Reviewerly/exHarmony huggingface dataset:
Stemming Algorithm for the Indonesian Language: A Scientometric View

Stemming is the process of cutting affixes, both prefixes and suffixes from a term to get the root of the word that has affixes. Stemming can be done in any language, especially in Indonesia Language. Indonesian which is rooted in Malay and Sanskrit has a bigger influence on Language. The algorithm of stemming that has developed is the one that is based on the dictionary of a root word. This kind of algorithm was started by Nazief- Adriani and it grew up to be Confix Stripping (CS), and then was perfected to be Enhanced Confix Stripping (ECS). A scientometric analysis is a mathematical method used to identify academic publications related to citations and scientific matters and is intended for use in libraries or other fields. In this research, the publication of which using stemming algorithm selected and collected using keywords related to Nazief Adriani and ECS. The publications are collected from dimension.ai (an online database for advance scientific research). From those publication we did the analysis bibliometric help by the VOS Viewer application using the analysis technique of co-authorship and citation technique, and all done. The result are, from 310 publications that have keywords Nazief Adriani and 119 publications containing the keyword ECS, it was found that the owning documents will be the reference sources of the next research in which the most citation and co-authorship are found in Indonesia.
A Simple Lossless Algorithm for On-Board Satellite Hyperspectral Data Compression

As resolution of on-board imaging spectrometer keeps improving, data acquisition rate increases and resource limited satellite environment necessitates for computationally simple data compression methods to meet timing, bandwidth and resource requirements with error resilience. This letter proposes a new lossless, prediction based algorithm for on-board satellite hyperspectral data compression that utilizes spectral as well as spatial correlation and at the same time, is computationally less complex. Concept of non-binary tree traversal is used with nearest neighbor method and implemented using neighbor driven decision making in pre-processing stage. Previously processed pixels are used to minimize the prediction residual, which makes more than 80% calculations causal in nature and thereby reducing the computational complexity of the algorithm. The prediction residual is then encoded using sample adaptive Golomb coding in band-sequential order. CCSDS corpus of data for hyperspectral images is used for evaluating the performance of the algorithm. The proposed method shows reduced computational complexity and lesser data dependencies compared to the CCSDS 123.0-B-1 standard when similar spectral vicinity is considered, and comparable compression performance compared to other state-of-the-art on-board lossless compression methods.
Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification

Few-shot fine-grained image classification has attracted considerable attention in recent years for its realistic setting to imitate how humans conduct recognition tasks. Metric-based few-shot classifiers have achieved high accuracies. However, their metric function usually requires two arguments of vectors, while transforming or reshaping three-dimensional feature maps to vectors can result in loss of spatial information. Image reconstruction is thus involved to retain more appearance details: the test images are reconstructed by different classes and then classified to the one with the smallest reconstruction error. However, discriminative local information, vital to distinguish sub-categories in fine-grained images with high similarities, is not well elaborated when only the base features from a usual embedding module are adopted for reconstruction. Hence, we propose the novel local content-enriched cross-reconstruction network (LCCRN) for few-shot fine-grained classification. In LCCRN, we design two new modules: the local content-enriched module (LCEM) to learn the discriminative local features, and the cross-reconstruction module (CRM) to fully engage the local features with the appearance details obtained from a separate embedding module. The classification score is calculated based on the weighted sum of reconstruction errors of the cross-reconstruction tasks, with weights learnt from the training process. Extensive experiments on four fine-grained datasets showcase the superior classification performance of LCCRN compared with the state-of-the-art few-shot classification methods. Codes are available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lutsong/LCCRN</uri> .
Robust Collision Avoidance for Mobile Robots in the Presence of Moving Obstacles

This paper is concerned with the collision avoidance for mobile robots with uncertain dynamics in the presence of obstacles that can considerably change their velocities over time. To address this problem, we propose a robust collision-avoidance method based on the continuous-control-obstacles one. The proposed method uses an arbitrary-order overdamped low-pass filter to generate sufficiently smooth position commands for the robot and a high-order sliding mode differentiator to robustly estimate the obstacles’ maximum accelerations. Based on these estimates, we define a set of possible future positions for the obstacles according to how each one is changing its velocity to calculate a robust position command for the robot. The method has been numerically evaluated using a conventional quadcopter flying among moving obstacles and has been shown to be effective in providing collision avoidance and velocity constraints satisfaction.
EnergyAuction: Oracle Blockchain-Based Energy Trading System for Microgrids

Nowadays, many households are adopting distributed energy resources (DERs), such as photovoltaics (PV) systems and energy storage systems (ESS) which enable each house to generate and consume energy. Peer-to-peer (P2P) energy trading is a new energy trading management among prosumers and consumers in the distribution power system, allowing excess energy to be traded locally. This work aims to design and implement an oracle blockchain-based system for local energy trading among smart homes in a microgrid. The main focus is the interaction between distributed P2P networks such as blockchain and oracle networks. Our implementation consists of a private Ethereum blockchain network and a Chainlink oracle network. Smart contracts enable prosumers and consumers to trade energy in an open auction while requesting external energy data from our testbed API through the oracle network for the settlement process.
What If Ethics Got in the Way of Generative AI?

The OpenAI Generative Pre-trained Transformer ChatGPT has sparked a firestorm of commentary. Responses are both both favorable and unfavorable, often revolving around ethics. Generative AI, based on trained Large Language Models (LLM) definitely have some underlying ethical issues. As they are likely here to stay however, ethics are best exercised in how these tools are applied and their data are verified.
An Efficient, Secured, and Infinitely Scalable Consensus Mechanism for Peer-to-Peer Energy Trading Blockchain

Peer-to-Peer (P2P) energy trading has the potential to create a more efficient and sustainable electricity market. It can relieve the load on the power grid during peak usage periods. However, such a P2P energy market is decentralized and vulnerable to numerous cyber attacks. Decentralized blockchain technology has been proposed to solve these problems. The consensus mechanism is the core of blockchain. It determines the effectiveness and safety of blockchain for energy trading. However, consensus mechanisms applied to energy trading today are traditional consensus. Due to their high latency and significant computational power, they cannot be directly implemented in P2P energy trading. Therefore, we propose a novel Block Alliance Consensus (BAC) mechanism. The BAC breaks through the blockchain impossible triangle in the energy trading scenario by reasonably pursuing decentralization. We achieve infinite scalability via sharding. Within each shard, we substitute the Hashgraph for conventional methods to further improve the throughput and transaction speed. We design a cross-shard method for transactions between different shards. We implement the energy trading blockchain (ETB) and BAC consensus mechanism on the Hyperledger Fabric platform. The experiments show that our ETB is not limited to the impossibility triangle like other consensus. Our BAC mechanism achieves infinite scalability while ensuring high levels of security.
A Blockchain Based Secure Decentralized Transaction System for Energy Trading in Microgrids

Energy markets are being transformed rapidly all over the world due to an increased integration of renewable energy sources. Blockchain technology is emerging as a prime contender, as it can provide a secure and efficient transactional platform for such markets. In a typical microgrid energy market, the consumers and prosumers belonging to the microgrid have the ability to trade energy in a peer-to-peer fashion. However, the existing energy markets suffer from multiple issues security and privacy issues. To handle the aforementioned issues, this research work proposes a blockchain based secure Decentralized Transaction System (DTS) for energy trading in microgrids. The proposed system comprises of a secure market model that facilitates energy trade between energy users. A simplistic energy exchange mechanism has been formulated that ensures data integrity and privacy of the participating energy users. A prosumer centric consensus mechanism has been employed to incentivize the prosumers and ensure the availability of energy in the microgrid at all times. An efficient and dynamic pricing mechanism has been used to reduce the supply and demand disparity. A comprehensive trust model based on commitments has been adopted for ensuring the reliability of the participating energy user. Additionally, a hardware based access control mechanism has been utilized to make the proposed DTS a physical and cyber secure system. Other than this, a framework of smart contracts has been deployed to provide a comprehensive solution that ensures privacy, security, anonymity, auditability and confidentiality of the generated energy information. To demonstrate its practicality, the system has been implemented on Ethereum platform. The proposed DTS is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Goerli Test Network.
Accelerated Federated Learning with Dynamic Model Partitioning for H-IoT

In the Healthcare Internet of Things (H-IoT), Federated Learning (FL) is a promising solution for processing huge amounts of medical data. At present, FL applied in H-IoT still faces many challenges such as low training efficiency and high data privacy risk. In this work, we develop a three-layer FL architecture, which introduces split learning to both prevent the leakage of medical data and improve training efficiency according to the inherent properties of Neural Networks (NN). Moreover, we formulate a long-term optimization problem with the goal of accelerating training speed of models in H-IoT. Then, an online model partitioning algorithm namely Privacyaware Model Partitioning Algorithm (PMPA) is derived based on Lyapunov optimization theory that enables mobile devices of the FL architecture to efficiently train local models and protect the data privacy. Furthermore, the simulation results show that compared with traditional FL, the local training delay of the proposed algorithm can be reduced by 28.94% and 39.89%, respectively.
Real-Time Super-Resolution: A New Mechanism for XR over 5G-Advanced

Extended Reality (XR) has attracted great attention from both academic and industry, for providing users with an immersive experience anywhere. Nowadays, XR video streaming service is evolving to high definition (HD), which results in massive data traffic with more stringent latency requirement. Due to the two characteristics, it is challenging to support the commercial use for XR service in the current New Ratio (NR) network. In this paper, we propose a real-time super-resolution (RTSR) framework for XR HD video transmission. The basic idea is to utilize the overfitting feature of Deep Neural Network (DNN) to learn the non-linear mapping between low-definition (LD) video frames and HD video frames. The cloud XR server can transmit the LD frames together with the dedicated super resolution (SR) models instead of sending HD frames directly. The receiver can recover the HD frames locally with the inferencing ability of SR model. In addition, by introducing the online training and layered transmission strategy, the SR model update period can be adaptively adjusted according to the scenario changes, which also reduces the transmission overhead. Simulation results demonstrate the superiority of our proposed RTSR, which can save up to 50% traffic and increase the XR capacity about 40% compared with the conventional SR scheme. In terms of the system capacity, our results show that the average number of UEs can reach about 23 per cell under the common settings of Dense Urban.
Measurement-based Modulation Classification in Unlicensed Millimeter-Wave Bands

Automatic modulation classification (AMC) facilitates adaptive modulation schemes, leading to the minimization of pilot signals, thus affecting spectral efficiency and reducing the power consumption in wireless communications systems. Since high-frequency heterogeneous and adaptive networks are established as future projections, AMC will also play a critical role in the millimeter-wave (mmWave) band communications. This study proposes multi-channel convolutional long short-term deep neural network (MCLDNN) model for AMC in mmWave bands. The performance of the proposed method is evaluated under real conditions based on a measurement campaign. 802.11ad signals are utilized for the measurements in 57.24 GHz to 59.40 GHz band. The classification performance of the proposed model is compared with that of well-known deep-learning methods, i.e., convolutional neural network and convolutional long short-term deep neural network. The measurement results imply the robustness of the proposed method to real-life conditions and its superiority against contemporary networks, especially in low signal-to-noise ratio (SNR) region.
DQN based Blockchain Data Storage in Resource-constrained IoT System

Applying blockchain technology in the Internet of Things (IoT) systems to realize massive data storage is a promising technology, which can eliminate the dependence of IoT on central servers and protect data security effectively. However, in legacy IoT systems, it is hardly to store a complete blockchain at all nodes due to the limited storage space. In order to securely store massive data in the resource-constrained IoT systems, we design a blockchain-assisted distributed storage mechanism, by which multiple nodes collaboratively store a complete blockchain. Meanwhile, we design a deep reinforcement learning (DRL) based algorithm to obtain the optimal block allocation strategy for the collaborative storage. The optimal strategy can maximize the number of storage blocks so as to maximize the utilization of the limited storage space under the premise of meeting the requirements of user query delay and reliability. The simulation results show that compared with the random search algorithm and the policy gradients algorithm, the proposed Deep Q Network algorithm can achieve the maximum number of storage blocks in the same storage space, and the fastest storage speed.
Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification

RGB-Infrared person re-identification (RGB-IR ReID) aims to associate people across disjoint RGB and IR camera views. Currently, state-of-the-art performance of RGB-IR ReID is not as impressive as that of conventional ReID. Much of that is due to the notorious modality bias training issue brought by the single-modality ImageNet pre-training, which might yield RGB-biased representations that severely hinder the cross-modality image retrieval. This paper makes first attempt to tackle the task from a pre-training perspective. We propose a self-supervised pre-training solution, named Modality-Aware Multiple Granularity Learning (MMGL), which directly trains models from scratch only on multi-modal ReID datasets, but achieving competitive results against ImageNet pre-training, without using any external data or sophisticated tuning tricks. First, we develop a simple-but-effective ‘permutation recovery’ pretext task that globally maps shuffled RGB-IR images into a shared latent permutation space, providing modality-invariant global representations for downstream ReID tasks. Second, we present a part-aware cycle-contrastive (PCC) learning strategy that utilizes cross-modality cycle-consistency to maximize agreement between semantically similar RGB-IR image patches. This enables contrastive learning for the unpaired multi-modal scenarios, further improving the discriminability of local features without laborious instance augmentation. Based on these designs, MMGL effectively alleviates the modality bias training problem. Extensive experiments demonstrate that it learns better representations (+8.03% Rank-1 accuracy) with faster training speed (converge only in few hours) and higher data efficiency (<5% data size) than ImageNet pre-training. The results also suggest it generalizes well to various existing models, losses and has promising transferability across datasets. The code will be released at https://github.com/hansonchen1996/MMGL.
Cross-Level Multi-Modal Features Learning With Transformer for RGB-D Object Recognition

Object recognition, one of the main goals of robot vision, is a vital prerequisite for service robots to perform domestic tasks. Thanks to the rich sense of information provided by RGB-D sensors, RGB-D-based object recognition has received increasing attention. However, the existing works focus on collaborative RGB and depth data for object recognition, while ignoring the influence of depth image quality on recognition performance. Moreover, in real-world scenarios, there are many objects with strong similarity from certain observation angles, which poses a challenge for the service robot to recognize objects accurately. In this paper, we propose CNN-TransNet, a novel end-to-end Transformer-based architecture with convolutional neural networks (CNNs) for RGB-D object recognition. In order to deal with the effect of high inter-class similarity, discriminative multi-modal feature representations are generated by learning and relating multi-modal features at multiple levels. Besides, we employ a multi-modal fusion and projection (MMFP) module to reweight the contribution of each modality to address the problem of poor-quality depth image. Our proposed approach achieves state-of-the-art performance on three datasets (including Washington RGB-D Object Dataset, JHUIT-50, and Object Clutter Indoor Dataset), with accuracy of 95.4%, 98.1%, and 94.7%, respectively. The results demonstrate the effectiveness and superiority of the proposed model in RGB-D object recognition task.
Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E.
3D Gaze Vis: Sharing Eye Tracking Data Visualization for Collaborative Work in VR Environment

Conducting collaborative tasks, e.g., multi-user game, in virtual reality (VR) could enable us to explore more immersive and effective experience. However, for current VR systems, users cannot communicate properly with each other via their gaze points, and this would interfere with users' mutual understanding of the intention. In this study, we aimed to find the optimal eye tracking data visualization, which minimized the cognitive interference and improved the understanding of the visual attention and intention between users. We designed three different eye tracking data visualizations: gaze cursor, gaze spotlight and gaze trajectory in VR scene for a course of human heart, and found that gaze cursor from doctors could help students learn complex 3D heart models more effectively. To further explore, two students as a pair were asked to finish a quiz in VR environment, with sharing gaze cursors with each other, and obtained more efficiency and scores. It indicated that sharing eye tracking data visualization could improve the quality and efficiency of collaborative work in the VR environment.
Four-Term Recurrence for Fast Krawtchouk Moments Using Clenshaw Algorithm

Krawtchouk polynomials (KPs) are discrete orthogonal polynomials associated with the Gauss hypergeometric functions. These polynomials and their generated moments in 1D or 2D formats play an important role in information and coding theories, signal and image processing tools, image watermarking, and pattern recognition. In this paper, we introduce a new four-term recurrence relation to compute KPs compared to their ordinary recursions (three-term) and analyse the proposed algorithm speed. Moreover, we use Clenshaw’s technique to accelerate the computation procedure of the Krawtchouk moments (KMs) using a fast digital filter structure to generate a lattice network for KPs calculation. The proposed method confirms the stability of KPs computation for higher orders and their signal reconstruction capabilities as well. The results show that the KMs calculation using the proposed combined method based on a four-term recursion and Clenshaw’s technique is reliable and fast compared to the existing recursions and fast KMs algorithms.