nils-herrmann/clean_scirepeval huggingface dataset:
Bands Sensitive Convolutional Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification deals with the problem of pixel-wise spectrum labelling. Traditional HSI classification algorithms focus on two major stages: feature extraction and classifier design. Though studied for decades, HSI classification hasn't been perfectly solved. One of the main reasons relies on the fact that features extracted by embedding methods can hardly match an ad hoc classifier. Recently, deep learning methods achieve an end-to-end mechanism and can learn features suitable for classification from the raw data. Inspired by the newly proposed work on deep learning for HSI classification, in this paper, we propose to build a deep convolutional network based on the analysis of spectral band discriminative characteristics. More specifically, we first split the spectrum bands into groups based on their correlation relationships. Then we build a band variant CNN submodel, where each group is modelled by one of those submodels. Meanwhile, a conventional CNN model is also learned globally on the spatial-spectral space, to maintain robustness of submodel changes. Lastly, we concatenate the global CNN model and band-specific CNN submodels to one unique model. In this way, global robustness and band variance are mixed together. Experiments on publicly available datasets demonstrate the great performance of the proposed method.
Gender and language-variety Identification with MicroTC

In this notebook, we describe our approach to cope with the Author Profiling task on PAN17 which consists of both gender and language identification for Twitter’s users. We used our MicroTC (μTC) framework as the primary tool to create our classifiers. μTC follows a simple approach to text classification; it converts the problem of text classification to a model selection problem using several simple text transformations, a combination of tokenizers, a term-weighting scheme, and finally, it classifies using a Support Vector Machine. Our approach reaches accuracies of 0.7838, 0.8054, 0.7957, and 0.8538, for gender identification; and for language variety, it achieves 0.8275, 0.9004, 0.9554, and 0.9850. All these, for Arabic, English, Spanish, and Portuguese languages, respectively.
The use of unlabeled data to improve supervised learning for text summarization

With the huge amount of information available electronically, there is an increasing demand for automatic text summarization systems. The use of machine learning techniques for this task allows one to adapt summaries to the user needs and to the corpus characteristics. These desirable properties have motivated an increasing amount of work in this field over the last few years. Most approaches attempt to generate summaries by extracting sentence segments and adopt the supervised learning paradigm which requires to label documents at the text span level. This is a costly process, which puts strong limitations on the applicability of these methods. We investigate here the use of semi-supervised algorithms for summarization. These techniques make use of few labeled data together with a larger amount of unlabeled data. We propose new semi-supervised algorithms for training classification models for text summarization. We analyze their performances on two data sets - the Reuters news-wire corpus and the Computation and Language (cmp_lg) collection of TIPSTER SUMMAC. We perform comparisons with a baseline - non learning - system, and a reference trainable summarizer system.
A 6.25Gbps Feed-forward Equalizer in 0.18μm CMOS Technology for SerDes

This paper presents a 6.25Gbps feed-forward equalizer (FFE) to reduce the inter-symbol-interference (ISI) in high-speed transmission backplane. The 3-tap fractionally spaced equalizer consists of a delay line, multiplier & summer cells and an output circuit. Active-inductive peaking circuit and capacitor-degenerated circuit are used in the delay line and the output stage respectively to meet the bandwidth demand. The proposed FFE has been implemented in TSMC 0.18μm CMOS technology with a whole area of 0.5mm×0.51mm including I/O pads. The power consumption of the core circuit is 31.7mW under 1.8V power supply. Post simulation results show that the distorted signal through a 24-inch backplane is well recovered by this equalizer.
Meta-analysis of Cohen’s kappa

Cohen’s κ is the most important and most widely accepted measure of inter-rater reliability when the outcome of interest is measured on a nominal scale. The estimates of Cohen’s κ usually vary from one study to another due to differences in study settings, test properties, rater characteristics and subject characteristics. This study proposes a formal statistical framework for meta-analysis of Cohen’s κ to describe the typical inter-rater reliability estimate across multiple studies, to quantify between-study variation and to evaluate the contribution of moderators to heterogeneity. To demonstrate the application of the proposed statistical framework, a meta-analysis of Cohen’s κ is conducted for pressure ulcer classification systems. Implications and directions for future research are discussed.
An Experimental Comparison of RGB, YIQ, LAB, HSV, and Opponent Color Models

The increasing availability of affordable color raster graphics displays has made it important to develop a better understanding of how color can be used effectively in an interactive environment. Most contemporary graphics displays offer a choice of some 16 million colors; the user's problem is to find the right color.
Folklore has it that the RGB color space arising naturally from color display hardware is user-hostile and that other color models such as the HSV scheme are preferable. Until now there has been virtually no experimental evidence addressing this point.
We describe a color matching experiment in which subjects used one of two tablet-based input techniques, interfaced through one of five color models, to interactively match target colors displayed on a CRT.
The data collected show small but significant differences between models in the ability of subjects to match the five target colors used in this experiment. Subjects using the RGB color model matched quickly but inaccurately compared with those using the other models. The largest speed difference occurred during the early convergence phase of matching. Users of the HSV color model were the slowest in this experiment, both during the convergence phase and in total time to match, but were relatively accurate. There was less variation in performance during the second refinement phase of a match than during the convergence phase.
Two-dimensional use of the tablet resulted in faster but less accurate performance than did strictly one-dimensional usage.
Significant learning occurred for users of the Opponent, YIQ, LAB, and HSV color models, and not for users of the RGB color model.
Timing of light exposure affects mood and brain circuits

Temporal organization of physiology is critical for human health. In the past, humans experienced predictable periods of daily light and dark driven by the solar day, which allowed for entrainment of intrinsic circadian rhythms to the environmental light–dark cycles. Since the adoption of electric light, however, pervasive exposure to nighttime lighting has blurred the boundaries of day and night, making it more difficult to synchronize biological processes. Many systems are under circadian control, including sleep–wake behavior, hormone secretion, cellular function and gene expression. Circadian disruption by nighttime light perturbs those processes and is associated with increasing incidence of certain cancers, metabolic dysfunction and mood disorders. This review focuses on the role of artificial light at night in mood regulation, including mechanisms through which aberrant light exposure affects the brain. Converging evidence suggests that circadian disruption alters the function of brain regions involved in emotion and mood regulation. This occurs through direct neural input from the clock or indirect effects, including altered neuroplasticity, neurotransmission and clock gene expression. Recently, the aberrant light exposure has been recognized for its health effects. This review summarizes the evidence linking aberrant light exposure to mood.
Image Edge Detection : A Review

Edge detection is important part of image processing for object detection. So it becomes extremely important to have a good understanding of edge detection algorithms. An edge is the real or imagined line that marks the limit and divides of plane, object or appearance from other places or things. This means that if the edges in an image can be identified accurately, all of the objects can be located and basic properties can be measured. This paper introduces a classification of most important and commonly used edge detection algorithms, namely Sobel, Robert, Prewitt, Laplacian of Gaussian, Canny, Ant colony Optimization. Ant colony optimization, Canny, Edge Detection, Image Processing, Laplacian of Gaussian, Prewitt, Robert, Sobel.
Information Technology, Materiality, and Organizational Change: A Professional Odyssey

We begin with a retrospective reflection on the first author’s research career, which in large part is devoted to research about the implications of information technology (IT) for organizational change. Although IT has long been associated with organizational change, our historical review of the treatment of technology in organization theory demonstrates how easily the material aspects of organizations can disappear into the backwaters of theory development. This is an unfortunate result since the material characteristics of IT initiatives distinguish them from other organizational change initiatives. Our aim is to restore materiality to studies of IT impact by tracing the reasons for its disappearance and by offering options in which IT’s materiality plays a more central theoretical role. We adopt a socio-technical perspective that differs from a strict sociomaterial perspective insofar as we wish to preserve the ontological distinction between material artifacts and their social context of use. Our analysis proceeds using the concept of “affordance” as a relational concept consistent with the socio-technical perspective. We then propose extensions of organizational routines theory that incorporate material artifacts in the generative system known as routines. These contributions exemplify two of the many challenges inherent in adopting materiality as a new research focus in the study of IT’s organizational impacts.
Semantic description, discovery and integration for the Internet of Things

To share and publish the domain knowledge of IoT objects, the development of a semantic IoT model based directory system that manages meta-data and relationships of IoT objects is required. Many researches focus on static relationships between IoT objects. However, because complex relationships between various resources change with time in an IoT environment, an efficient method for updating the meta-data is required. Thus, we propose an IoT-DS as the IoT directory that supports semantic description, discovery, and integration of IoT objects. Firstly, we introduce a semantic IoT component model to establish a shared conceptualization. Secondly, we present general cases of relationships to efficiently interact between IoT-DS and IoT objects. Thirdly, we construct IoT-DS as a Web portal. Finally, we verify in our evaluation study that the query processing time and communication workload imposed by the proposed approach are reduced.
Dermoscopic patterns of acral melanocytic nevi and melanomas in a white population in central Italy.

To investigate the dermoscopic features of acral melanocytic lesions in a white population in central Italy.



Retrospective review.



University dermatology department.



Six hundred fifty-one Italian subjects, ranging in age from 6 months to 78 years.



We retrospectively investigated all digital dermoscopic images of acral melanocytic lesions included in our database from January 1996 to May 2005.



We retrieved digital images of 723 benign acral melanocytic lesions in 641 patients (235 males and 406 females; mean age, 26.5 years) and of 10 acral melanomas in 10 patients (7 males and 3 females; mean age, 65 years). Individual lesions were located on the soles (n=520), fingers (n=146), and palms (n=67). Among acral nevi, the parallel furrow (42.1%) was the most common pattern, followed by the latticelike (14.9%), nontypical (13.7%), fibrillar (10.8%), homogeneous (9.3%), globular (5.4%), and reticular (2.1%) patterns. The frequency of distribution of the latticelike, nontypical, fibrillar, and homogeneous patterns significantly differed (P<.001, P=.03, P<.001, and P=.03, respectively) between anatomical sites. Also, 13 acral nevi (1.8%), mainly located on the fingers, showed a new combined pattern (transition pattern) consisting of a brownish black network associated with a parallel furrow or latticelike pattern. All 10 acral melanomas showed a multicomponent dermoscopic pattern.



In our series of acral nevi, we observed 8 dermoscopic patterns, with varying distribution by anatomical site. Identification of a specific pattern is highly suggestive of the benign or the malignant nature of any given acral melanocytic lesion.
Business Analytics: Enabling Strategic Alignment and organisational Transformation

Business analytics systems can potentially create value and provide competitive advantage to organisations. We argue that business analytics systems can also enable alignment between business strategy and IT strategy and support organisational transformation. We explain how a data warehouse, standardised metrics and global reporting supported the strategic transformation of a diversified and multi-layered international mining company to a unified and integrated global organisation. We identify a number of factors critical to the success of the business analytics initiative and to strategic alignment and organisational transformation, including: the early definition of globally standard metrics and dimensions; a high quality technology infrastructure and high quality data; sustained senior management support together with strong leadership within the business analytics project; effective governance structures that support change management and continual renewal of business analytics capabilities; and hybrid people with a mix of technical, business and strong communication skills. A number of implications of the study are discussed and suggestions for future work are provided.
Pulsating torque minimization techniques for permanent magnet AC motor drives-a review

AbstructPermanent magnet ac (PMAC) motor drives are finding expanded use in high-performance applications where torque smoothness is essential. This paper reviews a wide range of motorand controller-based design techniques that have been described in the literature for minimizing the generation of cogging and ripple torques in both sinusoidal and trapezoidal PMAC motor drives. Sinusoidal PMAC drives generally show the greatest potential for pulsating torque minimization using wellknown motor design techniques such as skewing and fractional slot pitch windings. In contrast, trapezoidal PMAC drives pose more difficult tradeoffs in both the motor and controller design which may require compromises in drive simplicity and cost to improve torque smoothness. Controller-based techniques for minimizing pulsating torque typically involve the use of active cancellation algorithms which depend on either accurate tuning or adaptive control schemes for effectiveness. In the end, successful suppression of pulsating torque ultimately relies on an orchestrated systems approach to all aspects of the PMAC machine and controller design which often requires a carefully selected combination of minimization techniques.
Effects of energy drinks on the cardiovascular system

Throughout the last decade, the use of energy drinks has been increasingly looked upon with caution as potentially dangerous due to their perceived strong concentration of caffeine aside from other substances such as taurine, guarana, and L-carnitine that are largely unknown to the general public. In addition, a large number of energy drink intoxications have been reported all over the world including cases of seizures and arrhythmias. In this paper, we focus on the effect of energy drinks on the cardiovascular system and whether the current ongoing call for the products' sales and regulation of their contents should continue.
The Role of E-Commerce in Electronic Customer Relationship Management ( ECRM ) and the Factors Affecting It

The emerging of new technologies have been primary and tremendous effects on different facets of commercial organizations in today’s world. One of the facets of this matter is customer satisfaction and trust through this way.The senior organization and company’s managers evidently know that the success and also their survival depended upon their customer’s satisfaction. Customer satisfaction is also a key for them which are also so sensitive. ECRM or customer relationship management electronically has managed to help organizations in this field. In article, the contents, aims and the benefits of CRM and ECRM have been considered. Their considerations as recognizing the behavioral types of customers to customize and increase customer’s loyalties to organizations have also been emphasized.
Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks

In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.
What influences literacy outcome in children with speech sound disorder?

In this study, the authors evaluated literacy outcome in children with histories of speech sound disorder (SSD) who were characterized along 2 dimensions: broader language function and persistence of SSD. In previous studies, authors have demonstrated that each dimension relates to literacy but have not disentangled their effects.  Two groups of children (86 SSD and 37 controls) were recruited at ages 5-6 and were followed longitudinally. The authors report the literacy of children with SSD at ages 7-9, compared with controls and national norms, and relative to language skill and SSD persistence (both measured at age 5-6).



The SSD group demonstrated elevated rates of reading disability. Language skill but not SSD persistence predicted later literacy. However, SSD persistence was associated with phonological awareness impairments. Phonological awareness alone predicted literacy outcome less well than a model that also included syntax and nonverbal IQ.



Results support previous literature findings that SSD history predicts literacy difficulties and that the association is strongest for SSD + language impairment (LI). Magnitude of phonological impairment alone did not determine literacy outcome, as predicted by the core phonological deficit hypothesis. Instead, consistent with a multiple deficit approach, phonological deficits appeared to interact with other cognitive factors in literacy development.
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning

I the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution (policy) found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees.
Achieving IT-Business Strategic Alignment via Enterprise-Wide Implementation of Balanced Scorecards

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Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion

The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.