Reviewerly/exHarmony huggingface dataset:
Implementation of the real–virtual 3D scene-fused full-parallax holographic stereogram

This work focuses on the generation of three-dimensional (3D)-scene information as well as the fusion of real and virtual 3D scene information for the full-parallax holographic stereogram based on the effective perspective images’ segmentation and mosaicking (EPISM) method. The improved depth-image-based rendering (DIBR) method was used to generate the virtual viewpoint images of the real 3D scene, and the regularization and densification processing models of the degraded light field were established; as a result, the real sampling-light field was reconstructed. Combined with the computer-rendered virtual 3D scene information, a “real + virtual” light-field fusion method based on a pixel-affine-projection was proposed to realize the fusion of the real and virtual 3D scene. The fusion information was then processed by the EPISM encoding and was then holographically printed. The optical experiment results showed that the full-parallax holographic stereogram with the real–virtual scene-fused 3D scenes could be correctly printed and reconstructed, which validated the effectiveness of our proposed method.
Immersion positively affects learning in virtual reality games compared to equally interactive 2d games

Purpose This study isolates the effect of immersion on players’ learning in a virtual reality (VR)-based game about cellular biology by comparing two versions of the game with the same level of interactivityand different levels of immersion. The authors identify immersion and additional interactivity as two key affordances of VR as a learning tool. A number of research studies compare VR with two-dimensional or minimally interactive media; this study focuses on the effect of immersion as a result of the head mounted display (HMD). Design/methodology/approach In the game, players diagnose a cell by exploring a virtual cell and search for clues that indicate one of five possible types of cystic fibrosis. Fifty-one adults completed all aspects of the study. Players took pre and post assessments and drew pictures of cells and translation before and after the game. Players were randomly assigned to play the game with the HMD (stereoscopic view) or without the headset (non-stereoscopic view). Players were interviewed about their drawings and experiences at the end of the session. Findings Players in both groups improved in their knowledge of the cell environment and the process of translation. Players who experienced the immersive stereoscopic view had a more positive learning effect in the content assessment, and stronger improvement in their mental models of the process of translation between pre- and post-drawings compared to players who played the two-dimensional game. Originality/value This study suggests that immersion alone has a positive effect on conceptual understanding, especially in helping learners understand spatial environments and processes. These findings set the stage for a new wave of research on learning in immersive environments; research that moves beyond determining whether immersive media correlate with more learning, toward a focus on the types of learning outcomes that are best supported by immersive media.
Research and Development of Blockchain Recordkeeping at the National Archives of Korea

In 2019, the National Archives of Korea (NAK) developed a blockchain recordkeeping platform to conduct R&amp;D on recordkeeping approaches. This paper introduces two types of R&amp;D studies that have been conducted thus far. The first is the use of blockchain transaction audit trail technology to ensure the authenticity of audiovisual archives, i.e., the application of blockchain to a new system. The second uses blockchain technology to verify whether the datasets of numerous information systems built by government agencies are managed without forgery or tampering, i.e., the application of blockchain to an existing system. Government work environments globally are rapidly shifting from paper records to digital. However, the traditional recordkeeping methodology has not adequately kept up with these digital changes. Despite the importance of responding to digital changes by incorporating innovative technologies such as blockchain in recordkeeping practices, it is not easy for most archives to invest funds in experiments on future technologies. Owing to the Korean government’s policy of investing in digital transformation, NAK’s blockchain recordkeeping platform has been developed, and several R&amp;D tasks are underway. Hopefully, the findings of this study will be shared with archivists around the world who are focusing on the future of recordkeeping.
A Hybrid Cloud Approach for Efficient Data Storage and Security

Cloud computing these days, may be a rudimentary correction happening inside the space of information innovation. It plays a crucial part in terms of data putting away and diminishing the esteem to business visionaries. In any case, information security proceeds to be one in all the most elevated issues for cloud computing. To deliver the secure information capacity and recovery, a few procedures are anticipated be that as it may the bulk of them confront a few downsides that reduce the common sense of Cloud Computing. This work scrutinizes a half breed cloud approach for proficient information capacity and security. The aim is to propose a model to identify duplication and prevent it thereby using the cloud storage efficiently.
Negotiating anthropomorphism in the Ai-Da robot

Abstract The central interest of this paper is the anthropomorphic social robot Ai-Da (Aidan Meller Gallery/Oxford University), perceived as an actor in the interplay of cultural and representational gestures. These gestures determine how this robot is presented—that is, how its activities are articulated, interpreted and promoted. This paper criticises the use of a transhistorical discourse in the presentational strategies around this robot, since this discourse reinforces the so-called “myth of a machine”. The discussion focuses on the individuation and embodiment of this drawing robot. It is argued that the choice to provide Ai-Da with an evocative silicone face, coupled with an anthropomorphic body, is a socio-political decision that shapes public imaginaries about social robots in general.
Surrogate Object Detection Explainer (SODEx) with YOLOv4 and LIME

Due to impressive performance, deep neural networks for object detection in images have become a prevalent choice. Given the complexity of the neural network models used, users of these algorithms are typically given no hint as to how the objects were found. It remains, for example, unclear whether an object is detected based on what it looks like or based on the context in which it is located. We have developed an algorithm, Surrogate Object Detection Explainer (SODEx), that can explain any object detection algorithm using any classification explainer. We evaluate SODEx qualitatively and quantitatively by detecting objects in the COCO dataset with YOLOv4 and explaining these detections with LIME. This empirical evaluation does not only demonstrate the value of explainable object detection, it also provides valuable insights into how YOLOv4 detects objects.
Toward Avatar-Drone: A Human-Embodied Drone for Aerial Manipulation

This paper presents Avatar-Drone to advance aerial manipulation. The mobile-manipulating drone is a haptics-based human-in-the-loop platform. This is married to an immersive framework based on Unity and virtual (VR) and augmented (AR) reality. The net effect provides the operator a sense of presence and see, hear, and feel what the drone is doing in the work environment. Two scenarios involving package delivery illustrate efficacy of this marriage.
Deep Joint Source-Channel Coding for Transmission of Correlated Sources over AWGN Channels

We revisit the joint source-channel coding (JSCC) problem of transmitting correlated sources over the additive white Gaussian noise channel using deep learning methods. Specifically, we consider the design of JSCC schemes for transmitting multivariate Gaussian sources, and Gauss-Markov processes over noisy channels with bandwidth (BW) compression and low delay. We show that encoding and decoding schemes represented by deep neural networks can be optimized jointly to obtain good JSCC schemes. Specifically, we adopt sinusoidal representation networks (SIRENs) for the transmission of multivariate Gaussian sources. The new architecture not only provides similar performance as the state-of-the-art (SOTA) with higher flexibility, but also results in interpretable encoder mappings. For the transmission of Gauss-Markov sources, recurrent neural networks (RNNs) are implemented to extract temporal information without resorting to explicit decorrelation prior to transmission. Experimental results show improved performance compared with traditional schemes.
Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text

Chinese sentiment analysis (CSA) has always been one of the challenges in natural language processing due to its complexity and uncertainty. Transformer has been successfully utilized in the understanding of semantics. However, it captures the sequence features in the text through position encoding, which is naturally insufficient compared with the recurrent model. To address this problem, we propose T-E-GRU. T-E-GRU combines the powerful global feature extraction of Transformer encoder and the natural sequence feature extraction of GRU for CSA. The experimental evaluations are conducted on three real Chinese datasets, the experimental results show that T-E-GRU has unique advantages over recurrent model, recurrent model with attention and BERT-based model.
Towards Optimal Use of Exception Handling Information for Function Detection

Function entry detection is critical for security of binary code. Conventional methods heavily rely on patterns, inevitably missing true functions and introducing errors. Recently, call frames have been used in exception-handling for function start detection. However, existing methods have two problems. First, they combine call frames with heuristic-based approaches, which often brings error and uncertain benefits. Second, they trust the fidelity of call frames, without handling the errors that are introduced by call frames. In this paper, we first study the coverage and accuracy of existing approaches in detecting function starts using call frames. We found that although recursive disassembly with call frames can maximize coverage, using extra heuristic-based approaches does not improve coverage and actually hurts accuracy. Second, we unveil call-frame errors and develop the first approach to fix them, making their use more reliable.
A hybrid self-adaptive invasive weed algorithm with differential evolution

The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches.
Identification of clouds using an all-sky imager

With the increasing number of utility-scale photovoltaic power plants, the uncertainty and volatility in power generation increases in the electricity system. In the range of long to medium term forecasting, the literature and the industry have several tools and methods in process. Nevertheless, the current and future energy transition, for instance the shorter gate closer of energy markets, is increasing the need for tools to support the ultra-short-term forecasting. This paper proposes a tool to support the ultra-short-term forecasting using an all-sky imager to map the clouds in the sky and estimate a Cloud Index. Three threshold-based methodologies are presented and tested, they are able to detect the clouds and categorize them as thick clouds and thin clouds.
Scene Graph tO Image Generation with Contextualized Object Layout Refinement

Generating images from scene graphs is a challenging task that attracted substantial interest recently. Prior works have approached this task by generating an intermediate layout description of the target image. However, the representation of each object in the layout was generated independently, which resulted in high overlap, low coverage, and an overall blurry layout. We propose a novel method that alleviates these issues by generating the entire layout description gradually to improve inter-object dependency. We empirically show on the COCO-STUFF dataset that our approach improves the quality of both the intermediate layout and the final image. Our approach improves the layout coverage by almost 20 points, and drops object overlap to negligible amounts. Our code is available at github.com/yanivbenny/COLoR.
MediTrans—Patient‐centric interoperability through blockchain

Summary In order to ensure best health care experience for the user, the user should be able to access or grant access to his medical information as the requirement arises. Currently, when the data are residing in hospital proprietary Electronic Health Record (EHR), this is not possible. Hence there is a requirement for patient operated PHR where the details of the treatment of patient is with the patient. This paper proposes a blockchain‐based, secure interoperable patient‐centric data access management system—MediTrans. In the proposed model, the patient owns his/her treatment related data and stores it in a secure personal health record (PHR) cloud. The tool provides technique for secure transfer of patient related data from Hospital EHR to patient PHR Cloud. Patient can give controlled access of patient's medical data to Health Care Providers for a specific period of time. MediTrans proposes secure access management of data stored in PHR cloud to authorised person via Blockchain Technology and Ciphertext policy attribute‐based encryption technique. The proposed design was evaluated by developing a prototype on Ethereum Blockchain with OpenEMR as the healthcare system. Performance analysis was carried out by varying the size of the data and the number of users. The results obtained were promising, which gives clear insights on the feasibility of the approach taken.
Small stories as curated formats on social media: The intersection of affordances, values &amp; practices

In this article, I focus on the design of stories as a specific feature, integrated into the spatial architecture of platform affordances (i.e. on Snapchat, Instagram, Facebook, Weibo. I argue for the need to interrogate such stories as curated, socio-technical formats, that is, as recogniseable and normative co-patternings of media-afforded ways of telling & types of teller. I show a methodological and analytical way of doing so, underpinned by a technographic perspective to stories that tracks media affordances, discourses about stories as features and communicative practices. I illustrate this approach with reference to three directives (cf. Preferential conditions, prompts) to users that my analysis attested to (Georgakopoulou, 2019; Georgakopoulou et al., 2020). These directives shape the types of stories told (i.e. sharing life-in-the-moment), the audience's mode of engagement in them (i.e. quantified viewing) and the tellers' self-presentation (i.e. authenticity). I specifically focus on the first directive of sharing-life-in-the-moment and consider its implications for the kinds of (in)visibilities of specific types of tellings, tales and tellers that has the potential to create. This line of inquiry, I suggest programmatically, can be productively integrated into critical digital literacies work and, subsequently, feed into discussions with (language) education and other non-academic stakeholders.
How Much Are We Willing To Lose in Cyberspace? On the Tail Risk of Scam in the Market for Initial Coin Offerings

From an entrepreneurial perspective, Initial Coin Offering (ICO) has become an alternative way for attaining funding for business projects using the new evolving digital financial market for tokens. Unfortunately, the majority of all ICOs are subject to scam which casts doubt on this new innovative tool for acquiring funding. Using a unique intensively hand-collected data set covering more than 5000 ICOs which have been launched in the August 2014–December 2019 period, we could identify 1014 ICOs exhibiting data on raised funding whereof 576 turned out to be scams projects. The cumulative losses due to scam in the ICO market correspond to $10.12 billion which is 66% of our identified overall market capitalization and highlights the enormous societal impact of this criminal activity. One novel aspect of our study is that it employs a recently proposed methodology based on ‘plug-in estimation’ to quantitatively computing the risk associated with scam in the market for ICOs. Our results suggest that employing naïve statistics in risk management dramatically underestimates this risk. We argue that our findings have important implications for policy makers as they call for an urgent need for ICO market regulations from governments and regulatory agencies to protect investors.
LSB steganography detection in monochromatic still images using artificial neural networks

Abstract Embedding graphic content in multimedia through steganography is a useful and fast practice to hide information. However, detecting the use of this technique is complex and sometimes unsuccessful because variations are not visually perceptible. This article proposes the use of a binary classification model based on artificial neural networks to detect the presence of LSB steganography on monochromatic still images of 256x256 and 8 bits, based on the Standford Genome Project. The steganograms were generated by varying the payload from 0.1 to 0.5 to obtain image pairs of carriers and steganograms. For each steganogram, the following features were extracted from image histograms: kurtosis, skewness, standard deviation, range, median, harmonic mean, Hjorth mobility, and complexity. The results show that the classifier reaches a 91.45% accuracy in detecting LSB steganography when learning from all payloads, as well as a 96.78% individual classification accuracy in the best case with a payload of 0.5.
Information Security Management based on Risk Assessment and Analysis

Information security management becomes more and more important with rapid development of information technology (IT) and its wide applications. This paper studies information security management in IT operation and maintenance of enterprises. A framework for information security control based on risk assessment and analysis is proposed. An application example is also provided.
Meta-Learner for Amharic Sentiment Classification

The emergence of the World Wide Web facilitates the growth of user-generated texts in less-resourced languages. Sentiment analysis of these texts may serve as a key performance indicator of the quality of services delivered by companies and government institutions. The presence of user-generated texts is an opportunity for assisting managers and policy-makers. These texts are used to improve performance and increase the level of customers’ satisfaction. Because of this potential, sentiment analysis has been widely researched in the past few years. A plethora of approaches and tools have been developed—albeit predominantly for well-resourced languages such as English. Resources for less-resourced languages such as, in this paper, Amharic, are much less developed. As a result, it requires cost-effective approaches and massive amounts of annotated training data, calling for different approaches to be applied. This research investigates the performance of a combination of heterogeneous machine learning algorithms (base learners such as SVM, RF, and NB). These models in the framework are fused by a meta-learner (in this case, logistic regression) for Amharic sentiment classification. An annotated corpus is provided for evaluation of the classification framework. The proposed stacked approach applying SMOTE on TF-IDF characters (1,7) grams features has achieved an accuracy of 90%. The overall results of the meta-learner (i.e., stack ensemble) have revealed performance rise over the base learners with TF-IDF character n-grams.
A Trend-aware Investment Target Recommendation System with Heterogeneous Graph

Recent years have witnessed the booming of venture capital industry, which may not only bring huge benefits to investors, startups and emerging industries, but also lead to potential risk due to the difficulty to find proper investment targets, especially for inexperienced investors. Therefore, the investment target recommendation task has long been treated as a valuable issue to help the investors for the next investments based on their historical behavior and the market trend. However, due to the sequential features of historical invest records, which could be severely disturbed by the time-varying factors like market trend or short-term invest preference, it will be extremely difficult to accurately reveal the potential rule of investment. Moreover, the latent relationships between investors and targets (i.e., startups) or brands, which formed a heterogeneous graph to reflect the mutual influence within investors, have been largely ignored. To that end, in this paper, we propose a novel paradigm for investment target recommendation with integrating the sequential records and heterogeneous relation graph. Specifically, we aggregate information at the micro level through a time segmentation method, and then extract trends at the macro level. Meanwhile, we use relational graph convolutional networks to propagate the latent preferences through the various relationships. Based on the validations on the realworld dataset, by fusing above information, the Investment Target Recommendation System (ITRS) we proposed has achieved impressive results compared with competitive baseline methods, which indicates the significance of both sequential and structural information.
Urban Metric Maps for Small Unmanned Aircraft Systems Motion Planning

Low-altitude urban flight planning for small unmanned aircraft systems (UASs) accurate vehicle kinodynamics, environment maps, and risk models to assure that flight plans consider the urban landscape as well as airspace constraints. This paper presents a suite of motion planning metrics designed for small UAS urban flight and defines map-based and path-based metrics to holistically characterize motion plan quality. Proposed metrics are examined in the context of representative geometric, graph-based, and sampling-based motion planners applied to a multicopter small UAS. A novel multi-objective heuristic is proposed and applied for graph-based and sampling motion planners at four urban UAS flight altitude layers. Monte Carlo case studies in a New York City urban environment illustrate metric map properties and planner performance. Motion plans are evaluated as a function of planning algorithm, location, range, and flight altitude.