Reviewerly/exHarmony huggingface dataset:
Revocable identity-based encryption with server-aided ciphertext evolution

The utmost important problem in identity-based cryptosystems is the issue of user revocation. One of the existing solutions in the literature is to issue extra time keys periodically for every non-revoked user over public channels. Unfortunately, this solution is inefficient and very impractical when applying to the cloud. Because the scheme requires different time keys to allow data decryption for different time periods, and therefore the user has to keep a long list of time keys, which grows linearly with time. Furthermore, it is worth noting that ciphertexts produced prior to the revocation will remain available to the revoked users, which is undesirable for most application scenarios. To the best of our knowledge, there is no existing work that can solve both the aforementioned problems simultaneously in a practical manner. In this paper, we present an efficient solution called ciphertext evolution. The ciphertexts evolve to new ones with cloud's aid and the old ones are deleted. At any time, the data user has to utilize its current decryption key to decrypt ciphertexts in the cloud. So, all the past time keys become invalid and the user only needs to keep the current one. If the user is revoked, it cannot decrypt any ciphertext in the cloud because it does not have the current time key. We present generic and concrete constructions of revocable identity-based encryption with ciphertext evolution (RIBE-CE), which are proven based on the IND-CPA security model. Subsequently, we also extend RIBE-CE to the broadcast setting by giving generic and concrete constructions of revocable identity-based broadcast encryption with ciphertext evolution, which are secure under the IND-sID-CPA security model. Our schemes can be applied to the (group) data sharing, which is very practical and applicable to the cloud setting.
Collaborative Deep Learning for Medical Image Analysis with Differential Privacy

Deep learning algorithms, especially convolution neural networks, have attracted huge attention in the field of medical image analysis. A hospital could train a neural network to detect disease based on medical images possessing. However, the number of medical images would affect the results of training. If medical images of all hospitals are collected together, there's a risk of privacy leakage. In this paper, we apply collaborative deep learning to medical image analysis, which could help to improve the training effect. Besides, we also exploit differential privacy, the analytic Gaussian Mechanism, to prevent the leakage of information about medical images. We experiment on the Chest X-ray Images (Pneumonia) dataset. Results show that the analytic Gaussian Mechanism can protect the privacy of medical images effectively, while the influence on the results of training is small. The accuracy can be improved about 19\% via collaborative deep learning and can still remain about 18\% even when the analytic Gaussian Mechanism was used.
Deep Learning for Multi-Resident Activity Recognition in Ambient Sensing Smart Homes

Advances in smart home technology and IoT devices has enabled us for monitoring of human activities for their health status and efficient energy consumption. Machine learning has been a great tool for the prediction of human activities. However, Multi-resident activity recognition is still a challenge as there is no direct correlation between sensor values and resident activities. In this paper, we have displayed the state of art deep learning algorithms on the real-world ARAS multi-resident dataset, which consists of data from two houses each with two residents. We have used different variations of RNN on the dataset and measured their performance with fewer data and more data and with data generated with GAN.
Simultaneous tracking of a maneuvering ship and its wake using Gaussian processes

Maneuvering ship tracking has applications in maritime surveillance and the performance of tracking algorithms is affected by background clutter and ship-generated wake. Previous works consider the wake as a form of clutter with some specific spatial distribution. In this paper, the Kelvin wake, which assumes that the ship-generated wake is V-shaped, is modelled as an extended target with an irregular and time-varying shape. Thus, the problem of maneuvering ship tracking in the presence of wake becomes a special multiple target tracking problem, where the ship is a point target and the wake is an extended target. A Gaussian Processes based Multiple Model Joint Probabilistic Data Association (GP-MM-JPDA) method is proposed to estimate the ship state and the wake state simultaneously. Two versions of the GP-MM-JPDA are implemented. The first one can be applied to most multiple target tracking problems and the second one is designed especially for the ship tracking problem. Both versions can improve the ship tracking performance. In addition, the proposed methods can also infer the ship length using the estimated wake angle, which facilitates ship classification. Simulation results show the effectiveness of the proposed methods.
Usability of the size, spacing, and operation method of virtual buttons with virtual hand on head-mounted displays

Virtual reality (VR) allows users to see and manipulate virtual scenes and items through input devices, like head-mounted displays. In this study, the effects of button size, spacing, and operation method on the usability of virtual buttons in VR environments were investigated. Task completion time, number of errors, and subjective preferences were collected to test different levels of the button size, spacing, and operation method. The experiment was conducted in a desktop setting with Oculus Rift and Leap motion. A total of 18 subjects performed a button selection task. The optimal levels of button size and spacing within the experimental conditions are 25 mm and between 5 mm and 9 mm, respectively. Button sizes of 15 mm with 1-mm spacing were too small to be used in VR environments. A trend of decreasing task completion time and the number of errors was observed as button size and spacing increased. However, large size and spacing may cause fatigue, due to continuous extension of the arms. For operation method, the touch method took a shorter task completion time. However, the push method recorded a smaller number of errors, owing to the visual push-feedback. In this paper, we discuss advantages and disadvantages in detail. The results can be applied to many different application areas with VR HMD using virtual hand interaction.
Robust supervised sparse representation for face recognition

Sparse representation based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum l1-norm on coding coefficient vector, which is considered as an unsupervised sparsity constraint and usually requires high computational cost. On the other hand, supervised sparsity representation based method (SSR) realizes sparse representation classification with higher efficiency by multiple phases of representing a probe. Nevertheless, since previous SSR methods only deal with Gaussian noise, they cannot satisfy empirical face recognition application which faces wide variations. In this paper, we propose a robust supervised sparse representation (RSSR) model, which uses two-phase of robust representation to compute a sparse coding vector. Huber loss is employed as the fidelity term in the linear representation, which improves the competitiveness of correct class in the first phase. Then training samples with weak competitiveness are removed by supervised way. In the second phase, the competitiveness of correct class is further boosted by Huber loss. We compare the RSSR with other state-of-the-art methods under different conditions, including illumination variations, gesture changes, expressions, corruptions, and occlusions. Comprehensive experiments on four open databases demonstrate the robustness of RSSR and competitive performance is obtained in dealing with face images with occlusion or not.
Design and implementation of bilingual digital reader based on artificial intelligence and big data technology

Along with the development of computer and information technology and the arrival of the digital reading wave, more and more users have switched the way they meet their reading needs to digital reading systems, At the same time, a variety of digital reading systems have also been created. However, most digital reading systems focus on how to present a better reading style, but little research has been done on how to use artificial intelligence and big data technology to provide intelligent information services and user behavior analysis. In such a large environment, a digital reading system that can provide reading behavior collection and intelligent analysis, while providing intelligent reading analysis function, will have broad research prospects. The digital reading system studied in this paper can provide reading behavior analysis and intelligent recommendation service for professional users based on artificial intelligence and big data technology. At the same time, the system uses artificial intelligence technology to realize the functions of bilingual learning reading, new word induction records, etc., which can provide users with knowledge efficiency. In addition, the system utilizes big data technology to provide users with information services such as communication content exchange. The main innovations of this digital reading system are computer automatic clauses based on Chinese and English syntax features, data layering processing mechanism that takes into account the speed and quality of book analysis, and book encryption and decryption schemes across computer systems. The system is based on a C/S and B/S fusion architecture and includes a reading system based on PC and Android.It can present customized ePub electronic resources, and collect users’ reading behavior through mobile screen or mouse and other devices, then use artificial intelligence and big data technology to analyze user data, and finally generate user reading reports. At present, the whole system has been applied in many universities, and the reading level of students and the work efficiency of teachers have been greatly improved, which proves that this digital reading system has high practical value.
Blockchain-based multi-organization taxonomy for smart cities

With the tremendous development in distributed ledger technology, assimilation of tokenization in sustainable assets has been a proof of concept. This paper is a documentation of ERC20 standards where cryptocontracts are an evident implementation of WRC tokens. Issuing and earning of these token credits rely on aggregate hazardous waste released into water as a by-product and careful monitoring of water quality standards (after treatment) using IOT sensors. The minting of this token currency is put forth by exchanging ether. This research is twofold attempt to eliminate the differences between Small and Medium Enterprise and large-scale Enterprises and to establish a business ground with equal opportunity of earning credits based on recycled wastewater.
<i>RoboPlanner</i>: a pragmatic task planning framework for autonomous robots

Robotic automation has proliferated various industrial deployments including manufacturing, retail warehousing and logistics supply chains. In order for robots to advance to the next stage of cognitive autonomy, a robust framework for planning, execution and adaptation is needed. While there have been advances in abstract automated planning systems, they are still ill-suited to be applied within runtime robotic executions, which take place in uncertain environments. In this study, the authors provide a deliberative robotic planning and simulated execution framework called RoboPlanner that provides a pragmatic integration of automated planning, orchestration and adaptive deployments. This is coupled with an execution monitor and plan repair module, that allows reconfiguration to various template actions with runtime changes. Structured rules for re-planning in the case of state changes, unforeseen obstacles or execution failures are provided. They demonstrate their simulation framework on a realistic example of mobile pick & delivery robots in Industry 4.0 warehouses, that plan, execute, adapt and re-plan in sync with a knowledge base.
An Efficient, Accountable, and Privacy-Preserving Access Control Scheme for Internet of Things in a Sharing Economy Environment

The Internet of Things (IoT) has set off a new information technology revolution due to its convenience and efficiency. An IoT enables sharing economy, as more people are willing to share their own things (mostly mobile devices) to leverage the under-used value. In such a situation where owners and users are often not familiar with each other, an efficient access control mechanism is needed to deal with the trust issue and support service accountability to help owners accurately get their deserved profits. Besides, in such a sharing economy environment, the mobility of most shared IoT devices and their privacy preserving should also be taken into account. Regrettably, the existing schemes cannot achieve all of the aforementioned goals simultaneously and only few schemes were implemented to evaluate the claimed performance. In this article, we propose an efficient, accountable, and privacy-preserving access control solution for IoT in a sharing economy environment. In our scheme, we utilize the one-time signature to achieve anonymous authentication and let gateways store the signatures as service credentials for accountability. Meanwhile, we adopt the identity-based authentication to exclude malicious gateways and shared devices from the system and design a specialized protocol for those devices moving with the users. We conduct a detailed security analysis to show that our scheme can effectively defend against potential attacks, and also implement a prototype system to demonstrate that our design is indeed an efficient one.
Interpretable Approximation of a Deep Reinforcement Learning Agent as a Set of If-Then Rules

In many industrial applications, one of the major bottlenecks in using advanced learning-based methods (such as reinforcement learning) for controls is the lack of interpretability of the trained agent. In this paper, we present a methodology for translating a trained reinforcement learning agent into a set of simple and easy to interpret if-then rules by using the proven universal approximation property of the rules with fuzzy predicates. Proposed methodology combines the optimality of reinforcement learning with interpretability of the theory of approximate reasoning, thus making reinforcement learning-based solutions more accessible to industrial practitioners. The framework presented in this paper has the potential to help address the fundamental problem in widespread adoption of reinforcement learning in industrial applications.
Adaptive denoising of 3D volumetric MR images using local variance based estimator

Preservation of the anatomical structures during denoising of medical images is a very significant and challenging operation. Corruption of magnetic resonance image (MRI) by Rician noise is inherent to the acquisition process, affecting diagnosis. In this study, we present a novel filtering methodology that removes Rician noise from MRI by estimating the local noise variance, which drives the diffusion process of the filter. In our methodology, the adaptation of statistical edge stopping function captivates the preservation condition of the anatomical structure of the MRI images. The results obtained on synthetic/simulated MRI datasets (3D) and real MRI datasets confirm the accuracy and robustness of the proposed methodology. Compared to the benchmark approaches like BM4D, LTA3D, RNOLMMSE, ROLMMSE, MNL-tSVD and PRINLM3D, the optimized way of choosing the edge stopping functions, the automatic adjustment of the filtering coefficients and variance based local noise estimation technique lead to a qualitative and quantitative robust estimation performance, in case of both simulated and real datasets.
The tetrad effect in REE distribution patterns: A quantitative approach to genetic issues of argillic and propylitic alteration zones of epithermal Cu-Pb-Fe deposits related to andesitic magmatism (Khan Kandi District, NW Iran)

The impact of the tetrad effect on the accumulation of REE during base metal mineralization can be deduced from four segments of the normalized REE distribution patterns. Quantifying this effect in each part of the REE graph has proven to be a powerful tool when it comes to the investigation of the origin of ore deposits. In the current research, a mathematical method using a logarithmic scale has been designed to calculate each individual tetrad segment in the samples and tested for the alteration zone of the andesite-related epithermal base metal deposits in the Khan Kandi District, NW Iran. In contrast to previous approaches, the current method achieves a higher accuracy for normalized REE patterns of samples from the argillic and propylitic alteration zones of base metal deposits. Using the calculation technique presented here reveals that the values obtained for third and fourth tetrad effects follow different trends in argillic and propylitic samples. In conclusion, mineralizing fluids of low pH accountable for the development of argillic alterations are characterized by larger σ3 (third tetrad) values in fault zones. They decrease toward the margins of the fault zones where argillic alteration is predominant whereas in the propylitic alteration zones the trend is reversed. The tetrad effect in these alteration zones is controlled pH, temperature changes and the fluid-rock interaction, conducive to the decomposition of minerals in host and wall rocks. The behavior of geochemical pairs such as Y/Ho, Zr/Hf and Nb/Ta displays a non-CHARAC behavior highlighted by non-linear and parallel trends in the chondrite-normalized REE curves. The occurrence of conjugate convex and concave tetrad effects in the REE patterns are accounted for by the mixing of meteoric waters and hydrothermal fluids. The correlation between σ3 values and geochemical couples are indicative of a mineralogy that formed under a regime of strong fluid-rock interaction. The data obtained from samples in the central parts of the fault zones document this zone as the pathways for hydrothermal solutions.
Photonic Switched Optically Connected Memory: An Approach to Address Memory Challenges in Deep Learning

Deep learning has been revolutionizing many aspects of our society, powering various fields including computer vision, natural language processing, and activity recognition. However, the scaling trends for both datasets and model size are constraining system performance. Variability of memory requirements can lead to poor resource utilization. Reconfigurable photonic interconnects provide scalable solutions and enable efficient use of disaggregated memory resources. We propose a photonic switched optically connected memory system architecture that tackles the memory challenges while showing the functionality of optical switching for deep learning models. Our proposed system architecture utilizes a “lite” (de)serialization scheme for memory transfers via optical links to avoid network overheads and supports the dynamic allocation of remote memories to local processing systems. In order to test the feasibility of our proposal, we built an experimental testbed with a processing system and two remote memory nodes using silicon photonic switch fabrics and evaluated the system performance. The optical switching time is measured to be 119 μs and an overall 2.78 ms latency is achieved for the end-to-end reconfiguration. The collective results and existing high-bandwidth optical I/Os show the potential of integrating the photonic switched optically connected memory to state-of-the-art processing systems.
Blockchain Technology as a Mechanism for Digital Railway Ticketing

This paper introduces a novel digital ticketing platform using blockchain technology. Taking in a number of considerations by observing legacy ticketing systems and existing attempts at digital ticketing, we make use of IBM's Hyperledger Fabric framework to design an architecture that distributes the tickets across all participating organisations. We note the potential benefits this platform has. Governing organisations maintain their right to set the rules of the platform and access the data to generate statistics. Vending organisations share access to the same underlying tickets whilst preserving competition. The platform offers passengers a variety of ways to pay for and access their tickets, using a combination of legacy and modern methods. Furthermore, we note the platform has the potential to eradicate paper ticketing and surplus voucher cards.
A Lightweight Certificateless Aggregation Signature Scheme With Provably Security in the Standard Model

In an aggregate signature scheme, n signatures on n different messages from n users can be combined into a single signature. By verifying the signature, the verifier believes that n users did generate the n corresponding signatures. In the recent decade, numerous certificateless aggregate signature (CLAS) schemes have been introduced. There are two issues with these schemes. First, it was in the random oracles model (ROM) that the security proofs of these schemes were given. ROM is an idealized model. A signature scheme is not necessarily secure in real life even if it has proven to be safe in ROM. Second, the number of hash-to-point operations increases linearly with the number of signers in these schemes, so that are not suited for the computation-constrained devices (such as mobile devices). In this article, a new certificateless signature scheme is constructed. Based on it, a new CLAS scheme is proposed. Under the hypothesis that it is hard to solve a computation Diffie-Hellman problem, two schemes are proved to be secure in the standard model. The CLAS scheme needs only three pairing operations and does not need a hash-to-point operation, taken into account the computation cost, it is more efficient than previous CLAS schemes.
ORIGIN: Non-Rigid Network Alignment

Network alignment is a fundamental task in many high-impact applications. Most of the existing approaches either explicitly or implicitly consider the alignment matrix as a linear transformation to map one network to another, and might overlook the complicated alignment relationship across networks. On the other hand, node representation learning based alignment methods are hampered by the incomparability among the node representations of different networks. In this paper, we propose a unified semi-supervised deep model (ORIGIN) that simultaneously finds the non-rigid network alignment and learns node representations in multiple networks in a mutually beneficial way. The key idea is to learn node representations by the effective graph convolutional networks, which subsequently enable us to formulate network alignment as a point set alignment problem. The proposed method offers two distinctive advantages. First (node representations), unlike the existing graph convolutional networks that aggregate the node information within a single network, we can effectively aggregate the auxiliary information from multiple sources, achieving far-reaching node representations. Second (network alignment), guided by the high-quality node representations, our proposed non-rigid point set alignment approach overcomes the bottleneck of the linear transformation assumption. We conduct extensive experiments that demonstrate the proposed non-rigid alignment method is (1) effective, outperforming both the state-of-the-art linear transformation-based methods and node representation based methods, and (2) efficient, with a comparable computational time between the proposed multi-network representation learning component and its single-network counterpart.