Reviewerly/exHarmony huggingface dataset:
Efficient Privacy-preserving in IoMT with Blockchain and Lightweight Secret Sharing

Internet of medical things (IoMT) aggregates a series of smart medical devices and fully uses the collected health data to improve user experience, medical resource utilization, and full life cycle protection. However, privacy leakage, data loss, and inefficient sharing problems are still serious in the data-sharing process between different smart medical devices. This paper first introduces an efficient privacy-preserving model with blockchain to construct a secure data-sharing mechanism between different device nodes. This model utilizes distributed storage form to solve the centralized management problem and provides a fundamental secret reconstruction and retrieval framework. Then, a lightweight (t,n)-threshold secret sharing (t/n-SS) scheme is designed to strengthen the medical data-sharing security and efficiency. It utilizes the interleaving encode technology to decrease the length of original message into n small shares. These small shares are also suitable for data transmission and processing with a more energy-efficient way. It can protect privacy by destroying the data’s semantic meaning. Meanwhile, it only needs less than t(t≤n) shares to recover the original secrets, making the sharing process more efficient. Moreover, the performance evaluations of transaction processing in IoMT show that the proposed model is very stable. The simulation and performance evaluation results show that this t/n-SS scheme is energy efficient, storage saving, and strong fault tolerance than similar literature.
Scholarly social media profiles and libraries

This article aims to point out emerging roles and responsibilities for academic librarians with the potential of better integrating the library in the research process. In order to find out how to enhance the online reputation and discoverability of individual faculty members as well as their affiliated institutions, the authors worked side-by-side with researchers in the United States and Europe to explore, create, revise, and disambiguate scholarly profiles in various software applications. In an attempt to understand and organize scholarly social media, including new, alternative metrics, the authors reviewed and classified the major academic profile platforms, highlighting the overlapping elements, benefits, and drawbacks inherent in each. The consensus is that it would be time-consuming to keep one’s profile current and accurate on all of these platforms, given the plethora of underlying problems, also discussed in detail in the article. However, it came as a startling discovery that reluctance to engage with scholarly social media may cause a misrepresentation of a researcher’s academic achievements and may come with unforeseen consequences. The authors claim that current skills and competencies can secure an essential role for academic librarians in the research workflow by means of monitoring and navigating researcher profiles in scholarly social media in order to best represent the scholarship of their host institutions.
Coordinate Memory Deduplication and Partition for Improving Performance in Cloud Computing

Both limited main memory size and memory interference are considered as the major bottlenecks in virtualization environments.Memory deduplication, detecting pages with same content and being shared into one single copy, reduces memory requirements; memory partition, allocating unique colors for each virtual machine according to page color, reduces memory interference among virtual machines to improve performance.In this paper, we propose a coordinate memory deduplication and partition approach named CMDP to reduce memory requirement and interference simultaneously for improving performance in virtualization.Moreover, CMDP adopts a lightweight page behavior-based memory deduplication approach named BMD to reduce futile page comparison overhead meanwhile to detect page sharing opportunities efficiently.And a virtual machine based memory partition called VMMP is added into CMDP to reduce interference among virtual machines.According to page color, VMMP allocates unique page colors to applications, virtual machines and hypervisor.The experimental results show that CMDP can efficiently improve performance (by about 15.8%) meanwhile accommodate more virtual machines concurrently.
Exploring Correlations Among Tasks, Clusters, and Features for Multitask Clustering

Multitask clustering methods are proposed to improve performances of related tasks concurrently, because they explore the relationship among tasks via exploiting the coefficient matrix or the shared feature matrix. However, divergent effects of features in learning this relationship are seldom considered. To further improve performances, we propose a new multitask clustering approach through exploring correlations among tasks, clusters, and features based on effects of features on clusters. First, a Feature-Cluster (FeaCluster) matrix is introduced to capture the similarity and the distinct task-feature information simultaneously for each task. With the FeaCluster matrix, two affinities are calculated to constitute the interdependencies among tasks: the former is the graphical affinity based on feature-task and task-cluster correlations, while the latter is the reconstructive affinity. Here, the feature-task correlation considers effects of features on tasks, and the task-cluster correlation considers the overall effects of features on clusters. The reconstructive affinity is obtained by minimizing the reconstruction error when representing the FeaCluster matrix for a given task with a linear combination of others. The interdependencies among tasks allow transferring asymmetric shared information, exploring significant features and preserving key information when mapping data into the subspace. The experimental results on multiple data sets reveal that the proposed approach outperforms the state-of-the-art clustering methods in terms of accuracy and normal mutual information.
Low-Cost AES-128 Implementation for Edge Devices in IoT Applications

Internet of Things (IoT) is an ecosystem of connected edge devices that are accessible through the internet. Recent research focusses on adding more smartness and intelligence to these edge devices making them susceptible to various kinds of security threats. These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field. Since the edge devices are resource constrained, low-cost implementations of cryptographic algorithms are desirable. This work proposes a novel low-cost implementation of a versatile symmetric encryption algorithm namely Advanced Encryption Standard (AES) using time-multiplexed architectures for edge devices. The optimization is carried out in a four-fold manner on AES encryption/decryption hardware based on the resource sharing mechanism with a modified Substitution box achieving a maximum of 1.053[Formula: see text]GHz operating frequency. The aim of this work is to develop an area-power efficient AES architecture with a reasonable throughput suitable for resource constrained applications. The proposed architectures are synthesized on a Virtex-6 FPGA board and the ASIC performance results are obtained using 180[Formula: see text]nm SCL technology library. Implementation results of the proposed AES core integrated with an UART module are shown as a proof of concept.
Unbiased Implicit Variational Inference

We develop unbiased implicit variational inference (UIVI), a method that expands the applicability of variational inference by defining an expressive variational family. UIVI considers an implicit variational distribution obtained in a hierarchical manner using a simple reparameterizable distribution whose variational parameters are defined by arbitrarily flexible deep neural networks. Unlike previous works, UIVI directly optimizes the evidence lower bound (ELBO) rather than an approximation to the ELBO. We demonstrate UIVI on several models, including Bayesian multinomial logistic regression and variational autoencoders, and show that UIVI achieves both tighter ELBO and better predictive performance than existing approaches at a similar computational cost.
Hierarchical Context Enabled Recurrent Neural Network for Recommendation

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our interest drift assumption. As we suggest a new RNN structure, we support HCRNN with a complementary bi-channel attention structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.
Atomic Cross-chain Swaps: Development, Trajectory and Potential of Non-monetary Digital Token Swap Facilities

Since the introduction of Bitcoin in 2008, many other cryptocurrencies have been introduced and gained popularity. Lack of interoperability and scalability amongst these cryptocurrencies was - and still is - acting as a significant impediment to the general adoption of cryptocurrencies and coloured tokens. Atomic Swaps – a smart exchange protocol for cryptocurrencies - is designed to facilitate a wallet-to-wallet transfer enabling direct trades amongst different cryptocurrencies. Since swaps between cryptocurrencies are still relatively unknown, this article will investigate the operation and market development thus far and query the advantages they offer and the future challenges they face. The paper contains detailed literature and technology reviews, followed by the main analysis and findings.
Spin chains for two-qubit teleportation

Generating high-quality multiparticle entanglement between communicating parties is the primary resource in quantum teleportation protocols. To this aim, we show that the natural dynamics of a single spin chain is able to sustain the generation of two pairs of Bell states----possibly shared between a sender and a distant receiver---which can in turn enable two-qubit teleportation. In particular, we address a spin-$\frac{1}{2}$ chain with $XX$ interactions, connecting two pairs of spins located at its boundaries, playing the roles of sender and receiver. In the regime where both end pairs are weakly coupled to the spin chain, it is possible to generate at predefinite times a state that has vanishing infidelity with the product state of two Bell pairs, thereby providing nearly unit fidelity of teleportation. We also derive an effective Hamiltonian via a second-order perturbation approach that faithfully reproduces the dynamics of the full system.
Optical colour image encryption using spiral phase transform and chaotic pixel scrambling

In this study, we propose a new optical colour image encryption technique using spiral phase transform and chaotic pixel scrambling. For encryption, three channels of the colour image i.e. red, green and blue are first separated and modulated with three different structured phase masks. Spiral phase transform (SPT) with a particular order of modified spiral phase function (MSPF) is utilized for further processing. Random modulus decomposition is applied to the complex output after SPT to generate the private key for decryption. The pixels of the image are scrambled by using the chaotic Tinkerbell map for enhanced security. The order of MSPF, three structured phase masks, parameters of Tinkerbell mapping, and the private key generated during the encryption process serve as the security keys. The robustness of the proposed method is checked against various potential attacks. A series of numerical simulation results are presented to validate the proposed colour image encryption method.
What Makes a Good Conversation?: Challenges in Designing Truly Conversational Agents

Conversational agents promise conversational interaction but fail to deliver. Efforts often emulate functional rules from human speech, without considering key characteristics that conversation must encapsulate. Given its potential in supporting long-term human-agent relationships, it is paramount that HCI focuses efforts on delivering this promise. We aim to understand what people value in conversation and how this should manifest in agents. Findings from a series of semi-structured interviews show people make a clear dichotomy between social and functional roles of conversation, emphasising the long-term dynamics of bond and trust along with the importance of context and relationship stage in the types of conversations they have. People fundamentally questioned the need for bond and common ground in agent communication, shifting to more utilitarian definitions of conversational qualities. Drawing on these findings we discuss key challenges for conversational agent design, most notably the need to redefine the design parameters for conversational agent interaction.
Multi-scale Hierarchical Residual Network for Dense Captioning

&#x0D; &#x0D; &#x0D; Recent research on dense captioning based on the recurrent neural network and the convolutional neural network has made a great progress. However, mapping from an image feature space to a description space is a nonlinear and multimodel task, which makes it difficult for the current methods to get accurate results. In this paper, we put forward a novel approach for dense captioning based on hourglass-structured residual learning. Discriminant feature maps are obtained by incorporating dense connected networks and residual learning in our model. Finally, the performance of the approach on the Visual Genome V1.0 dataset and the region labelled MS-COCO (Microsoft Common Objects in Context) dataset are demonstrated. The experimental results have shown that our approach outperforms most current methods.&#x0D; &#x0D; &#x0D;
Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection

Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.
Security Awareness Level of Smartphone Users: An Exploratory Case Study

As smartphone technology becomes more and more mature, its usage extends beyond and covers also applications that require security. However, since smartphones can contain valuable information, they normally become the target of attackers. A physically lost or a hacked smartphone may cause catastrophic results for its owner. To prevent such undesired events, smartphone users should be aware of existing threats and countermeasures to be taken against them. Therefore, user awareness is a critical factor for smartphone security. This study investigates the awareness level of smartphone users for different security-related parameters and compares the awareness levels of different user groups categorized according to their demographic data. It is based on a survey study conducted on a population with a different range of age, education level, and IT security expertise. According to the obtained results, in general, the awareness level of participants is fairly low, which needs considerable improvement. In terms of age, the oldest group has the lowest level followed by the youngest group. Education level, in general, has a positive effect on the awareness level. Having knowledge about IT is another factor increasing the security awareness level of smartphone users.
Mobile Edge Computing, Blockchain and Reputation-based Crowdsourcing IoT Federated Learning: A Secure, Decentralized and Privacy-preserving System.

Internet-of-Things (IoT) companies strive to get feedback from users to improve their products and services. However, traditional surveys cannot reflect the actual conditions of customers' due to the limited questions. Besides, survey results are affected by various subjective factors. In contrast, the recorded usages of IoT devices reflect customers' behaviours more comprehensively and accurately. We design an intelligent system to help IoT device manufacturers to take advantage of customers' data and build a machine learning model to predict customers' requirements and possible consumption behaviours with federated learning (FL) technology. The FL consists of two stages: in the first stage, customers train the initial model using the phone and the edge computing server collaboratively. The mobile edge computing server's high computation power can assist customers' training locally. Customers first collect data from various IoT devices using phones, and then download and train the initial model with their data. During the training, customers first extract features using their mobiles, and then add the Laplacian noise to the extracted features based on differential privacy, a formal and popular notion to quantify privacy. After achieving the local model, customers sign on their models respectively and send them to the blockchain. We use the blockchain to replace the centralized aggregator which belongs to the third party in FL. In the second stage, miners calculate the averaged model using the collected models sent from customers. By the end of the crowdsourcing job, one of the miners, who is selected as the temporary leader, uploads the model to the blockchain. Besides, to attract more customers to participate in the crowdsourcing FL, we design an incentive mechanism, which awards participants with coins that can be used to purchase other services provided by the company.
Physically-based statistical simulation of rain sound

A typical rainfall scenario contains tens of thousands of dynamic sound sources. A characteristic of the large-scale scene is the strong randomness in raindrop distribution, which makes it notoriously expensive to synthesize such sounds with purely physical methods. Moreover, the raindrops hitting different surfaces (liquid or various solids) can emit distinct sounds, for which prior methods with unified impact sound models are ill-suited. In this paper, we present a physically-based statistical simulation method to synthesize realistic rain sound, which respects surface materials. We first model the raindrop sound with two mechanisms, namely the initial impact and the subsequent pulsation of entrained bubbles. Then we generate material sound textures (MSTs) based on a specially designed signal decomposition and reconstruction model. This allows us to distinguish liquid surface with bubble sound and different solid surfaces with MSTs. Furthermore, we build a basic rain sound (BR-sound) bank with the proposed raindrop sound clustering method based on a statistical model, and design a sound source activator for simulating spatial propagation in an efficient manner. This novel method drastically decreases the computational cost while producing convincing sound results. Various experiments demonstrate the effectiveness of our sound simulation model.
A Permissioned Blockchain-Based System for Verification of Academic Records

While academic institutions maintain records such as transcripts and certificates, they are often requested to share these records with other institutions at the request of students for credit transfer, or prerequisites for acceptance into new academic programs. While the transfer of academic records is a regular daily activity for the institutions, there is often significant overhead involved as the process of transfer and verification is extremely manual. The need for an automated end-to-end solution for the transfer and verification of academic records between institutions is on the edge to reduce wait times for students to transfer their records, as well as to provide a reliable verification method to avoid academic fraud. This paper presents a permissioned blockchain-based system to allow institutions to securely and dependably transfer and verify academic records at the student request. Permissioned blockchains, such as Hyperledger, provide a more scalable and cost-effective and private solution for enterprise applications. Our solution is comprised of a web interface for enrolling and requesting the transfer, with a backend using Hyperledger Fabric and Hyperledger Composer to retain the hash of the records on the blockchain for verification.
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment

Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution. While absent assumptions, domain adaptation is impossible, strict conditions, e.g. covariate or label shift, enable principled algorithms. Recently-proposed domain-adversarial approaches consist of aligning source and target encodings, often motivating this approach as minimizing two (of three) terms in a theoretical bound on target error. Unfortunately, this minimization can cause arbitrary increases in the third term, e.g. they can break down under shifting label distributions. We propose asymmetrically-relaxed distribution alignment, a new approach that overcomes some limitations of standard domain-adversarial algorithms. Moreover, we characterize precise assumptions under which our algorithm is theoretically principled and demonstrate empirical benefits on both synthetic and real datasets.