Reviewerly/exHarmony huggingface dataset:
An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement

The advent of architectures such as the Internet of Things (IoT) has led to the dramatic growth of data and the production of big data. Managing this often-unlabeled data is a big challenge for the real world. Hierarchical Clustering (HC) is recognized as an efficient unsupervised approach to unlabeled data analysis. In data mining, HC is a mechanism for grouping data at different scales by creating a dendrogram. One of the most common HC methods is Agglomerative Hierarchical Clustering (AHC) in which clusters are created bottom-up. In addition, ensemble clustering approaches are used today in complex problems due to the weakness of individual clustering methods. Accordingly, we propose a clustering framework using AHC methods based on ensemble approaches, which includes the clusters clustering technique and a novel similarity measurement. The proposed algorithm is a Meta-Clustering Ensemble scheme based on Model Selection (MCEMS). MCEMS uses the bi-weighting policy to solve the model selection associated problem to improve ensemble clustering. Specifically, multiple AHC individual methods cluster the data from different aspects to form the primary clusters. According to the results of different methods, the similarity between the instances is calculated using a novel similarity measurement. The MCEMS scheme involves the creation of meta-clusters by re-clustering of primary clusters. After clusters clustering, the number of optimal clusters is determined by merging similar clusters and considering a threshold. Finally, the similarity of the instances to the meta-clusters is calculated and each instance is assigned to the meta-cluster with the highest similarity to form the final clusters. Simulations have been performed on some datasets from the UCI repository to evaluate MCEMS scheme compared to state-of-the-art algorithms. Extensive experiments clearly prove the superiority of MCEMS over HMM, DSPA and WHAC algorithms based on Wilcoxon test and Cophenetic correlation coefficient.
Photonic neuromorphic computing using vertical cavity semiconductor lasers

Photonic realizations of neural network computing hardware are a promising approach to enable future scalability of neuromorphic computing. The number of special purpose neuromorphic hardware and neuromorphic photonics has accelerated on such a scale that one can now speak of a Cambrian explosion. Work along these lines includes (i) high performance hardware for artificial neurons, (ii) the efficient and scalable implementation of a neural network’s connections, and (iii) strategies to adjust network connections during the learning phase. In this review we provide an overview on vertical-cavity surface-emitting lasers (VCSELs) and how these high-performance electro-optical components either implement or are combined with additional photonic hardware to demonstrate points (i-iii). In the neurmorphic photonics context, VCSELs are of exceptional interest as they are compatible with CMOS fabrication, readily achieve 30% wall-plug efficiency, &gt;30 GHz modulation bandwidth and multiply and accumulate operations at sub-fJ energy. They hence are highly energy efficient and ultra-fast. Crucially, they react nonlinearly to optical injection as well as to electrical modulation, making them highly suitable as all-optical as well as electro-optical photonic neurons. Their optical cavities are wavelength-limited, and standard semiconductor growth and lithography enables non-classical cavity configurations and geometries. This enables excitable VCSELs (i.e. spiking VCSELs) to finely control their temporal and spatial coherence, to unlock terahertz bandwidths through spin-flip effects, and even to leverage cavity quantum electrodynamics to further boost their efficiency. Finally, as VCSEL arrays they are compatible with standard 2D photonic integration, but their emission vertical to the substrate makes them ideally suited for scalable integrated networks leveraging 3D photonic waveguides. Here, we discuss the implementation of spatially as well as temporally multiplexed VCSEL neural networks and reservoirs, computation on the basis of excitable VCSELs as photonic spiking neurons, as well as concepts and advances in the fabrication of VCSELs and microlasers. Finally, we provide an outlook and a roadmap identifying future possibilities and some crucial milestones for the field.
Hand Interfaces: Using Hands to Imitate Objects in AR/VR for Expressive Interactions

Augmented reality (AR) and virtual reality (VR) technologies create exciting new opportunities for people to interact with computing resources and information. Less exciting is the need for holding hand controllers, which limits applications that demand expressive, readily available interactions. Prior research investigated freehand AR/VR input by transforming the user’s body into an interaction medium. In contrast to previous work that has users’ hands grasp virtual objects, we propose a new interaction technique that lets users’ hands become virtual objects by imitating the objects themselves. For example, a thumbs-up hand pose is used to mimic a joystick. We created a wide array of interaction designs around this idea to demonstrate its applicability in object retrieval and interactive control tasks. Collectively, we call these interaction designs Hand Interfaces. From a series of user studies comparing Hand Interfaces against various baseline techniques, we collected quantitative and qualitative feedback, which indicates that Hand Interfaces are effective, expressive, and fun to use.
A survey and taxonomy of consensus protocols for blockchains

Blockchain is an emerging decentralized and distributed technology. Along with the beneficial features of decentralization, transparency, and security the consensus algorithms of blockchains form key building blocks for this technology. Consensus protocol/algorithm helps to provide a decentralized decision making process. An efficient consensus algorithm is inclusive that engages all the participants to make their decision based on the conflicts of the blockchain networks. These consensus decisions lead to better quality outcomes of the blockchains and help to obtain the finality. Rigorous research is in process to upgrade or optimize the existing consensus protocols. The optimized or enhanced consensus protocols objectify to be suitable for Internet-of-Thing (IoT) as the current versions of the protocols are not suitable for the resource-constrained environments due their complexity, hard configurations, mining techniques, high resource consumption, and explicit security loophole. In this paper, we present a survey of consensus protocols with a purpose to identify and discuss the existence of various consensus protocols available in literature. We emphasize on the genesis of the consensus protocols, particularly for Proof-of-X, byzantine fault tolerance, Paxos, and RAFT; we also include Directed Acyclic Graph (DAG) orientation of some contemporary algorithms. We discuss the variants of these genesis protocols. Our survey analyzes the advantages, disadvantages, and their applicability in IoTs. We enlist the categorical use of consensus algorithms in blockchains and other applications. Finally, we present several research trends and open issues emphasizing for consensus protocols emphasizing on IoTs. Compared to the other surveys in the field, our present survey objectifies to provide a more thorough summary of the most relevant protocols and application issues; this survey helps the researchers and the application developers to obtain an insight on the current status of the consensus protocols’ suitability to deliver the desired functionalities in IoTs. The notified disadvantages of each of the protocol provide future scope for the industries and academia. To the best of our knowledge, such a comprehensive and summarized survey of consensus protocols including DAG-based protocols is unavailable in the literature and thus, our contribution claims are significant.
Case-based reasoning system for fault diagnosis of aero-engines

Fault diagnosis in aero-engines typically requires an experienced expert for understanding and detecting the cause of faults. However, accurate and quick identification of fault parts is difficult for maintenance crews owing to the complexity of aero-engines. In this study, we developed a case-based reasoning (CBR) system with a highly accurate novel similarity measure for fault diagnosis of aero-engines by retrieving similar fault cases. The proposed CBR system is established based on 143 cases with the knowledge of correctly diagnosed and successfully resolved aero-engine faults, which constitutes the first tentative case base in the field of aero-engine fault diagnosis. The proposed case similarity measure for fault diagnosis of aero-engines integrates three local similarity measures associated with different attributes, especially among which a tree-based semantic similarity measure is proposed to define the relationship between the fault part and fault mode based on a semantic graph incorporated into the aero-engine tree structure. The proposed CBR system is evaluated using the k-nearest neighbors algorithm and 5-fold cross-validation. The system exhibited high retrieval accuracy with all cases collected from real-world scenarios of aero-engine fault diagnosis. Our study shows great promise that the experience-based decisions yielded from the results can aid in aero-engine maintenance and support services.
<i>mmPose-NLP</i>: A Natural Language Processing Approach to Precise Skeletal Pose Estimation Using mmWave Radars

In this article, we presented mmPose-NLP, a novel natural language processing (NLP) inspired sequence-to-sequence (Seq2Seq) skeletal key-point estimator using millimeter-wave (mmWave) radar data. To the best of our knowledge, this is the first method to precisely estimate up to 25 skeletal key points using mmWave radar data alone. Skeletal pose estimation is critical in several applications ranging from autonomous vehicles, traffic monitoring, patient monitoring, and gait analysis, to defense security forensics, and aid both preventative and actionable decision making. The use of mmWave radars for this task, over traditionally employed optical sensors, provides several advantages, primarily its operational robustness to scene lighting and adverse weather conditions, where optical sensor performance degrade significantly. The mmWave radar point-cloud (PCL) data are first voxelized (analogous to tokenization in NLP) and N frames of the voxelized radar data (analogous to a text paragraph in NLP) is subjected to the proposed mmPose-NLP architecture, where the voxel indices of the 25 skeletal key points (analogous to keyword extraction in NLP) are predicted. The voxel indices are converted back to real-world 3-D coordinates using the voxel dictionary used during the tokenization process. Mean absolute error (MAE) metrics were used to measure the accuracy of the proposed system against the ground truth, with the proposed mmPose-NLP offering < 3 cm localization errors in the depth, horizontal, and vertical axes. The effect of the number of input frames versus performance/accuracy was also studied for N = {1,2, ... ,10} . A comprehensive methodology, results, discussions, and limitations are presented in this article. All the source codes and results are made available on GitHub for further research and development in this critical yet emerging domain of skeletal key-point estimation using mmWave radars.
Giant atoms with time-dependent couplings

We study the decay dynamics of a two-level giant atom that is coupled to a waveguide with time-dependent coupling strengths. In the non-Markovian regime where the retardation effect cannot be ignored, we show that the dynamics of the atom depends on the atom-waveguide coupling strengths at an earlier time. This allows one to tailor the decay dynamics of the giant atom and even realize a stationary population revival with appropriate coupling modulations. Moreover, we demonstrate the possibility of simulating the quantum Zeno and quantum anti-Zeno effects in the giant-atom model with periodic coupling quenches. These results have potential applications in quantum information processing and quantum network engineering.
Exploiting Intra-Slice and Inter-Slice Redundancy for Learning-Based Lossless Volumetric Image Compression

3D volumetric image processing has attracted increasing attention in the last decades, in which one major research area is to develop efficient lossless volumetric image compression techniques to better store and transmit such images with massive amount of information. In this work, we propose the first end-to-end optimized learning framework for losslessly compressing 3D volumetric data. Our approach builds upon a hierarchical compression scheme by additionally introducing the intra-slice auxiliary features and estimating the entropy model based on both intra-slice and inter-slice latent priors. Specifically, we first extract the hierarchical intra-slice auxiliary features through multi-scale feature extraction modules. Then, an Intra-slice and Inter-slice Conditional Entropy Coding module is proposed to fuse the intra-slice and inter-slice information from different scales as the context information. Based on such context information, we can predict the distributions for both intra-slice auxiliary features and the slice images. To further improve the lossless compression performance, we also introduce two new gating mechanisms called Intra-Gate and Inter-Gate to generate the optimal feature representations for better information fusion. Eventually, we can produce the bitstream for losslessly compressing volumetric images based on the estimated entropy model. Different from the existing lossless volumetric image codecs, our end-to-end optimized framework jointly learns both intra-slice auxiliary features at different scales for each slice and inter-slice latent features from previously encoded slices for better entropy estimation. The extensive experimental results indicate that our framework outperforms the state-of-the-art hand-crafted lossless volumetric image codecs (e.g., JP3D) and the learning-based lossless image compression method on four volumetric image benchmarks for losslessly compressing both 3D Medical Images and Hyper-Spectral Images.
Language as Queries for Referring Video Object Segmentation

Referring video object segmentation (R-VOS) is an emerging cross-modal task that aims to segment the target object referred by a language expression in all video frames. In this work, we propose a simple and unified framework built upon Transformer, termed ReferFormer. It views the language as queries and directly attends to the most relevant regions in the video frames. Concretely, we introduce a small set of object queries conditioned on the language as the input to the Transformer. In this manner, all the queries are obligated to find the referred objects only. They are eventually transformed into dynamic kernels which capture the crucial object-level information, and play the role of convolution filters to generate the segmentation masks from feature maps. The object tracking is achieved naturally by linking the corresponding queries across frames. This mechanism greatly simplifies the pipeline and the end-to-end framework is significantly different from the previous methods. Extensive experiments on Ref-Youtube-vos, Ref-DAVIS17, A2D-Sentences and JHMDB-Sentences show the effectiveness of ReferFormer. On Ref-Youtube-vos, ReferFormer achieves 55.6 J&F with a ResNet-50 backbone without bells and whistles, which exceeds the previous state-of-the-art performance by 8.4 points. In addition, with the strong Video-Swin-Base backbone, ReferFormer achieves the best J&F of 64.9 among all existing methods. Moreover, we show the impressive results of 55.0 mAP and 43.7 mAP on A2D-Sentences and JHMDB-Sentences respectively, which significantly outperforms the previous methods by a large margin. Code is publicly available at https://github.com/wjn922/ReferFormer.
Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual–Inertial Approach

This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.
A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

This study presents an autoencoder-embedded optimization (AEO) algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems (MEPs). A huge search space can be compressed to an informative low-dimensional space by using an autoencoder as a dimension reduction tool. The search operation conducted in this low space facilitates the population with fast convergence towards the optima. To strike the balance between exploration and exploitation during optimization, two phases of a tailored teaching-learning-based optimization (TTLBO) are adopted to coevolve solutions in a distributed fashion, wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary process. Also, a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence speed. The proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to 200. As indicated in our experiments, TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base optimizer. Compared with the state-of-the-art algorithms for MEPs, AEO shows extraordinarily high efficiency for these challenging problems, thus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing

The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted great attention in remote sensing (RS). In this paper, we focus our attention on cross-modal text-image retrieval, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., image). Most of the existing cross-modal text-image retrieval systems in RS require a high number of labeled training samples and also do not allow fast and memory-efficient retrieval. These issues limit the applicability of the existing cross-modal retrieval systems for large-scale applications in RS. To address this problem, in this paper we introduce a novel unsupervised cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS. To this end, the proposed DUCH is made up of two main modules: 1) feature extraction module, which extracts deep representations of two modalities; 2) hashing module that learns to generate cross-modal binary hash codes from the extracted representations. We introduce a novel multi-objective loss function including: i) contrastive objectives that enable similarity preservation in intra- and inter-modal similarities; ii) an adversarial objective that is enforced across two modalities for cross-modal representation consistency; and iii) binarization objectives for generating hash codes. Experimental results show that the proposed DUCH outperforms state-of-the-art methods. Our code is publicly available at https://git.tu-berlin.de/rsim/duch.
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors

Face image super resolution (face hallucination) usu-ally relies on facial priors to restore realistic details and preserve identity information. Recent advances can achieve impressive results with the help of GAN prior. They ei-ther design complicated modules to modify the fixed GAN prior or adopt complex training strategies to finetune the generator. In this work, we propose a generative and controllable face SR framework, called GCFSR, which can re-construct images with faithful identity information without any additional priors. Generally, GCFSR has an encoder-generator architecture. Two modules called style modu-lation and feature modulation are designed for the multi-factor SR task. The style modulation aims to generate real-istic face details and the feature modulation dynamically fuses the multi-level encoded features and the generated ones conditioned on the upscaling factor. The simple and elegant architecture can be trained from scratch in an end-to-end manner. For small upscaling factors (≤8), GCFSR can produce surprisingly good results with only adversar-ialloss. After adding L1 and perceptual losses, GCFSR can outperform state-of-the-art methods for large upscalingfac-tors (16, 32, 64). During the test phase, we can modulate the generative strength via feature modulation by changing the conditional upscaling factor continuously to achieve various generative effects. Code is available at https://github.com/hejingwenhejingwen/GCFSR
Computationally expedient Photovoltaic power Forecasting: A LSTM ensemble method augmented with adaptive weighting and data segmentation technique

Photovoltaics (PVs) hold the promise of sustainable electricity production. However, PV output is significantly influenced by variations in terrestrial solar radiation and other weather factors, causing problems in unit commitment, economic power dispatch and reliable electricity distribution from grid-connected, hybrid and off-grid PV plants. Thus, accurate and consistent PV output prediction is crucial for utilities companies and researchers. Current initiatives enlist a plethora of forecasting techniques, including: statistical models, physical models, artificial neural networks and deep learning algorithms. Nevertheless, most of these approaches suffer from computational costs, complex models and uncertainties. Hence, this research employed an ensemble-based Long Short-Term Memory (LSTM) algorithm comprising 10 component LSTM models. The method compares the effects of different data segmentations (three-months to one-day) and is based on varying time-horizons (14-days to 5-mins) in order to compare the effects of seasonal and periodic variations on time-series data and PV output forecast. The comparison is then used to minimise uncertainty by implementing grid search technique. Subsequently, the model’s performance was evaluated using MAPE analysis for two cases involving erratic weather conditions and PV output in two specific days of the year 2020. From the evaluation, the best prediction was found to be for the two-week dataset with MAPE of 6.02. This approach combined with online data acquisition from the Yulara Solar System power plant in central Australia leads to a more practical, computationally economical and robust PV power generation forecasting technique.