nils-herrmann/clean_scirepeval huggingface dataset:
On ImageJ / Fiji / ActogramJ / JikesRVM Based Actigraphy Informatics Platform – A Simple Suggestion to Perform Actigraphy Analysis Using JikesRVM as the Java Virtual Machine ( JVM )

According to published literature : “Actigraphy is a non-invasive method of monitoring human rest/activity cycles. A small actigraph unit, also called an actimetry sensor is worn for a week or more to measure gross motor activity. The unit is usually, in a wrist-watch-like package, worn on the wrist. The movements the actigraph unit undergoes are continually recorded and some units also measure light exposure. The data can be later read to a computer and analysed offline; in some brands of sensors the data are transmitted and analysed in real time.”[1-9].We are interested in focusing on the above mentioned research topic as per the title of this communication.Interested in suggesting an informatics and computational framework in the context of Actigraphy using ImageJ/Actigraphy Plugin by using JikesRVM as the Java Virtual Machine.
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches

Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion). In many cases, insufficient external resources in the knowledge population hinder the subsequent statistical inference. The gap between these two processes can be reduced by an incremental population approach. We propose a new probabilistic knowledge graph factorisation method that benefits from the path structure of existing knowledge (e.g. syllogism) and enables a common modelling approach to be used for both incremental population and knowledge completion tasks. More specifically, the probabilistic formulation allows us to develop an incremental population algorithm that trades off exploitation-exploration. Experiments on three benchmark datasets show that the balanced exploitation-exploration helps the incremental population, and the additional path structure helps to predict missing information in knowledge completion.
Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining

Process mining is a research discipline that aims to discover, monitor and improve real processing using event logs. In this paper we describe a novel approach that (i) identifies partial process models by exploiting sequential pattern mining and (ii) uses the additional information about the activities matching a partial process model to train nested prediction models from event logs. Models can be used to predict the next activity and completion time of a new (running) process instance. We compare our approach with a model based on Transition Systems implemented in the ProM5 Suite and show that the attributes in the event log can improve the accuracy of the model without decreasing performances. The experimental results show how our algorithm improves of a large margin ProM5 in predicting the completion time of a process, while it presents competitive results for next activity prediction.
Impact of Organizational Culture on Organizational Performance : An Overview

This article is on defining and measuring of organizational culture and its impact on the organizational performance, through an analysis of existing empirical studies and models link with the organizational culture and performance. The objective of this article is to demonstrate conceptualization, measurement and examine various concepts on organization culture and performance. After analysis of wide literature, it is found that organizational culture has deep impact on the variety of organizations process, employees and its performance. This also describes the different dimensions of the culture. Research shows that if employee are committed and having the same norms and value as per organizations have, can increase the performance toward achieving the overall organization goals. Balance Scorecard is suggested tool to measure the performance in the performance management system. More research can be done in this area to understand the nature and ability of the culture in manipulating performance of the organization. Managers and leaders are recommended to develop the strong culture in the organization to improve the overall performance of the employees and organization.
Earthquake Structure Fourier Analysis K-means Clustering

A classification algorithm is developed for evaluating the damage state of buildings subjected to earthquakes. Nonlinear response history analysis is used to generate the time histories of each building subjected to each earthquake. This report summarizes the analysis procedure used to extract data and describes the different classification algorithms that are developed to predict damage state. Support vector machines (SVM), multinomial logistic regression and k-nearest neighbors (KNN) are considered for the classification algorithm. Features are selected from building parameters, ground motion parameters and combinations of both. Four damage states are estimated: minimal damage, moderate damage, severe damage and collapse. The support vector machine classifier yields the most accurate results for evaluating the damage state with an overall accuracy of 71.2%. This is in part due to the highly nonlinear nature of this problem. The accuracy of classifying a damage state with a misclassification tolerance of ±1 is 95.2%.
Neuro-memristive Circuits for Edge Computing: A review

The volume, veracity, variability, and velocity of data produced from the ever increasing network of sensors connected to Internet pose challenges for power management, scalability, and sustainability of cloud computing infrastructure. Increasing the data processing capability of edge computing devices at lower power requirements can reduce several overheads for cloud computing solutions. This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices. We discuss why the neuromorphic architectures are useful for edge devices and show the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
Video Pixel Networks

We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a fourdimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.
Using corpora in machine-learning chatbot systems

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Teaching and Developing Vocabulary : Key to Long-Term Reading Success

It seems almost impossible to overstate the power of words; they literally have changed and will continue to change the course of world history. Perhaps the greatest tools we can give students for succeeding, not only in their education but more generally in life, is a large, rich vocabulary and the skills for using those words. Our ability to function in today’s complex social and economic worlds is mightily affected by our language skills and word knowledge. In addition to the vital importance of vocabulary for success in life, a large vocabulary is more specifically predictive and reflective of high levels of reading achievement. The Report of the National Reading Panel (2000), for example, concluded, “The importance of vocabulary knowledge has long been recognized in the development of reading skills. As early as 1924, researchers noted that growth in reading power relies on continuous growth in word knowledge” (pp. 4–15). Vocabulary or Vocabularies?
Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

It is well established that autism spectrum disorders (ASD) have a strong genetic component; however, for at least 70% of cases, the underlying genetic cause is unknown. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes—so-called sporadic or simplex families—we sequenced all coding regions of the genome (the exome) for parent–child trios exhibiting sporadic ASD, including 189 new trios and 20 that were previously reported. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19), for a total of 677 individual exomes from 209 families. Here we show that de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD. Moreover, 39% (49 of 126) of the most severe or disruptive de novo mutations map to a highly interconnected β-catenin/chromatin remodelling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes: CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3 and SCN1A. Combined with copy number variant (CNV) data, these results indicate extreme locus heterogeneity but also provide a target for future discovery, diagnostics and therapeutics.
EnAcq: energy-efficient GPS trajectory data acquisition based on improved map matching

Todays versatile mobile devices such as smartphones are increasingly popular platforms for trajectory-based applications such as vehicle tracking, route navigation and geotagged video acquisition. On these battery-powered devices employing a trajectory data acquisition approach that reduces the amount of energy spent but still provides accurate location information is essential for these applications' usability. This paper presents EnAcq, a novel energy-efficient GPS trajectory data acquisition scheme based on improved map matching that addresses two key challenges: providing highly accurate trajectory data and reducing energy consumption. To improve the precision of trajectory data, EnAcq utilizes an improved Hidden Markov Model (HMM)-based map matching algorithm which can find candidate matches for each GPS location sample point without using the traditionally necessary range query and determine the most likely route the mobile device (e.g., in a vehicle) has travelled. To avoid unnecessary energy consumption, EnAcq adopts an adaptive GPS sampling method which adjusts the sampling period based on the device's current motion state. On a public real-world dataset, we demonstrate via experimental results that EnAcq is able to yield accurate trajectory data while avoiding unnecessary energy consumption.
Herbal Hepatotoxicity: Clinical Characteristics and Listing Compilation

Herb induced liver injury (HILI) and drug induced liver injury (DILI) share the common characteristic of chemical compounds as their causative agents, which were either produced by the plant or synthetic processes. Both, natural and synthetic chemicals are foreign products to the body and need metabolic degradation to be eliminated. During this process, hepatotoxic metabolites may be generated causing liver injury in susceptible patients. There is uncertainty, whether risk factors such as high lipophilicity or high daily and cumulative doses play a pathogenetic role for HILI, as these are under discussion for DILI. It is also often unclear, whether a HILI case has an idiosyncratic or an intrinsic background. Treatment with herbs of Western medicine or traditional Chinese medicine (TCM) rarely causes elevated liver tests (LT). However, HILI can develop to acute liver failure requiring liver transplantation in single cases. HILI is a diagnosis of exclusion, because clinical features of HILI are not specific as they are also found in many other liver diseases unrelated to herbal use. In strikingly increased liver tests signifying severe liver injury, herbal use has to be stopped. To establish HILI as the cause of liver damage, RUCAM (Roussel Uclaf Causality Assessment Method) is a useful tool. Diagnostic problems may emerge when alternative causes were not carefully excluded and the correct therapy is withheld. Future strategies should focus on RUCAM based causality assessment in suspected HILI cases and more regulatory efforts to provide all herbal medicines and herbal dietary supplements used as medicine with strict regulatory surveillance, considering them as herbal drugs and ascertaining an appropriate risk benefit balance.
Co-Occurrence Matrix Analysis-Based Semi-Supervised Training for Object Detection

One of the most important factors in training object recognition networks using convolutional neural networks (CNN) is the provision of annotated data accompanying human judgment. Particularly, in object detection or semantic segmentation, the annotation process requires considerable human effort. In this paper, we propose a semi-supervised learning (SSL)-based training methodology for object detection, which makes use of automatic labeling of un-annotated data by applying a network previously trained from an annotated dataset. Because an inferred label by the trained network is dependent on the learned parameters, it is often meaningless for re-training the network. To transfer a valuable inferred label to the unlabeled data, we propose a re-alignment method based on co-occurrence matrix analysis that takes into account one-hot-vector encoding of the estimated label and the correlation between the objects in the image. We used an MS-COCO detection dataset to verify the performance of the proposed SSL method and deformable neural networks (D-ConvNets)  as an object detector for basic training. The performance of the existing state-of-the-art detectors (D-ConvNets, YOLO v2 , and single shot multi-box detector (SSD) ) can be improved by the proposed SSL method without using the additional model parameter or modifying the network architecture.
Predicting HDD failures from compound SMART attributes

Hard Disk Drives (HDD s) sometimes fail with no apparent reason; some SMART (Self-Monitoring, Analysis and Reporting Technology) attributes present strong correlations with drive failures[3], yet a drive may also fail without (supposedly) any previous indication. Most of the host systems today utilize alert-methods which are reactive by nature-a drive is indicated to fail when some SMART attribute exceeds its vendor defined threshold for valid operation[4]. This approach does not take the cross correlation between different attributes into account and the fact that thresholds vary across different vendors.
 Unlike more conventional studies that focused on reliability statistics such as the annualized failure rate (AFR) at the population level[5], , we use machine learning (ML) algorithms that attempt to predict the failure of individual drives. Former ML approaches applied to the drive prediction failure domain include methods for dealing with sequential data, such as sliding windows and hidden Markov models , or anomaly detection algorithms, adhering to the often-low proportion of failed drives in the population  and .
 We present a mechanism that performs a sophisticated samples aggregation inside a distributed database, allowing for the efficient extraction of compound features representing the behavior of the drive during a continuous time window prior its failure.
 We use here an open source dataset from BACKBLAZE, comprising extensive SMART information collected daily from a large drive population at its data center Thus we can use not only the last cumulative SMART counts but also include new features extracted over different time windows of drives last operational days. These capture the dynamics of the drives attributes, for example their growth rate which is highly indicative of the drive s failure probability [7]. Our results suggest that the use of compound features reduce the amount of false positives, which is primary performance measure for algorithms in the drive reliability domain[3], by as much as 60%.
 In a consecutive evaluation scenario a final decision is made based on a collection of the most recent data samples. This way we are able to capture more soon-to-fail drives at low cost to the false-positive rate. On average, a drive is predicted to fail long enough in advance (30 days) to allow for the modification of business strategy in fields such as drive replacement logistics. This approach, when implemented in a production environment may have a direct effect on business savings related to drive logistics.
Enterprise Architecture Principles: Literature Review and Research Directions

This paper presents a literature review on enterprise architecture principles. It consists of eleven articles published in journals and conference proceedings on enterprise architecture. The results show that there are various gaps in the research literature: No accepted definition of enterprise architecture principles has emerged yet. There is disagreement on the question of how to describe and formalize the connection of architecture goals, principles, and implications. A detailed conceptual framework that could serve as a basis for conducting quantitative research is still lacking. Business principles, IT principles and enterprise architecture principles are often mixed up. Research into generic design principles is still in its infancy. Our review illustrates the necessity to conduct more in-depth research on enterprise architecture principles. We describe conceptual foundations and provide guidance for further research in this field.
Assessment in Education : Principles , Policy & Practice

This article is a review of the literature on classroom formative assessment. Several studies show firm evidence that innovations designed to strengthen the frequent feedback that students receive about their learning yield substantial learning gains. The perceptions of students and their role in self-assessment are considered alongside analysis of the strategies used by teachers and the formative strategies incorporated in such systemic approaches as mastery learning. There follows a more detailed and theoretical analysis of the nature of feedback, which provides a basis for a discussion of the development of theoretical models for formative assessment and of the prospects for the improvement of practice.
Image understanding for iris biometrics: A survey

This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.
Exploring the Link Between Behavioural Information Security Governance and Employee Information Security Awareness

This paper explores the relation between a set of behavioural information security governance factors and employees’ information security awareness. To enable statistical analysis between proposed relations, data was collected from two different samples in 24 organisations: 24 information security executives and 240 employees. The results reveal that having a formal unit with explicit responsibility for information security, utilizing coordinating committees, and sharing security knowledge through an intranet site significantly correlates with dimensions of employees’ information security awareness. However, regular identification of vulnerabilities in information systems and related processes is significantly negatively correlated with employees’ information security awareness, in particular managing passwords. The effect of behavioural information security governance on employee information security awareness is an understudied topic. Therefore, this study is explorative in nature and the results are preliminary. Nevertheless, the paper provides implications for both research and practice.
Adaptive Comanagement and Its Relationship to Environmental Governance

We provide a systematic review of the adaptive comanagement (ACM) literature to (i) investigate how the concept of governance is considered and (ii) examine what insights ACM offers with reference to six key concerns in environmental governance literature: accountability and legitimacy; actors and roles; fit, interplay, and scale; adaptiveness, flexibility, and learning; evaluation and monitoring; and, knowledge. Findings from the systematic review uncover a complicated relationship with evidence of conceptual closeness as well as relational ambiguities. The findings also reveal several specific contributions from the ACM literature to each of the six key environmental governance concerns, including applied strategies for sharing power and responsibility and value of systems approaches in understanding problems of fit. More broadly, the research suggests a dissolving or fuzzy boundary between ACM and governance, with implications for understanding emerging approaches to navigate social-ecological system change. Future research opportunities may be found at the confluence of ACM and environmental governance scholarship, such as identifying ways to build adaptive capacity and encouraging the development of more flexible governance arrangements.
PointPillars: Fast Encoders for Object Detection from Point Clouds

Object detection in point clouds is an important aspect of many robotics applications such as autonomous driving. In this paper we consider the problem of encoding a point cloud into a format appropriate for a downstream detection pipeline. Recent literature suggests two types of encoders; fixed encoders tend to be fast but sacrifice accuracy, while encoders that are learned from data are more accurate, but slower. In this work we propose PointPillars, a novel encoder which utilizes PointNets to learn a representation of point clouds organized in vertical columns (pillars). While the encoded features can be used with any standard 2D convolutional detection architecture, we further propose a lean downstream network. Extensive experimentation shows that PointPillars outperforms previous encoders with respect to both speed and accuracy by a large margin. Despite only using lidar, our full detection pipeline significantly outperforms the state of the art, even among fusion methods, with respect to both the 3D and bird’s eye view KITTI benchmarks. This detection performance is achieved while running at 62 Hz: a 2 4 fold runtime improvement.  These benchmarks suggest that PointPillars is an appropriate encoding for object detection in point clouds.