snu-aidas/HVMap-dataset huggingface dataset:
Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews

The Web has become an excellent source for gathering consumer opinions (more specifically, consumer reviews) about products. Consumer reviews are essential for retailers and product manufacturers to understand the general responses of customers to their products and improve their marketing campaigns or products accordingly. In addition, consumer reviews enable retailers to recognize the specific preferences of each customer, which facilitates effective marketing decisions. As the number of consumer reviews expands, it is essential and desirable to develop an efficient and effective sentiment analysis technique that is capable of extracting product features stated in consumer reviews (i.e., product feature extraction) and determining the sentiments (positive or negative semantic orientations) of consumers for these product features (i.e., opinion orientation identification). Product feature extraction is critical to sentiment analysis, because its effectiveness significantly affects the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore, this study concentrates on product feature extraction from consumer reviews. C.-P. Wei (&) Institute of Service Science, College of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, ROC e-mail: cpwei@mx.nthu.edu.tw Y.-M. Chen Department of Information Management, College of Management, National Sun Yat-Sen University, Kaohsiung, Taiwan, ROC C.-S. Yang Department of Information Management, College of Informatics, Yuan-Ze University, Chung-Li, Taiwan, ROC e-mail: csyang@saturn.yzu.edu.tw C. C. Yang College of Information Science and Technology, Drexel University, Philadelphia, PA, USA e-mail: chris.yang@ischool.drexel.edu 123 Inf Syst E-Bus Manage (2010) 8:149–167 DOI 10.1007/s10257-009-0113-9
3D human face description: landmarks measures and geometrical features

Morphometric measures and geometrical features are widely used to descript faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their charateristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various morphometric measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.
Comparison of orthodontic treatment outcomes in adults with skeletal open bite between conventional edgewise treatment and implant-anchored orthodontics.

INTRODUCTION
Open bites are known to be difficult malocclusions to treat. Generally, with conventional edgewise treatment, incisor extrusion rather than molar intrusion is observed. Recently, the use of miniscrews as anchorage has markedly increased. In this study, orthodontic treatment outcomes after conventional edgewise treatment and implant-anchored treatment were investigated by cephalometric analysis and several occlusal indexes. In addition, the stability of these patients 2 years after the retention phase was also analyzed.


METHODS
Thirty adults (15 for nonimplant treatment [non-IA group] and 15 for implant-anchored treatment [IA group]) were our subjects. Cephalometric analysis, peer assessment rating, discrepancy index, and objective grading system were used.


RESULTS
From the cephalometric values in the non-IA group, open-bite patients were generally treated by extrusion of the maxillary and mandibular incisors that resulted in clockwise rotation of the mandibular plane angle. In the IA group, intrusion of the maxillary and mandibular molars that resulted in counterclockwise rotation was noted. Furthermore, in the IA group, the soft-tissue analysis showed decreases in the facial convexity and the inferior labial sulcus angle that resulted in the disappearance of incompetent lips. In the retention phase, extrusion of the mandibular molars was observed in the IA group. From the objective grading system evaluation, significant reductions of overbite in canines and premolars were observed in both groups. Furthermore, less stability was observed in the IA group compared with the non-IA group according to the total objective grading system score.


CONCLUSIONS
Ideal occlusion can be achieved in adults with severe open bite with both conventional edgewise and implant-anchored orthodontic treatment. However, absolute intrusion of the molars and improvement in esthetics might be achieved more effectively by using miniscrews as an anchorage device. In addition, since a significant amount of tooth movement occurs with miniscrews, careful attention is required during the retention phase.
AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security

Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our systemutilizes Redundant ResidueNumber System (RRNS)with newmethod of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS,we introduce AR-RRNSmethod for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data. © 2017 Elsevier B.V. All rights reserved.
Complimentary Memory Technologies to Reform the Homogeneity , Energy Landscape , and Volatility of Future Memory Hierarchies

With the limits of transistor size rapidly approaching and the memory wall approaching us at unfathomable speeds, a need to revisit our existing memory technologies has arisen. Fortunately, there are new non-volatile memory (NVM) technologies on the horizon showing great promise. In this paper we will examine some of the new NVM technologies being developed. We will present the basic characteristics of these new technologies as well as the properties that make them unique and the possibilities for the future they offer. Keywords—non-volatile; volatile; memory; memristor; RRAM; MRAM; SRAM; STT-RAM; PCM; PRAM
Heart rate variability, prefrontal neural function, and cognitive performance: the neurovisceral integration perspective on self-regulation, adaptation, and health.

BACKGROUND
In the present paper, we describe a model of neurovisceral integration in which a set of neural structures involved in cognitive, affective, and autonomic regulation are related to heart rate variability (HRV) and cognitive performance.


METHODS
We detail the pathways involved in the neural regulation of the cardiovascular system and provide pharmacological and neuroimaging data in support of the neural structures linking the central nervous system to HRV in humans. We review a number of studies from our group showing that individual differences in HRV are related to performance on tasks associated with executive function and prefrontal cortical activity. These studies include comparisons of executive- and nonexecutive-function tasks in healthy participants, in both threatening and nonthreatening conditions. In addition, we show that manipulating resting HRV levels is associated with changes in performance on executive-function tasks. We also examine the relationship between HRV and cognitive performance in ecologically valid situations using a police shooting simulation and a naval navigation simulation. Finally, we review our studies in anxiety patients, as well as studies examining psychopathy.


CONCLUSION
These findings in total suggest an important relationship among cognitive performance, HRV, and prefrontal neural function that has important implications for both physical and mental health. Future studies are needed to determine exactly which executive functions are associated with individual differences in HRV in a wider range of situations and populations.
The effect of "open access" on citation impact: An analysis of ArXiv's condensed matter section

This article statistically analyses how the citatio n impact of articles deposited in the Condensed Matter section of the preprint server ArX iv (hosted by Cornell University), and subsequently published in a scientific journal, compares to that of articles in the same journal that were not deposited in that archive. Its principal aim is to further illustrate and roughly estimate the effect of two factors, ‘early view’ and ‘quality bias’, upon differences in citation impact between these two se ts of papers, using citation data from Thomson Scientific’s Web of Science. It presents es imates for a number of journals in the field of condensed matter physics. In order to discriminate between an ‘open access’ effect and an early view effect, longitudinal citat ion data was analysed covering a time period as long as 7 years. Quality bias was measure d by calculating ArXiv citation impact differentials at the level of individual aut hors publishing in a journal, taking into account co-authorship. The analysis provided evide nce of a strong quality bias and early view effect. Correcting for these effects, there is in a sample of 6 condensed matter physics journals studied in detail, no sign of a general ‘open access advantage’ of paper s deposited in ArXiv. The study does provide evidence that ArXiv accelerates citation, due to the fact that that ArXiv makes papers arlier available rather than that it makes papers freely available.
A SIMULATION AND REGRESSION TESTING FRAMEWORK FOR AUTONOMOUS VEHICLES

x
Effect of horseback riding versus a dynamic and static horse riding simulator
on sitting ability of children with cerebral palsy: a randomized controlled
trial

[Purpose] A randomized controlled trial was conducted to investigate the immediate effects of horseback riding (HR) and a dynamic (DHS) and static (SHS) horse riding simulator (OSIM uGallop, Taiwan) on sitting ability of children with cerebral palsy. [Subjects and Methods] Thirty children with cerebral palsy were recruited and randomly assigned into three groups. Children received 30 minutes of exercise according to their assigned group. The Segmental Assessment of Trunk Control (SATCo) and Gross Motor Function Measure-66 (GMFM-66) sitting dimension were used to assess children in all groups both before and after the interventions. [Results] Sitting abilities were significantly improved after all interventions. Horseback riding showed the most improvement, followed by the dynamic and static horse riding simulator groups. Horseback riding also showed a significant improvement in the GMFM sitting dimension. [Conclusion] Horseback riding was the best intervention for promoting sitting ability of children with spastic cerebral palsy. However, a dynamic horse riding simulator can be a good surrogate for horseback riding when horseback riding is not available.
Graphical Figure Classification Using Data Fusion for Integrating Text and Image Features

This paper describes a multimodal (image + text) learning approach for automatically identifying three graphical figure types commonly found in biomedical literature, namely, diagrams, statistical figures and flow charts. The goal is to improve retrieval of figures from biomedical journal articles. In this article, we describe a data fusion approach to combine information from both text and image sources, believed to contain complementary information. Text information about the image is extracted from the figure caption. The data fusion process includes a hybrid of evolutionary algorithm (EA) and Binary Particle Swarm Optimization (BPSO) called method applied to find an optimal subset of extracted image features. Chi-square statistic and information gain metric are used to select the optimal subset of extracted text features, which along with image features are input to Multi-Layer Perceptron Neural Network classifiers, whose outputs are characterized as fuzzy sets to determine the final classification result. Evaluation performed on 1707 figure images extracted from a test subset of Biome Central® journals extracted from U.S. National Library of Medicine's PubMed Central ® repository yielded classification accuracy as high as 96.1%.
RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments

RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment applications will become available at cost below $100. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. The robotics and computer vision communities have developed a variety of techniques for 3D mapping based on laser range scans [8, 11], stereo cameras [7], monocular cameras [3], and unsorted collections of photos [4]. While RGB-D cameras provide the opportunity to build 3D maps of unprecedented richness, they have drawbacks that make their application to 3D mapping difficult: They provide depth only up to a limited distance (typically less than 5m), depth values are much noisier than those provided by laser scanners, and their field of view (∼ 60◦) is far more constrained than that of specialized cameras or laser scanners typically used for 3D mapping (∼ 180◦). In our work, we use a camera developed by PrimeSense [10]. The key insights of this investigation are: first, that existing frame matching techniques are not sufficient to provide robust visual odometry with these cameras; second, that a tight integration of depth and color information can yield robust frame matching and loop closure detection; third, that building on best practice techniques in SLAM and computer graphics makes it possible to build and visualize accurate and extremely rich 3D maps with such cameras; and, fourth, that it will be feasible to build complete robot navigation and interaction systems solely based on cheap depth cameras.
Time-of-Flight sensor calibration for accurate range sensing

Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToFcameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data. 2010 Elsevier Inc. All rights reserved.
Brand Name Logo Recognition of Fast Food and Healthy Food among Children

The fast food industry has been increasingly criticized for creating brand loyalty in young consumers. Food marketers are well versed in reaching children and youth given the importance of brand loyalty on future food purchasing behavior. In addition, food marketers are increasingly targeting the Hispanic population given their growing spending power. The fast food industry is among the leaders in reaching youth and ethnic minorities through their marketing efforts. The primary objective of this study was to determine if young children recognized fast food restaurant logos at a higher rate than other food brands. Methods Children (n = 155; 53% male; 87% Hispanic) ages 4–8 years were recruited from elementary schools and asked to match 10 logo cards to products depicted on a game board. Parents completed a survey assessing demographic and psychosocial characteristics associated with a healthy lifestyle in the home. Results Older children and children who were overweight were significantly more likely to recognize fast food restaurant logos than other food logos. Moreover, parents’ psychosocial and socio-demographic characteristics were associated with the type of food logo recognized by the children. Conclusions Children’s high recognition of fast food restaurant logos may reflect greater exposure to fast food advertisements. Families’ socio-demographic characteristics play a role in children’s recognition of food logos.
Understanding microblog continuance usage intention: an integrated model

Purpose – The purpose of this paper is to explore the factors affecting users’ continuous microblog usage intention. In recent years, the number of microblog users has gradually declined. This research can reveal microblog users’ needs and provide the improvement direction of microblog services. Design/methodology/approach – By integrating Wixom and Todd’s theoretical framework, the Uses and Gratifications Theory and the DeLone and McLean Information System Success Model, a conceptual model is proposed. In this model, gratification is defined as a kind of behavioral attitude, and satisfaction is viewed as an object-based attitude. The survey data were collected online and analyzed using the partial least squares method. Findings – The results suggest that users’ continuance intention (behavioral intention) is jointly determined by users’ gratification (behavioral-based attitude) and their habitual microblog usage behavior. Likewise, gratification is positively affected by satisfaction (object-based attitude) which is a joint function of system quality and information quality (object-based beliefs). Originality/value – In this research, Wixom and Todd’s principle is applied as the basic theoretical framework; gratification is viewed as a behavior attitude and user satisfaction is identified as an object-based attitude. This research model is a new lens for continuance usage research.
Forecasting market demand for new telecommunications services: an introduction

The marketing team of a new telecommunications company is usually tasked with producing forecasts for diverse stakeholders with different needs. Consequently, those outside marketing often realize neither the many reasons for developing forecasts nor the marketing theory used and the challenges involved in doing so. Based on our three decades of experience working with telecommunications operators around the world we seek to redress this situation by presenting a discussion of the issues involved in demand forecasting for new communications services. 2002 Elsevier Science Ltd. All rights reserved.
Security behaviors of smartphone users

Purpose – This paper aims to report on the information security behaviors of smartphone users in an affluent economy of the Middle East. Design/methodology/approach – A model based on prior research, synthesized from a thorough literature review, is tested using survey data from 500 smartphone users representing three major mobile operating systems. Findings – The overall level of security behaviors is low. Regression coefficients indicate that the efficacy of security measures and the cost of adopting them are the main factors influencing smartphone security behaviors. At present, smartphone users are more worried about malware and data leakage than targeted information theft. Research limitations/implications – Threats and counter-measures co-evolve over time, and our findings, which describe the state of smartphone security at the current time, will need to be updated in the future. Practical implications – Measures to improve security practices of smartphone users are needed urgently. The findings indicate that such measures should be broadly effective and relatively costless for users to implement. Social implications – Personal smartphones are joining enterprise networks through the acceptance of Bring-Your-Own-Device computing. Users’ laxity about smartphone security thus puts organizations at risk. Originality/value – The paper highlights the key factors influencing smartphone security and compares the situation for the three leading operating systems in the smartphone market.
06-025 Multi-agent reinforcement learning : A survey ∗

Multi-agent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, economics. Many tasks arising in these domains require that the agents learn behaviors online. A significant part of the research on multi-agent learning concerns reinforcement learning techniques. However, due to different viewpoints on central issues, such as the formal statement of the learning goal, a large number of different methods and approaches have been introduced. In this paper we aim to present an integrated survey of the field. First, the issue of the multi-agent learning goal is discussed, after which a representative selection of algorithms is reviewed. Finally, open issues are identified and future research directions are outlined. Keywords—multi-agent systems, reinforcement learning, game theory, distributed control