Reviewerly/exHarmony huggingface dataset:
Full‐field deformation measurement by videogrammetry using self‐adaptive window matching

Abstract Full‐field deformation measurement of specific materials is an important issue in civil engineering materials testing. This paper presents a flexible videogrammetric scheme to measure full‐field deformation of an artificial rock‐like material under uniaxial compression. In this scheme, two high‐speed cameras were used to measure the spatial morphological changes of the material surface, which was sprayed with a speckle pattern. A robust self‐adaptive window matching strategy is then proposed to extract accurate displacements of tracking points. Finally, three‐dimensional (3D) point clouds and full‐field deformation can be calculated by photogrammetric and spatiotemporal analysis. Simulation and empirical tests demonstrated that the self‐adaptive window matching strategy used with high‐speed videogrammetric speckle image sequences can detect more subtle deformation and avoid more mismatches than a fixed‐window matching strategy.
Big Dreams With Big Data! Use of Clinical Informatics to Inform Biomarker Discovery

As the complexity of biomedical data increases, so do the opportunities to leverage them to advance science and clinical care. Electronic health records form a rich but complex source of large amounts of data gathered during routine clinical care. Through the use of codified and free-text concepts identified using clinical informatics tools such as natural language processing, disease phenotyping can be performed with a high degree of accuracy. Technologies such as genome sequencing, gene expression profiling, proteomic and metabolomic analyses, and electronic devices and wearables are generating large amounts of data from various populations, cell types, and disorders (big data). However, to make these data useable for the next step of biomarker discovery, precision medicine, and clinical practice, it is imperative to harmonize and integrate these diverse data sources. In this article, we introduce important building blocks for precision medicine, including common data models, text mining and natural language processing, privacy-preserved record linkage, machine learning for predictive modeling, and health information exchange.
Towards identifying and preventing behavioral side channel attack on recording attack resilient unaided authentication services

Side channel attacks, based on the human behavior, have not received much attention in the domain of recording attack resilient unaided authentication services (RARUAS) that purely rely on human visual perception but not on hidden auxiliary channels. In this paper, for the first time, we have made an extensive analysis to show - how human behavior during the login can weaken the claimed security standard of RARUAS. We identify this threat as behavioral side channel attack. To make situation more alarming, our investigation revealed that the identified threat model is capable of reducing the claimed session resiliency of any RARUAS by a significant extent. For dealing with this threat model, the latter part of our proposal introduces a novel defense strategy that reduces attackers' efficiency and improves the session resiliency. The subsequent study indicates that by nature of its design, the proposed defense strategy does not make any significant impact on the usability standard. To validate our claims, we have made a thorough experimental study to show that the proposed defense strategy is truly deployable in practice for improving the situation against the behavioral side channel attack.
Pedestrian Detection and Distance Estimation Using Thermal Camera in Night Time

In this paper, we propose a method to detect a pedestrian in real time in a low illumination environment and estimate the distance from the camera using a smart phone based thermal camera. Thermal cameras use equipment that can be attached to low-cost smartphones and which use cameras for image processing in real-time. A pedestrian detector is created using a multi-stage cascade learning device to detect pedestrians in a low-illuminated environment, and the pedestrian area is detected using this detector. Then, the distance is estimated by calculating the position of the pedestrian detected in the real-world 3D environment in the 2D thermal image by calculating the parameters detected by the thermal imaging camera in advance. Experimental results show that the detection accuracy of pedestrians is about 91% and the accuracy of distance estimation is 95%. In this way, the proposed method can be applied to the image sensing system in real time in a low-illuminance environment such as nighttime.
CNN Training for Face Photo based Gender and Age Group Prediction with Camera

It appears that CNN for camera-based age and gender prediction is usually trained with RGB color images. However, it is difficult to say that CNN trained with RGB color images always produces good results in an environment where testing is performed with camera rather than with image files. With experiments, we observe that in camera-based testing CNN trained with grayscale images shows better gender and age group prediction accuracy than CNN trained with RGB color images.
A unified smart surveillance system incorporating adaptive foreground extraction and deep learning-based classification

The smart surveillance system is one of the most important services provided in a smart city. The smart surveillance applications are equipped with camera sensors to automatically detect and identify potential regions through automated object detection methods. Usually, such methods require high-complexity image processing techniques and algorithms. Hence, the design of low-complexity automated object detection algorithms becomes an important topic in this area. We consider a foreground extraction and deep learning to achieve these goals. A novel unified technique is proposed to detect a moving object from the surveillance videos based on CPU (central processing units). Unlike most of the existing methods that are relying on pixel information, we use a block-based texture and spatial-temporal information. We use this method to determine the area of the detected moving object(s) only. Furthermore, the area will be processed through a deep learning-based image classification in GPU (graphics processing units) in order to ensure high efficiency and accuracy. It cannot only help to detect object rapidly and accurately, but also can reduce big data volume needed to be stored in smart surveillance systems.
Affective judgement of social touch on a hand associated with hand embodiment

Social touch constitutes a critical component of human interactions. A gentle tap on the hand, for instance, can sometimes create emotional bonding and reduce interpersonal distance in social interactions. Evidence of tactile empathy suggests that touch can be experienced through both physical sensation and observation, yet vicarious perception of observed touch on an object as a function of the object's conceptual representation (e.g., Is this object identified as mine? Does this object feel like part of me?) remains less explored. Here we examined the affective judgement of social touch when the illusory sense of ownership over a dummy hand was manipulated through the rubber-hand illusion. When the same social touch was performed on either the real or the dummy hand, we found a similar sense of perceived pleasantness between the felt and observed touch, but only when the dummy hand was embodied; when it was not, the perceived pleasantness of the observed touch was lesser (an "embodiment effect"; Experiment 1). In addition, we found that the embodiment effect associated with the observed touch was insensitive to the way in which embodiment was manipulated (Experiment 2), and that this effect was specific to social but not neutral touch (Experiment 3). Taken together, our findings suggest a role of embodiment in the affective component of observed social touch and contribute to our understanding of tactile empathy for objects.
Does Financial Permeation Induce Economic Growth? Evidence from SAARC Countries

This study empirically inspects the question of whether financial permeation promotes economic growth. The fixed effects regression technique is employed to examine the effects of financial permeation on economic growth. The empirical analysis is conducted with annual data from 2004 to 2016 with four proxies for financial permeation. Empirical results show that all the proxies of financial permeation have significant positive impacts on economic growth, measured by GDP per capita, of SAARC countries. We also observed that global financial crisis has significant negative effects on economic growth. Empirical findings of this study imply that the governments of SAARC countries should undertake more financial permeation policies to make the economic growth sustainable. The present study contributes to empirical literature of the similar issue by reapproving (or else) the outcomes of past researches done across the domain.
Improving Document Similarity Calculation Using Cosine-Similarity Graphs

Data mining information using various indices and determining candidates that can be judged as having the same tendency based on similarity between documents is common. The accuracy of similarity largely depends on a sufficient amount of data and requires advanced analysis using natural language processing. In this paper, we present an approach for filtering based on the cosine similarity graph and clustering between candidates. For filtering candidates, we focus on the inflection point of the graphs when plotting by sorting similarities in descending order. By clustering among higher similarities, we aim at filtering candidates that cannot be removed without analyzing advanced natural language processing. The proposed method was applied to movie reviews and sightseeing location reviews written in Japanese. Although this study is a work in progress, it shows that candidates can be recommended without having to manually apply natural language processing, such as preparing stopwords for each category.
Security is an architectural design constraint

In state-of-the-art design paradigm, time, space and power efficiency are considered the primary design constraints. Quite often, this approach adversely impacts the security of the overall system, especially when security is adopted as a countermeasure after some vulnerability is identified. In this position paper, we motivate the idea that security should also be considered as an architectural design constraint in addition to time, space and power. We show that security and efficiency objectives along the three design axes of time, space and power are in fact tightly coupled while identifying that security stands in direct contrast with them across all layers of architectural design. We attempt to prove our case utilizing a proof-by-evidence approach wherein we refer to various works across literature that explicitly imply the eternal conflict between security and efficiency. Thus, security has to be treated as a design constraint from the very beginning. Additionally, we advocate a security-aware design flow starting from the choice of cryptographic primitives, protocols and system design.
Assessing Author Identifiers: Preparing for a Linked Data Approach to Name Authority Control in an Institutional Repository Context

Linked data solutions for name authority control in digital libraries are an area of growing interest, particularly among institutional repositories (IRs). This article first considers the shift from traditional authority files to author identifiers, highlighting some of the challenges and possibilities. An analysis of author name strings in Duke University's open access repository, DukeSpace, is conducted to identify a suitable source of author URIs for Duke's newly launched repository for research data. Does one of the three prominent international authority sources—Library of Congress Name Authority File (LCNAF), Virtual International Authority File (VIAF), and Open Researcher and Contributor Identifier (ORCID)—demonstrate the most comprehensive uptake? Finally, recommendations surrounding a technical approach to leveraging author Uniform Resource Identifiers (URIs) at Duke are briefly considered.
User‐Guided Facial Animation through an Evolutionary Interface

Abstract We propose a design framework to assist with user‐generated content in facial animation — without requiring any animation experience or ground truth reference. Where conventional prototyping methods rely on handcrafting by experienced animators, our approach looks to encode the role of the animator as an Evolutionary Algorithm acting on animation controls, driven by visual feedback from a user. Presented as a simple interface, users sample control combinations and select favourable results to influence later sampling. Over multiple iterations of disregarding unfavourable control values, parameters converge towards the user's ideal. We demonstrate our framework through two non‐trivial applications: creating highly nuanced expressions by evolving control values of a face rig and non‐linear motion through evolving control point positions of animation curves.
Tracking User Application Activity by using Machine Learning Techniques on Network Traffic

A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user's Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user's network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.
Walking Without Friends: Publishing Anonymized Trajectory Dataset Without Leaking Social Relationships

Trajectory data has been widely collected via mobile devices and publicly released for academic research and commercial purposes. One primary concern of publishing such a dataset is the privacy issue. Previous protection schemes mainly focus on preventing re-identification attack, which utilizes the uniqueness of trajectories. However, the correlation between trajectories, which has not been given much attention to before, could also give rise to serious privacy leakage. Recent studies have proved that it is possible to identify social relationship, de-anonymize trajectories or even infer user’s locations by analyzing the correlation between users’ trajectories. We identify the serious privacy problem of social relationship leakage caused by what we call social relationship attack and aim to protect social relationship information, which cannot be protected by existing algorithms. We contribute to the design of a new privacy model and an effective system to deal with social relationship attack and re-identification attack simultaneously while maintaining high data utility. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SlidingWindow</i> algorithm to merge trajectories according to their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social-aware distance</i> , which concerns both the spatiotemporal distance and social proximity. Evaluations of two trajectory datasets under different scenarios demonstrate that our system provides more than 1.84 times privacy protection at the cost of only 2.5% data utility loss.
Increasing the effectiveness of handwritten Manipuri Meetei-Mayek character recognition using multiple-HOG-feature descriptors

Detection and reading of the text from natural images is a difficult computer vision task, which is essential in a variety of emerging applications. Document character recognition is one such problem, which has been widely studied and documented by many machine learning and computer vision researchers, which is practically used for solving applications like recognizing handwritten digits. In this paper, a new approach for efficiently extracting cognition out of a total of 56 different classes of Handwritten Manipuri Meetei-Mayek (HMMM) (an Indian language) is described. Although character recognition algorithms have been researched and developed for other Indian scripts, no research work has been reported so far for recognizing all the characters of the Manipuri Meetei-Mayek (MMM). The work begins with a thorough analysis of the recognition task using a single hidden layer type Multilayer Perceptron Feedforward Artificial Neural Network with Histogram of Oriented Gradient (HOG) feature descriptors. After reviewing the level of accuracy and time it takes to train the network, the limitations are experimentally removed using multiple-sized cell grids using HOG descriptors. HOG, being a gradient-based descriptor, is very efficient in data discrimination and very stable with illumination variation. For efficient classification of the HOG features of the MMM, a linear multiclass support vector machine (SVM) classifier has been proposed for classifying the different offline characters because of its simplicity and speed. The classification based on linear multiclass SVM yielded a very high overall accuracy of 96.928%
Simultaneous Localization and Segmentation of Fish Objects Using Multi-task CNN and Dense CRF

We propose a deep learning tool to localize fish objects in benthic underwater videos on a frame by frame basis. The deep network predicts fish object spatial coordinates and simultaneously segments the corresponding pixels of each fish object. The network follows a state of the art inception resnet v2 architecture that automatically generates informative features for object localization and mask segmentation tasks. Predicted masks are passed to dense Conditional Random Field (CRF) post-processing for contour and shape refinement. Unlike prior methods that rely on motion information to segment fish objects, our proposed method only requires RGB video frames to predict both box coordinates and object pixel masks. Independence from motion information makes our proposed model more robust to camera movements or jitters, and makes it more applicable to process underwater videos taken from unmanned water vehicles. We test the model in actual benthic underwater video frames taken from ten different sites. The proposed tool can segment fish objects despite wide camera movements, blurred underwater resolutions, and is robust to a wide variety of environments and fish species shapes.
EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems

Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an event tracker enhanced GEP, which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on a GEP schema theory. Experiment results also confirm that EGEP outperforms the GEP with a shorter computation time in an evolution.
Applications for Persuasive Technologies in Participatory Design Processes

This paper studies the possibilities to support participatory design processes with persuasive technologies. Drawing on extant work by Elizabeth Sanders, it introduces a comprehensive framework for participatory design which highlights challenges for collaborative attitudes and behavior. To see how persuasive technologies can answer these challenges, the paper reviews the proceedings of the PERSUASIVE conferences since 2010. It identifies 186 application cases, which can be related to twelve different technology platforms. Following a design science research approach, the paper introduces an online navigator to explore different scenarios to support participatory design processes resulting from the findings of the literature review. The navigator can be applied together with Fogg's eight-step design process for creating persuasive technology. To evaluate the navigator, two artificial evaluation iterations are conducted: ex ante a criteria-based analysis to show its theoretical efficacy and ex post problem-centered interviews with experts to gain further insights. The results contribute to a better understanding of the role that persuasive technologies can play in participatory design activities.
High performance hybrid cognitive framework for bio-facial signal fusion processing for the disease diagnosis

In today’s world, every human’s life is affected by the several numbers of diseases which increases day-by-day due to the unpredicted growth of the pathogens. This leads to the increase in the death rate of the humans in which 70% take place without the proper knowledge of the diagnosis of diseases and care-taking mechanism. Numerous methods have been proposed for the diagnosis of the diseases or predetermination of the diseases. We propose a new method for diagnosing the disease through the fusion of bio-signal and the facial expression codes. The new algorithm which is based on the Cognitive Extreme Learning Machines (CELM) has been implemented for the classification of different facial expressions in accordance with the symptoms of the diseases and relates the results for their diagnosis. Again, the Cognitive Rule Engine has been used for the incorporation for the predetermination and diagnosis. The proposed method has been compared with the existing intelligent learning algorithms and the results are proved to be more accurate in terms of the recognition rate, and training speed.
A Comparative Analysis of Static Code Analysis Tools that check Java Code Adherence to Java Coding Standards

Java programming language is considered highly important due to its extensive use in the development of web, desktop as well as handheld devices applications. Implementing Java Coding standards on Java code has great importance as it creates consistency and as a result better development and maintenance. Finding bugs and standard's violations is important at an early stage of software development than at a later stage when the change becomes impossible or too expensive. In the paper, some tools and research work done on Coding Standard Analyzers is reviewed. These tools are categorized based on the type of rules they cheeked, namely: style, concurrency, exceptions, and quality, security, dependency and general methods of static code analysis. Finally, list of Java Coding Standards Enforcing Tools are analyzed against certain predefined parameters that are limited by the scope of research paper under study. This review will provide the basis for selecting a static code analysis tool that enforce International Java Coding Standards such as the Rule of Ten and the JPL Coding Standards. Such tools have great importance especially in the development of mission/safety critical system. This work can be very useful for developers in selecting a good tool for Java code analysis, according to their requirements.