Reviewerly/exHarmony huggingface dataset:
Fair payments for verifiable cloud services using smart contracts

With the advent of cloud computing, outsourcing a computation has become a common practice. A client, to have greater confidence in computations performed by the cloud, should be able to verify the correctness of the results returned. Two approaches are followed to verify the correctness of the results: (1) Proof-based verifiable computing and (2) Replication-based Verifiable Computing. The first approach uses cryptography techniques, whereas the second approach follows game-theoretic methods. Although both approaches are almost perfect solutions to verify the results, they do not discuss fairness in verifiable computing. In this paper, we consider the problem of fairness in verifiable cloud computing, which means that the cloud gets the payment from a client for an outsourced computation if and only if the client receives the correct output of the computation. Most of the existing solutions rely on a trusted third-party to realise fairness. Motivated by the recent advances in blockchain technology and smart contracts, we propose protocols for different verifiable computing techniques using smart contracts. We first present a smart-contract based solution for fair proof-based verifiable computation and show that the overhead of using smart-contract is almost negligible when both client and cloud are honest. Then we show two different solutions depending upon the number of clouds hired to achieve fairness in replication-based verifiable computing. We have designed our protocols in the Blockchain model of cryptography and proved their security under universally composable theory. We also show the financial and transactional cost analysis of proposed contracts by implementing them in solidity and running them on Ethereum Blockchain.
A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI

Prostate Cancer (PCa) is the most common oncological disease in Western men. Even though a growing effort has been carried out by the scientific community in recent years, accurate and reliable automated PCa detection methods on multiparametric Magnetic Resonance Imaging (mpMRI) are still a compelling issue. In this work, a Deep Neural Network architecture is developed for the task of classifying clinically significant PCa on non-contrast-enhanced MR images. In particular, we propose the use of Conditional Random Fields as a Recurrent Neural Network (CRF-RNN) to enhance the classification performance of XmasNet, a Convolutional Neural Network (CNN) architecture specifically tailored to the PROSTATEx17 Challenge. The devised approach builds a hybrid end-to-end trainable network, CRF-XmasNet, composed of an initial CNN component performing feature extraction and a CRF-based probabilistic graphical model component for structured prediction, without the need for two separate training procedures. Experimental results show the suitability of this method in terms of classification accuracy and training time, even though the high-variability of the observed results must be reduced before transferring the resulting architecture to a clinical environment. Interestingly, the use of CRFs as a separate postprocessing method achieves significantly lower performance with respect to the proposed hybrid end-to-end approach. The proposed hybrid end-to-end CRF-RNN approach yields excellent peak performance for all the CNN architectures taken into account, but it shows a high-variability, thus requiring future investigation on the integration of CRFs into a CNN.
A Framework for Supply Chain Traceability Based on Blockchain Tokens

Tracing products and processes across complex supply chain networks has become an integral part of current supply chain management practices. However, the effectiveness and efficiency of existing supply chain traceability mechanisms are hindered by several barriers including lack of data interoperability and information sharing, opportunistic behaviour, lack of transparency and visibility and cyber-physical threats, to name a few. In this paper, we propose a forensics-by-design supply chain traceability framework with audit trails for integrity and provenance guarantees based on malleable blockchain tokens. This framework also provides the establishment of different granularity levels for tracing products across the entire supply chain based on their unique characteristics, supply chain processes and stakeholders engagement. To showcase the applicability of our proposal, we develop a functional set of smart contracts and a local private blockchain. The benefits of our framework are further discussed, along with fruitful areas for future research.
Semi-Supervised Learning for Fine-Grained Classification With Self-Training

Semi-supervised learning is a machine learning approach that tackles the challenge of having a large set of unlabeled data and few labeled ones. In this paper we adopt a semi-supervised self-training method to increase the amount of training data, prevent overfitting and improve the performance of deep models by proposing a novel selection algorithm that prevents mistake reinforcement which is a common thing in conventional self-training models. The model leverages, unlabeled data and specifically, after each training, we first generate pseudo-labels on the unlabeled set to be added to the labeled training samples. Next, we select the top-k most-confident pseudo-labeled images from each unlabeled class with their pseudo-labels and update the training data, and retrain the network on the updated training data. The method improves the accuracy in two-fold; bridging the gap in the appearance of visual objects, and enlarging the training set to meet the demands of deep models. We demonstrated the effectiveness of the model by conducting experiments on four state-of-the-art fine-grained datasets, which include Stanford Dogs, Stanford Cars, 102-Oxford flowers, and CUB-200-2011. We further evaluated the model on some coarse-grain data. Experimental results clearly show that our proposed framework has better performance than some previous works on the same data; the model obtained higher classification accuracy than most of the supervised learning models.
An accelerated correlation filter tracker

Recent visual object tracking methods have witnessed a continuous improvement in the state-of-the-art with the development of efficient discriminative correlation filters (DCF) and robust deep neural network features. Despite the outstanding performance achieved by the above combination, existing advanced trackers suffer from the burden of high computational complexity of the deep feature extraction and online model learning. We propose an accelerated ADMM optimisation method obtained by adding a momentum to the optimisation sequence iterates, and by relaxing the impact of the error between DCF parameters and their norm. The proposed optimisation method is applied to an innovative formulation of the DCF design, which seeks the most discriminative spatially regularised feature channels. A further speed up is achieved by an adaptive initialisation of the filter optimisation process. The significantly increased convergence of the DCF filter is demonstrated by establishing the optimisation process equivalence with a continuous dynamical system for which the convergence properties can readily be derived. The experimental results obtained on several well-known benchmarking datasets demonstrate the efficiency and robustness of the proposed ACFT method, with a tracking accuracy comparable to the start-of-the-art trackers.
Scalable Privacy-Preserving Query Processing over Ethereum Blockchain

Blockchain technologies have recently received considerable attention, partly due to the success of cryptocurrency applications such as Bitcoin and Ethereum. As the adoption of blockchain technologies by various sectors increases, there is a demand for tools that enable regulatory enforcement, which include monitoring, examining and ensuring compliance of the data stored by the blockchain systems, all in a privacy preserving way. Current blockchain solutions store transactions in append-only and immutable fashion without any indexing, which contributes to limited and inefficient queries. Additionally, there is no support for privacy-preserving query processing. To address these issues, in this paper, we propose a system that can provide auditors, enforcing regulatory compliance, with efficient, scalable and richer blockchain query processing over Hadoop and synchronized Ethereum clients. The system additionally ensures auditors' privacy by utilizing cryptography techniques over semi-trusted servers to protect the auditors' identities, queries and their results.
Research and Discussion on Image Recognition and Classification Algorithm Based on Deep Learning

Machine learning, as the core of artificial intelligence, is also the fundamental reason for computer intelligence, and has been widely used in the field of artificial intelligence. With the improvement of computer's ability to process data, deep learning has been highlighted in the field of machine learning. More and more researchers have joined in the theoretical research and applied research of deep learning. And image recognition and classification are very important applications. This paper first compares deep learning with traditional machine learning methods, then introduces the development process of deep learning, studies and analyses the network structure of deep learning such as deep belief network, convolution neural network and recursive neural network, expounds the application of deep learning in image recognition and classification, and puts forward the deep learning in image recognition and classification. The problems encountered in the application of recognition and classification and the corresponding solutions are discussed. Finally, the research status of in-depth learning in image recognition and classification is summarized and prospected.
Goal-biased Bidirectional RRT based on Curve-smoothing

In this paper, a goal-biased bidirectional Rapidly-exploring Random Trees (RRTs) algorithm based on curve-smoothing is newly proposed. The main contribution of this work is that the two-parts of the rapidly-exploring random trees generated in the bidirectional search process are smoothly connected by Bézier curves, so that the whole path satisfies kinematic constraints. Comparative experimental results with the naive RRT algorithm are presented to demonstrate that the proposed algorithm can achieve superior performance in terms of higher success rate, shorter search time, shorter path length and fewer number of the search nodes. Finally, in order to simulate the motion of the robot in a real environment, we track the trajectory through a controller under the visual robot simulation platform V-Rep.
Corpora Annotated with Negation: An Overview

Negation is a universal linguistic phenomenon with a great qualitative impact on natural language processing applications. The availability of corpora annotated with negation is essential to training negation processing systems. Currently, most corpora have been annotated for English, but the presence of languages other than English on the Internet, such as Chinese or Spanish, is greater every day. In this study, we present a review of the corpora annotated with negation information in several languages with the goal of evaluating what aspects of negation have been annotated and how compatible the corpora are. We conclude that it is very difficult to merge the existing corpora because we found differences in the annotation schemes used, and most importantly, in the annotation guidelines: the way in which each corpus was tokenized and the negation elements that have been annotated. Differently than for other well established tasks like semantic role labeling or parsing, for negation there is no standard annotation scheme nor guidelines, which hampers progress in its treatment.
Stacked DeBERT: All attention in incomplete data for text classification

In this paper, we propose Stacked DeBERT, short for Stacked Denoising Bidirectional Encoder Representations from Transformers. This novel model improves robustness in incomplete data, when compared to existing systems, by designing a novel encoding scheme in BERT, a powerful language representation model solely based on attention mechanisms. Incomplete data in natural language processing refer to text with missing or incorrect words, and its presence can hinder the performance of current models that were not implemented to withstand such noises, but must still perform well even under duress. This is due to the fact that current approaches are built for and trained with clean and complete data, and thus are not able to extract features that can adequately represent incomplete data. Our proposed approach consists of obtaining intermediate input representations by applying an embedding layer to the input tokens followed by vanilla transformers. These intermediate features are given as input to novel denoising transformers which are responsible for obtaining richer input representations. The proposed approach takes advantage of stacks of multilayer perceptrons for the reconstruction of missing words' embeddings by extracting more abstract and meaningful hidden feature vectors, and bidirectional transformers for improved embedding representation. We consider two datasets for training and evaluation: the Chatbot Natural Language Understanding Evaluation Corpus and Kaggle's Twitter Sentiment Corpus. Our model shows improved F1-scores and better robustness in informal/incorrect texts present in tweets and in texts with Speech-to-Text error in the sentiment and intent classification tasks.
Hybrid Blockchain Design for Privacy Preserving Crowdsourcing Platform

Blockchain has been treated as one of the most promising technologies to promote crowdsourcing by providing new nice features, such as decentralization and accountability. Unfortunately, some inherent limitations of blockchain have been rarely addressed by the most existing works when applying blockchain into crowdsourcing, which becomes the performance bottleneck of crowdsourcing systems. In this paper, we propose a novel hybrid blockchain crowdsourcing platform to achieve decentralization and privacy preservation. Our platform integrates with a hybrid blockchain structure, dual-ledgers, and dual consensus algorithms to ensure secure communication between the requesters and the workers. Moreover, the smart contract and zero-knowledge proof are employed to ensure automatic operation of the tasks and the protection users' privacy, respectively. Finally, we conduct experiments to confirm the efficiency of the adopted consensus algorithm on our platform by comparing it with the state-of-the-art.
Doodle-Based Authentication Technique Using Augmented Reality

The emergence of augmented reality (AR) and virtual reality (VR) has revolutionized the trends in computing devices and modern technologies drastically. With this revolution, there is a need to extend existing architectures of security to serve as the key protective feature in all computing devices. In this experimental study, the aim is to develop a novel authentication technique with a fusion of graphical doodle password approach and AR environments. The mash-up of both doodle passwords and AR in a 3D space gives a promising direction to set off to a modern, more usable, and satisfying authentication techniques. The proposed approach works on real-time size and coordinate matching of doodles in an AR environment for the authentication of users. The creation of doodle passwords in an AR space is carried on by touch-gesture-recognition on a smartphone. The usability and usefulness of the proposed technique is evaluated by conducting an extensive survey, based on tasks and user experience assessments. The randomized-post-test-only study model is used to conduct experimentation that is also followed by the analysis of security parameters with the help of confusion matrix. The obtained results predict the use of AR during the authentication process more satisfying for users, where the proposed technique is useful, usable, and secure in comparison to the existing authentication approaches. This paper also highlights the importance of research needed for the utilization of modern techniques during the creation of security frameworks.
An efficient steganographic technique for hiding data

Steganography is the technique for hiding data and aims to hide data in such a way that any eavesdropper cannot observe any changes in the original media. The least significant bit (LSB) is one of the most public techniques in steganography. The classical technique is LSB substitution. The main idea of this technique is to directly alter some LSB of the cover image with the secret data. The essential drawback of the available LSB techniques is that increasing the capacity of the stego image leads to decreasing its quality. Therefore, the goal of the proposed method is to enhance the capacity taking high visual quality into consideration. To achieve this goal, some LSB of the cover image are inverted depending on the secret data for embedding instead of replacing LSB with the secret data. First, the maximum and minimum values in the secret data are determined then subtract all values of the secret data from this maximum value. Finally, make a division for the results and embed the new results into the cover image to obtain the stego image. The results show that the proposed method gives high capacity and good imperceptibility in comparison with the previous methods.
Combining novelty and popularity on personalised recommendations via user profile learning

Recommender systems have been widely used by large companies in the e-commerce segment as aid tools in the search for relevant contents according to the user’s particular preferences. A wide variety of algorithms have been proposed in the literature aiming at improving the process of generating recommendations; in particular, a collaborative, diffusion-based hybrid algorithm has been proposed in the literature to solve the problem of sparse data, which affects the quality of recommendations. This algorithm was the basis for several others that effectively solved the sparse data problem. However, this family of algorithms does not differentiate users according to their profiles. In this paper, a new algorithm is proposed for learning the user profile and, consequently, generating personalised recommendations through diffusion, combining novelty with the popularity of items. Experiments performed in well-known datasets show that the results of the proposed algorithm outperform those from both diffusion-based hybrid algorithm and traditional collaborative filtering algorithm, in the same settings.
Improved Mapping of Quantum Circuits to IBM QX Architectures

Quantum computers are becoming a reality today due to the rapid progress made by researchers in the last years. In the process of building quantum computers, IBM has developed several versions-starting from 5-qubit architectures like IBM QX2 and IBM QX4 to larger 16- or 20-qubit architectures. These architectures support arbitrary rotations of a single qubit and a controlled negation (CNOT) involving two qubits. The two qubit operations come with added coupling-map restrictions that only allow specific physical qubits to be the control and target qubits of the operation. In order to execute a quantum circuit on the IBM QX architecture, CNOT gates must satisfy the so-called coupling constraints of the architecture. Previous works addressed this issue with the objective of reducing the number of gates and the circuit depth. However, in this article, we show that further improvements are possible. To this end, we present a general approach for further improving the number of gate operations and depth of the mapped circuit. The proposed approach encompasses the selection of physical qubits, determining initial and local permutations efficiently to obtain the final circuit mapped to the given IBM QX architecture. Through experiments, improvements are observed over existing methods in terms of the number of gates and circuit depth.
VR with Older Adults: Participatory Design of a Virtual ATM Training Simulation

In this paper we report on a study conducted with a group of older adults in which they engaged in participatory design workshops to create a VR ATM training simulation. Based on observation, recordings and the developed VR application we present the results of the workshops and offer considerations and recommendations for organizing opportunities for end users, in this case older adults, to directly engage in co-creation of cutting-edge ICT solutions. These include co-designing interfaces and interaction schemes for emerging technologies like VR and AR. We discuss such aspects as user engagement and hardware and software tools suitable for participatory prototyping of VR applications. Finally, we present ideas for further research in the area of VR participatory prototyping with users of various proficiency levels, taking steps towards developing a unified framework for co-design in AR and VR.
Proximity Neighbor Selection in Blockchain Networks

Blockchains have attracted a great deal of attention as a technology for the distributed management of register information at multiple nodes without a centralized system. However, they possess the drawbacks of low transaction throughput and long approval time. These problems can be addressed by shortening the block generation interval; however, shortening this interval alone has the effect of increasing the frequency of forks. In this study, we aim to shorten the block generation interval without increasing the fork generation rate by improving the network topology of the nodes and shortening the propagation time. We propose a neighbor node selection method forming a network topology with a short block propagation time. A blockchain simulator is used to demonstrate the effect of the proposed neighbor node selection method on the propagation delay of the network. This result indicates that the proposed method improves block propagation time.
A Multi-Dimension Question Answering Network for Sarcasm Detection

Sarcasm is a form of figurative language where the literal meaning of words cannot hold, and instead the opposite interpretation is intended in a text. Sarcasm detection is a significant task to mine fine-grained information, which is a much more difficult challenge for sentiment analysis. Both industry and academia have realized the importance of sarcasm detection. However, most existing methods do not work very well. Using a neural architecture, we propose a novel multi-dimension question answering (MQA) network in order to detect sarcasm. MQA not only introduces the abundant semantic information to understand the ambiguity of sarcasm by multi-dimension representations, but also builds the conversation context information by deep memory question answering network based on bidirectional LSTM and attention mechanism to discover sarcasm. The experimental results show that our model has ability to obviously outperform other state-of-the-art methods, and then further examples also verifies the advancement and effectiveness of our proposed network for detecting sarcasm.