Reviewerly/exHarmony huggingface dataset:
Dynamic Heterogeneous User Generated Contents-Driven Relation Assessment via Graph Representation Learning

Cross-domain decision-making systems are suffering a huge challenge with the rapidly emerging uneven quality of user-generated data, which poses a heavy responsibility to online platforms. Current content analysis methods primarily concentrate on non-textual contents, such as images and videos themselves, while ignoring the interrelationship between each user post’s contents. In this paper, we propose a novel framework named community-aware dynamic heterogeneous graph embedding (CDHNE) for relationship assessment, capable of mining heterogeneous information, latent community structure and dynamic characteristics from user-generated contents (UGC), which aims to solve complex non-euclidean structured problems. Specifically, we introduce the Markov-chain-based metapath to extract heterogeneous contents and semantics in UGC. A edge-centric attention mechanism is elaborated for localized feature aggregation. Thereafter, we obtain the node representations from micro perspective and apply it to the discovery of global structure by a clustering technique. In order to uncover the temporal evolutionary patterns, we devise an encoder–decoder structure, containing multiple recurrent memory units, which helps to capture the dynamics for relation assessment efficiently and effectively. Extensive experiments on four real-world datasets are conducted in this work, which demonstrate that CDHNE outperforms other baselines due to the comprehensive node representation, while also exhibiting the superiority of CDHNE in relation assessment. The proposed model is presented as a method of breaking down the barriers between traditional UGC analysis and their abstract network analysis.
Indigenous knowledge and local practices for disaster risk reduction: A study of Kailali district

Disasters which are resulted due to the natural or anthropogenic environmental damage processes such as deforestation, desertification, loss of biodiversity, soil erosion etc. and are increasing day by day. As such there are potentialities for outbreaks and their high risk at any places at any time. There are many literatures which suggest the possibility of disaster risk reduction and early warning through local indigenous knowledge and practices. The present study is an attempt to explore, evaluate andanalyse the local knowledge and skills that are being practiced and used to reduce the risk of various types of natural hazards. Kailali district lying in the Sudur Paschim Province of Nepal is chosen as the study area. Chure rural municipality lying within Hill areas and Tikapur urban municipality lying within Terai plain areas of the district were selected as the research sites to evaluate the landslide and flood related local indigenous knowledge and practices respectively. Using a multi-assessment methodology of collecting information through targeted group discussions, key informant interviews, household surveys, and field observation methods, an attempt has been made to analyse the data to explore indigenous local knowledge, skills, and traditional practices related to disaster risk and mitigation in the study area. The indigenous knowledge, skills and practices on disaster risk reduction and early warning system have been developed by generations and tested in the absence of the scientific developments. However, many communities have lost their local knowledge, skills, and traditional practices because of non transfer by the senior citizens to the young generations. The findings of the study indicate that indigenous and local knowledge and practices are valuable resources that can support in the process of disaster management, prevention, preparedness, and cost-effective disaster risk reduction. Therefore, the researchers view that it would be wise decision focusing the policy framework by integrating indigenous and local knowledge, wisdom, and skills of the local people.
Branched Structure Component for a Video Game Scenario Prototype Generator

The task of automating the routine work of computer game writers, narrative designers, set forth in earlier works, has been continued in the presented work. The issues of visualization of branching narrative structures of computer games are considered, the analysis of various approaches to visualization of the plot and other important components of a video game, such as, for example, automatic balancing of quantitative parameters, is carried out. The paper presents the chosen technological stack and gives specific solutions for storage in the form of a structured scenario, allowing the generation of continuing story branches and testing the narrative prototyping stage using the automatically generated text novel.
Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors.
Non-spectroscopic sensing enabled by an electro-optical reservoir computer

A fully bio-inspired apparatus to perform non-spectroscopic sensing to discriminate and determine the constituent concentrations of a chemical mixture is proposed. Here, fully bio-inspired means that it is comprised of a biomimetic sensor and a neuromorphic signal processor. The sensor is similar to the human eye in terms of its trichromat architecture and overlapping spectral response. The information processor is a neuromorphic system based on an electro-optical implementation of a reservoir computer. A time-stepping signal algorithm based on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi class="MJX-tex-caligraphic" mathvariant="script">Z</mml:mi> </mml:mrow> </mml:math> -bilinear transformation has been developed to realistically simulate the electro-optical reservoir computer; this demonstrates the discrimination and chemical concentration determination tasks. We believe such an apparatus offers potential benefits in areas in which chemical composition needs to be monitored in real time, for example in chemical processing, and food-beverage (fermentation/brewing) and environmental monitoring.
A Cubic Convolution Interpolation-Based Chroma Subsampling Method for Bayer and RGBW CFA Raw Images

The Bayer and RGBW color filter array (CFA) raw images, denoted by <inline-formula> <tex-math notation="LaTeX">$I^{Bayer}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$I^{RGB}$ </tex-math></inline-formula>, respectively, have been widely used in consumer markets. In the demosaicking-first compression scheme, chroma subsampling is necessary prior to compressing <inline-formula> <tex-math notation="LaTeX">$I^{RGB}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$I^{RGBW}$ </tex-math></inline-formula>. Several linear interpolation-based chroma subsampling methods have been developed for <inline-formula> <tex-math notation="LaTeX">$I^{Bayer}$ </tex-math></inline-formula>, but no nonlinear interpolation-based chroma subsampling methods have targeted the above two CFA image types simultaneously. In this paper, we first propose a nonlinear interpolation-based, namely the cubic convolution interpolation-based (CCI-based), <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> block-distortion function for each <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> CFA block <inline-formula> <tex-math notation="LaTeX">$B^{CbCr}$ </tex-math></inline-formula>. Next, using the Cauchy-Schwarz inequality, we prove that the proposed block-distortion function is a convex function in the real domain, which further serves as the base of the initial subsampled chroma solution. Then, a CCI-based iterative method is proposed to improve the initial subsampled chroma solution. The results of comprehensive experimental tests using Bayer and RGBW CFA images created from the three RGB full-color datasets, namely IMAX, SCI (screen content images), and CI (classical images), demonstrate that on the Versatile Video Coding (VVC) platform VTM-11.0, the proposed method achieves substantial quality enhancement and quality-bitrate tradeoff merits of the reconstructed Bayer and RGBW CFA images compared with existing chroma subsampling methods.
A Banking Platform to Leverage Data Driven Marketing with Machine Learning

Payment data is one of the most valuable assets that retail banks can leverage as the major competitive advantage with respect to new entrants such as Fintech companies or giant internet companies. In marketing, the value behind data relates to the power of encoding customer preferences: the better you know your customer, the better your marketing strategy. In this paper, we present a B2B2C lead generation application based on payment transaction data within the online banking system. In this approach, the bank is an intermediary between its private customers and merchants. The bank uses its competence in Machine Learning driven marketing to build a lead generation application that helps merchants run data driven campaigns through the banking channels to reach retail customers. The bank's retail customers trade the utility hidden in its payment transaction data for special offers and discounts offered by merchants. During the entire process banks protects the privacy of the retail customer.
Please Explain: Key Questions for Explainable AI research from an Organizational perspective

There is growing interest in explanations as an ethical and technical solution to the problem of 'opaque' AI systems. In this essay we point out that technical and ethical approaches to Explainable AI (XAI) have different assumptions and aims. Further, the organizational perspective is missing from this discourse. In response we formulate key questions for explainable AI research from an organizational perspective: 1) Who is the 'user' in Explainable AI? 2) What is the 'purpose' of an explanation in Explainable AI? and 3) Where does an explanation 'reside' in Explainable AI? Our aim is to prompt collaboration across disciplines working on Explainable AI.
Local quantum state marking

We propose the task of local state marking (LSM), where some multipartite quantum states chosen randomly from a known set of states are distributed among spatially separated parties without revealing the identities of the individual states. The collaborative aim of the parties is to correctly mark the identities of states under the restriction that they can perform only local quantum operations (LOs) on their respective subsystems and can communicate with each other classically (CC)---popularly known as the operational paradigm of LOCC. While mutually orthogonal states can always be marked exactly under global operations, this is in general not the case under LOCC. We show that the LSM task is distinct from the vastly explored task of local state distinguishability (LSD)---perfect LSD always implies perfect LSM, whereas we establish that the converse does not hold in general. We also explore entanglement-assisted marking of states that are otherwise locally unmarkable, and we report intriguing entanglement-assisted catalytic LSM phenomena.
Future Needs of the Cybersecurity Workforce

Expected growth of the job market for cyber security professionals in both the US and the UK remains strong for the foreseeable future. While there are many roles to be found in cyber security, that vary from penetration tester to chief information security officer (CISO). One job of particular interest is security architect. The rise in Zero Trust Architecture (ZTA) implementations, especially in the cloud environment, promises an increase in the demand for these security professionals. A security architect requires a set of knowledge, skills, and abilities covering the responsibility for integrating the various security components to successfully support an organization’s goals. In order to achieve the goal of seamless integrated security, the architect must combine technical skills with business, and interpersonal skills. Many of these same skills are required of the CISO, suggesting that the role of security architect may be a professional stepping-stone to the role of CISO. We expected degreed programs to offer courses in security architecture. Accredited university cyber security programs in the United Kingdom (UK) and the United States of America (USA) were examined for course offerings in security architecture. Results found the majority of programs did not offer a course in security architecture. Considering the role of the universities in preparing C-suite executives, the absence of cyber security architecture offerings is both troubling and surprising.
Classification of Acute Lymphoblastic Leukemia through the Fusion of Local Descriptors

Leukemia is characterized by an abnormal proliferation of leukocytes in the bone marrow and blood, which is usually detected by pathologists using a microscope to examine a blood smear. Leukemia identification and diagnosis in advance are a trending topic in medical applications for decreasing the death toll of individuals with Acute Lymphoblastic Leukemia (ALL). It is critical to analyze the white blood cells for the identification of ALL for which the blood smear images are utilized. This paper discusses and presents a micro-pattern descriptor, called Local Directional Number Pattern along with Multi-scale Weber Local Descriptor for feature extraction task to determine cancerous and noncancerous blood cells. A balanced dataset with 260 blood smear images from the ALL-IDB2 dataset was used as training data. Consequently, a proposed model was constructed by applying different individual and combined feature extraction methods, and fed into the machine learning classifiers (Decision Tree, Ensemble, K-Nearest Neighbors, Naïve Bayes, and Random Forest) to determine cancerous and noncancerous blood cells. Experimental results indicate that the developed feature fusion technique assured a reasonable performance compared to other existing works with a testing average accuracy of 97.69 ± 1.83% using Ensemble classifier.
The Quantified Pandemic

In this chapter, I present a sociocultural analysis of how automated decision-making (ADM) tools and related software were deployed or anticipated in response to the COVID-19 crisis during the first year of the pandemic. These technologies included apps used to monitor people in quarantine and self-isolation, contact tracing apps, surveillance drones, digitised temperature checking devices, apps for delivering COVID test results, software for identifying ‘at risk’ patients and for selecting recipients of vaccines, and digital vaccine ‘passport’ apps, as well as automated symptom checker apps, platforms and chatbots designed to help people determine whether they were infected with the novel coronavirus or needed to seek medical attention. Building on scholarship in critical public health, technocultures and critical data studies, I identify and discuss the social and political contexts and effects of these technologies. I demonstrate that despite techno-utopian promissory narratives routinely promoting their advantages, while some of these technologies have assisted with COVID-19 surveillance, control and medical care, many have failed. Furthermore, the deployment of these technologies has in many cases exacerbated existing socioeconomic disadvantage and stigmatisation, excluded some social groups and populations from economic support or healthcare and flouted human rights relating to privacy and freedom of movement.
Calculation of Gibbs partition function with imaginary time evolution on near-term quantum computers

Abstract The Gibbs partition function is an important quantity in describing statistical properties of a system in thermodynamic equilibrium. There are several proposals to calculate the partition functions on near-term quantum computers. However, the existing schemes require many copies of the Gibbs states to perform an extrapolation for the calculation of the partition function, which could be costly performed on the near-term quantum computers. We propose a scheme to calculate the Gibbs function with the imaginary time evolution. After preparing Gibbs states with different temperatures by using the imaginary time evolution, we measure the overlap between them on a quantum circuit, which allows us to calculate the Gibbs partition function. Our scheme requires only 2 N qubits to calculate the Gibbs function of N qubits.
Enhancing Time-Frequency Analysis with Zero-Mean Preprocessing

Side-channel analysis is a critical threat to cryptosystems on the Internet of Things and in relation to embedded devices, and appropriate side-channel countermeasure must be required for physical security. A combined countermeasure approach employing first-order masking and desynchronization simultaneously is a general and cost-efficient approach to counteracting side-channel analysis. With the development of side-channel countermeasures, there are plenty of advanced attacks introduced to defeat such countermeasures. At CARDIS 2013, Belgarric et al. first proposed time-frequency analysis, a promising attack regarding the complexity of computation and memory compared to other attacks, such as conventional second-order side-channel analysis after synchronization. Nevertheless, their time-frequency analysis seems to have lower performance than expected against some datasets protected by combined countermeasures. It is therefore required to study the factors that affect the performance of time-frequency analysis. In this paper, we investigate Belgarric et al.’s time-frequency analysis and conduct a mathematical analysis in regard to the preprocessing of frequency information for second-order side-channel analysis. Based on this analysis, we claim that zero-mean preprocessing enhances the performance of time-frequency analysis. We verify that our analysis is valid through experimental results from two datasets, which are different types of first-order masked Advanced Encryption Standard (AES) software implementations. The experimental results show that time-frequency analysis with zero-mean preprocessing seems to have an enhanced or complementary performance compared to the analysis without preprocessing.
Machine vision-based conveyor belt tear detection in a harsh environment

Abstract This work proposes a recognition method to improve the visual detection rate of longitudinal conveyor belt tears related to fog pollution and uneven illumination. It integrates the Haar feature with a modified dark channel defogging algorithm. Firstly, the dark channel algorithm was used to eliminate fog interference with belt images, and the processing time was reduced from 14.5 ms to 5 ms by calculating a single necessary image channel. The Haar feature was used to replace the traditional geometric features due to its high-efficiency feature expression and illumination robustness; this provided a strong feature basis for the learning and training of the classifiers. Next, the classifiers were trained and strengthened by the AdaBoost algorithm, and the strengthened classifiers were cascaded by the Cascade algorithm to improve the recognition ability. Finally, experiments were carried out under mixed fog pollution and uneven illumination. The results show that the average accuracy, recall, precision and false positive rate of the proposed method are approximately 98.4%, 96.9%, 99.9% and 0.1%, respectively, while the recognition period was only 52.3 ms. This shows strong adaptability and robustness to a harsh environment and demonstrates that the proposed method has important application value.
Ethical Hacking: Skills to Fight Cybersecurity Threats

Ethical hacking education prepares future information security professionals with the tools and skills to fight and prevent cybersecurity threats. Businesses, schools, governments, and individuals have an increasing concern to keep their systems, networks, and data secure from outside threats. Most information security technologies use a defensive approach to keep client’s data safe; however, ethical hacking provides one of the only proactive/aggressive methods for information security professionals to utilize. Teaching inexperienced information security professionals these aggressive tactics can be viewed as a double- edged sword. Since they are the same methods used by malicious hackers, educating new security professionals will undoubtedly educate more malicious hackers.
Enhancing multi-class web video categorization model using machine and deep learning approaches

&lt;p&gt;&lt;span&gt;With today’s digital revolution, many people communicate and collaborate in cyberspace. Users rely on social media platforms, such as Facebook, YouTube and Twitter, all of which exert a considerable impact on human lives. In particular, watching videos has become more preferable than simply browsing the internet because of many reasons. However, difficulties arise when searching for specific videos accurately in the same domains, such as entertainment, politics, education, video and TV shows. This problem can be solved through web video categorization (WVC) approaches that utilize video textual information, visual features, or audio approaches. However, retrieving or obtaining videos with similar content with high accuracy is challenging. Therefore, this paper proposes a novel mode for enhancing WVC that is based on user comments and weighted features from video descriptions. Specifically, this model uses supervised learning, along with machine learning classifiers (MLCs) and deep learning (DL) models. Two experiments are conducted on the proposed balanced dataset on the basis of the two proposed algorithms based on multi-classes, namely, education, politics, health and sports. The model achieves high accuracy rates of 97% and 99% by using MLCs and DL models that are based on artificial neural network (ANN) and long short-term memory (LSTM), respectively.&lt;/span&gt;&lt;/p&gt;