Reviewerly/exHarmony huggingface dataset:
The Future of Fashion Films in Augmented Reality and Virtual Reality

This chapter provides insights into the future of fashion film with respect to augmented reality and virtual reality technologies. The question: How does augmented reality and virtual reality influence the future of fashion film? Is therefore considered. It is important to analyze the influence of those technologies on fashion films to assess the potential for fashion retailers and in best case gain first-mover advantages. To answer the stated research question, the literature research was conducted to gain insights about the topic and its influence toward fashion filming. Explanation of augmented reality and virtual reality is provided as well as implications in the retail sector regarding fashion films. Moreover, company examples already using this approach have been compiled. Furthermore, an empirical research part was conducted including a survey method based on an online survey design. The questionnaire is based on what has been revealed in the literature to gain in-depth insides and approval. The data gained indicated that augmented reality and virtual reality influence the future of fashion film in various ways. The findings highlight how important those technologies can be in order to enhance customer experience and engagement. Regarding the research question, the conclusion can be drawn that it is highly Important for fashion managers to take future developments like augmented reality and virtual reality into account to stay competitive and satisfy the requirements of modern consumers.
Artificial Intelligence and Turing Test

This chapter introduces the concept of artificial intelligence. Firstly, the artificial intelligence was subject to the interest of researcher Alan Turing. He proposed three different, but evolved tests of artificial intelligence called: (i) Original imitation game, (ii) Standard Turing test, (iii) Extended Turing test. In spite of some deficiencies, these tests are used as a golden standard in the evolving artificial intelligence evaluation.
LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information Retrieval

Resource selection is a key task in distributed information retrieval. There are many factors that affect the performance of resource selection. Learning to rank methods can effectively combine features and are widely used for document ranking in web search. But few of them are explored for resource selection. In this paper, we propose a resource selection algorithm based on learning to rank called LTRRS. By analyzing the factors affecting the effectiveness of resource selection, we extract multi-scale features including term matching features, topical relevance features and central sample index (CSI) based features. By training LambdaMART learning to rank model, we directly optimize NDCG metric of resource ranking list in LTRRS. Experiments on the Sogou-QCL dataset show that LTRRS algorithm can significantly outperform the baseline methods in NDCG and precision metrics.
Comparing novel object learning in humans, models, and monkeys

Humans readily learn to identify novel objects, and it has been hypothesized that plasticity in visual cortex supports this behavior. Contributing to this view are reports of experience-driven changes in the properties of neurons at many levels of visual cortex, from V1 to inferotemporal cortex (IT). Here, we ask if object learning might instead be explained by a simple model in which a static set of IT-like visual features is followed by a perceptron learner. Specifically, we measured human (268 subjects; 170,000+ trials) and nonhuman primate (NHP; 2 subjects, 300,000+ trials) behavior across a battery of 29 visuomotor association tasks that each required the subject to learn to discriminate between a pair of synthetically-generated, never-before-seen 3D objects (58 distinct objects). Objects were rendered at varying scales, positions, and rotations; superimposed on naturalistic backgrounds; and presented for 200 msec. We then approximated the visual system’s IT response to each image using models of ventral stream processing (i.e. specific deep neural networks trained on ImageNet categorization), and we applied a reward-based, perceptron learner to the static set of features produced at the penultimate layer of each model. We report that our model is sufficient to explain both human and NHP rates of learning on these tasks. Additionally, we show humans, NHPs, and this model share the same pattern of performance over objects, but that NHPs reach criterion performance ~10× as slowly as humans (human t = 139, NHP t = 1149), suggesting humans have similar but more rapid learning mechanisms than their NHP cousins in this domain. Taken together, these results suggest the possibility that object learning is mediated by plasticity in a small population of “readout” neurons that learn and execute weighted sums of activity across an upstream sensory population representation (IT) that is largely stable.
Finite <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>N</mml:mi></mml:math> corrections to white hot string bits

String bit systems exhibit a Hagedorn transition in the $N\to\infty$ limit. However, there is no phase transition when $N$ is finite (but still large). We calculate two-loop, finite $N$ corrections to the partition function in the low temperature regime. The Haar measure in the singlet-restricted partition function contributes pieces to loop corrections that diverge as $\mathcal{O}(N)$ when summed over the mode numbers. We study how these divergent pieces cancel each other out when combined. The properly normalized two loop corrections vanish as $\mathcal{O}(N^{-1})$ for all temperatures below the Hagedorn temperature. The coefficient of this $1/N$ dependence decreases with temperature and diverges at the Hagedorn pole.
A Face Recognition Algorithm Based on Optimal Feature Selection

To achieve accurate and robust face recognition, this paper designs a face recognition algorithm based on optimal feature selection.The algorithm is denoted as GRA-LSSVM, because it integrates grey relational analysis (GRA) with least squares support vector machine (LSSVM).Firstly, the target face image was segmented into several subblocks.Next, the global features of the face were extracted from each subblock by kernel principal component analysis (PCA).After that, the GRA algorithm was introduced to determine the features that contribute greatly to face recognition.These features were integrated into an eigenvector.Finally, a face classifier by the LSSVM based on the "one-to-many" principle, and simulated with multiple face databases.The simulation shows the GRA-LSSVM derived the optimal feature subset for face recognition, and thus outperformed other face recognition algorithms in accuracy and speed.The research provides an effective and advanced method for face recognition.
Comparative Study of Sky Diffuse Irradiance Models Applied to Photovoltaic Systems

The increasing energy demand and the search for greener energy resources are expanding interests on photovoltaic systems. These systems need an accurate climatic and irradiation data in order to precisely estimate the energy yield of PV systems. However, most of the available irradiance data, are only for the horizontal plane. The precise estimation of the total irradiance incident on the surface of photovoltaic modules is one of the most important steps in the performance analysis of PV systems. In order to estimate the irradiance on a tilted surface from the irradiance data on the horizontal plane, there are many models available in the literature, they can be classified in isotropic and anisotropic models. This study aims to access the performance of an isotropic and three anisotropic models, which are used by PV system simulation softwares, and the impact of each of them on the estimated energy generation. The results are compared with measured energy data collected at Politec®, Aracariguama in Brazil. Results showed that positioning the module at the correct tilted angle and facing north can optimise the global irradiance incident on the module's surface. Hay & Davies model presented the lowest Mean Bias Error and Root Mean Squared Error while also showing a correlation coefficient close to 1.
PENERAPAN PROGRAM PEMBELAJARAN SHOW AND TELL DALAM GERAKAN LITERASI SEKOLAH DI SD MUHAMMADIYAH CONDONGCATUR

This study aims to describe 1) the urgency of thelearning program show and tell in the literacy movement at SD Muhammadiyah Condongcatur; 2) the application oflearning programs show and tell in the literacy movement at SD Muhammadiyah Condongcatur; 3) supporting and inhibiting factors for thelearning program show and tell in the literacy movement at SD Muhammadiyah Condongcatur.This type of research uses qualitative descriptive research. This research is the vice principal, teachers, students, and library officers. Data collection uses triangulation techniques and sources. The data analysis techniques use Model Miles and Huberman.The results of the study can be summarized as follows. 1) The urgency of thelearning program show and tell in the literacy movement at SD Muhammadiyah Condongcatur is very important students due tolearning show and tell train students to understand what information is read, then convey, have information to make products, add insight, share information, and train self-confidence. 2) Application oflearning show and tell through 3 stages, namely planning, implementation, and evaluation. 3) Factors supporting learninghow and tell, among others, principals support the success oflearning programs show and tell, competent subject teachers, and enthusiasm in implementinglearning programs show and tell, schools provide adequate facilities and infrastructure, students are active subjects inlearning show and tell. The inhibiting learning show and tell factor include lack of time, with teacher supervision and there is no readiness of students when they turn to show and tell.
ÜNİVERSİTE RESMİ İNTERNET SİTELERİNİN GÖRSEL AÇIDAN MUKAYESELİ İNCELEMESİ

Nowadays, thanks to the rapidly developing communication technologies, it is possible to reach and communicate information, documents, people, organizations with the touch of a keyboard without the concept of time and space from anywhere in the world by using internet technology that connects millions of people with a common network. As a result of the advanced technologies of the 21st century, the digital age has transformed corporate websites into a gateway to the world; In addition to being at a high level of usability, the website should be able to provide continuous flow of information to the people with its visual designs that reflect the corporate identity and thus reflect the corporate identity, and satisfy the expectations of people to reach information. In this context, the official website of the university, multi-language support, student support line, the activity of departments and programs, candidate student platform, academic achievement announcements, content adequacy, campus life and city life information, graduate portal usability as well as graphic design in the context of corporate color, photography and the use of video, event calendar, timeliness, layout, simplicity, quality and quantity of individuals to meet the needs of the website use. In this research, the official websites of 70 public and private universities of domestic and foreign origin were examined, personal evaluation was made according to 16 different elements and comparative results were tried to be reached according to the average grades obtained from the evaluation results.
Automatic 3D Medical Image Segmentation with Densely-Connected Volumetric ConvNets

The Automated medical image segmentation in 3D medical images play an important role in many clinical applications, such as diagnosis of prostatitis, medical image cancer and enlarged medical image.However, it is still a challenging task due to the complex background, lacking of clear boundary and various shape and texture between the slices.In this paper, we propose a novel 3D convolutional neural network with densely-connected layers to automatically segment the medical image.Compared with other methods, our method has three compelling advantages.First, our model can effectively detect the medical image region in a volume-to-volume manner by utilizing the 3D convolution rather than the 2D convolution, which can fully exploit both spatial and region information.Second, the proposed network architecture alleviates the vanishing-gradient problem, strengthens the information propagation between layers, overcomes the problem of overfitting and makes the network deeper by adopting a densely-connected manner.Third, besides the densely-connected manner inside each block, we also adopt the long connections strategy between blocks.
Design synthesis: An act of Research through Design

We present a reflective practice wherechallenges of assembling, making sense of anddrawing conclusions from co-created materials wereaddressed through a process of design synthesis thatimproved the clarity and meaning making during theinterpretation process. In this paper, we illustrate ourpoint by presenting a set of design research artifactsresulting from design synthesis: a manifesto, a scalemodel, a set of storyboards and illustrated characters. Inspired by the arts and creative practice inother disciplines such as film making, we adaptedthese methods as a means of transforming participants’ contributions into inspirational resources forinteractive design. This process encouraged theproduction of new creative and active forms ofdocumentation and enabled us to handle interpretation in a way that embraced the inspirational andprovisional nature of our creative and participatoryprocesses. By doing so, we broaden the current practice of documentation in design and show how ourprocess of design synthesis can serve the purpose ofco-creation. Finally, we encourage design practitioners to adapt and develop design-based methods tofilter and externalize insights, making their thinkingtangible for them and others so they can be collectively discussed, tested and reflected upon to inspirefuture ideas.
A Comparative Evaluation of Visual and Natural Language Question Answering over Linked Data

With the growing number and size of Linked Data datasets, it is crucial to make the data accessible and useful for users without knowledge of formal query languages. Two approaches towards this goal are knowledge graph visualization and natural language interfaces. Here, we investigate specifically question answering (QA) over Linked Data by comparing a diagrammatic visual approach with existing natural language-based systems. Given a QA benchmark (QALD7), we evaluate a visual method which is based on iteratively creating diagrams until the answer is found, against four QA systems that have natural language queries as input. Besides other benefits, the visual approach provides higher performance, but also requires more manual input. The results indicate that the methods can be used complementary, and that such a combination has a large positive impact on QA performance, and also facilitates additional features such as data exploration.
Attitude to the Digital Learning Environment inUkrainian Universities

Needs of digital transformation requires specific flexibility frommodern universities to ensure the society demands implementation through innovative teaching and IC-technologies. Modern universities create a digitallearning environment to support studying activities. This research presents anexperts’ estimate of the current condition and perspectives of universities digitalstudying environments in Ukraine. We verified the theoretical model structureof the university digital studying environments by means of the empirical datafactor analysis. We studied the components of the existing learning environment and enabling environment and compared them to the results of our previous research. We proved the digital learning environment theoretical model wascorrect. We proved that visions of students and teachers correspond to the keytrends accelerating higher education technology adoption. We assume the digital learning environment development benefits overcoming significant challenges impeding higher education technology adoption.
CCTV-Based Multi-Factor Authentication System

Many security systems rely solely on solutions based on Artificial Intelligence, which are weak in nature. These security solutions can be easily manipulated by malicious users who can gain unlawful access. Some security systems suggest using fingerprint-based solutions, but they can be easily deceived by copying fingerprints with clay. Image-based security is undoubtedly easy to manipulate, but it is also a solution that does not require any special training on the part of the user. In this paper, we propose a multi-factor security framework that operates in a three-step process to authenticate the user. The motivation of the research lies in utilizing commonly available and inexpensive devices such as onsite CCTV cameras and smartphone camera and providing fully secure user authentication. We have used technologies such as Argon2 for hashing image features and physically unclonable identification for secure device-server communication. We also discuss the methodological workflow of the proposed multi-factor authentication framework. In addition, we present the service scenario of the proposed model. Finally, we analyze qualitatively the proposed model and compare it with state-of-the-art methods to evaluate the usability of the model in real-world applications.
Variational Regularization for Multi-Channel Image Denoising

Image restoration from noisy observations is an inverse problem. Total variation (TV) is widely used to regularize this problem. TV preserves object boundaries better than a quadratic regularizer; however, it performs poor in low-textured image regions because it generates undesirable staircase artefacts. Furthermore, TV can preserve sharp horizontal and vertical edges; however, it causes the unnecessary smoothing of edges at an angle other than 0o or 90o. This problem arises because TV minimizes the gradient magnitude. Therefore, to preserve sharp boundaries, the design of an efficient variational regularizer is crucial. This paper presents a novel regularizer for the denoising of multi-channel vector valued image. The proposed regularizer uses horizontal, vertical as well as diagonal derivatives, and imposes the intensity continuity of partial image derivatives at each pixel of the underlying image. Experiments reveal that the proposed regularizer preserves edges and object boundaries better than TV based regularizers. This regularizer is also able to reduce undesirable staircase artefacts produced by TV in flat image regions.
Model-based Integration of Unstructured Web Data Sources using Graph Representation of Document Contents

Unstructured or semi-structured documents on the web are often used as a media for publishing structured, domain-specific data which is not available from other sources. Integration of such documents as a data source to a standard information system is still a challenging problem because of the very loose structure of the input documents and usually missing semantic annotation of the published data. In this paper, we propose an approach to data integration that exploits the domain model of the target information system. First, we propose a graph-based model of the input document that allows to interpret the contained data in different alternative ways. Further, we propose a method of aligning the document model with the target domain model by evaluating all possible mappings between the two models. Finally, we demonstrate the applicability of the proposed approach on a sample domain of public transportation timetables and we present the preliminary results achieved with real-world documents available on the web.
Vectorized linear approximations for attacks on SNOW 3G

SNOW 3G is a stream cipher designed in 2006 by ETSI/SAGE, serving in 3GPP as one of the standard algorithms for data confidentiality and integrity protection. It is also included in the 4G LTE standard. In this paper we derive vectorized linear approximations of the finite state machine in SNOW3G. In particular,we show one 24-bit approximation with a bias around 2−37 and one byte-oriented approximation with a bias around 2−40. We then use the approximations to launch attacks on SNOW 3G. The first approximation is used in a distinguishing attack resulting in an expected complexity of 2172 and the second one can be used in a standard fast correlation attack resulting in key recovery in an expected complexity of 2177. If the key length in SNOW 3G would be increased to 256 bits, the results show that there are then academic attacks on such a version faster than the exhaustive key search. (Less)
TiPoint: detecting fingertip for mid-air interaction on computational resource constrained smartglasses.

Smartglasses mostly rely on hardware interfaces such as touch-pad and buttons, which are often cumbersome and counter-intuitive to use. Furthermore, smartglasses feature cheap and low-power hardware preventing the use of advanced pointing techniques. To overcome these issues, we introduce TiPoint, a freehand mid-air interaction technique. TiPoint uses the monocular camera embedded in smartglasses to detect the user's hand, enabling intuitive and non-intrusive interaction. We introduce a light-weight algorithm for fingertip detection, which is especially suited for the limited hardware specifications and the short battery life time of smartglasses. Our evaluation shows that TiPoint as a mid-air non-intrusive interface delivers a better experience for users and smart glasses interactions, with users completing typical tasks 1.82 times faster than when using the original hardware.
Auditing Indian Elections

Electronic Voting Machines (EVMs) used in the 2019 General Elections in India were fitted with printers to produce Voter-Verifiable Paper Audit Trails (VVPATs). VVPATs allow voters to check whether their votes were recorded as they intended. However, confidence in election results requires more: VVPATs must be preserved inviolate and then actually used to check the reported election result in a trustworthy way that the public can verify. A full manual tally from the VVPATs could be prohibitively expensive and time-consuming; moreover, it is difficult for the public to determine whether a full hand count was conducted accurately. We show how Risk-Limiting Audits (RLAs) can provide high confidence in Indian election results. Compared to full hand recounts, RLAs typically require manually inspecting far fewer VVPATs when the outcome is correct, and are much easier for the electorate to observe in adequate detail to determine whether the result is trustworthy. We show how to apply two RLA strategies, ballot-level comparison and ballot polling, to General Elections in India. Our main result is a novel method for combining RLAs in constituencies to obtain an RLA of the overall parliamentary election result.
53.1:<i>Invited Paper:</i>Opportunities and Hurdles for AR and VR Technology

VR and AR near‐to‐eye (NTE) technologies offer the potential for an ultimate large display experience with extreme portability at low‐cost for medical, workstation, entertainment, and communications applications, among others. However, AR and VR have yet to achieve even a small fraction of this potential to date. Seven recent technical advances are among key developments that may finally bring virtual imaging to the display market forefront.