Reviewerly/exHarmony huggingface dataset:
Infrared thermal image denoising with symmetric multi-scale sampling network

We propose an infrared thermal image denoising method based on the residual learning with a symmetric multi-scale (SM) encoder-decoder sampling structure (SMEDS). The U-shape-based SMEDS is designed to extract SM information from different layers and focus on decoding recovery with attention acting on the upper and lower levels. Specifically, SMEDS consists of SM encoder-decoder blocks, cascaded residual blocks, and attention recovery modules. An attention-guided reconstruction unit (AGRU) and SSIM loss are jointly used to supervise image reconstruction and enhance visual perception. A novel Gaussian-Poisson-Stripe noise (GPSN) model is developed to simulate real-world noise. To verify the effectiveness of SMEDS, ablation studies are conducted. Extensive experiments demonstrate that the proposed method performs well on synthetic and real-world noisy images and outperforms previously reported infrared image denoising methods. The proposed method has great potential applications in areas such as remote sensing, infrared medical imaging, and navigation.
Blockchain Framework of UiTM Postgraduate Tracking System (UPTrackS) for Good Governance Practice

Traditional bureaucracy in universities deters good governance. Moving to digital bureaucracy, like digital governance that uses blockchain technology, improves transparency, accountability, and efficiency. A digital architecture called UiTM Postgraduate Tracking System (UPTrackS) leverages blockchain technology to manage governance procedures. According to a poll on UPTrackS deployment, the system delivers sound governance, with a confidence rating of 7.9 out of 10. Enhancing the computer literacy skills of administrative employees and improving the user interface of the system they use are necessary steps to enhance efficiency. Additionally, implementing these changes can lead to better administrative practices that adhere to good governance principles.
Information Seeking in Information Management Practices: A systematic literature review

Information Seeking is one of the elements of Information Management Practices. Many authors use Information Seeking terms in various fields, such as health, medicine, business management and information management. This paper will provide insight into the research related to information seeking based on various studies in online databases. The main goal of this paper is to provide a systematic literature review of Information sought from the previous research from the year 2015 to the year 2020. The selected database consists of Emerald Insight, Science Direct and ProQuest. The search was done by using the keyword “information seeking”.
Quantum gate algorithm for reference-guided DNA sequence alignment

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ∼760MB of storage (National Institute of General Medical Sciences, n.d.). Today's most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed.
Cross-Cultural Professional Communication: Blended Learning in the Digital Age

In the Digital Age the focus of the modern educational process has radically shifted to give way to new forms and methods of educational activity due to the introduction of the digital technologies in the education system. Digital technologies open new vistas for the education system reform to introduce new methods to the professionally oriented L2 teaching necessary for effective cross-cultural communication. Blended learning is the first stage of the education system reform to meet the challenges of the time. It is evident that the modern education system requires optimization of its methods and activities. To achieve this, it is necessary to analyze and systematize the professionally oriented L2 curriculum, outline the problematic aspects, introduce innovative digital technologies in order to create effective IOT (Individual Educational Trajectory) for each individual student. Information, communication and digital technologies integrate text, graphic and video images, speech and music, making education multimodal. L2 teaching at the History Faculty has taken into account both hard skills and soft skills to equip history students with deep knowledge and expand their outlook by developing their critical thinking, creativity, research competence to prepare them for independent scholarly, academic, pedagogical and social activities.
Small Object Detection on Drone-view Images from Multiple UAVs Combination

Remote sensing technology has made significant advancements, leading to the widespread adoption of unmanned aerial vehicles (UAVs) in critical tasks such as smart cities, traffic surveillance, and disaster assistance. However, the identification of objects from drone-view images poses substantial challenges: 1) small object; 2) scale variance; 3) non-uniform distribution; 4) occlusion. To address these challenges, this paper introduces a novel strategy for remote sensing data collection via multiple UAVs combination, which can obtain drone-view images from various scales, angles and multi-regions in real time. Then we perform small object detection on drone-view images via a novel network structure, called Query-YOLOX, which combines the Cascade Sparse Query mechanism and the YOLOX anchor-free detector. Our novel Query-YOLOX model demonstrates remarkable advancements in small object detection for drone-view images by substantially reducing computation costs while preserving high detection accuracy. The experimental results conducted on the VisDrone2019 dataset validate the superiority of our approach, which now stands as the state-of-the-art solution for efficient and precise small object detection in drone-view imagery.
Multi-Scale Feature Fusion Network for Remote Sensing Image Captioning

Remote sensing image captioning (RSIC) has inspired spread attention because of its ability to flexibly generate sentences from input images. However, existing methods still have limitations in describing complex, multi-scale remote sensing image scenes. To solve this problem, we propose a multi-scale feature fusion network (MS-Net). Firstly, the features of different layers are fused to extract multi-scale feature. Further, we use the transformer encoder adaptively to fuse image features at different scales. Extensive experiments are conducted on benchmark dataset, namely NWPU-captions to verify the performance of MS-Net.
Combined Machine Learning and weather models for photovoltaic production forecasting in microgrid systems

Given the high stochasticity of solar irradiance, the accurate forecasts of photovoltaic plants production can contribute to correctly manage power systems resources. In microgrids and grids, the availability of thorough forecasts allow to suitably operate generation, load and storage resources, avoiding grid imbalance conditions. In this work authors investigate the possibility to employ a Machine Learning approach that leverages the PV power forecast obtained by weather model and past observed generated power data. The proposed approach permits to obtain at least 3.7% improvement in photovoltaic production forecasting, demonstrating the methodology effectiveness.
GBCloud: a Robust Cloud Model for Processing Multi-dimensional Data Using Granular-ball Computing

Cloud model is an important analysis tool in knowledge engineering. However, it is mainly applied in one or two dimensional data and small-scale data because of complex fitting for its parameters, so it is not suitable to be applied in analysis in multi-dimensional data, which limits its application ability. In this paper, we proposed the granular-ball cloud model using granular-ball to composing the data space and generating a cloud model in each granular-ball. As the distribution in each granular-ball becomes simple, the cloud model can effectively fit the data distribution, resulting a considerable improvement of performance of cloud model in processing multi-dimensional data, especially enhancing its robustness. As an example, we use the granular-ball cloud model as an oversampling method for addressing imbalanced problems. The experimental results on some benchmark data sets demonstrate its effectiveness in comparison with many popular or the state-of-the-art algorithms.The implementation of the proposed GBCloud in programming language Python is available at https://github.com/syxiaa/GBCloud.
General Form of Almost Instantaneous Fixed-to-Variable-Length Codes

A general class of the almost instantaneous fixed-to-variable-length (AIFV) codes is proposed, which contains every possible binary code we can make when allowing finite bits of decoding delay. The contribution of the paper lies in the following. (i) Introducing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> -bit-delay AIFV codes, constructed by multiple code trees with higher flexibility than the conventional AIFV codes. (ii) Proving that the proposed codes can represent any uniquely-encodable and uniquely-decodable variable-to-variable length codes. (iii) Showing how to express codes as multiple code trees with minimum decoding delay. (iv) Formulating the constraints of decodability as the comparison of intervals in the real number line. The theoretical results in this paper are expected to be useful for further study on AIFV codes.
Inspecting End-to-End Encrypted Communication Differentially for the Efficient Identification of Harmful Media

Due to the immense benefits of guaranteeing user privacy, popular messaging platforms have shown enthusiasm for deploying End-to-End Encryption (E2EE). However, E2EE could be misused for bypassing media moderation, opening a shortcut for the viral spreading of harmful media. Private hash-matching techniques are proposed to identify harmful content in E2EE. Unfortunately, the pioneering solution incurs prohibitively high latency due to redundant user-cloud interactions for a private inspection. In this paper, we design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Entbergen</i> for efficient inspection of E2EE media by differentially handling harmless and harmful ingredients. For this, a novel Private-2D BloOm filter with Fuzzy Query (PBO-FQ) is designed for local, agile, and private media hash matching. It is proposed as the first structure that adapts inverted index and differential privacy (DP) towards seamless integration of sketch and mask encoding. With PBO-FQ, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Entbergen</i> can instantly filter out harmless media and only pays attention to the small-scale counterparts by scrutinizing them privately based on homomorphic encryption. Security analysis shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Entbergen</i> can effectively fulfil the desired privacy requirements. Extensive evaluations demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Entbergen</i> is sufficiently efficient (w.r.t. computation and communication overhead) for working on mobile devices and can easily scale to real-world inspection with a large database.
Height and Punishment: Toward Accountable IoT Blockchain With Network Sanitization

A Blockchain network consists of a distributed ledger and a set of nodes participating in the network. The consistency in the ledger’s state is maintained by a Blockchain consensus mechanism. This property is essential since it is assumed that the participants in the network do not trust each other. Most Blockchain consensus algorithms are unsuitable for resource-constrained nodes due to their power consumption, storage, network overhead, and computation requirements. Furthermore, many consensus methods do not address the issue of data accountability, which guarantees that an entity will be judged on its performance or conduct concerning a duty they have taken. This study proposes a novel consensus mechanism termed Proof of Block Height ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PoBH</i> ) that prioritizes accountability for reporting IoT data transactions and considers factors such as low computation cost and energy consumption. The approach relies on a pre-existing comparable deterministic value based on the height of the committed blocks. The model identifies a malicious intent using a set of predicates and consequently incurs a computation penalty to keep such nodes away from the network. As a preventive measure, the model also seeks to increment this penalty appropriately by making the network free from dishonest nodes for as long as possible, thus sanitizing the network. Importantly, the developed method does not augment the basic hierarchy of an IoT network. A formal analysis of the mechanism’s operation shows that the resource and time required to cheat the system grows exponentially with the number of faulty blocks a node tries to append to the shared ledger. The factors affecting the mechanism’s performance, scalability, and effort required (by malicious nodes) to stay within the network are also quantitatively evaluated.
Efficient Byzantine-Robust and Privacy-Preserving Federated Learning on Compressive Domain

Data privacy and resistance against poisoning attack (Byzantine-robustness) are two critical concerns of Federated Learning (FL). Addressing the two issues simultaneously is challenging, since privacy-preserving mechanism tends to make the data be indistinguishable, whereas Byzantine-robustness methods require access for the data to make comprehensive analysis. To solve this problem, in this paper, we propose a novel defender for privacy-ensured Byzantine-robust FL on compressive domain. Unlike existing works that mainly using computation-intensive techniques, our method leverages Compressive Sensing (CS) as a lightweight encryption to protect the data privacy, while maintaining the possibility of Byzantine-robustness analysis on the encrypted (compressive) model update (i.e., gradient). Our key insight is that the cosine similarity can be approximately measured on the compressive measurements of any two normalized vectors, thus makes it be feasible to identify the malicious gradients on the CS compressive domain. We theoretically prove the correctness of our method. Notably, due to the dimensionality reduction of CS, the computation and communication overhead of our system can be significantly reduced. This makes our scheme be fit for applying in the applications with resource-constrained devices, such as Internet of Things (IoT). Experimental results demonstrate the effectiveness and efficiency of our method.
Human-Guided Reinforcement Learning With Sim-to-Real Transfer for Autonomous Navigation

Reinforcement learning (RL) is a promising approach in unmanned ground vehicles (UGVs) applications, but limited computing resource makes it challenging to deploy a well-behaved RL strategy with sophisticated neural networks. Meanwhile, the training of RL on navigation tasks is difficult, which requires a carefully-designed reward function and a large number of interactions, yet RL navigation can still fail due to many corner cases. This shows the limited intelligence of current RL methods, thereby prompting us to rethink combining RL with human intelligence. In this paper, a human-guided RL framework is proposed to improve RL performance both during learning in the simulator and deployment in the real world. The framework allows humans to intervene in RL's control progress and provide demonstrations as needed, thereby improving RL's capabilities. An innovative human-guided RL algorithm is proposed that utilizes a series of mechanisms to improve the effectiveness of human guidance, including human-guided learning objective, prioritized human experience replay, and human intervention-based reward shaping. Our RL method is trained in simulation and then transferred to the real world, and we develop a denoised representation for domain adaptation to mitigate the simulation-to-real gap. Our method is validated through simulations and real-world experiments to navigate UGVs in diverse and dynamic environments based only on tiny neural networks and image inputs. Our method performs better in goal-reaching and safety than existing learning- and model-based navigation approaches and is robust to changes in input features and ego kinetics. Furthermore, our method allows small-scale human demonstrations to be used to improve the trained RL agent and learn expected behaviors online.
Compression and Cryptography Algorithms for 3D Remote Sensing Point Cloud Data Based on 3D-TCICM and ECC

This paper proposes an asymmetric encryption and compression method based on three-dimensional Tent-Cubic-ICMIC map (3D-TCICM) and elliptic curve encryption (ECC) for 3D remote sensing point cloud. Due to the large size, rich data and high security of remote sensing point cloud, an encryption and compression method with high computing efficiency and high security is required. The point cloud is first divided into blocks, then compressed by point cloud library (PCL) using octree, and then the resulting data stream is encrypted. During encryption, a chaotic system 3D-TCICM with excellent chaotic behavior is proposed. Its initial values are generated by point cloud data and ECC algorithm. The sender and receiver only hold the public key or private key respectively, which further improves the security of the algorithm. The encryption factors are also generated for subsequent encryption operations, which improves the plaintext correlation. The encryption algorithm includes innovative scrambling algorithm between multiple blocks (MBS), helix diffusion in GF( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{8}$</tex-math></inline-formula> ) field and multiple S-boxes substitution, in which different S-boxes are provided for each data block. Simulation results indicate that the proposed scheme surpasses existing algorithms in terms of security, randomness, and plaintext correlation, all while maintaining a lower computational complexity. The algorithm exhibits robustness against various attacks such as differential, statistical, chosen-plaintext, known-plaintext, chosen-ciphertext, noise, and cropping attacks.
SAR Despeckling Using Multiobjective Neural Network Trained With Generic Statistical Samples

Synthetic Aperture Radar (SAR) images are impaired by the presence of speckle. Despite the deep interest of scholars in the last decades, SAR image despeckling is still an open issue. Among different approaches, recently, many Deep Learning (DL) methods have been proposed following both supervised and unsupervised training approaches. There are two main challenges within the supervised framework: training data, and cost functions. Our approach builds training datasets which are varied and realistic using a multi-category Generalized Gaussian Coherent SAR simulator. It allows modeling a variety of SAR scenarios beyond the fully developed speckle hypothesis, which is only valid in homogeneous areas. Such multi-category simulated speckle is then applied to a noise-free reference obtained by multi-looking a temporal stack of actual SAR images in order to obtain the noisy input. We design an effective multi-objective cost function that accounts for texture, edge, and statistical properties preservation. We show the superiority of our approach assessing numerically and quantitatively its performance with three different SAR datasets.
Robust Reflection Removal With Flash-Only Cues in the Wild

We propose a simple yet effective reflection-free cue for robust reflection removal from a pair of flash and ambient (no-flash) images. The reflection-free cue exploits a flash-only image obtained by subtracting the ambient image from the corresponding flash image in raw data space. The flash-only image is equivalent to an image taken in a dark environment with only a flash on. This flash-only image is visually reflection-free and thus can provide robust cues to infer the reflection in the ambient image. Since the flash-only image usually has artifacts, we further propose a dedicated model that not only utilizes the reflection-free cue but also avoids introducing artifacts, which helps accurately estimate reflection and transmission. Our experiments on real-world images with various types of reflection demonstrate the effectiveness of our model with reflection-free flash-only cues: our model outperforms state-of-the-art reflection removal approaches by more than 5.23 dB in PSNR. We extend our approach to handheld photography to address the misalignment between the flash and no-flash pair. With misaligned training data and the alignment module, our aligned model outperforms our previous version by more than 3.19 dB in PSNR on a misaligned dataset. We also study using linear RGB images as training data.
Design and Implementation of Fragmented Clouds for Evaluation of Distributed Databases

In this paper, we present a Fragmented Hybrid Cloud (FHC) that provides a unified view of multiple geographically distributed private cloud datacenters. FHC leverages a fragmented usage model in which outsourcing is bi-directional across private clouds that can be hosted by static and mobile entities. The mobility aspect of private cloud nodes has important impacts on the FHC performance in terms of latency and network throughput that are reversely proportional to time-varying distances among different nodes. Mobility also results in intermittent interruption among computing nodes and network links of FHC infrastructure. To fully consider mobility and its consequences, we implemented a layered FHC that leverages Linux utilities and bash-shell programming. We also evaluated the impact of the mobility of nodes on the performance of distributed databases as a result of time-varying latency and bandwidth, downsizing and upsizing cluster nodes, and network accessibility. The findings from our extensive experiments provide deep insights into the performance of well-known big data databases, such as Cassandra, MongoDB, Redis, and MySQL, when deployed on a FHC.