nils-herrmann/clean_scirepeval huggingface dataset:
The dual process model of coping with bereavement: rationale and description.

There are shortcomings in traditional theorizing about effective ways of coping with bereavement, most notably, with respect to the so-called "grief work hypothesis." Criticisms include imprecise definition, failure to represent dynamic processing that is characteristic of grieving, lack of empirical evidence and validation across cultures and historical periods, and a limited focus on intrapersonal processes and on health outcomes. Therefore, a revised model of coping with bereavement, the dual process model, is proposed. This model identifies two types of stressors, loss- and restoration-oriented, and a dynamic, regulatory coping process of oscillation, whereby the grieving individual at times confronts, at other times avoids, the different tasks of grieving. This model proposes that adaptive coping is composed of confrontation--avoidance of loss and restoration stressors. It also argues the need for dosage of grieving, that is, the need to take respite from dealing with either of these stressors, as an integral part of adaptive coping. Empirical research to support this conceptualization is discussed, and the model's relevance to the examination of complicated grief, analysis of subgroup phenomena, as well as interpersonal coping processes, is described.
Text-based CAPTCHA strengths and weaknesses

We carry out a systematic study of existing visual CAPTCHAs based on distorted characters that are augmented with anti-segmentation techniques. Applying a systematic evaluation methodology to 15 current CAPTCHA schemes from popular web sites, we find that 13 are vulnerable to automated attacks. Based on this evaluation, we identify a series of recommendations for CAPTCHA designers and attackers, and possible future directions for producing more reliable human/computer distinguishers.
System dynamics in software project management: towards the development of a formal integrated framework

Successful software development is becoming increasingly important as software basedsystems are at the core of a company`s new products. However, recent surveys show that most projects fail to meet their targets highlighting the inadequacies of traditional project management techniques to cope with the unique characteristics of this field. Despite the major breakthroughs in the discipline of software engineering, improvement of management methodologies has not occurred, and it is now recognised that the major opportunities for better results are to be found in this area. Poor strategic management and related human factors have been cited as a major cause for failures in several industries. Traditional project management techniques have proven inadequate to incorporate explicitly these higher-level and softer issues. System Dynamics emerged as a methodology for modelling the behaviour of complex socio-economic systems. There has been a number of applications to project management, and in particular in the field of software development. This new approach provides the opportunity for an alternative view in which the major project influences are considered and quantified explicitly. Grounded on a holistic perspective it avoids consideration of the detail required by the traditional tools and ensures that the key aspects of the general project behaviour are the main priority. However, if the approach is to play a core role in future of software project management it needs to embedded within the traditional decision-making framework. The authors developed a conceptual integrated model, the PMIM, which is now being tested and improved within a large on-going software project. Such a framework should specify the roles of system dynamics models, how they are to be used within the traditional management process, how they exchange information with the traditional models, and a general method to support model development. This paper identifies the distinctive contribution of System Dynamics to software management, proposes a conceptual model for an integrated management framework, and discusses its underlying principles.  News Join our email list to receive details of when new research papers are published and the quarterly departmental newsletter. To subscribe send a blank email to managementsciencesubscribe@egroups.com. Details of our research papers can be found at www.mansci.strath.ac.uk/papers.html. Management Science, University of Strathclyde, Graham Hills Building, 40 George Street, Glasgow, Scotland.  mgtsci@mansci.strath.ac.uk Tel: +44 (0)141 548 3613 Fax: +44 (0)141 552 6686
Generalized Zero-Shot Learning via Synthesized Examples

We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero-shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.
Understanding Principal Component Analysis Using a Visual Analytics Tool

Principle Component Analysis (PCA) is a mathematical procedure widely used in exploratory data analysis, signal processing, etc. However, it is often considered a black box operation whose results and procedures are difficult to understand. The goal of this paper is to provide a detailed explanation of PCA based on a designed visual analytics tool that visualizes the results of principal component analysis and supports a rich set of interactions to assist the user in better understanding and utilizing PCA. The paper begins by describing the relationship between PCA and single vector decomposition (SVD), the method used in our visual analytics tool. Then a detailed explanation of the interactive visual analytics tool, including advantages and limitations, is provided.
Rethinking Machine Learning Development and Deployment for Edge Devices

Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As the algorithms become mature and efficient, more and more ML inference is moving out of datacenters/cloud and deployed on edge devices. This model deployment process can be challenging as the deployment environment and requirements can be substantially different from those during model development. In this paper, we propose a new ML development and deployment approach that is specially designed and optimized for inference-only deployment on edge devices. We build a prototype and demonstrate that this approach can address all the deployment challenges and result in more efficient and high-quality solutions.
The neuroscience of placebo effects: connecting context, learning and health

Placebo effects are beneficial effects that are attributable to the brain–mind responses to the context in which a treatment is delivered rather than to the specific actions of the drug. They are mediated by diverse processes — including learning, expectations and social cognition — and can influence various clinical and physiological outcomes related to health. Emerging neuroscience evidence implicates multiple brain systems and neurochemical mediators, including opioids and dopamine. We present an empirical review of the brain systems that are involved in placebo effects, focusing on placebo analgesia, and a conceptual framework linking these findings to the mind–brain processes that mediate them. This framework suggests that the neuropsychological processes that mediate placebo effects may be crucial for a wide array of therapeutic approaches, including many drugs.
CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System

Recently, Internet of Things (IoT) and cloud computing (CC) have been widely studied and applied in many fields, as they can provide a new method for intelligent perception and connection from M2M (including man-to-man, man-to-machine, and machine-to-machine), and on-demand use and efficient sharing of resources, respectively. In order to realize the full sharing, free circulation, on-demand use, and optimal allocation of various manufacturing resources and capabilities, the applications of the technologies of IoT and CC in manufacturing are investigated in this paper first. Then, a CC- and IoT-based cloud manufacturing (CMfg) service system (i.e., CCIoT-CMfg) and its architecture are proposed, and the relationship among CMfg, IoT, and CC is analyzed. The technology system for realizing the CCIoT-CMfg is established. Finally, the advantages, challenges, and future works for the application and implementation of CCIoT-CMfg are discussed.
An Evaluation of the Human-Interpretability of Explanation

The evaluation of interpretable machine learning systems is challenging, as explanation is almost always a means toward some downstream task. In this work, we carefully control a number of properties of logic-based explanations (overall length, number of repeated terms, etc.) to determine their effect on human ability to perform three basic tasks: simulating the system’s response, verification of a suggested response, and counterfactual reasoning. Our findings about how each of these properties affect the ability of humans to perform each task provide insights on how we might construct regularizers to optimize for task performance.
Deep Structures of Collaboration

Collective intelligence (CI), a group’s capacity to perform a wide variety of tasks, is a key factor in successful collaboration. Group composition, particularly diversity and member social perceptiveness, are consistent predictors of CI, but we have limited knowledge about the mechanisms underlying their effects. To address this gap, we examine how physiological synchrony, as an indicator of coordination and rapport, relates to CI in computer-mediated teams, and if synchrony might serve as a mechanism explaining the effect of group composition on CI. We present results from a laboratory experiment where 120 dyads completed the Test of Collective Intelligence (TCI) together online and rated their group satisfaction, while wearing physiological sensors. The first 60 dyads communicated via video and audio in study 1, while the next 60 dyads communicated via audio only in study 2. In study 1, we find that synchrony in facial expressions and synchrony in standard deviation of loudness in speech (both indicative of shared experience) was associated with CI and synchrony in electrodermal activity (indicative of shared arousal) with group satisfaction. Furthermore, various forms of synchrony mediated the effect of member diversity and social perceptiveness on CI and group satisfaction. In study 2, synchrony in facial expressions no longer had an effect on CI, but synchrony in standard deviation of loudness in speech continued to positively effect CI. Our results have important implications for online collaborations and distributed teams.
Subspace Learning for Facial Age Estimation Via Pairwise Age Ranking

Age is one of the important biometric traits for reinforcing the identity authentication. The challenge of facial age estimation mainly comes from two difficulties: (1) the wide diversity of visual appearance existing even within the same age group and (2) the limited number of labeled face images in real cases. Motivated by previous research on human cognition, human beings can confidently rank the relative ages of facial images, we postulate that the age rank plays a more important role in the age estimation than visual appearance attributes. In this paper, we assume that the age ranks can be characterized by a set of ranking features lying on a low-dimensional space. We propose a simple and flexible subspace learning method by solving a sequence of constrained optimization problems. With our formulation, both the aging manifold, which relies on exact age labels, and the implicit age ranks are jointly embedded in the proposed subspace. In addition to supervised age estimation, our method also extends to semi-supervised age estimation via automatically approximating the age ranks of unlabeled data. Therefore, we can successfully include more available data to improve the feature discriminability. In the experiments, we adopt the support vector regression on the proposed ranking features to learn our age estimators. The results on the age estimation demonstrate that our method outperforms classic subspace learning approaches, and the semi-supervised learning successfully incorporates the age ranks from unlabeled data under different scales and sources of data set.
Learning to Filter Object Detections

Most object detection systems consist of three stages. First, a set of individual hypotheses for object locations is generated using a proposal generating algorithm. Second, a classifier scores every generated hypothesis independently to obtain a multi-class prediction. Finally, all scored hypotheses are filtered via a non-differentiable and decoupled non-maximum suppression (NMS) post-processing step. In this paper, we propose a filtering network (FNet), a method which replaces NMS with a differentiable neural network that allows joint reasoning and rescoring of the generated set of hypotheses per image. This formulation enables end-to-end training of the full object detection pipeline. First, we demonstrate that FNet, a feed-forward network architecture, is able to mimic NMS decisions, despite the sequential nature of NMS. We further analyze NMS failures and propose a loss formulation that is better aligned with the mean average precision (mAP) evaluation metric. We evaluate FNet on several standard detection datasets. Results surpass standard NMS on highly occluded settings of a synthetic overlapping MNIST dataset and show competitive behavior on PascalVOC2007 and KITTI detection benchmarks.
Green Marketing and Its Impact on Consumer Buying Behavior

This study aims to give information about the effect of green marketing on customers purchasing behaviors. First of all, environment and environmental problems, one of the reason why the green marketing emerged, are mentioned, and then the concepts of green marketing and green consumer are explained. Then together with the hypothesis developed literature review has been continued and studies conducted on this subject until now were mentioned. In the last section, moreover, questionnaire results conducted on 540 consumers in Istanbul are evaluated statistically. According to the results of the analysis, environmental awareness, green product features, green promotion activities and green price affect green purchasing behaviors of the consumers in positive way. Demographic characteristics have moderate affect on model.
Malleability of the blockchain’s entropy

Trustworthy generation of public random numbers is necessary for the security of a number of cryptographic applications. It was suggested to use the inherent unpredictability of blockchains as a source of public randomness. Entropy from the Bitcoin blockchain in particular has been used in lotteries and has been suggested for a number of other applications ranging from smart contracts to election auditing. In this Arcticle, we analyse this idea and show how an adversary could manipulate these random numbers, even with limited computational power and financial budget.
MRI Tumor Segmentation with Densely Connected 3D CNN

In this paper, we develop a new densely connected 3D Convolutional Neural Network (CNN) to segment subregions in primary brain tumors, so-called high-grade gliomas. These densely connected 3D CNNs have shortcut connections which have recently gained attention in designing deep CNNs due to improved performance in various pattern recognition applications. The network architecture we developed segments sub-regions of the glioma using four types of magnetic resonance imaging (MRI) sequences. The four sequence types are Fluid Attenuated Inversion Recovery (FLAIR), native (T1), post contrast T1-weighted (T1ce) and T2-weighted (T2) which are used to segment the tissue classes non-enhancing tumor (NET), peritumoral edema (ED) and GDenhancing tumor (ET). This task is highly biased, since lesion areas only take up a small portion of the whole scan. To deal with this data imbalance, we extract 3D patches and propose a densely connected 3D CNN model to classify the 3D patches. In addition to the patch network, we devise a novel cascaded training schema that efficiently segments MRI scans and achieves a good performance. We are able to achieve the following Dice coefficients (NET: 0.5545, ED: 0.7986, ET: 0.8675). Our results are comparable to the reported state-of-the-art results, which demonstrates the effectiveness of our networks.
Lattice Boltzmann Methods for Computational Fluid Dynamics

The Lecture Notes are used for the a short course on the theory and applications of the lattice Boltzmann methods for computational uid dynamics taugh by the author at Institut f ur Computeranwendungen im Bauingenieurwesen (CAB), Technischen Universitat Braunschweig, during August 7  2003. The lectures cover the basic theory of the lattice Boltzmann equation and its applications to hydrodynamics. Lecture One brie y reviews the history of the lattice gas automata and the lattice Boltzmann equation and their connections. Lecture Two provides an a priori derivation of the lattice Boltzmann equation, which connects the lattice Boltzmann equation to the continuous Boltzmann equation and demonstrates that the lattice Boltzmann equation is indeed a special nite di erence form of the Boltzmann equation. Lecture Two also includes the derivation of the lattice Boltzmann model for nonideal gases from the Enskog equation for dense gases. Lecture Three studies the generalized lattice Boltzmann equation with multiple relaxation times. A summary is provided at the end of each Lecture. Lecture Four discusses the uid-solid boundary conditions in the lattice Boltzmann methods. Applications of the lattice Boltzmann mehod to particulate suspensions, turbulence ows, and other ows are also shown. An Epilogue on the rationale of the lattice Boltzmann method is given. Some key references in the literature is also provided.
An Algorithm for Learning Shape and Appearance Models without Annotations

This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. It is based on the idea that having a more accurate shape and appearance model leads to more accurate image registration, which in turn leads to a more accurate shape and appearance model. This leads naturally to an iterative scheme, which is based on a probabilistic generative model that is fit using Gauss-Newton updates within an EM-like framework. It was developed with the aim of enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance basis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle “missing data”, which allows it to be cross-validated according to how well it can predict left-out voxels. The Email address: j.ashburner@ucl.ac.uk (John Ashburner) Preprint submitted to Medical Image Analysis July 30, 2018 ar X iv :1 80 7. 10 73 1v 1 [ cs .C V ] 2 7 Ju l 2 01 8
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization

Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but it is still not clear enough why such training techniques work well in practice and how we can maximize their benefit in the presence of two conflicting objectives—optimizing to true data distribution and preventing overfitting by regularization. This paper addresses the above issues by 1) interpreting that the conventional training methods with regularization by noise injection optimize the lower bound of the true objective and 2) proposing a technique to achieve a tighter lower bound using multiple noise samples per training example in a stochastic gradient descent iteration. We demonstrate the effectiveness of our idea in several computer vision applications.
Preliminary System Dynamics Maps of the Insider Cyber-threat Problem

Twenty five researchers from eight institutions and a variety of disciplines, viz. computer science, information security, knowledge management, law enforcement, psychology, organization science and system dynamics, found each other February 2004 in the “System Dynamics Modelling for Information Security: An Invitational Group Modeling Workshop” at Software Engineering Institute, Carnegie Mellon University. The exercise produced preliminary system dynamics models of insider and outsider cyber attacks that motivated five institutions, viz. Syracuse University, TECNUN at University of Navarra, CERT/CC at Carnegie Mellon University, University at Albany and Agder University College, to launch an interdisciplinary research proposal (Improving Organizational Security and Survivability by Suppression of Dynamic Triggers). This paper discusses the preliminary system dynamic maps of the insider cyber-threat and describes the main ideas behind the research proposal.
The Wearable Eletronic Rescue System for Home Alone Elderly- Labview & Arduino Evaluation

The socio-economic condition & various health hazards are the main suffering at old age. To combat this situation, we have tried to develop and fabricate one wearable electronic rescue system for elderly especially when he is at home alone. The system can detect abnormal condition of heart as well as sudden accidental fall at home. The system has been developed using Arduino Microcontroller and GSM modem. The entire program and evaluation has been developed under LabView platform. The prototype was built and trialed successfully.