nils-herrmann/clean_scirepeval huggingface dataset:
Mindfulness based cognitive therapy for psychiatric disorders: A systematic review and meta-analysis

Mindfulness- based Cognitive Therapy (MBCT) is a meditation program based on an integration of Cognitive behavioural therapy and Mindfulness-based stress reduction. The aim of the present work is to review and conduct a meta-analysis of the current findings about the efficacy of MBCT for psychiatric patients. A literature search was undertaken using five electronic databases and references of retrieved articles. Main findings included the following: 1) MBCT in adjunct to usual care was significantly better than usual care alone for reducing major depression (MD) relapses in patients with three or more prior depressive episodes (4 studies), 2) MBCT plus gradual discontinuation of maintenance ADs was associated to similar relapse rates at 1year as compared with continuation of maintenance antidepressants (1 study), 3) the augmentation of MBCT could be useful for reducing residual depressive symptoms in patients with MD (2 studies) and for reducing anxiety symptoms in patients with bipolar disorder in remission (1 study) and in patients with some anxiety disorders (2 studies). However, several methodological shortcomings including small sample sizes, non-randomized design of some studies and the absence of studies comparing MBCT to control groups designed to distinguish specific from non-specific effects of such practice underscore the necessity for further research.
Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series

We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the intensive care unit (ICU) of a major urban medical center, our data consists of multivariate time series of observations. The data is irregularly sampled, leading to missingness patterns in re-sampled sequences. In this work, we show the remarkable ability of RNNs to make effective use of binary indicators to directly model missing data, improving AUC and F1 significantly. However, while RNNs can learn arbitrary functions of the missing data and observations, linear models can only learn substitution values. For linear models and MLPs, we show an alternative strategy to capture this signal. Additionally, we evaluate LSTMs, MLPs, and linear models trained on missingness patterns only, showing that for several diseases, what tests are run can be more predictive than the results themselves.
The psychological consequences of money.

Money has been said to change people's motivation (mainly for the better) and their behavior toward others (mainly for the worse). The results of nine experiments suggest that money brings about a self-sufficient orientation in which people prefer to be free of dependency and dependents. Reminders of money, relative to nonmoney reminders, led to reduced requests for help and reduced helpfulness toward others. Relative to participants primed with neutral concepts, participants primed with money preferred to play alone, work alone, and put more physical distance between themselves and a new acquaintance.
PENTOS: Penetration testing tool for Internet of Thing devices

The Internet of Thing (IoT) technology has been growing rapidly with many implementations. However, because of its ability to perform tasks and handle the sensitive information and also the paucity of user security awareness, IoT devices contain many potential risks and are the new target of attacks. In this paper, we develop a penetration testing system for IoT devices called PENTOS in order to increase the user security awareness. The system comes with the GUI running on Kali Linux which is specifically designed for ethical hacking. PENTOS automatically gathers the information of the target IoT device through the wireless communication, which are WiFi and Bluetooth. The system allows users to perform various kinds of penetration testing on their IoT devices such as the password attack, web attack, and wireless attack in order to gain the privilege access by multiple algorithms. Moreover, the system also provides the basic security guidance according to the OWASP's Top 10 IoT Vulnerabilities to educate users and increase the security awareness. After the penetration testing, the system then summarizes the results of all attacking modules and gives the recommendations for the secure deployment to avoid possible threats.
Small-signal modeling of a three-phase isolated inverter with both voltage and frequency droop control

In conventional power system operation, droop control methods are used to facilitate load sharing among different generation sources. This method compensates for both active and reactive power imbalances by adjusting the output voltage magnitude and frequency of the generating unit. Both P-ω and Q-V droops have been used in synchronous machines for decades. Similar droop controllers were used in this study to develop a control algorithm for a three-phase isolated (islanded) inverter. Controllers modeled in a synchronous dq reference frame were simulated in PLECS and validated with the hardware setup. A small-signal model based on an averaged model of the inverter was developed to study the system's dynamics. The accuracy of this mathematical model was then verified using the data obtained from the experimental and simulation results. This validated model is a useful tool for the further dynamic analysis of a microgrid.
A Modest Approach to Checking Probabilistic Timed Automata

Probabilistic timed automata (PTA) combine discrete probabilistic choice, real time and nondeterminism. This paper presents a fully automatic tool for model checking PTA with respect to probabilistic and expected reachability properties. PTA are specified in Modest, a high-level compositional modelling language that includes features such as exception handling, dynamic parallelism and recursion, and thus enables model specification in a convenient fashion. For model checking, we use an integral semantics of time, representing clocks with bounded integer variables. This makes it possible to use the probabilistic model checker PRISM as analysis backend. We describe details of the approach and its implementation, and report results obtained for three different case studies.
Domain-specific Question Generation from a Knowledge Base

Question generation has been a research topic for a long time, where a big challenge is how to generate deep and natural questions. To tackle this challenge, we propose a system to generate natural language questions from a domain-specific knowledge base (KB) by utilizing rich web information. A small number of question templates are first created based on the KB and instantiated into questions, which are used as seed set and further expanded through the web to get more question candidates. A filtering model is then applied to select candidates with high grammaticality and domain relevance. The system is able to generate large amount of in-domain natural language questions with considerable semantic diversity and is easily applicable to other domains. We evaluate the quality of the generated questions by human judgments and the results show the effectiveness of our proposed system.
Peer and self assessment in massive online classes

Peer and self-assessment offer an opportunity to scale both assessment and learning to global classrooms. This article reports our experiences with two iterations of the first large online class to use peer and self-assessment. In this class, peer grades correlated highly with staff-assigned grades. The second iteration had 42.9% of students’ grades within 5% of the staff grade, and 65.5% within 10%. On average, students assessed their work 7% higher than staff did. Students also rated peers’ work from their own country 3.6% higher than those from elsewhere. We performed three experiments to improve grading accuracy. We found that giving students feedback about their grading bias increased subsequent accuracy. We introduce short, customizable feedback snippets that cover common issues with assignments, providing students more qualitative peer feedback. Finally, we introduce a data-driven approach that highlights high-variance items for improvement. We find that rubrics that use a parallel sentence structure, unambiguous wording, and well-specified dimensions have lower variance. After revising rubrics, median grading error decreased from 12.4% to 9.9%.
Unified particle physics for real-time applications

We present a unified dynamics framework for real-time visual effects. Using particles connected by constraints as our fundamental building block allows us to treat contact and collisions in a unified manner, and we show how this representation is flexible enough to model gases, liquids, deformable solids, rigid bodies and cloth with two-way interactions. We address some common problems with traditional particle-based methods and describe a parallel constraint solver based on position-based dynamics that is efficient enough for real-time applications.
Feature learning based on SAE-PCA network for human gesture recognition in RGBD images

Coming with the emerging of depth sensors link Microsoft Kinect, human hand gesture recognition has received ever increasing research interests recently. A successful gesture recognition system has usually heavily relied on having a good feature representation of data, which is expected to be task-dependent as well as coping with the challenges and opportunities induced by depth sensor. In this paper, a feature learning approach based on sparse auto-encoder (SAE) and principle component analysis is proposed for recognizing human actions, i.e. finger-spelling or sign language, for RGB-D inputs. The proposed model of feature learning is consisted of two components: First, features are learned respectively from the RGB and depth channels, using sparse auto-encoder with convolutional neural networks. Second, the learned features from both channels is concatenated and fed into a multiple layer PCA to get the final feature. Experimental results on American sign language (ASL) dataset demonstrate that the proposed feature learning model is significantly effective, which improves the recognition rate from 75% to 99.05% and outperforms the state-of-the-art. & 2014 Elsevier B.V. All rights reserved.
Multirobot Coordination With Periodic Connectivity: Theory and Experiments

We examine the scenario in which a mobile network of robots must search, survey, or cover an environment and communication is restricted by relative location. While many algorithms choose to maintain a connected network at all times while performing such tasks, we relax this requirement and examine the use of periodic connectivity, where the network must regain connectivity at a fixed interval. We propose an online algorithm that scales linearly in the number of robots and allows for arbitrary periodic connectivity constraints. To complement the proposed algorithm, we provide theoretical inapproximability results for connectivity-constrained planning. Finally, we validate our approach in the coordinated search domain in simulation and in real-world experiments.
Critical Success Factors for Business Intelligence Systems

June 11, 2009  August 6, 2009  September 14, 2009 The implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of critical success factors (CSFs) for management reference because the BI market has been driven mainly by the IT industry and vendors. This research seeks to bridge the gap that exists between academia and practitioners by investigating the CSFs influencing BI systems success. The study followed a two-stage qualitative approach. Firstly, the authors utilised the Delphi method to conduct three rounds of studies. The study develops a CSFs framework crucial for BI systems implementation. Next, the framework and the associated CSFs are delineated through a series of case studies. The empirical findings substantiate the construct and applicability of the framework. More significantly, the research further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results.
Customer Experience Creation : Determinants , Dynamics and Management Strategies

Peter C. Verhoef a,∗, Katherine N. Lemon b, A. Parasuraman c, Anne Roggeveen d, Michael Tsiros c, Leonard A. Schlesinger d a University of Groningen, Faculty of Economics and Business, P.O. Box 800, NL-9700 AV Groningen, The Netherlands b Boston College, Carroll School of Management, Fulton Hall 510, 140 Commonwealth Avenue, Chestnut Hill, MA 02467 United States c University of Miami, School of Business Administration, P.O. Box 24814, Coral Gables, FL 33124, United States d Babson College, 231 Forest Street, Wellesley, Massachusetts, United States
Mixed feature selection based on granulation and approximation

Feature subset selection presents a common challenge for the applications where data with tens or hundreds of features are available. Existing feature selection algorithms are mainly designed for dealing with numerical or categorical attributes. However, data usually comes with a mixed format in real-world applications. In this paper, we generalize Pawlak’s rough set model into d neighborhood rough set model and k-nearest-neighbor rough set model, where the objects with numerical attributes are granulated with d neighborhood relations or k-nearest-neighbor relations, while objects with categorical features are granulated with equivalence relations. Then the induced information granules are used to approximate the decision with lower and upper approximations. We compute the lower approximations of decision to measure the significance of attributes. Based on the proposed models, we give the definition of significance of mixed features and construct a greedy attribute reduction algorithm. We compare the proposed algorithm with others in terms of the number of selected features and classification performance. Experiments show the proposed technique is effective.
Data Mining with R: Learning with Case Studies

 Covers the main data mining techniques through carefully selected case studies  Describes code and approaches that can be easily reproduced or adapted to your own problems  Requires no prior experience with R  Includes introductions to R and MySQL basics  Provides a fundamental understanding of the merits, drawbacks, and analysis objectives of the data mining techniques  Offers data and R code on www.liaad.up.pt/~ltorgo/DataMiningWithR/
Quick Layered Response ( QLR ) Codes

We propose a new 3D barcode standard, Quick Layered Response (QLR) codes, and implemented encoding and decoding on Android devices. QLR codes are Quick Response (QR) codes superimposed in the RGB color space, and increase the capacity by a factor of 3. This advantage can be used to decrease the area needed to represent data by a factor of 3 or to increase the readable distance. QLR codes can be encoded and decoded in linearly independent RGB basis colors, meaning attractive color schemes can be chosen. We also investigate the inclusion of modern error coding schemes in QLR codes.
The factors influencing members' continuance intentions in professional virtual communities - a longitudinal study

The advance of internet technology has stimulated the rise of professional virtual communities (PVCs). The objective of PVCs is to encourage people to exploit or explore knowledge through websites. However, many virtual communities have failed due to the reluctance of members to continue their participation in these PVCs. Motivated by such concerns, this study formulates and tests a theoretical model to explain the factors influencing individuals’ intention to continue participating in PVCs’ knowledge activities. Drawing from the information system and knowledge management literatures, two academic perspectives related to PVC continuance are incorporated in the integrated model. This model posits that an individual’s intention to stay in a professional virtual community is influenced by a contextual factor and technological factors. Specifically, the antecedents of PVC members’ intention to continue sharing knowledge include social interaction ties capital and satisfaction at post-usage stage. These variables, in turn, are adjusted based on the confirmation of pre-usage expectations. A longitudinal study is conducted with 360 members of a professional virtual community. Results indicate that the contextual factor and technological factors both exert significant impacts on PVC participants’ continuance intentions.
Word Embedding Composition for Data Imbalances in Sentiment and Emotion Classification

Text classification often faces the problem of imbalanced training data. This is true in sentiment analysis and particularly prominent in emotion classification where multiple emotion categories are very likely to produce naturally skewed training data. Different sampling methods have been proposed to improve classification performance by reducing the imbalance ratio between training classes. However, data sparseness and the small disjunct problem remain obstacles in generating new samples for minority classes when the data are skewed and limited. Methods to produce meaningful samples for smaller classes rather than simple duplication are essential in overcoming this problem. In this paper, we present an oversampling method based on word embedding compositionality which produces meaningful balanced training data. We first use a large corpus to train a continuous skip-gram model to form a word embedding model maintaining the syntactic and semantic integrity of the word features. Then, a compositional algorithm based on recursive neural tensor networks is used to construct sentence vectors based on the word embedding model. Finally, we use the SMOTE algorithm as an oversampling method to generate samples for the minority classes and produce a fully balanced training set. Evaluation results on two quite different tasks show that the feature composition method and the oversampling method are both important in obtaining improved classification results. Our method effectively addresses the data imbalance issue and consequently achieves improved results for both sentiment and emotion classification.
An Experimental Comparison of Several Clustering and Initialization Methods

We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation–Maximization (EM) algorithm, a “winner take all” version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarchical agglomerative clustering. We learn naive-Bayes models with a hidden root node, using high-dimensional discrete-variable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality.
Detecting abnormal operating performance : The empirical power and specification of test statistics

This research evaluates methods used in event studies that employ accounting-based measures of operating performance. We examine the choice of an accounting-based performance measure, a statistical test, and a model of expected operating performance. We document the impact of these choices on the test statistics designed to detect abnormal operating performance. We find that commonly used research designs yield test statistics that are misspecified in cases where sample firms have performed either unusually well or poorly. In this sampling situation, the test statistics are only well specified when sample firms are matched to control firms of similar pre-event performance.  Operating performance; Event studies; Return on assets; Return on sales JEL &ss$cation: G30
Theoretical, Statistical, and Practical Perspectives on Pattern-based Classification Approaches to the Analysis of Functional Neuroimaging Data

The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These brain-reading analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.
LIFESTYLE FINDER: Intelligent User Profiling Using Large-Scale Demographic Data

taking data about a user’s likes and dislikes and generating a general profile of the user. These profiles can be used to retrieve documents matching user interests; recommend music, movies, or other similar products; or carry out other tasks in a specialized fashion. This article presents a fundamentally new method for generating user profiles that takes advantage of a large-scale database of demographic data. These data are used to generalize user-specified data along the patterns common across the population, including areas not represented in the user’s original data. I describe the method in detail and present its implementation in the LIFESTYLE FINDER agent, an internet-based experiment testing our approach on more than 20,000 users worldwide.
Mobile Attacks and Defense

In this paper smartphones are discussed. Today's smartphone are more common than computers. In fact, smart phones are simply computers with extra hardware-namely, a GSM (Global System for Mobile Communications) radio and a baseband processor to control it. These extra features are great, but with the power they provide, there's also a threat. Today, smartphones are becoming targets of attackers in the same way PCs have been for many years. This paper focus on the security models of two smart phone operating systems: Apple's iOS and Google's Android. These two have a special place in my heart because I was the first to publicly exploit both of them.