nils-herrmann/clean_scirepeval huggingface dataset:
Efficient query answering in probabilistic RDF graphs

In this paper, we tackle the problem of efficiently answering queries on probabilistic RDF data graphs. Specifically, we model RDF data by probabilistic graphs, and an RDF query is equivalent to a search over subgraphs of probabilistic graphs that have high probabilities to match with a given query graph. To efficiently processqueries on probabilistic RDF graphs, we propose effective pruning mechanisms, structural and probabilistic pruning. For the structural pruning, we carefully design synopses for vertex/edge labels by considering their distributions and other structural information, in order to improve the pruning power. For the probabilistic pruning, we derive a cost model to guide the pre-computation of probability upper bounds such that the query cost is expected to be low. We construct an index structure that integrates synopses/statistics for structural and robabilistic pruning, and propose an efficient approach to answer queries on probabilistic RDF graph data. The efficiency of our solutions has been verified through extensive experiments.
Virtual InfiniBand clusters for HPC clouds

High Performance Computing (HPC) employs fast interconnect technologies to provide low communication and synchronization latencies for tightly coupled parallel compute jobs.  HPC clusters have a fixed capacity and static runtime environments; they cannot elastically adapt to dynamic workloads, and provide a limited selection of applications, libraries, and system software. In contrast, a cloud model for HPC clusters promises more flexibility, as it provides elastic virtual clusters to be available on-demand. This is not possible with physically owned clusters.
 In this paper, we present an approach that makes it possible to use InfiniBand clusters for HPC cloud computing. We propose a performance-driven design of an HPC IaaS layer for InfiniBand, which provides throughput and latency-aware virtualization of nodes, networks, and network topologies, as well as an approach to an HPC-aware, multi-tenant cloud management system for elastic virtualized HPC compute clusters.
Facial Expression Recognition in Older Adults using Deep Machine Learning

Facial Expression Recognition is still one of the challenging fields in pattern recognition and machine learning science. Despite efforts made in developing various methods for this topic, existing approaches lack generalizability and almost all studies focus on more traditional hand-crafted features extraction to characterize facial expressions. Moreover, effective classifiers to model the spatial and temporary patterns embedded in facial expressions ignore the effects of facial attributes, such as age, on expression recognition even though research indicates that facial expression manifestation varies with ages. Although there are large amount of benchmark datasets available for the recognition of facial expressions, only few datasets contains faces of older adults. Consequently the current scientific literature has not exhausted this topic. Recently, deep learning methods have been attracting more and more researchers due to their great success in various computer vision tasks, mainly because they avoid a process of feature definition and extraction which is often very difficult due to the wide variability of the facial expressions. Based on the deep learning theory, a neural network for facial expression recognition in older adults is constructed by combining a Stacked Denoising Auto-Encoder method to pre-train the network and a supervised training that provides a fine-tuning adjustment of the network. For the supervised classification layer, the -class softmax classifier was implemented, where is the number of expressions to be recognized. The performance are evaluated on two benchmark datasets (FACES and Lifespan), that are the only ones that contain facial expressions of the elderly. The achieved results show the superiority of the proposed deep learning approach compared to the conventional non-deep learning based facial expression recognition methods used in this context.
Central Lip Lift as Aesthetic and Physiognomic Plastic Surgery: The Effect on Lower Facial Profile.

A central lip lift was introduced to Westerners in 1980s. However, no studies have been conducted on the facial aesthetic and physiognomic perspectives of a central lip lift in the Asian population.



The authors presented the central lip lift as aesthetic and physiognomic treatment in Asians and explained its effect on lower facial profile.



A retrospective chart review was performed in 202 cases of asians. The authors analyzed patient age, cause of long philtrum, purpose of the treatment, and postoperative satisfaction. The authors then performed an anthropometric assessment and a photographic analysis.



The vertical disproportion of the lower face was improved after the treatment, and there was significant shortening of the philtrum length (P < .001) and an increase in a visible upper vermilion (P < .001). In Westerners, a long philtrum was mainly caused by the aging process. Aging patients (range, 40-59 years) underwent the central lip lift for upper lip rejuvenation. In contrast, in Asia, a long philtrum was primarily caused by bone retraction after an orthognathic surgery or orthodontic procedure. Young patients (range, 20-39 years old) underwent the central lip lift to correct a relatively lengthened philtrum after 2-jaw surgery. Furthermore, about half of the patients (52.0%) underwent the central lip lift for facial physiognomic improvement.



In today's multiracial society, plastic surgeons planning a central lip lift in Asian patients should consider both aesthetic and physiognomic perspectives. Regardless of the aesthetic outcome, the surgeon should strive to maximize patient satisfaction.
Factors Affecting Computer Crime Protection Behavior

This research aimed to investigate factors that affect computer crime protection behavior, based on the protection motivation theory. Personal factors were considered, including: conscientious personality, perceived value of data, prior experience, and environmental factors. In addition, other factors were evaluated, including: subjective norm, security knowledge, and safeguard costs. These factors are mediated by threat appraisal and coping appraisal. The data were collected from 600 personal computer users by use of a questionnaire. Data were analyzed using structural equation modeling. Findings showed that all factors had significant effects on the computer crime protection behavior. In addition, the results showed that security knowledge, one of the environmental factors, had the strongest effects on coping appraisal which subsequently had the strongest impact on protection behavior.
Engineering Quality and Reliability in Technology-Assisted Review

The objective of technology-assisted review ("TAR") is to find as much relevant information as possible with reasonable effort. Quality is a measure of the extent to which a TAR method achieves this objective, while reliability is a measure of how consistently it achieves an acceptable result. We are concerned with how to define, measure, and achieve high quality and high reliability in TAR. When quality is defined using the traditional goal-post method of specifying a minimum acceptable recall threshold, the quality and reliability of a TAR method are both, by definition, equal to the probability of achieving the threshold. Assuming this definition of quality and reliability, we show how to augment any TAR method to achieve guaranteed reliability, for a quantifiable level of additional review effort. We demonstrate this result by augmenting the TAR method supplied as the baseline model implementation for the TREC 2015 Total Recall Track, measuring reliability and effort for 555 topics from eight test collections. While our empirical results corroborate our claim of guaranteed reliability, we observe that the augmentation strategy may entail disproportionate effort, especially when the number of relevant documents is low. To address this limitation, we propose stopping criteria for the model implementation that may be applied with no additional review effort, while achieving empirical reliability that compares favorably to the provably reliable method. We further argue that optimizing reliability according to the traditional goal-post method is inconsistent with certain subjective aspects of quality, and that optimizing a Taguchi quality loss function may be more apt.
A 2–11 GHz 7-Bit High-Linearity Phase Rotator Based on Wideband Injection-Locking Multi-Phase Generation for High-Speed Serial Links in 28-nm CMOS FDSOI

Pushed by the ever-increasing demand of internet traffic, high-speed serial interfaces are expected to reach 400-Gb/s aggregate data rates in near future. At receiver (RX) side, phase rotators (PRs) are key blocks to align the phase of the local clock to the transitions of the incoming data and to sample the eye in the optimal position. Small phase step and high linearity are paramount in preserving the horizontal time margin, tightened by the reduced symbol duration at 25 Gb/s and beyond. Interpolation of  $\pi $ /4-spaced signals is a viable means of improving linearity at high resolution, provided multi-phase signals with low phase error are available. An injection-locked ring oscillator (ILRO) with a mixed analog and digital calibration loop is proposed for high accuracy multi-phase generation over a wide frequency range and against large voltage and temperature variations. A phase detector (PD) based on two passive mixers measures the quadrature error and continuously tunes the oscillator to achieve low phase error. Concurrently, a window comparator monitors the PD output and drives digital coarse calibration in background. Two test chips have been fabricated in 28-nm CMOS fully depleted silicon on insulator technology. The stand-alone ILRO demonstrates 0.2–11.7 GHz frequency range with better than 1.5° quadrature phase error over ±20% supply and −40 °C to +120 °C temperature variations. Power consumption is scalable from 3 to 15 mW. When the ILRO drives the 7-bit PR, it demonstrates differential and integral non-linearity within 0.5 and 1.1 LSB, respectively, across the 2–11 GHz frequency range with 18.6-mW maximum power dissipation. Measured performances compare favorably against the state of the art and meet the requirements of >25 Gb/s multi-standard I/O RXs.
Study on WeChat User Behaviors of University Graduates

WeChat was published in China by Tencent company since Jan. 21st 2011. After 100 million users in Mar. 2012, Tencent announced that the number of WeChat's users reached 300 million in Jan 15th 2013. WeChat has become one of the most heated topics within the last 2 years and its every move is attracting attention from various academia, industries, and enterprises. This study selected 50 university graduates as object and analyzed their We Chat using patterns through investigation and survey with statistic methods. The author designed the methods of user study and invited users to participate in the user study at first. Second, by using the collected data from university graduates from 2013 to Mar 2014, the author analyzed the quantities and composition of their contacts in We Chat, figured out the source, type and goal of the public platform they are following, recorded the time, type, form and time relativity of their first 100 posts in the "Moments" function. Through analyzing the data, the author intended to study the real situation in which university graduates virtually socialize and communicate with each other using WeChat and the popularity of WeChat on building social network with strangers. Also, this paper has certain contribution to how to improve the function and service of WeChat. What's more, it analyzed both positive and negative influence of We Chat on students' socialization.
Conditional Generative Adversarial Networks for Commonsense Machine Comprehension

Recently proposed Story Cloze Test [Mostafazadeh et al., 2016] is a commonsense machine comprehension application to deal with natural language understanding problem. This dataset contains a lot of story tests which require commonsense inference ability. Unfortunately, the training data is almost unsupervised where each context document followed with only one positive sentence that can be inferred from the context. However, in the testing period, we must make inference from two candidate sentences. To tackle this problem, we employ the generative adversarial networks (GANs) to generate fake sentence. We proposed a Conditional GANs (CGANs) in which the generator is conditioned by the context. Our experiments show the advantage of the CGANs in discriminating sentence and achieve state-of-the-art results in commonsense story reading comprehension task compared with previous feature engineering and deep learning methods.
SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling

Applications in biotechnology such as gene expression analysis and image processing have led to a tremendous development of statistical methods with emphasis on reliable solutions to severely underdetermined systems. Furthermore, interpretations of such solutions are of importance, meaning that the surplus of inputs has been reduced to a concise model. At the core of this development are methods which augment the standard linear models for regression, classification and decomposition such that sparse solutions are obtained. This toolbox aims at making public available carefully implemented and well-tested variants of the most popular of such methods for the MATLAB programming environment. These methods consist of easy-to-read yet efficient implementations of various coefficient-path following algorithms and implementations of sparse principal component analysis and sparse discriminant analysis which are not available in MATLAB. The toolbox builds on code made public in 2005 and which has since been used in several studies.
Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans

Lung cancer is one of the most common causes of death among all cancer-related diseases (Cancer Research UK in Cancer mortality for common cancers.  2017). It is primarily diagnosed by performing a scan analysis of the patient’s lung. This scan analysis could be of X-ray, CT scan, or MRI. Automated classification of lung cancer is one of the difficult tasks, attributing to the varying mechanisms used for imaging patient’s lungs. Image processing and machine learning approaches have shown a great potential for detection and classification of lung cancer. In this paper, we have demonstrated effective approach for detection and classification of lung cancer-related CT scan images into benign and malignant category. Proposed approach firstly processes these images using image processing techniques, and then further supervised learning algorithms are used for their classification. Here, we have extracted texture features along with statistical features and supplied various extracted features to classifiers. We have used seven different classifiers known as k-nearest neighbors classifier, support vector machine classifier, decision tree classifier, multinomial naive Bayes classifier, stochastic gradient descent classifier, random forest classifier, and multi-layer perceptron (MLP) classifier. We have used dataset of 15750 clinical images consisting of both 6910 benign and 8840 malignant lung cancer related images to train and test these classifiers. In the obtained results, it is found that accuracy of MLP classifier is higher with value of 88.55% in comparison with the other classifiers.
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods

We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at http://winobias.org.
An egocentric computer vision based co-robot wheelchair

Motivated by the emerging needs to improve the quality of life for the elderly and disabled individuals who rely on wheelchairs for mobility, and who might have limited or no hand functionality at all, we propose an egocentric computer vision based co-robot wheelchair to enhance their mobility without hand usage. The co-robot wheelchair is built upon a typical commercial power wheelchair. The user can access 360 degrees of motion direction as well as a continuous range of speed without the use of hands via the egocentric computer vision based control we developed. The user wears an egocentric camera and collaborates with the robotic wheelchair by conveying the motion commands with head motions. Compared with previous sip-n-puff, chin-control and tongue-operated solutions to hands-free mobility, this egocentric computer vision based control system provides a more natural human robot interface. Our experiments show that this design is of higher usability and users can quickly learn to control and operate the wheelchair. Besides its convenience in manual navigation, the egocentric camera also supports novel user-robot interaction modes by enabling autonomous navigation towards a detected person or object of interest. User studies demonstrate the usability and efficiency of the proposed egocentric computer vision co-robot wheelchair.
The role of prediction algorithms in the MavHome smart home architecture

The goal of the Managing an Adaptive Versatile Home (MavHome) project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. In order to achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. In this article, we introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms that are central to home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data. THE ROLE OF PREDICTION ALGORITHMS IN THE MAVHOME SMART HOME ARCHITECTURE
Copy-Move Image Forgery Detection Using Local Binary Pattern and Neighborhood Clustering

This paper introduces a copy-move image forgery detection method based on local binary patterns (LBP) and neighborhood clustering. In the proposed method, an image is first decomposed into three color components. LBP histograms are then calculated from overlapping blocks from each component. The histogram distance between the blocks is calculated and the block-pairs having the minimal distance are retained. If the retained block-pairs are present in all the three color components, they are selected as primary candidates. 8-connected neighborhood clustering is then applied to refine the candidates. The proposed method shows significant improvement in reducing the false positive rates over some recent related methods.
Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems

Robots and autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability and using reinforcement learning to learn that the kill switch deprives it of long-term reward and learn to act to disable the switch or otherwise prevent a human operator from using the switch. This is referred to as the big red button problem. We present a technique which prevents a reinforcement learning agent from learning to disable the big red button. Our technique interrupts the agent or robot by placing it in a virtual simulation where it continues to receive reward. We illustrate our technique in a simple grid world environment.
Recurrent Capsule Network for Relations Extraction: A Practical Application to the Severity Classification of Coronary Artery Disease

Coronary artery disease (CAD) is one of the leading causes of cardiovascular disease deaths. CAD condition progresses rapidly, if not diagnosed and treated at an early stage may eventually lead to an irreversible state of the heart muscle death. Invasive coronary arteriography is the gold standard technique for CAD diagnosis. Coronary arteriography texts describe which part has stenosis and how much stenosis is in details. It is crucial to conduct the severity classification of CAD. In this paper, we propose a recurrent capsule network (RCN) to extract semantic relations between clinical named entities in Chinese coronary arteriography texts, through which we can automatically find out the maximal stenosis for each lumen to inference how severe CAD is according to the improved method of Gensini. Experimental results on the corpus collected from Shanghai Shuguang Hospital show that our proposed RCN model achieves a F1-score of 0.9641 in relation extraction, which outperforms the baseline methods.
Intensive care unit environment may affect the course of delirium

Delirium is a common disorder in intensive care unit (ICU) patients. It is unclear whether ICU environment affects delirium. We investigated the influence of ICU environment on the number of days with delirium during ICU admission. In this prospective before–after study, ICU delirium was compared between a conventional ICU with wards and a single-room ICU with, among others, improved daylight exposure. We included patients admitted for more than 24 h between March and June 2009 (ICU with wards) or between June and September 2010 (single-room ICU). Patients who remained unresponsive throughout ICU admission were excluded. The presence of delirium in the preceding 24 h was assessed daily with the confusion assessment method for the ICU (CAM-ICU) by research physicians combined with evaluation of medical and nursing charts. The number of days with delirium was investigated with Poisson regression analysis. We included 55 patients (449 observation days) in the ICU with wards and 75 patients (468 observation days) in the single-room ICU. After adjusting for confounding, the number of days with delirium decreased by 0.4 days (95 % confidence interval 0.1–0.7) in the single-room ICU (p = 0.005). The incidence of delirium during ICU stay was similar in the ICU with wards (51 %) and in the single-room ICU (45 %, p = 0.53). This study is the first to show that ICU environment may influence the course of delirium in ICU patients.
Experiments with crowdsourced re-annotation of a POS tagging data set

Crowdsourcing lets us collect multiple annotations for an item from several annotators. Typically, these are annotations for non-sequential classification tasks. While there has been some work on crowdsourcing named entity annotations, researchers have largely assumed that syntactic tasks such as part-of-speech (POS) tagging cannot be crowdsourced. This paper shows that workers can actually annotate sequential data almost as well as experts. Further, we show that the models learned from crowdsourced annotations fare as well as the models learned from expert annotations in downstream tasks.