Reviewerly/exHarmony huggingface dataset:
Applying Social Network Analysis to Model and Handle a Cross-Blockchain Ecosystem

In recent years, the huge growth in the number and variety of blockchains has prompted researchers to investigate the cross-blockchain scenario. In this setting, multiple blockchains coexist, and wallets can exchange data and money from one blockchain to another. The effective and efficient management of a cross-blockchain ecosystem is an open problem. This paper aims to address it by exploiting the potential of Social Network Analysis. This general objective is declined into a set of activities. First, a social network-based model is proposed to represent such a scenario. Then, a multi-dimensional and multi-view framework is presented, which uses such a model to handle a cross-blockchain scenario. Such a framework allows all the results found in the past research on Social Network Analysis to be applied to the cross-blockchain ecosystem. Afterwards, this framework is used to extract insights and knowledge patterns concerning the behavior of several categories of wallets in a cross-blockchain scenario. To verify the goodness of the proposed framework, it is applied on a real dataset derived from Multichain, in order to identify various user categories and their “modus operandi”. Finally, a new centrality measure is proposed, which identifies the most significant wallets in the ecosytem. This measure considers several viewpoints, each of which addresses a specific aspect that may make a wallet more or less central in the cross-blockchain scenario.
ConMLP: MLP-Based Self-Supervised Contrastive Learning for Skeleton Data Analysis and Action Recognition

Human action recognition has drawn significant attention because of its importance in computer vision-based applications. Action recognition based on skeleton sequences has rapidly advanced in the last decade. Conventional deep learning-based approaches are based on extracting skeleton sequences through convolutional operations. Most of these architectures are implemented by learning spatial and temporal features through multiple streams. These studies have enlightened the action recognition endeavor from various algorithmic angles. However, three common issues are observed: (1) The models are usually complicated; therefore, they have a correspondingly higher computational complexity. (2) For supervised learning models, the reliance on labels during training is always a drawback. (3) Implementing large models is not beneficial to real-time applications. To address the above issues, in this paper, we propose a multi-layer perceptron (MLP)-based self-supervised learning framework with a contrastive learning loss function (ConMLP). ConMLP does not require a massive computational setup; it can effectively reduce the consumption of computational resources. Compared with supervised learning frameworks, ConMLP is friendly to the huge amount of unlabeled training data. In addition, it has low requirements for system configuration and is more conducive to being embedded in real-world applications. Extensive experiments show that ConMLP achieves the top one inference result of 96.9% on the NTU RGB+D dataset. This accuracy is higher than the state-of-the-art self-supervised learning method. Meanwhile, ConMLP is also evaluated in a supervised learning manner, which has achieved comparable performance to the state of the art of recognition accuracy.
A behavioural hierarchical analysis framework in a smart home: Integrating HMM and probabilistic model checking

Smart homes offer great convenience for people living alone and assistance for physically impaired inhabitants. Robust behavioural analysis technology is one of the keys to maximizing the role of the Smart Home. Typically, when it comes to the behavioural analysis of its inhabitants, most researchers have acquired it through data collection from sensors, cameras, and portable Bluetooth sensors. However, a gap in research exists concerning activity recognition in the context of the users physical location in the environment. In this paper, we propose a hierarchical framework based on Hidden Markov Model (HMM) and suggest dividing the behavioural sequence analysis into two layers: spatial transfer and sensor transfer. In addition, we apply probabilistic model checking to verify the properties of each module's state transfer and obtain the probability of occurrence of the corresponding behavioural sequence. By integrating an implicit Markov model and probabilistic model checking, we effectively analyse the composition and probability of occurrence of three arbitrary sequences of complex behaviours. Finally, anomaly detection and behavioural guidance are discussed based on the proposed behavioural analysis methods.
Remote Sensing Image Super-Resolution via Multiscale Enhancement Network

In recent years, remote sensing images have attracted a lot of attention because of their special value. However, images acquired by satellite sensors are usually low-resolution (LR), so remote sensing images are much more difficult to infer high-frequency details from compared with ordinary digital images, which means they cannot meet the needs of certain downstream tasks. In this letter, we propose a multiscale enhancement network (MEN), which uses multiscale features of remote sensing images to enhance the network’s reconstruction capability. Specifically, the network extracts the coarse features of LR remote sensing images using convolutional layers. Then, these features are fed into the multiscale enhancement module (MEM) proposed by this network, which uses a combination of convolutional layers with multiple convolutional kernel sizes to refine the extraction of multiscale features, and finally, the final reconstructed image is generated by the reconstruction module. Extensive experiments show that MEN achieves significant reconstruction advantages in both objective and subjective aspects.
Real-Time Face Detection and Face Recognition: Study of Approaches

This study demonstrates various face detection and recognition techniques which have been studied till now and compares them on basis of their merits and demerits, discusses their methodologies of working and put forward a core idea of how face detection is done, mentioning about very basic term, so that any person who is not that good in technology can understand it and dive deep into this field. Some of the face detection techniques we will look into will be geometric-based face detection, feature-based face detection, and Haar-like feature based-face detection, giving special emphasis on Haar-like features-based face detection. In face recognition techniques, we looked into the lazy learner's face recognition approach, neural networks face recognition approach, and holistic face recognition approaches. Objective of the study is also to demonstrate about preparing a model which detects faces in a real-time environment. Face detection, nowadays, is the most primary check in any security system. So, automation in detecting faces will prove helpful. This model, rather than any other model, works on real-time data provided. Our model works on the fundamentals of the K-nearest neighbors algorithm, Haar cascade classifier (an object detection technique), and OpenCV (an open-source python library for computer vision, machine learning, and image processing).
Implementing artificial intelligence education for middle school technology education in Republic of Korea

Abstract The purpose of this study is multifold: First, to develop an educational program using artificial intelligence (AI) in middle school free semester system of South Korea. Second, to verify the program’s effectiveness, the study clarified the definition of AI and AI education and considered their meaning in technology education. This study used three steps: preparation, development, and improvement. In the preparation step, this study set the theme and purpose of the AI program and selected the free semester activity type “theme selection activity.” After analyzing the technology curriculum and extracting AI-related elements in the development step, this study laid out the program for 16 h of class time. In the improvement step, to augment the program’s validity, the researcher revised and supplemented it as a whole through expert consultation. This research differentiated the developed program from the AI education program of other subjects and specialized it, focusing on the specificity of technology education. The study emphasized the social impact of the latest technology, ethics of AI, physical computing using AI, and technological problem-solving activity using AI. The final developed program was applied to the students, and students participated in a pretest and posttest. The study used the PATT and AI competency test tools. The PATT results showed a significant increase in the mean of both constructs in “interest in technology” and “career aspirations in technology.” In AI competency, the mean of two constructs increased significantly in “social impact of AI” and “AI performance.” In particular, “AI performance” showed the largest increase. There was no statistically significant change in “interaction with AI.” The study results confirmed that the developed AI program was effective in technology education and career exploration, which is the primary purpose of the free semester. In addition, it was possible to confirm the technology educational value of the AI education program centered on technological problem-solving. These research results have implications for bringing AI into technology education.
Blockchain-enabled authentication platform for the protection of 3D printing intellectual property: a conceptual framework study

3D printing (3DP) has enjoyed rapid growth yet has also prompted ethical and social concerns.For example, the ability to print unethical objects and intellectual property (IP) infringement.This paper follows the dual-cycle information system design model and implements a multi-method to propose a blockchain-enabled digital platform solution to protect 3DP digital assets' IP.It combines the advantages of patented watermarking technology and blockchain technology, including encryption, authentication, and transaction services of 3DP designs according to users' needs.The platform may help promote the standardised development of the 3DP industry and the international digital assets' IP protection process.
Effects of button colour and background on augmented reality interfaces

This study aims to understand the usability of common button colours and background choices in the existing augmented reality (AR) interface. AR is increasingly used in dynamic situations, such as driving using an AR head-up display. However, studies regarding the usability of AR interfaces, especially about the colours, are scarce. Twenty-four participants performed button selection tasks in an experiment using Microsoft HoloLens. Three measures (task completion time, the number of errors, and button colour visibility were collected for the 12 experimental conditions (3 backgrounds by 4 button colours). Significant differences were found in completion time between backgrounds and blue button visibility. On a moving background, the most effective button colour is orange, and the most minor effective button colour is blue. The results of this study can be used for colour choices in designing AR applications and devices.
Design of High-Throughput Mixed-Precision CNN Accelerators on FPGA

Convolutional Neural Networks (CNNs) reach high accuracies in various application domains, but require large amounts of computation and incur costly data movements. One method to decrease these costs while trading accuracy is weight and/or activation word-length reduction. Thereby, layer-wise mixed-precision quantization allows for more efficient results while inflating the design space. In this work, we present an in-depth quantitative methodology to efficiently explore the design space considering the limited hardware resources of a given FPGA. Our holistic exploration approach vertically traverses the various design entry levels from the architectural down to the logic level, and laterally covers optimization from processing elements to dataflow for an efficient mixed-precision CNN accelerator. Our resulting hardware accelerators implement truly mixed-precision operations that enable efficient execution of layer-wise and channel-wise quantized CNNs. Mapping feed-forward and identity-shortcut-connection mixed-precision CNNs result in competitive accuracy-throughout trade-offs: 245 frames/s with 87.48% Top-5 accuracy for ResNet-18 and 92.9% Top-5 accuracy with 1.13 TOps/s for ResNet-152, respectively. Thereby, the required memory footprint for parameters is reduced by 4.9 × and 9.4 × compared to the respective floating-point baseline.
IMC-NLT: Incomplete multi-view clustering by NMF and low-rank tensor

Multi-view data obtained from different perspectives are becoming increasingly available. As such, researchers can use this data to explore complementary information. However, such real-world data are often incomplete. Existing algorithms for incomplete multi-view clustering (IMC) have some limitations, such as the ineffective use of valuable information hidden in the data, oversensitivity to model parameters, and ineffective handling of samples with incomplete views. To overcome these limitations, we present a novel algorithm for incomplete multi-view clustering using Non-negative matrix factorization and a low-rank tensor (IMC-NLT). In particular, IMC-NLT first uses a low-rank tensor to retain view features with a unified dimension. Using a consistency measure, IMC-NLT captures a consistent representation across multiple views. Finally, IMC-NLT incorporates multiple learning into a unified model such that hidden information can be extracted effectively from incomplete views. We conducted comprehensive experiments on five real-world datasets to validate the performance of IMC-NLT. The overall experimental results demonstrate that the proposed IMC-NLT performs better than several baseline methods, yielding stable and promising results.
Software-hardware co-design for accelerating large-scale graph convolutional network inference on FPGA

Inspired by convolutional neural networks, graph convolutional networks (GCNs) have been proposed for processing non-Euclidean graph data and successfully been applied in recommendation systems, smart traffic, etc. However, subject to the sparsity and irregularity of GCN models, the complex execution pattern of large-scale GCN poses huge challenges to the efficient inference on general purpose CPUs and GPUs, such as workload imbalance and irregular memory access. Therefore, we propose a software-hardware co-design framework for low-latency GCN inference on field programmable gate array. Specifically, at the algorithm level, we propose an attention-mechanism-based graph sparsification approach to reduce the redundant relation in the graph structure and alleviate irregularity without losing accuracy. Then, at the hardware design level, based on the sparsified graph, we propose a two-stage hardware architecture that supports the two phases with a distinct execution mode in the GCN. In order to achieve low-latency computation, edge-level and feature-level parallelism are exploited in the aggregation phase. In addition, a graph partition strategy is exploited to efficiently improve data reuse. The experimental results demonstrate that our proposed framework can achieve 739× speedup compared to CPU, 13.7× speedup compared to GPU on average and 6.8× speedup compared to state-of-the-art accelerators.
A Spark-Based Parallel Implementation of Arithmetic Optimization Algorithm

Arithmetic optimization algorithm (AOA) is a recent population-based metaheuristic widely used for solving optimization problems. However, the emerging large-scale optimization problems pose a great challenge for AOA due to its prohibitive computational cost to traverse the huge solution space effectively. This article proposes a parallel Spark-AOA using Scala on Apache Spark computing platform. Spark-AOA leverages the intrinsic parallel nature of the population-based AOA and the native iterative in-memory computation support of Spark through resilient distributed datasets (RDD) to accelerate the optimization process. Spark-AOA divides the solutions population into several subpopulations that are distributed into multiple RDD partitions and manipulated concurrently. Simulation experiments on different benchmark functions with up to 1,000-dimension and three engineering design problems demonstrate that Spark-AOA outperforms considerably standard AOA and Spark-based implementations of two recent metaheuristics both in terms of run-time and solution quality.
Late acceptance hill climbing aided chaotic harmony search for feature selection: An empirical analysis on medical data

In today’s era of data-driven digital society, there is a huge demand for optimized solutions that essentially reduce the cost of operation, thereby aiming to increase productivity. Processing a huge amount of data, like the Microarray based gene expression data, using machine learning and data mining algorithms has certain limitations in terms of memory and time requirements. This would be more concerning, when a dataset comes with redundant and non-important information. For example, many report-based medical datasets have several non-informative attributes which mislead the classification algorithms. To this end, researchers have been developing several feature selection algorithms that try to discard the redundant information from the raw datasets before feeding them to machine learning algorithms. Metaheuristic based optimization algorithms provide an excellent option to solve feature selection problems. In this paper, we propose a music-inspired harmony search (HS) algorithm based wrapper feature selection method. At the beginning, we use a chaotic mapping to initialize the population of the HS algorithm in order to better coverage of the search space. Further to complement the inferior exploitation of the HS algorithm, we integrate it with the Late Acceptance Hill Climbing (LAHC) method. Thus the combination of these two algorithms provides a good balance between the exploration and exploitation of the HS algorithm. We evaluate the proposed feature selection method on 15 UCI datasets and the obtained results are found to be better than many state-of-the-art methods both in terms of the classification accuracy and the number of features selected. To evaluate the effectiveness of our algorithm, we utilize a combination of precision, recall, F1 score, fitness value, and execution time as performance indicators. These metrics enable us to obtain a comprehensive assessment of the algorithm’s abilities and limitations. We also apply our method on 3 microarray based gene expression datasets used for prediction of cancer to ensure the scalability and robustness as a feature selection method in real-life scenarios. In addition to this, we test our approach using the COVID-19 dataset, and it performs better than several metaheuristic based optimization techniques.
A unified speech enhancement approach to mitigate both background noises and adversarial perturbations

Deep learning has been vastly applied in classification systems of speech signals. However, adversarial examples crafted by adding small perturbations to input speech signals can make those classification systems produce incorrect results. Beyond detecting and discarding adversarial examples when securing the classification systems, we consider to purify the input adversarial examples to normal ones in this paper. Speech enhancement is a technique known as removing background noises to enhance voice quality. By treating adversarial perturbations as noises, we hereby propose a unified speech enhancement approach to mitigate both background noises and adversarial perturbations. The approach is named by Speech Enhancement and Adversarial Defense Network (SEADNet), which performs model-level fusion of conventional speech enhancement and adversarial defenses. In SEADNet, a learnable noise-adding module is firstly created to adaptively putting noises to the input speech to destroy the functions of adversarial perturbations. Subsequently, a speech enhancement network is deployed to reconstruct clean speech from noisy inputs. Finally, an adversarial example detection module is designed as a discriminator to guide the reconstructed speech in the previous speech enhancement network to mitigate adversarial perturbations. The overall network is optimized by minimizing a joint loss function. Experiments are conducted on the tasks of speech enhancement and automatic speaker verification with benchmark datasets. The proposed SEADNet performs well on both tasks of conventional noise mitigation and adversarial defenses compared to the related competitive baselines.
Solving DC power flow problems using quantum and hybrid algorithms

Power flow calculation plays an important role in planning, operation, and control of the power system. The quantum HHL algorithm can achieve theoretical exponential speedup over classical algorithms on DC power flow calculation. Since the qubit resources in the Noisy Intermediate-scale Quantum (NISQ) era are limited, it is important to discuss the performance considering this limitation. The phase estimation of DC power flow problems is imperfect. This work is carried out under the assumption of imperfect phase estimation. The performance of the HHL algorithm is systematically investigated with different accuracy and redundant qubits. In order to further reduce the required qubit resources, a hybrid quantum–classical algorithm is proposed. By comparing errors of the HHL and hybrid algorithms in the DC power flow calculation of the PJM 5-bus system, it is found that the hybrid algorithm can achieve comparable precision with fewer qubits than HHL by increasing the number of phase estimation modules, which may make the hybrid algorithm a feasible route in the NISQ era.
APSM-jSO: A novel jSO variant with an adaptive parameter selection mechanism and a new external archive updating mechanism

A novel jSO variant named APSM-jSO is proposed in this study by making simple and effective modifications to improve its performance. There are three main differences between APSM-jSO and jSO. First, a novel adaptive selection mechanism (APSM) for selecting entries from the historical memory is designed to fully utilize the better entries in the historical memory. Subsequently, the first in-first out (FIFO) method is utilized to update the external archive for maintaining the population diversity and avoiding overuse of the external archive. Finally, a new mutation strategy adopting rank-based selective pressure (RSP) is used to enhance the exploitation of APSM-jSO. APSM-jSO is evaluated using the IEEE CEC 2018 test suite in comparison with five state-of-the-art DE-based variants (ELSHADE-SPACMA, EB-LSHADE, LSHADE-RSP, mL-SHADE, and MadDE) and five winners of the IEEE CEC competitions (AGSK, APGSK-IMODE, EBOwithCMAR, HSES, and IMODE). The results demonstrate that APSM-jSO outperforms jSO and five DE-based algorithms, is superior to four top winners of the IEEE CEC competitions (AGSK, APGSK-IMODE, HSES, and IMODE), and is not inferior to the top method of the IEEE CEC 2017 competition (EBOwithCMAR). The conclusion is that APSM-jSO effectively further enhances the overall performance of jSO, and is an excellent jSO variant. The MATLAB source code of APSM-jSO can bedownloaded from https://github.com/Yintong-Li/APSM-jSO.