Reviewerly/exHarmony huggingface dataset:
Machine-learning-based equalization for short-reach transmission: neural networks and reservoir computing

The substantial increase in communication throughput driven by the ever-growing machine-to-machine communication within a data center and between data centers is straining the short-reach communication links. To satisfy such demand - while still complying with the strict requirements in terms of energy consumption and latency - several directions are being investigated with a strong focus on equalization techniques for intensity- modulation/direct-detection (IM/DD) transmission. In particular, the key challenge equalizers need to address is the inter-symbol interference introduced by the fiber dispersion when making use of the low-loss transmission window at 1550 nm. Standard digital equalizers such as feed-forward equalizers (FFEs) and decision-feedback equalizers (DFEs) can provide only limited compensation. Therefore more complex approaches either relying on maximum likelihood sequence estimation (MLSE) or using machine-learning tools, such as neural network (NN) based equalizers, are being investigated. Among the different NN architectures, the most promising approaches are based on NNs with memory such as time-delay feedforward NN (TD-FNN), recurrent NN (RNN), and reservoir computing (RC). In this work, we review our recent numerical results on comparing TD-FNN and RC equalizers, and benchmark their performance for 32-GBd on-off keying (OOK) transmission. A special focus will be dedicated to analyzing the memory properties of the reservoir and its impact on the full system performance. Experimental validation of the numerical findings is also provided together with reviewing our recent proposal for a new receiver architecture relying on hybrid optoelectronic processing. By spectrally slicing the received signal, independently detecting the slices and jointly processing them with an NN-based equalizer (wither TD-FNN or RC), significant extension reach is shown both numerically and experimentally.
Boosting Semi-Supervised Face Recognition With Noise Robustness

Although deep face recognition benefits significantly from large-scale training data, a current bottleneck is the labelling cost. A feasible solution to this problem is semi-supervised learning, exploiting a small portion of labelled data and large amounts of unlabelled data. The major challenge, however, is the accumulated label errors through auto-labelling, compromising the training. In this paper, we present an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling. Specifically, we introduce a multi-agent method, named GroupNet (GN), to endow our solution with the ability to identify the wrongly-labelled samples and preserve the clean samples. We show that GN alone achieves the leading accuracy in traditional supervised face recognition even when the noisy labels take over 50% of the training data. Further, we develop a semi-supervised face recognition solution, named Noise Robust Learning-Labelling (NRoLL), which is based on the robust training ability empowered by GN. It starts with a small amount of labelled data and consequently conducts high-confidence labelling on a large amount of unlabelled data to boost further training. The more data is labelled by NRoLL, the higher confidence is with the label in the dataset. To evaluate the competitiveness of our method, we run NRoLL with a rough condition that only one-fifth of the labelled MSCeleb is available and the rest is used as unlabelled data. On a wide range of benchmarks, our method compares favorably against the state-of-the-art methods.
3D Point Clouds Data Super Resolution-Aided LiDAR Odometry for Vehicular Positioning in Urban Canyons

LiDAR odometry algorithms are extensively studied for vehicular positioning. However, achieving high-precision positioning using low-cost 16-channel LiDAR in urban canyons remains a challenging problem due to the limited point cloud density from low-cost LiDAR and excessive dynamic surrounding objects. To fill this gap, this paper proposes enriching sparse 3D point clouds to denser clouds via a novel deep learning-based superresolution (SR) algorithm before its utilization in 3D LiDAR odometry. We validate the effectiveness of the proposed method using the KITTI dataset and a challenging dataset collected in an urban canyon (with complex environmental structures and dynamic objects) of Hong Kong. We conclude that significantly denser point clouds are achieved with considerable accuracy. In addition, significantly improved performance of 3D LiDAR odometry is obtained in the evaluated dataset collected in an urban canyon of Hong Kong.
Examining Students’ Intention to Use Augmented Reality in a Project-Based Geometry Learning Environment

The aim of this study is to examine factors that affect secondary students' behavioural intention to use GeoGebra Augmented Reality to support a projectbased geometry learning environment.To achieve this purpose, we adopted technology acceptance model (TAM).A total of 54 students from two lower secondary schools in Indonesia were involved in this study.The students enrolled in secondary mathematics classes and attended six lessons of a project-based geometry learning assisted with GeoGebra Augmented Reality.Descriptive analysis, multiple regression and independent T-test were employed to analysed data.The findings of this study suggested that GeoGebra Augmented Reality was well accepted by the secondary school students.It was found that perceived of usefulness (PU) is the strongest factor of students' behavioural intention (BI).Contrary to the previous studies, this study showed that attitude toward using (AT) has no direct effect on behaviour intention (BI).We elaborate how the findings of this study could improve the understanding of AR acceptance by students and highlight important guideline for educational AR developers, researchers, and educators.
Diverse Adversaries for Mitigating Bias in Training

Adversarial learning can learn fairer and less biased models of language than standard methods.However, current adversarial techniques only partially mitigate model bias, added to which their training procedures are often unstable.In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another.Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and the stability of training.
TAGNet: Triplet-Attention Graph Networks for Hashtag Recommendation

Hashtag is an important advertising tool and a must-have feature for social media nowadays. In the past, many hashtag recommendation methods have been proposed from different perspectives of images, texts, and users. However, most previous works consider neither the mutual influence between multi-modalities, nor the visual similarity between images. In this paper, we devise a novel model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Triplet-Attention Graph Network</i> (TAGNet). The rationale behind our method is that visually similar images share some common hashtags. Therefore, we build an image graph, and apply a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aggregated Graph Convolution</i> (AGC) module to propagate information in a collective way. Furthermore, it is noted that text and user also have rich content information within posts, and we hence propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Triplet Attention</i> (TA) module to incorporate multi-modalities into node features. Experiments on the large-scale dataset collected from Instagram show that TAGNet achieved significant improvement in recall rate over the best state-of-the-art method.
Facial Expression Recognition Using CNN with Keras

Facial Expressions are an essential feature of non-verbal communication, as we move towards the digitization the human computer interactions plays a vital role.The emotional changes results change in the expressions.This paper elaborates development of Deep Convolutional Neural Network Model using tf.keras for building and training Deep Learning Model.The aim is to classify facial image into one of the seven face detection classifiers using open CV and one of its classifiers for drawing the boundary box around the face to detect the correct expression.For training the CNN models we have used 48x48 grey-scale images from Kaggle's ICMP 2013-Fecial Expression Recognition (FER) datasetThe FER dataset is divided into two folders called test and train, further divided into separate folder each containing one of the seven types of FER dataset.To understand the spread of the distribution of the class data augmentation method is used to generate minority classes.To reduce over fitting of the models, dropout and batch normalization is used.We are using atom optimizer and softmax activation function as it is a multiclass classification problem.It is a categorical cross entropy and matrix that we are training this for accuracy based on the parameters to evaluate the performance of the developed CNN model by looking at the training epoch history.
CrossWalk: Fairness-Enhanced Node Representation Learning

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on developing algorithmic tools to assess and mitigate such unfairness. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node representation learning algorithm, such as DeepWalk and Node2Vec. The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups’ peripheries or (2) connect different groups in the network. CrossWalk pulls nodes that are near groups’ peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural properties of the graph. Extensive experiments show the effectiveness of our algorithm to enhance fairness in various graph algorithms, including influence maximization, link prediction and node classification in synthetic and real networks, with only a very small decrease in performance.
Entropy-based evolutionary diversity optimisation for the traveling salesperson problem

Computing diverse sets of high-quality solutions has gained increasing attention among the evolutionary computation community in recent years. It allows practitioners to choose from a set of high-quality alternatives. In this paper, we employ a population diversity measure, called the high-order entropy measure, in an evolutionary algorithm to compute a diverse set of high-quality solutions for the Traveling Salesperson Problem. In contrast to previous studies, our approach allows diversifying segments of tours containing several edges based on the entropy measure. We examine the resulting evolutionary diversity optimisation approach precisely in terms of the final set of solutions and theoretical properties. Experimental results show significant improvements compared to a recently proposed edge-based diversity optimisation approach when working with a large population of solutions or long segments.
What Blockchain Are We Talking About? An Analytical Framework for Understanding Blockchain Applications in Agriculture and Food

With the many promises it holds in addressing problems concerning information exchange and digital transactions in multi-actor processes, blockchain technology (BCT) has gained considerable traction in the agrifood sector. Governments, international organisations, private companies, consortia of public and private actors are launching various blockchain projects for improving transparency, traceability, and many other key issues in the agrifood sector. This has resulted in a large number of use cases. It is often unclear, however, what and how technical, social and economic aspects were considered in different usecases. Due to the novelty and elusiveness of the technology, there is no ready-to-use analytical framework or guideline yet for assessing its applicability and choosing the right technical and organisational setup. As a result, many stakeholders have to grope in the dark when designing and implementing their “blockchain application.” Based on studies of use cases worldwide and experiences in organizing different blockchain pilots in the Netherlands, this paper seeks to address this problem by providing an overview of the choices to be made at three layers of a blockchain application: the ledger, the governance structure and the ecosystem. This can serve as a reference framework for understanding different blockchain applications and choosing key parameters for new use cases in the agrifood sector. It is expected that such demystification of the blockchain will contribute to more realistic and effective application of the technology to pressing problems in agriculture and food.
Visual Secure Image Encryption Scheme Based on Compressed Sensing and Regional Energy

The network security transmission of digital images needs to solve the dual security problems of content and appearance. In this paper, a visually secure image compression and encryption scheme is proposed by combining compressed sensing (CS) and regional energy. The plain image is compressed and encrypted into a secret image by CS and zigzag confusion. Then, according to the regional energy, the secret image is embedded into a carrier image to obtain the final visual secure cipher image. A method of hour hand printing (HHP) scrambling is proposed to increase the pixel irrelevance. Regional energy embedding reduce the damage to the visual quality of carrier image, and the different embedding positions between images greatly enhances the security of the encryption algorithm. Furthermore, the hyperchaotic multi-character system (MCS) is utilized to construct measurement matrix and control pixels. Simulation results and security analyses demonstrate the effectiveness, security and robustness of the propose algorithm.
A Comprehensive Study of Kernel (Issues and Concepts) in Different Operating Systems

Various operating systems (OS) with numerous functions and features have appeared over time. As a result, they know how each OS has been implemented guides users' decisions on configuring the OS on their machines. Consequently, a comparative study of different operating systems is needed to provide specifics on the same and variance in novel types of OS to address their flaws. This paper's center of attention is the visual operating system based on the OS features and their limitations and strengths by contrasting iOS, Android, Mac, Windows, and Linux operating systems. Linux, Android, and Windows 10 are more stable, more compatible, and more reliable operating systems. Linux, Android, and Windows are popular enough to become user-friendly, unlike other OSs, and make more application programs. The firewalls in Mac OS X and Windows 10 are built-in. The most popular platforms are Android and Windows, specifically the novelist versions. It is because they are low-cost, dependable, compatible, safe, and easy to use. Furthermore, modern developments in issues resulting from the advent of emerging technology and the growth of the cell phone introduced many features such as high-speed processors, massive memory, multitasking, high-resolution displays, functional telecommunication hardware, and so on.
Blockchain Implementation Method for Interoperability between CBDCs

Central Bank Digital Currency (CBDC) is a digital currency issued by a central bank. Motivated by the financial crisis and prospect of a cashless society, countries are researching CBDC. Recently, global consideration has been given to paying basic income to avoid consumer sentiment shrinkage and recession due to epidemics. CBDC is coming into the spotlight as the way to manage the public finance policy of nations comprehensively. CBDC is studied by many countries. The bank of the Bahamas released Sand Dollar. Each country’s central bank should consider the situation in which CBDCs are exchanged. The transaction of the CDDB is open data. Transaction registers CBDC exchange information of the central bank in the blockchain. Open data on currency exchange between countries will provide information on the flow of money between countries. This paper proposes a blockchain system and management method based on the ISO/IEC 11179 metadata registry for exchange between CBDCs that records transactions between registered CBDCs. Each country’s CBDC will have a different implementation and time of publication. We implement the blockchain system and experiment with the operation method, measuring the block generation time of blockchains using the proposed method.
Developing a framework for designing humanitarian blockchain projects

Blockchain technology promises to improve the efficiency, transparency, and accountability of humanitarian operations. Yet at the same time, especially the humanitarian context with its characteristic volatility poses unique challenges to any technology. Most prominent are the humanitarian principles that are fundamental to humanitarian operations. These ethical principles are set to protect the most vulnerable populations. Designing blockchain projects in the humanitarian context therefore requires a systematic framework that helps humanitarians make critical choices. While some design instructions can be found for commercial applications, the humanitarian context requires different design principles and guidelines. To address the lack of a design framework for humanitarian blockchain projects, in this paper, we design and validate guidelines for humanitarian blockchain-projects. We use data from two humanitarian blockchain pilots in Jordan and Kenya to design our framework. Thereafter, we benchmark its applicability and relevance against another pilot in Vanuatu. Our framework highlights the need to consider infrastructure, end-users, ethics, stakeholders, and privacy in contexts, scalability and in/out mechanisms in technology, and knowledge/skills and intellectual property in organisation-related design requirements.
Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine

Sentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively.
Recommender Systems in E-learning

In this era when every aspect of society is accelerating, people are always seeking improvement to stay competitive in their careers. E-learning systems fit into the ever challenging situation and provide learners with remote learning opportunities and abundant learning resources. Facing with the numerous resources online, users need support in deciding which course to take, thus recommender systems are applied in E-learning to provide learners with personalized services by automatically identifying their preferences. This position paper systematically discusses the main recommendation techniques employed in in E-learning and identifies new research directions. Three main recommendation techniques are reviewed in this paper: content-based, collaborative filtering-based and knowledge-based recommendations. The basic mechanism of these technique together with how they are used to fulfill the specific requirements in the context of E-learning are highlighted and presented. The observations in this paper could support researchers and practitioners to better understand the current development and future directions of recommender systems in E-learning.
Unsupervised Multi-hop Question Answering by Question Generation

Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive.We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multihop question-answer pairs, i.e., unsupervised multi-hop QA.We propose MQA-QG, an unsupervised framework that can generate human-like multi-hop training data from both homogeneous and heterogeneous data sources.MQA-QG generates questions by first selecting/generating relevant information from each data source and then integrating the multiple information to form a multi-hop question.Using only generated training data, we can train a competent multi-hop QA which achieves 61% and 83% of the supervised learning performance for the HybridQA and the HotpotQA dataset, respectively.We also show that pretraining the QA system with the generated data would greatly reduce the demand for human-annotated training data.
Blockchain and its application to accounting

Purpose: In recent years Bitcoin has revolutionized the financial world. Its birth was made possible thanks to an innovative combination of certain pre-existing technology in computer networks and cryptography, which led to the existence of a transparent, reliable and immutable record of transactions, a large shared ledger: Blockchain. Several studies analyse the application or future application of Blockchain in different industries. The application of Blockchain in accounting is one of the most debated, since it is a shared accounting record and it is anticipated that its application could change the mission of accountants or even auditors. Therefore, the main objective of this work is to analyze the existing literature on the importance of Blockchain and its possible impact on accounting.Design/methodology/approach: The review of the literature on blockchain and its importance was carried out. Also we have analyzed the triple entry and the so-called accounting ledger, trying to clarify some existing terminological confusion, and we have faced what could be the future of this technology.Findings: The review of the literature implies that Blockchain, after technical improvement and development, will involve an important transformation of the traditional accounting system, with the consequent modification of the work of accountants and auditors.Originality/value: One of the main contributions of this study is its importance for academic literature, being one of the first to analyze the impact of Blockchain in traditional accounting.