Reviewerly/exHarmony huggingface dataset:
Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection

Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithm (QAOA) constitutes a promising candidate for demonstrating tangible quantum advantages based on NISQ devices. In this paper, we consider the maximum likelihood (ML) detection problem of binary symbols transmitted over a multiple-input and multiple-output (MIMO) channel, where finding the optimal solution is exponentially hard using classical computers. Here, we apply the QAOA for the ML detection by encoding the problem of interest into a level- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> QAOA circuit having <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2p$ </tex-math></inline-formula> variational parameters, which can be optimized by classical optimizers. This level- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> QAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> consecutive rounds. More explicitly, we first encode the optimal solution of the ML detection problem into the ground state of a problem Hamiltonian. Using the quantum adiabatic evolution technique, we provide both analytical and numerical results for characterizing the evolution of the eigenvalues of the quantum system used for ML detection. Then, for level-1 QAOA circuits, we derive the analytical expressions of the expectation values of the QAOA and discuss the complexity of the QAOA based ML detector. Explicitly, we evaluate the computational complexity of the classical optimizer used and the storage requirement of simulating the QAOA. Finally, we evaluate the bit error rate (BER) of the QAOA based ML detector and compare it both to the classical ML detector and to the classical minimum mean squared error (MMSE) detector, demonstrating that the QAOA based ML detector is capable of approaching the performance of the classical ML detector.
Analysis of Machine Learning and Deep Learning Frameworks for Opinion Mining on Drug Reviews

Abstract Opinion mining from medical forums such as health check-ups is sparking growing interest and a stimulating area for natural language processing. This allows for a better understanding of patient health status and drug reactions while generating new knowledge for health care professionals and drug manufacturers, which helps improve the quality of service and produce more effective treatments. In this paper, the researchers present a framework of opinions classification of drug reviews. The objective of this work is to find the best model for analyzing patients’ emotions about drugs. In this sense, the researchers oppose classical text vectorization methods (bag of words, term frequency-inverse document frequency) and word embedding methods (Word2vec, GloVe) for classical opinion mining face to modern machine learning tools with the Convolutional Neural Network (CNN), the Recurrent Neural Networks (Long Short-term Memory and Bidirectional Long Short-Term Memory). Experiments results show that the best model for drug reviews was achieved by CNN based on the Skip-gram model (85% accuracy). Experiments have led to conclude that the performance of a given model will depend on the type of dataset used, on feature representation and better collaboration between classifiers and feature extraction methods.
Exploring stewardship: A precursor to voluntary security behaviors

The security of organizations' information resources is often threatened by employee non-compliance to security policy or negligence. Though technical and procedural controls for curtailing security violations and motivating secure behaviors have been explored in the literature, security violations persist. Given the significant influence individuals wield on the welfare of organizations' security and the dynamic nature of security threats, we explore voluntary security behaviors through the mechanism of stewardship. Drawing from the stewardship and information security literature, we develop and test a stewardship model of voluntary security behaviors using a sample of 409 working individuals. The results show that organizational support and identification influence stewardship, which in turn affects voluntary security behaviors. Our findings have several theoretical and practical implications for increasing stewardship and voluntary security behaviors in the organization.
Defining the Non-user: A Classification of Reasons for Non-use

With human-centered design becoming more widely implemented within various industries, the user’s needs are being taken into account more than ever. The user of a product or service has been very well defined over the last decades. What is often overlooked is the user’s counterpart: the non-user. Integrating non-users into modern development projects provides great additional value, since addressing their needs can turn a previously inactive group into new users. First, current definitions of non-users need to be compiled in order to analyze and evaluate them within the context of product development. A contribution is to be made toward creating a comprehensive definition of the non-user that can be applied to product development. Current literature mixes definitions of non-use with reasons for non-use. An analytical differentiation between the two needs to be made in order to comprehensively define the non-user and reasons for non-use. On the basis of the “non-user map” (Augustin et al. in Proceedings of the Design Society: DESIGN Conference 1:1853–1862, 2020 []), which classifies different types of non-users based on frequency of use and degree of self-determination, this paper seeks to define and classify the different reasons for non-use. These reasons can be divided into interconnected groups that show the relationships in between each category. With non-users and their reasons for non-use comprehensively defined, proposals for their integration into product development can be made. With a new concept of the widely used design tool “persona” (Cooper, The inmates are running the Asylum. why high-tech products drive us crazy and how to restore the sanity, Sams, 2004 []), the “non-persona,” now the non-user of a product or service can be included in the development process as well as the user in order to give a more comprehensive understanding of possible requirements for a product or service.
TCD-CF: Triple cross-domain collaborative filtering recommendation

Recently, data sparsity is still one of the critical problems faced by recommendation systems. Although many existing methods based on cross-domain can alleviate it to a certain extent, these methods only use the information of single-domain (e.g., user-side, item-side and rating-side) or dual-domain (e.g., user-rating-side, user-item-side and item-rating-side) to make recommendations, which results in performance degradation. In this paper, we propose a triple cross-domain collaborative filtering method to alleviate data sparsity, named TCD-CF. In TCD-CF method, the triple-side intrinsic characteristics are first obtained by using the joint nonnegative matrix factorization to integrate the user-side, item-side and rating-side domain knowledge. Then the extended codebook (as knowledge to transfer) based on these intrinsic characteristics is constructed by using the orthogonal nonnegative matrix tri-factorization. Finally, the codebook-based transfer method for cross-system CF is applied into the source domain and target domain to predict the missing ratings and perform recommendation in the target domain. Extensive experiments on two real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods for the cross-domain recommendation task.
Communication channels for exchanging agricultural information among Tanzanian farmers: A meta-analysis

This study investigates how communication channels to exchange agricultural information were chosen. Specifically, it identifies the communication channels used by farmers in Tanzania and determines the factors influencing the choice of communication channels for exchanging agricultural information. The study employs a meta-analysis review methodology in identifying, evaluating and interpreting studies relevant to the topic of interest. The results indicate that radio, mobile phones, television, fellow farmers, agricultural extension agents and newspapers were the commonly used communication channels for transferring agricultural information. Moreover, the channels’ influence, availability, affordability, communication network coverage, and the resources and facilities needed to use a particular communication channel were found to influence the choice of channels. It is concluded that understanding the audience, the characteristics surrounding messages and choosing appropriate communication channels are important for enhancing access to agricultural information. It is recommended that agricultural information providers should understand the factors surrounding communication channels before disseminating agricultural information.
Quantum-Driven Energy-Efficiency Optimization for Next-Generation Communications Systems

The advent of deep-learning technology promises major leaps forward in addressing the ever-enduring problems of wireless resource control and optimization, and improving key network performances, such as energy efficiency, spectral efficiency, transmission latency, etc. Therefore, a common understanding for quantum deep-learning algorithms is that they exploit advantages of quantum hardware, enabling massive optimization speed ups, which cannot be achieved by using classical computer hardware. In this respect, this paper investigates the possibility of resolving the energy efficiency problem in wireless communications by developing a quantum neural network (QNN) algorithm of deep-learning that can be tested on a classical computer setting by using any popular numerical simulation tool, such as Python. The computed results show that our QNN algorithm can be indeed trainable and that it can lead to solution convergence during the training phase. We also show that the proposed QNN algorithm exhibits slightly faster convergence speed than its classical ANN counterpart, which was considered in our previous work. Finally, we conclude that our solution can accurately resolve the energy efficiency problem and that it can be extended to optimize other communications problems, such as the global optimal power control problem, with promising trainability and generalization ability.
Punjabi Children Speech Recognition System Under Mismatch Conditions Using Discriminative Techniques

It is a very difficult challenge to recognize children's speech on Automatic Speech Recognition (ASR) systems built using adult speech. In such ASR tasks, a significant deteriorated recognition efficiency is observed, as noted by several earlier studies. It is primarily related to the significant inconsistency between the two groups of speakers in the auditory and linguistic attributes. One of the numerous causes of conflict found is that the adult and child speaker vocal organs are of substantially different dimensions. Discriminatory approaches are noted for dealing extensively with the effects emerging from these differences. Specific parameter variations have been introduced with boosted parameters and iteration values to achieve the optimum value of the acoustic models boosted maximum mutual information (bMMI) and feature-space bMMI (fbMMI). Experimental results demonstrate that the feature space discriminative approaches have achieved a significant reduction in the Word Error Rate (WER). This is also shown that fbMMI achieves better performance than the bMMI and fMMI. Recognition of children and the elderly will need even more studies if we are to examine these age groups features in existing and future speech recognition systems.
iDEV: exploring and exploiting semantic deviations in ARM instruction processing

ARM has become the most competitive processor architecture. Many platforms or tools are developed to execute or analyze ARM instructions, including various commercial CPUs, emulators, and binary analysis tools. However, they have deviations when processing the same ARM instructions, and little attention has been paid to systematically analyze such semantic deviations, not to mention the security implications of such deviations. In this paper, we conduct an empirical study on the ARM Instruction Semantic Deviation (ISDev) issue. First, we classify this issue into several categories and analyze the security implications behind them. Then, we further demonstrate several novel attacks which utilize the ISDev issue, including stealthy targeted attacks and targeted defense evasion. Such attacks could exploit the semantic deviations to generate malware that is specific to certain platforms or able to detect and bypass certain detection solutions. We have developed a framework iDEV to systematically explore the ISDev issue in existing ARM instructions processing tools and platforms via differential testing. We have evaluated iDEV on four hardware devices, the QEMU emulator, and five disassemblers which could process the ARMv7-A instruction set. The evaluation results show that, over six million instructions could cause dynamic executors (i.e., CPUs and QEMU) to present different runtime behaviors, and over eight million instructions could cause static disassemblers yielding different decoding results, and over one million instructions cause inconsistency between dynamic executors and static disassemblers. After analyzing the root causes of each type of deviation, we point out they are mostly due to ARM unpredictable instructions and program defects.
Efficient and Privacy-Preserving Contact Tracing System for Covid-19 using Blockchain

COVID-19 has been endangering people’s lives and causing a huge burden on the healthcare sector. To limit the fast spread of the virus, contact tracing systems are currently adopted worldwide to identify the close contacts of positive cases during the incubation period of the virus and notify them to quarantine. However, the existing systems are vulnerable to security and privacy attacks and suffer from large communication and computation overheads. To address these limitations, in this paper, we propose an efficient and privacy-preserving contact tracing system based on a consortium Blockchain. Instead of depending on one entity to run the system, our system is run in a decentralized fashion by multiple health authorities that construct the Blockchain network. The utilization of Blockchain secures our system against the problems of centralized architecture. Besides, our system is designed to thwart the false reporting (panic) attack, while preserving the privacy of the users against identification and social graph disclosure attacks. The performance evaluation of our system demonstrates its efficiency in terms of communication and computation.
Robust discriminative feature subspace analysis for kinship verification

Kinship verification using single image of a person is a challenging task for real-world applications. In this paper, we propose a novel robust discriminative feature subspace analysis (RDFSA) method to address single sample per person (SSPP) problem in kinship verification. The proposed RDFSA method takes advantages of facial symmetry and patch-based analysis to extract discriminative features for kinship verification. Each face image is firstly divided into two halves about bilateral symmetry axis, and each halved face is then partitioned into equal sized non-overlapping patches. Multiple image-sets are formed by grouping these patches according to their positions at each halved face. Then, an SSPP is formulated as an RDFSA problem and a feature subspace is learned by maximizing inter-class separation and minimizing intra-class variance for different patches in each image-set. For a given test image pair, similarity is computed for each feature subspace and majority voting strategy is employed to determine if a given image pair is kin related or not. Proposed RDFSA method is extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results show that RDFSA achieves competitive accuracy on all kinship datasets while performing kinship verification under unconstrained environment.
Automated parameter choice with exploratory landscape analysis and machine learning

The performance of an evolutionary algorithm on a particular optimization problem highly depends on the choice of the algorithm's parameters. Fitness landscape analysis can help learn important characteristics of the optimization problem that may influence the optimal parameter values. Using this knowledge for parameter choice can be an important step in designing fitness-aware algorithms.
Towards the reversal of poverty and income inequality setbacks due to COVID-19: the role of globalisation and resource allocation

This study contributes to the policy discourse aimed at addressing the welfare setbacks imposed implicitly by the coronavirus pandemic. To this end, we draw macrodata for the period 1990 – 2019 to examine whether globalisation and resource allocation matter for reducing poverty and income inequality in the Middle East and North Africa (MENA). Robust evidence from the GMM estimator shows that: (1) economic globalisation reduces both poverty and income inequality, (2) though social globalisation heightens income inequality in the region, it has no significant effect on poverty; (3) compared to the effect on poverty, economic globalisation is remarkable in reducing income inequality in the presence of efficient resource allocation. Policy recommendations are provided in line with Agenda 2030 and the rise in social globalisation of the region.
3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.
Fraudulent traffic detection in online advertising with bipartite graph propagation algorithm

Online advertising fraud has brought huge economic losses to global advertisers. We propose and compare a set of graph models to detect fraudulent Internet Protocol addresses (IPs) in online advertising. We use the classic label propagation algorithm (LPA) on the IP-campaign bipartite graph as our baseline. Based on two behavioral patterns of fraudulent IPs, the power-law distribution and behavioral similarity, we propose two new methods to calculate IP fraud scores and three new methods to calculate campaign fraud scores. Their combinations produce six variations of LPA. Finally, we integrate the six variations with the random forest algorithm. Results show that all six variations outperform the basic bipartite graph model. The integrated random forest algorithm outperforms the basic bipartite graph model in precision, ROC, F1 score, and kappa by 17.9%, 15.0%, 17.9%, and 23.1%, respectively.
Universal 3-Dimensional Perturbations for Black-Box Attacks on Video Recognition Systems

Widely deployed deep neural network (DNN) models have been proven to be vulnerable to adversarial perturbations in many applications (e.g., image, audio and text classifications). To date, there are only a few adversarial perturbations proposed to deviate the DNN models in video recognition systems by simply injecting 2D perturbations into video frames. However, such attacks may overly perturb the videos without learning the spatio-temporal features (across temporal frames), which are commonly extracted by DNN models for video recognition. To our best knowledge, we propose the first black-box attack framework that generates universal 3-dimensional (U3D) perturbations to subvert a variety of video recognition systems. U3D has many advantages, such as (1) as the transfer-based attack, U3D can universally attack multiple DNN models for video recognition without accessing to the target DNN model; (2) the high transferability of U3D makes such universal black-box attack easy-to-launch, which can be further enhanced by integrating queries over the target model when necessary; (3) U3D ensures human-imperceptibility; (4) U3D can bypass the existing state-of-the-art defense schemes; (5) U3D can be efficiently generated with a few pre-learned parameters, and then immediately injected to attack real-time DNN-based video recognition systems. We have conducted extensive experiments to evaluate U3D on multiple DNN models and three large-scale video datasets. The experimental results demonstrate its superiority and practicality.
Efficient Object Embedding for Spliced Image Retrieval

Detecting spliced images is one of the emerging challenges in computer vision. Unlike prior methods that focus on detecting low-level artifacts generated during the manipulation process, we use an image retrieval approach to tackle this problem. When given a spliced query image, our goal is to retrieve the original image from a database of authentic images. To achieve this goal, we propose representing an image by its constituent objects based on the intuition that the finest granularity of manipulations is oftentimes at the object-level. We introduce a framework, object embeddings for spliced image retrieval (OE-SIR), that utilizes modern object detectors to localize object regions. Each region is then embedded and collectively used to represent the image. Further, we propose a student-teacher training paradigm for learning discriminative embeddings within object regions to avoid expensive multiple forward passes. Detailed analysis of the efficacy of different feature embedding models is also provided in this study. Extensive experimental results show that the OE-SIR achieves state-of-the-art performance in spliced image retrieval.