binding in drowning cases is also discussed in this article.
Physical Human Activity Recognition Using Wearable Sensors

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.
Compact and Broadband CB-CPW-to-SIW Transition Using Stepped-Impedance Resonator With 90$^{\circ}$-Bent Slot

In this paper, a compact and broadband conductor-backed coplanar waveguide (CB-CPW) to substrate integrated waveguide (SIW) transition using a stepped-impedance resonator (SIR) with a 90°-bent slot is proposed. The proposed transition can achieve a 36.8% 15-dB fractional bandwidth, which almost covers the S-band (2.6-3.95 GHz). Compared to the CB-CPW-to-SIW transition using the single-section quarter-wavelength transformer, transition using the SIR with the 90°-bent slot can increase the 15-dB fractional bandwidth from 23.44% to 36.8% and reduce the physical length from 14 to 7.2 mm. In order to verify the simulation results, back-to-back transitions are fabricated and measured, and the measurement results are in good agreement with those of simulation.
Novel Microneedle Patches for Active Insulin Delivery are Efficient in Maintaining Glycaemic Control: An Initial Comparison with Subcutaneous Administration

Good glycaemic control is essential to minimize the risk for diabetes-induced complications. Also, compliance is likely to be higher if the procedure is simple and painless. This study was designed to validate painless intradermal delivery via a patch-like microneedle array. Diabetes was induced by an intravenous injection of streptozotocin (50 mg/kg bw) in adult male Sprague Dawley rats. Plasma insulin and blood glucose were measured before, during and after subcutaneous or intradermal (microneedles) infusion of insulin (0.2 IU/h) under Inactin-anaesthesia. Before insulin administration, all animals displayed a pronounced hyperglycaemia (19 ± 1 mM; 359 mg/dl). Administration of insulin resulted in a reduced plasma glucose independently of administration route (subcutaneous 7.5 ± 4.2, n = 9, and intradermal 11 ± 1.8, n = 9 after 240 min), but with less errors of the mean in the intradermal group. In the intradermal group, plasma insulin was increased in all latter measurements (72 ± 22, 81 ± 34, and 87 ± 20 μIU/ml), as compared to the first measurement (26 ± 13). In the subcutaneous group, plasma insulin was elevated during the last measurement (to 154 ± 3.5 μIU/ml from 21 ± 18). This study presents a novel possibility of insulin delivery that is controllable and requires minimal training. This treatment strategy could improve compliance, and thus be beneficial for patients’ glycaemic control.
Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research

In this work, we introduce a dataset of video annotated with high quality natural language phrases describing the visual content in a given segment of time. Our dataset is based on the Descriptive Video Service (DVS) that is now encoded on many digital media products such as DVDs. DVS is an audio narration describing the visual elements and actions in a movie for the visually impaired. It is temporally aligned with the movie and mixed with the original movie soundtrack. We describe an automatic DVS segmentation and alignment method for movies, that enables us to scale up the collection of a DVS-derived dataset with minimal human intervention. Using this method, we have collected the largest DVS-derived dataset for video description of which we are aware. Our dataset currently includes over 84.6 hours of paired video/sentences from 92 DVDs and is growing.
Accurate face localisation for faces under active near-IR illumination

In this paper we propose a novel approach to accurate face localisation for faces under near-infrared (near-IR) illumination. The circular shape of the bright pupils is a scale and rotation invariant feature which is exploited to quickly detect pupil candidates. As the first step of face localisation, a rule-based pupil detector is employed to find candidate pupil edges from the edge map. Candidate eye centres for each eye are selected from the neighborhood of corresponding pupil regions and sorted based on the similarity to eye templates. Two support vector machine (SVM) classifiers based on eye appearance are employed to validate those candidates for each eye individually. Finally candidates are further validated in pair by an SVM classifier based on global face appearance. In the experiment on a near-IR face database with 40 subjects and 48 images per subject, 96.5% images are accurately localised using the proposed approach
A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy

This paper presents a language-independent ontology (LION) construction method that uses tagged images in an image folksonomy. Existing multilingual frameworks that construct an ontology deal with concepts translated on the basis of parallel corpora, which are not always available; however, the proposed method enables LION construction without parallel corpora by using visual features extracted from tagged images as the alternative. In the proposed method, visual similarities in tagged images are leveraged to aggregate synonymous concepts across languages. The aggregated concepts take on intrinsic semantics of themselves, while they also hold distinct characteristics in different languages. Then relationships between concepts are extracted on the basis of visual and textual features. The proposed method constructs a LION whose nodes and edges correspond to the aggregated concepts and relationships between them, respectively. The LION enables successful image retrieval across languages since each of the aggregated concepts can be referred to in different languages. Consequently, the proposed method removes the language barriers by providing an easy way to access a broader range of tagged images for users in the folksonomy, regardless of the language they use.
Data Security and Privacy for Outsourced Data in the Cloud

Although outsourcing data to cloud storage has become popular, the increasing concerns about data security and privacy in the cloud limits broader cloud adoption. Ensuring data security and privacy, therefore, is crucial for better and broader adoption of the cloud. This tutorial provides a comprehensive analysis of the state-of-the-art in the context of data security and privacy for outsourced data. We aim to cover common security and privacy threats for outsourced data, and relevant novel schemes and techniques with their design choices regarding security, privacy, functionality, and performance. Our explicit focus is on recent schemes from both the database and the cryptography and security communities that enable query processing over encrypted data and access oblivious cloud storage systems.
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing

Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for increasing accuracy and coverage of entity type information in KBs. We rely on two metaviews: language and representation. For language, we consider high-resource and lowresource languages from Wikipedia. For representation, we consider representations based on the context distribution of the entity (i.e., on its embedding), on the entity’s name (i.e., on its surface form) and on its description in Wikipedia. The two metaviews language and representation can be freely combined: each pair of language and representation (e.g., German embedding, English description, Spanish name) is a distinct view. Our experiments on entity typing with fine-grained classes demonstrate the effectiveness of multiview learning. We release MVET, a large multiview – and, in particular, multilingual – entity typing dataset we created. Monoand multilingual finegrained entity typing systems can be evaluated on this dataset.
Full-space spectrum-sharing strategy for TDD/FDD massive MIMO cognitive radio systems

This paper presents a new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems, where two CR base stations (CBS) are employed at the adjacent sides of each cell to provide a full-space coverage for the whole cell. Thanks to the high spatial resolution of massive antennas, CRs are distinguished by their angular information and their uplink/downlink channels are also represented with reduced parameter dimensions by the proposed two-dimensional spatial basis expansion model (2D-SBEM). To improve the spectral efficiency and the scheduling probability of CRs, a greedy CR scheduling algorithm is designed for the dual CBS system. As the proposed strategy is mainly based on angular information and since the angle reciprocity holds for two frequency carriers with moderate distance, it can be applied for both TDD and FDD systems.
The Stressometer: A Simple, Valid, and Responsive Measure of Psychological Stress in Inflammatory Bowel Disease Patients.

Psychological stress is associated with inflammatory bowel disease [IBD], but the nature of this relationship is complex. At present, there is no simple tool to screen for stress in IBD clinical practice or assess stress repeatedly in longitudinal studies. Our aim was to design a single-question 'stressometer' to rapidly measure stress and validate this in IBD patients.



In all, 304 IBD patients completed a single-question 'stressometer'. This was correlated with stress as measured by the Depression Anxiety Stress Scales [DASS-21], quality of life, and disease activity. Test-retest reliability was assessed in 31 patients who completed the stressometer and the DASS-21 on two occasions 4 weeks apart.



Stressometer levels correlated with the DASS-21 stress dimension in both Crohn's disease [CD] (Spearman's rank correlation coefficient [rs] 0.54; p < 0.001) and ulcerative colitis [UC] [rs 0.59; p < 0.001]. Stressometer levels were less closely associated with depression and anxiety [rs range 0.36 to 0.49; all p-values < 0.001]. Stressometer scores correlated with all four Short Health Scale quality of life dimensions in both CD and UC [rs range 0.35 to 0.48; all p-values < 0.001] and with disease activity in Crohn's disease [rs 0.46; p < 0.001] and ulcerative colitis [rs 0.20; p = 0.02]. Responsiveness was confirmed with a test-retest correlation of 0.43 [p = 0.02].



The stressometer is a simple, valid, and responsive measure of psychological stress in IBD patients and may be a useful patient-reported outcome measure in future IBD clinical and research assessments.
Management strategies for patients with varicose veins (C2-C6): results of a worldwide survey.

This study evaluated how patient characteristics and duplex ultrasound findings influence management decisions of physicians with specific expertise in the field of chronic venous disease.



Worldwide, 346 physicians with a known interest and experience in phlebology were invited to participate in an online survey about management strategies in patients with great saphenous vein (GSV) reflux and refluxing tributaries. The survey included two basic vignettes representing a 47 year old healthy male with GSV reflux above the knee and a 27 year old healthy female with a short segment refluxing GSV (CEAP classification C2sEpAs2,5Pr in both cases). Participants could choose one or more treatment options. Subsequently, the basic vignettes were modified according to different patient characteristics (e.g. older age, morbid obesity, anticoagulant treatment, peripheral arterial disease), clinical class (C4, C6), and duplex ultrasound findings (e.g. competent terminal valve, larger or smaller GSV diameter, presence of focal dilatation). The authors recorded the distribution of chosen management strategies; adjustment of strategies according to characteristics; and follow up strategies.



A total of 211 physicians (68% surgeons, 12% dermatologists, 12% angiologists, and 8% phlebologists) from 36 different countries completed the survey. In the basic case vignettes 1 and 2, respectively, 55% and 40% of participants proposed to perform endovenous thermal ablation, either with or without concomitant phlebectomies (p < .001). Looking at the modified case vignettes, between 20% and 64% of participants proposed to adapt their management strategy, opting for either a more or a less invasive treatment, depending on the modification introduced. The distribution of chosen management strategies changed significantly for all modified vignettes (p < .05).



This study illustrates the worldwide variety in management preferences for treating patients with varicose veins (C2-C6). In clinical practice, patient related and duplex ultrasound related factors clearly influence therapeutic options.
Perception, Guidance, and Navigation for Indoor Autonomous Drone Racing Using Deep Learning

In autonomous drone racing, a drone is required to fly through the gates quickly without any collision. Therefore, it is important to detect the gates reliably using computer vision. However, due to the complications such as varying lighting conditions and gates seen overlapped, traditional image processing algorithms based on color and geometry of the gates tend to fail during the actual racing. In this letter, we introduce a convolutional neural network to estimate the center of a gate robustly. Using the detection results, we apply a line-of-sight guidance algorithm. The proposed algorithm is implemented using low cost, off-the-shelf hardware for validation. All vision processing is performed in real time on the onboard NVIDIA Jetson TX2 embedded computer. In a number of tests our proposed framework successfully exhibited fast and reliable detection and navigation performance in indoor environment.
A Cognitive Loop for Assistive Robots - Connecting Reasoning on Sensed Data to Acting

The deployment of assistive robots in everyday life scenarios and their capability of providing an effective and useful support for independent living is an open and challenging research problem. The development of suitable robot control systems requires effective solutions for addressing issues concerning performance, reliability, flexibility and proactivity. In this work, we propose an AI-based cognitive architecture aiming at integrating knowledge representation with automated planning and execution techniques in order to endow assistive robots with proactivity and self-configuration capabilities.