Reviewerly/exHarmony huggingface dataset:
Australian Users’ Perspective of Green Blockchain Technology Adoption in Businesses

In this research, our aim is to understand Australia’s perception of how Blockchain Technology can provide sustainability to businesses today and whether, therefore, it can be considered as Green Technology. This research will determine the relationship between Blockchain Technology and sustainability by perusing the relevant literature and will examine this relationship by analyzing the gathered survey responses. This research will also help readers to understand the business advantages and challenges associated with Blockchain Technology. Firstly, this paper presents the history of sustainability and explores the current knowledge about this concept. This is followed by an introduction to Corporate Social Responsibility (CSR) that looks at what it is, why it is important, what types and roles it entails, and the positive and negative factors associated with it. Further, the Triple Bottom Line (TBL) concept is introduced and explained. The concept of Green IT is discussed and evaluated for its effectiveness in helping organisations become energy efficient and making their surroundings greener and carbon-less. Then, Blockchain Technology is defined in detail: its types, how it works, smart contracts, advantages and disadvantages are discussed in depth. Additionally, the implementation of Blockchain Technology in different industries is discussed, giving an insight into the ways that Blockchain Technology can bring sustainability to businesses and hence can be considered as Green IT. Moreover, apart from the evaluation of the relationship between sustainability and Blockchain Technology found in the literature, we analyse the online survey data, applying the principal component analysis via IBM SPSS Statistics 25 software that offers more insights and accurate results. The survey was completed by 101 respondents in Australia who hold academic degrees, and the analysed survey data generated various factors for business advantages and challenges. The most important business advantages that an organisation could achieve with Blockchain Technology were improved TBL, a reduction in resource consumption, and better ‘green’ practices. However, blockchain technology faces several challenges associated with strict governance and environmental compliances. Finally, this paper acknowledges the limitations of the research and suggests future research advantages. The research questions regarding the relationship between Blockchain Technology and sustainability will be answered. Additionally, recommendations are offered, and conclusion are drawn. It is anticipated that this research and its findings are intended will benefit public and private organisations, relevant researchers and other individuals who have an interest in Blockchain Technology as a sustainable technology, and in exploring the potential of Blockchain Technology in different industries.
BUNET: Blind Medical Image Segmentation Based on Secure UNET

The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data. To ensure efficient machine learning as a service schemes while protecting data confidentiality, in this work, we propose blind UNET (BUNET), a secure protocol that implements privacy-preserving medical image segmentation based on the UNET architecture. In BUNET, we efficiently utilize cryptographic primitives such as homomorphic encryption and garbled circuits (GC) to design a complete secure protocol for the UNET neural architecture. In addition, we perform extensive architectural search in reducing the computational bottleneck of GC-based secure activation protocols with high-dimensional input data. In the experiment, we thoroughly examine the parameter space of our protocol, and show that we can achieve up to 14x inference time reduction compared to the-state-of-the-art secure inference technique on a baseline architecture with negligible accuracy degradation.
K-MEANS CLUSTER ANALYSIS OF SUB-DISTRICTS IN SIDOARJO BASED ON LONG-TERM CONTRACEPTIVE METHOD

The success of the Family Planning program can be measured from the ratio of the use of the Long-Term Contraceptive Method by the couples of childbearing age in each region. The approach used in the Family Planning program is a regional approach, so this study aimed to group sub-districts based on the proportion users of the Long-Term Contraceptive Method by couples of childbearing age in Sidoarjo in 2018. This study was non-reactive study, which is a type of research using secondary data. The data source came from the Report of the Office of Women's Empowerment and Child Protection, Family Planning Sidoarjo in 2018. This study used cross-sectional design with the K-Means Clustering model data analysis. The results of this study produced three regional clusters: Cluster 1 with a low success rate sub-districts category with 8 sub-districts; Cluster 2 with a medium success rate category that has 6 subdistrict members; and Cluster 3 with a high success rate category consisting of 4 sub-districts in Sidoarjo. Based on the study results, broad accessibility was needed to obtain information so that the level of the Long-Term Contraceptive Method users in Sidoarjo increases.
Okkhor: A Synthetic Corpus of Bangla Printed Characters

Bangla is the fifth most-spoken native language in the world. Despite having such a large number of speakers, the resources related to development of language processing solutions are very limited. To realize the full potential of Machine Learning (ML) and Artificial Intelligence (AI) solutions for computer vision and Natural Language Processing (NLP), a complete and standardized fully-annotated corpus is an essential prerequisite. Specifically, development of Optical Character Recognition systems (OCRs) for printed characters, an important resource for language automatic and digitization, requires a large corpus with high coverage and variability of fonts, representing the nuances of the language usage, which does not exist for Bangla. In this paper, we present a novel synthetic corpus of over 5 million printed Bangla characters containing 60 alphanumeric characters, 10 vowel modifiers, 159 compound characters, which corresponds to 229 different classes of both Unicode and ASCII encodings. This is entirely novel work, since there exists no such corpus currently for the Bangla language.
Natural Answer Generation via Graph Transformer

Natural Answer Generation (NAG), which generates natural answer sentences for the given question, has received much attention in recent years. Compared with traditional QA systems, NAG could offer specific entities fluently and naturally, which is more user-friendly in the real world. However, existing NAG systems usually utilize simple retrieval and embedding mechanism, which is hard to tackle complex questions. They suffer issues containing knowledge insufficiency, entity ambiguity, and especially poor expressiveness during generation. To address these challenges, we propose an improved knowledge extractor to retrieve supporting graphs from the knowledge base, and an extending graph transformer to encode the supporting graph, which considers global and variable information as well as the communication path between entities. In this paper, we propose a framework called G-NAG, including a knowledge extractor, an incorporating encoder, and an LSTM generator. Experimental results on two complex QA datasets demonstrate the efficiency of G-NAG compared with state-of-the-art NAG systems and transformer baselines.
DeepBanglaNet: A Deep Convolutional Neural Network to Recognize Bengali Handwritten Digits

Classifying handwritten digits is one of the most trending topics of research in the study of the automated text recognition system. The problem is more challenging in the case of Bengali digits due to additional complexities arising from similarity among various digits along with a wide variety of styles of hand-writings. In this paper, an end-to-end deep convolutional neural network, named as DeepBanglaNet, is proposed to classify Bengali handwritten digits. The proposed network utilizes various state-of-the-art optimization algorithms for eliminating vanishing/exploding gradient problems while extracting the global features effectively required for proper recognition of handwritten digits. This results in a very efficient model providing state-of-the-art accuracy of 99.43% on the NumtaDB database and outperforms all other existing models in all traditional evaluation metrics.
Quasi Monte Carlo time-frequency analysis

We study signal processing tasks in which the signal is mapped via some generalized time-frequency transform to a higher dimensional time-frequency space, processed there, and synthesized to an output signal. We show how to approximate such methods using a quasi-Monte Carlo (QMC) approach. We consider cases where the time-frequency representation is redundant, having feature axes in addition to the time and frequency axes. The proposed QMC method allows sampling both efficiently and evenly such redundant time-frequency representations. Indeed, 1) the number of samples required for a certain accuracy is log-linear in the resolution of the signal space, and depends only weakly on the dimension of the redundant time-frequency space, and 2) the quasi-random samples have low discrepancy, so they are spread evenly in the redundant time-frequency space. One example of such redundant representation is the localizing time-frequency transform (LTFT), where the time-frequency plane is enhanced by a third axis. This higher dimensional time-frequency space improves the quality of some time-frequency signal processing tasks, like the phase vocoder (an audio signal processing effect). Since the computational complexity of the QMC is log-linear in the resolution of the signal space, this higher dimensional time-frequency space does not degrade the computation complexity of the proposed QMC method. The proposed QMC method is more efficient than standard Monte Carlo methods, since the deterministic QMC sample points are optimally spread in the time-frequency space, while random samples are not.
Learning with mitigating random consistency from the accuracy measure

Abstract Human beings may make random guesses in decision-making. Occasionally, their guesses may generate consistency with the real situation. This kind of consistency is termed random consistency. In the area of machine leaning, the randomness is unavoidable and ubiquitous in learning algorithms. However, the accuracy (A), which is a fundamental performance measure for machine learning, does not recognize the random consistency. This causes that the classifiers learnt by A contain the random consistency. The random consistency may cause an unreliable evaluation and harm the generalization performance. To solve this problem, the pure accuracy (PA) is defined to eliminate the random consistency from the A. In this paper, we mainly study the necessity, learning consistency and leaning method of the PA. We show that the PA is insensitive to the class distribution of classifier and is more fair to the majority and the minority than A. Subsequently, some novel generalization bounds on the PA and A are given. Furthermore, we show that the PA is Bayes-risk consistent in finite and infinite hypothesis space. We design a plug-in rule that maximizes the PA, and the experiments on twenty benchmark data sets demonstrate that the proposed method performs statistically better than the kernel logistic regression in terms of PA and comparable performance in terms of A. Compared with the other plug-in rules, the proposed method obtains much better performance.
Self-supervised Pairing Image Clustering and Its Application in Cyber Manufacturing

Artificial intelligence is being increasingly applied in manufacturing to maximize industrial productivity. Image clustering, as a fundamental research direction in unsupervised learning, has been used in various fields. Since no label information is required in clustering, it can perform a preliminary analysis of the data while saving lots of manpower. In this paper, we propose a novel end-to-end clustering network called Self-supervised Pairing Image Clustering (SPIC) for industrial application, which produces clustering prediction for input images in an advanced pair classification network. For training this network, a self-supervised pairing module is built to form balanced pairs accurately and efficiently without label information. Since the existence of trivial solutions cannot be avoided in most of unsupervised learning methods, two additional information theoretic-constraints regularize the training that ensures the clustering prediction to be unambiguous and close to the real data distribution during training. Experimental results indicate that the proposed SPIC outperforms the state-of-art approaches on manufacturing datasets-NEU and DAGM. It also shows the execellent generalization capability on other genral public datasets, such as MNIST, Omniglot, CIFAR10, and CIFAR100.
Using Data Mining for Infrastructure and Safety Violations Discovery in Cities

In city planning and maintenance, the ability to quickly identify infrastructure violations - such as missing or misplaced fire hydrants - can be crucial for maintaining a safe city; it can even save lives. In this work, we aim to provide an analysis of such violations, and to demonstrate the potential of data-driven approaches for quickly locating and addressing them. We conduct an analytical study based upon data from the city of Beer-Sheva's public records of fire hydrants, bomb shelters, and other public facilities. The results of our analysis are presented along with an interactive exploration tool, which allows for easy exploration and identification of the different facilities around the city that violate regulations.
The evolving role of library collections in the broader information ecosystem

Academic libraries operate in a complex information environment that lies increasingly beyond the borders of library-provided search tools and collections. Consumer web search engines facilitate discovery, and an abundance of content is available through open access sources and academic social networks as well as consumer-focused web publications and digital media libraries. Libraries need to select the content and tools that they buy with these realities in mind. Libraries should use their purchasing power to support open access, select book and journal content bundles with compelling value, and leverage demand-driven purchasing. They should advocate for more comprehensive discovery tools and in-the-flow reference products and eliminate redundant search products. Finally, they should align purchased resources closely with the curriculum and provide high-quality data sources along with the expertise to use such sources.
Challenges and opportunities of Blockchain technology in the energy sector

This article analyzes entrepreneurial start-up initiatives that have developed energy solutions based on Block chain and DLT (Distributed Ledger Technologies) and wide-ranging projects. The most innovative cases summarized by a swot analysis were examined. Finally, a concept of a possible application of Block chain technology is presented to manage the flexibility applied to a microgrid from the DSO point of view.
A Study on the Mechanism of Knowledge Acquisition for Establishing Education System for Marine Fisheries Resources

This study is to establish an education system for assessing and managing marine fisheries resources for achieving sustainable fisheries, which are known to be at a significantly degraded state. Typical contemporary methodologies for fish stock assessment and management were reviewed. The mechanism and process of knowledge acquisition were analyzed and restructured, based on Zhang (2019)
Skill Based Course Recommendation System

In today's world, students face an immense repertoire of options relating to the number of courses that they may choose from. To make this seemingly massive choice relatively easy to make, many authors have created their own recommender systems to map students to the courses that are best suited for them. However, they are not widely used as they give good results only for the dataset that they consider. In our paper, we have mapped the current students to their alumni based on multiple criterion. Afterwards, unlike other papers that used k-means, we used c-means and fuzzy clustering to arrive at a better solution to predict an elective course for the student. Since all of this is done on a broad actual dataset, the results can be applied anywhere in a real world scenario.
Quantum Pliers Cutting the Blockchain

Recent years have seen the continuous evolution of technology, which has led to the definition of frameworks such as the internet of things (IoT) and Industry 4.0. These paradigms are producing enormous quantities of data every single day. These data are subject to data analysis, shared publicly or kept secret. Traditionally, this task was carried out using databases. With the advent of Blockchain and other distributed ledger technologies (DLTs), instead, these data have a new way of being stored and shared (or kept private). The last actor role in this play is acted by quantum computers, since sufficiently large quantum computers are expected to seriously threaten the security and integrity of DLTs thanks to their totally different way of representing information. This paper aims to investigate the real threats for blockchain due to quantum computing and review post-quantum DLT solutions for traceability applications.
Human Action Recognition Using STIP Evaluation Techniques

The activities of human can be classified into human actions, interactions, object–human interactions and group actions. The recognition of actions in the input video is very much useful in computer vision technology. This system gives application to develop a model that can detect and recognize the actions. The variety of HAR applications are surveillance environment systems, healthcare systems, military, patient monitoring system (PMS), etc., that involve interactions between electronic devices such as human–computer interfaces with persons. Initially, collecting the videos containing actions or interactions was performed by the humans. The given input videos were converted into number of frames, and then these frames were undergone preprocessing stage using by applying median filter. The noise of the given input frame is reduced by applying the median filter of the neighboring pixels. Through frames, desired features were extracted. The actions of the person which is recognised from the system is going to extract further. There are three spatial–temporal interest point (STIP) techniques such as Harris SPIT, Gabor SPIT and HOG SPIT used for feature extraction from video frames. SVM algorithm is applied for classifying the extracted feature. The action recognition is based on the colored label identified by classifier. The system performance is measured by calculating the classifier performance which is the accuracy, sensitivity and specificity. The accuracy represents the classifier reliability. The specificity and sensitivity represent how exactly the classifier categorizes its features to each correct category and how the classifier rejects the features that are not belonging to the particular correct category.
Towards boosting the OpenMBEE platform with model-code consistency

Eventual consistency between design and implementation is imperative for the quality and maintainability of software systems. Towards achieving this consistency, engineers can analyze the gaps between models and corresponding code to gain insights into differences between design and implementation. Due to the different levels of abstraction of the involved artifacts, this analysis is a complex task to automate. We study an industrial MBSE setting where we aim to provide model-code gap analysis between SysML system models and corresponding C/C++ code through structural consistency checks. To this end, we propose an extension of the OpenMBEE platform, to include code as one of the synchronized development artifacts in addition to models and documentation. In this paper, we outline our initial research idea to include code as a view in this platform and we propose to explicitly link the code to generated documentation, and thereby to the model.
A 3D Measuring Endoscope for Use in Sheet-Bulk Metal Forming: Design, Algorithms, Applications and Results

Sheet-bulk metal forming, as a novel and innovative production process, represents a possible approach for the realization of present and future requirements in the context of production technology through the combination of different, forming processes. Due to the metrological requirements of modern, digital and largely automated production plants, the development of new types of measuring instruments for use in industrial environments is driven. Within the scope of the TCRC 73, a new type of compact and flexible endoscopic 3D sensor was developed in subproject B6, enabling measurements of stressed structures inside a sheet-bulk metal forming plant. This serves the purpose of preventing failure and obtaining a better understanding of the process. This contribution provides a description of the optical design, the principal components, sensor head configurations, as well as a description of algorithms and models and demonstrates them by reference to applications.
Guided Search 6.0: An upgrade with five forms of guidance, three types of functional visual fields, and two, distinct search templates

The Guided Search (GS) model of visual search was published 30 years ago. The core idea of GS is that search becomes more efficient when deployments of attention are guided by preattentive information. As new data about search accumulated, GS needed modification. Revisions have been numbered so that outdated ideas that seemed reasonable in 1989 don’t need to be defended in 2020. This talk on the new GS6 will focus on three topics: 1) Five Sources of Guidance: Early versions of GS focused on top-down (user-driven) and bottom-up (salience) guidance by basic features (color, orientation, etc). Subsequent research adds guidance by history of search, “value” of the target, and, most importantly, scene structure and meaning. 2) Three “Functional Visual Fields”: Visual and attentional processing are better nearer the point of fixation. This fact is captured in the idea of a Functional Visual Field (FVF) surrounding fixation. The FVF is typically treated as a single thing (notably in medical image perception). In fact, it is important to distinguish at least three, distinct co-occurring FVFs governing visual resolution, overt oculomotor exploration, and covert attentional selection. 3) In order to search, you need a representation of search target(s). This search “template” is usually discussed as though it (like the FVF) is a single mental representation. However, it is important in GS6 to distinguish between two representations: A “guiding template” that helps to direct attention to candidate targets and a “target template” that allows us to determine if a candidate is, indeed, the target. GS6 proposes that the guiding template resides in Working Memory while the target template resides in Activated Long Term Memory. I will present new data on these three topics and show how they contribute to the overall structure of Guided Search 6.0.