Reviewerly/exHarmony huggingface dataset:
SqueezeLight: Towards Scalable Optical Neural Networks with Multi-Operand Ring Resonators

Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption. However, due to high area cost and lack of efficient sparsity exploitation, previous ONN designs fail to provide scalable and efficient neuromorphic computing, which hinders the practical implementation of photonic neural accelerators. In this work, we propose a novel design methodology to enable a more scalable ONN architecture. We propose a nonlinear optical neuron based on multi-operand ring resonators to achieve neuromorphic computing with a compact footprint, low wavelength usage, learnable neuron balancing, and built-in nonlinearity. The structured sparsity is exploited to support more efficient ONN engines via a fine-grained structured pruning technique. A robustness-aware learning method is adopted to guarantee the variation-tolerance of our ONN. Simulation and experimental results show that the proposed ONN achieves one-order-of-magnitude improvement in compactness and efficiency over previous designs with high fidelity and robustness.
A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

Nowadays, solving constrained engineering problems related to optimization approaches is an attractive research topic. The chaotic krill herd approach is considered as one of most advanced optimization techniques. An advanced hybrid technique is exploited in this paper to solve the challenging problem of estimating the largest domain of attraction for nonlinear systems. Indeed, an intelligent methodology for the estimation of the largest stable equilibrium domain of attraction established on quadratic Lyapunov functions is developed. The designed technique aims at computing and characterizing a largest level set of a Lyapunov function that is included in a particular region, satisfying some hard and delicate algebraic constraints. The formulated optimization problem searches to solve a tangency constraint between the LF derivative sign and constraints on the level sets. Such formulation avoids possible dummy solutions for the nonlinear optimization solver. The analytical development of the solution exploits the Chebyshev chaotic map function that ensures high search space capabilities. The accuracy and efficiency of the chaotic krill herd technique has been evaluated by benchmark models of nonlinear systems. The optimization solution shows that the chaotic krill herd approach is effective in determining the largest estimate of the attraction domain. Moreover, since global optimality is needed for proper estimation, a bound type meta-heuristic optimization solver is implemented. In contrast to existing strategies, the synthesized technique can be exploited for both rational and polynomial Lyapunov functions. Moreover, it permits the exploitation of a chaotic operative optimization algorithm which guarantees converging to an expanded domain of attraction in an essentially restricted running time. The synthesized methodology is discussed, with several examples to illustrate the advantageous aspects of the designed approach.
Neural Collision Clearance Estimator for Batched Motion Planning

We present a neural network collision checking heuristic, ClearanceNet, and a planning algorithm, CN-RRT. ClearanceNet learns to predict separation distance (minimum distance between robot and workspace) with respect to a workspace. CN-RRT then efficiently computes a motion plan by leveraging three key features of ClearanceNet. First, CN-RRT explores the space by expanding multiple nodes at the same time, processing batches of thousands of collision checks. Second, CN-RRT adaptively relaxes its clearance requirements for more difficult problems. Third, to repair errors, CN-RRT shifts its nodes in the direction of ClearanceNet's gradient and repairs any residual errors with a traditional RRT, thus maintaining theoretical probabilistic completeness guarantees. In configuration spaces with up to 30 $$^\circ $$ of freedom, ClearanceNet achieves 845x speedup over traditional collision detection methods, while CN-RRT accelerates motion planning by up to 42% over a baseline and finds paths up to 36% more efficient. Experiments on an 11 degree of freedom robot in a cluttered environment confirm the method's feasibility on real robots.
Can Blockchain Strengthen the Energy Internet?

Emergence of the Energy Internet (EI) demands restructuring of traditional electricity grids to integrate heterogeneous energy sources, distribution network management with grid intelligence and big data management. This paradigm shift is considered to be a breakthrough in the energy industry towards facilitating autonomous and decentralized grid operations while maximizing the utilization of Distributed Generation (DG). Blockchain has been identified as a disruptive technology enabler for the realization of EI to facilitate reliable, self-operated energy delivery. In this paper, we highlight six key directions towards utilizing blockchain capabilities to realize the envisaged EI. We elaborate the challenges in each direction and highlight the role of blockchain in addressing them. Furthermore, we summarize the future research directive in achieving fully autonomous and decentralized electricity distribution networks, which will be known as Energy Internet.
Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

This paper introduces an approach for multi-human 3D pose estimation and tracking based on calibrated multi-view. The main challenge lies in finding the cross-view and temporal correspondences correctly even when several human pose estimations are noisy. Compare to previous solutions that construct 3D poses from multiple views, our approach takes advantage of temporal consistency to match the 2D poses estimated with previously constructed 3D skeletons in every view. Therefore cross-view and temporal associations are accomplished simultaneously. Since the performance suffers from mistaken association and noisy predictions, we design two strategies for aiming better correspondences and 3D reconstruction. Specifically, we propose a part-aware measurement for 2D-3D association and a filter that can cope with 2D outliers during reconstruction. Our approach is efficient and effective comparing to state-of-the-art methods; it achieves competitive results on two benchmarks: 96.8% on Campus and 97.4% on Shelf. Moreover, we extends the length of Campus evaluation frames to be more challenging and our proposal also reach well-performed result. The code will be available at https://git.io/JO4KE.
Predicate order and coherence in copredication

This article proposes that predicate order and coherence relations are the two major determining factors in copredication licensing, resolving a long-standing puzzle over the criteria for constructing acceptable copredications. The effects of predicate ordering are claimed to be anchored around semantic complexity, such that copredications with semantically Simple–Complex predicate orderings are more acceptable than the reverse. This motivates a parsing bias, termed Incremental Semantic Complexity. Particular ways of implementing this parsing bias are discussed. The effect of predicate coherence is claimed to be anchored around a sense of causality and featural commonality. Lastly, a hierarchy of possible copredications is outlined (the Copredication Hierarchy), helping to delimit the modelling of copredications to a greater extent than has previously been possible.
Multi-orientation scene text detection with scale-guided regression

Existing multi-orientation scene text detection methods generally contain two crucial components: regression prediction for text bounding boxes and classification prediction for text/non-text. However, these methods always regard classification prediction and regression prediction as two independent procedures, neglecting fully exploring their mutual relations. Based on this key observation, we propose an innovative Scale-Guided Regression Module (SRM), specially for multi-orientation scene text detection. Equipped with width-guided kernels and height-guided kernels of different sizes, our SRM can generate a series of scale feature maps of candidate texts by capturing their shape information in classification prediction. The scale feature maps are used to predict the width and height of candidate texts, which can serve as guides for regressing bounding boxes. In this way, the procedures of classification and regression can be coherently integrated. In addition, we adopt IoU loss to train our network and then integrate IoU loss and l1-smooth loss for fine-tuning. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. Notably, our method achieves significant improvement of performance on long texts, e.g., on MSRA-TD500, our method outperforms Basemodel with a great margin (4.86% in terms of Recall).
Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning

A class of machine learning problem where each instance may either belong to one or more than one class simultaneously is known as Multi-label classification problem. Unlike other classification problems there exist several challenges and the important among them is learning the label correlation when labels are missing. In this paper, we present a unified learning system that addresses the aforementioned issue, and suggest a novel multi-label classifier termed as Multi-label classification with Missing Labels using Label Correlation and Robust Structural Learning (MLLCRS-ML). The propose classifier considers label-specific features along with utilizing the structural property of data and pairwise label correlation (both positive and negative label correlations) for recovering the missing labels. We have use hinge loss function which ensures less sensitivity towards outliers and the accelerated proximal gradient method (APG) to efficiently solve the underlying optimization problem. Experimental results on several benchmark data sets show that our propose approach MLLCRS-ML is as competitive as other state-of-the-art approaches.
CapsuleRRT: Relationships-aware Regression Tracking via Capsules

Regression tracking has gained more and more attention thanks to its easy-to-implement characteristics, while existing regression trackers rarely consider the relationships between the object parts and the complete object. This would ultimately result in drift from the target object when missing some parts of the target object. Recently, Capsule Network (CapsNet) has shown promising results for image classification benefits from its part-object relationships mechanism, while CapsNet is known for its high computational demand even when carrying out simple tasks. Therefore, a primitive adaptation of CapsNet to regression tracking does not make sense, since this will seriously affect speed of a tracker. To solve these problems, we first explore the spatial-temporal relationships endowed by the CapsNet for regression tracking. The entire regression framework, dubbed CapsuleRRT, consists of three parts. One is S-Caps, which captures the spatial relationships between the parts and the object. Meanwhile, a T-Caps module is designed to exploit the temporal relationships within the target. The response of the target is obtained by STCaps Learning. Further, a prior-guided capsule routing algorithm is proposed to generate more accurate capsule assignments for subsequent frames. Apart from this, the heavy computation burden in CapsNet is addressed with a knowledge distillation pose matrix compression strategy that exploits more tight and discriminative representation with few samples. Extensive experimental results show that CapsuleRRT performs favorably against state-of-the-art methods in terms of accuracy and speed.
A Super Resolution Algorithm Based on Attention Mechanism and SRGAN Network

Image super-resolution reconstruction uses a specific algorithm to restore the low resolution blurred image in the same scene to a high resolution image. In recent years, with the vigorous development of deep learning, this technology has been widely used in many fields. In the field of image super-resolution reconstruction, more and more methods based on deep learning have been studied. According to the principle of GAN, a pseudo high-resolution image is generated by the generator, and then the discriminator calculates the difference between the image and the real high-resolution image to measure the authenticity of the image. Based on SRGAN (super resolution general adverse network), this paper mainly makes three improvements. First, it introduces the attention channel mechanism, that is, it adds Ca (channel attention) module to SRGAN network, and increases the network depth to better express high frequency features; Second, delete the original BN (batch normalization) layer to improve the network performance; Third, modify the loss function to reduce the impact of noise on the image. The experimental results show that the proposed method is superior to the current methods in both quantitative and qualitative indicators, and promotes the recovery of high-frequency detail information. The experimental results show that the proposed method improves the artifact problem and improves the PSNR (peak signal-to-noise ratio) on set5, set10 and bsd100 test sets.
Autonomous Parking Trajectory Planning With Tiny Passages: A Combination of Multistage Hybrid A-Star Algorithm and Numerical Optimal Control

This paper introduces an autonomous parking trajectory planning method in an unstructured environment with narrow passages.The proposed hierarchical trajectory planner consists of a graph search layer and a numerical optimal control layer.The contribution mainly lies in the graph search layer, wherein a multistage hybrid A * algorithm is proposed to handle narrow passages formed by obstacles in the cluttered environment.In the multistage hybrid A * algorithm, a 2-dim A * search is conducted to find a global route that connects the starting and goal points.Along the derived global route, subtle segments that traverse narrow passages are extracted.Thereafter, the hybrid A * algorithm is used to plan kinematically feasible subpaths that connect the boundary points of each subtle segment.The hybrid A * algorithm is also used to find linking paths that connect adjacent subpaths.Combining all the subpaths and linking paths in a sequence yields a coarse path, which is converted into a coarse trajectory by attaching a time-optimal velocity profile to it.The coarse trajectory is fed into the numerical optimization layer as the initial guess.Simulation results indicate that the hierarchical trajectory planner runs much faster than prevalent ones in dealing with unstructured environments with narrow passages.
A Model for Representing the Color and Depth Metric Characteristics of Objects in an Image

In general, a person receives information from the surrounding world with the help of the senses, processes it through the brain, using the knowledge gained earlier, and then transmits and accepts it, again using the senses. In other words, these information processes (receiving, processing, transmitting information), a person implements in a sign-signal form. The considered information processes are closely interrelated. For example, the processing and transmission of information is impossible without its storage, and to save the processed information, it must be transmitted. In this paper, the possibility of storing in the form of a single data structure not only information about the color of each pixel corresponding to the object of the scene fixed on it, but also the depth of its position is considered. A model is constructed for storing rangefinder information for each point of the three-dimensional scene in the image. An image generation algorithm is proposed for this model.
Impact of Heterogeneity and Risk Aversion on Task Allocation in Multi-Agent Teams

Cooperative multi-agent decision-making is a ubiquitous problem with many real-world applications. In many practical applications, it is desirable to design a multi-agent team with a heterogeneous composition where the agents can have different capabilities and levels of risk tolerance to address diverse requirements. While heterogeneity in multi-agent teams offers benefits, new challenges arise including how to find optimal heterogeneous team compositions and how to dynamically distribute tasks among agents in complex operations. In this work, we develop an artificial intelligence framework for multi-agent heterogeneous teams to dynamically learn task distributions among agents through reinforcement learning. The framework extends Decentralized Partially Observable Markov Decision Processes (Dec-POMDP) to be compatible to model various types of heterogeneity. We demonstrate our approach with a benchmark problem on a disaster relief scenario. The effect of heterogeneity and risk aversion in agent capabilities and decision-making strategies on the performance of multi-agent teams in uncertain environments is analyzed. Results show that a well-designed heterogeneous team outperforms its homogeneous counterpart and possesses higher adaptivity in uncertain environments.
A blockchain-based forensic model for financial crime investigation: the embezzlement scenario

The financial crime landscape is evolving along with the digitization in financial services. In this context, laws and regulations cannot efficiently cope with a fast-moving industry such as finance, which translates in late adoption of measures and legal voids, providing a fruitful landscape for malicious actors. In parallel, blockchain technology and its promising features such as immutability, verifiability, and authentication, enhance the opportunities of financial forensics. In this paper, we focus on an embezzlement scheme and we provide a forensic-by-design methodology for its investigation. In addition, the feasibility and adaptability of our approach can be extended and embrace digital investigations on other types of schemes. We provide a functional implementation based on smart contracts and we integrate standardised forensic flows and chain of custody preservation mechanisms. Finally, we discuss the benefits and challenges of the symbiotic relationship between blockchain and financial investigations, along with future research directions.
Cybersecurity Analysis: Investigating the Data Integrity and Privacy in AWS and Azure Cloud Platforms

The information technology field remains dominant in terms of adopting modern technologies. Additionally, there is increased adoption of the technologies in diverse realms and industries. One such rapidly emerging technology stands with the advancement of cloud computing. Today, cloud platforms are being sought after by a significant number of users and organizations to leverage their operations and productivity. The technology continues to gain more attention in the IT-Business arena. Cloud platforms offer greater flexibility in supporting real-time computation and arises as a more robust framework delivering offerings over the Internet. Amazon Windows Services (AWS) and Microsoft Azure are two key cloud platforms that allow users to utilize the cloud as a source of data storage, access, and retrieval. In the modern period of rapid global technological change, AWS and Azure cloud solutions are broadly adopted as public storage platforms for bulk information systems. Both server platforms offer private, public, hybrid, and community offerings to distinct organizations.Further, the two cloud technologies can be upgraded to mitigate against information imposition. Different security features allow diverse users to allocate a mutual foundation for storing and accessing data. Systematic appraisals among various characteristics, including platform independence, bulk employment, client requirements, security, size of data size, and other associated assets, are considered.This study is geared towards examining the infrastructure, platform, and data security issues that occur in cloud technologies, specifically with the application of Azure and AWS. Individual users and entities desire to experience the agility and scalability attributed to cloud technology platforms. These clients seek to develop and execute novice applications faster with the reduced cost through migration to the cloud platform. Amazon AWS and Microsoft Azure support distinct database management systems. The cloud providers portray different architectures, patterns of resource management, and degrees of complexity of the information systems’ enhancers, impacting scalability, performance, and price. While the cloud platforms continue to provide immense data security solutions, major challenges limit their growth and application. Future research needs to focus on more robust solutions and best practices to maintain cloud data security and integrity.
RS-DPSNet: Deep Plane Sweep Network for Rolling Shutter Stereo Images

Since the rolling shutter (RS) camera successively exposes each scanline, accurately reconstructing scene depth from an RS stereo image pair remains a great challenge. Directly applying the deep-learning-based depth estimation methods tailored for the global shutter (GS) stereo images leads to undesirable RS depth results due to inherent flaws in the network structure. In this letter, we fill this gap by developing an end-to-end RS-stereo-aware plane sweep network to improve the accuracy of the classic GS-based algorithm ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> DPSNet) in estimating the RS depth map. Specifically, we derive the RS-stereo-aware plane sweep model and further produce a more accurate and efficient cost volume through the effective incorporation of this model within DPSNet. Furthermore, to enable learning-based approaches to address the depth estimation problem in the context of RS stereo images, we contribute the first RS stereo dataset, CARLA-RSS. Experimental results demonstrate that our proposed pipeline achieves state-of-the-art performance.
Multimodal grid features and cell pointers for scene text visual question answering

This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is ×5 faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link.