Reviewerly/exHarmony huggingface dataset:
2 Bordered constructions of self-dual codes from group rings

Self-dual codes, which are codes that are equal to their orthogonal, are a widely studied family of codes. Various techniques involving circulant matrices and matrices from group rings have been used to construct such codes. Moreover, families of rings have been used, together with a Gray map, to construct binary self-dual codes. In this paper, we introduce a new bordered construction over group rings for self-dual codes by combining many of the previously used techniques. The purpose of this is to construct self-dual codes that were missed using classical construction techniques by constructing self-dual codes with different automorphism groups. We apply the technique to codes over finite commutative Frobenius rings of characteristic 2 and several group rings and use these to construct interesting binary self-dual codes. In particular, we construct some extremal self-dual codes of length 64 and 68, constructing 30 new extremal self-dual codes of length 68.
An Intelligent Ubiquitous Activity Aware Framework for Smart Home

The proposed Ubiquitous Activity Aware Framework aims to leverage the immense potential that lies in the application of Activity Centered Computing in an Internet of Things (IoT) environment for the future of technology-based living spaces, for instance, in Smart Homes and Smart Cities. The scientific contribution in this work aims to address both the shortage of caregivers and the global challenges for reducing the burden of healthcare related costs for treating elderly people. Elderly people face problems like motor disabilities, physical limitations, memory issues and so on, which limit their abilities to perform Activities of Daily Living (ADLs) on their own. The challenge in this context is to develop technology-based solutions to sustain active aging and independent living of the aging population. Mild Cognitive Impairment (MCI) is commonly found in the aging population and there is a looming need for early detection of MCI to address the increasing healthcare related costs associated with the elderly population. To ensure smooth functioning of the world, the proposed framework addresses the aforementioned challenges through development of a long-term, economically viable and feasible assistive solution that takes a holistic approach in terms of analyzing user interactions during ADLs, to track, study, analyze and detect potential symptoms of MCI and other anomalies in elderly behavior, for preventive and timely care, with an aim to reduce these costs incurred to the healthcare industry in the context of contributing towards independent living of the aging population in Smart Homes.
An adaptive algorithm for dealing with data stream evolution and singularity

The analysis of representative features for multi-source and massive data streams, from which implicit knowledge is mined, has become a research hotspot in the era of rapid development of data industry. In response to the weak adaptability of traditional algorithms, an adaptive algorithm GNG-L is proposed based on growing neural gas (GNG) for monitoring and tracking the drift and singularity of real-time data stream in non-stationary environments, which includes three mechanisms, namely weight adaptation, neuron deletion and generation. Firstly, the mechanism of weight adaptation is proposed by analyzing the changes of the local characteristics for data streams, which ensures the network topology is adjusted accurately and quickly. Secondly, the adaptive deletion mechanism removes neural nodes that are no longer updated due to the evolution of data streams. Finally, the generation mechanism is trigged when the new feature of data stream evolution needs to be described in new regions of the feature space. The proposed model has been validated based on a number of data sets, and the results show that the algorithm proposed in this paper can effectively track changes of data sets in non-stationary environments.
Deep supervised feature selection for social relationship recognition

Social relationships link everyone in human society. Exploring social relationships in still images promotes researches of behaviors or characteristics among persons. Previous literature has discovered that face and body attributes can provide effective semantic information for social relationship recognition. However, they ignore that attributes contribute much differently to the recognition accuracy, and these multi-source attributes may contain redundancies and noises. This work aims to promote social relationship recognition accuracy by abstracting multi-source attribute features more efficiently. To this end, we propose a novel Deep Supervised Feature Selection (DSFS) framework to recognize social relationships in photos, which fuses the deep learning algorithm with l2,1-norm to learn a discriminative feature subset from multi-source features by leveraging the face and body attributes. Experimental results on PIPA-relation dataset qualitatively demonstrate the effectiveness of the proposed DSFS framework.
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning

Machine learning algorithms operating on mobile networks can be characterized into three different categories. First is the classical situation in which the end-user devices send their data to a central server where this data is used to train a model. Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model. Third is the federated learning setting in which, at any given time t, a certain number of active end users train with their own local data along with feedback provided by the central server and then send their newly estimated model parameters to the central server. The server, then, aggregates these new parameters, updates its own model, and feeds the updated parameters back to all the end users, continuing this process until it converges.The main objective of this work is to provide an information-theoretic framework for all of the aforementioned learning paradigms. Moreover, using the provided framework, we develop upper and lower bounds on the generalization error together with bounds on the privacy leakage in the classical, distributed and federated learning settings.
Semantic Segmentation of Marine Remote Sensing Based on a Cross Direction Attention Mechanism

With the development of remote sensing technology, the semantic segmentation and recognition of various things in the ocean have become more and more frequent. Due to the wide variety of marine things and the large differences in morphology, it has brought greater difficulties to the recognition of marine remote sensing images. In order to obtain better segmentation results of ocean remote sensing images, this paper proposes an cross attention mechanism(Horizontal and Vertical) of exponential operation combined with multi-scale convolution algorithm. Among them, the cross attention mechanism and expanded distribution weight coefficient mentioned in this paper are first proposed. First, Input the marine remote sensing image features into an cross attention mechanism algorithm of exponential operation to obtain feature weight coefficients and joint weight coefficients in multiple directions; Then, the features with weight coefficients are input into the multi-access convolutional layer and the multi-scale dilated convolutional layer respectively for deep feature mining; Then the above steps are repeated twice, and finally the semantic segmentation of marine remote sensing images is achieved by fusing multiple deep-level features afterwards. Experiments were conducted on three public marine remote sensing data sets, and the results proved the effectiveness of our proposed cross attention mechanism of extended operation algorithm. The F values of the MAMC model on Beach, Island and Sea ice data sets have reached 99.4%, 91.25%, 87.08% respectively. Compared with other models, the effect is significantly improved, and proved the powerful performance of the algorithm in the semantic segmentation of marine remote sensing images.
Analysis of Anemia Using Data Mining Techniques with Risk Factors Specification

Deficiency in healthy Red Blood Cells (RBC) leads to insufficient oxygen to be carried to whole blood tissues. Many reasons cause such an issue like iron or vitamin deficiency which is known as Anemia. Pregnant women, children under the age of 6, people with a low vitamin diet and losing their blood due to surgery or injury are at risk that will tend to have anemia. Such a disease can be diagnosed by blood test called Complete Blood Count (CBC), which evaluates Hemoglobin levels of patient's blood. Undiagnosed or untreated left disease, such as anemia, can cause health problems such as severe fatigue and pregnancy complications. Different types of anemia, especially those associated with iron or vitamin deficiency, can be ameliorated, especially when detected at an early stage. In this paper, four techniques, Bayesian Network (BN), Naive Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) have been applied to predict anemia based on 539 data, with 10 attributes, collected from laboratories. The LR has given better results compared to other considered techniques. In addition, attribute evaluators such as information gain have been applied to demonstrate the high performance of the system with minimum characteristics.
McPoRA: A Multi-chain Proof of Rapid Authentication for Post-Blockchain Based Security in Large Scale Complex Cyber-Physical Systems

Internet of Things (IoT) research is in full swing to integrate recent technologies such as the blockchain to enhance system security. However, the blockchain faces many limitations, such as resource demands, energy requirements, scalability, and high latency. This article presents a novel post-blockchain structure that integrates a multi-blockchain in one ledger using a directed acyclic graph (DAG) structure called a multi-chain. The multi-chain structure resolves the issues of scalability and storage and is a candidate to replace the traditional blockchain in IoT applications. This article also introduces a new consensus algorithm called "Multi-Chain Proof of Rapid Authentication" (McPoRA) to improve latency, which is a crucial factor in IoT resource constrained devices. McPoRA is approx. 4000X faster than proof-of-work (PoW) and 55X faster than Proof-of-Authentication (PoAh).
Accelerating the Training Process of Convolutional Neural Networks for Image Classification by Dropping Training Samples Out

Stochastic gradient descent and other adaptive optimization methods have been proved effective for training deep neural networks. Within each epoch of these methods, the whole training set is involved to train the model. In general, large training data sets have data redundancy among their training samples. In this paper, we present an algorithm to reduce the training time consumption of CNN by dropping certain samples out, which is called the greedy DropSample. For the absence of certain training samples, the distribution of networks’ activations is biased during training. By correcting the mean and variance of batch-normalization layers, this issue is solved. Experimental results over several data sets demonstrate the efficiency of the proposed method. The results show that this method could decrease the training time of multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) significantly. Despite the reduced number of training samples, the accuracies of networks are similar, or even better.
An AI-based Visual Attention Model for Vehicle Make and Model Recognition

With the increasing highlighted security concerns in Intelligent Transportation System (ITS), Vehicle Make and Model Recognition (VMMR) has attracted a lot of attention in recent years. The VMMR method can be widely used in suspicious vehicle recognition, urban traffic monitoring, and the automated driving system. With the development of the Vehicle-to-Everything (V2X) technology, the vehicle information recognized by the AI-based VMMR method can be shared among vehicles and other participants within the transportation system, and can help the police fast locate the suspicious vehicles. VMMR is complicated due to the subtle visual differences among vehicle models. In this paper, we propose a novel Recurrent Attention Unit (RAU) to expand the standard Convolutional Neural Network (CNN) architecture for VMMR. The proposed RAU learns to recognize the discriminative part of a vehicle on multiple scales and builds up a connection with the prominent information in a recurrent way. RAU is a modular unit. It can be easily applied to different layers of the vanilla CNN architectures to boost their performance on VMMR. The efficiency of our models is tested on three challenging VMMR benchmark datasets, i.e., Stanford Cars, CompCars, and CompCars Surveillance. The proposed ResNet101-RAU achieves the best recognition accuracy of 93.81% on the Stanford Cars dataset and 97.84% on the CompCars dataset.
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition

Existing deep active learning algorithms achieve impressive sampling efficiency on natural language processing tasks. However, they exhibit several weaknesses in practice, including (a) inability to use uncertainty sampling with black-box models, (b) lack of robustness to labeling noise, and (c) lack of transparency. In response, we propose a transparent batch active sampling framework by estimating the error decay curves of multiple feature-defined subsets of the data. Experiments on four named entity recognition (NER) tasks demonstrate that the proposed methods significantly outperform diversification-based methods for black-box NER taggers, and can make the sampling process more robust to labeling noise when combined with uncertainty-based methods. Furthermore, the analysis of experimental results sheds light on the weaknesses of different active sampling strategies, and when traditional uncertainty-based or diversification-based methods can be expected to work well.
Detection of panoramic vision pedestrian based on deep learning

As the AI (artificial intelligence) develops, driverless vehicle technology is widely concerned, and the important problem that needs to be solved in driverless technology is the detection of pedestrians in the panoramic vision of the vehicle, so the pedestrian detection technology of panoramic vision is explored based on the deep learning. Recently, anchor-free and one-stage detectors have been introduced into this area. However, their accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of anchor-free detectors and the accuracy of two-stage ones simultaneously, some adaptations based on a detector, CSP (center and scale prediction) are proposed. The original CSP of pedestrian detector is improved to make the training process more robust. For example, we use SN layers to replace all BN layers and ResNet-101 is used as backbone based on the research of deep learning. The main contributions of our paper are: (1) we improve the robustness of CSP and make it easier to train. (2) we propose a novel method to predict width, namely compressing width. (3) we achieve the second best performance on CityPersons benchmark, i.e. 9.3% log-average miss rate (MR) on reasonable set, 8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and one-stage detector can still have high accuracy. (4) we explore some capabilities of switchable normalization which are not mentioned in its original paper. This study will provide important theoretical support and practical basis for pedestrian detection
Neural Mutual Information Estimation for Channel Coding: State-of-the-Art Estimators, Analysis, and Performance Comparison

Deep learning based physical layer design, i.e., using dense neural networks as encoders and decoders, has received considerable interest recently. However, while such an approach is naturally training data-driven, actions of the wireless channel are mimicked using standard channel models, which only partially reflect the physical ground truth. Very recently, neural network based mutual information (MI) estimators have been proposed that directly extract channel actions from the input-output measurements and feed these outputs into the channel encoder. This is a promising direction as such a new design paradigm is fully adaptive and training data-based. This paper implements further recent improvements of such MI estimators, analyzes theoretically their suitability for the channel coding problem, and compares their performance. To this end, a new MI estimator using a "reverse Jensen" approach is proposed.
WGAN-based Autoencoder Training Over-the-air

The practical realization of end-to-end training of communication systems is fundamentally limited by its accessibility of the channel gradient. To overcome this major burden, the idea of generative adversarial networks (GANs) that learn to mimic the actual channel behavior has been recently proposed in the literature. Contrarily to handcrafted classical channel modeling, which can never fully capture the real world, GANs promise, in principle, the ability to learn any physical impairment, enabled by the data-driven learning algorithm. In this work, we verify the concept of GAN-based autoencoder training in actual over-the-air (OTA) measurements. To improve training stability, we first extend the concept to conditional Wasserstein GANs and embed it into a state-of-the-art autoencoder-architecture, including bit-wise estimates and an outer channel code. Further, in the same framework, we compare the existing three different training approaches: model-based pre-training with receiver finetuning, reinforcement learning (RL) and GAN-based channel modeling. For this, we show advantages and limitations of GAN-based end-to-end training. In particular, for non-linear effects, it turns out that learning the whole exploration space becomes prohibitively complex. Finally, we show that the training strategy benefits from a simpler (training) data acquisition when compared to RL-based training, which requires continuous transmitter weight updates. This becomes an important practical bottleneck due to limited bandwidth and latency between transmitter and training algorithm that may even operate at physically different locations.
Latent groups of cybersecurity preparedness in Europe: Sociodemographic factors and country-level contexts

The increase of people's online presence creates additional cybersecurity threats. Cybersecurity preparedness is increasingly important to minimize and mitigate cybersecurity-related victimization. This study explores how cybersecurity preparedness is shaped by socio-structural factors across thirty European samples. We analyzed survey items measuring purchase behavior, user's security settings, software settings, and trust in websites from the 2014 Eurobarometer (N=26,879). The findings of latent class analysis show three groups based on members’ cybersecurity preparedness: uninformed users, disciplined users, and cautious users. The determinants of cybersecurity preparedness were examined using two-level hierarchical logit modeling. While sociodemographic factors modestly contribute to cybersecurity preparedness at an individual level, country-level economic status (GDP per capita) and cybersecurity preparedness (Global Cybersecurity Index) appear to be significant for individual-level cybersecurity preparedness. In countries with higher cybersecurity preparedness and economic status, individuals tend to show a higher level of cybersecurity preparedness. Implications for government agencies and corporations are discussed.
Connecting User Experience to Learning in an Evaluation of an Immersive, Interactive, Multimodal Augmented Reality Virtual Diorama in a Natural History Museum &amp; the Importance of Story

Reported are the findings of user experience and learning outcomes from a July 2019 study of an immersive, interactive, multimodal augmented reality (AR) application, used in the context of a museum. The AR Perpetual Garden App is unique in creating an immersive multisensory experience of data. It allowed scientifically naïve visitors to walk into a virtual diorama constructed as a data visualization of a springtime woodland understory and interact with multimodal information directly through their senses. The user interface comprised of two different AR data visualization scenarios reinforced with data based ambient bioacoustics, an audio story of the curator's narrative, and interactive access to plant facts. While actual learning and dwell times were the same between the AR app and the control condition, the AR experience received higher ratings on perceived learning. The AR interface design features of "Story" and "Plant Info" showed significant correlations with actual learning outcomes, while "Ease of Use" and "3D Plants" showed significant correlations with perceived learning. As such, designers and developers of AR apps can generalize these findings to inform future designs.
A Blockchain Based Architecture for IoT Data Sharing Systems

Blockchain as an emerging distributed protocol has been widely used in IoT systems. Using blockchain as an IoT data sharing protocol provides precious features including consistency, reliability, and traceability. However, such combination brings high cryptography overhead and consensus latency when sharing data from large number of IoT sensors. To address these issues, we propose a novel blockchain based architecture for IoT data sharing systems. In this architecture, data messages signed by IoT sensors are packaged into data blocks and distributed to the blockchain network. We propose a data block structure with identity-based aggregate signature to protect data reliability from malicious sink nodes and reduce the communication, storage, and computing cost of signatures. We also present a multiple state chain structure with a new consensus algorithm which cuts back consensus phases and accelerate the consensus process. Finally, we evaluate the proportion of data in a block and the blockchain consensus latency, which shows a better performance than PBFT in this scenario.
A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm

Hitherto, urbanization reached unprecedented spreading, various problems in the field increase from day to day, and makes the urban phenomena more dynamic and more complex. Therefore, it is important to call in experts and provide all stuff to establish urban projects’ plans, which often need to be achieved in a brief time. Actually, decision-makers need more and more updated plans and even sustainable solutions to convey eventual urban changes with maintaining intrinsic features of urban areas, such as coverage, inter-dependency, and coherency. Due to decision-makers yearnings and the short time allocated to planners, urban project planning remains an exhausting task; it resorts to arbitrary choices to find a good match of projects according to the intended situations. On the other hand, it should take care of the available resources like funds, land, water, energy, underground, and raw materials, which ought to be rationally exploited, and preserved for future generations. In this paper, the proposed intelligent decision support system (IDSS) aims to find out the best urban plans that fit urban projects to appropriate areas. It also employs the holonic approach to model complex and large-scale urban systems, where agents of each level apply a new multi-objective ant colony optimization algorithm called BKPACS for the urban project planning problem, which is viewed as a bounded knapsack problem (BKP). To produce global optimal urban plans, the main algorithm called H-MACO coordinates between the different levels of this holonic system. The experimental results on a set of urban projects about a province of Algeria show good quality plans produced in less time.