Reviewerly/exHarmony huggingface dataset:
Medical Data Sharing Scheme Based on Attribute Cryptosystem and Blockchain Technology

Electronic medical data have significant advantages over paper-based patient records when it comes to storage and retrieval. However, most existing medical data sharing schemes have security risks, such as being prone to data tampering and forgery, and do not support the ability to verify the authenticity of the data source. To solve these problems, we propose a medical data sharing scheme based on attribute cryptosystem and blockchain technology in this paper. First, the encrypted medical data are stored in the cloud, and the storage address and medical-related information are written into the blockchain, which can ensure efficient storage and eliminate the possibility of irreversible modification of the data. Second, the proposed scheme combines attribute-based encryption (ABE) and attribute-based signature (ABS), which achieves the sharing of medical data in many-to-many communications. The ABE achieves data privacy and fine-grained access control, and the ABS verifies the authenticity of the source of the medical data while protecting the signer's identity. Moreover, the data user outsources most of the operations of medical data ciphertext decryption to the cloud service provider (CSP), which can greatly reduce the computational burden. Finally, results of the analysis show that our scheme satisfies the requirements for confidentiality and unforgeability in the random oracle model, and that the proposed scheme offers higher computational performance than other similar schemes.
Universal Deep Neural Network Compression

We consider compression of deep neural networks (DNNs) by weight quantization and lossless source coding for memory-efficient deployment. Whereas the previous work addressed non-universal scalar quantization and entropy source coding, we for the first time introduce universal DNN compression by universal vector quantization and universal source coding. In particular, the proposed scheme utilizes universal lattice quantization, which randomizes the source by uniform random dithering before lattice quantization and can perform near-optimally on any source without relying on knowledge of the source distribution. Moreover, we present a method of fine-tuning vector quantized DNNs to recover any accuracy loss due to quantization. From our experiments, we show that the proposed scheme compresses the MobileNet and ShuffleNet models trained on ImageNet with the state-of-the-art compression ratios of 10.7 and 8.8, respectively.
FragNet: Writer Identification Using Deep Fragment Networks

Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet has two pathways: feature pyramid which is used to extract feature maps and fragment pathway which is trained to predict the writer identity based on fragments extracted from the input image and the feature maps on the feature pyramid. We conduct experiments on four benchmark datasets, which show that our proposed method can generate efficient and robust deep representations for writer identification based on both word and page images.
Distributed Ledger Technology Review and Decentralized Applications Development Guidelines

The Distributed Ledger Technology (DLT) provides an infrastructure for developing decentralized applications with no central authority for registering, sharing, and synchronizing transactions on digital assets. In the last years, it has drawn high interest from the academic community, technology developers, and startups mostly by the advent of its most popular type, blockchain technology. In this paper, we provide a comprehensive overview of DLT analyzing the challenges, provided solutions or alternatives, and their usage for developing decentralized applications. We define a three-tier based architecture for DLT applications to systematically classify the technology solutions described in over 100 papers and startup initiatives. Protocol and Network Tier contains solutions for digital assets registration, transactions, data structure, and privacy and business rules implementation and the creation of peer-to-peer networks, ledger replication, and consensus-based state validation. Scalability and Interoperability Tier solutions address the scalability and interoperability issues with a focus on blockchain technology, where they manifest most often, slowing down its large-scale adoption. The paper closes with a discussion on challenges and opportunities for developing decentralized applications by providing a multi-step guideline for decentralizing the design and implementation of traditional systems.
xDBAuth: Blockchain Based Cross Domain Authentication and Authorization Framework for Internet of Things

The innovation of ubiquitous and pervasive computing helps service-oriented organizations in the realization of a virtual coalition. The virtual coalition is a set of IoT domains i.e., smart homes and smart hospitals that are linked together through communication lines to share resources. Such virtual coalitions need secure cross-domain permission delegation and access control mechanisms. In existing approaches, permission delegation and access control are performed at the resource owner domain or by a single trusted third party. This single trusted third party may fail to work or compromise. Therefore, it will collapse either the whole system or the security of the system. We propose xDBAuth, a decentralized Blockchain (BC) based permission delegation and access control framework for the Internet of Things (IoT). Also, we proposed a hierarchy of local and global smart contracts that perform permission delegation and access control for both internal and external user/IoT devices. Additionally, the proposed framework preserves an external user’s privacy by allowing them to get authentication in their parent IoT domains. During authentication, Proof-of-Authenticity/Integrity (PoAI) mechanism is used to find and retrieve user/IoT device platform hashes stored on local BC. After successful authentication, BC authorizes the user/IoT device based on the validation of delegation policies stored on BC. We implemented the proposed framework using Node.js. The results show that the proposed xDBAuth is a lightweight framework with less computational overhead. xDBAuth produces high throughput in an environment having a large number of concurrent requests.
Harris Hawks optimization with information exchange

The basic Harris Hawks optimization algorithm cannot take full advantage of the information sharing capability of the Harris Hawks while cooperatively searching for prey, and it is difficult to balance the exploration and development capacities of this algorithm. These factors limit the Harris Hawks optimization algorithm, such as in terms of premature convergence and ease of falling into a local optimum. To this end, an improved Harris Hawks optimization algorithm based on information exchange is proposed to optimize the continuous function and its application to engineering problems. First, an individual Harris Hawk obtains information from the shared area of cooperative foraging and the location area of collaborators, thereby realizing information exchange and sharing. Second, a nonlinear escaping energy factor with chaos disturbance is designed to better balance the local searching and the global searching of the algorithm. Finally, a numerical experiment is conducted with four benchmark test functions and five CEC-2017 real-parameter numerical optimization problems as well as seven practical engineering problems. The results show that the proposed algorithm outperforms the basic Harris Hawks optimization algorithm and other intelligence optimization algorithms in terms of the convergence rate, solution accuracy, and robustness.
Meta Learning for End-To-End Low-Resource Speech Recognition

In this paper, we proposed to apply meta learning approach for low-resource automatic speech recognition (ASR). We formulated ASR for different languages as different tasks, and meta-learned the initialization parameters from many pretraining languages to achieve fast adaptation on unseen target language, via recently proposed model-agnostic meta learning algorithm (MAML). We evaluated the proposed approach using six languages as pretraining tasks and four languages as target tasks. Preliminary results showed that the proposed method, MetaASR, significantly outperforms the state-of-the-art multitask pretraining approach on all target languages with different combinations of pretraining languages. In addition, since MAML's model-agnostic property, this paper also opens new research direction of applying meta learning to more speech-related applications.
Sentimental Short Sentences Classification by Using CNN Deep Learning Model with Fine Tuned Word2Vec

Continues growth of social networking web users, people daily shared their ideas and opinions in the form of texts, images, videos, and speech. Text categorization is still a crucial issue because these huge texts received from the heterogeneous sources and different mindset peoples. The shared opinion is to be incomplete, inconsistent, noisy and also in different languages form. Currently, NLP and deep neural network methods are widely used to solve such issues. In this way, Word2Vec word embedding and Convolutional Neural Network (CNN) method have to be implemented for effective text classification. In this paper, the proposed model perfectly cleaned the data and generates word vectors from pre-trained Word2Vec model and use CNN layer to extract better features for short sentences categorization.
A perspective on machine learning in turbulent flows

The physical complexity and the large number of degrees of freedom that can be resolved today by direct numerical simulations of turbulent flows, and by the most sophisticated experimental techniques, require new strategies to reduce and analyse the data so generated, and to model the turbulent behaviour. We discuss a few concrete examples for which the turbulence data have been analysed by machine learning tools. We also comment on work in neighbouring fields of physics, particularly astrophysical (and astronomical) work, where Big Data has been the paradigm for some time. We discuss unsupervised, semi-supervised and supervised machine learning methods to direct numerical simulations data of homogeneous isotropic turbulence, Rayleigh-Bénard convection, and the minimal flow unit of a turbulent channel flow; for the last case, we discuss in some detail the application of echo state networks, this being one implementation of reservoir computing. The paper also provides a brief perspective on machine learning applications more broadly.
An Augmented Reality Collaborative Product Design Cloud-Based Platform in the Context of Learning Factory

In the modern market scheme, Market 4.0, there is an evident need for improved quality, increased quantities and lower delivery times of products. However, customers seek for highly personalized products. Consequently, production engineers must include customers in the product design phase so as to meet their unique requirements. On the contrary, during the fourth industrial revolution, cutting-edge digital technologies, such as Augmented Reality (AR) and Cloud technologies have reached a high level of maturity. AR is a digital technology, capable of super-imposing digital information to the user’s physical environment. Furthermore, with the adoption of the Learning Factory concept, academia contributes to the development of new skills and competencies, for the new generation of engineers, that align with the ever-increasing global market demands. As such, in this paper, a framework enabling collaborative product design based on AR is presented, which acts as the digital thread for connecting engineers to customers. The participants will design and develop a Cloud platform for supporting file exchange and storage. Moreover, the Cloud platform can be realized as the communication layer between the stakeholders. In addition to that, an end-user application for the product design, customization and visualization with the use of AR will also be developed. Finally, the proposed framework will be tested in vitro, in a machine shop, in order to gather useful insight into the skills and competencies acquired by the participants during the development of the project.
ViVo

In this paper, we perform a first comprehensive study of mobile volumetric video streaming. Volumetric videos are truly 3D, allowing six degrees of freedom (6DoF) movement for their viewers during playback. Such flexibility enables numerous applications in entertainment, healthcare, education, etc. However, volumetric video streaming is extremely bandwidth-intensive. We conduct a detailed investigation of each of the following aspects for point cloud streaming (a popular volumetric data format): encoding, decoding, segmentation, viewport movement patterns, and viewport prediction. Motivated by the observations from the above study, we propose ViVo, which is to the best of our knowledge the first practical mobile volumetric video streaming system with three visibility-aware optimizations. ViVo judiciously determines the video content to fetch based on how, what and where a viewer perceives for reducing bandwidth consumption of volumetric video streaming. Our evaluations over real wireless networks (including commercial 5G), mobile devices and users indicate that ViVo can save on average 40% of data usage (up to 80%) with virtually no drop in visual quality.
Conversational receptiveness: Improving engagement with opposing views

We examine “conversational receptiveness” – the use of language to communicate one’s willingness to thoughtfully engage with opposing views. We develop an interpretable machine-learning algorithm to identify the linguistic profile of receptiveness (Studies 1A-B). We then show that in contentious policy discussions, government executives who were rated as more receptive - according to our algorithm and their partners, but not their own self-evaluations - were considered better teammates, advisors, and workplace representatives (Study 2). Furthermore, using field data from a setting where conflict management is endemic to productivity, we show that conversational receptiveness at the beginning of a conversation forestalls conflict escalation at the end. Specifically, Wikipedia editors who write more receptive posts are less prone to receiving personal attacks from disagreeing editors (Study 3). We develop a “receptiveness recipe” intervention based on our algorithm. We find that writers who follow the recipe are seen as more desirable partners for future collaboration and their messages are seen as more persuasive (Study 4). Overall, we find that conversational receptiveness is reliably measurable, has meaningful relational consequences, and can be substantially improved using our intervention (183 words).
A privacy protection scheme of smart meter for decentralized smart home environment based on consortium blockchain

In order to ensure the reasonable dispatch and utilization of power in smart homes, decentralized smart homes need to send power information to the power control center, and the power control center need to send control instructions to smart homes. However, the bi-directional interaction between smart homes and the power control center may lead to a series of security problems, such as smart meters of smart homes collect fine-grained real-time power consumption data and upload it to the control center, which will lead to the leakage of users’ privacy. The existing schemes protect users’ privacy by signcryption and data aggregation usually, and elliptic curve bilinear pairing or exponential operation are used in most of them, whose computation costs is high. In addition, the existing schemes for centralized storage of power consumption data may result in some security problems, such as single point failure, malicious tampering of data, etc. Aiming at the problems above, a privacy protection scheme of smart meter for decentralized smart home environment based on consortium blockchain is proposed, elliptic curve point multiplication operation is used in the certificateless aggregated ring signcryption algorithm, which reduces the computing and communication costs; distributed storage of users’ data with consortium blockchain is used to solve the problem of single point failure and data tampering. Theoretical analysis proves that the scheme not only guarantees users’ privacy, but also has characteristics of confidentiality and unforgeability, and experiments show that the scheme has lower computing and communication costs.
An integrated assessment of land-use change impact, seasonal variation of pollution indices and human health risk of selected toxic elements in sediments of River Atuwara, Nigeria

River sediments contain environmental fingerprints that provide useful ecological information. However, the geochemistry of River Atuwara sediments has received less attention over the years. One hundred and twenty-six sediments from 21 locations were collected over a two-season period from River Atuwara, and a detailed investigation of the land use and land cover (LULC) change between 1990 and 2019, analysis of selected toxic and potentially toxic metal(oid)s (TPTM) (Cu, As, Cd, Pb, Ni, Cr, Zn, Fe, Co and Al) using ICP-OES, pollution index assessment, potential source identification (using center log-transformation approach), potential ecological, and human health risk assessment were conducted. The results of the LULC change revealed that the built-up area increased by 95.58 km2, at an average rate of 3.186 km2/year over the past 30 years. The mean concentration of metal(oid)s increased in the order of Cd < As < Cr < Pb < Co < Ni < Cu < Zn < Fe < Al, and Cd < As < Cr < Co < Pb < Ni < Cu < Zn < Fe < Al during the dry and wet seasons, respectively. Meanwhile, the statistical analysis of the data spectrum inferred possible contamination from lithological and anthropogenic sources. According to the pollution load index, 90.48% of the sediment samples are polluted by the metal(oid)s. Potential ecological risk assessment identified Ni, As, and Cd as problematic to the ecological community of River Atuwara. Regarding the metal-specific hazard quotient via ingestion route, the risks are in order of Co ≫ As ≫ Pb > Cr > Cd > Al > Ni > Cu > Zn > Fe for both seasons and the carcinogenic risk for children via ingestion route presented a value higher than the safe limits for As, Cd, Cr, and Ni during both seasons. This outcome highlights the need for prompt action towards the restoration of environmental quality for communities surrounding River Atuwara.
An Adaptive Wireless Virtual Reality Framework in Future Wireless Networks: A Distributed Learning Approach

Wireless virtual reality (VR) is predicted to become a killer application in 5G and beyond, which provides an immersive experience and revolutionizes the way people communicate. It is well-known that rendering is the key performance bottleneck in wireless VR systems, especially for VR games. However, real-time rendering and data correlation are ignored by most researchers. In this paper, we propose an adaptive VR framework that enables high-quality wireless VR in future mmWave-enabled wireless networks with mobile edge computing (MEC), where real-time VR rendering tasks can be offloaded to MEC servers adaptively and the caching capability of MEC servers enables further performance improvement. First, we formulate the addressed problem to maximize the quality of experience (QoE) of the users, where association, adaptive offloading mode selection, and caching policy are jointly optimized. Considering the high complexity of the addressed problem, we then propose a distributed learning approach consisting of an offline training phase and an online running phase, which maintains scalability and adaptation capability. The offline phase is based on deep reinforcement learning (DRL) while the latter utilizes game theory. At last, simulation results show the superiority of the proposed algorithm over the other baseline algorithms in terms of QoE utility values, latency, and convergence time.
A Smart Chatbot Architecture based NLP and Machine Learning for Health Care Assistance

A chatbot or conversational agent is a software that can communicate with a human by using natural language. One of the essential tasks in artificial intelligence and natural language processing is the modeling of conversation. Since the beginning of artificial intelligence, its been the hardest challenge to create a good chatbot. Although chatbots can perform many tasks, the primary function they have to play is to understand the utterances of humans and to respond to them appropriately. In the past, simple statistic methods or handwritten templates and rules were used for the constructions of chatbot architectures. With the increasing learning capabilities, end-to-end neural networks have taken the place of these models in around 2015. Especially now, the encoder-decoder recurrent model is dominant in the modeling of conversations. This architecture is taken from the neural machine translation domain, and it performed very well there. Until now, plenty of features and variations are introduced that have remarkably enhanced the conversational capabilities of chatbots.
"I am just terrified of my future"  Epistemic Violence in Disability Related Technology Research

Technology for disabled people is often developed by non-disabled populations, producing an environment where the perspectives of disabled researchers - particularly when they clash with normative ways of approaching accessible technology - are denigrated, dismissed or treated as invalid. This epistemic violence has manifest material consequences for our lives as disabled researchers engaging with work on our own states of being. Through a series of vignettes, we illustrate our experiences and the associated pain that comes with such engagement as well as the consequences of pervasive dehumanization of ourselves through existing works. Our aim is to identify the epistemic injustice disabled people experience within HCI, to question the epistemological base of knowledge production leading to said injustice and to take ownership of a narrative that all too often is created without our participation.
<scp>Senti‐eSystem</scp>: A sentiment‐based <scp>eSystem</scp>‐using hybridized fuzzy and deep neural network for measuring customer satisfaction

Summary In the competing era of online industries, understanding customer feedback and satisfaction is one of the important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing customer feedback is beneficial, as it provides an advantage way of unveiling customer interests. The proposed system, namely Senti‐eSystem , aims at the development of sentiment‐based eSystem using hybridized Fuzzy and Deep Neural Network for Measuring Customer Satisfaction to assist business organizations for improving the quality of their services and products. The proposed approach initially deploys a Bidirectional Long Short Term Memory with attention mechanism to predict the sentiment polarity that is positive and negative, followed by Fuzzy logic approach to determine the customer satisfaction level, which further strengthens the capabilities of the proposed approach. The system achieves an accuracy of 92.86%, outperforming the previous state‐of‐art lexicon‐based approaches. Moreover, the effectiveness of the proposed system is also validated by applying the statistical test.