Reviewerly/exHarmony huggingface dataset:
Blockchain-Inspired Secure Computation Offloading in a Vehicular Cloud Network

With the emergence of computation-intensive vehicular applications, computation offloading based on mobile-edge computing (MEC) has become a promising paradigm in resource-constrained vehicular cloud networks (VCNs). However, when doing computation offloading in a VCN, malicious service providers can cause serious security concerns on the content offloading. To address that in this article, a blockchain-based secure computation offloading scheduling scheme is proposed. It embraces the blockchain-based trust management paradigm and smart contract-enabled deep reinforcement learning (DRL) algorithm. As for the trust management, the long-term reputation and short-term trust variability are jointly considered. Specifically, a novel three-valued subjective logic (3VSL) scheme is adopted to obtain a more comprehensive reputation, and the statistics of behavioral transitions can provide a short-term trust variability to timely capture the malicious behaviors. In addition, to securely update, validate, and store the trust information, we propose a hierarchical blockchain framework that comprises vehicular blockchain, roadside unit (RSU) blockchain, and cloud blockchain. Furthermore, a smart contract-enabled DRL algorithm is proposed to implement the secure and intelligent computation offloading scheduling in a VCN. Simulations are conducted to verify the effectiveness of the proposed scheme.
Multilevel 2-D Quantum Wavelet Transforms

Wavelet transform is being widely used in classical image processing. One-dimension quantum wavelet transforms (QWTs) have been proposed. Generalizations of the 1-D QWT into multilevel and multidimension have been investigated but restricted to the quantum wavelet packet transform (QWPTs), which is the direct product of 1-D QWPTs, and there is no transform between the packets in different dimensions. A 2-D QWT is vital for image processing. We construct the multilevel 2-D QWT's general theory. Explicitly, we built multilevel 2-D Haar QWT and the multilevel Daubechies D4 QWT, respectively. We have given the complete quantum circuits for these wavelet transforms, using both noniterative and iterative methods. Compared to the 1-D QWT and wavelet packet transform, the multilevel 2-D QWT involves the entanglement between components in different degrees. Complexity analysis reveals that the proposed transforms offer exponential speedup over their classical counterparts. Also, the proposed wavelet transforms are used to realize quantum image compression. Simulation results demonstrate that the proposed wavelet transforms are significant and obtain the same results as their classical counterparts with an exponential speedup.
Enhancing ICT for quality education in sub-Saharan Africa

This research assesses the relevance of information and communication technology (ICT) in primary education quality in a panel of 49 Sub-Saharan African countries for the period 2000–2012. The empirical evidence is based on Two Stage Least Squares (2SLS) and Instrumental Quantile regressions (IQR). From the 2SLS: (i) mobile phone and internet penetration rates reduce poor quality education and enhancing internet penetration has a net negative effect of greater magnitude. From the IQR: (i) with the exception of the highest quantile for mobile phone penetration and top quantiles for internet penetration, ICT consistently has a negative effect on poor education quality with a non-monotonic pattern. (ii) Net negative effects are exclusively apparent in the median and top quantiles of internet-related regressions. It follows that enhancing internet penetration will benefit countries with above-median levels of poor education quality while enhancing internet penetration is not immediately relevant to reducing poor education quality in countries with below-median levels of poor education quality.
All-Day Object Tracking for Unmanned Aerial Vehicle

Unmanned aerial vehicle (UAV) has facilitated a wide range of real-world applications and attracted extensive research in the mobile computing field. Specially, developing real-time robust visual onboard trackers for all-day aerial maneuver can remarkably broaden the scope of intelligent deployment of UAV. However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers’ capabilities to be deployed in the dark. In darkness, the conditions can be more complex and harsh, easily posing inferior robust tracking or even tracking failure. To this end, this work proposes a novel discriminative correlation filter-based tracker with illumination adaptive and anti-dark capability, namely ADTrack. ADTrack firstly exploits image illuminance information to enable adaptability of the model to the given light condition. Then, by virtue of an efficient enhancer, ADTrack carries out image pretreatment where a target aware mask is generated. Benefiting from the mask, ADTrack aims to solve a novel dual regression problem where dual filters are online trained with mutual constraint. Besides, this work also constructs a UAV nighttime tracking benchmark UAVDark135. Exhaustive experiments on authoritative benchmarks and onboard tests are implemented to validate the superiority and robustness of ADTrack in all-day conditions.
Techniques for Automated Machine Learning

Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a problem description, its task type, and datasets. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we portray AutoML as a bi-level optimization problem, where one problem is nested within another to search the optimum in the search space, and review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter tuning (AutoMHT), and automated deep learning (AutoDL). Stateof- the-art techniques in the three categories are presented. The iterative solver is proposed to generalize AutoML techniques. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.
Technology Entrepreneurship in Developing Countries: Role of Telepresence Robots in Healthcare

Developing countries represent about three-quarters of the world's population and have become the main driver for the global population and economic growth. Technology entrepreneurship plays an important role in sustainable development of vulnerable social groups and element. These efforts can channel technological benefits to underdeveloped regions. We discuss technology entrepreneurship in developing countries, with a focus on mobile health (mHealth) and telepresence robots. Specifically, we discuss how mHealth can help aged and disadvantaged developing country populations cope with normal life difficulties and challenges-but also in the extreme case and threat of a pandemic. Using past practices and lessons in telepresence robots, we propose FLEXTRA, a flexible robot mechanism to support instant video calls, smart medicine dispensation, and remote control. We also illustrate the applications of FLEXTRA in healthcare, e.g., fighting against COVID-19.
Differential Equation Invariance Axiomatization

This article proves the completeness of an axiomatization for differential equation invariants described by Noetherian functions. First, the differential equation axioms of differential dynamic logic are shown to be complete for reasoning about analytic invariants. Completeness crucially exploits differential ghosts, which introduce additional variables that can be chosen to evolve freely along new differential equations. Cleverly chosen differential ghosts are the proof-theoretical counterpart of dark matter. They create new hypothetical state, whose relationship to the original state variables satisfies invariants that did not exist before. The reflection of these new invariants in the original system then enables its analysis. An extended axiomatization with existence and uniqueness axioms is complete for all local progress properties, and, with a real induction axiom, is complete for all semianalytic invariants. This parsimonious axiomatization serves as the logical foundation for reasoning about invariants of differential equations. Indeed, it is precisely this logical treatment that enables the generalization of completeness to the Noetherian case.
A blockchain technology based trust system for cloud manufacturing

Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus "trust" is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and the provider should ensure that the payment will be made on time. In this paper, a novel Blockchain Technology (BCT)-based trust system called "Blocktrust" is proposed to address the trust problem of the CM. First, the CM framework that contains the digital firm, capability pool, and digital certificate issuing units is developed, and then, the private blocktrust peer-to-peer network is proposed and implemented based on Hyperledger fabric. Finlay, the feasibility of the blocktrust is examined under different testing scenarios. The reason for using a private network instead of the public is placing restrictions on who is allowed to participate in the network and also enjoying a network with fast transaction speed. Experiments show that the proposed blocktrust embedded CM is credible and practical.
The Impact of Tourism Development on Economic Growth in Sub-Saharan Africa

This study examines the dynamic impact of tourism development on economic growth in sub-Saharan Africa (SSA) using the Generalised Method of Moments and data covering the period from 2002 to 2018. The increasingly important role of tourism and the limelight the tourism sector has been enjoying of late, on the one hand, and on the other, the lack of sufficient coverage of tourism-growth nexus studies in Africa in general and in SSA in particular, motivated this study. Unlike most of the known panel data-based studies on tourism development and economic growth, this study has split the sub-Saharan African countries into low-income and middle-income categories. The results of the study show that tourism expenditure negatively affects economic growth, while tourism receipts have the opposite effect in SSA. The findings are robust to the low-income sub-sample, while only the effect of tourism expenditure is robust in the middle-income sub-sample.
Zooming into the pandemic! A forensic analysis of the Zoom Application

The global pandemic of COVID-19 has turned the spotlight on video conferencing applications like never before. In this critical time, applications such as Zoom have experienced a surge in its user base jump over the 300 million daily mark (ZoomBlog, 2020). The increase in use has led malicious actors to exploit the application, and in many cases perform Zoom Bombings. Therefore forensically examining Zoom is inevitable. Our work details the primary disk, network, and memory forensic analysis of the Zoom video conferencing application. Results demonstrate it is possible to find users' critical information in plain text and/or encrypted/encoded, such as chat messages, names, email addresses, passwords, and much more through network captures, forensic imaging of digital devices, and memory forensics. Furthermore we elaborate on interesting anti-forensics techniques employed by the Zoom application when contacts are deleted from the Zoom application's contact list.
Mining Optimal Policies: A Pattern Recognition Approach to Model Analysis

This project spawned from an admission control problem we were working on for a major hospital in the Boston area. We tried to incorporate various aspects of the problem in a model, which resulted in a complex optimization problem that was difficult to solve analytically. Although numerical solutions could be computed, we were looking for insights to characterize simple policies that could be used in practice. We then came up with the idea of using machine learning to analyze solutions as a mean for obtaining such insights, an idea we thought could be interesting by itself. The motivating problem is an ongoing separate work.
Experimental exploration of five-qubit quantum error-correcting code with superconducting qubits

Quantum error correction is an essential ingredient for universal quantum computing. Despite tremendous experimental efforts in the study of quantum error correction, to date, there has been no demonstration in the realisation of universal quantum error-correcting code, with the subsequent verification of all key features including the identification of an arbitrary physical error, the capability for transversal manipulation of the logical state and state decoding. To address this challenge, we experimentally realise the [5, 1, 3] code, the so-called smallest perfect code that permits corrections of generic single-qubit errors. In the experiment, having optimised the encoding circuit, we employ an array of superconducting qubits to realise the [5, 1, 3] code for several typical logical states including the magic state, an indispensable resource for realising non-Clifford gates. The encoded states are prepared with an average fidelity of [Formula: see text] while with a high fidelity of [Formula: see text] in the code space. Then, the arbitrary single-qubit errors introduced manually are identified by measuring the stabilisers. We further implement logical Pauli operations with a fidelity of [Formula: see text] within the code space. Finally, we realise the decoding circuit and recover the input state with an overall fidelity of [Formula: see text], in total with 92 gates. Our work demonstrates each key aspect of the [5, 1, 3] code and verifies the viability of experimental realisation of quantum error-correcting codes with superconducting qubits.
BCNet: Bidirectional collaboration network for edge-guided salient object detection

The boundary quality is a key factor determining the success of accurate salient object detection (SOD). A number of edge-guided SOD methods have been proposed to improve the boundary quality, but shown unsatisfactory performance due to the lack of a comprehensive consideration of multi-level feature fusion and multi-type feature aggregation. To resolve this issue, we propose a novel Bidirectional Collaboration Network (BCNet), which integrates effective multi-level feature fusion and multi-type feature aggregation into a unified edge-guided SOD framework. Specifically, we first utilize multiple Consistency Saliency Maximization (CSM) modules to propagate the highest level semantic representations in a top-down progressive pathway to generate both global edge representations and a series of region representations. Multiple Bounded Feature Fusion (BFF) modules are then utilized to refine the region features with the edge features. The CSM and BFF modules enable robust multi-level feature fusion and multi-type feature aggregation with only little extra computation, which allows a high computational efficiency. Finally, BCNet is jointly trained with edge and region losses in an end-to-end manner. Extensive comparisons are conducted with 17 state-of-the-art methods on five challenging benchmark datasets. Thanks to the use of CSM and BFF modules, our BCNet outperforms existing deep learning based SOD methods, including the latest edge-guided ones, in terms of both detection accuracy and processing speed.
Visible-infrared cross-modality person re-identification based on whole-individual training

Visible-infrared cross-modality person re-identification (VI-ReID) aims to search person images across cameras of different modalities, which can make up for the problem that ReID cannot be performed through visible images in a dark environment. The difficulty of VI-ReID task is the huge discrepancy between the visible modality and the infrared modality. In this paper, a novel whole-individual training (WIT) model is proposed for VI-ReID, which is based on the idea of pulling in the whole and distinguishing the individuals. Specifically, the model is divided into a whole part and an individual part. Two loss functions are developed in the whole part, namely center maximum mean discrepancy (CMMD) loss and intra-class heterogeneous center (ICHC) loss. Ignoring identity difference and treating each modality as a whole, the CMMD loss pulls in the centers of the two modalities. Ignoring modality difference and treating each identify as a whole, the ICHC loss pulls images with the same identity to its cross-modality center. In the individual part, a cross-modality triplet (CMT) loss is employed, which can distinguish the pedestrian images with different identities. The WIT model can help the network identify pedestrian images in an all-round way. Experiments show that the VI-ReID performance of the proposed method is better than existing technologies on two most popular benchmark datasets SYSU-MM01 and RegDB.
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly efficient multi-level malignant tissue detection through the designed adversarial CAC-UNet. A patch-level model with a pre-prediction strategy and a malignancy area guided label smoothing is adopted to remove the negative WSIs, with which to lower the risk of false positive detection. For the selected key patches by multi-model ensemble, an adversarial context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve robust segmentation. In CAC-UNet, mirror designed discriminators are able to seamlessly fuse the whole feature maps of the skillfully designed powerful backbone network without any information loss. Besides, a mask prior is further added to guide the accurate segmentation mask prediction through an extra mask-domain discriminator. The proposed scheme achieves the best results in MICCAI DigestPath2019 challenge1 on colonoscopy tissue segmentation and classification task. The full implementation details and the trained models are available at https://github.com/Raykoooo/CAC-UNet.
Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency

Abstract South Lampung is a regency with the capital of Kalianda which has an area of 2,007.01 km 2 that dominates the agricultural area. Based on the data of corn crops in the South Lampung Regency Agriculture Office through BPS (Central Bureau of Statistics), showing several areas with corn crops that vary in number. Therefore, a grouping of potential corn-producing regions is required to know which areas produce large or small amounts of corn. The distribution of crops is usually done based on the name of the corn-producing sub-district. The K-Means clustering method is one of the data mining methods that is non-hierarchical clustering that groups data in the form of one or more clusters. Data that have the same characteristics are grouped in one cluster and the remaining is grouped into another cluster so that the data that is in one cluster has a small degree of variation. So the authors tried to apply the K-Means clustering method from the corn crop data of the last 2 years to produce feasibility information from each sub-district.
Deep Learning based Semantic Communications: An Initial Investigation

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which makes joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to provide a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and can achieve better performance, especially in the low signal-to-noise ratio (SNR) regime, as demonstrated by the extensive simulation results.
Comprehensive analysis of MQTT 5.0 susceptibility to network covert channels

Message Queuing Telemetry Transport (MQTT) is a publish-subscribe protocol which is currently popular in Internet of Things (IoT) applications. Recently its 5.0 version has been introduced and ensuring that it is capable of providing services in a secure manner is of great importance. It must be noted that holistic security analysis should also evaluate protocol’s susceptibility to network covert channels. That is why in this paper we present a systematic overview of potential data hiding techniques that can be applied to MQTT 5.0. We are especially focusing on network covert channels that, in order to exchange secrets, exploit characteristic features of this MQTT version. Finally, we develop proof-of-concept implementations of the chosen data hiding techniques and conduct their performance evaluation in order to assess their feasibility in practical setups.