Reviewerly/exHarmony huggingface dataset:
A 3D Vision Cone Based Method for Collision Free Navigation of a Quadcopter UAV among Moving Obstacles

In the near future, it’s expected that unmanned aerial vehicles (UAVs) will become ubiquitous surrogates for human-crewed vehicles in the field of border patrol, package delivery, etc. Therefore, many three-dimensional (3D) navigation algorithms based on different techniques, e.g., model predictive control (MPC)-based, navigation potential field-based, sliding mode control-based, and reinforcement learning-based, have been extensively studied in recent years to help achieve collision-free navigation. The vast majority of the 3D navigation algorithms perform well when obstacles are sparsely spaced, but fail when facing crowd-spaced obstacles, which causes a potential threat to UAV operations. In this paper, a 3D vision cone-based reactive navigation algorithm is proposed to enable small quadcopter UAVs to seek a path through crowd-spaced 3D obstacles to the destination without collisions. The proposed algorithm is simulated in MATLAB with different 3D obstacles settings to demonstrate its feasibility and compared with the other two existing 3D navigation algorithms to exhibit its superiority. Furthermore, a modified version of the proposed algorithm is also introduced and compared with the initially proposed algorithm to lay the foundation for future work.
Does technological inclusion reduce financial constraints on small and medium sized enterprises? The case of Vietnam

This study investigates whether the use of information technology capabilities reduces financial constraints on SMEs. Using the instrumental variable technique with a Vietnamese panel dataset, the estimates provide evidence that the utilization of technology facilitates loan applications and increases approval rates. In addition, while taking advantage of technology does not help extend loan terms, it does increase the amount of formal loans. Further analysis reveals several potential channels of transmission. First, technology use promotes e-business, and improves firm productivity. Also, although the adoption of technology does not immediately improve the transparency of the business environment, it does promote innovative activities.
Recognition and classification of mathematical expressions using machine learning and deep learning methods

The advent of various machine learning methods can add a distinct dimension to the domain of recognition. The realm of pattern recognition has been deeply influenced by the ongoing trend of artificial learning-based methodologies. The two-dimensional structure of mathematical symbols and expressions makes recognition tasks more difficult, particularly for mathematical expressions. This paper delves into recognition approaches based on machine learning and deep learning. The leading recognition algorithms from both categories, SVM, and CNN, have been deployed to recognize the Hasyv2 dataset. The competent accuracies of 62.3% and 76.21% have been given by SVM and CNN, respectively.
Bifurcated particle swarm optimizer with topology learning particles

The flying speed and trajectory of particles are subject to several factors, including neighborhood structures, inertia weight, and acceleration coefficients. This paper improves particle swarm optimizer by exploiting these factors. Specifically, it proposes an approach to adjust the neighborhood structures of particles adaptively. The search task is divided between two groups of particles, termed even and uneven, to perform a vigorous in-depth search. Each particle group pursues a different objective and conducts its search in a different manner. Even particles adaptively adjust their number of attractors and neighborhood radiuses to experience various flying trajectories and paces. Each uneven particle follows a single even one for a while until it is assigned to another even particle. Uneven particles are responsible for performing fine-grained searches in the vicinity of their associated even particles as well as their previously experienced locations. A tree structure is utilized to implement the neighborhood structures of the proposed method. In the presented structure, particles can experience large neighborhoods by choosing their attractors from higher levels of the tree. The proposed method is experimentally investigated on the comprehensive CEC2013 benchmark set and two challenging real-world problems: non-uniform circular antenna array synthesis and image segmentation. The comparison results with advanced particle swarm optimization algorithms demonstrate that search bifurcation and topology adjustment can significantly improve particle swarm optimization. Experimental results also indicate that the proposed method can be successfully employed for solving challenging real-world problems with various characteristics.
Evaluation of Deep Neural Network Compression Methods for Edge Devices Using Weighted Score-Based Ranking Scheme

The demand for object detection capability in edge computing systems has surged. As such, the need for lightweight Convolutional Neural Network (CNN)-based object detection models has become a focal point. Current models are large in memory and deployment in edge devices is demanding. This shows that the models need to be optimized for the hardware without performance degradation. There exist several model compression methods; however, determining the most efficient method is of major concern. Our goal was to rank the performance of these methods using our application as a case study. We aimed to develop a real-time vehicle tracking system for cargo ships. To address this, we developed a weighted score-based ranking scheme that utilizes the model performance metrics. We demonstrated the effectiveness of this method by applying it on the baseline, compressed, and micro-CNN models trained on our dataset. The result showed that quantization is the most efficient compression method for the application, having the highest rank, with an average weighted score of 9.00, followed by binarization, having an average weighted score of 8.07. Our proposed method is extendable and can be used as a framework for the selection of suitable model compression methods for edge devices in different applications.
PPDet: A novel infrared pedestrian detection network in a per-pixel prediction fashion

Infrared images are suitable for pedestrian detection under poor lighting conditions. However, the lack of feature information about pedestrians in infrared images and the diversities of pedestrians make infrared pedestrian detection difficult. To address this issue, this paper proposes a novel infrared pedestrian detection network in a per-pixel prediction fashion. The proposed network consists of three parts, including backbone, neck and head: 1) The backbone expands the receptive field of the network and solves the network degradation problem caused by network deepening; 2) The neck improves feature representation by fusing multi-scale feature information; 3) By designing multiple cascaded heads, pixel-by-pixel prediction of multi-scale pedestrians is achieved. The proposed method is compared with a variety of object detection algorithms on the SCUT dataset, and it achieves better performance in infrared pedestrian detection.
Efficient Spatio-Temporal Contrastive Learning for Skeleton-Based 3-D Action Recognition

In this paper, we propose a simple yet effective self-supervised method called spatio-temporal contrastive learning (ST-CL) for 3D skeleton-based action recognition. ST-CL acquires action-specific features by regarding the spatio-temporal continuity of motion tendency as the supervisory signal. To yield effective representations, ST-CL first designs some novel contrastive proxy tasks by providing different spatio-temporal observation scenes for the same 3D action and pulling them together in the embedding space. Second, three key components are devised in the action encoding to efficiently extract representations in contrastive tasks: (1) Information Representation introduces the awareness of joint type when analyzing motion dynamics. (2) Non-local GCN learns a data-driven graph topology structure and promotes a spatial message passing among long-range joints in each frame. (3) Multi-Scale TCN makes larger receptive fields for capturing richer longe-range temporal dynamics amomg adjacent frames. In ST-CL, these effective proxy tasks yield useful representations and efficient action encoding further enhances the representation capacity. As validated on four large-scale datasets, ST-CL is a strong baseline with high performance and efficiency for the contrastive learning study of the skeleton data. Compared to previous self-supervised methods, the proposed ST-CL achieves significant improvement consistently with a smaller model size and better training efficiency.
A mixed-methods study of novice teachers’ technology integration: Do they leverage their TPACK knowledge once entering the profession?

Research indicates educators benefit from developing Technological Pedagogical Content Knowledge (TPACK), a combination of constructs that inform the design of pedagogically-sound technology-integrated instruction. Applying a mixed-methods explanatory sequential study design, we examined if and how novice K-12 teachers, who developed TPACK during the same teacher education program, transferred that knowledge to their full-time teaching upon graduation. A total of 50 participants completed a survey that explored their perspectives of technology integration. Interviews were conducted with 20 participants to further examine the survey results. Three significant findings were identified: (1) participants perceived technology integration as important to their teaching, (2) a disconnect existed between teachers' perceived importance of technology-integrated activities and actual integration, and (3) persistent barriers continued to challenge teachers' integration of technology.
ADAM-sense: Anxiety-displaying activities recognition by motion sensors

In the field of Human Activity Recognition (HAR), human activities are recognized based on sensors' streaming data. HAR has been utilized widely in various fields of application where the studies of human behaviors are conducted such as healthcare, personal care, aged care, and several other domains. This approach can also be beneficial in the field of psychiatry where the patients suffer from mental, emotional, and behavioral disorder. According to American Psychiatric Association (APA), the most common form of mental disorder is Anxiety Disorder (AD) effecting 30% of the adult population at some point in their life. In this paper, a HAR based method is proposed to recognize some behaviors pertaining to anxiety display. To make such model, a novel dataset of anxious behaviors is also created with unique features using motion sensors of smartphone and Inertial Measurement Unit (IMU). Several deep learning-based models are created and compared against random forest and gradient boost algorithms, where a deep model comprising Convolution Neural Network (CNN) and Long-Short Term Memory (LSTM) is shown to perform better than other models and could recognize anxiety-related behaviors with over 92% accuracy.
Towards the Mobile Detection of Cervical Lesions: A Region-Based Approach for the Analysis of Microscopic Images

Given the current prevalence and impact of cervical cancer worldwide, many technological developments focused on automating the screening process have arisen recently. Nonetheless, there is still a clear need for affordable, portable and automated IoT-based solutions to expand the coverage of current cervical screening programs worldwide. This is particularly relevant for lower-resource countries, which account for 88% of all cervical cancer-related deaths. This work proposes a low-cost, smartphone-based microscopy device for the analysis of liquid-based cytology samples, through autonomous image acquisition and automated identification of cervical lesions. Different deep learning models for object detection were separately optimised and compared to select the most adequate network architecture.Transfer learning from a similar application domain - conventional cytology - was also investigated as a way of improving the robustness of the analysis pipeline, as well as overcoming the limitations of the mobile-acquired image dataset specifically collected and manually annotated by specialists under the scope of this work. In this process, a detection performance benchmark in the SIPAKMED dataset - test mean average precision (mAP) of 0.37798 and average recall (AR) of 0.63651 - was reported for the first time. Although further improvements are required for its integration in a computer-aided diagnosis system sufficiently reliable for deployment in a clinical context, the explored approach exhibits promising results (cross-validation mAP of 0.20315, AR of 0.46572 and analysis time of 4 minutes per cytological sample), corresponding to a step forward in the development of a cost-effective mobile IoT framework that supports cervical lesion screening.
Blind Digital Image Watermarking using Henon Chaotic Map and Elliptic Curve Cryptography in Discrete Wavelets with Singular Value Decomposition

Copyright protection of digital images has become vital in the modern era where multimedia content is the dominant form of traffic in networked multimedia communication systems. Hence, there is a need for building technologies around robustness and computational efficiency for watermark embedding and extraction through state-of-the-art cryptographic algorithms. In this paper, we have proposed a blind image watermarking system that applies Henon chaotic map for watermark embedding and Elliptic Curve Cryptography (ECC) for securely sending the algorithmic parameters to the receiver. Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) are utilised for hybrid decomposition of the host image. The metrices used for the evaluation of the robustness and imperceptibility of this watermarking system are the Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NC) and the Central Processing Unit (CPU) runtime was recorded in order to assess the system’s execution speed.
An efficient ant colony optimization framework for HPC environments

Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants’ solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale).
A differential privacy-based classification system for edge computing in IoT

The blooming Internet of Things (IoTs) devices have brought many new types of sensing applications and methods to the traditional cloud-enabled IoTs framework. Hence, the new network framework becomes bidirectional gradually, in which IoTs devices can also perform moderate computation tasks instead of being solely used as data harvesters. This new coming framework known as edge computing significantly improves the traditional cloud-enabled network latency and dependency by shifting part of computation back to “local”. However, new security risks emerge when the edge computing shifts data and models back to the IoTs devices. Acies, a differential privacy based privacy-preserving classification system for edge computing is proposed to secure the classification models offloaded to edge devices. Acies supports popular classifiers such as Nearest Neighborhood, Support Vector Machine and Sparse Representation Classifier with a variety of feature selection methods. According to our evaluation on different datasets, classification models with Acies can be private and maintain high utility. Acies achieves reliable privacy protection under reconstruction attacks with minimal impact on classification accuracy (2% ∽ 5%) only. Acies outperforms the naive input dataset perturbation methods by up to 30% higher classification accuracy when the privacy requirements of the applications is high (the privacy budget ε is less than 2).
Robust Detection of Machine-induced Audio Attacks in Intelligent Audio Systems with Microphone Array

With the popularity of intelligent audio systems in recent years, their vulnerabilities have become an increasing public concern. Existing studies have designed a set of machine-induced audio attacks, such as replay attacks, synthesis attacks, hidden voice commands, inaudible attacks, and audio adversarial examples, which could expose users to serious security and privacy threats. To defend against these attacks, existing efforts have been treating them individually. While they have yielded reasonably good performance in certain cases, they can hardly be combined into an all-in-one solution to be deployed on the audio systems in practice. Additionally, modern intelligent audio devices, such as Amazon Echo and Apple HomePod, usually come equipped with microphone arrays for far-field voice recognition and noise reduction. Existing defense strategies have been focusing on single- and dual-channel audio, while only few studies have explored using multi-channel microphone array for defending specific types of audio attack. Motivated by the lack of systematic research on defending miscellaneous audio attacks and the potential benefits of multi-channel audio, this paper builds a holistic solution for detecting machine-induced audio attacks leveraging multi-channel microphone arrays on modern intelligent audio systems. Specifically, we utilize magnitude and phase spectrograms of multi-channel audio to extract spatial information and leverage a deep learning model to detect the fundamental difference between human speech and adversarial audio generated by the playback machines. Moreover, we adopt an unsupervised domain adaptation training framework to further improve the model's generalizability in new acoustic environments. Evaluation is conducted under various settings on a public multi-channel replay attack dataset and a self-collected multi-channel audio attack dataset involving 5 types of advanced audio attacks. The results show that our method can achieve an equal error rate (EER) as low as 6.6% in detecting a variety of machine-induced attacks. Even in new acoustic environments, our method can still achieve an EER as low as 8.8%.
Advances in human action, activity and gesture recognition

A set of advanced approaches and models on human action, activity, gesture, and behavior recognition related aspects along with associated challenges are summarized in this note. Notably, Video-based human activity recognition, sensor-based activity analysis, skeleton-based activity recognition, assisted daily living, anomaly detection, facial expression and emotion analysis, gesture and sign language are highlighted in the works. This special issue also introduced six new datasets, while exploring a total of 55 different datasets in its 21 research articles. Apart from the the areas covered in this issue, research on multi-modal analysis, action quality assessment, real-time applications, and activity and behavior computing on edge devices are some of the dominant future challenges to deal with. We firmly believe that the original works and ideas presented in this special issue will serve as helpful references for the relevant research communities in the journey towards a brighter future.
Modeling and Measuring for Emotion Communication based on DIKW

Traditional expression of emotion generally uses tone of voice or modality to express emotion content of a sender. There are limitations in expression space and personal differences in cognition, which can lead to misunderstanding. To solve these problems, we propose an emotion communication method based on Data, Information, Knowledge and Wisdom (DIKW). Firstly, the content expression of a sender transforms into Data, Information and Knowledge type resources by conceptualizing and abstracting for personalised services. In order to reduce unnecessary input costs, analysis of a sender purpose regulates the range of Data, Information, Knowledge and the transmission range of Data, Information, Knowledge. The order of priority for transferring them is in descending order of weight. Then, reshaping emotion content forms a topology that matches a receiver cognition ability to achieve a certain level of accuracy and efficiency transmission. Finally, using value-oriented assessment of emotion communications and blocking intrusions achieves a certain accuracy and efficiency and secure transmission.
Bilateral Temporal Re-Aggregation for Weakly-Supervised Video Object Segmentation

Weakly-supervised video object segmentation is an emerging video task to track and segment the target given a simple bounding box label, which requires the method to fully catch and utilize the target information. Most existing approaches only rely on the guidance of a single frame and ignore the interaction between different frames when gathering information, making them hard to achieve reliable target representation. In this paper, we propose to capture the temporal dependencies and gather information from multiple frames through bilateral temporal re-aggregation. We explore three schemes to build the aggregation: 1) a two-stage re-aggregation mechanism is applied to provide target prior to the current frame, which obtains more valid feature matching and information aggregation; 2) a query-memory bilateral aggregation module is proposed to aggregate features from an unlimited amount of past frames and enable the mutual perception between different frames to validate the gathered information; 3) we guide the learning of aggregation modules through a novel cross-task representation distillation, transferring the knowledge from a semi-supervised model to our weakly-supervised model without increasing the inference latency. These schemes collaboratively build an efficient and competent aggregation process, thus we can fully exploit the video context to make the inference. Experimental results on four benchmarks show that our method achieves superior performance than previous methods and still maintains the efficiency ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$e.g$ </tex-math></inline-formula> ., overall scores of 70.4% and 72.5% on the YouTube-VOS and DAVIS 2017 validation sets, respectively).