Reviewerly/exHarmony huggingface dataset:
Cryptographic Solutions for Credibility and Liability Issues of Genomic Data

In this work, we consider a scenario that includes an individual sharing his genomic data (or results obtained from his genomic data) with a service provider. In this scenario, (i) the service provider wants to make sure that received genomic data (or results) in fact belongs to the corresponding individual (and computed correctly), (ii) the individual wants to provide a digital consent along with his data specifying whether the service provider is allowed to further share his data, and (iii) if his data is shared without his consent, the individual wants to determine the service provider that is responsible for this leakage. We propose two schemes based on homomorphic signature and aggregate signature that links the information about the legitimacy of the data to the consent and the phenotype of the individual. Thus, to verify the data, each party also needs to use the correct consent and phenotype of the individual who owns the data.
Students vs. Professionals in Assisted Requirements Tracing: How Could We Train Our Students?

Abstract Students vs. Professionals in Assisted Requirements Tracing: How Could We Train Our Students?AbstractAssisted requirements tracing (ART) skills are essential for new college graduates joining thesoftware industry as their initial assignments involve substantial tracing-related activities.Although studying human analysts in ART is an emerging research trend, how students mightbehave differently from software professionals is yet to be investigated. In this paper, wecompare the performances, processes, and strategies between students and software professionalsin carrying out ART tasks for an unfamiliar system. We observe that both students andprofessionals performing ART activities follow a generic four-phase problem solving process:define the problem, develop a plan, implement the plan, and evaluate the solution. We find thatstudents show significant deficiency in the overall problem solving process, whereasprofessionals follow unique and effective tracing techniques in defining the problem, and indeveloping and implementing the plan. We identify the improvement areas and propose a set oflearning activities for software engineering students to enhance their tracing skills. Weimplement two learning activities in a Software Requirements Engineering course and report ourexperience. Our study contributes to the improvement of training students in performing ARTand other information-intensive tasks in software engineering.
Innovation in Construction: New Course Development Within a Construction Management Curriculum

Abstract Innovation in Construction: New Course Development Within a Construction Management CurriculumThe U.S. construction industry has long been saddled with a reputation as being resistant to theadoption of innovation. Countless scholarly papers and articles in the popular press haveidentified this trait and bemoaned its effects on the construction industry and the country as awhole. It has been observed that failure to adopt innovation has serious ramifications because itincreases inefficiency by lessening build quality, increasing construction costs and increasingwaste. Ultimately critics assert these negatives have far reaching national implications as theycan adversely affect the United States’ financial well-being by reducing employment, slowingeconomic growth and lessening overall global competitiveness.To address these concerns and encourage a more innovation-receptive mentality within the upperclassmen of a construction management degree program, a new course was developed,“Innovation in Construction”.The goals of the course include:  Provide the student with a critical eye towards inherent problems embedded within the construction industry.  Enable the student to distinguish a valid innovation from a simple novelty.  Recognize the importance innovation plays in the construction industry.  Evaluate innovations by utilizing a risk benefit analysis-like approach.This paper will describe the development of this course, the special challenges associated with acourse topic that falls outside traditional construction management curricula, and the inherentreluctance to adopt change in any form – both on a personal and professional level. The courseattempts to reconcile the domestic industry’s penchant for conservatism with the global pressurefor advanced capabilities and on-going improvement. Including the course themes, the paperwill present assignments, tools, research sources, and course content.
Tapping the User Experience to Design a Better Library for Engineering and Textiles Students and Faculty

Abstract Tapping the User Experience to Design a Better Library for Engineering and Textiles Students and FacultyIn recent years, many academic libraries have embraced the “user experience” (or “UX”)movement, which involves performing research on library users to learn about the totality oftheir experience using libraries, including attitudes, motivations, and emotions. This movement,which has its roots in website design and applied anthropology, has gained traction in the librarycommunity, where focus on the user has always been of primary importance, but ever evolvinguse of technologies and learning styles has called for a continual commitment to collectingfeedback from users. This feedback often challenges old assumptions and provides informationabout the life of the user beyond the walls of the library.In 2010, the North Carolina State University Libraries began construction of a new librarybuilding to serve the Colleges of Engineering and Textiles on NC State’s Centennial Campus.Slated to open in January 2013, the James Hunt Library is meant to redefine the library of the21st century in terms of services, technology and learning spaces. Understanding the faculty andstudents in these colleges is critical to the success of this new building, and as a result, userexperience research has been a significant part of the planning process. A major product of thisresearch has been the creation of design “personas,” profiles of archetypical users that helppersonalize user groups to librarians and administrators envisioning space uses and designingservices.This paper will highlight the ways we have collected data on faculty and students in the Collegesof Engineering and Textiles. It will detail the persona-creation process as it was adapted forspace planning in an academic library setting. It will discuss how such user research bringsdeeper understanding to the ways students use spaces and technologies and revealed gaps instudent needs. We will also highlight reusable tools for gathering user experience data andcreating personas available through the web-based Learning Space Toolkit, an IMLS-grantfunded collaborative project between the NCSU Libraries and two design consultancy firms.
The Differential Effects of Causes on Categorization and Similarity

Does categorization involve more than the similarity of an item to a category prototype or other category members? Rips (1989) argues yes, because categorization and similarity ratings sometimes diverge, indicating that they are based on different factors. However, Smith and Sloman (1994) suggest that such categorization/similarity dissociations may be limited to special conditions. We examined the effect of causal relationships between category attributes on categorization and similarity, and found that causal knowledge had a much larger effect on categorization than on similarity. This result was obtained with stimuli rich in characteristic attributes and without participants thinking aloud, that is, in just those conditions where Smith and Sloman found categorization to be solely similarity-based. Thus, the categorization/similarity dissociation demonstrated by Rips is alive and well, and the need for an account of categorization that goes beyond similarity is again highlighted.
Cost-Based Heuristics and Node Re-Expansions across the Phase Transition

Recent work aimed at developing a deeper understanding of suboptimal heuristic search has demonstrated that the use of a cost-based heuristic function in the presence of large operator cost ratio and the decision to allow re-opening of visited nodes can have a significant effect on search effort. In parallel research, phase transitions in problem solubility have proved useful in the study of problem difficulty for many computational problems and have recently been shown to exist in heuristic search problems. In this paper, we show that the impact on search effort associated with a larger operator cost ratio and the number of node re-expansions is concentrated almost entirely in the phase transition region. Combined with previous work connecting local minima in the search space with such behavior, these observations lead us to hypothesize a relationship between the phase transition and the existence of local minima.
International Service as a Means of Improving Retention of Engineering Students

One of the most significant challenges facing engineering education is the chronic problem of inclusion and retention of underrepresented minority (URM) groups.One group requiring unique retention efforts is students who are making satisfactory grades when they decide to withdraw.Many of these students have a difficult time connecting the work of introductory engineering classes with the local, national and international societal issues that they value.Service learning has been integrated into many non-engineering curricula with success in developing skills and promoting social engagement.In engineering settings it can also be used to highlight the link between engineering and society at moments when students seek such a connection.The authors have established an international service club with activities developing in Peru.The paper presents an overview of some of the international service activities currently underway which have resulted in good participation, and exceptionally strong interest by URM students.Students will be surveyed as to their desire to integrate service in their careers and ways that involvement in the club has fulfilled these desires.On outline of such a survey instrument is presented.
Advances in Handwritten Keyword Indexing and Search Technologies

Many extensive manuscript collections are available in archives and libraries all over the world, but their textual contents remain practically inaccessible, buried under thousands of terabytes worth of high-resolution images. If perfect or sufficiently accurate text-image transcripts were available, textual content could be indexed directly for plaintext access using conventional information retrieval systems. But the results of fully automated transcriptions generally lack the level of accuracy needed for reliable text indexing and search purposes. Additionally, manual or even computer-assited transcription is entierely unsustainable when dealing with the extensive image collections typically considered for indexing. This paper explains how accurate indexing and search commands can be implemented directly on the digital images themselves without the need to explicitly resort to image transcripts. Results obtained using the proposed techniques on several relevant historical data sets are presented, clearly supporting the considerable potential of these technologies.
A Pixon-based Image Segmentation Method Considering Textural Characteristics of Image

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper we proposed a textured-based method to segment an input image into regions. In our method an entropy-based textured map of image is extracted, followed by an histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achieving more robustness against unwanted noises, a low-pass filtering technique is successfully used to smooth the image. As the next step, the appropriate pixons are extracted and delivered to a fuzzy c-mean clustering stage to obtain the final image segments. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other methods.
Diagrams to Augment Cognition

Diagrams, such as maps, charts, graphs, and widely used as cognitive tools to promote memory and information processing, serving a variety of situated roles. One reason for the effectiveness of diagrams is that they map real or conceptual elements and relations to graphic elements and spatial relations in diagrammatic space. Diagrams, such as maps, charts, graphs, and widely used as cognitive tools to promote memory and information processing, serving a variety of situated roles. They offload limited capacity working memory; they promote the use of space in inference and reasoning, they provide common ground for collaborative design. Diagrams can be used to organize and convey information and instructions, as in route maps and assembly directions. Diagrams can also function to aid inference and promote creativity. The goal is to come up with new ideas, ideas not anticipated by the designer of the diagram. In both cases, diagrams are inevitably replete with ambiguity. In the former, context disambiguates, instilling clarity and avoiding confusion.
A fully automated, transparent, reproducible, and blind protocol for sequential analyses

Despite many cultural, methodological, and technical improvements, one of the major obstacle to results reproducibility remains the pervasive low statistical power. In response to this problem, a lot of attention has recently been drawn to sequential analyses. This type of procedure has been shown to be more efficient (to require less observations and therefore less resources) than classical fixed-N procedures. However, these procedures are submitted to both intrapersonal and interpersonal biases during data collection and data analysis. In this tutorial, we explain how automation can be used to prevent these biases. We show how to synchronise open and free experiment software programs with the Open Science Framework and how to automate sequential data analyses in R. This tutorial is intended to researchers with beginner experience with R but no previous experience with sequential analyses is required.
Producing 3-D Audio

Arguably the most rapidly expanding market for audio production is that of 3-D audio – 360° spatial audio with representation of height. Such playback is becoming commonplace in cinemas, and multi-speaker home setups are following. As these mature, greater convenience and enhanced functionality will likely increase domestic uptake.In parallel, headphone-based 3D is experiencing an unprecedented rate of development. Headphones with spaced multiple drivers are emerging, accelerometers are being incorporated to facilitate head-tracking-driven audio panning, and binaural algorithms are steadily improving. The principal exponents of such playback are virtual and augmented reality. While gaming will lead this market, soon the applications will proliferate into many areas of daily life – from productivity to education, through social networking to music playback, and advertising will pervade these new media.All of these applications require the production of audio, and this chapter explores the associated implications for the music producer. 3D offers an exciting new paradigm for music, since the conventions associated with stereo and horizontal surround are increasingly outmoded by the greater options associated with perception of height.This chapter will consider relevant technologies and identify key production-practice, underpinned by a framework of scientific research to help define the emergent praxis of 3-D audio production. It will consider opportunities, applications and limitations, all of which combine to introduce and help define a new approach to music production that will increasingly pervade into the future.
Low-resolution and open-set face recognition via recursive label propagation based on statistical classification

In video surveillance, the captured face images are usually suffered from low-resolution (LR), besides, not all the probe images have mates in the gallery under the premise that only a single frontal high-resolution (HR) face image per subject. To address this problem, a novel face recognition framework called recursive label propagation based on statistical classification (ReLPBSC) has been proposed in this paper. Firstly, we employ VGG to extract robust discriminative feature vectors to represent each face. Then we select the corresponding LR face in the probe for each HR gallery face by similarity. Based on the picked HR–LR pairs, ReLPBSC is implemented for recognition. The main contributions of the proposed approach are as follows: (i) Inspired by substantial achievements of deep learning methods, VGG is adopted to achieve discriminative representation for LR faces to avoid the super-resolution steps; (ii) the accepted and rejected threshold parameters, which are not fixed in face recognition, can be achieved with ReLPBSC adaptively; (iii) the unreliable subjects never enrolled in the gallery can be rejected automatically with designed methods. Experimental results in [Formula: see text] pixels resolution show that the proposed method can achieve 86.64% recall rate while keeping 100% precision.
Medical Image Processor and Repository – MIPAR

The recent progress in medical image analysis has resulted in image-guided therapy, virtual reality, and augmented reality, other innovations that have greatly improved healthcare delivery, improved the quality of life, and saved lives. The advances in Internet and network technology have produced web technologies which make it possible to offer platform or software as a service via the web, making it possible for end users to access computer resources or specialized computer tools remotely. However, the high cost of image acquisition, the limited availability of medical image analysts, and the limited collaborative efforts between medical experts and scientists are major challenges for medical image analysis in the developing world. The aim of this project was to devise a medical image e-infrastructure called Medical Image Processor and Analysis (MIPAR) to contain a repository of medical images acquired from Africa and a platform for processing medical images. The backend of MIPAR which is resident on a High-Performance Computing infrastructure was built using FutureGateway, a framework for building science gateway. The image upload and download module was built upon the framework of Open Access Repository with the front end developed using HTML, CSS and BootStrap. JavaScript and JQuery were used for scripting. User's access to the server is controlled with HTTP response and a Client-Server Architecture, while the image processing tools on the server side communicate with PHP using Representational State Architecture (REST) API. MIPAR was tested using brain MRI images. Images were submitted remotely via MIPAR's web interface and kept in the repository. A 3D image of approximately 43MB was uploaded within 43 seconds. Downloading of the same image took approximately 30 seconds. To test the image processing facility, our request for brain extraction of the same image was successfully completed within 60 seconds. MIPAR allows users to donate, download and process medical images at no cost. It is our hope that such useful and unique tools will encourage collaboration, improve diagnosis, improve patient management, and promote open science in Africa.
MEFUASN: A Helpful Method to Extract Features using Analyzing Social Network for Fraud Detection

Fraud detection is one of the ways to cope with damages associated with fraudulent activities that have become common due to the rapid development of the Internet and electronic business. There is a need to propose methods to detect fraud accurately and fast. To achieve to accuracy, fraud detection methods need to consider both kind of features, features based on user level and features based on network level. In this paper a method called MEFUASN is proposed to extract features that is based on social network analysis and then both of obtained features and features based on user level are combined together and used to detect fraud using semi-supervised learning. Evaluation results show using the proposed feature extraction as a pre-processing step in fraud detection improves the accuracy of detection remarkably while it controls runtime in comparison with other methods.
Recognizing Human Actions in Low-Resolution Videos: An Approach Based on the Dempster–Shafer Theory

To address the problem that many existing approaches are not appropriate for action recognition in low-resolution (LR) videos, this paper presents a framework based on the Dempster–Shafer (DS) theory for this purpose. In the framework, artificial neural networks (ANNs) are firstly trained for every class with training samples, and then basic belief assignments (BBAs) for underlying classes are computed with the trained ANNs. The resulted BBAs are fused from all frames in the whole video sequentially by frame-by-frame based on DS’s rule of fusion. Action recognition is last performed with a threshold-based decision making. We conducted experiments on extensive testing data with various levels of video resolution. Results reveal that the proposed framework: (1) shows outperforming recognition performances compared with state-of-the-art classifications, respectively, such as sequence matching, voting-based strategy and bag-of-words (BoW) method; and (2) can achieve a low observational latency in recognition.
Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). SNR may be improved by averaging functional information in a large region of interest when it is functionally homogeneous. We propose a novel method for automatic segmentation of DCE image sequences into functionally homogeneous regions, called DCE-HiSET. Using an observation model which depends on one parameter a and is justified a posteriori, DCE-HiSET is a hierarchical clustering algorithm. It uses the p-value of a multiple equivalence test as dissimilarity measure and consists of two steps. The first exploits the spatial neighborhood structure to reduce complexity and takes advantage of the regularity of anatomical features, while the second recovers (spatially) disconnected homogeneous structures at a larger (global) scale. Given a minimal expected homogeneity discrepancy for the multiple equivalence test, both steps stop automatically by controlling the Type I error. This provides an adaptive choice for the number of clusters. Assuming that the DCE image sequence is functionally piecewise constant with signals on each piece sufficiently separated, we prove that DCE-HiSET will retrieve the exact partition with high probability as soon as the number of images in the sequence is large enough. The minimal expected homogeneity discrepancy appears as the tuning parameter controlling the size of the segmentation. DCE-HiSET has been implemented in C++ for 2D and 3D image sequences with competitive speed.