nils-herrmann/clean_scirepeval huggingface dataset:
Coping With Systems Risk: Security Planning Models for Management Decision Making

The likelihood that the firm’s information systems are insufficiently protected against certain kinds of damage or loss is known as “systems risk.” Risk can be managed or reduced when managers are aware of the full range of controls available and implement the most effective controls. Unfortunately, they often lack this knowledge and their subsequent actions to cope with systems risk are less effective than they might otherwise be. This is one viable explanation for why losses from computer abuse and computer disasters today are uncomfortably large and still so potentially devastating after many years of attempting to deal with the problem. Results of comparative qualitative studies in two information services Fortune 500 firms identify an approach that can effectively deal with the problem. This theory-based security program includes: (1) use of a security risk planning model, (2) education/training in security awareness, and (3) Countermeasure Matrix analysis.
Trajectory Planning of a Six-DOF Robot Based on a Hybrid Optimization Algorithm

This work adopts a standard Denavit–Hartenberg method to model a PUMA 560 spot welding robot as the object of study. The forward and inverse kinematics solutions are then analyzed. To address the shortcomings of the ant colony algorithm, factors from the particle swarm optimization and the genetic algorithm are introduced into this algorithm. Subsequently, the resulting hybrid algorithm and the ant colony algorithm are used to conduct trajectory planning in the shortest path. Experimental data and simulation result show that the hybrid algorithm is significantly better in terms of initial solution speed and optimal solution quality than the ant colony algorithm. The feasibility and effectiveness of the hybrid algorithm in the trajectory planning of a robot are thus verified.
A Low-Phase-Noise 77-GHz FMCW Radar Transmitter With a 12.8-GHz PLL and a  $\times$6 Frequency Multiplier

In this letter, a 77-GHz transmitter (TX) with a 12.8-GHz phase-locked-loop (PLL) and a $\times$ 6 frequency multiplier is presented for a FMCW radar sensor in a 65-nm CMOS process. To realize the low-phase-noise TX, a voltage controlled oscillator (VCO) with an excellent phase noise performance at a lower fundamental frequency (12.8 GHz) is designed and scaled up ( $\times$ 6) for the desired target frequency (77 GHz). The measured FMCW modulation range with an external triangular chirp signal (1-ms sweep time) is 601 MHz. The output power and the total DC power consumption of the TX are 8.9 dBm and 116.7 mW, respectively. Here, a good phase noise level of -91.16 dBc/Hz at a 1-MHz offset frequency from a 76.81-GHz carrier is achieved.
The beliefs behind the teacher that influences their ICT practices

This paper explores teacher beliefs that influence the ways Information and Communications Technologies (ICT) are used in learning contexts. Much has been written about the impact of teachers' beliefs and attitudes to ICT as 'barriers' to ICT integration (Ertmer, Ottenbreit-paper takes a closer look at the types of beliefs that influence ICT practices in classrooms and the alignment of these beliefs to current pedagogical reform in Australia. The paper draws on data collected through the initial phase of a research project that involved an Industry Collaborative of four Catholic primary schools (prep-grade 7). Data are drawn from teacher surveys, interviews and document analysis. The results present specific links between ICT beliefs that are informing teachers' practices. ICT beliefs and practices are aligned to reform agenda for digital pedagogies. The findings of this research inform teacher ICT practice and requirements for ICT professional development.
Fast and robust segmentation of white blood cell images by self-supervised learning.

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets.
Autonomous HVAC Control, A Reinforcement Learning Approach

Recent high profile developments of autonomous learning thermostats by companies such as Nest Labs and Honeywell have brought to the fore the possibility of ever greater numbers of intelligent devices permeating our homes and working environments into the future. However, the specific learning approaches and methodologies utilised by these devices have never been made public. In fact little information is known as to the specifics of how these devices operate and learn about their environments or the users who use them. This paper proposes a suitable learning architecture for such an intelligent thermostat in the hope that it will benefit further investigation by the research community. Our architecture comprises a number of different learning methods each of which contributes to create a complete autonomous thermostat capable of controlling a HVAC system. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. Our results show that the learning thermostat can achieve cost savings of 10% over a programmable thermostat, whilst maintaining high occupant comfort standards.
A general framework for estimating the relative pathogenicity of human genetic variants

Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation–Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.
Criterion-Related Validity of Sit-and-Reach Tests for Estimating Hamstring and Lumbar Extensibility: a Meta-Analysis.

The main purpose of the present meta-analysis was to examine the scientific literature on the criterion-related validity of sit-and-reach tests for estimating hamstring and lumbar extensibility. For this purpose relevant studies were searched from seven electronic databases dated up through December 2012. Primary outcomes of criterion-related validity were Pearson´s zero-order correlation coefficients (r) between sit-and-reach tests and hamstrings and/or lumbar extensibility criterion measures. Then, from the included studies, the Hunter- Schmidt´s psychometric meta-analysis approach was conducted to estimate population criterion- related validity of sit-and-reach tests. Firstly, the corrected correlation mean (rp), unaffected by statistical artefacts (i.e., sampling error and measurement error), was calculated separately for each sit-and-reach test. Subsequently, the three potential moderator variables (sex of participants, age of participants, and level of hamstring extensibility) were examined by a partially hierarchical analysis. Of the 34 studies included in the present meta-analysis, 99 correlations values across eight sit-and-reach tests and 51 across seven sit-and-reach tests were retrieved for hamstring and lumbar extensibility, respectively. The overall results showed that all sit-and-reach tests had a moderate mean criterion-related validity for estimating hamstring extensibility (rp = 0.46-0.67), but they had a low mean for estimating lumbar extensibility (rp = 0. 16-0.35). Generally, females, adults and participants with high levels of hamstring extensibility tended to have greater mean values of criterion-related validity for estimating hamstring extensibility. When the use of angular tests is limited such as in a school setting or in large scale studies, scientists and practitioners could use the sit-and-reach tests as a useful alternative for hamstring extensibility estimation, but not for estimating lumbar extensibility. Key PointsOverall sit-and-reach tests have a moderate mean criterion-related validity for estimating hamstring extensibility, but they have a low mean validity for estimating lumbar extensibility.Among all the sit-and-reach test protocols, the Classic sit-and-reach test seems to be the best option to estimate hamstring extensibility.End scores (e.g., the Classic sit-and-reach test) are a better indicator of hamstring extensibility than the modifications that incorporate fingers-to-box distance (e.g., the Modified sit-and-reach test).When angular tests such as straight leg raise or knee extension tests cannot be used, sit-and-reach tests seem to be a useful field test alternative to estimate hamstring extensibility, but not to estimate lumbar extensibility.
A digital family calendar in the home: lessons from field trials of LINC

Digital family calendars have the potential to help families coordinate, yet they must be designed to easily fit within existing routines or they will simply not be used. To understand the critical factors affecting digital family calendar design, we extended LINC, an inkable family calendar to include ubiquitous access, and then conducted a month-long field study with four families. Adoption and use of LINC during the study demonstrated that LINC successfully supported the families' existing calendaring routines without disrupting existing successful social practices. Families also valued the additional features enabled by LINC. For example, several primary schedulers felt that ubiquitous access positively increased involvement by additional family members in the calendaring routine. The field trials also revealed some unexpected findings, including the importance of mobility---both within and outside the home---for the Tablet PC running LINC.
Tailoring of K 0 . 8 Al 0 . 7 Fe 0 . 15 Si 2 . 25 O 6 Leucite Based Dental Ceramic Material

Potassium based ceramic materials composed from leucite in which 5 % of Al is exchanged with Fe and 4 % of hematite was synthesized by mechanochemical homogenization and annealing of K2O-SiO2-Al2O3-Fe2O3 mixtures. Synthesized material was characterized by X-ray Powder Diffraction (XRPD) and Scanning Electron Microscopy coupled with Energy Dispersive X-ray spectroscopy (SEM/EDX). The two methods are in good agreement in regard to the specimen chemical composition suggesting that a leucite chemical formula is K0.8Al0.7Fe0.15Si2.25O6. Rietveld structure refinement results reveal that about 20 % of vacancies exist in the position of K atoms.
From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering

The application of information retrieval techniques to search tasks in software engineering is made difficult by the lexical gap between search queries, usually expressed in natural language (e.g. English), and retrieved documents, usually expressed in code (e.g. programming languages). This is often the case in bug and feature location, community question answering, or more generally the communication between technical personnel and non-technical stake holders in a software project. In this paper, we propose bridging the lexical gap by projecting natural language statements and code snippets as meaning vectors in a shared representation space. In the proposed architecture, word embeddings are first trained on API documents, tutorials, and reference documents, and then aggregated in order to estimate semantic similarities between documents. Empirical evaluations show that the learned vector space embeddings lead to improvements in a previously explored bug localization task and a newly defined task of linking API documents to computer programming questions.
Design and validation of heuristic algorithms for simulation-based scheduling of a semiconductor Backend facility

In this paper a simulation-based scheduling system is discussed which was developed for a semiconductor Backend facility. Apart from the usual dispatching rules it uses heuristic search strategies for the optimization of the operating sequences. In practice hereby multiple objectives have to be considered, e. g. concurrent minimization of mean cycle time, maximization of throughput and due date compliance. Because the simulation model is very complex and simulation time itself is not negligible, we emphasize to increase the convergence of heuristic optimization methods, consequentially reducing the number of necessary iterations. Several realized strategies are presented.
Design and analysis of BLDC motor for aerospace application using FEM

In this paper, a 3 phase 16 pole, 1500 rpm surface mounted inner rotor radial BLDC motor for aerospace industry is designed and analysed using the finite element method (FEM). BLDC motors are still classified as special motors. Analysis of these motors are a tedious task due to lack of analysing tools and the opportunities available for the developed motor. Analytical calculations to obtain the motor parameters are verified using Opera 18 R2 simulations and ensures a high value of accuracy and efficiency.
Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation. Specifically, we use unsupervised motion-based segmentation on videos to obtain segments, which we use as pseudo ground truth to train a convolutional network to segment objects from a single frame. Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed pretext tasks studied in the literature. Indeed, our extensive experiments show that this is the case. When used for transfer learning on object detection, our representation significantly outperforms previous unsupervised approaches across multiple settings, especially when training data for the target task is scarce.
Second International Nurse Rostering Competition (INRC-II) - Problem Description and Rules -

In this paper, we provide all information to participate to the Second International Nurse Rostering Competition (INRC-II). First, we describe the problem formulation, which, differently from INRC-I, is a multi-stage procedure. Second, we illustrate all the necessary infrastructure do be used together with the participant’s solver, including the testbed, the file formats, and the validation/simulation tools. Finally, we state the rules of the competition. All update-to-date information about the competition is available at http://mobiz.vives.be/inrc2/.
Impedance characteristics of deep brain stimulation electrodes in vitro and in vivo.

The objective of this study was to quantify the electrode-tissue interface impedance of electrodes used for deep brain stimulation (DBS). We measured the impedance of DBS electrodes using electrochemical impedance spectroscopy in vitro in a carbonate- and phosphate-buffered saline solution and in vivo following acute implantation in the brain. The components of the impedance, including the series resistance (R(s)), the Faradaic resistance (R(f)) and the double layer capacitance (C(dl)), were estimated using an equivalent electrical circuit. Both R(f) and C(dl) decreased as the sinusoidal frequency was increased, but the ratio of the capacitive charge transfer to the Faradaic charge transfer was relatively insensitive to the change of frequency. R(f) decreased and C(dl) increased as the current density was increased, and above a critical current density the interface impedance became nonlinear. Thus, the magnitude of the interface impedance was strongly dependent on the intensity (pulse amplitude and duration) of stimulation. The temporal dependence and spatial non-uniformity of R(f) and C(dl) suggested that a distributed network, with each element of the network having dynamics tailored to a specific stimulus waveform, is required to describe adequately the impedance of the DBS electrode-tissue interface. Voltage transients to biphasic square current pulses were measured and suggested that the electrode-tissue interface did not operate in a linear range at clinically relevant current amplitudes, and that the assumption of the DBS electrode being ideally polarizable was not valid under clinical stimulating conditions.
Depression among caregivers of stroke survivors.

We aimed to assess the prevalence of depressive symptoms among caregivers of stroke survivors and to determine which patient- or stroke-related factors are associated with and can be used to predict caregiver depression during an 18-month follow-up after stroke.



We examined 98 caregivers of 100 consecutive patients experiencing their first ischemic stroke in Helsinki University Central Hospital. The caregivers were interviewed at the acute phase and at 6 months and 18 months. Depression was assessed with the Beck Depression Inventory. The neurological, functional, cognitive, and emotional status of the patients was assessed 5x during the follow-up with a comprehensive test battery.



A total of 30% to 33% of all caregivers were depressed during the follow-up; the rates were higher than those of the patients. At the acute phase, caregiver depression was associated with stroke severity and older age of the patient, and at 18 months the older age of the patient was associated with depression of the spouses. In later follow-up, caregiver depression was best predicted by the caregiver's depression at acute phase.



Identifying those caregivers at highest risk for poor emotional outcome in follow-up requires not only assessment of patient-related factors but also interview of the caregiver during the early poststroke period.
First Japanese case of congenital generalized hypertrichosis with a copy number variation on chromosome 17q24.

Hypertrichosis is defined as excessive hair growth compared with individuals of the same age, race and sex . Congenital generalized hypertrichosis (CGH) is a rare syndrome characterized by universal hair overgrowth which is not caused by androgen stimulation . In addition to hypertrichosis, patients with CGH can have other associated abnormalities, such as cardiomyopathy and gingival hyperplasia . CGH can show X-linked recessive (OMIM 307150), autosomal dominant or autosomal recessive (OMIM 135400) inheritance trait, and recent advances in molecular genetics have gradually disclosed the pathogenesis of CGH [3– 6]. Of these findings, it has been reported that autosomal dominant form of CGH with or without gingival hyperplasia is caused by copy number variations (CNVs) on chromosome (Ch) 17q24.2-q24.3 [4]. Interestingly, SOX9 gene, which is known to play a role in maintenance of hair follicle (HF) stem cells [7,8], is located close to the region of the CNVs, and it has been demonstrated that a CNV on Ch17q24.2-q24.3 resulted in reduced expression of the SOX9mRNA, most likely due to a position effect . Furthermore, autosomal recessive form of CGH has been shown to be caused by a loss-of-function mutation in ABCA5 gene encoding a member of the ABC transporter [6]. Importantly, the ABCA5 gene resides on Ch17q24.3, where the CNVs occurred in patients with autosomal
A Framework for the Evaluation and Management of Network Centrality

Network-analysis literature is rich in node-centrality measures that quantify the centrality of a node as a function of the (shortest) paths of the network that go through it. Existing work focuses on defining instances of such measures and designing algorithms for the specific combinatorial problems that arise for each instance. In this work, we propose a unifying definition of centrality that subsumes all path-counting based centrality definitions: e.g., stress, betweenness or paths centrality. We also define a generic algorithm for computing this generalized centrality measure for every node and every group of nodes in the network. Next, we define two optimization problems: k-Group Centrality Maximization and k-Edge Centrality Boosting. In the former, the task is to identify the subset of k nodes that have the largest group centrality. In the latter, the goal is to identify up to k edges to add to the network so that the centrality of a node is maximized. We show that both of these problems can be solved efficiently for arbitrary centrality definitions using our general framework. In a thorough experimental evaluation we show the practical utility of our framework and the efficacy of our algorithms.
Why would online gamers share their innovation-conducive knowledge in the online game user community? Integrating individual motivations and social capital perspectives

0747-5632/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.chb.2010.11.022 ⇑ Corresponding author.  +82 2 958 3598; fax: E-mail addresses: Hau.Augustine@gmail.com (Y.S t.ac.kr (Y.-G. Kim). 1 Tel.: +82 2 958 3614; fax: 82 2 958 3599. The user community has been an important external source of a firm’s product or service innovation. Users’ innovation-conducive knowledge sharing enables the community to work as a vital source of innovation. But, traditional economic theories of innovation seem to provide few explanations about why such knowledge sharing takes place for free in the user community. Therefore, this study investigates what drives community users to freely share their innovation-conducive knowledge, using the theory of planned behavior. Based on an empirical analysis of the data from 1244 members of a South Korean online game user community, it reveals that intrinsic motivation, shared goals, and social trust are salient factors in promoting users’ innovation-conducive knowledge sharing. Extrinsic motivation and social tie, however, were found to affect such sharing adversely, contingent upon whether a user is an innovator or a non-innovator. The study illustrates how social capital, in addition to individual motivations, forms and influences users’ innovation-conducive knowledge sharing in the online gaming context.