nils-herrmann/clean_scirepeval huggingface dataset:
gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data

Clustering is an important technique in microarray data analysis, and mining three-dimensional (3D) clusters in gene-sample-time (simply GST) microarray data is emerging as a hot research topic in this area. A 3D cluster consists of a subset of genes that are coherent on a subset of samples along a segment of time series. This kind of coherent clusters may contain information for the users to identify useful phenotypes, potential genes related to these phenotypes and their expression rules. TRICLUSTER is the state-of-the-art 3D clustering algorithm for GST microarray data. In this paper, we propose a new algorithm to mine 3D clusters over GST microarray data. We term the new algorithm gTRICLUSTER because it is based on a more general 3D cluster model than the one that TRICLUSTER is based on. gTRICLUSTER can find more biologically meaningful coherent gene clusters than TRICLUSTER can do. It also outperforms TRICLUSTER in robustness to noise. Experimental results on a real-world microarray dataset validate the effectiveness of the proposed new algorithm.
HGMF: Hierarchical Group Matrix Factorization for Collaborative Recommendation

Matrix factorization is one of the most powerful techniques in collaborative filtering, which models the (user, item) interactions behind historical explicit or implicit feedbacks. However, plain matrix factorization may not be able to uncover the structure correlations among users and items well such as communities and taxonomies. As a response, we design a novel algorithm, i.e., hierarchical group matrix factorization (HGMF), in order to explore and model the structure correlations among users and items in a principled way. Specifically, we first define four types of correlations, including (user, item), (user, item group), (user group, item) and (user group, item group); we then extend plain matrix factorization with a hierarchical group structure; finally, we design a novel clustering algorithm to mine the hidden structure correlations. In the experiments, we study the effectiveness of our HGMF for both rating prediction and item recommendation, and find that it is better than some state-of-the-art methods on several real-world data sets.
Discovering Teachers' Attitudes toward Use of Information and Communication Technology (ICT) in Preschool

Information and Communication Technology (ICT) has a great potential for enriching learning experience especially in early childhood education. The need to integrate ICT at preschool level has become greater. The teacher as a mediator influences the use of ICT in teaching and learning. The aim of the research is to investigate teachers' attitudes toward use of ICT and examine whether there are a relationship between teachers' attitude with teachers' background. Descriptive analysis was used to determine the teachers' attitudes toward the use of ICT. Correlation analysis was used to determine the relationship between teachers' attitude and teachers' background. This result from this research will contributes to the body of knowledge regarding teachers' attitudes with ICT usage. Further research in this field is very important since Malaysia wants to make the utilization of ICT in preschool as a culture.
Scene Recognition Using Mid-level features from CNN

In this project we try to explore how the features extracted from the activation of a deep convolutional neural network trained in a supervised fashion on the ImageNet dataset can be used to classify in nivel generic tasks such as scene recognition. We use the mid-level features from the pretrained CNN hypothesising that they contain semantic information as relevant for the task of scene recognition. We consider a spatial pyramid kind of setting for the image, which represents ImageNet objects of scale upto the size of the image. We present that the results, obtained on MIT-67 Scene Dataset and 15 scene dataset, are quite impressive as compared to the state-of-the-art.
Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks

Distant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error propagation. Recently, a neural network architecture has been proposed to automatically extract features for relation classification. However, this approach follows the traditional expressed-at-least-once assumption, and fails to make full use of information across different sentences. Moreover, it ignores the fact that there can be multiple relations holding between the same entity pair. In this paper, we propose a multi-instance multi-label convolutional neural network for distantly supervised RE. It first relaxes the expressed-at-least-once assumption, and employs cross-sentence max-pooling so as to enable information sharing across different sentences. Then it handles overlapping relations by multi-label learning with a neural network classifier. Experimental results show that our approach performs significantly and consistently better than state-of-the-art methods.
Use of Web 2 . 0 technologies in K-12 and higher education : The search for evidence-based practice

Evidence-based practice in education entails making pedagogical decisions that are informed by relevant empirical research evidence. The main purpose of this paper is to discuss evidence-based pedagogical approaches related to the use of Web 2.0 technologies in both K-12 and higher education settings. The use of such evidence-based practice would be useful to educators interested in fostering student learning through Web 2.0 tools. A comprehensive literature search across the Academic Search Premier, Education Research Complete, ERIC, and PsycINFO databases was conducted. Empirical studies were included for review if they specifically examined the impact of Web 2.0 technologies on student learning. Articles that merely described anecdotal studies such as student perception or feeling toward learning using Web 2.0, or studies that relied on student self-report data such as student questionnaire survey and interview were excluded. Overall, the results of our review suggested that actual evidence regarding the impact of Web 2.0 technologies on student learning is as yet fairly weak. Nevertheless, the use of Web 2.0 technologies appears to have a general positive impact on student learning. None of the studies reported a detrimental or inferior effect on learning. The positive effects are not necessarily attributed to the technologies per se but to how the technologies are used, and how one conceptualizes learning. It may be tentatively concluded that a dialogic, constructionist, or coconstructive pedagogy supported by activities such as Socratic questioning, peer review and self-reflection appeared to increase student achievement in blog-, wiki-, and 3-D immersive virtual world environments, while a transmissive pedagogy supported by review activities appeared to enhance student learning using podcast.
Limited Gradient Descent: Learning With Noisy Labels

Label noise may handicap the generalization of classifiers, and the effective learning of the main pattern from samples with noisy labels is an important issue. Recent studies have shown that deep neural networks tend to prioritize the learning of simple patterns over the memorization of noise patterns. This suggests the need for a method to search for the best generalization that learns the main pattern until noise begins to be memorized. An intuitive idea is to use a supervised approach to find the stop timing of learning by, for example, employing a clean verification set. In practice, however, a clean verification set is sometimes difficult to obtain. To solve this problem, we propose an unsupervised method called limited gradient descent to estimate the best stop timing. We modified the labels of a few samples in a noisy dataset to be almost false labels, creating a reverse pattern. By monitoring the learning progresses of the noisy samples and the reverse samples, we could determine the stop timing of learning. In this paper, we also provide some sufficient conditions on learning with noisy labels. Experimental results on CIFAR-10 demonstrate that our approach has a similar generalization performance to supervised methods. For uncomplicated datasets, such as MNIST, we add a relabeling strategy to further improve generalization and achieve stateof-the-art performance.
The human amygdala and the emotional evaluation of sensory stimuli

A wealth of animal data implicates the amygdala in aspects of emotional processing. In recent years, functional neuroimaging and neuropsychological studies have begun to refine our understanding of the functions of the amygdala in humans. This literature offers insights into the types of stimuli that engage the amygdala and the functional consequences that result from this engagement. Specific conclusions and hypotheses include: (1) the amygdala activates during exposure to aversive stimuli from multiple sensory modalities; (2) the amygdala responds to positively valenced stimuli, but these responses are less consistent than those induced by aversive stimuli; (3) amygdala responses are modulated by the arousal level, hedonic strength or current motivational value of stimuli; (4) amygdala responses are subject to rapid habituation; (5) the temporal characteristics of amygdala responses vary across stimulus categories and subject populations; (6) emotionally valenced stimuli need not reach conscious awareness to engage amygdala processing; (7) conscious hedonic appraisals do not require amygdala activation; (8) activation of the amygdala is associated with modulation of motor readiness, autonomic functions, and cognitive processes including attention and memory; (9) amygdala activations do not conform to traditional models of the lateralization of emotion; and (10) the extent and laterality of amygdala activations are related to factors including psychiatric status, gender and personality. The strengths and weakness of these hypotheses and conclusions are discussed with reference to the animal literature.
A Distributed Test System Architecture for Open-source IoT Software

In this paper, we discuss challenges that are specific to testing of open IoT software systems. The analysis reveals gaps compared to wireless sensor networks as well as embedded software. We propose a testing framework which (a) supports continuous integration techniques, (b) allows for the integration of project contributors to volunteer hardware and software resources to the test system, and (c) can function as a permanent distributed plugtest for network interoperability testing. The focus of this paper lies in open-source IoT development but many aspects are also applicable to closed-source projects.
A Novel Transformer Structure for High power, High Frequency converter

Power transformer structure is a key factor for the high power, high frequency converter performance which includes efficiency, thermal performance and power density. This paper proposes a novel transformer structure for the kilo-watt level, high frequency converter which is reinforce insulation needed for the secondary side to primary side. The transformer has spiral-wound primary layers using TIW (triple insulation wire) and PCB-winding secondary layers. All the windings are arranged by full interleaving structure to minimize the leakage inductance and eddy current loss. Further more, the secondary rectifiers and filter capacitors are mounted in PCB-winding secondary layers to further minimize the termination effect. A 1.2 KW (O/P: 12 V/100 A, I/P: 400 V) Mega Hz LLC converter prototype employed the proposed transformer structure is constructed, and over 96% efficiency achieved.
3D deeply supervised network for automated segmentation of volumetric medical images

While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN.
A Study on Evaluation of E-Government Service Quality

Service quality is the highest requirement by users, especially for the service in electronic government. During the past decades, it has become a major area of academic investigation. Considering this issue, there are a lot of researches that evaluated the dimensions and e-service contexts. This study also identified the dimensions of service quality, but focuses on a new concept and provides a new methodology in developing measurement scales of e-service quality such as information quality, service quality and organization quality. Finally, this study will suggest a key factor to evaluate e-government service quality better. E-government, e-service, e-service quality, dimensionality.
Methods for Designing Multiple Classifier Systems

In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called “overproduce and choose” paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.
A Review of Platelet-Rich Plasma: History, Biology, Mechanism of Action, and Classification.

Platelet-rich plasma (PRP) is currently used in different medical fields. The interest in the application of PRP in dermatology has recently increased. It is being used in several different applications as in tissue regeneration, wound healing, scar revision, skin rejuvenating effects, and alopecia. PRP is a biological product defined as a portion of the plasma fraction of autologous blood with a platelet concentration above the baseline. It is obtained from the blood of patients collected before centrifugation. The knowledge of the biology, mechanism of action, and classification of the PRP should help clinicians better understand this new therapy and to easily sort and interpret the data available in the literature regarding PRP. In this review, we try to provide useful information for a better understanding of what should and should not be treated with PRP.
Identify Online Store Review Spammers via Social Review Graph

Online shopping reviews provide valuable information for customers to compare the quality of products, store services, and many other aspects of future purchases. However, spammers are joining this community trying to mislead consumers by writing fake or unfair reviews to confuse the consumers. Previous attempts have used reviewers’ behaviors such as text similarity and rating patterns, to detect spammers. These studies are able to identify certain types of spammers, for instance, those who post many similar reviews about one target. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like normal reviewers, and thus cannot be detected by the available techniques.
 In this article, we propose a novel concept of review graph to capture the relationships among all reviewers, reviews and stores that the reviewers have reviewed as a heterogeneous graph. We explore how interactions between nodes in this graph could reveal the cause of spam and propose an iterative computation model to identify suspicious reviewers. In the review graph, we have three kinds of nodes, namely, reviewer, review, and store. We capture their relationships by introducing three fundamental concepts, the trustiness of reviewers, the honesty of reviews, and the reliability of stores, and identifying their interrelationships: a reviewer is more trustworthy if the person has written more honesty reviews; a store is more reliable if it has more positive reviews from trustworthy reviewers; and a review is more honest if many other honest reviews support it. This is the first time such intricate relationships have been identified for spam detection and captured in a graph model. We further develop an effective computation method based on the proposed graph model. Different from any existing approaches, we do not use an review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.
Quality expectations of machine translation

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself. 1 Machine Translation Today Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. I will examine the reasons for this later in this chapter, but one inference is very clear: those people using MT in those use-cases must already be satisfied with the level of quality emanating from the MT systems they are deploying, otherwise they would stop using them. That is not the same thing at all as saying that MT quality is perfect, far from it. The many companies and academic researchers who develop and deploy MT engines today continue to strive to improve the quality of the translations produced. This too is an implicit acceptance of the fact that the level of quality is sub-optimal – for some use-cases at least – and can be improved. If MT system output is good enough for some areas of application, yet at the same time system developers are trying hard to improve the level of translations produced by their engines, then translation quality – whether produced by a machine or by a human – needs to be measurable. Note that this applies also to translators who complain that MT quality is too poor to be used in their workflows; in order to decide that with some certainty – rather than rejecting MT out-of-hand merely as a knee-jerk reaction to the onset of this new technology – the impact of MT on translators’ work needs to be measurable. In Way (2013), I appealed to two concepts, which are revisited here, namely:
Development of VLF receiver for remote sensing of low atmospheric activities

VLF (very low frequency) can be used to probe lower atmospheric activities. To detect lower atmospheric activities, two types of VLF receiver were developed in this project. The first type is called SID (sudden ionospheric disturbances) receiver which is used to detect the solar flares activities that affect communication systems. The second one is called natural radio receiver which is used to detect natural radio signals such as sferics, whistlers emitted by the activities of lightning discharges. A wire loop antena was developed to receive VLF signals from outer space for the receivers that has been developed. In the process of developing the VLF receiver, MULTISIM software. It was used to simulate the receiver's circuit developed for this purpose. The simulation results was adopted to design a prototype for the circuit for experiments carried out to measure the performance and the ability of the receivers in receiving VLF signals. Lab equipment, such as signal generators and oscilloscopes, were used to test the performance of the receivers. The receivers developed will contribute to the research of D-layer ionospheric activities.
PLL type estimator applied in PMSM sensorless control for speed and position

This paper presents a novel estimation algorithm for speed and position of Permanent Magnet Synchronous Machine (PMSM) based on Phase Locked Loop (PLL) technique. The invention in this sensorless control scheme combines the PLL methodology with a modified PMSM mathematical model expressed in the arbitrary rotating frame. Only stator voltages and currents are required in the algorithm estimating modified stator and rotor magnetic flux. The control strategy principle is based on the advantage of PLL-type structure to successfully track rotor flux with the required stability and accuracy. Simulation results demonstrate the effectiveness and performance analysis of the proposed position and speed estimation method.
A 95% Efficient Normally-Off GaN-on-Si HEMT Hybrid-IC Boost-Converter with 425-W Output Power at 1 MHz

A 2:1 351 V hard-switched boost converter was constructed using high-voltage GaN high-electron-mobility transistors grown on Si substrates and GaN Schottky diodes grown on Sapphire substrates. The high speed and low on-resistance of the GaN devices enables extremely fast switching times and low losses, resulting in a high conversion efficiency of 95% with 425-W output power at 1 MHz. The boost converter has a power density of 175 W/in3. To our knowledge, these results are the best reported on GaN devices, and the highest for 1MHz switching.