Reviewerly/exHarmony huggingface dataset:
A Study on the Method of Calculating S&amp;T ODA Statistics

It is important for the developing countries to secure the technological capacity using the international aids of development countries. It is also known that the Science and Technology(S&T) ODA have a greater impact on the economic growth of recipient countries than other sectors such as education, health and energy. However, there is not an officially recognized definition of S&T ODA and the researches on S&T related ODA are not enough.In this study, the authors define the S&T ODA concept and present the method of calculating S&T ODA statistics using Creditor Reporting System (CRS) purpose code, channel code, project description and ODA disbursement. S&T ODA statistics were calculated using the original ODA statistics provided by the OECD Development Assistance Committee (DAC).It is found out that the spending on S&T ODA in major countries (Korea, USA, Japan, Germany, France, UK) is about 4 billion dollars (2017), which is 4% (2017) of the total ODA spending. Korea's spending on S&T ODA was about 39 million dollars (2017), and it is far below than education (190 million dollar), health care (120 million dollar), agriculture, forestry and fisheries (100 million dollar) and administration (80 million dollar) sectors.In this research, the method of calculating S&T ODA was presented and S&T ODA trend can be found using authors’ methodology. It is hoped that the methodology is further developed and refined and officially recognized concerned international authorities. In this way, it could provide valuation information for S&T ODA which is necessary to strengthen the technology capacity building of developing countries.
Measuring Model Understandability by means of Shapley Additive Explanations

In this work we link the understandability of machine learning models to the complexity of their SHapley Additive exPlanations (SHAP). Thanks to this reframing we introduce two novel metrics for understandability: SHAP Length and SHAP Interaction Length. These are model-agnostic, efficient, intuitive and theoretically grounded metrics that are anchored in well-established game-theoretic and psychological principles. We show how these metrics resonate with other model-specific ones and how they can enable a fairer comparison of epistemically different models in the context of Explainable Artificial Intelligence. In particular, we quantitatively explore the understandability-performance tradeoff of different models which are applied to both classification and regression problems. Reported results suggest the value of the new metrics in the context of automated machine learning and multi-objective optimisation.
Mapping of aeolian deposits of an industrial site in the arid region using the TIR bands of ASTER and study of physicochemical characters and stabilization of sand erosion

This study describes the spectral emissive character of silicate and carbonate minerals of aeolian deposits and maps the deposits and sand encroachments that occurred in and around the site 5/6 of Qatar Fertilizer Company (QAFCO), Qatar using thermal infrared (TIR) bands of ASTER. The results of studies show that the quartz and unaltered silicates have spectral features between 8.12 and 9.27 µm, and the calcite and dolomite have emissivity minima near 11.4 and 11.2 µm, respectively. The mapping of deposits, dunes, and carbonate formations using the bands, and quartz index (QI), and carbonate index (CI) displayed their occurrence, distribution, and direction of sand movement from NW to SE. The sand encroachment was mapped using high spatial resolution satellite data of WorldView-2. The study of physicochemical characteristics of field samples showed the occurrence of sand grains up to 99.81% and the XRD and geochemical analyses represented the presence of quartz, calcite, dolomite, albite, and halite minerals in the deposits. In addition, the bacterial strains isolated from the samples indicated high urease activity leading to precipitation of carbonate minerals via microbially induced calcite precipitation (MICP) processes, and demonstrated high potential for utilization for sand stabilization of the QAFCO site.
3D-VR Based Color Design Method for Interior Space in Iot Applications

Virtual reality technology has grown in popularity as the economy and society have changed dramatically, and it is now being used in a wide range of applications. Interactive virtual technology’s deployment in the field of art has a wide range of possibilities due to its interaction, immersion, and real-time capabilities. The advantages of this technology, extremely in interiors and design, are unrivalled by other technologies. Virtual reality interior design can help designers and consumers understand the influence of “what you see is what you get,” while also expecting people to feel the visual concept in the 3D content of this model. The research and studies of interior decoration are conducted in this paper using visually stunning virtual reality technology, with the viewing public housing developments as the research object, creating a conceptual framework for successful research and innovation of interior design assisted by visually stunning Virtual reality (VR) technology. In this paper we applied the reliability to check the accuracy of questionnaire and descriptive statistics which summarises all the data.
UStark: underwater image domain-adaptive tracker based on Stark

Though the open-air tracker has achieved an advanced level, its design is still a challenging task for degraded underwater images. Using underwater enhancement technology can improve the performance of underwater trackers. However, most underwater image enhancement methods focus on improving the visual effect rather than serving the tracker better. Therefore, we intended to explore a simple but powerful image domain-adaptive method to improve Stark’s performance by enhancing Stark’s input images. Specifically, it consists of underwater image adaptation network (UIAN) with double heads and adaptation block based on scene estimation (ABSE) that consists of three independent image processing modules without deep learning. UIAN is used to predict the category of image domain and parameters of ABSE. ABSE decodes the parameters and sequentially process underwater images in each module. The training of UIAN is independent of the training of the tracker. After training the class prediction head of UIAN first, freezing its weights, by initializing and tracking in one enhanced image and computing tracker’s loss, the parameter head can be trained to make sure UIANs hyperparameters can match the Stark tracker. The UStark proposed can adaptively process clear and degraded underwater images. Compared with Stark, UStark has improved the accuracy and success rate in typical underwater environments by 3.7% and 1.5% (blue), 5% and 3.4% (yellow), and 5.4% and 3.3% (dark), respectively. In addition, compared with other underwater image enhancement methods, our method can enhance the performance of the tracker in more categories of underwater images.
A Fine-Grained Image Classification and Detection Method Based on Convolutional Neural Network Fused with Attention Mechanism

Due to the existence of attention system, people pay attention to the distinguishable area of the image, rather than directly receiving and processing the information of the whole image. This natural advantage makes attention mechanism widely used in fine-grained image classification. The research goal of fine-grained image classification task often is to differentiate subclass objects belonging to the same basic category. The difficulty of classification is that there are only slight local differences between different categories, but there may be large feature differences within the same category. At the same time, complex background features also bring interference factors to image recognition. In order to further extract discriminant regional features, this paper proposes a fine-grained image classification method WSFF-BCNN based on weak supervision feature fusion from two aspects: the improvement of the loss function in the training process of convolution neural network and the refinement of fine-grained image feature extraction. It uses the mixed attention of channel domain and spatial domain to obtain the detailed description information in the feature to highlight the response of the corresponding channel and spatial location in the feature map and pay attention to the attention characteristics of different dimensions. The original images of different sizes are input into the improved bilinear model to obtain multi-scale features. The large-scale features can represent the spatial location information of key areas, and the small-scale features represent the low-level features of the image. The backbone network of bilinear network uses ResNet50 to extract features and sample and zoom and uses bilinear pooling to fuse features of different scales to obtain a rich image feature representation.
Dynamic Reduced-Round TLS Extension for Secure and Energy-Saving Communication of IoT Devices

The security of wireless Internet of Things (IoT) communication is a complex challenge due to not only growing attack surfaces and threats but also the limitations of energy consumption. As a significant portion of the IoT market is composed of both security- and energy-critical sectors, e.g., smart homes and e-health, there is a pressing demand for solutions to secure billions of IoT devices while minimizing energy footprint. To this end, this article proposes a transport layer security (TLS) extension to integrate a lightweight and self-monitored mechanism that dynamically balances communication security and power consumption according to the IoT device’s current battery level. Integrated within the TLSv1.3 protocol, the secure extension automatically adjusts the encryption round number of the negotiated cipher according to an operator-defined policy while ensuring the minimum required security level. A Proof-of-Concept (PoC) has been implemented on the wolfSSL library and a real-world IoT platform, on which the performance of the proposed mechanism has been reported for various lightweight ciphers. The results showed an energy reduction of encryptions by 57.1% and a battery saving of 9.4% when encrypting at 4 kBps with reduced-round encryption, demonstrating the potential of the proposed extension into the TLS protocol.
Defending Against Co-Residence Attack in Energy-Efficient Cloud: An Optimization Based Real-Time Secure VM Allocation Strategy

Resource sharing among users serves as the foundation of cloud computing, which, however, may also cause vulnerabilities to diverse co-residence attacks launched by malicious virtual machines (VM) residing in the same physical server with the victim VMs. In this paper, we aim to defend against such co-residence attacks through a secure, workload-balanced, and energy-efficient VM allocation strategy. Specifically, we model the problem as an optimization problem by quantifying and minimizing three key factors: (1) the security risks, (2) the power consumption and (3) the unbalanced workloads among different physical servers. Furthermore, this work considers a realistic environmental setting by assuming a random number of VMs from different users arriving at random timings, which requires the optimization solution to be continuously evolving. As the optimization problem is NP-hard, we propose to first cluster VMs in time windows, and further adopt the Ant Colony Optimization (ACO) algorithm to identify the optimal allocation strategy for each time window. Comprehensive experimental results based on real world cloud traces validates the effectiveness of the proposed scheme.
Analysis of Network-level Key Exchange Protocols in the Post-Quantum Era

A Virtual Private Network (VPN) ensures the confidentiality and integrity of data transferred between two endpoints, even if the means of transport are insecure. One popular protocol is Internet Protocol Security (IPSec) which operates at OSI layer 3 and protects all protocols at higher layers. Cryptographic keys in IPSec are negotiated using the Internet Key Exchange (IKE) protocol. IKE negotiates security parameters for IPSec sessions. In particular, the IKEv2 protocol uses the Elliptic Curve Diffie-Hellman (ECDH) algorithm to establish a secret key shared between two nodes on a network. Although solving such a problem remains difficult with current computing power, it is believed that generic quantum computers will be able to solve this problem, which implies that the security of IKEv2 is compromised. There are, however, several cryptographic systems that are trusted to be resistant to attacks by quantum computers. This family of cryptosystems is known as quantum-resistant cryptography (QRC). In this paper, after highlighting the requirements for a secure key exchange protocol, we briefly review the QRC solutions that have been proposed in the recent literature.
Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks

Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users’ privacy. However, the training process might significantly occupy a long time due to the nature of the used data for training, which leads to higher energy consumption and therefore impacts the model convergence. To tackle this issue, we propose a data-driven federated edge learning scheme that tends to select suitable participating nodes based on quality data and energy. First, we design an unsupervised data-aware splitting scheme that partitions the node’s local data into diverse samples used for training. We incorporate a similarity index to select quality data that enhances the training performance. Then, we propose a heuristic participating nodes selection scheme to minimize the communication and computation energy consumption, as well as the amount of communication rounds. The obtained results show that the proposed scheme substantially outperforms the vanilla FEEL in terms of energy consumption and the number of communication rounds.
The Impact of Social Media on the Teaching and Learning of EFL Speaking Skills during the COVID-19 Pandemic

The shift to teaching online during the COVID-19 pandemic has resulted in the extensive use of social media in the English language classroom. The current study investigated the impact of social media on the speaking skills of tertiary-level EFL students during the pandemic. Adopting a qualitative framework, data was collected by using in-depth interviews with both students and lecturers. Findings generally point to the benefits of using social media in the speaking classroom. However, accounting for students&rsquo; cultural differences is the most important aspect during the lessons. Implications on the pedagogical awareness of lecturers are underscored.
Two-stage low-light image enhancement network with an attention mechanism and cross-stage connection

The purpose of low-light image enhancement is to improve image quality assessment by human visual perception and bolster the performance of subsequent visual tasks. It is necessary to consider not only the complexity of illumination in a real scene but also the issues of color distortion after image enhancement. We propose a two-stage network to solve these problems. In the first stage, we use a two-branch encoder–decoder subnetwork to learn multiscale features of the image, in which the brightness encoder branch is used to improve the network’s attention to a low-light region and improve the problem of uneven illumination. In the second stage, we utilize the detail recovery subnetwork to preserve details. In addition, we introduce an attention mechanism and cross-stage connection between the two stages to utilize the features learned by different subnetworks effectively. Extensive experiment results demonstrate that the proposed method outperforms several state-of-the-art methods in terms of quantitative metrics and the visual effect. Thus, our network enhances low-light images with high quality.
A Novel Unsupervised Domain Adaption Method for Depth-Guided Semantic Segmentation Using Coarse-to-Fine Alignment

Domain adaptation methods in machine learning deal with the domain shift issue by aligning source and target data representation. This paper proposes a novel domain adaptation method for semantic segmentation that exploits the Fourier transform on chromatic space to improve the quality of style transfer, and generates pseudo-labels for self-training by combining the results from different teachers obtained at different rounds of self-training. Our method also applies class-level adversarial learning to achieve a more fine-grained alignment between the two domains, and a late fusion with a depth-estimation model to improve its segmentation outputs. Experiments show that our method yields superior performance in terms of accuracy compared to other existing state-of-the-art methods.
Voice Conversion Based on Deep Neural Networks for Time-Variant Linear Transformations

This paper describes a novel framework of voice conversion to improve the conversion performance against the amount of training data. In voice conversion, deep neural networks are used as conversion models that map source to target features. In this framework, it generally needs a larger amount of training data and bigger models to build more accurate conversion models. This condition, however, will reduce the usability of voice conversion. In this paper, in order to improve the conversion performance versus the amount of training data, a top-down knowledge is introduced into models as prior. We expect that we can take advantage of top-down knowledge we have instead of preparing a large amount of data. In the proposed method, the conversion process of features is restricted to time-variant linear transformation on cepstral space. It explicitly utilizes an attribute of voice conversion i.e. homo-domain mapping, which is not common in automatic speech recognition or text-to-speech synthesis. In other words, in VC, the input and output are on the same feature domain. In addition, it also makes it possible to explicitly consider the physical difference between speakers such as the difference of vocal tract length. The assumption of the homo-domain mapping is related to conversion methods based on spectral differentials, and then the relation is discussed in the paper. Experiments demonstrate the effectiveness of our proposal and the way that the constraint of linear transformation works is investigated.
SMART: Vision-Based Method of Cooperative Surveillance and Tracking by Multiple UAVs in the Urban Environment

UAV surveillance and tracking have attracted great enthusiasm in intelligent transportation, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the urban environment, such as occlusion, view change, and background clutter. Ignoring these uncertain factors often leads to a reduction in surveillance performance and tracking quality. This study devotes to improving the cooperative surveillance capability of multi-UAV formation by designing different cooperative strategies in the urban environment. To be specific, a novel cooperative architecture is designed to control the observation locations of multiple UAVs throughout the formation process. For different types of interference, we introduce a novel target recognition rate of each UAV as the decision factor and design corresponding cooperative strategies to guarantee the accuracy of cooperative surveillance. Based on this architecture, we develop a vision-based method of cooperative surveillance and tracking by multiple UAVs (SMART) whose objective function is the motion cost and flight reliability of UAVs to ensure that each UAV can be in the optimal surveillance location for the target. The proposed SMART skillfully integrates the strict, elastic, and flight constraint strategies. During the execution of the multi-UAV formation, the inherent safety constraints of multiple UAVs and the designed strategies are used to solve the quadratic optimization model to adjust the locations of these UAVs. To demonstrate the superiority of our method, we conduct a 3D simulation urban environment and devise several experiments to analyze the performance of SMART on it. The experimental results demonstrate that SMART can not only maintain the high cooperative flight capability, but also provide high flexibility and fault tolerance.
A Domain-Guided Model for Facial Cartoonlization

Dear Editor, This work investigates the issue of facial cartoonlization under the condition of lacking training data. We propose a domain-guided model (DGM) to realize facial cartoonlization for different kinds of faces. It includes two parts: 1) a domain-guided model that contains four different interface networks and can embed an image from a facial domain to a cartoon domain independently; and 2) a one-to-one tutoring strategy that uses a sub-model as a teacher to train other interface networks and can yield fine-grained cartoon faces. Extensive qualitative and quantitative experimental results validate our proposed method, and show that DGM can yield fine-translated results for different kinds of faces and outperforms the state of the art.
Efficient and Secure Federated Learning With Verifiable Weighted Average Aggregation

Federated learning allows a large number of participants to collaboratively train a global model without sharing participant's local data. Participants train local models with their local data and send gradients to the cloud server for aggregation. Unfortunately, as a third party, the cloud server cannot be fully trusted. Existing research has shown that a compromised cloud server can extract sensitive information of participant's local data from gradients. In addition, it can even forge the aggregation result to corrupt the global model without being detected. Therefore, in a secure federated learning system, both the privacy and aggregation correctness of the uploaded gradients should be guaranteed. In this article, we propose a secure and efficient federated learning scheme with verifiable weighted average aggregation. By adopting the masking technique to encrypt both weighted gradients and data size, our scheme can support the privacy-preserving weighted average aggregation of gradients. Moreover, we design the verifiable aggregation tag and propose an efficient verification method to validate the weighted average aggregation result, which greatly improves the performance of the aggregation verification. Security analysis shows that our scheme is provably secure. Extensive experiments demonstrate the efficiency of our scheme compared with the state-of-the-art approaches.
Multigroup spatial shift models for thermal infrared tracking

Many trackers use attention mechanisms to enhance the details of feature maps. However, most attention mechanisms are designed based on RGB images and thus cannot be effectively adapted to infrared images. The features of infrared images are weak, and the attention mechanism is difficult to learn. Most thermal infrared trackers based on Siamese networks use traditional cross-correlation techniques, which ignore the correlation between local parts. To address these problems, this paper proposes a Siamese multigroup spatial shift (SiamMSS) network for thermal infrared tracking. The SiamMSS network uses a spatial shift model to enhance the details of feature maps. First, the feature map is divided into four groups according to the channel, moving unit wise in four directions of the two dimensions of height and width. Next, the sample and search image features are cross-correlated using the graph attention module cross-correlation method. Finally, split attention is used to fuse multiple response maps. Results of experiments on challenging benchmarks, including VOT-TIR2015, PTB-TIR, and LSOTB-TIR, demonstrate that the proposed SiamMSS outperforms state-of-the-art trackers. The code is available at lvlanbing/SiamMSS (github.com).