Reviewerly/exHarmony huggingface dataset:
Exploring the governance and implementation of sustainable development initiatives through blockchain technology

Societies at large still grapple to categorize digital space as a phenomenon. At the same time, scientists and developers are searching for innovative methods to better understand how the fundamental shifts caused by digital change will affect the future of humanity over the coming decades. Interdisciplinary governance research at the intersection of technological and environmental foresight is urgently needed to minimize the risks of technological change and explore how digitalization may support, hinder or re-shape sustainability transformations. In this article, we focus on the case of ‘blockchain’ or distributed-ledger technology (DLT) to investigate how recent digital technologies may support the implementation of sustainable development initiatives. Our investigation is centered on areas of public administration and governance which will most likely see an adoption of DLT over the next two decades, such as digital identity, social service provision, and innovative climate finance. To allow for a meaningful comparison of various use cases, we propose four guiding questions that can help researchers, decision-makers and practitioners to determine whether DLT might be an appropriate choice for the sustainability-related task at hand. Moreover, we illustrate how the initial design and subsequent implementation of DLTs may support more centralized or networked modes of governance.
On the Quantum versus Classical Learnability of Discrete Distributions

Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consider the following task: Given samples from some unknown discrete probability distribution, output with high probability an efficient algorithm for generating new samples from a good approximation of the original distribution. Our primary result is the explicit construction of a class of discrete probability distributions which, under the decisional Diffie-Hellman assumption, is provably not efficiently PAC learnable by a classical generative modelling algorithm, but for which we construct an efficient quantum learner. This class of distributions therefore provides a concrete example of a generative modelling problem for which quantum learners exhibit a provable advantage over classical learning algorithms. In addition, we discuss techniques for proving classical generative modelling hardness results, as well as the relationship between the PAC learnability of Boolean functions and the PAC learnability of discrete probability distributions.
Chaotic image encryption algorithm based on block theory and reversible mixed cellular automata

In this paper, a new chaotic image encryption algorithm based on block theory and reversible mixed cellular automata model is proposed. Different from traditional confusion-diffusion structure, this paper employs new three-tier architecture, i.e. diffusion-confusion-diffusion. The first diffusion operation adopts the traditional diffusion method, which aims to avoid direct operation on plaintext and further enhance the encryption effect. The next scrambling and diffusion operations are based on image blocks and a new dynamical system, namely reversible mixed cellular automata model. The scrambling operation is divided into two parts, inter block scrambling and intra block scrambling, all the above operations are based on pixel level. Then, diffusion operation is performed and its implementation is based on the evolution of constructed reversible mixed cellular automata with balanced rules. In addition, the initial values and parameters of the adopted chaotic system, two dimensional (2D) Logistic map, are obtained using SHA 256 hash function related to plaintext image. The use of hash function makes the chaotic sequences completely different when encrypt different images, which enhances the ability to resist known-plaintext and chosen-plaintext attacks. Simulation results and performance analysis bear out that the encryption algorithm proposed in this paper has higher security performance and can resist different attacks efficiently.
Physical Activity Recognition With Statistical-Deep Fusion Model Using Multiple Sensory Data for Smart Health

Nowadays, enhancing the living standard with smart healthcare via the Internet of Things is one of the most critical goals of smart cities, in which artificial intelligence plays as the core technology. Many smart services, deployed according to wearable sensor-based physical activity recognition, have been able to early detect unhealthy daily behaviors and further medical risks. Numerous approaches have studied shallow handcrafted features coupled with traditional machine learning (ML) techniques, which find it difficult to model real-world activities. In this work, by revealing deep features from deep convolutional neural networks (DCNNs) in fusion with conventional handcrafted features, we learn an intermediate fusion framework of human activity recognition (HAR). According to transforming the raw signal value to pixel intensity value, segmentation data acquired from a multisensor system are encoded to an activity image for deep model learning. Formulated by several novel residual triple convolutional blocks, the proposed DCNN allows extracting multiscale spatiotemporal signal-level and sensor-level correlations simultaneously from the activity image. In the fusion model, the hybrid feature merged from the handcrafted and deep features is learned by a multiclass support vector machine (SVM) classifier. Based on several experiments of performance evaluation, our fusion approach for activity recognition has achieved the accuracy over 96.0% on three public benchmark data sets, including Daily and Sport Activities, Daily Life Activities, and RealWorld. Furthermore, the method outperforms several state-of-the-art HAR approaches and demonstrates the superiority of the proposed intermediate fusion model in multisensor systems.
Cardiac MRI Segmentation With a Dilated CNN Incorporating Domain-Specific Constraints

Semantic segmentation of cardiac MR images is a challenging task due to its importance in medical assessment of heart diseases. Having a detailed localization of specific regions of interest such as Right and Left Ventricular Cavities and Myocardium, doctors can infer important information about the presence of cardiovascular diseases, which are today a major cause of death globally. This paper addresses the problem of semantic segmentation in cardiac MR images using a dilated Convolutional Neural Network. Opting for dilated convolutions allowed us to work in full resolution throughout the network's layers, preserving localization accuracy, while maintaining a relatively small number of trainable parameters. To assist the network's training process we designed a custom loss function. Furthermore, we developed new augmentation techniques and also adapted existing ones, to cope for the lack of sufficient training images. Consequently, the training set increases not only by amount, but by substance as well, and the network trains quickly and efficiently without overfitting. Our pre- and post-processing steps are also crucial to the whole process. We apply our methodology for the Right and Left Ventricles (RV, LV) and also the Myocardium (MYO) according to the Automated Cardiac Diagnosis Challenge (ACDC) with promising results. Submitting our algorithm's predictions to the Post-2017-MICCAI-challenge testing phase, we achieved similar scores (average Dice coefficient 0.916) on the test data set compared to the state of the art featured in the ACDC leaderboard, but with significantly fewer parameters than the leading method. Our approach outperforms other methods featuring dilated convolutions in this challenge up until now.
SurveilEdge: Real-time Video Query based on Collaborative Cloud-Edge Deep Learning

The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> with multiple edge devices and a public Cloud, and conduct extensive experiments using real-world surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7× less bandwidth cost and 5.4× faster query response time than the cloud-only solution; and can improve query accuracy by up to 43.9% and achieve 15.8× speedup respectively, in comparison with edge-only approaches.
Revisiting the nexus among foreign direct investment, corruption and growth in developing and developed markets

This study examines the dynamic association among foreign direct investment (FDI), corruption and economic growth. The study applies a panel vector autoregressive (PVAR) model to the context of the generalized method of moments estimation technique to determine the relationships in 54 developed and developing countries over the period of 1996–2018. The findings suggest that the control of corruption negatively (positively) affects inward FDI and economic advancement in developing (developed) countries, suggesting that weak (strong) institutional quality and higher (lower) corruption boost investments and economic development. The study further finds that economic growth and corruption have a positive bidirectional relationship for developing countries and negative unidirectional association for developed countries. Besides, the bidirectional linkage of FDI with corruption and economic growth is observed in both developed and developing countries. The findings are elicited through a series of robustness tests, including two-step system generalized method of moments. The results provide policy implications to government and regulatory authorities.
AI-Powered Blockchain - A Decentralized Secure Multiparty Computation Protocol for IoV

The rapid advancements in autonomous technologies have paved way for vehicular networks. In particular, Vehicular Ad-hoc Network (VANET) forms the basis of the future of Intelligent Transportation System (ITS). ITS represents the communication among vehicles by acquiring and sharing the data. Though congestion control is enhanced by Internet of Vehicles (IoV), there are various security criteria where entire communication can lead to many security and privacy challenges. A blockchain can be deployed to provide the IoV devices with the necessary authentication and security feature for the transfer of data. Blockchain based IoV mechanism eliminates the single source of failure and remains secure at base despite having strong security, the higher level layers and applications are susceptible to attacks. Artificial Intelligence (AI) has the potential to overcome several vulnerabilities of current blockchain technology. In this paper, we propose an AI-Powered Blockchain which provides auto coding feature for the smart contracts making it an intelligent contract. Moreover, it speeds up the transaction verification and optimises energy consumption. The results show that intelligent contracts provide higher security compared to smart contracts considering range of different scenarios.
Do We Trust in AI? Role of Anthropomorphism and Intelligence

AI applications are radically transforming the manner in which service providers and consumers interact. We explore how the humanness of AI applications affects consumers’ trust in these applications. Qualitative evidence collected with focus groups provides fresh insights into the roles of anthropomorphism and intelligence, as key constructs representing humanness. Our findings reveal the consumers’ perspective on the nuances of these constructs pertaining to services enabled by AI applications. It also extends current understanding of the phenomenon of the “uncanny valley,” by identifying conditions under which consumers experience discomfort and uneasiness as AI humanness increases in service environments.
Deep Human Activity Recognition With Localisation of Wearable Sensors

Automatic recognition of human activities using wearable sensors remains a challenging problem due to high variability in inter-person gait and movements.Moreover, finding the best on-body location for a wearable sensor is also critical though it provides valuable context information that can be used for accurate recognition.This article addresses the problem of classifying motion signals generated by multiple wearable sensors for the recognition of human activity and localisation of the wearable sensors.Unlike existing methods that used the raw accelerometer and gyroscope signals for extracting time and frequency-based features for activity inference, we propose to create frequency images for the raw signals and show this representation to be more robust.The frequency image sequences are generated from the accelerometer and gyroscope signals from seven different body parts.These frequency images serve as the input to our proposed two-stream Convolutional Neural Networks (CNN) for predicting the human activity and the location of the sensor generating the activity signal.We show that the complementary information collected by both accelerometer and gyroscope sensors can be leveraged to develop an effective classifier that can accurately predict the performed human activity.We evaluate the performance of the proposed method using the cross-subjects approach and show that it achieves an impressive F1-score of 0.90 on a publicly available real-world human activity dataset.This performance is superior to that reported by another stateof-the-art method on the same dataset.Moreover, we also experimented with the datasets from different body locations to predict the best position for the underlying task.We show that shin and waist are the best places on the body for placing sensors and this could help other researchers to collect higher quality activity data.We plan to publicly release the generated frequency images from all sensor positions and activities and our implementation code with the publication.
A socio-technical framework for digital contact tracing

In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods. We illustrate its usage with three applications, and with the European Data Protection Board (EDPB) guidelines, highlighting their limitations.
Minimal Variance Sampling with Provable Guarantees for Fast Training of Graph Neural Networks

Sampling methods (e.g., node-wise, layer-wise, or subgraph) has become an indispensable strategy to speed up training large-scale Graph Neural Networks (GNNs). However, existing sampling methods are mostly based on the graph structural information and ignore the dynamicity of optimization, which leads to high variance in estimating the stochastic gradients. The high variance issue can be very pronounced in extremely large graphs, where it results in slow convergence and poor generalization. In this paper, we theoretically analyze the variance of sampling methods and show that, due to the composite structure of empirical risk, the variance of any sampling method can be decomposed intoembedding approximation variance in the forward stage andstochastic gradient variance in the backward stage that necessities mitigating both types of variance to obtain faster convergence rate. We propose a decoupled variance reduction strategy that employs (approximate) gradient information to adaptively sample nodes with minimal variance, and explicitly reduces the variance introduced by embedding approximation. We show theoretically and empirically that the proposed method, even with smaller mini-batch sizes, enjoys a faster convergence rate and entails a better generalization compared to the existing methods.
Assessment of Augmented Reality in Manual Wiring Production Process with Use of Mobile AR Glasses

Digitalization of production environment, also called Industry 4.0 (the term invented by Wahlster Wolfgang in Germany) is now one of the hottest topics in the computer science departments at universities and companies. One of the most significant topics in this area is augmented reality (AR). The interest in AR has grown especially after the introduction of the Microsoft HoloLens in 2016, which made this technology available for researchers and developers all around the world. It is divided into numerous subtopics and technologies. These wireless, see-through glasses give a very natural human-machine interface, with the possibility to present certain necessary information right in front of the user's eyes as 3D virtual objects, in parallel with the observation of the real world, and the possibility to communicate with the system by simple gestures and speech. Scientists noted that in-depth studies connected to the effects of AR applications are presently sparse. In the first part of this paper, the authors recall the research from 2019 about the new method of manual wiring support with the AR glasses. In the second part, the study (tests) for this method carried out by the research team is described. The method was applied in the actual production environment with consideration of the actual production process, which is manual wiring of the industrial enclosures (control cabinets). Finally, authors deliberate on conclusions, technology's imperfections, limitations, and future possible development of the presented solution.
Neural Subgraph Isomorphism Counting

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting is NP-complete and requires more global inference to oversee the whole graph. To make it scalable for large-scale graphs and patterns, we propose a learning framework that augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize intermediate states of subgraph isomorphism searching for global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different representation and interaction modules. A mutagenic compound dataset, MUTAG, is also used to evaluate neural models and demonstrate the success of transfer learning. While the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in linear time compared to the exponential time of the original NP-complete problem. Experimental results show that learning based subgraph isomorphism counting can speed up the traditional algorithm, VF2, 10-1,000 times with acceptable errors. Domain adaptation based on fine-tuning also shows the usefulness of our approach in real-world applications.
Two-stage three-way enhanced technique for ensemble learning in inclusive policy text classification

With the development of the social economy, small and medium-sized enterprises (SMEs) play a vital role in promoting economic development. Multiple local governments in China are developing policy recommended platforms in order to help SMEs better understand the inclusive policy. However, these online platforms manually extract the key information from the inclusive policy texts, which takes a lot of time and causes low efficiency. The policy text is composed of some paragraphs and each paragraph corresponds to a topic. When we classify the paragraphs into different topics, there exists a decision risk of text misclassification. Therefore, we design two-stage based three-way enhanced technique to automatically classify these text paragraphs into the predefined categories. At the first stage, by using ensemble learning algorithms, we construct an ensemble convolution neural network (CNN) model in order to ensure the generalization ability and stability of text classification results. Meanwhile, we develop a new weight determination method to integrate the prediction results of all base classifiers according to the accuracy and classification confidence. With the help of three-way decisions (3WD), we assign the samples with poor resolution to the boundary area for secondary classification, which can reduce the decision risk. At the second stage, in order to classify the boundary region samples and improve the overall classification results, we further utilize traditional machine learning method as the secondary classifier. Finally, we develop some comparison experiments to verify our proposed method. The experimental results show that the two-stage three-way enhanced classification framework is valid and obtains a better performance. Our proposed method can effectively support the designment of policy recommended platforms and serve SMEs.
Video super-resolution based on a spatio-temporal matching network

Deep spatio-temporal neural networks have shown promising performance for video super-resolution (VSR) in recent years. However, most of them heavily rely on accuracy motion estimations. In this paper, we propose a novel spatio-temporal matching network (STMN) for video super-resolution, which works on the wavelet domain to reduce dependence on motion estimations. Specifically, our STMN consists of three major components: a temporal fusion wavelet network (TFWN), a non-local matching network (NLMN), and a global wavelet domain residual connection (GWDRC). TFWN adaptively extracts temporal fusion wavelet maps via three 3d convolutional layers and a discrete wavelet transform (DWT) decomposition layer. The extracted temporal fusion wavelet maps are rich in spatial information and knowledge of different frequencies from consecutive frames, which are feed to NLMN for learning deep wavelet representations. NLMN integrates super-resolution and denoising into a unified module by pyramidally stacking non-local matching residual blocks (NLMRB). At last, GWDRC reconstructs the super-resolved frames from the deep wavelet representations by using global wavelet domain residual information. Consequently, our STMN can efficiently enhance reconstruction quality by capturing different frequencies wavelet representations in consecutive frames, and does not require any motion compensation. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our method compared with state-of-the-art methods.