Reviewerly/exHarmony huggingface dataset:
Design and development of an immersive virtual reality news application: a case study of the SARS event

In recent years, virtual reality (VR) technologies have been applied to the field of journalism, where the concept of immersive VR news has been proposed. However, despite the fanfare, strong response, and sensational effect caused by its advent, immersive VR news remains a novel journalism paradigm that faces new challenges in its production process. Currently, there is a lack of a unified design framework, and, since most studies in this area have focused on non-interactive VR news, the understanding of the effects of more interactive VR technologies on the news consumer remains inadequate. In this study, we propose a more practical design framework for immersive VR news products. Following this framework, we designed a VR news application and conducted user evaluation in terms of media effects and user experience. Based on the experimental findings, which demonstrated that non-interactive VR news products resulted in a distracting user experience and less immersion, while interactive VR news offered improved media effects and user experience, we then derived concrete design guidelines for immersive VR news design. Finally, we highlight that this study provides a theoretical and practical reference framework for the further study of VR news.
Sorting methods and adaptive thresholding for histogram based reversible data hiding

This paper presents a histogram based reversible data hiding (RDH) scheme, which divides image pixels into different cell frequency bands to sort them for data embedding. Data hiding is more efficient in lower cell frequency bands because it provides prediction that is more accurate. Using pixel existence probability for some pixels of ultra-low cell frequency band, another sorting is performed. Employing these two novel sorting methods, we determine smooth areas of an image more efficient than other schemes. The smoother area of the image is selected for data embedding, the less distortion of the marked image may be achieved. In another proposal, we introduce hiding intensity analysis to determine optimum prediction error to embed data. In comparison with methods that sequentially choose prediction error, this analysis results in better quality of the marked image. In effect, the proposed scheme increases the hiding capacity for a specific level of the distortion comparing to existent RDH algorithms. Experimental results confirm that the proposed algorithm outperforms state of the art ones.
Zipper Stack: Shadow Stacks Without Shadow

Return-Oriented Programming (ROP) is a typical attack technique that exploits return addresses to abuse existing code repeatedly. Most of the current return address protecting mechanisms (also known as the Backward-Edge Control-Flow Integrity) work only in limited threat models. For example, the attacker cannot break memory isolation, or the attacker has no knowledge of a secret key or random values. This paper presents a novel, lightweight mechanism protecting return addresses, Zipper Stack, which authenticates all return addresses by a chain structure using cryptographic message authentication codes (MACs). This innovative design can defend against the most powerful attackers who have full control over the program's memory and even know the secret key of the MAC function. This threat model is stronger than the one used in related work. At the same time, it produces low-performance overhead. We implemented Zipper Stack by extending the RISC-V instruction set architecture, and the evaluation on FPGA shows that the performance overhead of Zipper Stack is only 1.86%. Thus, we think Zipper Stack is suitable for actual deployment.
End-to-end trainable network for superpixel and image segmentation

Image segmentation and superpixel generation have been studied for many years, and they are still active research topics in computer vision. Although many advanced computer vision algorithms have been used for image segmentation and superpixel generation, there is no end-to-end trainable algorithm that generates superpixels and segment images simultaneously. We propose an end-to-end trainable network to solve this problem. We train a differentiable clustering algorithm module to produce accurate superpixels. Based on the generated superpixels, the superpixel pooling operation is performed to obtain superpixel features, and then we calculate the similarity of two adjacent superpixels. If the similarity is greater than the preset threshold, we merge the two superpixels. Finally, we get the segmented image. We conduct our experiments in the BSDS500 dataset and get good results.
Unsupervised Visual–Textual Correlation Learning With Fine-Grained Semantic Alignment

With the rapid growth of multimedia data on the Internet, there has been a rapid rise in the demand for visual-textual cross-media retrieval between images and sentences. However, the heterogeneous property of visual and textual data brings huge challenges to measure the cross-media similarity for retrieval. Although existing methods have achieved great progress with the strong learning ability of the deep neural network, they rely heavily on the scale of training data with manual annotation, that is, either pairwise image-sentence annotation or category annotation as supervised information for visual-textual correlation learning, which are extremely labor and time consuming to collect. Without any pairwise or category annotation, it is highly challenging to construct a correlation between images and sentences due to their inconsistent distributions and representations. But people can naturally understand the correlation between visual and textual data in high-level semantic, and those images and sentences containing the same group of semantic concepts can be easily matched in human brain. Inspired by the above human cognitive process, this article proposes an unsupervised visual-textual correlation learning (UVCL) approach to construct correlations without any manual annotation. The contributions are summarized as follows: 1) unsupervised semantic-guided cross-media correlation mining is proposed to bridge the heterogeneous gap between visual and textual data. We measure the semantic matching degree between images and sentences, and generate descriptive sentences according to the concepts extracted from images to further augment the training data in an unsupervised manner. Therefore, the approach can exploit the semantic knowledge within both visual and textual data to reduce the gap between them for further correlation learning and 2) unsupervised visual-textual fine-grained semantic alignment is proposed to learn cross-media correlation by aligning the fine-grained visual local patches and textual keywords with fine-grained soft attention as well as semantic-guided hard attention, and the results can effectively highlight the fine-grained semantic information within both images and sentences to boost visual-textual alignment. Extensive experiments are conducted to perform visual-textual cross-media retrieval in unsupervised setting without any manual annotation on two widely used datasets, namely, Flickr-30K and MS-COCO, which verify the effectiveness of our proposed UVCL approach.
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings

We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. The model achieves this goal by further associating each relation with two relation-aware rotations, which are used to rotate quaternion embeddings of the head and tail entities, respectively. Experimental results show that our proposed model produces state-of-the-art performances on well-known benchmark datasets for knowledge graph completion. Our code is available at: https://github.com/daiquocnguyen/QuatRE.
Hybrid Genetic Manta Ray Foraging Optimization and Its Application to Interval Type 2 Fuzzy Logic Control of An Inverted Pendulum System

Abstract This paper presents an improvised version of Manta-Ray Foraging Optimization (MRFO) by using components in Genetic Algorithm (GA). MRFO is a recent proposed algorithm which based on the behaviour of manta rays. The algorithm imitates three foraging strategies of this cartilaginous fish, which are chain foraging, cyclone foraging and somersault foraging to find foods. However, this optimization algorithm can be improved in its strategy which increases its accuracy. Thus, in this proposed improvement, mutation and crossover strategy from GA were adopted into MRFO. Crossover operation is a convergence action which is purposely to pull the agents towards an optimum point. At the meanwhile, mutation operation is a divergence action which purposely to spread out the agents throughout wider feasible region. Later, the algorithms were performed on several benchmark functions and statically tested by using Wilcoxon signed-rank test to know their performances. To test the algorithm with a real application, the algorithms were applied to an interval type 2 fuzzy logic controller (IT2FLC) of an inverted pendulum system. Result of the test on benchmark functions shows that GMRFO outperformed MRFO and GA and it shows that it provides a better parameter of the control system for a better response.
Haar FCM with DEMATEL Technique to Analyze the Solid Waste Management

An interesting attempt is taken to form a hybrid model by integrating Fuzzy Cognitive Map (FCM), the Delphi Method, and the DEMATEL method through the Haar ranking of Hexagonal Fuzzy Number. To examine this model, the problem of solid waste management is taken. Solid waste arises from human and animal activities can generate major health problems and horrible living environment when they are not dumped of safely and appropriately. The proper disposal of solid waste helps to reduce the terrible impacts on both human health and environment to sustain economic growth and better quality of life. Therefore, this present study analyzes the solid waste management through the Haar FCM with DEMATEL technique.
Two-Level Stackelberg Game for IoT Computational Resource Trading Mechanism: A Smart Contract Approach

To support the increasing computation-intensive applications in the Internet of Things (IoT), edge computing is introduced to provide mobile devices computing resources for performing low-latency tasks. Therefore, how to design an effective and secure computing resource allocation mechanism is attracting increasing attention. A lot of works have been done to design an effective computational resource market for IoT, but the problems of vulnerability and inefficiency still exist. In this article, we propose a two-level Stackelberg game-based computing resource trading mechanism for mobile IoT devices with a credit-based payment approach, which is implemented by smart contracts on blockchain. In our model, the Stackelberg game consists of two levels, i.e., leader-level and user-level. In the leader-level, the computing service provider (CSP) and its agent constitute a composite leader. The agent purchases computing resource from CSP on credit and acts as a broker among leader-level and user-level reselling these computing resources to users. In the user-level, every user experiences social externality, which means users are interdependent. The leader-level subgame makes credit payment easier by making loaning and trading become a joint credit payment. The user-level subgame makes the market more active and closer to reality by introducing social externality. Besides, smart contracts can prevent malicious behaviors such as delay payment. We also conduct equilibrium analysis and prove the existence and uniqueness of the Nash equilibrium in our Stackelberg game-based model. Finally, we conduct numerical experiments to evaluate the cost of smart contracts and the performance of each entity with the proposed pricing mechanism.
A Circular Target Feature Detection Framework Based on DCNN for Industrial Applications

This article presents a novel target detection method, which is named as circular target feature detection framework based on a deep convolutional neural network (DCNN). The central proposition of this method uses the optimized DCNN architecture to detect the target and locate the position of the circle accurately in the image field of view. In this article, a Hough transform based on threshold processing (HTP) is embedded into the optimized DCNN architecture, which calculates the center positions and radius of all circles by training the circular samples for each detected rectangular frame. It can efficiently identify small circular target materials in the industry and screen out unqualified particles. The experimental results show that the boundary information of the circles is obtained clearly from the complex noise background images, thereby accurately determining the location of the circle. It has some advantages over only using a specific circular recognition algorithm. We proposed the new study on HTP-DCNN, which has extremely high accuracy in the field of machine vision positioning with circles for industrial applications.
On the necessary and sufficient conditions for delineating forward- and backward-graded knowledge structures from skill maps

In recent years two classes of knowledge structures received particular attention. They are, respectively, forward-graded and backward-graded knowledge structures. Their importance is related to the fact that the basic local independence model applied to such structures turns out to be locally unidentifiable. Spoto et al. (2012) established some sufficient conditions for a skill multimap to delineate forward- and/or backward graded knowledge structures. Drawing upon some of the theoretical results contained in that article, the present theoretical note aims at providing necessary and sufficient conditions for a skill map to delineate a forward- and/or backward graded knowledge structure.
Effects of particulate matters and climate condition on photovoltaic system efficiency in tropical climate region

The design of the photovoltaic system comprises numerous factors, which can affect the scale of system installation. This research study investigated additional factors to common parameters that are used in fundamental computations, especially factors related to tropical climates. Moreover, pollution problems, such as Particulate Matter 2.5 microns (PM2.5) is currently becoming critical across these regions. Therefore, we also investigated the influence of PM2.5, ambient temperature and other factors towards the efficiency of electricity-generation from photovoltaic systems in a tropical climate zone. The parameters were collected using a data logger and the data was analyzed. The direct and indirect influences on the electricity produced from the photovoltaic system was described using the R2-value obtained from structural equation modeling. The results showed that ambient temperature and PM2.5 factors resulted in a direct negative effect on the electrical production in the tropical climate area. The equation could explain 59.0% of the factors affecting energy production; therefore can be applied to improve the development of system designs in tropical climate regions.
The scrutinizing look on the impending proliferation of mandatory ORCID use from the perspective of data protection, privacy and freedom of science

ORCID stands for the Open Researcher & Contributor ID. It is both the digital identifier (number) for scientists and researchers, and the name of the non-profit organization. ORCID wanted to solve the problem of name ambiguity by assigning a unique identifier to the interested researchers. It was started several years ago as a voluntary community-based initiative. Currently ORCID has become an important player in science and publishing. The commonly observed situation now is that authors are asked or even forced (by publishers, universities or funding agencies) to register in ORCID, even if they do not want to. Therefore, the paper presents a use-case and discusses if there are any possible threats from ORCID’s growing position and the policy to make it mandatory. The paper also raises the question if it is ethical to force anyone (including researchers) to open and register in ORCID or any other service.
An efficient eCK secure identity based Two Party Authenticated Key Agreement scheme with security against active adversaries

A Two-Party Authenticated Key Agreement (2-PAKA) protocol facilitates two communicating entities to equally contribute to the establishment of a shared session key. IDentity-based 2-PAKA (ID-2-PAKA) protocols are widely researched, since it eliminates the need for explicit public-key verification using digital certificates. Over the years, ID-2-PAKA protocols with perfect forward secrecy and Key Generation Center forward secrecy were devised, to circumvent the inherent key escrows in identity based cryptosystems. Nevertheless, cryptanalysis of the recent ID-2-PAKA schemes reveals that many of the protocols are insecure. We reconstruct the possible attacks against the schemes and propose a secure escrowless pairing-free ID-2-PAKA protocol. The proposed scheme is proven secure in the modified extended Canetti-Krawczyk model, which captures all the desirable security attributes of ID-2-PAKA protocols, including, public key replacement attack resilience. Comparative analysis of the protocol with other pairing-free ID-2-PAKA schemes suggests that the proposed scheme offers a fine trade-off between efficiency and security.
Conditional Mutual Information Estimation for Mixed, Discrete and Continuous Data

Fields like public health, public policy, and social science often want to quantify the degree of dependence between variables whose relationships take on unknown functional forms. Typically, in fact, researchers in these fields are attempting to evaluate causal theories, and so want to quantify dependence after conditioning on other variables that might explain, mediate or confound causal relations. One reason conditional mutual information is not more widely used for these tasks is the lack of estimators which can handle combinations of continuous and discrete random variables, common in applications. This article develops a new method for estimating mutual and conditional mutual information for data samples containing a mix of discrete and continuous variables. We prove that this estimator is consistent and show, via simulation, that it is more accurate than similar estimators.
Transfer Learning Code Vectorizer based Machine Learning Models for Software Defect Prediction

Software development life cycle comprises of planning, design, implementation, testing and eventually, deployment. Software defect prediction can be used in the initial stages of the development life cycle for identifying defective modules. Researchers have devised various methods that can be used for effective software defect prediction. The prediction of the presence of defects or bugs in a software module can facilitate the testing process as it would enable developers and testers to allocate their time and resources on modules that are prone to defects. Transfer learning can be used for transferring knowledge obtained from one domain into the other. In this paper, we propose Transfer Learning Code Vectorizer, a novel method that derives features from the text of the software source code itself and uses those features for defect prediction. We focus on the software code and convert it into vectors using a pre-trained deep learning language model. These code vectors are subsequently passed through machine and deep learning models. Further, we compare the results of using deep learning on the text of the software code versus the usage of software metrics for prediction of defects. In terms of weighted F1 scores, the experiments show that applying the proposed TLCV method outperforms the other machine learning techniques by 9.052%.
Detection and characterization of cloud enhancement events for solar irradiance using a model-independent, statistically-driven approach

Cloud enhancement events are instances in which the cloud pattern increases global horizontal irradiance on the ground above levels higher than would otherwise be expected during clear sky with cloudless conditions, and in general, these events are characterized by high irradiances occurring during highly changing transient conditions. Even though it is a well-known although infrequent phenomenon, currently no consensus exists as to its formal definition. However, several studies point out to a need for a better understanding of this phenomenon due to their effects on mathematical models or photovoltaic systems behavior during these instances, as results show significant deviations with respect to the expected results in normal conditions. Due to these issues, the present work intends to explore cloud enhancement definitions and detection methods, for which a novel model-independent, statistically-driven approach is proposed and compared to three other models found in the literature. Assessments are made regarding the main aspects of the phenomenon: frequency of occurrence, clearness index, diffuse fraction and its relationship with solar resource variability. Data from eight measurements sites throughout Chile, with different climatic characteristics is used. To further support the analysis made, monthly maximum expected clear sky irradiance for each site was estimated through satellite-based data as well as with estimation from ground measurements. Finally, the highest irradiances are presented for each measurement site reporting, the record irradiances for one-minute data thermopile and photodiode pyranometers.
Anomaly Detection Using Hierarchical Temporal Memory (HTM) in Crowd Management

Effective crowd management during events helps to avoid overcrowding that could lead to serious incidents and fatalities. Such application domain generates spatial and temporal resolution that demands diverse sophisticated mechanisms to measure, extract and process the data to produce a meaningful abstraction. Crowd management includes modelling the movements of a crowd to project effective mechanisms that support quick emersion from a dangerous and fatal situation. Internet of Things (IoT) technologies, machine learning techniques and communication methods can be used to sense the crowd density and offer early detection of such events or even better prediction of potential accidents to inform the management authorities. Different machine learning methods have been applied for crowd management; however, the rapid advancement in deep hierarchal models that learns from a continuous stream of data has not been fully investigated in this context e.g. Hierarchical Temporal Memory (HTM) has shown good potential for application domains that require online learning and modelling temporal information. In this paper, we propose a new HTM framework for crowd management. The proposed framework aims to detect anomalies in crowd movements and to predict potential overcrowding.