Reviewerly/exHarmony huggingface dataset:
Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach

The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people’s goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between robot trajectory optimization and human intent inference. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive human models with both continuous and categorical uncertainty. The efficacy of our approach is demonstrated with simulated driving examples.
An Innovative Blockchain-Based Secured Logistics Management Architecture: Utilizing an RSA Asymmetric Encryption Method

Purpose: The recent development in logistics due to the dawn of Logistics 4.0 has made global logistics providers more dependent on intelligent technologies. In this era, these technologies assist in data collection and transmission of logistical data and pose many security and privacy threats in logistics management systems. The customer’s private information, which is shared among the logistics stakeholders for optimal operation, faces unauthorized access due to a lack of privacy. This, amongst others, is a critical problem that needs to be addressed with blockchain. Blockchain is a disruptive technology that is transforming different sectors, and it has the potential to provide a solution to the issues mentioned above, with its unique features such as immutability, transparency, and anonymity. Method: This study designed a blockchain-based logistics management architecture on a decentralized peer-2-peer network using Ethereum smart contracts. The proposed system deployed the Rivest–Shamir–Adleman (RSA) asymmetric encryption method to protect the logistics system from cyber-attacks and secure customers’ private information from unauthorized access. Findings: Furthermore, the security and privacy of the proposed system are evaluated based on the theorem. The proof shows that the system can provide security to the logistics system and privacy to customers’ private data. The performance evaluation is based on throughput and latency. It shows that the proposed system is better than the baseline system, and the comparatives analysis shows that the proposed system is more secure and efficient than the existing systems. Implication and Limitation: The proposed system offers a better solution to the security/privacy of the logistics management system and provides recommendations to key stakeholders involved in the logistics industry while adopting blockchain technology. Apart from the study’s methodological limitation, it is also limited by a lack of reference materials.
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer

In recent years, there has been tremendous progress in the field of semantic image segmentation. However, one remaining challenging problem is that segmentation models do not generalize to unseen domains. To overcome this problem, one either has to label lots of data covering the whole variety of possible domains, which is often infeasible in practice, or apply unsupervised domain adaptation (UDA), only requiring labeled source data. In this work, we focus on UDA and additionally address the case of adapting not only to a single domain, but to a sequence of target domains. This requires mechanisms preventing the model from forgetting its previously learned knowledge. To adapt a segmentation model to a target domain, we follow the idea of utilizing light-weight style transfer to convert the style of labeled source images into the style of the target domain, while retaining the source content. To mitigate the distributional shift between the source and the target domain, the model is fine-tuned on the transferred source images in a second step. Existing light-weight style transfer approaches relying on adaptive instance normalization (AdaIN) or Fourier transformation (FDA) still lack performance and do not substantially improve upon common data augmentation, such as color jittering. The reason for this is that these methods do not focus on region- or class-specific differences, but mainly capture the most salient style. Therefore, we propose a simple and light-weight framework that incorporates two class-conditional AdaIN layers. To extract the class-specific target moments needed for the transfer layers, we use unfiltered pseudo-labels, which we show to be an effective approximation compared to real labels. We extensively validate our approach (CACE) on a synthetic sequence and further propose a challenging sequence consisting of real domains. CACE outperforms existing methods visually and quantitatively.
Multi-Agent Path Finding with Mutex Propagation

Mutex propagation is a form of efficient constraint propagation popularly used in AI planning to tightly approximate the reachable states from a given state. We utilize this idea in the context of Multi-Agent Path Finding (MAPF). When adapted to MAPF, mutex propagation provides stronger constraints for conflict resolution in Conflict-Based Search (CBS), a popular optimal MAPF algorithm, and provides it with the ability to identify and reason with symmetries in MAPF. While existing work identifies a limited form of symmetries using rectangle reasoning and requires the manual design of symmetry-breaking constraints, mutex propagation is more general and allows for the automated design of symmetry-breaking constraints. Our experimental results show that CBS with mutex propagation is capable of outperforming CBSH with rectangle reasoning, a state-of-the-art variant of CBS, with respect to runtime and success rate.
Proper Reuse of Image Classification Features Improves Object Detection

A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task. In object detection specifically, the feature backbone is typically initialized with ImageNet classifier weights and fine-tuned on the object detection task. Recent works show this is not strictly necessary under longer training regimes and provide recipes for training the backbone from scratch. We investigate the opposite direction of this end-to-end training trend: we show that an extreme form of knowledge preservation-freezing the classifier-initialized backbone— consistently improves many different detection models, and leads to considerable resource savings. We hypothesize and corroborate experimentally that the remaining detector components capacity and structure is a crucial factor in leveraging the frozen backbone. Immediate applications of our findings include performance improvements on hard cases like detection of long-tail object classes and computational and memory resource savings that contribute to making the field more accessible to researchers with access to fewer computational resources.
Clothes Retrieval Using M-AlexNet With Mish Function and Feature Selection Using Joint Shannon’s Entropy Pearson’s Correlation Coefficient

The online retrieval of images related to clothes is a crucial task because finding the exact items like the query image from a large amount of data is extremely challenging.However, large variations in clothes images degrade the retrieval accuracy of visual searches.Another problem with retrieval accuracy is high dimensions of feature vectors obtained from pre-trained deep CNN models.This research is an effort to enhance the training and test accuracy of clothes retrieval by using two different means.Initially, features are extracted using the modified AlexNet (M-AlexNet) with little modification in which ReLU activation function is replaced with a self-regularized Mish activation function because of its non-monotonic nature.The M-AlexNet with Mish is trained on CIFAR-10 dataset using SoftMax classifier.Another contribution is to reduce the dimensions of feature vectors obtained from M-AlexNet.The dimensions of features are reduced by selecting the top k ranked features and removing some of the dissimilar features using the proposed Joint Shannon's Entropy Pearson Correlation Coefficient (JSE-PCC) technique to enhance the clothes retrieval performance.To calculate the efficacy of suggested methods, the comparison is performed with other deep CNN models such as baseline AlexNet, VGG-16, VGG-19, and ResNet50 on DeepFashion2, MVC, and the proposed Clothes Image Dataset (CID).Extensive experiments indicate that AlexNet with Mish attains 85.15%, 82.04%, and 83.65% accuracy on DeepFashion2, MVC, and 83.65% on CID datasets respectively.Hence, M-AlexNet and the proposed feature selection technique surpassed the results with a margin of 5.11% on DeepFashion2, 1.95% on MVC, and 3.51% CID datasets.
Conflict-Based Search for Multi-Robot Motion Planning with Kinodynamic Constraints

Multi-robot motion planning (MRMP) is the fundamental problem of finding non-colliding trajectories for multiple robots acting in an environment, under kinodynamic constraints. Due to its complexity, existing algorithms are either incomplete, or utilize simplifying assumptions. This work introduces Kinodynamic Conflict-Based Search (K-CBS), a decentralized MRMP algorithm that is general, scalable, and probabilistically complete. The algorithm takes inspiration from successful solutions to the discrete analogue of MRMP over finite graphs, known as Multi-Agent Path Finding (MAPF). Specifically, we adapt ideas from Conflict-Based Search (CBS)-a popular decentralized MAPF algorithm-to the MRMP setting. The novelty of our approach is that we work directly in the continuous domain, without discretization. In particular, the kinodynamic constraints are treated natively. K-CBS plans for each robot individually using a low-level planner and grows a conflict tree to resolve collisions between robots by defining constraints. The low-level planner can be any sampling-based, tree-search algorithm for kinodynamic robots, thus lifting existing planners for single robots to the multi-robot setting. We show that K-CBS inherits the (probabilistic) completeness of the low-level planner. We illustrate the generality and performance of K-CBS in several case studies and benchmarks.
BRNet: Exploring Comprehensive Features for Monocular Depth Estimation

Self-supervised monocular depth estimation has achieved encouraging performance recently. A consensus is that high-resolution inputs often yield better results. However, we find that the performance gap between high and low resolutions in this task mainly lies in the inappropriate feature representation of the widely used U-Net backbone rather than the information difference. In this paper, we address the comprehensive feature representation problem for self-supervised depth estimation by paying attention to both local and global feature representation. Specifically, we first provide an in-depth analysis of the influence of different input resolutions and find out that the receptive fields play a more crucial role than the information disparity between inputs. To this end, we propose a bilateral depth encoder that can fully exploit detailed and global information. It benefits from more broad receptive fields and thus achieves substantial improvements. Furthermore, we propose a residual decoder to facilitate depth regression as well as save computations by focusing on the information difference between different layers. We named our new depth estimation model Bilateral Residual Depth Network (BRNet). Experimental results show that BRNet achieves new state-of-the-art performance on the KITTI benchmark with three types of self-supervision. Codes are available at: https://github.com/wencheng256/BRNet .
XAI-3DP: Diagnosis and Understanding Faults of 3-D Printer With Explainable Ensemble AI

Additive manufacturing is one of the most widely used techniques in the domain of manufacturing. Three-dimensional (3-D) printers are one of those systems that made additive manufacturing easier. Fused-deposition-modeling-based 3-D printers provide cost-effective 3-D models. Like other mechanical systems, 3-D printers also face faults that damage the printing of the system. Hence, proper maintenance is required. The data-driven-based approach of the diagnosis of the fault in 3-D printers is proposed in this letter. Data are collected for three scenarios—1) healthy condition, 2) bed failure, and 3) arm failure. The ensemble learning model of Random Forest and XGBoost has been implemented with a ratio of 0.54 and 0.46, and a result of 99.75% accuracy is achieved, compared to 96% and 98% alone, respectively. The black box machine learning model is then further explained using the Shapley additive explanations library for the interpretation of the prediction, such that the trustworthiness of the artificial intelligence model gets improved.
An abstraction-refinement framework for verifying strategic properties in multi-agent systems with imperfect information

We investigate the verification of Multi-Agent Systems against strategic properties expressed in Alternating-time Temporal Logic under the assumptions of imperfect information and perfect recall. To this end, we develop a three-valued semantics for concurrent game structures upon which we define an abstraction method. We prove that concurrent game structures with imperfect information admit perfect information abstractions that preserve three-valued satisfaction. Furthermore, to deal with cases in which the value of a specification is undefined, we develop a novel automata-theoretic technique for the linear-time logic (LTL), then apply it to finding “failure” states. The latter can then be fed into a refinement procedure, thus providing a sound, albeit incomplete, verification method. We illustrate the overall procedure in a variant of the Train Gate Controller scenario and a simple voting protocol under imperfect information and perfect recall. We also present an implementation of our procedure and provide preliminary experimental results.
Selectively reflective edge detection system based on cholesteric liquid crystal

Optical analog computing has attracted extensive interest in image processing and optical engineering in recent decades. Here, we propose a reflective optical analog computing system based on a cholesteric liquid crystal (CLC), which simplifies the traditional optical analog computing system by taking advantage of the CLC reflecting the light with specified circular polarization and provides a new, to the best of our knowledge, idea for the integration of optical analog computing systems. Meanwhile, we present results in which a section of an insect foot is observed using the reflective optical analog computing system, which may develop valuable applications in biomedical imaging.
A Blockchain Peer-to-peer Energy Trading System for Microgrids

Traditional power grids have been the major source of electricity for several households and industries for a number of years. However, with that kind of supply, every member of the grid is affected whenever there is a fault on the transmission line connecting them. With that in mind, microgrids, usually powered by numerous distributed electrical sources, were introduced to curb this problem, and as such, energy users have also become producers themselves. Nonetheless, the generation of power by distributed sources brings about unpredictability on the network and, in essence, problems in energy sharing. Peer-to-peer (P2P) energy trading has several advantages and has been introduced to mitigate energy sharing problems. With networked energy trading comes the issue of trust, as several prosumers are concerned about their privacy and security in such environments. Therefore, this work leverages the advantages of blockchain in proposing a secure energy trading platform for all parties involved. Coupled with certificateless signcryption, an immutable energy trading market is designed, and its use case is applicable in smart cities. A thorough security analysis was performed, and the efficiency of our proposed solution is backed by numerical results.
AI in Computational Pathology of Cancer: Improving Diagnostic Workflows and Clinical Outcomes?

Histopathology plays a fundamental role in the diagnosis and subtyping of solid tumors and has become a cornerstone of modern precision oncology. Histopathological evaluation is typically performed manually by expert pathologists due to the complexity of visual data. However, in the last ten years, new artificial intelligence (AI) methods have made it possible to train computers to perform visual tasks with high performance, reaching similar levels as experts in some applications. In cancer histopathology, these AI tools could help automate repetitive tasks, making more efficient use of pathologists’ time. In research studies, AI methods have been shown to have an astounding ability to predict genetic alterations and identify prognostic and predictive biomarkers directly from routine tissue slides. Here, we give an overview of these recent applications of AI in computational pathology, focusing on new tools for cancer research that could be pivotal in identifying clinical biomarkers for better treatment decisions.
City-scale holographic traffic flow data based on vehicular trajectory resampling

Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole city, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data (FCD).
Quantum computing for fusion energy science applications

This is a review of recent research exploring and extending present-day quantum computing capabilities for fusion energy science applications. We begin with a brief tutorial on both ideal and open quantum dynamics, universal quantum computation, and quantum algorithms. Then, we explore the topic of using quantum computers to simulate both linear and nonlinear dynamics in greater detail. Because quantum computers can only efficiently perform linear operations on the quantum state, it is challenging to perform nonlinear operations that are generically required to describe the nonlinear differential equations of interest. In this work, we extend previous results on embedding nonlinear systems within linear systems by explicitly deriving the connection between the Koopman evolution operator, the Perron–Frobenius evolution operator, and the Koopman–von Neumann evolution (KvN) operator. We also explicitly derive the connection between the Koopman and Carleman approaches to embedding. Extension of the KvN framework to the complex-analytic setting relevant to Carleman embedding, and the proof that different choices of complex analytic reproducing kernel Hilbert spaces depend on the choice of Hilbert space metric are covered in the appendixes. Finally, we conclude with a review of recent quantum hardware implementations of algorithms on present-day quantum hardware platforms that may one day be accelerated through Hamiltonian simulation. We discuss the simulation of toy models of wave–particle interactions through the simulation of quantum maps and of wave–wave interactions important in nonlinear plasma dynamics.
An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN)

Sentiment analysis is one of the effective techniques for mining the opinion from shapeless data contains text like review of the products, review of the movie. Sentiment analysis is used as a key to gather response from consumers, reviews of brands, marketing analyses, and political campaigns. In the subject of natural processing, performing sentiment analysis using the data obtained from Twitter is considered as a new study in these days. The dataset is gathered using the Twitter API and the Twitter package. The analysis of Twitter data is a process which takes place automatically by text data analysis to determine the view of public on the specified topic. Here, an improvised sentimental analysis model is proposed to identify the polarity of the tweets such as positive, neutral and negative. In this paper, stochastic gradient descent (SGD) algorithm uses stochastic gradient neural network (SGNN) to categorize the sentiment analysis on basis of tweets provided by the Twitter users and the proposed stochastic gradient descent optimization Algorithm based on stochastic gradient neural network (SGDOA-SGNN) provides better performance when compared with the existing Forest–Whale Optimization Algorithm based on deep neural network F-WOA-DNN model.
DCT-Former: Efficient Self-Attention with Discrete Cosine Transform

Since their introduction the Transformer architectures emerged as the dominating architectures for both natural language processing and, more recently, computer vision applications. An intrinsic limitation of this family of “fully-attentive” architectures arises from the computation of the dot-product attention, which grows both in memory consumption and number of operations as $$O(n^2)$$ where n stands for the input sequence length, thus limiting the applications that require modeling very long sequences. Several approaches have been proposed so far in the literature to mitigate this issue, with varying degrees of success. Our idea takes inspiration from the world of lossy data compression (such as the JPEG algorithm) to derive an approximation of the attention module by leveraging the properties of the Discrete Cosine Transform. An extensive section of experiments shows that our method takes up less memory for the same performance, while also drastically reducing inference time. Moreover, we assume that the results of our research might serve as a starting point for a broader family of deep neural models with reduced memory footprint. The implementation will be made publicly available at https://github.com/cscribano/DCT-Former-Public .