Reviewerly/exHarmony huggingface dataset:
Towards Pattern-aware Privacy-preserving Real-time Data Collection

Although time-series data collected from users can be utilized to provide services for various applications, they could reveal sensitive information about users. Recently, local differential privacy (LDP) has emerged as the state-of-art approach to protect data privacy by perturbing data locally before outsourcing. However, existing works based on LDP perturb each data point separately without considering the correlations between consecutive data points in time-series. Thus, the important patterns of each time-series might be distorted by existing LDP-based approaches, leading to severe degradation of data utility. In this paper, we focus on real-time data collection under a honest-but-curious server, and propose a novel pattern-aware privacy-preserving approach, called PatternLDP, to protect data privacy while the pattern of time-series can still be preserved. To this end, instead of providing the same level of privacy protection at each data point, each user only samples remarkable points in time-series and adaptively perturbs them according to their impacts on local patterns. In particular, we propose a pattern-aware sampling method based on Piecewise Linear Approximation (PLA) to determine whether to sample and perturb current data point. To reduce the utility loss caused by pattern change after perturbation, we propose an importance-aware randomization mechanism to adaptively perturb sampled data locally while achieving better trade-off between privacy and utility. A novel metric-based w-event privacy is introduced to measure the privacy protection degree for pattern-rich time-series. We prove that PatternLDP can provide the above privacy guarantee, and extensive experiments on real-world datasets demonstrate that PatternLDP outperforms existing mechanisms and can effectively preserve the important patterns.
Event driven Recommendation System for E-commerce using Knowledge based Collaborative Filtering Technique

The Internet is changing the method of selling and purchasing items. Nowadays online trading replaces offline trading. The items offered by the online system can influence the nature of buying customers. The recommendation system is one of the basic tools to provide such an environment. Several techniques are used to design and implement the recommendation system. Every recommendation system passes from two phases similarity computation among the users or items and correlation between target user and items. Collaborative filtering is a common technique used for designing such a system. The proposed system uses a knowledge base generated from knowledge graph to identify the domain knowledge of users, items, and relationships among these, knowledge graph is a labelled multidimensional directed graph that represents the relationship among the users and the items. Almost every existing recommendation system is based on one of feature, review, rating, and popularity of the items in which users’ involvement is very less or none. The proposed approach uses about 100 percent of users’ participation in the form of activities during navigation of the web site. Thus, the system expects under the users’ interest that is beneficial for both seller and buyer. The proposed system relates the category of items, not just specific items that may be interested in the users. We see the effectiveness of this approach in comparison with baseline methods in the area of recommendation system using three parameters precision, recall, and NDCG through online and offline evaluation studies with user data, and its performance is better than all other baseline systems in all aspects.
Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses

Designing a high-quality control is crucial for reliable quantum computation. Among the existing approaches, closed-loop leaning control is an effective choice. Its efficiency depends on the learning algorithm employed, thus deserving algorithmic comparisons for its practical applications. Here, we assess three representative learning algorithms, including GRadient Ascent Pulse Engineering (GRAPE), improved Nelder-Mead (NMplus) and Differential Evolution (DE), by searching for high-quality control pulses to prepare the Bell state. We first implement each algorithm experimentally in a nuclear magnetic resonance system and then conduct a numerical study considering the impact of some possible significant experimental uncertainties. The experiments report the successful preparation of the high-fidelity target state with different convergence speeds by the three algorithms, and these results coincide with the numerical simulations when potential uncertainties are negligible. However, under certain significant uncertainties, these algorithms possess distinct performance with respect to their resulting precision and efficiency. This study provides insight to aid in the practical application of different closed-loop learning algorithms in realistic physical scenarios.
A feature-supervised generative adversarial network for environmental monitoring during hazy days

The adverse haze weather condition has brought considerable difficulties in vision-based environmental applications. While, until now, most of the existing environmental monitoring studies are under ordinary conditions, and the studies of complex haze weather conditions have been ignored. Thence, this paper proposes a feature-supervised learning network based on generative adversarial networks (GAN) for environmental monitoring during hazy days. Its main idea is to train the model under the supervision of feature maps from the ground truth. Four key technical contributions are made in the paper. First, pairs of hazy and clean images are used as inputs to supervise the encoding process and obtain high-quality feature maps. Second, the basic GAN formulation is modified by introducing perception loss, style loss, and feature regularization loss to generate better results. Third, multi-scale images are applied as the input to enhance the performance of discriminator. Finally, a hazy remote sensing dataset is created for testing our dehazing method and environmental detection. Extensive experimental results show that the proposed method has achieved better performance than current state-of-the-art methods on both synthetic datasets and real-world remote sensing images.
Collaborative Optimization Scheduling of Cloud Service Resources Based on Improved Genetic Algorithm

With the wide application of cloud computing technology, the services provided by cloud systems have become increasingly diverse, thus these systems are required to solve tasks of high variety and complexity, with a tremendously extensive amount of task data involved.That is why reasonable scheduling system resources are particularly important in cloud computing research.In this paper, a cloud computing system needs to take into account a wider range of cloud service resource types and collaborative optimization scheduling issues in order to solve the tasks at hand.Firstly, a new adaptive genetic algorithm (NAGA) was proposed.By improving the crossover mutation genetic operator, the algorithm was able to save excellent individuals as much as possible, enhance the algorithm's optimization ability, and greatly reduce the probability of the algorithm falling into the local optimal solution.Secondly, focusing on the main factors affecting service quality, such as task completion time, system load, and network bandwidth, an upgraded fitness operator method for the cloud resource collaborative optimization scheduling problem is set forth.Finally, an algorithm of cloud service resources based on an improved genetic algorithm (OSIG) is proposed.Experiments on the CloudSim cloud computing simulation platform demonstrate that the OSIG algorithm proposed in this paper can effectively optimize the resource scheduling strategy, shorten the task completion time, facilitate the system load balancing, and boost the system's service quality.The theoretical analysis was consistent with the experimental results.
Non-perturbative effects in corrections to quantum master equations arising in Bogolubov–van Hove limit

We study the perturbative corrections to the { Gorini-Kossakowski-Sudarshan-Lindblad equation which arises in the weak coupling limit}. The spin-boson model in the rotating wave approximation at zero temperature is considered. We show that the perturbative part of the density matrix satisfies the time-independent Gorini-Kossakowski-Sudarshan-Lindblad equation for arbitrary order of the perturbation theory (if all the moments of the reservoir correlation function are finite). But to reproduce the right asymptotic precision at long times, one should use { an initial condition different} from the one for exact dynamics. Moreover, we show that the initial condition for this master equation even fails to be a density matrix under certain resonance conditions.
Topic Detection Based on Sentence Embeddings and Agglomerative Clustering with Markov Moment

The paper is dedicated to solving the problem of optimal text classification in the area of automated detection of typology of texts. In conventional approaches to topicality-based text classification (including topic modeling), the number of clusters is to be set up by the scholar, and the optimal number of clusters, as well as the quality of the model that designates proximity of texts to each other, remain unresolved questions. We propose a novel approach to the automated definition of the optimal number of clusters that also incorporates an assessment of word proximity of texts, combined with text encoding model that is based on the system of sentence embeddings. Our approach combines Universal Sentence Encoder (USE) data pre-processing, agglomerative hierarchical clustering by Ward’s method, and the Markov stopping moment for optimal clustering. The preferred number of clusters is determined based on the “e-2” hypothesis. We set up an experiment on two datasets of real-world labeled data: News20 and BBC. The proposed model is tested against more traditional text representation methods, like bag-of-words and word2vec, to show that it provides a much better-resulting quality than the baseline DBSCAN and OPTICS models with different encoding methods. We use three quality metrics to demonstrate that clustering quality does not drop when the number of clusters grows. Thus, we get close to the convergence of text clustering and text classification.
Multimodal Hand Gesture Classification for the Human–Car Interaction

The recent spread of low-cost and high-quality RGB-D and infrared sensors has supported the development of Natural User Interfaces (NUIs) in which the interaction is carried without the use of physical devices such as keyboards and mouse. In this paper, we propose a NUI based on dynamic hand gestures, acquired with RGB, depth and infrared sensors. The system is developed for the challenging automotive context, aiming at reducing the driver’s distraction during the driving activity. Specifically, the proposed framework is based on a multimodal combination of Convolutional Neural Networks whose input is represented by depth and infrared images, achieving a good level of light invariance, a key element in vision-based in-car systems. We test our system on a recent multimodal dataset collected in a realistic automotive setting, placing the sensors in an innovative point of view, i.e., in the tunnel console looking upwards. The dataset consists of a great amount of labelled frames containing 12 dynamic gestures performed by multiple subjects, making it suitable for deep learning-based approaches. In addition, we test the system on a different well-known public dataset, created for the interaction between the driver and the car. Experimental results on both datasets reveal the efficacy and the real-time performance of the proposed method.
A Secure and Lightweight Fine-Grained Data Sharing Scheme for Mobile Cloud Computing

With the explosion of various mobile devices and the tremendous advancement in cloud computing technology, mobile devices have been seamlessly integrated with the premium powerful cloud computing known as an innovation paradigm named Mobile Cloud Computing (MCC) to facilitate the mobile users in storing, computing and sharing their data with others. Meanwhile, Attribute Based Encryption (ABE) has been envisioned as one of the most promising cryptographic primitives for providing secure and flexible fine-grained "one to many" access control, particularly in large scale distributed system with unknown participators. However, most existing ABE schemes are not suitable for MCC because they involve expensive pairing operations which pose a formidable challenge for resource-constrained mobile devices, thus greatly delaying the widespread popularity of MCC. To this end, in this paper, we propose a secure and lightweight fine-grained data sharing scheme (SLFG-DSS) for a mobile cloud computing scenario to outsource the majority of time-consuming operations from the resource-constrained mobile devices to the resource-rich cloud servers. Different from the current schemes, our novel scheme can enjoy the following promising merits simultaneously: (1) Supporting verifiable outsourced decryption, i.e., the mobile user can ensure the validity of the transformed ciphertext returned from the cloud server; (2) resisting decryption key exposure, i.e., our proposed scheme can outsource decryption for intensive computing tasks during the decryption phase without revealing the user's data or decryption key; (3) achieving a CCA security level; thus, our novel scheme can be applied to the scenarios with higher security level requirement. The concrete security proof and performance analysis illustrate that our novel scheme is proven secure and suitable for the mobile cloud computing environment.
Group codes over fields are asymptotically good

Group codes are right or left ideals in a group algebra of a finite group over a finite field. Following the ideas of a paper on binary group codes by Bazzi and Mitter in 2006, we prove that group codes over finite fields of any characteristic are asymptotically good.
Blockchain-Based Internet of Vehicles Privacy Protection System

With the development of wireless local area networks and intelligent transportation technologies, the Internet of Vehicles is considered to be an effective method to alleviate the severe situation of the current transportation system. The vehicles in the Internet of Vehicles system build the Vehicular Ad Hoc Networks through wireless communication technology and dynamically provide different services through the real-time driving information broadcast by the vehicles. Vehicle drivers can control the distance, planning the driving route, between vehicles according to the current traffic environment, which improves the overall safety and efficiency of the traffic system. Due to the particularity of the Internet of Vehicles system service, vehicles need to broadcast their location information frequently. Attackers can collect and analyze vehicle broadcast information to steal privacy and even directionally track the owner through the driving trajectory, bringing serious security risks. This paper proposes a blockchain-based privacy protection system for the Internet of Vehicles. The system combines the blockchain with the Internet of Vehicles system to design a safe and efficient two-way authentication and key agreement algorithm through encryption and signature algorithm, which also solves the central dependency problem of the traditional Internet of Vehicles system.
Named Entity Recognition in Chinese Medical Literature Using Pretraining Models

The medical literature contains valuable knowledge, such as the clinical symptoms, diagnosis, and treatments of a particular disease. Named Entity Recognition (NER) is the initial step in extracting this knowledge from unstructured text and presenting it as a Knowledge Graph (KG). However, the previous approaches of NER have often suffered from small-scale human-labelled training data. Furthermore, extracting knowledge from Chinese medical literature is a more complex task because there is no segmentation between Chinese characters. Recently, the pretraining models, which obtain representations with the prior semantic knowledge on large-scale unlabelled corpora, have achieved state-of-the-art results for a wide variety of Natural Language Processing (NLP) tasks. However, the capabilities of pretraining models have not been fully exploited, and applications of other pretraining models except BERT in specific domains, such as NER in Chinese medical literature, are also of interest. In this paper, we enhance the performance of NER in Chinese medical literature using pretraining models. First, we propose a method of data augmentation by replacing the words in the training set with synonyms through the Mask Language Model (MLM), which is a pretraining task. Then, we consider NER as the downstream task of the pretraining model and transfer the prior semantic knowledge obtained during pretraining to it. Finally, we conduct experiments to compare the performances of six pretraining models (BERT, BERT-WWM, BERT-WWM-EXT, ERNIE, ERNIE-tiny, and RoBERTa) in recognizing named entities from Chinese medical literature. The effects of feature extraction and fine-tuning, as well as different downstream model structures, are also explored. Experimental results demonstrate that the method of data augmentation we proposed can obtain meaningful improvements in the performance of recognition. Besides, RoBERTa-CRF achieves the highest F 1-score compared with the previous methods and other pretraining models.
Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Image contrast enhancement is a very important phase for processing of digital images. The main goal of image contrast enhancement is to improve the visual quality by improving the contrast level of images which were distorted or degraded due to casual acquisition of images. The most popular method to perform this task is Histogram Equalization (HE). However, the exhaustive approach taken during HE is an algorithmically complex task. In this paper, we have considered image contrast enhancement as an optimization problem, where a new meta-heuristic algorithm, called Barnacles Mating Optimizer (BMO) is used to find the optimal solution for this optimization problem. A grey level mapping technique is used here to convert an image to a solution of the optimization problem. The algorithm has been evaluated on five publicly available datasets: Kodak, MIT-Adobe FiveK images, H-DIBCO 2016, and H-DIBCO 2018. It is also applied on some standard images like Boy, Lena, Lifting body and Zebra. The obtained results clearly display the effectiveness of the proposed method. The results obtained on the Kodak images are compared with many state-of-the-art methods present in the literature, and the comparison proves the superiority of the proposed method. To test the applicability of BMO in solving real world problems, we have applied it as a pre-processing step in binarization of H-DIBCO 2016 and H-DIBCO 2018 datasets. The source code of this work is available at https://github.com/ahmed-shameem/Projects.
Robust Hand Shape Features for Dynamic Hand Gesture Recognition Using Multi-Level Feature LSTM

This study builds robust hand shape features from the two modalities of depth and skeletal data for the dynamic hand gesture recognition problem. For the hand skeleton shape approach, we use the movement, the rotations of the hand joints with respect to their neighbors, and the skeletal point-cloud to learn the 3D geometric transformation. For the hand depth shape approach, we use the feature representation from the hand component segmentation model. Finally, we propose a multi-level feature LSTM with Conv1D, the Conv2D pyramid, and the LSTM block to deal with the diversity of hand features. Therefore, we propose a novel method by exploiting robust skeletal point-cloud features from skeletal data, as well as depth shape features from the hand component segmentation model in order for the multi-level feature LSTM model to benefit from both. Our proposed method achieves the best result on the Dynamic Hand Gesture Recognition (DHG) dataset with 14 and 28 classes for both depth and skeletal data with accuracies of 96.07% and 94.40%, respectively.
Internet of Things, cybersecurity and governing wicked problems: learning from climate change governance

The implementation of the Internet of Things (IoT) is central to what the World Economic Forum has coined the ‘Fourth Industrial Revolution’; a technological revolution built upon cyber-physical systems that will blur the lines between the physical, digital and biological spheres. Novel interconnections will emerge as a result, challenging traditional relations and modes of governance. However, a central feature of the IoT is that the implications of cyber (in)security are no longer abstract. The IoT also returns us to the world of kinetic effects in international relations; more familiar territory for IR. The resulting cooperation and coordination challenges are transboundary in nature, occur at multiple levels across sectors, between institutions, and will impact all actors, both public and private, in complex, often highly politicised ways. In this article we argue that advances in global climate governance appear to be offering an early model of a consensual rules-based approach within the existing international order that provides space for advancing agility, flexibility, and polycentrism to meet the demands of ‘wicked problems’ like the cybersecurity of the IoT. Perhaps one of the most important lessons to be drawn across from climate governance is the role of robust mechanisms for knowledge exchange – specifically between the technical and policy communities.
A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Financial statement fraud has been on the increase in the past two decades and includes prominent scandals such as Enron, WorldCom and more recently in South Africa, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different stock exchanges across the world. During this time, there has been an increase in the literature on applying automated methods to detecting financial statement fraud using publicly available data. This paper provides a survey of the literature on automated financial statement fraud detection and identifies current gaps in the literature. The paper highlights a number of important considerations in the implementation of financial statement fraud detection decision support systems, including 1) the definition of fraud, 2) features used for detecting fraud, 3) region of the case study, dataset size and imbalance, 4) algorithms used for detection, 5) approach to feature selection / feature engineering, 6) treatment of missing data, and 7) performance measure used. The current study discusses how these and other implementation factors could be approached within the South African context.
Training Simulators in Vocational Education: Pedagogical and Technological Aspects

This article provides a review of literature on using training simulators in vocational education and training and explores the vocational pedagogy approaches to describing training simulators as a tool for vocational skill development. We examine simulators applied in medical, engineering and teacher training education, present a general taxonomy of practice-oriented training models, and analyze the role of simulators as specific learning media. This review can be useful for developing and implementing initiatives within the Human Resources and Education component of the Digital Economy National Program (in particular, integrating simulators in vocational and higher education).