Reviewerly/exHarmony huggingface dataset:
Deep Learning based Channel Code Recognition using TextCNN

The following topics are dealt with: radio spectrum management; cognitive radio; cellular radio; learning (artificial intelligence); wireless channels; telecommunication computing; radiofrequency interference; 5G mobile communication; signal detection; mobile computing.
Application of LA ICP-MS analysis of arsenopyrite to gold metallogeny of the Meguma Terrane, Nova Scotia, Canada

Investigation of gold metallogeny in the Paleozoic Meguma terrane (Canada) is conducted through LA-ICP-MS analysis of arsenopyrite collected from eight slate-belt style vein gold deposits using a novel approach integrating elemental distribution maps and their derived elemental paragenesis with multi-element binary plots. The data reveal two distinct gold events: 1) an early event characterized by a Co-Ni-Mo-Sb-Se elemental association related to initial growth of arsenopyrite that reflects the presence of invisible gold (>10 ppm); and 2) a second event, spatially associated with late fracture sets, that is characterized by an Al-Ti-V-Mn element association and reflects either remobilization or upgrading of primary invisible gold and is manifest as visible gold. The results of this study indicate a complex and protracted history of gold mineralization which has important ramifications for the Meguma gold deposits, as well as other orogenic gold districts globally. In the case of the Meguma Terrane, it involves an initial gold event that is followed by element mobilization and, in the case of precious metals (Au, Ag), an upgrading through a zone refining process. In addition, the variable coupling and decoupling of elements is only revealed using in-situ derived LA-ICP-MS data.
M-NAS: Meta Neural Architecture Search

Neural Architecture Search (NAS) has recently outperformed hand-crafted networks in various areas. However, most prevalent NAS methods only focus on a pre-defined task. For a previously unseen task, the architecture is either searched from scratch, which is inefficient, or transferred from the one obtained on some other task, which might be sub-optimal. In this paper, we investigate a previously unexplored problem: whether a universal NAS method exists, such that task-aware architectures can be effectively generated? Towards this problem, we propose Meta Neural Architecture Search (M-NAS). To obtain task-specific architectures, M-NAS adopts a task-aware architecture controller for child model generation. Since optimal weights for different tasks and architectures span diversely, we resort to meta-learning, and learn meta-weights that efficiently adapt to a new task on the corresponding architecture with only several gradient descent steps. Experimental results demonstrate the superiority of M-NAS against a number of competitive baselines on both toy regression and few shot classification problems.
Generative Attention Networks for Multi-Agent Behavioral Modeling

Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.
Leveraging Class Hierarchy in Fashion Classification

The online commerce market has been growing rapidly, spurring interest in the deep fashion domain from the research community. Among various tasks in the fashion domain, the classification problem is the vital one, because metadata extraction through fashion classification has tremendous industrial value. A flurry of recent deep-learning based models have been proposed for the task and have showed great performances but they fail to capture the hierarchical nature of fashion annotations, such as 'pant' and 'skirt' both having 'bottom' as the superordinate. In this preliminary work, we propose a novel fashion classification model that works in a hierarchical manner. Experimental results on large fashion datasets show that our intuition, taking into account hierarchical dependencies between class labels, can help improve performance.
Comparative Analysis of Resampling Techniques under Noisy Imbalanced Datasets

Classification is mainly challenged by the problems in the dataset. When dataset have uneven distribution of data among classes, then class imbalance problem arise. Class imbalance with noise creates immense effect on classification of instances of classes. The main focus of this article is to provide the detail comparative analysis of seven Resampling techniques under 16 noisy imbalanced datasets using C4.5 classifier. The performance evaluation is done by using AUC, F1 score, G-mean. Based on the evaluation, article inferred that SMOTE-ENN perform better than rest of resampling techniques.
A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension

Context: Several studies investigated the impact of anti-patterns (i.e., "poor" solutions to recurring design problems) during maintenance activities and reported that anti-patterns significantly affect the developers' effort required to edit files. However, before developers edit files, they must understand the source code of the systems. This source code must be easy to understand by developers. Objective: In this work, we provide a complete assessment of the impact of two instances of two anti-patterns, Blob or Spaghetti Code, on program comprehension. Method: We analyze the impact of these two anti-patterns through three empirical studies conducted at Polytechnique Montr\'eal (Canada) with 24 participants; at Carlton University (Canada) with 30 participants; and at University Basilicata (Italy) with 79 participants. Results: We collect data from 372 tasks obtained thanks to 133 different participants from the three universities. We use three metrics to assess the developers' comprehension of the source code: (1) the duration to complete each task; (2) their percentage of correct answers; and, (3) the NASA task load index for their effort. Conclusions: We report that, although single occurrences of Blob or Spaghetti code anti-patterns have little effect on code comprehension, two occurrences of either Blob or Spaghetti Code significantly increases the developers' time spent in their tasks, reduce their percentage of correct answers, and increase their effort. Hence, we recommend that developers act on both anti-patterns, which should be refactored out of the source code whenever possible. We also recommend further studies on combinations of anti-patterns rather than on single anti-patterns one at a time.
Bat algorithm (BA): review, applications and modifications

Generally, Metaheuristic algorithms such as ant colony optimization, Elephant herding algorithm, particle swarm optimization, bat algorithms becomes a powerful methods for solving optimization problems. This paper provides a timely review of the bat algorithm and its new variants.Bat algorithm (BA) is a Swarm based metaheuristic algorithm developed in 2010 by Xin-She Yang, BA has been inspired by the foraging behavior of micro bats, algorithm carries out the search process using artiï¬cial bats as search agents mimicking the natural pulse loudness and emission rate of real bats. It has become a powerful swarm intelligence method for solving optimization prob-lems over continuous and discrete spaces. Nowadays, it has been successfully applied to solve problems in almost all areas of opti-mization, and it found to be very efficient. As a result, the literature has expanded significantly, a wide range of diverse applications and case studies has been made base on the bat algorithm.Â
An Improved Network Traffic Classification Model Based on a Support Vector Machine

Network traffic classification based on machine learning is an important branch of pattern recognition in computer science. It is a key technology for dynamic intelligent network management and enhanced network controllability. However, the traffic classification methods still facing severe challenges: The optimal set of features is difficult to determine. The classification method is highly dependent on the effective characteristic combination. Meanwhile, it is also important to balance the experience risk and generalization ability of the classifier. In this paper, an improved network traffic classification model based on a support vector machine is proposed. First, a filter-wrapper hybrid feature selection method is proposed to solve the false deletion of combined features caused by a traditional feature selection method. Second, to balance the empirical risk and generalization ability of support vector machine (SVM) traffic classification model, an improved parameter optimization algorithm is proposed. The algorithm can dynamically adjust the quadratic search area, reduce the density of quadratic mesh generation, improve the search efficiency of the algorithm, and prevent the over-fitting while optimizing the parameters. The experiments show that the improved traffic classification model achieves higher classification accuracy, lower dimension and shorter elapsed time and performs significantly better than traditional SVM and the other three typical supervised ML algorithms.
Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.
A Content-Based Method for Sybil Detection in Online Social Networks via Deep Learning

Online social networks (OSNs) are generally susceptible to Sybil attack, which causes a series of cybersecurity problems and privacy violations.Malicious attackers can create massive Sybils and further utilize those fake identities to launch various Sybil attacks.Therefore, Sybil detection in OSNs has become an urgent security research problem for both academia and industries.The existing content-based methods to detect Sybils base on the combination of manual-design features and machine learning algorithms, which requires lots of professional experiences and human effort.These methods divide the Sybil detection problem into two piece-wise sub-problems, which prevents us from getting the optimal solution.In this work, we propose a novel content-based method to detect Sybils.The proposed method is an end-to-end classification model that extracts features directly from the input data, and then output the classification results in a unified framework.The proposed method includes three main parts: first, the self-normalizing convolutional neural network (CNN) is adopted to extract lower features from the multi-dimensional input data; second, the bidirectional self-normalizing LSTM network (bi-SN-LSTM) is developed to extract higher features from the compressed feature map sequence; third, the dense layer and softmax classifier are stacked to output the classification results.Unlike the traditional bidirectional long short-term memory network (bi-LSTM), the proposed bi-SN-LSTM network utilizes SELU as the activation function of its recurrent step, which provides unbounded changes to the state value.Through the case study of the real-world dataset, the comparison experiments demonstrate that our method significantly outperforms other state-of-the-art methods.INDEX TERMS Convolutional neural network (CNN), deep learning (DL), long short-term memory (LSTM), online social networks (OSNs), sybil detection.
Characterizing Cloud-to-User Latency as Perceived by AWS and Azure Users Spread over the Globe

With the growing adoption of cloud infrastructures to deliver a variety of IT services, monitoring cloud network performance has become crucial. However, cloud providers only disclose qualitative info about network performance, at most. This hinders efficient cloud adoption, resulting in no performance guarantees, uncertainties about the behavior of hosted services, and sub-optimal deployment choices. In this work, we focus on cloud-to-user latency, i.e. the latency of network paths interconnecting datacenters to worldwide-spread cloud users accessing their services. In detail, we performed a 14-day measurement campaign from 25 vantage points deployed via Planetlab infrastructure (emulating spatially- spread users) and considering services running in distinct locations on the infrastructures of the two most popular public-cloud providers, namely Amazon Web Services and Microsoft Azure. Our experimentation allows to provide an in-depth performance characterization (based on multiple probing methods and fine-grained sampling rate) of such networks as perceived by users spread worldwide. Results show the presence of both spatial and temporal latency trends. Finally, by evaluating the advantages of multi- cloud deployments, our results also provide useful guidelines to cloud customers.
A STUDY OF INTERNET OF THINGS ENABLED HEALTHCARE ACCEPTANCE IN MALAYSIA

The power of Internet of Things (IoT) has disrupted the traditional business models of industries, especially in healthcare industry. The emerging of connected sensors and devices, and the advancement of wireless technology, cloud computing, and data analytics, have been transforming the healthcare from case-based paid service to value-based care service. These value-based care services is known as IoT enabled healthcare applications. The aim of the IoT enabled healthcare is to eventually provide a low-cost, advanced technology, and high accessibility care services for patients and consumers. Some developed countries have been implementing the IoT enabled applications in healthcare sector and obtained satisfactory result. In this paper, Diffusion of Innovations (DOI) model was applied to study the IoT diffusion in public and Technology Acceptance Model (TAM) was used to study the degree of IoT is accepted in public. In addition, survey questionnaire was developed to collect data from the public and consequently identify the barriers for Malaysia to embrace the IoT into healthcare sector. After going through the data collection, IoT enabled healthcare application is accepted by most of the respondents. IoT healthcare application is reaching the decision stage that Malaysian need more persuasion to encourage them to make the decision of using the IoT healthcare application. Lastly, six major barriers for service provider and public to adopt IoT healthcare application were then identified in this paper.
Cybersecurity Risks of Blockchain Technology

Blockchain technology has become a paradigm shift to digital transactions.It has brought massive potentials in many fields, such as financial services, energy, healthcare and Internet of Things.As often occurs with innovative technologies, it has suffered from several critical Cybersecurity threats and vulnerabilities.The complicated relation between Cybersecurity risk management and companies strategic and operational objectives which make identifying, analyzing, and controlling the relevant risk events as a major challenge.In this paper, the researchers classify those incidents against the Cybersecurity vulnerabilities in Blockchain technology and explain the methods of risk measures according to the Information Security Risk Assessment (ISRA) Models.
Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets.
Improving the MILP-based Security Evaluation Algorithm against Differential/Linear Cryptanalysis Using A Divide-and-Conquer Approach

In recent years, Mixed Integer Linear Programming (MILP) has been widely used in cryptanalysis of symmetric-key primitives. For differential and linear cryptanalysis, MILP can be used to solve two kinds of problems: calculation of the minimum number of differentially/linearly active S-boxes, and search for the best differential/linear characteristics. There are already numerous papers published in this area. However, the efficiency is not satisfactory enough for many symmetric-key primitives. In this paper, we greatly improve the efficiency of the MILP-based search algorithm for both problems. Each of the two problems for an r-round cipher can be converted to an MILP model whose feasible region is the set of all possible r-round differential/linear characteristics. Generally, high-probability differential/linear characteristics are likely to have a low number of active S-boxes at a certain round. Inspired by the idea of a divide-and-conquer approach, we divide the set of all possible differential/linear characteristics into several smaller subsets, then separately search them. That is to say, the search of the whole set is split into easier searches of smaller subsets, and optimal solutions within the smaller subsets are combined to give the optimal solution within the whole set. In addition, we use several techniques to further improve the efficiency of the search algorithm. As applications, we apply our search algorithm to five lightweight block ciphers: PRESENT, GIFT-64, RECTANGLE, LBLOCK and TWINE. For each cipher, we obtain better results than the best-known ones obtained from the MILP method. For the minimum number of differentially/linearly active S-boxes, we reach 31/31, 16/15, 16/16, 20/20 and 20/20 rounds for the five ciphers respectively. For the best differential/linear characteristics, we reach 18/18, 15/13, 15/14, 16/15 and 15/16 rounds for the five ciphers respectively.
Trust information network in social Internet of things using trust-aware recommender systems

Social Internet of things is one of the most up-to-date research issues in the applications of Internet of things technologies. In social Internet of things, accuracy and reliability are standard features to discerning decisions. We assume that decision support systems based on social Internet of things could leverage research from recommender systems to achieve more stable performance. Therefore, we propose a trust-aware recommender systems suitable for social Internet of things. Trust-aware recommender systems adapt the concept of social networking service and utilize social interaction information. Trust information not only improves recommender systems from opinion spam problems but also more accurately predicts users’ preferences. We confirm that the performance of a recommender system becomes more improved when implicit trust is able to satisfy the properties of trust in the social Internet of things environment. The structure and amount of social link information are context-sensitive, so applying the concept of trust into social Internet of things environments requires a method to optimize implicit and explicit trust with minimal social link information. Our proposed method configures an asymmetric implicit trust network utilizing user–item rating matrix and transforms trust propagation metrics for a directional and weighted trust network. Through experiments, we confirm that the proposed methods enable higher accuracy and wider coverage compared to the existing recommendation methods.
Automatic DenseNet Sparsification

As a classic and well-performed deep convolutional neural network, DenseNet links every layer to each of its preceding layers via skip connections. However, the dense connectivity of the links leads to much redundance, consuming lots of computational resources. In this paper, to automatically prune redundant skip connections in DenseNet, we introduce a novel reinforcement learning method called automatic DenseNet sparsification (ADS). In ADS, we use adjacent matrix to represent dense connections in DenseNet, and design an agent using recurrent neural networks (RNNs) to sparsify the matrix, i. e. removing redundant skip connections in DenseNet. The validation accuracies of the sparsified DenseNets are used as rewards to update the agent, which promotes the agent to generate sparsified DenseNets with high performance. Extensive experiments demonstrate the effectiveness of ADS: The performance of the sparsified DenseNet surpasses not only the original DenseNet but related models; Moreover, the sparsified DenseNet has strong transferability when it is applied to new tasks. More importantly, ADS is very efficient. For the compression of a 40-layer DenseNet, it takes less than 1 day on a single GPU.
Research on Charity System Based on Blockchain

Abstract The charity organizations in China lack transparency and the supervision to them is difficult to achieve, which has a negative impact on the willingness of the people to donate. Blockchain as a underlying technology of Bitcoin system provides a new solution for the charity system in terms of technology. This paper proposed a charity system based on blockchain technology and expounds the design pattern, architecture and operational process of the platform. Some core functions of the charity platform have been realized and verified on Ethereum in this article. We hope to increase the transparency of charities to enhance the public’s trust in charities and promote the development of philanthropy by blockchain-based charity system.