snu-aidas/HVMap-dataset huggingface dataset:
Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks

Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10±0.015 on the held-out test data (mean ± standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.
Resuming the discussion of AMSTAR: What can (should) be made better?

BACKGROUND
Evidence syntheses, and in particular systematic reviews (SRs), have become one of the cornerstones of evidence-based health care. The Assessment of Multiple Systematic Reviews (AMSTAR) tool has become the most widely used tool for investigating the methodological quality of SRs and is currently undergoing revision. The objective of this paper is to present insights, challenges and potential solutions from the point of view of a group of assessors, while referring to earlier methodological discussions and debates with respect to AMSTAR.


DISCUSSION
One major drawback of AMSTAR is that it relies heavily on reporting quality rather than on methodological quality. This can be found in several items. Furthermore, it should be acknowledged that there are now new methods and procedures that did not exist when AMSTAR was developed. For example, the note to item 1 should now refer to the International Prospective Register of Ongoing Systematic Reviews (PROSPERO). Furthermore, item 3 should consider the definition of hand-searching, as the process of reviewing conference proceedings using the search function (e.g. in Microsoft Word or in a PDF file) does not meet the definition set out by the Cochrane Collaboration. Moreover, methods for assessing the quality of the body of evidence have evolved since AMSTAR was developed and should be incorporated into a revised AMSTAR tool. Potential solutions are presented for each AMSTAR item with the aim of allowing a more thorough assessment of SRs. As the AMSTAR tool is currently undergoing further development, our paper hopes to add to preceding discussions and papers regarding this tool and stimulate further discussion.
Adaptation and coaching of periodic motion primitives through physical and visual interaction

In this paper we propose and evaluate a control system to (1) learn and (2) adapt robot motion for continuous non-rigid contact with the environment. We present the approach in the context of wiping surfaces with robots. Our approach is based on learning by demonstration. First an initial periodicmotion, covering the essence of the wiping task, is transferred from a human to a robot. The system extracts and learns one period of motion. Once the user/demonstrator is content with the motion, the robot seeks and establishes contactwith a given surface,maintaining a predefined force of contact through force feedback. The shape of the surface is encoded for the complete period ofmotion, but the robot can adapt to a different surface, perturbations or obstacles. The novelty stems from the fact that the feedforward component is learned and encoded in a dynamic movement primitive. By using the feedforward component, the feedback component is greatly reduced if not completely canceled. Finally, if the user is not satisfied with the periodic pattern, he/she can change parts of motion through predefined gestures or through physical contact in a manner of a tutor or a coach. The complete system thus allows not only a transfer ofmotion, but a transfer ofmotionwithmatching correspondences, i.e. wiping motion is constrained to maintain physical contact with the surface to be wiped. The interface for both learning and adaptation is simple and intuitive and allows for fast and reliable knowledge transfer to the robot. Simulated and real world results in the application domain of wiping a surface are presented on three different robotic platforms. Results of the three robotic platforms, namely a 7 degree-of-freedom Kuka LWR-4 robot, the ARMAR-IIIa humanoid platform and the Sarcos CB-i humanoid robot, depict different methods of adaptation to the environment and coaching. © 2015 Elsevier B.V. All rights reserved. ⇤ Corresponding author. E-mail addresses: andrej.gams@ijs.si (A. Gams), tadej.petric@ijs.si (T. Petri£), martin.do@kit.edu (M. Do), bojan.nemec@ijs.si (B. Nemec), xmorimo@atr.jp (J. Morimoto), asfour@kit.edu (T. Asfour), ales.ude@ijs.si (A. Ude).
20.7 A 13.8µW binaural dual-microphone digital ANSI S1.11 filter bank for hearing aids with zero-short-circuit-current logic in 65nm CMOS

This paper presents an ANSI S1.11 1/3-octave filter-bank chip for binaural hearing aids with two microphones per ear. Binaural multimicrophone systems significantly suppress noise interference and preserve interaural time cues at the cost of significantly higher computational and power requirements than monophonic single-microphone systems. With clock rates around the 1MHz mark, these systems are ideal candidates for low-power implementation through charge-recovery design. At such low clock frequencies, however, charge-recovery logic suffers from short-circuit currents that limit its theoretical energy efficiency [1]. The chip described in this paper is designed in 65nm CMOS using a new charge-recovery logic, called zero-short-circuit-current (ZSCC) logic, that drastically reduces short-circuit current. It processes 4 input streams at 1.75MHz with a charge recovery rate of 92%, achieving 9.7× lower power per input compared with the 40nm monophonic single-input chip that represents the published state of the art [2].
The Evolution of Culturally-Variable Sex Differences : Men and Women Are Not Always Different , but When They Are ... It Appears Not to Result from Patriarchy or Sex Role Socialization

© Springer International Publishing Switzerland 2014 T. K. Shackelford, R. D. Hansen (eds.), The Evolution of Sexuality, Evolutionary Psychology DOI 10.1007/978-3-319-09384-0_11 D. P. Schmitt () Department of Psychology, Bradley University, 75 Bradley Hall, Peoria, IL 61625, USA e-mail: dps@bradley.edu Just like all other sexually reproducing species, male and female humans are more similar than different. Even so, men’s and women’s psychological traits sometimes differ in important ways, both in terms of typical or average levels (Buss 1989; Del Giudice 2009; Ellis 2011) and in terms of variability (Archer and Mehdikhani 2003; Borkenau et al. 2013; Lippa 2009). Sex differences in numerous traits have been well-established as moderate to large in size1 and as culturally pervasive. For example, sex differences in negative emotion-related traits have been documented across several meta-analyses (Feingold 1994; Miettunen et al. 2007; Whissell 1996), integrative neuroscientific reviews (Hyde et al. 2008; Stevens and Hamann 2012), and large cross-cultural surveys (Costa et al. 2001; Hopcroft and McLaughlin 2012; Lippa 2009; McCrae and Terracciano 2005; Schmitt et al. 2008; Van de Velde et al. 2010). Using a multivariate approach, Del Giudice et al. (2012) documented across 16 personality traits—ranging from dominance and liveliness to perfectionism and tension—that sex differences in personality are astonishingly large, with only 10 % overlap in men’s and women’s overall distributions. Beyond sex differences in personality traits, psychologists have uncovered dozens of ways that men and women differ in affect, behavior, and cognition across most cultures (Archer 2014; Browne 1998; Mealey 2000). In one comprehensive review, Ellis (2011) identified 63 psychological sex differences that have been replicated across multiple cultures and at least 10 studies, with not a single replication failure (probably an overly strict exclusionary criterion; see Schmitt et al. 2014). In another wide-ranging review, Archer (2014) reported culturally-pervasive sex differences are reliably found in the assessment of negative emotions (e.g., fear, anxiety, depression), anti-social behaviors (e.g., aggression, violence, criminality), cognitive abilities (e.g., mental rotation, object location,
A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies

In this part of the paper, we review qualitative model representations and search strategies used in fault diagnostic systems. Qualitative models are usually developed based on some fundamental understanding of the physics and chemistry of the process. Various forms of qualitative models such as causal models and abstraction hierarchies are discussed. The relative advantages and disadvantages of these representations are highlighted. In terms of search strategies, we broadly classify them as topographic and symptomatic search techniques. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Various forms of topographic and symptomatic search strategies are discussed. # 2002 Published by Elsevier Science Ltd.
Beyond binary choices: Integrating individual and social creativity

The power of the unaided individual mind is highly overrated. Although society often thinks of creative individuals as working in isolation, intelligence and creativity result in large part from interaction and collaboration with other individuals. Much human creativity is social, arising from activities that take place in a context in which interaction with other people and the artifacts that embody collective knowledge are essential contributors. This paper examines: (1) how individual and social creativity can be integrated by means of proper collaboration models and tools supporting distributed cognition; (2) how the creation of shareable externalizations (“boundary objects”) and the adoption of evolutionary process models in the construction of meta-design environments can enhance creativity and support spontaneous design activities (“unselfconscious cultures of design”); and (3) how a new design competence is emerging—one that requires passage from individual creative actions to synergetic activities, from the reflective practitioner to reflective communities, and from given tasks to personally meaningful activities. The paper offers examples in the context of collaborative design and art practice, including urban planning, interactive art, and open source. In the effort to draw a viable path “beyond binary choices”, the paper points out some major challenges for the next generation of socio-technical environments to further increase the integration of individual and social creativity.
Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring

The promising potential of the emerging Internet of Things (IoT) technologies for interconnected medical devices and sensors has played an important role in the next-generation healthcare industry for quality patient care. Because of the increasing number of elderly and disabled people, there is an urgent need for a real-time health monitoring infrastructure for analyzing patients’ healthcare data to avoid preventable deaths. Healthcare Industrial IoT (HealthIIoT) has significant potential for the realization of such monitoring. HealthIIoT is a combination of communication technologies, interconnected apps, Things (devices and sensors), and people that would function together as one smart system to monitor, track, and store patients’ healthcare information for ongoing care. This paper presents a HealthIIoT-enabled monitoring framework, where ECG and other healthcare data are collected by mobile devices and sensors and securely sent to the cloud for seamless access by healthcare professionals. Signal enhancement, watermarking, and other related analytics will be used to avoid identity theft or clinical error by healthcare professionals. The suitability of this approach has been validated through both experimental evaluation, and simulation by deploying an IoT-driven ECG-based health monitoring service in the cloud. © 2016 Elsevier B.V. All rights reserved.
Selective mutism and comorbidity with developmental disorder/delay, anxiety disorder, and elimination disorder.

OBJECTIVES
To assess the comorbidity of developmental disorder/delay in children with selective mutism (SM) and to assess other comorbid symptoms such as anxiety, enuresis, and encopresis.


METHOD
Subjects with SM and their matched controls were evaluated by a comprehensive assessment of the child and by means of a parental structured diagnostic interview with focus on developmental history. Diagnoses were made according to DSM-IV.


RESULTS
A total of 54 children with SM and 108 control children were evaluated. Of the children with SM, 68.5% met the criteria for a diagnosis reflecting developmental disorder/delay compared with 13.0% in the control group. The criteria for any anxiety diagnosis were met by 74.1% in the SM group and for an elimination disorder by 31.5% versus 7.4% and 9.3%, respectively, in the control group. In the SM group, 46.3% of the children met the criteria for both an anxiety diagnosis and a diagnosis reflecting developmental disorder/delay versus 0.9% in the controls.


CONCLUSIONS
SM is associated with developmental disorder/delay nearly as frequently as with anxiety disorders. The mutism may conceal developmental problems in children with SM. Children with SM often meet diagnostic criteria for both a developmental and an anxiety disorder.
CREDIT CARD FRAUD DETECTION BASED ON BEHAVIOR MINING

NIMISHA PHILIP, SHERLY K.K Department of Computer Science & Engineering, Toc H Institute of Science & Technology email:nimishavellapally@gmail.com Department of Information Technology, Toc H Institute of Science & Technology, Ernakulam, Kerala, India shrly_shilu@yahoo.com Abstract — Globalization and increased use of the Internet for online shopping has resulted in a considerable proliferation of credit card transactions throughout the world.Higher acceptability and convenience of credit cards for purchases has not only given personal comfort to customers but also attracted a large number of attackers. As a result, credit card payment systems must be supported by efficient fraud detection capability for minimizing unwanted activities by adversaries. Most of the well known algorithms for fraud detection are based on supervised training Every cardholder has a certain shopping behavior, which establishes an activity profile for him. Existing FDS try to capture behavioral patterns as rules which are static .This becomes ineffective when cardholder develops new patterns of behavior Here, we propose a unsupervised method to dynamically profile behavior pattern of customer Then the incoming transactions are compared against the user profile to indicate the anomalies, based on which the corresponding warnings are outputted. A FP tree based pattern matching algorithm is used to evaluate how unusual the new transactions are.
Information security management needs more holistic approach: A literature review

Information technology has dramatically increased online business opportunities; however these opportunities have also created serious risks in relation to information security. Previously, information security issues were studied in a technological context, but growing security needs have extended researchers’ attention to explore the management role in information security management. Various studies have explored different management roles and activities, but none has given a comprehensive picture of these roles and activities to manage information security effectively. So it is necessary to accumulate knowledge about various managerial roles and activities from literature to enable managers to adopt these for a more holistic approach to information security management. In this paper, using a systematic literature review approach, we synthesised literature related to management’s roles in information security to explore specific managerial activities to enhance information security management. We found that numerous activities of management, particularly development and execution of information security policy, awareness, compliance training, development of effective enterprise information architecture, IT nformation architecture infrastructure management, business and IT alignment and human resources management, had a significant impact on the quality of management of information security. Thus, this research makes a novel contribution by arguing that a more holistic approach to information security is needed and we suggest the ways in which managers can play an effective role in information security. This research also opens up many new avenues for further research in this area. © 2015 Elsevier Ltd. All rights reserved.
CPLR: Collaborative pairwise learning to rank for personalized recommendation

Compared with explicit feedback data, implicit feedback data is easier to be collected and more widespread. However, implicit feedback data is also more difficult to be analyzed due to the lack of negative examples. Bayesian personalized ranking (BPR) is a well-known algorithm for personalized recommendation on implicit feedback data due to its high performance. However, it (1) treats all the unobserved feedback the same as negative examples which may be just caused by unseen, (2) treats all the observed feedback the same as positive examples which may be just caused by noisy action, and (3) assumes the preferences of users are independent which is difficult to achieve in reality. To solve all these problems, we propose a novel personalized recommendation algorithm called collaborative pairwise learning to rank (CPLR), which considers the influence between users on the preferences for both items with observed feedback and items without. To take these information into consideration, we try to optimize a generalized AUC instead of the standard AUC used in BPR. CPLR can be seen as a generalized BPR. Besides BPR, several extension algorithms of BPR, like social BPR (SBPR) and group preference based BPR (GBPR), are special cases of CPLR. Extensive experiments demonstrate the promise of our approach in comparison with traditional collaborative filtering methods and state-of-the-art pairwise learning to rank algorithms. Compared with the performance of baseline algorithms on five real-world data sets, the improvements of CPLR are over 17%, 23% and 22% for Pre@5, MAP and NDCG, respectively. © 2018 Published by Elsevier B.V.
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

After [15], [31], [19], [8], [25], [5], minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push-relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.
Exploring the factors affecting MOOC retention: A survey study

Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Corporate Sustainability : First Evidence on Materiality

An increasing number of companies make sustainability investments, and an increasing number of investors integrate sustainability performance data in their capital allocation decisions. To date however, the prior academic literature has not distinguished between investments in material versus immaterial sustainability issues. We develop a novel dataset by hand-mapping data on sustainability investments classified as material for each industry into firm-specific performance data on a variety of sustainability investments. This allows us to present new evidence on the value implications of sustainability investments. Using calendar-time portfolio stock return regressions we find that firms with good performance on material sustainability issues significantly outperform firms with poor performance on these issues, suggesting that investments in sustainability issues are shareholder-value enhancing. Further, firms with good performance on sustainability issues not classified as material do not underperform firms with poor performance on these same issues, suggesting investments in sustainability issues are at a minimum not value-destroying. Finally, firms with good performance on material issues and concurrently poor performance on immaterial issues perform the best. These results speak to the efficiency of firms’ sustainability investments, and also have implications for asset managers who have committed to the integration of sustainability factors in their capital allocation decisions.  Mozaffar Khan is a visiting Associate Professor at Harvard Business School. George Serafeim is the Jakurski Family Associate Professor of Business Administration at Harvard Business School. Aaron Yoon is a doctoral student at Harvard Business School. We are grateful for comments from seminar participants at National University of Singapore and Harvard Business School. We are grateful for financial support from the Division of Faculty Research and Development at Harvard Business School. George Serafeim has served on the Standards Council of SASB. Contact emails: mkhan@hbs.edu; gserafeim@hbs.edu; ayoon@hbs.edu.