Reviewerly/exHarmony huggingface dataset:
Digital Image Correlation Method Based on SURF in Airship Envelope Measurement

Digital image correlation (DIC) based on computer vision is introduced to the measurement of airship envelope because the material has the characteristics of small modulus and large strain range which bring some difficulties to the traditional contact methods. This paper presents a proposed DIC initial value estimation algorithm by introducing SURF (Speeded Up Robust Feature) method to improve the correlation and system automation, which makes the instability and the initial value sensitivity problems of further optimization algorithm solved. Firstly, SURF is used to detect the feature points in the images. Then, the SURF feature points are associated with sub-regions by selecting N feature points, which means that per sub-region is represented by few feature points. Finally, the selected points in the images before and after the deformation are accurately matched so that displacement and strain initial values are obtained for Newton-Raphson (N-R) method. The algorithm is verified by simulated speckle experiment and airship envelope tensile experiment and is compared with the traditional initial value estimation method. The results show that the proposed algorithm can provide more stable and accurate initial value estimation which not only reduces operational complexity but also speeds up the matching process.
Content-Based Image Retrieval Using Extended Local Tetra Patterns

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
The Multi-Stage Experience: the Simulated Work Task Approach to Studying Information Seeking Stages.

This experience paper shines more light on a simulated work taskapproach to studying information seeking stages. This explicit multistage approach was first utilized in Huurdeman, Wilson, and Kamps (2016) to investigate the utility of search user interface (SUI) features at different macro-level stages of complex tasks. We focus on the paper’s terminology, research design, methodology and use of previous resources. Finally, based on our experience, we reflect on the potential for re-using our multistage approach and on general barriers to re-use in an Interactive Information Retrieval research context.
Automated Face Detection Using Skin Color Segmentation and Viola-Jones Algorithm

Viola-Jones algorithm can be categorized as one of an established and effective method (feature-based approach) for detecting face. It consists of three main processes which are Haar features, Adaboost and Cascading. These processes involved scanning the patterns of human face through all the pixels in the image. In the empirical experiment by using this algorithm, some region in the image which is supposed to be non-face region is detected as face due to the similarity of human face features. In addition, this approach only search for pattern (feature) but leaving the color information. Therefore, this paper proposes hybridization of skin color segmentation in prior to Viola-Jones algorithm. Several images with different kinds of environment (different light conditions, different face orientation) and various people ethnics (Malay, Chinese, Indian, etc.) are tested using the improved algorithm and the original Viola-Jones algorithm as well. In average, experimental results reveal the combination of YCbCr color and Viola-Jones algorithm is the best model (average accuracy is ~88%) to detect human face in various conditions. Other color model such as HSV and benchmarked algorithm are having slightly low detection rates due to some false face detection. Despite of this, this research also found that both YCbCr and HSV color model are having some limitation when dealing with darker face and lightning condition since the skin color become slightly out of range from normal skin color distribution.
Fractal Image Compression Techniques

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Toward “quantum supremacy” with photons

We develop single-photon sources that simultaneously combines high purity, efficiency, and indistinguishability. We demonstrate entanglement among 12 single photons. We construct high-performance multi-photon boson sampling machines to race against classical computers.
JudaicaLink: A Knowledge Base for Jewish Culture and History

JudaicaLink is a novel resource which provides a knowledge base of Jewish culture and history. It is based on multilingual domain-specific information from encyclopedias and general-purpose knowledge bases such as The Integrated Authority File (GND) of the German National Library. JudaicaLink can be used for contextualization of metadata, i.e., entity resolution within and linking of metadata to improve resource access and to provide richer context to the user. Many resources for contextualization, particularly specialized resources for the given domain, are only available in unstructured form. General-purpose resources like DBpedia are hard to use due to their sheer size while only a very small subset of the data is actually relevant. Therefore, JudaicaLink aims at integrating relevant subsets of various data sources to function as a single hub for the contextualization process. JudaicaLink is freely available on the Web as Linked Open Data. In this paper, we explain how JudaicaLink is built, how it can be accessed by users, as well as its architecture, technical implementation, applications and relations to Jewish culture.
Medical Diagnosis Based on Nonlinear Manifold Discriminative Projection.

In recent years, medical diagnosis based on machine learning has become popular in the interdiscipline research of computer science and medical science. It is closely related with classification, which is one of the important problems in machine learning. However, the traditional classification algorithms can hardly appropriately solve high-dimensional medical datasets. Manifold learning as nonlinear dimensionality reduction algorithm can efficiently process high dimensional medical datasets. In this paper, we propose an algorithm based on Nonlinear Manifold Discriminative Projection (NMDP). Our algorithm incorporates the label information of medical data into the unsupervised LLE method, so that the transformed manifold becomes more discriminative. Then we apply the discriminant mapping to the unlabeled test data for classification. Experimental results show that our method exhibits promising classification performance on different medical data sets.
Diagnóstico de gestão do conhecimento: o caso de uma organização de pesquisa e desenvolvimento

Knowledge is recognized as an important resource for organizations to become competitive in their market. The management of this resource consists of a practice adopted by organizations that search for efficiency and effectiveness of their processes. This research carried out a knowledge management diagnosis in a research and development organization in Florianópolis. It is an applied research, with a qualitative and descriptive approach. Knowledge management diagnostic model proposed by Bukowitz and Williams (2002) was applied to five leaders of a knowledge-intensive research and development organization. The findings showed that knowledge management of the organization presented low evaluation in the perception of the managers. Most below-average results are found in sections of tacit level of knowledge management process (Get, Learn and Contribute). Only the Use section was better judged by leaders. At strategic level, we highlight the Evaluate section with the lowest result and the Build/Hold section as the best evaluated section. Due to this diagnosis, an action program was developed aiming at improvement in those sections with low evaluation.
LibGuides: Integrating Library Content into Blackboard for SAU Faculty: Home

A guide for SAU faculty on how to add library e-books, articles, videos and more to your Blackboard courses
A novel accuracy assessment model for video stabilization approaches based on background motion

In this paper, we propose a new accuracy measurement model for the video stabilization method based on background motion that can accurately measure the performance of the video stabilization algorithm. Undesired residual motion present in the video can quantitatively be measured by the pixel by pixel background motion displacement between two consecutive background frames. First of all, foregrounds are removed from a stabilized video, and then we find the two-dimensional flow vectors for each pixel separately between two consecutive background frames. After that, we calculate a Euclidean distance between these two flow vectors for each pixel one by one, which is regarded as a displacement of each pixel. Then a total Euclidean distance of each frame is averaged to get a mean displacement for each pixel, which is called mean displacement error, and finally we calculate the average mean displacement error. Our experimental results show the effectiveness of our proposed method.
LibGuides: Engineering Profession: Common First Year (FEBE1000): Library Services

This LibGuide provides relevant information for all FEBE1000A Introduction to the Engineering Profession students.
Socialification: Social Software Elements Analysis and Design

The goal of this paper is to initiate a ‎conversation on the undergraduate teaching of ‎social software analysis and design in applications ‎which are non-social-media specific. This course ‎covers the topics required to strategically ‎‎“socialify” organizational applications to engage ‎users in the most productive way for the ‎organization. To capture this effort, we suggest the ‎term “socialification” which means the use of social ‎software design features in non-social-media ‎applications. We provide some background and ‎course goals and learning objectives as well as a ‎course structure. We then discuss issues to consider ‎when implementing a course in social software ‎elements development. We also cover the theoretical ‎grounding related to the interdisciplinary process ‎and explain how it contributes to the design of the ‎course. ‎
Do Consumers’ Ethical Judgments Matter for Purchase Intentions in Online Gray Markets? The Mediating Role of Trust: An Abstract

Firms around the world confront new pressures with the trade of their branded products in unauthorized distribution channels known as gray markets. Gray markets emerge when firms sell their products in different markets at different prices. Differences in prices may stem from exchange rate fluctuations, promotional costs, brand owners’ multitiered pricing schemes, tax differences, or differing consumer preferences. Unlike counterfeits, gray market products are genuine in that they have been manufactured by or for, or are under license from, the brand owner. Unsurprisingly, gray markets create an array of problems for companies and consumers. Companies invest a considerable amount of time, money, and effort in manufacturing and promoting the goodwill of their products, which reflects in selling prices. Gray marketers take advantage of the market demand and reputation created and maintained by these companies and their authorized channel partners without investing the capital associated with establishing market demand and such reputation. Additionally, gray markets erode consumer goodwill because a product meant for a different market varies from the same product designed for domestic consumption in a variety of ways such as health and safety codes, regional tastes, warranties, and languages. Goodwill is further damaged when brand owners refuse to honor gray market products’ warranties and create a consumer perception of substandard customer service. An online environment has fueled the growth of online gray market activities due to the Internet’s ease of use, accessibility, and ability to connect consumers with product suppliers worldwide. Efforts by brand owners to contain gray market activities have mostly been unsuccessful. Given that online purchase activities have experienced a drastic increase over the years, many nonprofit organizations such as AGMA (Alliance for Gray Market and Counterfeit Abatement) and BBB (Better Business Bureau) and popular press such as Forbes, Fortune, and The Economist are shedding light on gray markets and educating consumers about the pitfalls of gray market purchases and a gray market’s cost to nations’ economies. In this paper, we explore consumer perceptions about the ethicality of gray markets and whether their ethical judgments have an influence on their purchase intention in an online shopping context. We further explore the role of trust in this dynamic relationship. We draw on the Hunt-Vitell (H-V) theory of ethics to explain the hypothesized relationships in our conceptual model (Hunt and Vitell 1986, 2006). We test the hypothesized relationships in an experimental study with 114 subjects and discuss the findings. References Available Upon Request
A Persistent Web IDentifier (PWID) URN Namespace

This document specifies a Uniform Resource Name (URN) for PersistentWeb IDentifiers for web material in web archives using the'pwid' namespace identifier. The main purpose of the standard isto support specification of references that are not covered by otherreference techniques: to support references to material in webarchives with restricted access. Furthermore, it supports persistenttechnology agnostic references to web archives in general, in a formthat can work as an algorithmic basis for finding web archiveresources in general. An additional important benefit is that thestandard can be used for specifying web collections, which can thenform a persistent computational basis for the extract of the archivedcollection parts. The PWID URN is designed to meet requirements forproper referencing needed by researchers. Therefore, it is designed asgeneral, global, sustainable, humanly readable, technology agnostic,persistent and precise web references for web materials in webarchives.
7 steps to mitigating your cyber risk

Cybersecurity is a real and serious issue, but taking these seven steps can drastically lower your practice’s exposure to a cyberattack.
Defining thematic relevance of messages in the task of online social networks monitoring in providing information-psychological security

Online social networks (OSN) are actively used for implementation of illegal and destructive activities including propaganda of drugs, suicide, terrorism. There is the task of detecting such materials for providing information-psychological security of a person. For dealing with it automated monitoring of OSN is required. Monitoring includes search in unstructured users’ text messages using keywords characterizing the object of monitoring. There is a problem of thematic relevance during process of OSN monitoring as well as in information retrieval systems. It happens because of language phenomenon of lexical ambiguity. The task of illegal content detecting is complicated by using of slang and jargon in communications, it does not allow to use existing effective approaches to word sense disambiguation. For fixing the problem of topical relevance author suggests to use topic models based on contexts of keywords. Additional multidimensional scaling for contexts in semantic space and subsequent clustering allow to make sense induction of posts from OSN. Condition for classifying a message as illegal content is proposed. Developed technique was tested on  The National  Corpus of Russian and The General Internet-Corpus of Russian.
Synthesizing Virtual-Real Artworks Using Sun Orientation Estimation.

The illumination effect is essential for the realistic results in images which are created by inserting virtual objects into real scene. For outdoor scenes, automatic estimation of sun orientation condition from a single outdoor image is fundamental for inserting 3D models to a single image. Traditional methods for outdoor sun orientation estimation often use handcraft illumination features or cues. These cues heavily rely on the experiences of human and pre-processing progresses using other image understanding technologies such as shadow and sky detection, geometry recovery and intrinsic image decomposition, which limit their performances. We propose an end to end way of outdoor sun orientation estimation via a novel deep convolutional neural network (DCNN), which directly outputs the orientation of the sun from an outdoor image. Our proposed SunOriNet contains a contact layer that directly contacts the intermediate feature maps to the high-level ones and learns hierarchical features automatically from a large-scale image dataset with annotated sun orientations. The experiments reveal that our DCNN can well estimate sun orientation from a single outdoor image. The estimation accuracy of our method outperforms model state-of-the-art DCNN based methods.
Comparison of Wearable and Non-wearable Technologies on Global CO2 Levels

Many leading analysts would agree that, had it not been for kernels, the refinement of su- perblocks might have never occurred. Given the current status of “fuzzy” symmetries, theorists daringly desire the analysis of e-business. Our focus in this paper is on whether robots and their entanglement with human nature, seen in bionic creatures or similar, can be a decisive influence on the global CO2 levels due to their replacement of non-robotic wearables such as clothes. We therefore explored a novel system for the refinement of SCSI disks (robotic wearables).&#x0D; Aims: To compare and calculate the influence of non-robotic and robotic wearable technology and its influence on global CO2 levels.&#x0D; Study Design: Comparative Study.&#x0D; Place and duration of Study: Institute for Applied Basic Industrial Research IFABIR, between March 2018 and April 2018.&#x0D; Methodology: We included 40 participants (38 men, 2 women; age range 18-20 years) with wearable technology incorporated or on their body, with the target group representing the use of these technologies in reality. Personal observations by participants led to statistical analysis about their validity and were, if granted, further analyzed and calculated by us and compared to existing numbers.&#x0D; Results: The climactic analysis of the first two experiments showed aligning results with g(n) = n. Furthermore, simulating red-black trees rather than emulating them in software produced less discretized, more reproducible results. Similarly, these energy observations contrast to those seen in earlier work.&#x0D; Conclusion: Our paper clearly showed that non-invasive, robotic wearables can have a positive influence on global CO2 levels, if they stand in alliance with global geographical trends and vegetation.
Library: PRISM: Home

PRISM (formerly known as DSpace) is University of Calgary's Digital Repository used to collect, preserve and disseminate the academic works of faculty, students, and research groups.
Batch Dictionary Learning with Augmented Orthogonal Matching Pursuit.

Dictionary learning is often incorporated in classification method, which can obtain a new representation under the learned dictionary to achieve better classification performance. In this paper, we propose a novel Batch Dictionary Learning model with augmented orthogonal matching pursuit classification. Batch Dictionary Learning model is capable of improving the dictionary by removing the redundancy of over-complete dictionary, thus the learned optimal dictionary is more suitable for classification. To solve the optimization target, we improve the traditional orthogonal matching pursuit (OMP) algorithm and propose an augmented orthogonal matching pursuit algorithm (AOMP) to solve the objective function. Superior experimental results demonstrate that our proposed model outperform the other state-of-the-art classification algorithms on real-world dataset.
An Overview of Various Classification Concepts of Web Page Content

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Efficient Network Reliability Computation in Uncertain Graphs.

Network reliability is an important metric to evaluate the connectivity among given vertices in uncertain graphs. Since the network reliability problem is known as #P-complete, existing studies have used approximation techniques. In this paper, we propose a new sampling-based approach that efficiently and accurately approximates network reliability. Our approach improves efficiency by reducing the number of samples based on stratified sampling. We theoretically guarantee that our approach improves the accuracy of approximation by using lower and upper bounds of network reliability, even though it reduces the number of samples. To efficiently compute the bounds, we develop an extended BDD, called S2BDD. During constructing the S2BDD, our approach employs dynamic programming for efficiently sampling possible graphs. Our experiment with real datasets demonstrates that our approach is up to 51.2 times faster than the existing sampling-based approach with higher accuracy.