nils-herrmann/clean_scirepeval huggingface dataset:
Innovations in the Field of Child Abuse and Neglect Prevention : A Review of the Literature

ii Acknowledgments I would like to thank all of the experts I interviewed during the process of writing this paper. Their deep understanding of the history and visions for the future of child maltreatment prevention provided a strong foundation for this work and guided the direction and structure of the project overall. I am grateful for how generous they were with their time and wisdom. The names and affiliations of all the professionals that participated in an interview are listed in Appendix A. Additionally, I would like to thank my mentor, Dr. Deborah Daro. She is a creative and sophisticated thinker, an eloquent and articulate writer, and a passionate and inspiring leader moving child abuse and neglect prevention efforts forward. Lastly, I would like to thank Anne Clary and Matthew Brenner for lending their editing expertise, and the greater Chapin Hall community for their support. Trend #2: Social context and culture can protect the developing child and strengthen parental capacity in important ways that can buffer against individual and contextual risk factors.. Trend #6: Implementation science offers program managers effective research frameworks to monitor and strengthen the service delivery process and to improve the odds of replicating model programs with fidelity and quality.. Trend #7: Maximizing population-level change requires new understanding of how to construct and sustain effective state systems, local community collaboration, and robust community-based Trend #8: New technologies offer important, cost-effective opportunities for advancing our reach into new populations and supporting direct service providers.. 1 Introduction Child maltreatment prevention policy and practice have been driven by a continuous stream of innovation generated by researchers, advocates, and practitioners working across disciplines to improve child safety and well-being. Settlement homes were established in the late 19 th century, social work was professionalized in the early 20 th century, child abuse injuries were identified and findings published by pediatric radiologist John Caffey and pediatrician Henry Kempe in the 1960s, and the federal government passed the Child Abuse Prevention and Treatment Act of 1972. Progress in the field has always been determined by the diligence of thought leaders in a multitude of professions and the collaboration of diverse and unlikely partners (Myers, 2011, p. 1166). Today, the nation faces an excruciating economic climate, and many parents find it hard to meet their children's most basic needs. Simultaneously, political pressure is mounting to cut social services and reduce government spending, and, as a …
An Artificial Bee Colony-Based COPE Framework for Wireless Sensor Network

In wireless communication, network coding is one of the intelligent approaches to process the packets before transmitting for efficient information exchange. The goal of this work is to enhance throughput by using the intelligent technique, which may give comparatively better optimization. This paper introduces a biologically-inspired coding approach called Artificial Bee Colony Network Coding (ABC-NC), a modification in the COPE framework. The existing COPE and its variant are probabilistic approaches, which may not give good results in all of the real-time scenarios. Therefore, it needs some intelligent technique to find better packet combinations at intermediate nodes before forwarding to optimize the energy and maximize the throughput in wireless networks. This paper proposes ABC-NC over the existing COPE framework for the wireless environment.
Why can LDA be performed in PCA transformed space?

PCA plus LDA is a popular framework for linear discriminant analysis (LDA) in high dimensional and singular case. In this paper, we focus on building a theoretical foundation for this framework. Moreover, we point out the weakness of the previous LDA based methods, and suggest a complete PCA plus LDA algorithm. Experimental results on ORL face image database indicate that the proposed method is more e6ective than the previous ones. ? 2002  on behalf of Pattern Recognition Society.
A low-voltage, low quiescent current, low drop-out regulator

The demand for low voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDOs are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDOs at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 μA. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V respectively.
Applying MIPVU Metaphor Identification Procedure on Czech

This paper represents the current state of the research project aimed at modifying the MIPVU protocol for metaphor annotation for usage on Czech-language texts. Three annotators were trained to use metaphor identification procedure MIPVU and annotated 2 short text excerpts of about 600 tokens length, then the reliability of annotation was measured using Fleiss’ kappa. The resultant interannotator agreement of 0.70 was below kappa values reported by annotators of VU Amsterdam Metaphor Corpus (Steen et al., 2010) and very similar to the agreement that researchers (Badryzlova et al., 2013) got in their first reliability test with unmodified MIPVU procedure applied on Russian texts. Some modifications of the annotation procedure are proposed in order for it to be more suitable for Czech language. The modifications are based on the observations made by annotators in error analysis and by authors of similar projects aimed to transfer MIPVU procedure to Slavic/inflected languages. The functionality of the annotation procedure refinements now have to be tested in the second reliability test.
Log-DenseNet: How to Sparsify a DenseNet

Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet’s extreme connectivity pattern may hinder its scalability to high depths, and in applications like fully convolutional networks, full DenseNet connections are prohibitively expensive. This work first experimentally shows that one key advantage of skip connections is to have short distances among feature layers during backpropagation. Specifically, using a fixed number of skip connections, the connection patterns with shorter backpropagation distance among layers have more accurate predictions. Following this insight, we propose a connection template, Log-DenseNet, which, in comparison to DenseNet, only slightly increases the backpropagation distances among layers from 1 to (1 + log2 L), but uses only L log2 L total connections instead of O(L). Hence, Log-DenseNets are easier than DenseNets to implement and to scale. We demonstrate the effectiveness of our design principle by showing better performance than DenseNets on tabula rasa semantic segmentation, and competitive results on visual recognition.
Metamodel for Service Design and Service Innovation: Integrating Service Activities, Service Systems, and Value Constellations

This paper presents a metamodel that addresses service system design and innovation by traversing and integrating three essential layers, service activities, service systems, and value constellations. The metamodel's approach to service systems as service-inoperation is an alternative to another currently used approach that views service systems as systems of economic exchange. The metamodel addresses service science topics including basic concepts of service science, design thinking for service systems, decomposition within service systems, and integration of IT service architecture with
Analysis of Multiresolution Image Denoising Schemes Using Generalized Gaussian and Complexity Priors

Recent research on universal and minimax wavelet shrinkage and thresholding methods has demonstrated near{ideal estimation performance in various asymptotic frameworks. However, image processing practice has shown that universal thresholding methods are outperformed by simple Bayesian estimators assuming independent wavelet coeecients and heavy{tailed priors such as Generalized Gaussian distributions (GGDs). In this paper, we investigate various connections between shrinkage methods and MAP estimation using such priors. In particular, we state a simple condition under which MAP estimates are sparse. We also introduce a new family of complexity priors based upon Rissanen's universal prior on integers. One particular estimator in this class outperforms conventional wavelet{based MDL estimators. We develop analytical expressions for the shrinkage rules implied by GGD and complexity priors. This allows us to show the equivalence between universal hard thresholding, MAP estimation using a very heavy{ tailed GGD, and MDL estimation using one of the new complexity priors. Theoretical analysis supported by numerous practical experiments shows the robustness of some of these estimates against misspeciications of the prior { a basic concern in image processing applications.
Smile Esthetics – A Literature

Esthetic dental treatment involves artistic and subjective components design to create the illusion of beauty. An organised systematic approach is required to evaluate, diagnose and resolve esthetic problems predictably. Our ultimate goal is to achieve pleasing composition in the smile by creating an arrangement of various esthetic elements. One of the most important tasks in esthetic dentistry is creating harmonious proportions between the widths of maxillary anterior teeth when restoring or replacing these teeth This review article describes application of the Golden Proportion and Red Proportion in dentistry also the smile design for completely edentulous and for full mouth rehabilitation. Also the future scope for designing smile .
Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications

We describe a generalization of the PAC learning model that is based on statistical decision theory. In this model the learner receives randomly drawn examples, each example consisting of an instance x E X and an outcome J E Y, and tries to find a decision rule h: X + A, where h E X, that specifies the appropriate action a E A to take for each instance x in order to minimize the expectation of a loss I( y, a). Here X, Y, and A are arbitrary sets. I is a real-valued function, and examples are generated according to an arbitrary joint distribution on Xx Y. Special cases include the problem of learning a function from X into Y, the problem of learning the conditional probability distribution on Y given X (regression), and the problem of learning a distribution on X (density estimation). We give theorems on the uniform convergence of empirical loss estimates to true expected loss rates for certain decision rule spaces 2, and show how this implies learnability with bounded sample size, disregarding computational complexity. As an application, we give distribution-independent upper bounds on the sample size needed for learning with feedforward neural networks, Our theorems use a generalized notion of VC dimension that applies to classes of real-valued functions, adapted from Vapnik and Pollard’s work, and a notion of capacity and metric dimension for classes of functions that map into a bounded metric space. ‘(‘1 1992 Academic Press, Inc.
Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms

The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms. Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep. Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied. A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied. The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image. Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations. The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds. Twenty student observers were asked to detect the orientation of the spiculation in the image. There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4. The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.
MOS PARAMETER EXTRACTION AND OPTIMIZATION WITH GENETIC ALGORITHM

Extracting an optimal set of parameter values for a MOS device is great importance in contemporary technology is a complex problem. Traditional methods of parameter extraction can produce far from optimal solutions because of the presence of local optimum in the solution space. Genetic algorithms are well suited for finding near optimal solutions in irregular parameter spaces. In this study*, We have applied a genetic algorithm to the problem of device model parameter extraction and are able to produce models of superior accuracy in much less time and with less reliance on human expertise. MOS transistor’s parameters have been extracted and optimized with genetic algorithm. 0.35μm fabricated by C35 process have been used for the results of experimental studies of parameter extraction. Extracted parameters’ characteristic data results have been compared with measurement results. Different values of parameters of genetic algorithm, such as population size, crossover rate , and generation size are compared by different tests.
Rules or connections in past-tense inflections: what does the evidence rule out?

Pinker and colleagues propose two mechanisms - a rule system and a lexical memory - to form past tenses and other inflections. They predict that children's acquisition of the regular inflection is sudden; that the regular inflection applies uniformly regardless of phonological, semantic or other factors; and that the rule system is separably vulnerable to disruption. A connectionist account makes the opposite predictions. Pinker has taken existing evidence as support for his theory, but the review of the evidence presented here contradicts this assessment. Instead, it supports all three connectionist predictions: gradual acquisition of the past tense inflection; graded sensitivity to phonological and semantic content; and a single, integrated mechanism for regular and irregular forms, dependent jointly on phonology and semantics.
Adaptive scale based entropy-like estimator for robust fitting

In this paper, we propose a novel robust estimator, called ASEE (Adaptive Scale based Entropy-like Estimator) which minimizes the entropy of inliers. This estimator is based on IKOSE (Iterative Kth Ordered Scale Estimator) and LEL (Least Entropy-Like Estimator). Unlike LEL, ASEE only considers inliers' entropy while excluding outliers, which makes it very robust in parametric model estimation. Compared with other robust estimators, ASEE is simple and computationally efficient. From the experiments on both synthetic and real-image data, ASEE is more robust than several state-of-the-art robust estimators, especially in handling extreme outliers.
iPhone 3 GS Forensics : Logical analysis using Apple iTunes Backup Utility

The iPhone mobile is used worldwide due to its enhanced computing capabilities, increased storage capacity as well as its attractive touch interface. These characteristics made the iPhone a popular smart phone device. The increased use of the iPhone lead it to become a potential source of digital evidence in criminal investigations. Therefore, iPhone forensics turned into an essential practice for forensic and security practitioners today. This research aimed at investigating and examining the logical backup acquisition of the iPhone 3GS mobile device using the Apple iTunes backup utility. It was found that significant data of forensic value such as e-mail messages, text and multimedia messages, calendar events, browsing history, GPRS locations, contacts, call history and voicemail recording can be retrieved using this method of iPhone acquisition.
Neural Machine Translation Advised by Statistical Machine Translation

Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016; He et al. 2016). This is in contrast to conventional Statistical Machine Translation (SMT), which usually yields adequate but non-fluent translations. It is natural, therefore, to leverage the advantages of both models for better translations, and in this work we propose to incorporate SMT model into NMT framework. More specifically, at each decoding step, SMT offers additional recommendations of generated words based on the decoding information from NMT (e.g., the generated partial translation and attention history). Then we employ an auxiliary classifier to score the SMT recommendations and a gating function to combine the SMT recommendations with NMT generations, both of which are jointly trained within the NMT architecture in an end-to-end manner. Experimental results on Chinese-English translation show that the proposed approach achieves significant and consistent improvements over state-of-the-art NMT and SMT systems on multiple NIST test sets.
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method: (1) In a semi-automated setup, the user annotates some slices in the volume to be segmented. The network learns from these sparse annotations and provides a dense 3D segmentation. (2) In a fully-automated setup, we assume that a representative, sparsely annotated training set exists. Trained on this data set, the network densely segments new volumetric images. The proposed network extends the previous u-net architecture from Ronneberger et al. by replacing all 2D operations with their 3D counterparts. The implementation performs on-the-fly elastic deformations for efficient data augmentation during training. It is trained end-to-end from scratch, i.e., no pre-trained network is required. We test the performance of the proposed method on a complex, highly variable 3D structure, the Xenopus kidney, and achieve good results for both use cases.
Two-Handed Sign Language Recognition for Bangla Character Using Normalized Cross Correlation

Sign language detection and recognition (SLDR) using computer vision is a very challenging task. In respect to Bangladesh, sign language users are around 2.4 million . In this paper, we try to focus for communicating with those users by computer vision. In this respect, an efficient method is propose consists of two basic steps: (a) refinement and (b) recognition. Initially in refinement, a Red-Green-Blue (RGB) color model is adopted to select heuristically threshold value for detecting candidate regions (i.e. hand and wrist band sign regions). After the candidate regions are obtained by applying color segmentation, then procedures for refining the candidate region are followed by using two different color wrist band regions and filtering. Finally, statistically based template matching technique is used for recognition of hand sign regions. Various hand sign images are used to test the proposed method and results are presented to provide its effectiveness.
Object Recognition using 3D SIFT in Complex CT Volumes

The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.
Recent Themes in Case-Based Reasoning and Knowledge Discovery

Case-based reasoning (CBR) systems have tight connections with machine learning and knowledge discovery and often incorporate diverse knowledge discovery functionalities and algorithms. This article presents themes identified in work presented at recent workshops on synergies between CBR and knowledge discovery. Among the main themes appear Big Data, with cases involving signals, images, texts, and other complex types of data; similarity metric discovery, in the form of weight spaces, feature weights, and feature selection; adaptation knowledge; explainability and transparency; and user centeredness and interactivity. Researchers highlight the advantages of case-based reasoning in terms of its lazy learning, explainability, user centeredness, and interactivity when performing knowledge discovery, as well as how diverse knowledge discovery methods can improve CBR.
Consumer Trust in an Internet Store: A Cross-Cultural Validation

Original Article Download all figures Many have speculated that trust plays a critical role in stimulating consumer purchases over the Internet. Most of the speculations have rallied around U.S. consumers purchasing from U.S.–based online merchants. The global nature of the Internet raises questions about the robustness of trust effects across cultures. Culture may also affect the antecedents of consumer trust; that is, consumers in different cultures might have differing expectations of what makes a web merchant trustworthy. Here we report on a cross-cultural validation of an Internet consumer trust model. The model examined both antecedents and consequences of consumer trust in a Web merchant. The results provide tentative support for the generalizability of the model. Article activity alert Receive exclusive of f ers and updates f rom Oxf ord Academic