nils-herrmann/clean_scirepeval huggingface dataset:
Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO)

The 2-D maximum entropy method not only considers the distribution of the gray information, but also takes advantage of the spatial neighbor information with using the 2-D histogram of the image. As a global threshold method, it often gets ideal segmentation results even when the image s signal noise ratio (SNR) is low. However, its time-consuming computation is often an obstacle in real time application systems. In this paper, the image thresholding approach based on the index of entropy maximization of the 2-D grayscale histogram is proposed to deal with infrared image. The threshold vector (t, s), where t is a threshold for pixel intensity and s is another threshold for the local average intensity of pixels, is obtained through a new optimization algorithm, namely, the particle swarm optimization (PSO) algorithm. PSO algorithm is realized successfully in the process of solving the 2-D maximum entropy problem. The experiments of segmenting the infrared images are illustrated to show that the proposed method can get ideal segmentation result with less computation cost.
A Visualization of the Greater Trochanter and Peritrochanteric Soft Tissues.

The greater trochanter of the femur is a bony protuberance arising at the femoral neck and shaft interface. The greater trochanter has 4 distinct facets (anterior, superoposterior, lateral, and posterior) that serve for attachments of the abductor tendons and/or sites for bursae  (Figures 1 and 2). The gluteus minimus and medius muscles arise from the external iliac fossa and their corresponding tendons insert onto the greater trochanter (Figures 1-3). The gluteus medius muscle almost completely covers the gluteus minimus muscle. The gluteus minimus tendon attaches to the anterior facet (main insertion) (Figures 1-3) and to the anterior and superior hip joint capsule. From posterior to anterior, the gluteus medius tendon attaches to the superoposterior facet (main tendinous attachment), the inferior aspect of the lateral facet, and more anteriorly to the gluteus minimus tendon [2]. The posterior facet is devoid of tendon attachments (Figures 1-3). A variety of bursae have been described in the vicinity of the greater trochanter . The 3 most consistently identified bursae are the subgluteus minimus, subgluteus medius, and subgluteus maximus bursae. The subgluteus minimus bursa lies deep to the gluteus minimus tendon. The subgluteus medius bursa is located between the lateral insertion of the gluteus medius tendon and the superior part of the lateral facet (this portion of the lateral facet is devoid of tendon insertion and is known as the trochanteric bald spot)  . The largest bursa is the subgluteus maximus. This bursa covers the posterior facet and lies deep to the gluteus maximus muscle (Figure 4).
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients

High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features, we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9% We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time, which provides valuable insights into functional neuro-oncological applications.
Characterization of early smartwatch apps

Wearable smart devices are already amongst us. Currently, smartwatches are one of the key drivers of the wearable technology and are being used by a large population of consumers. This paper takes a first look at this increasingly popular technology with a systematic characterization of the smartwatch app markets. We conduct a large scale analysis of three popular smartwatch app markets: Android Wear, Samsung, and Apple, and characterize more than 14,000 smartwatch apps in multiple aspects such as prices, number of developers and categories. Our analysis shows that approximately 41% and 30% of the apps in Android Wear and Samsung app markets are Personalization apps that provide watch faces. Further, we provide a generic taxonomy for apps on all three platforms based on their packaging and modes of communication, that allow us to investigate apps with respect to privacy and security. Finally, we study the privacy risks associated with the app usage by identifying third party trackers integrated into these apps and personal information leakage through network traffic analysis. We show that a higher percentage of Apple apps (62%) are connected to third party trackers compared to Samsung (36%) and Android Wear (46%).
A hand gesture interface device

This paper reports on the development of a hand to machine interface device that provides real-time gesture, position and orientation information. The key element is a glove and the device as a whole incorporates a collection of technologies. Analog flex sensors on the glove measure finger bending. Hand position and orientation are measured either by ultrasonics, providing five degrees of freedom, or magnetic flux sensors, which provide six degrees of freedom. Piezoceramic benders provide the wearer of the glove with tactile feedback. These sensors are mounted on the light-weight glove and connected to the driving hardware via a small cable.
Applications of the glove and its component technologies include its use in conjunction with a host computer which drives a real-time 3-dimensional model of the hand allowing the glove wearer to manipulate computer-generated objects as if they were real, interpretation of finger-spelling, evaluation of hand impairment in addition to providing an interface to a visual programming language.
A general space vector PWM control algorithm for multilevel inverters

To solve the problem of computational complexity in multilevel inverters due to the large number of space vectors and redundant switching states, a simple and general space vector PWM algorithm is proposed. Based on this algorithm, the location of the reference voltage vector can be easily determined and the calculation of dwell times becomes very simple. More importantly, the proposed algorithm is general and can be directly applied to the cascaded H-bridge inverter of any voltage levels. In addition, a new switching sequence, large-small alternation (LSA), is proposed for the minimization of total harmonic distortion. To verify the algorithms, a 7-level cascaded H-bridge inverter drive system was constructed and experimental results are provided.
Marketing Research : The Role of Sentiment Analysis

This article promotes sentiment analysis as an alternative research technique for collecting and analyzing textual data on the internet. Sentiment analysis is a data mining technique that systematically evaluates textual content using machine learning techniques. As a research method in marketing, sentiment analysis presents an efficient and effective evaluation of consumer opinions in real time. It allows data collection and analysis from a very large sample without hindrances, obstructions and time delays. Through sentiment analysis, marketers collect rich data on attitudes and opinion in real time, without compromising reliability, validity and generalizability. Marketers also gather feedback on attitudes and opinions as they occur without having to invest in lengthy and costly market research activities. The paper proposes sentiment analysis as an alternative technique capable of triangulating qualitative and quantitative methods through innovative real time data collection and analysis. The paper concludes with the challenges marketers can face when using this technique in their research work.
DVL1 frameshift mutations clustering in the penultimate exon cause autosomal-dominant Robinow syndrome.

Robinow syndrome is a genetically heterogeneous disorder characterized by mesomelic limb shortening, genital hypoplasia, and distinctive facial features and for which both autosomal-recessive and autosomal-dominant inheritance patterns have been described. Causative variants in the non-canonical signaling gene WNT5A underlie a subset of autosomal-dominant Robinow syndrome (DRS) cases, but most individuals with DRS remain without a molecular diagnosis. We performed whole-exome sequencing in four unrelated DRS-affected individuals without coding mutations in WNT5A and found heterozygous DVL1 exon 14 mutations in three of them. Targeted Sanger sequencing in additional subjects with DRS uncovered DVL1 exon 14 mutations in five individuals, including a pair of monozygotic twins. In total, six distinct frameshift mutations were found in eight subjects, and all were heterozygous truncating variants within the penultimate exon of DVL1. In five families in which samples from unaffected parents were available, the variants were demonstrated to represent de novo mutations. All variant alleles are predicted to result in a premature termination codon within the last exon, escape nonsense-mediated decay (NMD), and most likely generate a C-terminally truncated protein with a distinct -1 reading-frame terminus. Study of the transcripts extracted from affected subjects' leukocytes confirmed expression of both wild-type and variant alleles, supporting the hypothesis that mutant mRNA escapes NMD. Genomic variants identified in our study suggest that truncation of the C-terminal domain of DVL1, a protein hypothesized to have a downstream role in the Wnt-5a non-canonical pathway, is a common cause of DRS.
A Systematic Literature Review of Open Government Data Research: Challenges, Opportunities and Gaps

Open Government Data (OGD) has been growing as a field of research in digital government. Using a systematic review method, this paper sought to capture the status of research in OGD over the last five years from 2012–2018. The key findings indicate that OGD is an emergent research area with no existing theoretical frameworks. The absence of design science methods suggests an opportunity to create digital government platforms based on OGD best practices. There are also strong legal concerns concerning the opening up of government data, the format of the data, and the privacy / security implications of such exposure. There is limited research on OGD for business benefit nor developing countries. We highlight some gaps for further research and identify potential opportunities.
Problem set up and motivation

Today, we analyze an application of random projection to compute approximate solutions of constrained least-squares problems. This method is often referred to as sketched least-squares. Suppose that we are given an observation vector y ∈ R n and matrix A ∈ R n×d , and that for some convex set C ⊂ R d , we would like to compute the constrained least-squares solution x LS : = argmin x∈C 1 2 y − Ax 2 2 : =f (x). In general, this solution may not be unique, but we assume throughout this lecture that uniqueness holds (so that n ≥ d necessarily). Different versions of the constrained least-squares problem arise in many applications: • In the simplest case of an unconstrained problem (C = R d), it corresponds to the usual least-squares estimator, which has been widely studied. Most past work on sketching least-squares has focused on this case. • When C is a scaled form of the 1-ball—that is, C = {x ∈ R d | x 1 ≤ R}—then the constrained problem is known as the Lasso. It is widely used for estimating sparse regression vectors. • The support vector machine for classification, when solved in its dual form, leads to a least-squares problem over a polytope C. Problems of the form (3.1) can also arise as intermediate steps of using Newton's method to solve a constrained optimization problem. The original problem can be difficult to solve if the first matrix dimension n is too large. Thus, in order to reduce both storage and computation requirements, a natural idea is to randomly project the original data to a lower-dimensional space. In particular, given a random sketch matrix S ∈ R m×n , consider the sketched least-squares problem x : = argmin x∈C 1 2 S(y − Ax) 2 2 : =g(x) .
Alveolar ridge preservation. A systematic review

The objective of this paper is to examine the effect of alveolar ridge preservation (ARP) compared to unassisted socket healing. Systematic review with electronic and hand search was performed. Randomised controlled trials (RCT), controlled clinical trials (CCT) and prospective cohort studies were eligible. Eight RCTs and six CCTs were identified. Clinical heterogeneity did not allow for meta-analysis. Average change in clinical alveolar ridge (AR) width varied between −1.0 and −3.5 ± 2.7 mm in ARP groups and between −2.5 and −4.6 ± 0.3 mm in the controls, resulting in statistically significantly smaller reduction in the ARP groups in five out of seven studies. Mean change in clinical AR height varied between +1.3 ± 2.0 and −0.7 ± 1.4 mm in the ARP groups and between −0.8 ± 1.6 and −3.6 ± 1.5 mm in the controls. Height reduction in the ARP groups was statistically significantly less in six out of eight studies. Histological analysis indicated various degrees of new bone formation in both groups. Some graft interfered with the healing. Two out of eight studies reported statistically significantly more trabecular bone formation in the ARP group. No superiority of one technique for ARP could be identified; however, in certain cases guided bone regeneration was most effective. Statistically, significantly less augmentation at implant placement was needed in the ARP group in three out of four studies. The strength of evidence was moderate to low. Post-extraction resorption of the AR might be limited, but cannot be eliminated by ARP, which at histological level does not always promote new bone formation. RCTs with unassisted socket healing and implant placement in the ARP studies are needed to support clinical decision making. This systematic review reports not only on the clinical and radiographic outcomes, but also evaluates the histological appearance of the socket, along with site specific factors, patient-reported outcomes, feasibility of implant placement and strength of evidence, which will facilitate the decision making process in the clinical practice.
Enriched environment treatment restores impaired hippocampal synaptic plasticity and cognitive deficits induced by prenatal chronic stress

Prenatal stress can cause long-term effects on cognitive functions in offspring. Hippocampal synaptic plasticity, believed to be the mechanism underlying certain types of learning and memory, and known to be sensitive to behavioral stress, can be changed by prenatal stress. Whether enriched environment treatment (EE) in early postnatal periods can cause a recovery from these deficits is unknown. Experimental animals were Wistar rats. Prenatal stress was evoked by 10 foot shocks (0.8 mA for 1s, 2-3 min apart) in 30 min per day at gestational day 13-19. After weaning at postnatal day 22, experimental offspring were given the enriched environment treatment through all experiments until tested (older than 52 days age). Electrophysiological and Morris water maze testing was performed at 8 weeks of age. The results showed that prenatal stress impaired long-term potentiation (LTP) but facilitated long-term depression (LTD) in the hippocampal CA1 region in the slices. Furthermore, prenatal stress exacerbated the effects of acute stress on hippocampal LTP and LTD, and also impaired spatial learning and memory in the Morris water maze. However, all these deficits induced by prenatal stress were recovered by enriched environment treatment. This work observes a phenomenon that may contribute to the understanding of clinically important interactions among cognitive deficit, prenatal stress and enriched environment treatment. Enriched environment treatment on early postnatal periods may be one potentially important target for therapeutic interventions in preventing the prenatal stress-induced cognitive disorders.
Automated Resolution of Semantic Heterogeneity in Multidatabases

A multidatabase system provides integrated access to heterogeneous, autonomous local databases in a distributed system. An important problem in current multidatabase systems is identification of semantically similar data in different local databases. The Summary Schemas Model (SSM) is proposed as an extension to multidatabase systems to aid in semantic identification. The SSM uses a global data structure to abstract the information available in a multidatabase system. This abstracted form allows users to use their own terms (imprecise queries) when accessing data rather than being forced to use system-specified terms. The system uses the global data structure to match the user's terms to the semantically closest available system terms. A simulation of the SSM is presented to compare imprecise-query processing with corresponding query-processing costs in a standard multidatabase system. The costs and benefits of the SSM are discussed, and future research directions are presented.
Plant-Mediated Synthesis of Silver Nanoparticles: Their Characteristic Properties and Therapeutic Applications

Interest in "green nanotechnology" in nanoparticle biosynthesis is growing among researchers. Nanotechnologies, due to their physicochemical and biological properties, have applications in diverse fields, including drug delivery, sensors, optoelectronics, and magnetic devices. This review focuses on the green synthesis of silver nanoparticles (AgNPs) using plant sources. Green synthesis of nanoparticles is an eco-friendly approach, which should be further explored for the potential of different plants to synthesize nanoparticles. The sizes of AgNPs are in the range of 1 to 100 nm. Characterization of synthesized nanoparticles is accomplished through UV spectroscopy, X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy, and scanning electron microscopy. AgNPs have great potential to act as antimicrobial agents. The green synthesis of AgNPs can be efficiently applied for future engineering and medical concerns. Different types of cancers can be treated and/or controlled by phytonanotechnology. The present review provides a comprehensive survey of plant-mediated synthesis of AgNPs with specific focus on their applications, e.g., antimicrobial, antioxidant, and anticancer activities.
Typicality Matching for Pairs of Correlated Graphs

In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network de-anonymization, study of biological data, and web graphs. An achievable region of graph parameters for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching. The algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs. Our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs. The result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest.
Digital citizenship within global contexts

EduSummIT 2013 featured a working group that examined digital citizenship within a global context. Group members recognized that, given today’s international, regional, political, and social dynamics, the notion of “global” might be more aspirational than practical. The development of informed policies and practices serving and involving as many sectors of society as possible is desirable since a growing world’s population, including students in classrooms, will have continued access to the Internet, mobile devices and social media. Action steps to guide technology integration into educational settings must address the following factors: national and local policies, bandwidth and technology infrastructure, educational contexts, cyber-safety and cyberwellness practices and privacy accountability. Finally, in the process of developing and implementing positive and productive solutions, as many key members and stakeholders as possible who share in—and benefit from—students’ digital lives should be involved, from families and educators to law enforcement authorities, from telecommunication organizations to local, provincial and national leaders.
Ego depletion: is the active self a limited resource?

Choice, active response, self-regulation, and other volition may all draw on a common inner resource. In Experiment 1, people who forced themselves to eat radishes instead of tempting chocolates subsequently quit faster on unsolvable puzzles than people who had not had to exert self-control over eating. In Experiment 2, making a meaningful personal choice to perform attitude-relevant behavior caused a similar decrement in persistence. In Experiment 3, suppressing emotion led to a subsequent drop in performance of solvable anagrams. In Experiment 4, an initial task requiring high self-regulation made people more passive (i.e., more prone to favor the passive-response option). These results suggest that the self's capacity for active volition is limited and that a range of seemingly different, unrelated acts share a common resource.
Disadvantage, inequality, and social policy.

Eliminating disparities in health is a primary goal of the federal government and many states. Our overarching objective should be to improve population health for all groups to the maximum extent. Ironically, enhancing population health and even the health of the disadvantaged can conflict with efforts to reduce disparities. This paper presents data showing that interventions that offer some of the largest possible gains for the disadvantaged may also increase disparities, and it examines policies that offer the potential to decrease disparities while improving population health. Enhancement of educational attainment and access to health services and income support for those in greatest need appear to be particularly important pathways to improved population health.
Multi-Agent Actor-Critic with Generative Cooperative Policy Network

We propose an efficient multi-agent reinforcement learning approach to derive equilibrium strategies for multi-agents who are participating in a Markov game. Mainly, we are focused on obtaining decentralized policies for agents to maximize the performance of a collaborative task by all the agents, which is similar to solving a decentralized Markov decision process. We propose to use two different policy networks: (1) decentralized greedy policy network used to generate greedy action during training and execution period and (2) generative cooperative policy network (GCPN) used to generate action samples to make other agents improve their objectives during training period. We show that the samples generated by GCPN enable other agents to explore the policy space more effectively and favorably to reach a better policy in terms of achieving the collaborative tasks.