Reviewerly/exHarmony huggingface dataset:
Pairing-Free Certificate-Based Searchable Encryption Supporting Privacy-Preserving Keyword Search Function for IIoTs

As a practical application of the Internet of Things (IoT) in the modern industry, industrial IoT (IIoT) enables industrial enterprises to accelerate the development. Nowadays, the cloud computing technology has been applied to data storage and processing in IIoTs, but how to protect data privacy in the cloud has become a challenge and technical issue. Recently, the certificate-based encryption with keyword search (CBEKS) was presented to handle the cloud ciphertext retrieval. By CBEKS, one can get back all desired ciphertexts from the cloud without decrypting the ciphertexts or leaking the search keywords. However, the existing CBEKS scheme uses the computationally expensive bilinear pairing, which is disgusted by the performance-limited IIoT smart devices. In this article, a pairing-free and privacy-preserving CBEKS scheme is developed. The experimental results show that it has an obvious advantage in the computation performance when compared with the pairing-based CBEKS scheme. In addition, our security proofs indicate that it is secure against keyword guessing attacks.
Informative missingness in electronic health record systems: the curse of knowing

Electronic health records provide a potentially valuable data source of information for developing clinical prediction models. However, missing data are common in routinely collected health data and often missingness is informative. Informative missingness can be incorporated in a clinical prediction model, for example by including a separate category of a predictor variable that has missing values. The predictive performance of such a model depends on the transportability of the missing data mechanism, which may be compromised once the model is deployed in practice and the predictive value of certain variables becomes known. Using synthetic data, this phenomenon is explained and illustrated.
A Light Blockchain-Powered Privacy-Preserving Organization Scheme for Ride Sharing Services

Ride-sharing is a service that enables drivers to share their trips with other riders, contributing to improving traffic congestion as well as assist in reducing Carbon Dioxide (CO2) emission and fuel consumption. It has come to the forefront in recent years as a Green service in large cities. However, the majority of existing ride-sharing services rely on a central third party, which makes them subject to a single point of failure and privacy disclosure concerns by both internal and external attackers. Moreover, they are vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users. There is also high service fees paid to the ride-sharing service provider. In this paper, we propose to decentralize ride-sharing services based on a public Blockchain. Our scheme enables drivers to propose ride-sharing services without relying on a trusted third party. To preserve location privacy, riders send cloaked ride requests to hide their exact pick-up/drop-off locations, and departure/arrival dates. Then, by using an off-line matching technique, drivers sends their offers encrypted to ensure data confidentiality. Upon receiving the ride-offers, the rider can find a ride match using some heuristics as well as the bid price included in the offer. To preserve anonymity, riders/drivers use pseudonyms that change per trip to ensure unlinkabilty. We envision the application of this technology in Green Internet of Things connected smart cities, where ride sharing services are common. Finally, we implement our scheme and deploy it in a test net of Ethereum. The experimental results show the applicability of our protocol.
Two-Stage Multi-Swarm Particle Swarm Optimizer for Unconstrained and Constrained Global Optimization

This paper presents a new two-stage multi-swarm particle swarm optimizer (TMPSO), which employs the multi-swarm method and takes two-stage different search strategies in the whole iteration process.This new optimizer includes two versions: unconstrained TMPSO (uTMPSO) and constrained TMPSO (cTMPSO) for unconstrained and constrained global optimizations respectively.For the uTMPSO version, TMPSO makes a certain number of sub-swarms in the first stage to iterate to increase the probability to find the global optimum.Further in the second stage, all the sub-swarms are merged into one large swarm to further refine the global best particle.In both these two stages, each sub-swarm of the first stage and the merged swarm of the second stage all employ a local three-stage multi-point particle swarm optimization (MpPSO) algorithm, which is enlightened by human decision-making and cusp catastrophe theory to enhance the local search ability.To solve constrained optimization problems, the uTMPSO is further upgraded to handle the constraints by using trial and error method to form the cTMPSO version, in which constraints violations are checked on each new created particle in the above uTMPSO procedures, and the violating ones are enforced to execute ''retreat'' operations, return into the feasible region and recreate new positions, which replaces the traditional penalty function method.This proposed uTMPSO is tested on two unconstrained optimization test functions benchmark set with 25 and 28 functions (including multimodal hybrid composition functions) respectively, and compared with other twelve particle swarm optimization variants.The test results show that uTMPSO has better performance and outperforms most compared algorithms.The cTMPSO is also tested on eight benchmark constrained optimization functions and five engineering application problems.
Practical Accuracy Estimation for Efficient Deep Neural Network Testing

Deep neural network (DNN) has become increasingly popular and DNN testing is very critical to guarantee the correctness of DNN, i.e., the accuracy of DNN in this work. However, DNN testing suffers from a serious efficiency problem, i.e., it is costly to label each test input to know the DNN accuracy for the testing set, since labeling each test input involves multiple persons (even with domain-specific knowledge) in a manual way and the testing set is large-scale. To relieve this problem, we propose a novel and practical approach, called PACE (which is short for P ractical AC curacy E stimation), which selects a small set of test inputs that can precisely estimate the accuracy of the whole testing set. In this way, the labeling costs can be largely reduced by just labeling this small set of selected test inputs. Besides achieving a precise accuracy estimation, to make PACE more practical it is also required that it is interpretable, deterministic, and as efficient as possible. Therefore, PACE first incorporates clustering to interpretably divide test inputs with different testing capabilities (i.e., testing different functionalities of a DNN model) into different groups. Then, PACE utilizes the MMD-critic algorithm, a state-of-the-art example-based explanation algorithm, to select prototypes (i.e., the most representative test inputs) from each group, according to the group sizes, which can reduce the impact of noise due to clustering. Meanwhile, PACE also borrows the idea of adaptive random testing to select test inputs from the minority space (i.e., the test inputs that are not clustered into any group) to achieve great diversity under the required number of test inputs. The two parallel selection processes (i.e., selection from both groups and the minority space) compose the final small set of selected test inputs. We conducted an extensive study to evaluate the performance of PACE based on a comprehensive benchmark (i.e., 24 pairs of DNN models and testing sets) by considering different types of models (i.e., classification and regression models, high-accuracy and low-accuracy models, and CNN and RNN models) and different types of test inputs (i.e., original, mutated, and automatically generated test inputs). The results demonstrate that PACE is able to precisely estimate the accuracy of the whole testing set with only 1.181%∼2.302% deviations, on average, significantly outperforming the state-of-the-art approaches.
Compression and Acceleration of Neural Networks for Communications

DL has achieved great success in signal processing and communications and has become a promising technology for future wireless communications. Existing works mainly focus on exploiting DL to improve the performance of communication systems. However, the high memory requirement and computational complexity constitute a major hurdle for the practical deployment of DL-based communications. In this article, we investigate how to compress and accelerate the neural networks (NNs) in communication systems. After introducing the deployment challenges for DL-based communication algorithms, we discuss some representative NN compression and acceleration techniques. Afterwards, two case studies for multiple-input-multiple- output (MIMO) communications, including DL-based channel state information feedback and signal detection, are presented to show the feasibility and potential of these techniques. We finally identify some challenges on NN compression and acceleration in DL-based communications and provide a guideline for subsequent research.
Heterogeneous Signcryption With Equality Test for IIoT Environment

The existing signcryption schemes with equality testing are aimed at a sole cryptosystem and not suitable for the sophisticated heterogeneous network of Industrial Internet of Things (IIoT). To deal with this challenge, we propose a heterogeneous signcryption scheme with equality test (HSC-ET) in this article. This scheme enables a sensor in public key infrastructure (PKI) to execute data encryption and deliver it to the semitrusted entity (cloud server). When a user in an identity-based cryptosystem (IBC) intends to search for some data stored on the cloud server. The delegated cloud server executes tests on ciphertexts for determining whether the same underlying plaintext exists between two ciphertexts. These two ciphertexts can be one signcrypted ciphertext and one encrypted ciphertext, or both encrypted/signcrypted ciphertext, thus achieving a flexible search to the ciphertext. HSC-ET is demonstrated to be secure by the rigorous and detailed analysis. The experimental simulation and analysis results show the efficiency of our scheme.
ABC-RuleMiner: User behavioral rule-based machine learning method for context-aware intelligent services

This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internet-of-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data - for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset. However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method “ABC-RuleMiner” that effectively identifies the redundancy in associations, and discovers a set of non-redundant behavioral rules (IF-THEN) for individual users by taking into account the precedence of relevant contexts. Our experiments on individuals’ contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.
Artificial intelligence and food security: swarm intelligence of AgriTech drones for smart AgriFood operations

The Sustainable Development Goals (SDGs) present the emerging need to explore new ways of AgriFood production and food security as ultimate targets
TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation

The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.
Potential Application of Blockchain Technology for Embodied Carbon Estimating in Construction Supply Chains

Carbon emissions are categorised as Embodied Carbon (EC) occurring in the production phase and Operational Carbon (OC) occurring in the operational phase of buildings. The current focus on producing zero-carbon buildings, emphasises reducing OC and ignores the importance of reducing EC emissions. This study focuses on EC. Methods available in EC estimating currently produce estimates that often do not complement each other. This makes it important to develop a robust and accurate methodology for estimating EC. Blockchain is an emerging technology that has significant potential for transaction processing in supply chains. The construction industry being the second least digitalised industry, the adoption of innovative technologies is predominantly important. This paper explores the potential application of blockchain for accurate estimation of EC in construction supply chains. A detailed literature review and expert interviews revealed that, compared to traditional information systems, blockchain systems could eliminate issues in EC estimating highlighting its potential credible application for EC estimating. Scalability was identified as a feature that was lacking in a blockchain system, however, for EC estimating, its impact was identified as minimal. It will be difficult to generalise the findings of the study due to interview based qualitative methodology adopted in this study along with the fact that blockchain is an emerging and fairly new technology. However, a similar process could be followed by other studies to compare blockchain with traditional information systems, to evaluate the suitability of blockchain technology to develop prototype systems.
Blockchain for energy sharing and trading in distributed prosumer communities

The decentralisation of energy supply and demand can contribute decisively to protecting the environment and climate of the planet by consuming electricity in the proximity of the generation source and avoiding losses in transmission and distribution. Supporting energy transactions with emerging intelligent technologies can advance the development of energy communities and accelerate the integration of renewable sources. Distributed energy solutions play an essential role as they are explicitly designed to produce, store and deliver green energy. Profiting with these benefits is essential, especially in the context of the current debate on stopping climate change. Several technologies such as waste heat recovery with intelligent algorithms can improve the energy distribution and provide significant resource savings. On the other hand, the usage of Blockchain technology in energy markets promises to incentivise the use of renewables and provide a reliable framework to monitor real-time information of energy production and consumption. Blockchain can also enable trading between independent agents and lead to the formation of more secured energy communities. In this paper, we demonstrate how Blockchain can be utilised to support the formation and use of energy communities. We propose a Blockchain-based energy framework as a mean to support energy exchanges in a community of prosumers. We demonstrate how smart contracts can manage energy transactions and enable a more secured trading environment between consumers and producers. We utilise data and models from a real fish processing industrial site in Milford Haven Port, South Wales, based on which we validate our research hypothesis.
Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades

In the software engineering, estimation of the effort, time and cost required for the development of software projects is an important issue. It is a very difficult task for project managers to predict the cost and effort needed in the premature stages of planning. Software estimation ahead of development can reduce the risk and increase the success rate of the project. Many traditional and machine learning methods are used for software effort estimation by researchers, but always it has been a challenge to predict the effort accurately. In this study, different Artificial Neural Network (ANN) used for effort estimation is discussed. It is observed that the prediction of software effort by using ANN is more precise and better compared to traditional methods such as Function point, Use-case methods and COCOMO etc. Models based on neural networks are competitive in nature as compared to statistical and traditional regression methods. This paper explains the overview of various ANN such as basic NN, higher order NN, and deep learning networks used by the researchers for software effort estimation.
Dynamic interdependence of cryptocurrency markets: An analysis across time and frequency

The extreme price swings and complexity in cryptocurrency markets drives multifarious research into co-movements, in both time and frequency, among cryptocurrencies. In this paper, we investigate the dynamics of multiscale interdependencies among five leading and liquid cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash) using wavelet-based analyses that account for the heterogeneous behaviour of crypto-traders and crypto-investors. The results provide evidence of high levels of dependency from 2016 to 2018 at daily frequency scales. The cross wavelet transforms demonstrate Ripple and Ethereum to be trivial origins of market contagion. The results of wavelet coherence confirm the short-run and long-run market integration among some cryptocurrency pairs. However, the coherence is found to fluctuate at higher frequencies and be significantly stable at lower frequencies. Furthermore, the switch in the lead and lag relations of cryptocurrency returns suggests alternating time and frequency interdependencies. Our findings are useful to scale-conscious traders and multi-prospect (various investment horizon) investors and portfolio managers.
Knowledge Guided Capsule Attention Network for Aspect-Based Sentiment Analysis

Aspect-based (aspect-level) sentiment analysis is an important task in fine-grained sentiment analysis, which aims to automatically infer the sentiment towards an aspect in its context. Previous studies have shown that utilizing the attention-based method can effectively improve the accuracy of the aspect-based sentiment analysis. Despite the outstanding progress, aspect-based sentiment analysis in the real-world remains several challenges. (1) The current attention-based method may cause a given aspect to incorrectly focus on syntactically unrelated words. (2) Conventional methods fail to identify the sentiment with the special sentence structure, such as double negatives. (3) Most of the studies leverage only one vector to represent context and target. However, utilizing one vector to represent the sentence is limited, as the natural languages are delicate and complex. In this paper, we propose a knowledge guided capsule network (KGCapsAN), which can address the above deficiencies. Our method is composed of two parts, a Bi-LSTM network and a capsule attention network. The capsule attention network implements the routing method by attention mechanism. Moreover, we utilize two prior knowledge to guide the capsule attention process, which are syntactical and n-gram structures. Extensive experiments are conducted on six datasets, and the results show that the proposed method yields the state-of-the-art.
The Prediction of Saliency Map for Head and Eye Movements in 360 Degree Images

By recording the whole scene around the capturer, virtual reality (VR) techniques can provide viewers the sense of presence. To provide a satisfactory quality of experience, there should be at least 60 pixels per degree, so the resolution of panoramas should reach 21600 × 10800. The huge amount of data will put great demands on data processing and transmission. However, when exploring in the virtual environment, viewers only perceive the content in the current field of view (FOV). Therefore if we can predict the head and eye movements which are important behaviors of viewer, more processing resources can be allocated to the active FOV. But conventional saliency prediction methods are not fully adequate for panoramic images. In this paper, a new panorama-oriented model, to predict head and eye movements, is proposed. Due to the superiority of computation in the spherical domain, the spherical harmonics are employed to extract features at different frequency bands and orientations. Related low- and high-level features including the rare components in the frequency domain and color domain, the difference between center vision and peripheral vision, visual equilibrium, person and car detection, and equator bias are extracted to estimate the saliency. To predict head movements, visual mechanisms including visual uncertainty and equilibrium are incorporated, and the graphical model and functional representation for the switch of head orientation are established. Extensive experimental results on the publicly available database demonstrate the effectiveness of our methods.
Cryptanalytic Extraction of Neural Network Models

We argue that the machine learning problem of model extraction is actually a cryptanalytic problem in disguise, and should be studied as such. Given oracle access to a neural network, we introduce a differential attack that can efficiently steal the parameters of the remote model up to floating point precision. Our attack relies on the fact that ReLU neural networks are piecewise linear functions, and thus queries at the critical points reveal information about the model parameters. We evaluate our attack on multiple neural network models and extract models that are $$2^{20}$$ times more precise and require $$100{\times }$$ fewer queries than prior work. For example, we extract a 100, 000 parameter neural network trained on the MNIST digit recognition task with $$2^{21.5}$$ queries in under an hour, such that the extracted model agrees with the oracle on all inputs up to a worst-case error of $$2^{-25}$$ , or a model with 4, 000 parameters in $$2^{18.5}$$ queries with worst-case error of $$2^{-40.4}$$ . Code is available at https://github.com/google-research/cryptanalytic-model-extraction .