Reviewerly/exHarmony huggingface dataset:
Consortium Blockchain-Based Decentralized Stock Exchange Platform

The global implementation architecture of the traditional stock market distributes responsibilities and data across different intermediaries, including financial and governmental organizations. Each organization manages its system and collaborates with the others to facilitate trading on the stock exchange platform, and typically buy-sell orders go through different parties before settlement. This design architecture that involves a complex chain of intermediaries has several limitations and shortcomings, such as a single point of failure, a longer time for financial settlements, and weak transparency. Blockchain technology consists of a network of computer nodes that securely share a common ledger without the need of having any kind of intermediaries. In this paper, we present a novel blockchain-based architecture for a fully decentralized stock market. Our architecture is based on a private Ethereum blockchain to create a consortium network leveraging organizations that are already involved in the traditional stock exchange to act as validating nodes. In our architecture, the stock exchange trading logic is completely implemented on a smart contract, while considering the existing governmental market regulations. Since the new platform does not introduce significant changes to the stock exchange trading logic and does not eliminate any of the traditional parties from the system, our proposal promotes efficient adoption and deployment of decentralized stock exchange platforms. In addition, we present a proof of concept implementation of the new architecture, including the smart contract for trade exchange, as well as a virtualization-based test network to assess the platform performance. The test network consists of virtual nodes that run the developed stock exchange smart contract where we measure the buy-sell orders throughput and latency under different network sizes and trading workload scenarios. The obtained results have shown that the proposed trading platform can reach a throughput of 311.8 tx/sec, which is equivalent to 89% of the optimal throughput when the sending rate is 350 tx/sec. This throughput is largely sufficient to meet the requirement of major stock exchanges, such as Singapore stock market.
Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images

Lossy image compression is pervasively conducted to save communication bandwidth, resulting in undesirable compression artifacts. Recently, extensive approaches have been proposed to reduce image compression artifacts at the decoder side; however, they require a series of architecture-identical models to process images with different quality, which are inefficient and resource-consuming. Besides, it is common in practice that compressed images are with unknown quality and it is intractable for existing approaches to select a suitable model for blind quality enhancement. In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images. Specifically, our approach blindly and progressively enhances the quality of compressed images through a dynamic deep neural network (DNN), in which an early-exit strategy is embedded. Then, our approach can automatically decide to terminate or continue enhancement according to the assessed quality of enhanced images. Consequently, slight artifacts can be removed in a simpler and faster process, while the severe artifacts can be further removed in a more elaborate process. Extensive experiments demonstrate that our RBQE approach achieves state-of-the-art performance in terms of both blind quality enhancement and resource efficiency.
Bayesian personalized ranking based on multiple-layer neighborhoods

Recommender systems are widely used on the Internet as tools for data analysis, processing and discovery. Traditional recommendation algorithms mostly exploit rating information in a simple way while ignoring some hidden information in ratings, thus restricting recommendation performance. This hidden information in ratings, such as similarities between rated items and items unrated by the same user, can unveil the relationships between users and items by using multiple layers to help find the preferences of users. To focus on this hidden information, we propose a new Bayesian Personalized Ranking algorithm based on multiple-layer neighborhoods (BPRN). We divide items into different sets based on the analysis of user-item relevance and give an order for the sets. Then, we use BPRN to obtain the fine-grained order of items in different sets and finally generate a personalized, sorted list for each user. We have used five real-world datasets to test the accuracy of BPRN and compare its performance with state-of-the-art models. Experiments show that our algorithm greatly improves the accuracy of the recommendation results. In addition, our algorithm distinctly alleviates the problems of data sparsity and cold-start users.
Multi-class Document Classification using Support Vector Machine (SVM) Based on Improved Naïve Bayes Vectorization Technique

At present several vectorization approaches are used to transform text documents into a numerical format.A huge number of features converted from text data from a single document take time to process vectorized data with large dimensions.To reduce the number of dimensions, this work uses an improved Naïve Bayes algorithm to vectorize documents according to a distribution of probabilities reflecting the probable categories to which the document that belongs.The improved Naïve Bayes vectorization used Laplace smoothing to ensure that posterior probabilities are never zero and logarithmic function to solve the result of the probability calculation that is too small that cannot be represented.The text classification algorithms based on the vector space model, such as the Support Vector Machine (SVM), use this probability distribution as the vectors to represent the document that is used to classify the documents.To validate the improvement of the Naïve Bayes vectorization technique, the results are compared to TF-IDF vectorization.The results showed that the transformation of data by improved Naïve Bayes vectorization technique reduces dimensionality and has contributed to better performance of the SVM classification approach.
Robust Multi-Task Learning With Flexible Manifold Constraint

Multi-Task Learning attempts to explore and mine the sufficient information within multiple related tasks for the better solutions. However, the performance of the existing multi-task approaches would largely degenerate when dealing with the polluted data, i.e., outliers. In this paper, we propose a novel robust multi-task model by incorporating a flexible manifold constraint (FMC-MTL) and a robust loss. Specifically speaking, multi-task subspace is embedded with a relaxed and generalized Stiefel Manifold for considering point-wise correlation and preserving the data structure simultaneously. In addition, a robust loss function is developed to ensure the robustness to outliers by smoothly interpolating between <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> ℓ2,1-norm and squared Frobenius norm. Equipped with an efficient algorithm, FMC-MTL serves as a robust solution to tackling the severely polluted data. Moreover, extensive experiments are conducted to verify the superiority of our model. Compared to the state-of-the-art multi-task models, the proposed FMC-MTL model demonstrates remarkable robustness to the contaminated data.
Tools for Wellbeing-Supportive Design: Features, Characteristics, and Prototypes

While research on wellbeing within Human-Computer Interaction (HCI) is an active space, a gap between research and practice persists. To tackle this, we sought to identify the practical needs of designers in taking wellbeing research into practice. We report on 15 semi-structured interviews with designers from four continents, yielding insights into design tool use generally and requirements for wellbeing design tools specifically. We then present five resulting design tool concepts, two of which were further developed into prototypes and tested in a workshop with 34 interaction design and HCI professionals. Findings include seven desirable features and three desirable characteristics for wellbeing-supportive design tools, including that these tools should satisfy the need for proof, buy-in, and tangibility. We also provide clarity around the notion of design for wellbeing and why it must be distinguished from design for positive emotions.
An Encoder-Decoder Network Based FCN Architecture for Semantic Segmentation

In recent years, the convolutional neural network (CNN) has made remarkable achievements in semantic segmentation. The method of semantic segmentation has a desirable application prospect. Nowadays, the methods mostly use an encoder-decoder architecture as a way of generating pixel by pixel segmentation prediction. The encoder is for extracting feature maps and decoder for recovering feature map resolution. An improved semantic segmentation method on the basis of the encoder-decoder architecture is proposed. We can get better segmentation accuracy on several hard classes and reduce the computational complexity significantly. This is possible by modifying the backbone and some refining techniques. Finally, after some processing, the framework has achieved good performance in many datasets. In comparison with the traditional architecture, our architecture does not need additional decoding layer and further reuses the encoder weight, thus reducing the complete quantity of parameters needed for processing. In this paper, a modified focal loss function is also put forward, as a replacement for the cross-entropy function to achieve a better treatment of the imbalance problem of the training data. In addition, more context information is added to the decode module as a way of improving the segmentation results. Experiments prove that the presented method can get better segmentation results. As an integral part of a smart city, multimedia information plays an important role. Semantic segmentation is an important basic technology for building a smart city.
A UTAUT Evaluation of WhatsApp as a Tool for Lecture Delivery During the COVID-19 Lockdown at a Zimbabwean University

Focusing through the lens of the (COVID-19) lockdown which was enforced on the 30th of March 2020, it became apparent that students from rural resource-constrained educational institutions had to adapt to sustainable online learning platforms from traditional content delivery. WhatsApp a social networking app, but due to its low data consumption, it became a de-facto teaching and learning tool for Lupane State University (LSU) students in Zimbabwe. Prior studies have focused on the use of WhatsApp as an alternative lecture delivery platform but very few have evaluated its role as the sole platform for lecture delivery. With no government or institutional support for data acquisition, students failed to utilise other e-learning platforms that were in place due to exorbitant data costs. This study seeks to evaluate the success of WhatsApp mediated teaching and learning at LSU during the COVID-19 pandemic. This was a randomized evaluation of weekly lecture delivery through WhatsApp to LSU students. A questionnaire based on the Unified Theory of Acceptance and Use of Technology’s main constructs was delivered to 200 students that were randomly selected. The results revealed that student’s attitudes, behavioral intention of using WhatsApp for learning as well as the platform’s usefulness were rated highly, implying high adoption. The positive perceptions suggest that it would be easy for the institution to formally integrate the platform to augment traditional lecture delivery or for use during an event that disrupts traditional face-to-face lecture delivery. Results revealed that WhatsApp can support 21st century learning through autonomous, collaborative and learner centred education.
Investigating object compositionality in Generative Adversarial Networks

Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work, we investigate object compositionality as an inductive bias for Generative Adversarial Networks (GANs). We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects. Using this general design as a backbone, we then propose two useful extensions to incorporate dependencies among objects and background. We extensively evaluate our approach on several multi-object image datasets and highlight the merits of incorporating structure for representation learning purposes. In particular, we find that our structured GANs are better at generating multi-object images that are more faithful to the reference distribution. More so, we demonstrate how, by leveraging the structure of the learned generative process, one can `invert' the learned generative model to perform unsupervised instance segmentation. On the challenging CLEVR dataset, it is shown how our approach is able to improve over other recent purely unsupervised object-centric approaches to image generation.
Cyber Threat Intelligence: A Product Without a Process?

Cyber threats have become a permanent threat to society. Over the last few years, accounts of hacking campaigns into public- and private-sector enterprises have drawn significant attention. In 2017...
iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching

Author(s): Kuo, Tsung-Ting; Jiang, Xiaoqian; Tang, Haixu; Wang, XiaoFeng; Bath, Tyler; Bu, Diyue; Wang, Lei; Harmanci, Arif; Zhang, Shaojie; Zhi, Degui; Sofia, Heidi J; Ohno-Machado, Lucila
MAS-Encryption and its Applications in Privacy-Preserving Classifiers

Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information retrieval, cloud security, and privacy protection.While FHE schemes do not require any interaction during computation, the key limitations are large ciphertext expansion and inefficiency.Thus, to overcome these limitations, we develop a novel cryptographic tool, MAS-Encryption (MASE), to support real-value input and secure computation on the multiply-add structure.The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two case study examples.Specifically, the first case study example is the privacy-preserving Naive Bayes classifier that can achieve minimal Bayes risk, and the other example is the privacy-preserving support vector machine.We prove that the constructed classifiers are secure and evaluate their performance using real-world datasets.Experiments show that our proposed MASE scheme and MASE based classifiers are efficient, in the sense that we achieve an optimal tradeoff between computation efficiency and communication interactions.Thus, we avoid the inefficiency of FHE based paradigm.
Decentralized and Privacy-Preserving Public Auditing for Cloud Storage Based on Blockchain

Cloud storage systems provide a flexible, convenient and friendly way for users to outsource data.However, users lose control of their data once outsourcing them to the cloud.Public auditing was introduced to ensure data integrity, in which a third-party auditor (TPA) is delegated to execute auditing tasks.In general, TPA generates and sends challenge information to the cloud server (CS), which proves data possession accordingly.However, the TPA may not perform public auditing protocol honestly or may even collude with CS to deceive users.Some existing public auditing schemes utilize blockchain to resist against the malicious TPA.However, the CS may guess the challenge messages and there is a risk that users' information may be leaked to the TPA during the process of auditing.In this paper, we propose a decentralized and privacy-preserving public auditing scheme based on blockchain (DBPA), in which a blockchain is utilized as an unpredictable source for the generation of (random) challenge information, and the auditor is required to record the audit process onto the blockchain.Due to the characteristics of blockchain, users can check the audit results publicly.Moreover, zero-knowledge proof is used in DBPA to protect user's privacy during the audit process so that the response information returned by the CS does not leak information about user's data.Security analysis and performance evaluation show that DBPA is secure and efficient.
Patient Similarity via Joint Embeddings of Medical Knowledge Graph and Medical Entity Descriptions

With the prevalence and growing volume of Electronic Health Records (EHRs), there has been increasing interest in mining EHRs for improving clinical decision support. The accurate identification of patients with similar conditions based on EHRs is a key step in personalized healthcare. Existing studies model EHRs by medical knowledge graph embedding to learn the latent embeddings of medical entities (e.g., patients, medications, diagnoses and procedures). However, such precisely structured data is usually limited in quantity and in scope. Therefore, to enhance the quality of the embeddings it is important to consider more widely available medical information such as medical entity descriptions. In this paper we propose a novel framework, called Deep Patient Similarity (DeepPS). Specifically, DeepPS incorporates medical entity descriptions by augmenting the embeddings of medical entities and relations with the embeddings of words, which leverages both information from medical knowledge graph structures and the contexts of medical entity descriptions. Furthermore, DeepPS employs the embeddings to patient similarity learning by leveraging Siamese Convolutional Neural Network (CNN) with Spatial Pyramid Pooling (SPP). Extensive experiments on real datasets are conducted to show superior performance of our proposed framework.
High Embedding Capacity Data Hiding Technique Based on EMSD and LSB Substitution Algorithms

Data hiding called steganography is a security technique to protect secret data throughout the transmission from malicious attackers. The purposes of steganography are to obtain good stego-image quality, high embedding-capacity, low computational complexity, visual imperceptibility, undetectability, and more security. In this paper, we offer a new hybrid image steganography technique based on least significant bit (LSB) substitution and enhanced modified signed digit (EMSD) algorithms. The proposed algorithm utilizes n adjacent cover image pixels to hide the secret data with EMSD algorithm, and least significant k-bit for LSB substitution algorithm. Hence, it has more embedding capacity than the EMSD algorithm and exploiting modification direction (EMD) based algorithms. We obtain that the stego-image quality is better than 43 dB when the payload is 2.404 bpp. The results of experiment represent that this algorithm ensures high embedding-capacity while preserving acceptable visual stego image quality that can be undetectable by human eyes. Also, the hybrid of the EMSD and LSB substitution algorithms is to difficult for malicious people to consolidate data by scrambling secret data bits.
De-Anonymisation Attacks on Tor: A Survey

Anonymity networks are becoming increasingly popular in today’s online world as more users attempt to safeguard their online privacy. Tor is currently the most popular anonymity network in use and provides anonymity to both users and services (hidden services). However, the anonymity provided by Tor is also being misused in various ways. Hosting illegal sites for selling drugs, hosting command and control servers for botnets, and distributing censored content are but a few such examples. As a result, various parties, including governments and law enforcement agencies, are interested in attacks that assist in de-anonymising the Tor network, disrupting its operations, and bypassing its censorship circumvention mechanisms. In this survey paper, we review known Tor attacks and identify current techniques for the de-anonymisation of Tor users and hidden services. We discuss these techniques and analyse the practicality of their execution method. We conclude by discussing improvements to the Tor framework that help prevent the surveyed de-anonymisation attacks.
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy

Mixed-initiative systems allow users to interactively provide feedback to potentially improve system performance. Human feedback can correct model errors and update model parameters to dynamically adapt to changing data. Additionally, many users desire the ability to have a greater level of control and fix perceived flaws in systems they rely on. However, how the ability to provide feedback to autonomous systems influences user trust is a largely unexplored area of research. Our research investigates how the act of providing feedback can affect user understanding of an intelligent system and its accuracy. We present a controlled experiment using a simulated object detection system with image data to study the effects of interactive feedback collection on user impressions. The results show that providing human-in-the-loop feedback lowered both participants’ trust in the system and their perception of system accuracy, regardless of whether the system accuracy improved in response to their feedback. These results highlight the importance of considering the effects of allowing end-user feedback on user trust when designing intelligent systems.
Exploring Underwater Target Detection Algorithm Based on Improved SSD

As the in-depth exploration of oceans continues, the accurate and rapid detection of fish, bionics and other intelligent bodies in an underwater environment is more and more important for improving an underwater defense system. Because of the low accuracy and poor real-time performance of target detection in the complex underwater environment, we propose a target detection algorithm based on the improved SSD. We use the ResNet convolution neural network instead of the VGG convolution neural network of the SSD as the basic network for target detection. In the basic network, the depthwise-separated deformable convolution module proposed in this paper is used to extract the features of an underwater target so as to improve the target detection accuracy and speed in the complex underwater environment. It mainly fuses the depthwise separable convolution when the deformable convolution acquires the offset of a convolution core, thus reducing the number of parameters and achieving the purposes of increasing the speed of the convolution neural network and enhancing its robustness through sparse representation. The experimental results show that, compared with the SSD detection model that uses the ResNet convolution neural network as the basic network, the improved SSD detection model that uses the depthwise-separated deformable convolution module improves the accuracy of underwater target detection by 11 percentage points and reduces the detection time by 3 ms, thus validating the effectiveness of the algorithm proposed in the paper.