Reviewerly/exHarmony huggingface dataset:
Parallel Ant Colony Optimization Algorithm for Finding the Shortest Path for Mountain Climbing

The problem of finding the shortest path between two nodes is a common problem that requires a solution in many applications like games, robotics, and real-life problems. Since its deals with a large number of possibilities. Therefore, parallel algorithms are suitable to solve this optimization problem that has attracted a lot of researchers from both industry and academia to find the optimal path in terms of runtime, speedup, efficiency, and cost compared to sequential algorithms. In mountain climbing, finding the shortest path from the start node under the mountain to reach the destination node is a fundamental operator, and there are some interesting issues to be studied in mountain climbing that cannot be found in a traditional two-dimensional space search. We present a parallel Ant Colony Optimization (ACO) to find the shortest path in the mountain climbing problem using Apache Spark. The proposed algorithm guarantees the security of the selected path by applying some constraints that take into account the secure slope angle for the path. A generated dataset with variable sizes is used to evaluate the proposed algorithm in terms of runtime, speedup, efficiency, and cost. The experimental results show that the parallel ACO algorithm significantly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(p &lt; 0.05)$ </tex-math></inline-formula> outperformed the best sequential ACO. On the other hand, the parallel ACO algorithm is compared with one of the most recent research from the literature for finding the best path for mountain climbing problems using the parallel A* algorithm with Apache Spark. The parallel ACO algorithm with Spark significantly outperformed the parallel A* algorithm.
A survey on computer vision based human analysis in the COVID-19 era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.
Spatial-temporal single object tracking with three-way decision theory

Trackers based on Siamese network have achieved positive performance in recent days. However, most of the existing siamese single object trackers only consider the spatial information in the template which was given in the first frame of the video but do not extract the affluent temporal information. In this paper, we propose a novel tracking framework based on a spatial-temporal network. Specifically, we introduce three-way decision theory into object tracking to avoid interference from complex situations such as occlusions, fast motions, and non-rigid deformation. Furthermore, our proposed method can generate more precise tracking results due to the discriminative correlation filters (DCF). Extensive tests and comparisons with numerous competitive trackers on demanding large-scale benchmarks, including OTB-2015, GOT-10k, LaSOT and VOT2018, TrackingNet, demonstrate that our tracker outperforms many state-of-the-art real-time techniques while operating at 22 frames per second.
Controlling corruption in African countries: innovation, financial inclusion and access to education as alternative measures

Purpose This study aims to examine the impact of financial inclusion and financial innovation on corruption, considering the moderating role of education, as well as identify the specific modality of digital inclusion and payments that contribute to corruption reduction. Design/methodology/approach The study uses a representative sample consisting of 46 African countries in three different years 2011, 2014 and 2017. On the data, feasible generalized least squares (FGLS), instrumental variables – two stages least squares (IV-2SLS) and two-stage generalized method of moments (IV-2GMM) model estimation methods were employed. Findings The results suggest that financial inclusion and education significantly reduce corruption. As well, the interaction between financial inclusion and education reduces corruption. Additionally, the authors find that the expansion of bank credit and the use of credit and debit cards are the specific modes of financial inclusion and digital payments that can contribute to corruption reduction. Research limitations/implications This study awakens policymakers in African countries about the need to consider education as an alternative measure to support financial inclusion and reduce the use of physical cash in transactions for an effective fight against corruption. Practical implications Regarding practical implications, the study shows that financial inclusion besides reducing poverty for households can contribute to macroeconomic stability in Africa. Originality/value The study uses a representative sample composed of 46 African countries and considers the role of education in moderating the relationship between financial inclusion and financial innovation on corruption. Furthermore, the study identifies the specific modality of financial inclusion and digital payments that contribute to corruption reduction.
A CFI Verification System based on the RISC-V Instruction Trace Encoder

Control-Flow Integrity (CFI) is used to check a program execution flow and detect whether it is correctly executed and not altered by software or physical attacks. This paper presents a CFI verification system for programs executed on RISC- V cores. Our solution is based on the RISC- V instruction Trace Encoder (TE). The TE provides information about the execution path of the user program. Two approaches are proposed. One is consistent with the RISC- V TE standard. It permits to detect instruction skip attacks on function calls, on their returns and on branch instructions. The second implies an evolution of the RISC- V TE specifications to detect more complex fault models as the corruption of any discontinuity instruction. We implemented both approaches on a RISC-V core and simulated their efficiency against Fault Injection Attacks (FIA). Compared to existing CFI solutions, our methodology does not modify the user application code nor the RISC- V compiler.
CPU-GPU Layer-Switched Low Latency CNN Inference

Convolutional Neural Networks (CNNs) inference on Heterogeneous Multi-Processor System-on-Chips (HMPSoCs) in edge devices represent cutting-edge embedded machine learning. Embedded CPU and GPU within an HMPSoC can both perform inference using CNNs. However, common practice is to run a CNN on the HMPSoC component (CPU or GPU) provides the best performance (lowest latency) for that CNN. CNNs are not monolithic and are composed of several layers of different types. Some of these layers have lower latency on the CPU, while others execute faster on the GPU. In this work, we investigate the reason behind this observation. We also propose an execution of CNN that switches between CPU and GPU at the layer granularity, wherein a CNN layer executes on the component that provides it with the lowest latency. Switching between the CPU and the GPU back and forth mid-inference introduces additional overhead (delay) in the inference. Regardless of overhead, we show in this work that a CPU-GPU layer switched execution results in, on average, having 4.72% lower CNN inference latency on the Khadas VIM 3 board with Amlogic A311D HMPSoC.
Trolling of female journalists on Twitter in Pakistan: an analysis

In this study, we examined the trolling of female journalists on Twitter in Pakistan. Through quantitative and qualitative content analysis, we found the female journalists received offensive comments in which the trolls mocked their gender, profession, and personal lives. Though the trolls affiliated with the government produced more hate, opposition political parties and ordinary people also engaged in varying levels of gender-based slurs. Moreover, the journalists critical of the government received more negative comments than the pro-government journalists. Our findings show the trolls adopted a targeted approach in which the highly conservative cultural values were exploited to put social pressure on journalists to dissuade them against exercising their democratic rights.
Reconceptualising Disabilities and Inclusivity for the Postdigital Era: Recommendations to Educational Leaders

Inclusive education is pivotal to sustainable development in different parts of the world. This perhaps accounts for its inclusion in the Sustainable Development Goals (SDGs) in SDG4, which targets inclusive education. Meanwhile, inclusive education has predominantly dimensioned physically challenged or impaired learners. However, with the outbreak of the COVID-19 pandemic and the sudden transition to online teaching and learning, the quest for a different dimension of inclusive education, especially with regard to technology, is on the increase. This paper investigates how inclusive education is presented in scholarly published articles in the South African context. Five phases of a scoping review, namely, identification, finding/searching, choosing, extraction/charting and collation, were adapted for the review. From a search using the terms “inclusive” AND “distance” AND “education” AND “disability” AND “South Africa”, a corpus of 73 scholarly published articles was identified. Using different selection criteria, such as specific context of the review, 55 articles were deleted. Thus, a final corpus of 18 articles was analysed. From the reviewed relevant literature, themes were generated after retrieved information had been coded and categorised. The review indicated that the focus on inclusive education in the South African context is directed towards physically impaired or challenged persons. The paper recommends that in the context of distance education and with the sudden transition to online teaching and learning, lack of access to technology such as computers and Wi-Fi, among others, can constitute a technological disability. Thus, inclusive education in the dimension of technological disability should be explored to enable the leadership of education systems in providing the required assistance.
Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm's searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems.
Returning to critical pedagogy in a world of datafication

The COVID-19 pandemic prompted a further extension of the sociotechnical logics of digital platforms to every realm of social life. Given the colonialist, oppressive and exploitative dynamics through which digital platforms work, several scholars supported the need to embrace an openly activist role to help individuals contrast the ways in which they are trapped in loops of dependency and trajectorism. Drawing on the results of 40 auto-ethnographic diaries, this paper showcases the usefulness of critical pedagogical techniques in enhancing critical awareness regarding hegemonic datafication structures, while also arguing that despite a good level of consciousness raising, it remains difficult for people to go beyond subalternity and make more concrete changes in personal and collective behaviors. We contend that to break persistent feelings of dependency, it is necessary to go further with a two-step process combining autoethnographic tools, aimed at increasing critical algorithmic awareness, with the development of data science skills that can help individuals acquiring more precise knowledge schemes and scaling down the power of giant corporations, thereby building individual and collective capacities to use data for developing counter-narratives about possible futures.
Comparative Study of ASEAN Research Productivity

Research productivity has become one of the main indicators used by higher education institutions (HEIs) as well as the country to support their innovation capability. This study purposely describes the research productivity among ASEAN countries, which is considered to be the world’s current economic hotspot. By using SciVal database to examine the literature over the last 10 years, we describe productivity, citation impact, and economic impact metrics. The findings indicate that Singapore is superior in terms of publication quality (citation) and patents while Malaysia is leading in terms of the number of scientific research. Interestingly, Indonesia’s scientific publication growth has the highest percentage. Furthermore, Engineering &amp; Technology and Life Sciences &amp; Medicine are the two major contributors to ASEAN research productivity. These subjects could be the major locomotives for ASEAN countries to use to sustain their competitiveness if the leaders can transform it into successful commercialization.
DTSC-Net: Semi-supervised 3D Biomedical Image Segmentation through Dual-Teacher Simplified Consistency

Although those deep learning networks have achieved remarkable performance in various biomedical image segmentation tasks, they rely on massive labeled data for training, which is time-consuming to acquire. To alleviate this challenging issue, semi-supervised learning (SSL) has shown the potential to simultaneously leverage between abundant unlabeled samples and limited labeled data. In this work, we propose a new SSL approach for left atrial and liver tumor segmentation called DTSC-Net. First, two teacher networks and one student network are optimized together through a supervised loss and two unsupervised consistency losses. Furthermore, pseudo labels are yielded by using the first teacher network on unlabeled data. In addition, the weights of the second teacher network are an exponential moving average of the student network’s weights. Moreover, the student network learns from the two teacher networks by minimizing a supervised segmentation loss, a pseudo-labeling unsupervised consistency (PUC) loss, and a dual-teacher unsupervised consistency (DUC) loss with respect to the targets of the two teacher networks. Experiment results show that our approach achieves state-of-the-art semi-supervised segmentation performance on the LA2018 and LiTS2017 datasets.
CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://github.com/ProgrammerHyy/CM-MLP.
Real-Time Mask Detection Based on SSD-MobileNetV2

After the outbreak of COVID-19, mask detection, as the most convenient and effective means of prevention, plays a crucial role in epidemic prevention and control. An excellent automatic real-time mask detection system can reduce a lot of work pressure for relevant staff. However, by analyzing the existing mask detection approaches, we find that they are mostly resource-intensive and do not achieve a good balance between speed and accuracy. And there is no perfect face mask dataset at present. In this paper, we propose a new architecture for mask detection. Our system uses SSD as the mask locator and classifier, and further replaces VGG-16 with MobileNetV2 to extract the features of the image and reduce a lot of parameters. Therefore, our system can be deployed on embedded devices. Transfer learning methods are used to transfer pre-trained models from other domains to our model. Data enhancement methods in our system such as MixUp effectively prevent overfitting. It also effectively reduces the dependence on large-scale datasets. By doing experiments in practical scenarios, the results demonstrate that our system performed well in real-time mask detection.
Variational quantum one-class classifier

Abstract One-class classification (OCC) is a fundamental problem in pattern recognition with a wide range of applications. This work presents a semi-supervised quantum machine learning algorithm for such a problem, which we call a variational quantum one-class classifier (VQOCC). The algorithm is suitable for noisy intermediate-scale quantum computing because the VQOCC trains a fully-parameterized quantum autoencoder with a normal dataset and does not require decoding. The performance of the VQOCC is compared with that of the one-class support vector machine (OC-SVM), the kernel principal component analysis (PCA), and the deep convolutional autoencoder (DCAE) using handwritten digit and Fashion-MNIST datasets. The numerical experiment examined various structures of VQOCC by varying data encoding, the number of parameterized quantum circuit layers, and the size of the latent feature space. The benchmark shows that the classification performance of VQOCC is comparable to that of OC-SVM and PCA, although the number of model parameters grows only logarithmically with the data size. The quantum algorithm outperformed DCAE in most cases under similar training conditions. Therefore, our algorithm constitutes an extremely compact and effective machine learning model for OCC.
Reversible attack based on adversarial perturbation and reversible data hiding in YUV colorspace

Recent research on using adversarial perturbation to prevent malicious models from accessing image data has led to the corruption of image data, making images useless in other fields, especially in digital forensics. To prevent malicious models from retrieving images and ensure that authorized models can recover original image data without distortion, the reversible attack technique is rising. However, attack ability, reversibility, and image visual quality are three major challenges for existing reversible attack techniques. In this paper, a novel reversible attack method based on adversarial perturbation and reversible data hiding in YUV colorspace is proposed. We first add adversarial perturbation into the luminance channel. Then, the luminance channel distortion caused by adversarial perturbation is embedded into chrominance channels by reversible data hiding to achieve the reversible attack. In particular, the class activation mapping module is introduced to narrow the perturbation region to reduce the amount of embedded data. Experimental results on the ImageNet dataset demonstrated that the proposed method achieves better attack ability and image visual quality and ensures that original images can be recovered without distortion.
A multi-view ensemble clustering approach using joint affinity matrix

In multi-view ensemble clustering, the correctly-partitioned data objects should be assigned with a higher weight, thereby helping to decrease the influence of incorrectly-partitioned data objects. Therefore, different data objects should be treated separately instead of being set the same view weight as traditional solutions. In this paper, a multi-view ensemble clustering approach is proposed using joint affinity matrix, which is generated by sample-level weight. Firstly, a new concept of core data objects is defined according to the influence index and Gaussian Mixed Model, and basic partitions and sample-level weights can be yielded for every view. Secondly, a joint affinity matrix, which maintains pairwise similarities of all data objects, is generated using the sample-level weights. Consequently, data objects can be effectively assigned to the correct partition. Thirdly, a multi-view ensemble clustering algorithm is proposed using the joint affinity matrix. In the end, experimental results on benchmark datasets validate the efficacy of the algorithm with state-of-the-art baselines.