Reviewerly/exHarmony huggingface dataset:
Quantum Hypercomputing and Communications: Overview and Future Applications

Today, like never before, we are witnessing a pervasive diffusion of ultra-broadband fixed-mobile connectivity, the deployment of 5G infrastructures and its integration with Cloud-Edge Computing. Moreover, Artificial Intelligence (AI) is at the center of this Digital Transformation. Sustainability of future scenarios will have to face several techno-economic challenges: the transmission and processing of enormous quantity of data with ultra-low latencies, automation of management processes, strict requirements of resilience, security and privacy, optimization of resources and energy, new markets dynamics. This is requiring a rethinking of the ways of doing computation and communications. As a matter of fact, a second revolution is underway with a growing interest - and several investments from Industries worldwide – and expected potential exploitations for meeting said evolutionary requirements and the sustainability. This paper provides a description of an innovative approach based on evanescent, electromagnetic waves and tunneling photons, for developing quantum computing systems, so called Quantum Hypercomputing. Some examples of potential applications are also provided.
Oppositional Glowworm Swarm based Vector Quantization Technique for Image Compression in Fiber Optic Communication

In recent times, fiber optic communication networks have become commonly applied for commercial as well as military applications. Fiber optic networks have gained popularity owing to the high data rate. At the same time, the generation of huge quantity of data at a faster rate poses a major challenge in the storing and transmission process. To resolve this issue, data compression approaches have been presented to reduce the quantity of transmitted data and thereby minimizes bandwidth utilization and memory. Vector quantization (VQ) is a commonly employed image compression technique and Linde Buzo Gray (LBG) is used to construct an optimum codebook to compress images. With this motivation, this paper presents a new oppositional glowworm swarm optimization based LBG (OGSO-LBG) technique for image compression in fiber optic communication. The OGSO algorithm involves the integration of oppositional based learning (OBL) concept into the GSO algorithm to boost its convergence rate. The OGSO-LBG algorithm produces the codebook at a faster rate with minimal computation complexity. In order to highlight the enhanced compression performance of the OGSO-LBG technique, a series of experiments were carried out and the results are examined under different dimensions.
Privacy Accounting and Quality Control in the Sage Differentially Private ML Platform

We present Sage, the first ML platform that enforces a global differential privacy (DP) guarantee across all models produced from a sensitive data stream. Sage extends the Tensorflow-Extended ML platform with novel mechanisms and DP theory to address operational challenges that arise from incorporating DP into ML training processes. First, to avoid the typical problem with DP systems of "running out of privacy budget" after a pre-established number of training processes, we develop block composition. It is a new DP composition theory that leverages the time-bounded structure of training processes to keep training models endlessly on a sensitive data stream while enforcing event-level DP on the stream. Second, to control the quality of ML models produced by Sage, we develop a novel iterative training process that trains a model on increasing amounts of data from a stream until, with high probability, the model meets developer-configured quality criteria.
Methodology of stakeholders’ behaviour modelling based on time

The methods presented in this article were created to model and describe the behaviour of the web users of a bank institution web portal. The source dataset is represented by a log file of the commercial bank web server. The analysis is oriented on examining the behaviour of visitors over an extended period (2009-2012). The years 2009-2010 represent the years of the financial crisis, and the years 2011-2012 represent the years after the financial crisis. The following method describes the sequence of steps necessary to pre-process the raw log file and model the web user behaviour using the multinomial logit model. The introduced methods can be used also for other domains in the case of appropriate data preparation.•Data preparation- data cleaning, user/session identification, path completion, variables determination;•Data analysis- model definition, parameters estimation, logits estimation, probabilities estimation;•Results evaluation- comparison of empirical and theoretical values in term of counts, probabilities and logits.
Moving Target Defense against Adversarial Machine Learning

As Machine Learning (ML) models are increasingly employed in a number of applications across a multitude of fields, the threat of adversarial attacks against ML models is also increasing. Adversarial samples crafted via specialized attack algorithms have been shown to significantly decrease the performance of ML models. Furthermore, it has also been found that adversarial samples generated for a particular model can transfer across other models, and decrease accuracy and other performance metrics for a model they were not originally crafted for. In recent research, many different defense approaches have been proposed for making ML models robust, ranging from adversarial input re-training to defensive distillation, among others. While these approaches operate at the model-level, we propose an alternate approach to defending ML models against adversarial attacks, using Moving Target Defense (MTD). We formulate the problem and provide preliminary results to showcase the validity of the proposed approach.
Security Issues of IoT in Healthcare Sector: A Systematic Review

Internet of Things (IoT) has become a new trend in the healthcare system which provides a high diversity of efficacious services for healthcare professionals as well as patients’ treatments for various diseases. In IoT healthcare system application design consists of devices, network, layered model, and Communication Application/Protocols. Mostly in studies, in fact, IoT provides several benefits, and it also facing different security challenges in their patient record hierarchy. Due to the fact of private and sympathetic data of patients, required complete security and privacy are a key issue in the formation of IoT-based systems in the healthcare sector. The aim of this study is to investigate the security-related issues of IoT in the health sector. In this study, we compiled searched articles that are published in well-reputed journals from 2015 to 2021 for constructing the summary of related challenges. We construct a table that analyzes the security-related issues of IoT in the healthcare sector. Moreover, an IoT healthcare architecture has been proposed that represents the effective patient discovery solution in the field of health care. Our study sketches the open and recent security issues of IoT in the healthcare sector.
Self-Supervised Video Super-Resolution by Spatial Constraint and Temporal Fusion

To avoid any fallacious assumption on the degeneration procedure in preparing training data, some self-similarity based super-resolution (SR) algorithms have been proposed to exploit the internal recurrence of patches without relying on external datasets. However, the network architectures of those “zero-shot” SR methods are often shallow. Otherwise they would suffer from the over-fitting problem due to the limited samples within a single image. This restricts the strong power of deep neural networks (DNNs). To relieve this problem, we propose a middle-layer feature loss to allow the network architecture to be deeper for handling the video super-resolution (VSR) task in a self-supervised way. Specifically, we constrain the middle-layer feature of VSR network to be as similar as that of the corresponding single image super-resolution (SISR) in a Spatial Module, then fuse the inter-frame information in a Temporal Fusion Module. Experimental results demonstrate that the proposed algorithm achieves significantly superior results on real-world data in comparison with some state-of-the-art methods.
Blind High-Order Modulation Recognition for Beyond 5G OSTBC-OFDM Systems via Projected Constellation Vector Learning Network

The blind modulation recognition (BMR) of high-order modulation types is a pressing task and needs to be raised in the calendar for the Beyond 5G (B5G) OSTBC-OFDM (Orthogonal Space-Time Block Coded-Orthogonal Frequency Division Multiplexing) system. In this letter, a BMR algorithm based on a project constellation vector which employs a temporal convolutional network (PCV-TCNet) is proposed to recognize 13 modulation formats, such as high-order 1024QAM and 2048QAM. Without any prior information, a zero-forcing blind equalization algorithm is leveraged to reconstruct the impaired signal. Furthermore, the learning content of PCV-TCNet is PCV features, which are transformed by the constellation diagram of the reconstructed signal. In addition, PCV-TCNet utilizes causal and dilated convolutions to accelerate the BMR process. The simulation results verify the proposed PCV-TCNet for recognizing the high-order modulation types in the B5G OSTBC-OFDM system and demonstrate its preferable recognition performance with the lowest complexity to existing methods.
On strong consistency of kernel k-means: A Rademacher complexity approach

We provide uniform concentration bounds on the kernel k-means clustering objective based on Rademacher complexity by posing the underlying problem as a risk minimization task. This approach results in state-of-the-art convergence rates on the excess risk besides the eventual establishment of strong consistency of cluster centers.
A Novel CTR Prediction Based Model Using xDeepFM Network

Nowadays, increasing people choose to buy items online and Taobao is the largest online shopping platform. The order and the content of ads on Taobao affects the platform's revenue and the consumers' experience. Therefore, CTR(click through rate) prediction is a useful tool for enterprises to get the customers' preferences With the development of technology, there are many machine learning algorithms proposed to predict CTR, such as generalized linear model, factorization machines and deep neural network. In our paper, we utilize the xDeepFM model, which is the combination of Compressed interfere network (CIN)and Deep neural network (DNN). In experimental periods, the AUC is the metric to evaluate the prediction model's performance. xDeepFM model outperforms SVM model and neural network model by 0.017 and 0.009 respectively, which means our xDeepFM model is effective for CTR forecast.
Performance Analysis of Hybrid Architectures of Deep Learning for Indonesian Sentiment Analysis

Sentiment analysis is one of the fields of Natural Language Processing that builds a system to recognize and extract opinions in the form of text into positive or negative sentiment. Nowadays, many researchers have developed methods that yield the best accuracy in performing analysis sentiment. Three particular models are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), which have deep learning architectures. CNN is used because of its ability to extract essential features from each sentence fragment, while LSTM and GRU are used because of their ability to memorize prior inputs. GRU has a more straightforward and more practical structure compared to LSTM. These models have been combined into hybrid architectures of LSTM-CNN, CNN-LSTM, and CNN-GRU. In this paper, we analyze the performance of the hybrid architectures for Indonesian sentiment analysis in e-commerce reviews. Besides all three combined models mentioned above, we consider one more combined model, which is GRU-CNN. We evaluate the performance of each model, then compare the accuracy of the standard models with the combined models to see if the combined models can improve the performance of the standard. Our simulations show that almost all of the hybrid architectures give better accuracies than the standard models. Moreover, the hybrid architecture of LSTM-CNN reaches slightly better accuracies than other hybrid architectures.
Multi-User Distillation of Common Randomness and Entanglement From Quantum States

We construct new protocols for the tasks of converting noisy multipartite quantum correlations into noiseless classical and quantum ones using local operations and classical communication (LOCC). The former task is known as common randomness (CR) distillation, and it requires offsetting the amount of classical communication against the randomness created. We obtain a new lower bound on the “distillable common randomness,” an operational measure of the total genuine (classical) correlations in a quantum state. Our proof relies on a generalization of communication for omniscience (CO) [Csiszár and Narayan, IEEE Trans. Inf. Theory 50:3047-3061, 2004], and our contribution here is a novel simultaneous decoder for the compression of correlated classical sources by random binning with quantum side information at the decoder. For the latter task, we derive two lower bounds on the rate at which Greenberger-Horne-Zeilinger (GHZ) states can be asymptotically distilled from any given pure state under LOCC. Our approach consists in “making coherent” the proposed CR distillation protocols and recycling of resources [Devetak <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> IEEE Trans. Inf. Theory 54(10):4587-4618, 2008]. The first lower bound is identical to a recent result by Vrana and Christandl [IEEE Trans. Inf. Theory 65(9):5945-5958, 2019], which is based on a combinatorial method to achieve the same rate. Our second lower bound generalises and improves upon this result, and unifies a number of other known lower bounds on GHZ distillation.
Research on transformer fault diagnosis based on sparrow algorithm optimization probabilistic neural network

research-article Research on transformer fault diagnosis based on sparrow algorithm optimization probabilistic neural network Share on Authors: Mingxia Chen Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Hanyu Shi Guilin University of Technology, China Guilin University of Technology, ChinaView Profile , Junjie Wu Guilin University of Technology, China Guilin University of Technology, ChinaView Profile Authors Info & Claims ICIIP 2021: 2021 6th International Conference on Intelligent Information ProcessingJuly 2021 Pages 254–259https://doi.org/10.1145/3480571.3480616Online:29 October 2021Publication History 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Multi-agent Epistemic Planning with Inconsistent Beliefs, Trust and Lies

Developing autonomous agents that can reason about the perspective of their (human or artificial) peers is paramount to realistically model a variety of real-world domains. Being aware of the state of mind of others is a key aspect in different fields—e.g., legal reasoning, business negotiations, ethical AI and explainable AI. In particular, in the area of Multi-Agent Epistemic Planning (MEP), agents must reach their goals by taking into account the knowledge and beliefs of other agents. Although the literature offers an ample spectrum of approaches for planning in this scenario, they often come with limitations. This paper expands previous formalization of MEP to enable representing and reasoning in presence of inconsistent beliefs of agents, trust relations and lies. The paper explores the syntax and semantics of the extended MEP framework, along with an implementation of the framework in the solver Epistemic Forward Planner (EFP). The paper reports formal properties about the newly introduced epistemic states update that have been also empirically tested via an actual implementation of the solver.
Dynamical Conventional Neural Network Channel Pruning by Genetic Wavelet Channel Search for Image Classification

Neural network pruning is critical to alleviating the high computational cost of deep neural networks on resource-limited devices. Conventional network pruning methods compress the network based on the hand-crafted rules with a pre-defined pruning ratio (PR), which fails to consider the variety of channels among different layers, thus, resulting in a sub-optimal pruned model. To alleviate this issue, this study proposes a genetic wavelet channel search (GWCS) based pruning framework, where the pruning process is modeled as a multi-stage genetic optimization procedure. Its main ideas are 2-fold: (1) it encodes all the channels of the pertained network and divide them into multiple searching spaces according to the different functional convolutional layers from concrete to abstract. (2) it develops a wavelet channel aggregation based fitness function to explore the most representative and discriminative channels at each layer and prune the network dynamically. In the experiments, the proposed GWCS is evaluated on CIFAR-10, CIFAR-100, and ImageNet datasets with two kinds of popular deep convolutional neural networks (CNNs) (ResNet and VGGNet). The results demonstrate that GNAS outperforms state-of-the-art pruning algorithms in both accuracy and compression rate. Notably, GNAS reduces more than 73.1% FLOPs by pruning ResNet-32 with even 0.79% accuracy improvement on CIFAR-100.
Where is the Light(ning) in the Taproot Dawn? Unveiling the Bitcoin Lightning (IP) Network

Proposed in 2016 and launched in 2018, the Bitcoin (BTC) Lightning Network (LN) can scale-up the capacity of the BTC blockchain network to process a significantly higher amount of transactions, in a faster, cheaper, and more privacy preserving manner. The number of LN nodes has been significantly increasing since 2018, and today there are more than twelve thousand nodes actively participating of so-called LN payment channels. The upcoming Taproot upgrade to the Bitcoin protocol would further boost the development and adoption of the LN. Taproot is the most significant upgrade to the Bitcoin network since the block size increase of 2017, and it will make LN transactions cheaper, more flexible, and more private. We focus on the characterization of the LN network topology, using network active measurements. By crawling the underlying P2P network supporting the Bitcoin LN over a span of 10-months, we unveil the LN in terms of size and location of its nodes as well as connectivity protocols, comparing it to the P2P IP network supporting the BTC blockchain. Among our findings, we show that IP addresses exposed by LN nodes correspond mainly to customer networks, even if most BTC nodes are actually deployed at major cloud providers, and that LN nodes significantly rely on anonymized networks and protocols such as Onion, with more than 40% of LN nodes connect through Tor.
Institutional Challenges of Economic Reforms: Tunisia during Democratic Transition

This paper focuses on the challenges of economic reform in post-revolution Tunisia. It underlines the importance of a mixed approach between political economy (positive) and normative economic analysis to implement an effective public policy reform agenda. The post-revolution situation underlines the need for a progressive consensual reform strategy to preserve the interests of different groups and overcome political and legislative polarization. Building an effective reform system in Tunisia will mean improving institutional public administration capacities, enhancing government leadership, and building an effective system of coordination, evaluation, and follow up.
BSO-CMA-ES: Brain Storm Optimization Based Covariance Matrix Adaptation Evolution Strategy for Multimodal Optimization

Recently, covariance matrix adaption evolution strategy (CMA-ES) and its variants have achieved great success in the continuous unimodal optimization tasks owing to its strong local search capabilities. However, it is precisely this capability that reduces the population diversity, which makes it unable to obtain the good performance on the multimodal optimization problems (MMOPs) aiming at locating all global optimal solutions during a single algorithm run. To address this problem, we first propose a swarm learning framework which is capable of collaboratively training multiple optimization algorithms (e.g., CMA-ES in this paper). Specifically, it introduces two objectives including individual objective and neighbor objective to balance the exploitation and exploration. The former guides each algorithm to locate at least one global optimal solution (exploitation), and the latter aims at maintaining the diversity of the different algorithms (exploration). Based on this framework, the brain storm optimization (BSO) is incorporated with multiple CMA-ES models, called BSO-CMA-ES, which makes the multiple CMA-ESs be collaboratively trained. To validate the effectiveness of the proposed method, several comparison algorithms are adopted and tested on typical MMOPs benchmark functions. Experimental results show that BSO-CMA-ES could obtain promising performance.
Robust Feature Extraction Using Embedded Gan in Image Retrieval Systems for SEMI-Supervised Data

A blend of unlabeled data (unsupervised) in large volume, along with a small number of labeled data (supervised), makes a semi-supervised dataset. Content-Based Image Retrieval (CBIR) systems are used to label/model such data. Based on the features extracted, the unsupervised query images get labeled upon the similarity measures with the reference labeled images (one image per class). Due to the insufficient availability of trained data, modeling a CNN to act as a feature extractor will be inefficient. Hence, pre-trained models, trained upon public databases (ImageNet) and have never been fine-tuned on the target dataset, will be used as a feature extractor, which lowers the confidence over the labeling task. The proposed method will put together a Generative Adversarial Network (GAN), with the chosen pre-trained model embedded inside the GAN as a discriminator, that will fine-tune the pre-trained model's weights in accordance with the unlabeled target data. These fine-tuned pre-trained models can then be used to extract features and will help to build a robust CBIR system. Abbreviations - CBIR, Content Based Image Retrieval; GAN, Generative Adversarial Network; CNN, Convolutional Neural Network.
A COVID-19 Prediction Model Based on Neural Network

The emergence and spread of COVID-19 has had a huge negative impact on society. The use of machine learning methods and big data technology to study the spread and development of the epidemic is a hot topic for many scholars. This paper proposes a model based on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{LSTM}+\text{BPNN}$</tex> neural network, which predicts the development trend of the new crown epidemic through the migration data of urban population flows, and proves the effectiveness of the model through a large number of experiments.