Reviewerly/exHarmony huggingface dataset:
The Bangkok Urbanscapes Dataset for Semantic Urban Scene Understanding Using Enhanced Encoder-Decoder With Atrous Depthwise Separable A1 Convolutional Neural Networks

Semantic segmentation is one of the computer vision tasks which is widely researched at present. It plays an essential role to adapt and apply for real-world use-cases, including the application with autonomous driving systems. To further study self-driving cars in Thailand, we provide both the proposed methods and the proposed dataset in this paper. In the proposed method, we contribute Deeplab-V3-A1 with Xception, which is an extension of DeepLab-V3&#x002B; architecture. Our proposed method as DeepLab-V3-A1 with Xception is enhanced by the different number of <inline-formula> <tex-math notation="LaTeX">$1\times1$ </tex-math></inline-formula> convolution layers on the decoder side and refining the image classification backbone with modification of the Xception model. The experiment was conducted on four datasets: the proposed dataset and three public datasets i.e., the CamVid, the cityscapes, and IDD datasets, respectively. The results show that our proposed strategy as DeepLab-V3-A1 with Xception performs comparably to the baseline methods for all corpora including measurement units such as mean IoU, F1 score, Precision, and Recall. In addition, we benchmark DeepLab-V3-A1 with Xception on the validation set of the cityscapes dataset with a mean IoU of 78.86&#x0025;. For our proposed dataset, we first contribute the Bangkok Urbanscapes dataset, the urban scenes in Southeast Asia. This dataset contains the pair of input images and annotated labels for 701 images. Our dataset consists of various driving environments in Bangkok, as shown for eleven semantic classes (Road, Building, Tree, Car, Footpath, Motorcycle, Pole, Person, Trash, Crosswalk, and Misc). We hope that our architecture and our dataset would help self-driving autonomous developers improve systems for driving in many cities with unique traffic and driving conditions similar to Bangkok and elsewhere in Thailand. Our implementation codes and dataset are available at <uri>https://kaopanboonyuen.github.io/bkkurbanscapes</uri>.
Optimizing MapReduce Task Scheduling on Virtualized Heterogeneous Environments Using Ant Colony Optimization

Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible when MapReduce workloads and virtual machines (VM) are highly heterogeneous. Therefore, in this paper, we addressed these heterogeneities and proposed an efficient MapReduce scheduler to improve resource utilization by placing the right combination of the map and reduce tasks in each VM in the virtual cluster. To achieve this, we transformed the MapReduce task scheduling problem into a 2-Dimensional (2D) bin packing model and obtained an optimal schedule using the ant colony optimization (ACO) algorithm. As an added advantage, our proposed ACO based bin packing (ACO-BP) scheduler minimized the makespan for a batch of jobs. To showcase the performance improvement, we compared our proposed scheduler with three existing schedulers that work well in a heterogeneous environment. As expected, results show that ACO-BP significantly outperformed the existing schedulers while dealing with workload and VM level heterogeneities.
Dual Learning-Based Graph Neural Network for Remote Sensing Image Super-Resolution

High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image interpretation, and single image super-resolution (SISR) reconstruction technology is becoming increasingly valuable and significant. The state-of-the-art deep-learning-based SISR methods have demonstrated remarkable advantages, while reconstructing complex texture details still remains a big challenge. Besides, as a typical ill-posed inverse problem, how to determine the optimal solution is another important topic. To address these problems, in this work, a dual learning-based graph neural network (DLGNN) is proposed, in which the GNN is utilized to consider the self-similarity patches in remote sensing imagery by aggregating cross-scale neighboring feature patches, and dual learning strategy is adopted to refine the reconstruction results by constraining the mapping process in terms of the loss function, transferring the typical ill-posed problem to a well-posed one. Abundant experiments on 3K VEHICLE&#x005F;SR datasets and Massachusetts Roads demonstrate the validity and outstanding performance for remote sensing image super-resolution tasks compared with other state-of-the-art super-resolution construction methods. Code is available at https://github.com/CUG-RS/DLGNN.
GA-based geometrically optimized topology robustness to improve ambient intelligence for future internet of things

This study presents an ambient intelligence oriented, topological robustness scheme for internet of things (IoT). The scheme primarily exploits the underlying geometric properties of scale-free IoT networks for a substantial improvement of genetic algorithm (GA) based state of the art robustness techniques. The geometrically optimized GA (Go-GA) is subsequently extended to traditional heuristics algorithms by proposing their geometrically optimized variants. All three techniques are comparatively evaluated over a simulated scale-free IoT architecture employing Schneider R as metric of robustness. The study follows a data-driven approach where information about nodes and edges is pulled from a central big data server, and topological robustness of a given scale-free IoT is tested against existing benchmarks. The proposed scheme aims to achieve convergence to global optima and conserve computational costs by efficient edge swapping (EES) and node removal based thresholding (NRT). Performance evaluations show that Go-GA outperforms state of the art variants of GA by a margin of 20% for Schneider R. Traditional techniques of hill climbing algorithm (HCA), simulated annealing algorithm (SAA) and ROSE also improve by a margin of 11%, 12% and 14% respectively with consideration of geometric aspects. Moreover, as the network size increases, a mere decline of 7.6% in robustness R is observed for Go-GA as compared to 18% degradation for classical algorithms.
<i>SEHC</i>: A Benchmark Setup to Identify Online Hate Speech in English

Thanks to the digital age, online speech and information may now be disseminated anonymously without regard for repercussions. Regulators face a unique problem with social media platforms because of the speed and volume of material and the lack of editorial supervision. The existing datasets on hate speech or offensive language identification lack diversity in the dataset’s content. In this article, we create a multi-domain hate speech corpus (MHC) of English tweets that includes hate speech against religion, nationality, ethnicity, and gender in general and cover diverse domains, such as current affairs, politics, terrorism, technology, natural disasters, and human/drugs trafficking. Each instance in our dataset is manually annotated as hate or non-hate. We use the existing state-of-the-art models and present a stacked-ensemble-based hate speech classifier (SEHC) to identify hate speech from Twitter data. Our results indicate that the proposed method may serve as a strong baseline for future studies using this dataset. (The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.iitp.ac.in/~ai-nlp-ml/resources.html#MHC</uri> .)
Blockchain-Enabled Approach for Big Data Processing in Edge Computing

The integration of blockchain with edge computing technology has emerged as a popular topic in recent years. Edge computing offers many advantages to IoT data computing by reducing the bandwidth in the cloud, whereas blockchain offers a promising solution to the data integrity issues of dynamic edge nodes. There is considerable technological mismatch, however, when employing a blockchain protocol for edge computing. Standard edge nodes usually deal with big data and require constant data exchange over time, whereas traditional blockchains prevent the storage of big data in a distributed network. This study presents a novel blockchain architecture called Recordchain, which is fully adapted to the edge computing environment. The innovative feature of Recordchain is its ability to locate actual data separately while providing security, scalability, and performance, thus addressing the storage utilization issues related to big data. The efficiency and performance of the proposed architecture is further demonstrated experimentally.
Few-shot learning with unsupervised part discovery and part-aligned similarity

Few-shot learning aims to recognize novel concepts with only a few examples. To this end, previous studies resort to acquiring a strong inductive bias via meta-learning on a group of similar tasks, which however needs a large labeled base dataset to sample training tasks. In this paper, we show that such inductive bias can be learned from a flat collection of unlabeled images, and instantiated as transferable representations among seen and unseen classes. Specifically, we propose a novel unsupervised Part Discovery Network (PDN) to learn transferable representations from unlabeled images, which automatically selects the most discriminative part from an input image and then maximizes its similarities to the global view of the input and other neighbors with similar semantics. To better leverage the learned representations for few-shot learning, we further propose Part-Aligned Similarity (PAS), the key of which is to measure image similarities based on a set of discriminative and aligned parts. We conduct extensive studies on five popular few-shot learning datasets to evaluate our approach. The experimental results show that our approach outperforms previous unsupervised methods by a large margin and is even comparable with state-of-the-art supervised methods.
Color stereo image encryption and local zero-watermarking schemes using octonion Hahn moments and modified Henon map

Discrete orthogonal moments (DOMs) are widely applied in the field of image processing. However, most existing DOMs are used for flat image analysis and are less suitable for color stereo image processing. In light of this limitation, this paper firstly presents a moments type called octonion Hahn moments (OHMs) for the compact analysis of color stereo images, and also for preserving the internal relations between all the color channels of the stereo image. OHMs are introduced in both Cartesian and polar coordinates. Secondly, we present a modified version of 2D Henon map that exhibits improved chaotic behavior over its original version. This version is introduced for potential deployment in the field of information security. Thirdly, we present an efficient color stereo image encryption scheme in the transform domain using the modified Henon’s map and OHMs that are defined in Cartesian space. This scheme guarantees a perfect reconstruction of the stereo image. Finally, we present a local zero-watermarking scheme for color stereo image copyrighting based on the proposed version of Henon’s map and OHMs that are defined in the polar coordinate space. This scheme is originally designed to be robust against image rotation attack. Compared to excellent existing encryption schemes, our scheme shows superior performance in terms of high security level and good robustness to various attacks (classical, statistical, noise addition, cropping, etc.). Moreover, the proposed zero-watermarking method shows stronger resistance to different types of attacks (geometric, common image processing, noise, desynchronization, etc.) in comparison to recent zero-watermarking methods.
Development of a companion questionnaire for “Did You Feel It?”: Assessing response in earthquakes where an earthquake early warning may have been received

Earthquake early warning (EEW) systems are relatively new technologies having first emerged as regional systems in the 1990s. Japan was the first nation to develop and implement a nationwide system in October 2007, and in the United States, ShakeAlert ® became available on the entire length of the US West Coast in May 2021. Assessing how EEW is perceived and utilized by alert recipients is considered essential. Such assessments are necessary to evaluate whether alert recipients are taking advantage of alert messages to initiate protective actions upon receipt of an alert, how they regard the usefulness of alerts, desirable thresholds for issuing alerts, and other aspects of these systems. Having information from users will also facilitate assessments of the success of earthquake preparedness educational programs such as the ShakeOut and whether annual drills which include information on EEW systems are resulting in behavioral response consistent with the content of these programs. Finally, information on EEW utilization will provide data useful to social scientists who study hazards to advance our understanding of behavioral response to warnings. Survey research in the aftermath of a significant earthquake in which an EEW has been issued is one obvious method of achieving these objectives and there already exist a number of survey instruments for this purpose. A related strategy and the goal of the present research is to develop a brief questionnaire, consistent with those already developed, as a supplement to the United States Geological Survey’s “Did You Feel It?” questionnaire that has provided earthquake intensities and information on behavioral response in earthquakes, both domestic and international, since 2004. Having the intensity level at each respondent’s location is essential for relating their perspectives and actions to the shaking they experienced.
LightRay: Lightweight network for prohibited items detection in X-ray images during security inspection

Object detection algorithms based on deep learning have been widely used in the intelligent detection of prohibited items in X-ray images during the security inspection process. However, security inspection is a process that emphasizes timeliness, and the low inference efficiency brought by overly complex deep neural networks is not allowed. In this paper, we propose a lightweight object detection framework based on the YOLOv4 algorithm, named LightRay. Where MobileNetV3 is applied as the feature extraction backbone. To effectively deal with the detection problem of prohibited items with small sizes in complex backgrounds, a feature enhancement network in shallow layers is proposed. Where, the Lightweight Feature Pyramid Network (LFPN) and the Convolutional Block Attention Module (CBAM) effectively achieve feature fusion of different scales, while strengthening the features of small objects. The experimental results show that the mAP of the lightRay model is 87.28% on the SIXray data set, while the FLOPs of the model are reduced to the original 1/5, and Params is reduced to 1/3 of the original. In addition, some ablation experiments confirm the ability of the LightRay model the detection of prohibited items with small sizes. The codes of LightRay are released at https://github.com/zhg-SZPT/LightRay.
Curve Fitting-Based Dynamic Path Planning and Tracking Control for Flexible Needle Insertion

Needle insertion is widely used in modern clinical practice, and flexible needles enable surgeons to adjust the needle insertion path to avoid obstacles and compensate for target drift. To increase the accuracy and safety of the needle insertion procedure, a path planning and tracking control method for flexible needle steering in dynamic environment is proposed in this paper. The path planning method is based on the artificial potential field algorithm and curve fitting algorithm, after several feasible paths with variant curvatures is obtained, they are furtherly optimized based on path length, obstacle clearance and control effort to select the best one. Simulations show that the proposed path planning method performs better than the current constant curvature method and the rapidly-exploring random tree (RRT) based method. By finely tuning the initial planned path, an intraoperative path replanning method is proposed, which are used to compensate for the drift of target and obstacles. Then to ensure the needle is inserted along the planned path, a tracking control method is adopted based on fuzzy logic algorithm. With the proposed intraoperative path planning and tracking control method, the robustness and accuracy of needle insertion in congested environment can be improved, and experiments also show that our proposed method achieves satisfactory insertion safety and targeting accuracy in dynamic environment. The obtained accuracy of 1.57 mm is sufficient for most clinical needle insertion applications.
A Blockchain-Based Framework to Enhance Anonymous Services with Accountability Guarantees

Anonymous service delivery has attracted the interest of research and the industry for many decades. To obtain effective solutions, anonymity should be guaranteed against the service provider itself. However, if the full anonymity of users is implemented, no accountability mechanism can be provided. This represents a problem, especially when referring to scenarios in which a user, protected by anonymity, may perform illegally when leveraging the anonymous service. In this paper, we propose a blockchain-based solution to the trade-off between anonymity and accountability. In particular, our solution relies on three independent parties (one of which is the service provider itself) such that only the collaboration of all three actors allows for the disclosure of the real identity of the user. In all other cases, anonymity is guaranteed. To show the feasibility of the proposal, we developed a prototype with user-friendly interfaces that minimize the client-side operations. Our solution is then also effective from the point of view of usability.
An Efficient and Privacy-Preserving Range Query over Encrypted Cloud Data

The growing power of cloud computing prompts data owners to outsource their databases to the cloud. In order to meet the demand of multi-dimensional data processing in big data era, multi-dimensional range queries, especially over cloud platform, have received extensive attention in recent years. However, since the third-party clouds are not fully trusted, it is popular for the data owners to encrypt sensitive data before outsourcing. It promotes the research of encrypted data retrieval. Nevertheless, most existing works suffer from single-dimensional privacy leakage which would severely put the data at risk. Up to now, although a few existing solutions have been proposed to handle the problem of single-dimensional privacy, they are unsuitable in some practical scenarios due to inefficiency, inaccuracy, and lack of support for diverse data. Aiming at these issues, this paper mainly focuses on the secure range query over encrypted data. We first propose an efficient and private range query scheme for encrypted data based on homomorphic encryption, which can effectively protect data privacy. By using the dual-server model as the framework of the system, we not only achieve multi-dimensional privacy-preserving range query but also innovatively realize similarity search based on MinHash over ciphertext domains. Then we perform formal security analysis and evaluate our scheme on real datasets. The result shows that our proposed scheme is efficient and privacy-preserving. Moreover, we apply our scheme to a shopping website. The low latency demonstrates that our proposed scheme is practical.
Application of Human–Computer Interaction Virtual Reality Technology to the Design of Ice and Snow Landscapes

Various artistic expressions of ice and snow landscapes are deeply loved by the public. To study the application of virtual reality (VR) technology in the design of ice and snow landscape based on human–computer interaction (HCI), Harbin ice and snow landscape is taken as the research object. By applying HCI technology and VR technology, the design of ice and snow landscape is explored. VR technology allows the public to appreciate related things through interactive technology. The application of HCI technology and VR technology in the design of ice and snow landscape has established a system with the theme of ice and snow design, and created a virtual world of ice and snow in the system, enabling designers to be in a VR environment. During the design process, creators can enjoy a more comprehensive experience, constantly looking for the best visual area to stimulate their innovative thinking and inspiration. The results found that the designed system can present a better ice and snow landscape for the public, promote the rapid and stable development of ice and snow sculptures and landscapes, and promote the development of the local economy to a certain extent. Besides, the system has certain reference significance for the use of VR technology to study ice and snow landscapes, and also provides new ideas for the design of other types of landscapes.
Accurate BDD-based unitary operator manipulation for scalable and robust quantum circuit verification

Quantum circuit verification is essential, ensuring that quantum program compilation yields a sequence of primitive unitary operators executable correctly and reliably on a quantum processor. Most prior quantum circuit equivalence checking methods rely on edge-weighted decision diagrams and suffer from scalability and verification accuracy issues. This work overcomes these issues by extending a recent BDD-based algebraic representation of state vectors to support unitary operator manipulation. Experimental results demonstrate the superiority of the new method in scalability and exactness in contrast to the inexactness of prior approaches. Also, our method is much more robust in verifying dissimilar circuits than previous work.
Cognitive Radios Equipped With Modulation and STBC Recognition Over Coded Transmissions

Signal recognition has recently emerged as a key ingredient for cognitive radios with defense and industrial uses. This letter area encompasses the recognition of a broad range of transmission aspects such as modulation format, space-time mapping, channel coding form, central frequency, and data rate. Each of these aspects has been extensively researched in the literature. Only a few works addressed co-recognition. To the best of the authors’ knowledge, there is a single study that is dedicated to the recognition of combined modulation and space-time block coding (STBC), with the constraints of having more receive antennas than transmit antennas and operating over frequency-flat channels. In this letter, we propose a novel algorithm that recognizes modulation and STBC simultaneously over unknown frequency-selective channels while relaxing the requirement of having more antennas at the receiver than at the transmitter. The mathematical treatments demonstrate how an iterative expectation-maximization strategy is simply used to create a maximum-likelihood solution for this problem. Additionally, we make use of soft information coupled with channel decoders to enhance the proposed algorithm’s recognition performance. The proposed design also incorporates the supplementary task of channel estimation as a part of its overall structure. According to the findings of the computational complexity analysis and simulation results, the proposed algorithm easily defeats the one documented in the literature.