nils-herrmann/clean_scirepeval huggingface dataset:
Towards a neural language model for signature extraction from forensic logs

Signature extraction is a critical preprocessing step in forensic log analysis because it enables sophisticated analysis techniques to be applied to logs. Currently, most signature extraction frameworks either use rule-based approaches or handcrafted algorithms. Rule-based systems are error-prone and require high maintenance effort. Hand-crafted algorithms use heuristics and tend to work well only for specialized use cases. In this paper we present a novel approach to extract signatures from forensic logs that is based on a neural language model. This language model learns to identify mutable and non-mutable parts in a log message. We use this information to extract signatures. Neural language models have shown to work extremely well for learning complex relationships in natural language text. We experimentally demonstrate that our model can detect which parts are mutable with an accuracy of 86.4%. We also show how extracted signatures can be used for clustering log lines.
Analysis of a Quadrotor with a Two-Degree-of-Freedom Robotic Arm

The classical kinematic approach used in analyzing systems of a small number of interconnected bodies is unsuitable for researching complex kinematics. Moreover, solving nonlinear algebraic equations that involve trigonometric functions is difficult. However, the computational kinematic approach involves using a set of algebraic kinematic equations that describe the joint connectivity between the bodies of a system as well as the specified motion trajectories. This paper presents an analytical approach for a quadrotor with a two-degree-of-freedom (DOF) robotic arm. The approach, which is based on the computational kinematic approach, was adopted to model algebraic equations to obtain the values of position, velocity, acceleration, and the trajectories of motion of the joint and end effector. MATLAB was employed to simulate and solve the kinematic equations. The results showed that the analysis approach is feasible for the quadrotor with a two-DOF robotic arm.
ProCom: Designing and Evaluating a Mobile and Wearable System to Support Proximity Awareness for People with Autism

People with autism are at risk for social isolation due to differences in their perception and engagement with the social world. In this work, we aim to address one specific concern related to socialization the understanding, awareness, and use of interpersonal space. Over the course of a year, we iteratively designed and tested a series of concepts for supporting children with autism in perceiving, understanding, and responding to physical proximity with other people. During this process, we developed ProCom, a prototype system for measuring proximity without requiring instrumentation of the environment or another person. We used a variety of low and high fidelity prototypes, culminating in ProCom, to assess the feasibility, utility, and challenges of this approach. The results of these iterative design engagements indicate that wearable assistive technologies can support people in developing awareness of physical proximity in social settings. However, challenges related to both personal and collective use remain
Data Mining in Healthcare and Biomedicine: A Survey of the Literature

As a new concept that emerged in the middle of 1990’s, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.
Teaching Computational Thinking Using Agile Software Engineering Methods: A Framework for Middle Schools

Computational Thinking (CT) has been recognized as one of the fundamental skills that all graduates should acquire. For this reason, motivational concerns need to be addressed at an early age of a child, and reaching students who do not consider themselves candidates for science, technology, engineering, and mathematics disciplines is important as well if the broadest audience possible is to be engaged. This article describes a framework for teaching and assessing CT in the context of K-12 education. The framework is based on Agile software engineering methods, which rely on a set of principles and practices that can be mapped to the activities of CT. The article presents as well the results of an experiment applying this framework in two sixth-grade classes, with 42 participants in total. The results show that Agile software engineering methods are effective at teaching CT in middle schools, after the addition of some tasks to allow students to explore, project, and experience the potential product before using the software tools at hand. Moreover, according to the teachers’ feedback, the students reached all the educational objectives of the topics involved in the multidisciplinary activities. This result can be taken as an indicator that it is possible to use computing as a medium for teaching other subjects, besides computer science.
Singer Traits Identification using Deep Neural Network

The author investigates automatic recognition of singers’ gender and age through audio features using deep neural network (DNN). Features of each singing voice, fundamental frequency and Mel-Frequency Cepstrum Coefficients (MFCC) are extracted for neural network training. 10,000 singing voice from Smule’s Sing! Karaoke app is used for training and evaluation, and the DNN-based method achieves an average recall of 91% for gender classification and 36% for age identification.
Obesity in children and young people: a crisis in public health.

Ten per cent of the world’s school-aged children are estimated to be carrying excess body fat , with an increased risk for developing chronic disease. Of these overweight children, a quarter are obese, with a significant likelihood of some having multiple risk factors for type 2 diabetes, heart disease and a variety of other co-morbidities before or during early adulthood. The prevalence of overweight is dramatically higher in economically developed regions, but is rising significantly in most parts of the world. In many countries the problem of childhood obesity is worsening at a dramatic rate. Surveys during the 1990s show that in Brazil and the USA, an additional 0.5% of the entire child population became overweight each year. In Canada, Australia and parts of Europe the rates were higher, with an additional 1% of all children becoming overweight each year. The burden upon the health services cannot yet be estimated. Although childhood obesity brings a number of additional problems in its train – hyperinsulinaemia, poor glucose tolerance and a raised risk of type 2 diabetes, hypertension, sleep apnoea, social exclusion and depression – the greatest health problems will be seen in the next generation of adults as the present childhood obesity epidemic passes through to adulthood. Greatly increased rates of heart disease, diabetes, certain cancers, gall bladder disease, osteoarthritis, endocrine disorders and other obesityrelated conditions will be found in young adult populations, and their need for medical treatment may last for their remaining life-times. The costs to the health services, the losses to society and the burdens carried by the individuals involved will be great. The present report has been written to focus attention on the issue and to urge policy-makers to consider taking action before it is too late. Specifically, the report:
High-quality speech coding with SampleRNN

We provide a speech coding scheme employing a generative model based on SampleRNN that, while operating at significantly lower bitrates, matches or surpasses the perceptual quality of state-of-theart classic wide-band codecs. Moreover, it is demonstrated that the proposed scheme can provide a meaningful rate-distortion trade-off without retraining. We evaluate the proposed scheme in a series of listening tests and discuss limitations of the approach.
GeaBase: A High-Performance Distributed Graph Database for Industry-Scale Applications

Graph analytics have been gaining tractions rapidly in the past few years. It has a wide array of application areas in the industry, ranging from e-commerce, social network and recommendation systems to fraud detection and virtually any problem that requires insights into data connections, not just data itself. In this paper, we present GeaBase, a new distributed graph database that provides the capability to store and analyze graph-structured data in real-time at massive scale. We describe the details of the system and the implementation, including a novel update architecture, called Update Center (UC), and a new language that is suitable for both graph traversal and analytics. We also compare the performance of GeaBase to a widely used open-source graph database Titan. Experiments show that GeaBase is up to 182x faster than Titan in our testing scenarios. We also achieves 22x higher throughput on social network workloads in the comparison.
Tree Adjoining Grammar Based Parser for a Hindi Text-to-Scene Conversion System

We have developed Preksha: a Hindi text visualizer. Preksha generates a 3D scene by understanding a given Hindi text. We propose the Earley's parsing algorithm using Tree adjoining grammar (TAG) for Hindi language understanding. A brief overview of the TAG formalism and the Earley's parsing algorithm is presented. Preksha's Hindi language parsing is explainedwith the Lisp notation. The maincontribution of this paper is to apply TAG parsing for morphologically-richfree-order language Hindi for automatic text visualization (ATV).
Histogram of Oriented Lines for Palmprint Recognition

Subspace learning methods are very sensitive to the illumination, translation, and rotation variances in image recognition. Thus, they have not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named histogram of oriented lines (HOL), which is a variant of histogram of oriented gradients (HOG). HOL is not very sensitive to changes of illumination, and has the robustness against small transformations because slight translations and rotations make small histogram value changes. Based on HOL, even some simple subspace learning methods can achieve high recognition rates.
Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: a report of four patients

Epileptic seizure prediction has steadily evolved from its conception in the 1970s, to proof-of-principle experiments in the late 1980s and 1990s, to its current place as an area of vigorous, clinical and laboratory investigation. As a step toward practical implementation of this technology in humans, we present an individualized method for selecting electroencephalogram (EEG) features and electrode locations for seizure prediction focused on precursors that occur within ten minutes of electrographic seizure onset. This method applies an intelligent genetic search process to EEG signals simultaneously collected from multiple intracranial electrode contacts and multiple quantitative features derived from these signals. The algorithm is trained on a series of baseline and preseizure records and then validated on other, previously unseen data using split sample validation techniques. The performance of this method is demonstrated on multiday recordings obtained from four patients implanted with intracranial electrodes during evaluation for epilepsy surgery. An average probability of prediction (or block sensitivity) of 62.5% was achieved in this group, with an average block false positive (FP) rate of 0.2775 FP predictions/h, corresponding to 90.47% specificity. These findings are presented as an example of a method for training, testing and validating a seizure prediction system on data from individual patients. Given the heterogeneity of epilepsy, it is likely that methods of this type will be required to configure intelligent devices for treating epilepsy to each individual's neurophysiology prior to clinical deployment.
Video Game Addiction and College Performance Among Males: Results from a 1 Year Longitudinal Study

This study explored the pattern of video game usage and video game addiction among male college students and examined how video game addiction was related to expectations of college engagement, college grade point average (GPA), and on-campus drug and alcohol violations. Participants were 477 male, first year students at a liberal arts college. In the week before the start of classes, participants were given two surveys: one of expected college engagement, and the second of video game usage, including a measure of video game addiction. Results suggested that video game addiction is (a) negatively correlated with expected college engagement, (b) negatively correlated with college GPA, even when controlling for high school GPA, and (c) negatively correlated with drug and alcohol violations that occurred during the first year in college. Results are discussed in terms of implications for male students' engagement and success in college, and in terms of the construct validity of video game addiction.
Modified image segmentation method based on region growing and region merging

Image segmentation is one of the basic concepts widely used in each and every fields of image processing. The entire process of the proposed work for image segmentation comprises of 3 phases: Threshold generation with dynamic Modified Region Growing phase (DMRG), texture feature generation phase and region merging phase. by dynamically changing two thresholds, the given input image can be performed as DMRG, in which the cuckoo search optimization algorithm helps to optimize the two thresholds in modified region growing. after obtaining the region growth segmented image, the edges are detected with edge detection algorithm. In the second phase, the texture feature is extracted using entropy based operation from the input image. In region merging phase, the results obtained from the texture feature generation phase is combined with the results of DMRG phase and similar regions are merged by using a distance comparison between regions. The proposed work is implemented using Mat lab platform with several medical images. the performance of the proposed work is evaluated using the metrics sensitivity, specificity and accuracy. the results show that this proposed work provides very good accuracy for the segmentation process in images.
Context Prediction for Unsupervised Deep Learning on Point Clouds

Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While massive point cloud datasets can be captured using modern scanning technology, manually labelling such large 3D point clouds for supervised learning tasks is a cumbersome process. This necessitates effective unsupervised learning methods that can produce representations such that downstream tasks require significantly fewer annotated samples. We propose a novel method for unsupervised learning on raw point cloud data in which a neural network is trained to predict the spatial relationship between two point cloud segments. While solving this task, representations that capture semantic properties of the point cloud are learned. Our method outperforms previous unsupervised learning approaches in downstream object classification and segmentation tasks and performs on par with fully supervised methods.
3D-Printing of Meso-structurally Ordered Carbon Fiber/Polymer Composites with Unprecedented Orthotropic Physical Properties

Here we report the first example of a class of additively manufactured carbon fiber reinforced composite (AMCFRC) materials which have been achieved through the use of a latent thermal cured aromatic thermoset resin system, through an adaptation of direct ink writing (DIW) 3D-printing technology. We have developed a means of printing high performance thermoset carbon fiber composites, which allow the fiber component of a resin and carbon fiber fluid to be aligned in three dimensions via controlled micro-extrusion and subsequently cured into complex geometries. Characterization of our composite systems clearly show that we achieved a high order of fiber alignment within the composite microstructure, which in turn allows these materials to outperform equivalently filled randomly oriented carbon fiber and polymer composites. Furthermore, our AM carbon fiber composite systems exhibit highly orthotropic mechanical and electrical responses as a direct result of the alignment of carbon fiber bundles in the microscale which we predict will ultimately lead to the design of truly tailorable carbon fiber/polymer hybrid materials having locally programmable complex electrical, thermal and mechanical response.
An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

Received Nov 14, 2017 Revised Jan 26, 2018  is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no contentbased preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previously defined web content mining and security techniques. To overcome the issues, Level-level Security Optimization & Content Visualization Algorithm is proposed to avoid the privacy issues during content sharing and data visualization. It adopts level by level privacy based on user requirement in the social network. It evaluates the privacy compatibility in the online social network environment to avoid security complexities. The mechanism divided into three parts namely like online social network platform creation, social network privacy, social network within organizational privacy and network controlling and authentication. Based on the experimental evaluation, a proposed method improves the privacy retrieval accuracy (PRA) 9.13% and reduces content retrieval time (CRT) 7 milliseconds and information loss (IL) 5.33%.
Approximate Bayesian Computation for Distance-Dependent Learning

The distance dependent Chinese restaurant process (ddCRP) and its hierarchical extensions provide a flexible framework for clustering data with temporal, spatial, or other non-exchangeable dependencies. The successful application of these models crucially depends on functions chosen to encode structural dependencies exhibited by the data. Designing such affinity functions is challenging and often involves significant trial and error. Here, we explore methods for learning these functions from human annotated data. Leveraging recent advances in approximate Bayesian computation (ABC) we design algorithms that are effective at learning affinity functions from collections of human annotated image and video partitions and at achieving competitive results on standard benchmarks.
PV generation enhancement with a virtual inertia emulator to provide inertial response to the grid

With high-penetration levels of renewable generating sources being integrated into the existing electric power grid, conventional generators are being replaced and grid inertial response is deteriorating. This technical challenge is more severe with photovoltaic (PV) generation than with wind generation because PV generation systems cannot provide inertial response unless special countermeasures are adopted. To enhance the inertial response, this paper proposes to synthesize a virtual inertia emulator (VIE) by using a battery energy storage system (BESS) and a three-phase grid-tied inverter to simulate a traditional generator such that the inertial response can be appropriately enhanced without sacrificing energy efficiency. Control systems for the VIE are presented in this paper, along with simulation results from PSCAD and MATLAB to validate the effectiveness of the proposed scheme.
A fast restoration method for atmospheric turbulence degraded images using non-rigid image registration

In this paper, a fast image restoration method is proposed to restore the true image from an atmospheric turbulence degraded video. A non-rigid image registration algorithm is employed to register all the frames of the video to a reference frame and determine the shift maps. The First Register Then Average And Subtract-variant (FRTAASv) method is applied to correct the geometric distortion of the reference frame. A performance comparison is presented between the proposed restoration method and the earlier Minimum Sum of Squared Differences (MSSD) image registration based FRTAASv method, in terms of processing time and accuracy. Simulation results show that the proposed method requires shorter processing time to achieve the same geometric accuracy.
Predicting Student Progress from Peer-Assessment Data

Predicting overall student performance and monitoring progress have attracted more attention in the past five years than before. Demographic data, high school grades and test result constitute much of the data used for building prediction models. This study demonstrates how data from a peer-assessment environment can be used to build student progress prediction models. The possibility for automating tasks, coupled with minimal teacher intervention, make peer-assessment an efficient platform for gathering student activity data in a continuous manner. The performances of the prediction models are comparable with those trained using other educational data. Considering the fact that the student performance data do not include any teacher assessments, the results are more than encouraging and shall convince the reader that peerassessment has yet another advantage to offer in the realm of automated student progress monitoring and supervision.