snu-aidas/HVMap-dataset huggingface dataset:
Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers.

OBJECTIVES
The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.


DESIGN
Observational.


METHODS
Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.


RESULTS
Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).


CONCLUSIONS
OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.
Humanoid robotics platforms developed in HRP

This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the r ns and is e ©
Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review.

BACKGROUND
Systematic literature reviews identify, select, appraise, and synthesize relevant literature on a particular topic. Typically, these reviews examine primary studies based on similar methods, e.g., experimental trials. In contrast, interest in a new form of review, known as mixed studies review (MSR), which includes qualitative, quantitative, and mixed methods studies, is growing. In MSRs, reviewers appraise studies that use different methods allowing them to obtain in-depth answers to complex research questions. However, appraising the quality of studies with different methods remains challenging. To facilitate systematic MSRs, a pilot Mixed Methods Appraisal Tool (MMAT) has been developed at McGill University (a checklist and a tutorial), which can be used to concurrently appraise the methodological quality of qualitative, quantitative, and mixed methods studies.


OBJECTIVES
The purpose of the present study is to test the reliability and efficiency of a pilot version of the MMAT.


METHODS
The Center for Participatory Research at McGill conducted a systematic MSR on the benefits of Participatory Research (PR). Thirty-two PR evaluation studies were appraised by two independent reviewers using the pilot MMAT. Among these, 11 (34%) involved nurses as researchers or research partners. Appraisal time was measured to assess efficiency. Inter-rater reliability was assessed by calculating a kappa statistic based on dichotomized responses for each criterion. An appraisal score was determined for each study, which allowed the calculation of an overall intra-class correlation.


RESULTS
On average, it took 14 min to appraise a study (excluding the initial reading of articles). Agreement between reviewers was moderate to perfect with regards to MMAT criteria, and substantial with respect to the overall quality score of appraised studies.


CONCLUSION
The MMAT is unique, thus the reliability of the pilot MMAT is promising, and encourages further development.
Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

IMPORTANCE
Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.


OBJECTIVE
To investigate network dysfunction in MDD through a meta-analysis of rsFC studies.


DATA SOURCES
Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.


STUDY SELECTION
Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.


DATA EXTRACTION AND SYNTHESIS
Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.


RESULTS
Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.


CONCLUSIONS AND RELEVANCE
Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.
Knowledge sharing in organisational contexts: a motivation-based perspective

Purpose – Facilitating knowledge sharing within organisations is a difficult task: the willingness of individuals to share and integrate their knowledge is one of the central barriers. This paper aims to develop a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing. Design/methodology/approach – The analysis builds on a three-category taxonomy of motivation, adding ‘‘hedonic’’ motivation to the traditional dichotomy of ‘‘extrinsic’’ and ‘‘intrinsic’’ motivation. It uses case studies gleaned from the literature to explore the interactive effects between the different motivators in two different types of knowledge-intensive organisations: professional bureaucracy and operating adhocracy. Findings – Within a professional bureaucracy, the social dilemma of knowledge sharing may be overcome through normative motivation, with provision of hedonic motivation through extrinsic incentives such as training and career progression. In an operating adhocracy where interdependent teamwork is vital, it may be overcome through normative alignment reinforced by intensive socialisation. Extrinsic motivators that align with hedonic motivation may also reinforce the propensity for knowledge sharing. In both organisational types, financial extrinsic incentives do not appear to be relevant on their own, and may ‘‘crowd out’’ other motivators. Research limitations/implications – The cases reported were chosen from the existing literature and, although many were not designed specifically to address motivational issues, suggestive conclusions are drawn. Most of the cases were drawn from organisations rooted in the Anglo-American context and thus care would be needed in generalising the findings to organisations in other contexts. Originality/value – The paper represents the first attempt to apply a three-category taxonomy of motivation to examine knowledge-sharing behaviour in organisations. It highlights the interaction between the different motivators and provides a basis to integrate further the work of social psychologists and socio-economists on incentives and motivation in the context of knowledge sharing.
xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs

In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED. Keywords— ezfiltration; air-gap; network; optical; covertchannel (key words)
A solution to the simultaneous localization and map building (SLAM) problem

The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1], [2], [3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including sub-optimal map-building algorithms and map management.
Complexity leadership theory : An interactive perspective on leading in complex adaptive systems

leadership theory: An interactive perspective on leading in complex adaptive systems" (2006). Management Department Faculty Publications. Paper 8. Traditional, hierarchical views of leadership are less and less useful given the complexities of our modern world. Leadership theory must transition to new perspectives that account for the complex adaptive needs of organizations. In this paper, we propose that leadership (as opposed to leaders) can be seen as a complex dynamic process that emerges in the interactive " spaces between " people and ideas. That is, leadership is a dynamic that transcends the capabilities of individuals alone; it is the product of interaction, tension, and exchange rules governing changes in perceptions and understanding. We label this a dynamic of adaptive leadership, and we show how this dynamic provides important insights about the nature of leadership and its outcomes in organizational fields. We define a leadership event as a perceived segment of action whose meaning is created by the interactions of actors involved in producing it, and we present a set of innovative methods for capturing and analyzing these contextually driven processes. We provide theoretical and practical implications of these ideas for organizational behavior and organization and management theory.
Analysis of users and non-users of smartphone applications

Purpose: Smartphones facilitate the potential adoption of new mobile applications. The purpose of this research is to study users and non-users of three selected mobile applications, and find out what really drives the intention to use these applications across users and non-users. Design/methodology/approach: The authors measured actual usage of mobile applications in a panel study of 579 Finnish smartphone users, using in-device measurements as an objective way to identify users and non-users. A web-based survey was used in collecting data to test an extended TAM model in explaining intention to use. Findings: Perceived technological barriers negatively affect behavioural control, reflecting people’s assessment of themselves being capable of using the services without trouble. Behavioural control is directly linked to perceived usefulness (except for games) and perceived enjoyment, as hypothesized. Perceived enjoyment and usefulness were generically found to explain intention to use applications for both users and for non-users. Research limitations/implications: With regards to the impact of social norms, the study finds that further research needs to be done in exploring its impact more thoroughly. The dataset of the research, consisting purely of male-dominated, young smartphone users, make the generalization of results difficult. Practical implications: There are differences regarding what drives the usage of different kinds of mobile applications. In this study, map applications and mobile Internet, are driven by more utilitarian motivations, whereas games are more hedonic. It is also clear that not everybody are using applications facilitated by smartphones, and therefore the presented approach of studying users and non-users separately provides a new approach to analyze adoption on a practical level. Originality/value: This research proves that models like TAM should not treat mobile services as a generic concept, but instead to specifically address individual mobile services. The research also demonstrates the unique value of combining objective usage measurements (reflecting actual behaviour) with traditional survey data in more comprehensively modelling service adoption. 2009 Elsevier Ltd. All rights reserved.
A Comparative Study of Feature Extraction Algorithms in Customer Reviews

The paper systematically compares two feature extraction algorithms to mine product features commented on in customer reviews. The first approach [17] identifies candidate features by applying a set of POS patterns and pruning the candidate set based on the log likelihood ratio test. The second approach [11] applies association rule mining for identifying frequent features and a heuristic based on the presence of sentiment terms for identifying infrequent features. We evaluate the performance of the algorithms on five product specific document collections regarding consumer electronic devices. We perform an analysis of errors and discuss advantages and limitations of the algorithms.
Data Preparation for Data Mining

Senior Editor: Diane D. Cerra Director of Production & Manufacturing: Yonie Overton Production Editor: Edward Wade Editorial Assistant: Belinda Breyer Cover Design: Wall-To-Wall Studios Cover Photograph: © 1999 PhotoDisc, Inc. Text Design & Composition: Rebecca Evans & Associates Technical Illustration: Dartmouth Publishing, Inc. Copyeditor: Gary Morris Proofreader: Ken DellaPenta Indexer: Steve Rath Printer: Courier Corp.
A robust-coded pattern projection for dynamic 3D scene measurement

This paper presents a new coded structured light pattern which permits to solve the correspondence problem by a single shot and without using geometrical constraints. The pattern is composed by the projection of a grid made by coloured slits in such a way that each slit with its two neighbours appears only once in the pattern. The technique proposed permits a rapid and robust 3D scene measurement, even with moving objects. Ó 1998 Elsevier Science B.V. All rights reserved.
From centrality to temporary fame: Dynamic centrality in complex networks

We develop a new approach to the study of the dynamics of link utilization in complex networks using records of communication in a large social network. Counter to the perspective that nodes have particular roles, we find roles change dramatically from day to day. “Local hubs” have a power law degree distribution over time, with no characteristic degree value. Our results imply a significant reinterpretation of the concept of node centrality in complex networks, and among other conclusions suggest that interventions targeting hubs will have significantly less effect than previously thought. © 2006 Wiley Periodicals, Inc. Complexity 12: 000 – 000, 2006
Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning

BACKGROUND
Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.


OBJECTIVE
We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.


METHODS
We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.


RESULTS
Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.


CONCLUSIONS
Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.
The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings

The increasing use of web 2.0 applications has generated numerous online user reviews. Prior studies have revealed the influence of user-generated reviews on the sales of products such as CDs, books, and movies. However, the influence of online user-generated reviews in the tourism industry is still largely unknown both to tourism researchers and practitioners. To bridge this knowledge gap in tourism management, we conducted an empirical study to identify the impact of online user-generated reviews on business performance using data extracted from a major online travel agency in China. The empirical findings show that traveler reviews have a significant impact on online sales, with a 10 percent increase in traveler review ratings boosting online bookings by more than five percent. Our results highlight the importance of online user-generated reviews to business performance in tourism. 2010 Elsevier Ltd. All rights reserved.
Interaction Design for Recommender Systems

1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu ABSTRACT Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems.