Reviewerly/exHarmony huggingface dataset:
DEM-AIA: Asymmetric inclination-aware trajectory planner for off-road vehicles with digital elevation models

Planning safe and effective trajectories for off-road unmanned ground vehicles (UGV) is a critical Artificial Intelligence (AI) challenge that can benefit from recent advances in digital elevation models (DEM) for readily capturing accurate terrain geometry. Considering path slopes is crucial to preserve stability and assess terrain traversability at feasible speeds to optimize travel time, which is highly dependent on direction (i.e., pitch and roll). In this article, we propose a new DEM-based asymmetric inclination-aware (DEM-AIA) trajectory planner for ground vehicles. The planner is an any-angle variant of the A⋆ algorithm that computes pitch and roll estimations for each segment crossing cell triangles in the line-of-sight. Furthermore, we define a non-linear velocity constraints function that integrates information about tip-over safety limitations, maximum uphill and downhill slopes for the vehicle, and asymmetric modulation of nominal flat-ground velocity for all pitch and roll combinations. The planner produces a time sub-optimal trajectory with feasible speed references for each segment crossing a cell triangle. Moreover, we provide an extensive experimental analysis of inclination-aware performance on simulated and real-world DEMs as well as a comparison with state-of-the-art path planners adapted to travel-time optimization. An executable version of the planner with parameterizable variations is publicly available.
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning

Stochastic Approximation ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</b> ) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity to deal with a large amount of data observed with uncertainties. An exemplar special case of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</b> pertains to the popular stochastic (sub)gradient algorithm which is the working horse behind many important applications. A lesser-known fact is that the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</b> scheme also extends to non-stochastic-gradient algorithms such as compressed stochastic gradient, stochastic expectation-maximization, and a number of reinforcement learning algorithms. The aim of this article is to overview and introduce the non-stochastic-gradient perspectives of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</b> to the signal processing and machine learning audiences through presenting a design guideline of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</b> algorithms backed by theories. Our central theme is to propose a general framework that unifies existing theories of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</b> , including its non-asymptotic and asymptotic convergence results, and demonstrate their applications on popular non-stochastic-gradient algorithms. We build our analysis framework based on classes of Lyapunov functions that satisfy a variety of mild conditions. We draw connections between non-stochastic-gradient algorithms and scenarios when the Lyapunov function is smooth, convex, or strongly convex. Using the said framework, we illustrate the convergence properties of the non-stochastic-gradient algorithms using concrete examples. Extensions to the emerging variance reduction techniques for improved sample complexity will also be discussed.
Hardware acceleration of number theoretic transform for zk‐SNARK

Abstract Zk‐SNARK unleashes the great potential of ZKP (zero‐knowledge proof) in the blockchain, distributed storage, and so forth. However, the proof‐generation of zk‐SNARK is excessively time intensive, making it a challenge to deploy a high‐performance zk‐SNARK in most real applications. As a result, NTT (Number Theoretic Transform), one of the most time‐consuming parts in proof‐generation, needs to be accelerated significantly. To address this issue, we propose a novel and efficient “data reordering" technique to enable a highly pipelined architecture, on which an FPGA‐based hardware accelerator is designed to support the large‐bitwidth and large‐scale NTT tasks in zk‐SNARK. This two‐level pipelined architecture can effectively reduce the data dependency and memory access requirements, meanwhile, can be flexibly scaled to different scales of FPGAs. To balance computing efficiency and flexibility, the OpenCL equipped with HLS is used to implement the heterogeneous acceleration system. We prototype the accelerator on the AMD‐Xilinx Alveo U50 card (UltraScale+ XCU50 FPGA). The evaluation results show that (1) our accelerator shows high scalability for different scales of FPGAs with a stable performance improvement; (2) it performs 1.95 faster than the one in PipeZK; (3) and it achieves 27.98 , 1.74 speedup and 6.9 , 6 energy efficiency improvement than AMD Ryzen 9 5900X single core and 12 cores respectively when integrated into the well‐known ZKP open‐source project, Bellman.
A Scalable Recommendation System Approach for a Companies — Seniors Matching

Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid content-based filtering (CBF) and similarity index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.
Knowledge Graph-Based Explainable Artificial Intelligence for Business Process Analysis

For critical operational decisions (e.g. consulting services), explanations and interpretable results of powerful Artificial Intelligence (AI) systems are becoming increasingly important. Knowledge graphs possess a semantic model that integrates heterogeneous information sources and represents knowledge elements in a machine-readable form. The integration of knowledge graphs and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other’s strengths. Our research aims at an explainable system with a specific knowledge graph architecture that generates human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable AI approach for business process analysis is focused. We design a framework of interpretation, show how interpretable models are generated by a single case study and evaluate the applicability of our approach by different expert interviews. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects.
The Characteristic Analysis of ESDD and NSDD Detection of Composite Insulators Based on Hyperspectral Technology

Pollution flashover of insulators has been viewed as a common issue existing in the area of power systems for a long term. However, traditional detection methods such as equivalent salt density and leakage current (LC) can hardly satisfy the requirements of the progressing smart grid. Herein, a novel highly-efficient method is proposed for classifying the pollution degree detection of insulators based on hyperspectral technology. The insulating sheets and actual insulators were set as the research object and their hyperspectral images are obtained. Moreover, the data for describing the characteristic wavelengths and the G color components of the samples were extracted from the images as the characterization of the equivalent salt deposit density (ESDD) and nonsoluble deposit density (NSDD) respectively. The ESDD and NSDD detection model is established based on the multiple linear regression (MLR) algorithm and the random forest (RF) algorithm. Ultimately, the proposed model reaches a detection accuracy of above 84% on the insulating sheets and 75% on the actual insulators, which can be viewed as acceptable. The hyperspectral imaging technique has considerable potential for the non-contact detection of ESDD and NSDD.
Research on Privacy Protection of Technology Service Transactions Based on Blockchain and Zero-Knowledge Proof

In view of the problem that the transaction privacy in the current blockchain technology service is easy to be leaked, a bulletproof alliance chain technology service transaction privacy protection mechanism is proposed. Firstly, this paper uses digital certificates as access mechanisms and stores them on the chain to ensure that the identity of technical service transactions is trusted. Secondly, the transaction data of the technical service user is hidden in the Pedersen commitment, and the Bulletproof is used to build the scope proof. Enable the verifier to conduct confidential verification of the legitimacy of the transaction without obtaining the sensitive information of the transaction, so as to ensure that the user’s transaction privacy is not disclosed. Finally, the security and privacy of the proposed privacy protection scheme are analyzed, and the comparison with other zero-knowledge proof schemes shows that the scheme has the advantages of strong privacy, scalability, and low storage cost.
Phonetic-Based Forward Online Transliteration Tool from English to Tamil Language

Transliteration is the process of mapping the character of one language to the character of some other language based on its phonetics. India is very much diverse in languages where people speak different languages. Though they speak different languages, it might be difficult for them to read the script of those many languages. In a situation like this, transliteration process plays a major role. It helps in various Natural Language Processing applications such as Information retrieval, Machine translation, Speech recognition. These are NLP applications which make the computer understand the natural language as to how human being interprets. It helps in translating technical terms and proper names from one language to another language. Moreover, transliteration works have been carried out in languages such as Japanese, Chinese and English. But when considering Indian languages, especially Tamil language, very few recognizable works have been carried out. In this paper, transliteration process is carried out on Unicode Tamil characters. The phonetics-based forward list processing is implemented for transliterating from English language to Tamil language which yields promising results.
Open set domain adaptation with latent structure discovery and kernelized classifier learning

Numerous visual domain adaptation methods have been proposed for transferring knowledge from a well-labeled source domain to an unlabeled but related target domain. Most of existing works are only geared to closed set domain adaptation, where an identical label space is shared between two domains. In this paper, we focus on a more realistic but challenging scenario, open set domain adaptation, where the target domain contains unknown classes that do not appear in the label space of source domain. The main task of open set domain adaptation is to simultaneously recognize the target images of known classes and those of unknown classes correctly. To achieve this goal, in this paper, we propose a novel open set domain adaptation method, which consists of two parts: latent structure discovery and kernelized classifier learning. In the first part, we employ an adaptive discriminative graph learning strategy to capture the intrinsic manifold structure of the source and target domain data in the latent feature space, such that the boundaries among all classes will be delineated more clearly. In the second part, the samples from the latent feature space are mapped into a high-dimensional kernel space to make them linearly separable, and a linear classifier is learned by jointly operating unknown target samples separating, known samples matching and local structure preserving. As the optimization problem is not convex with all variables, we devise an efficient iterative algorithm to solve it. The extensive experimental results on five image datasets confirm the superiority of the proposed method compared with the state-of-the-art traditional and deep competitors.
Hardware-in-the-loop Simulation for Real-time Autonomous Tracking and Landing of an Unmanned Aerial Vehicle

Collaborative exploration using unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has become increasingly popular in the past decade. This study addresses real-time autonomous tracking and landing of a UAV on a moving ground vehicle, which is fundamental for the UAV-UGV collaborative exploration. The method proposed in this study estimates the relative pose and velocity between a UAV and UGV, and uses model predictive control for UAV trajectory planning while considering the field of view of the camera onboard the UAV. We elaborate the hardware-in-the-loop simulator (HITL) with a physical companion computer, and confirm that the proposed method enables a UAV to land on a UGV traversing on rough terrain based on online computations in the HITL. Additionally, we present statistical analysis of the simulation results of typical and computationally demanding scenarios to elucidate the computational cost on the real machine.
Development of an Anthropomorphic Saxophonist Robot Using a Human-like holding Method

This paper presents WAS-X (Waseda Anthropomorphic Saxophonist robot X), a saxophonist robot designed following an “Instrument-centered” approach. WAS-X can play on a regular alto saxophone two octaves of music with a direct MIDI input control, but it is much smaller and simpler to control than its WAS predecessors and can be used in the future for more complex interaction studies. In developing this robot, we used a Design Thinking method with the goal of solving the “wicked problem” of finding a balanced solution between performance quality and dynamic movement freedom for interaction purposes. From a Design Thinking perspective, we replaced “User-centered design” with an innovative “Instrument-centered design” and explored different development alternatives, ultimately focusing on the human holding posture of the saxophone by observing saxophonists and following how beginners learn to play the saxophone.
Discrimination of Structures in a Plant Facility Based on Projected Image Created from Colored 3D Point Cloud Data

This paper describes a method for discrimination of structures in a plant facility by deep learning based on projected images created from a colored 3D point cloud data using a virtual camera system. In order to promote safe and secure decommissioning works, it is important to recognize a radiation condition in the workspace via calculation based on the measured sensor data. In our previous work, we proposed a structure discrimination method by 3D semantic segmentation network to obtain clues required for radiation dose simulation: shape regions for creating 3D shape model and structural category labels for assigning material information. However, in the evaluation, we confirmed that the network trained based on point geometric patterns had limited discrimination performance. In this paper, we introduce deep learning based on projected images created from colored 3D point cloud data to improve the accuracy of our structure discrimination method. The projected images are created by a virtual camera system, and after discrimination, predicted pixel-wise labels are back-projected into the 3D point cloud. In evaluation, we reported the discrimination performance and 2D-3D back projection result of our proposed method.
Acoustic Camera Pose Refinement Using Differentiable Rendering

Acoustic cameras, also known as 2D forward looking sonars, show high reliability in underwater environments as they can produce high resolution images even if the illumination is limited. However, due to the unique imaging principle, it is hard to estimate ground-truth-level extrinsic parameters even in a known 3D scene. Usually, there are methods such as direct measurements by rulers to acquire a rough pose with centimeter-level error. It is necessary to refine the pose to millimeter-level error. In this work, we develop a novel differentiable acoustic camera simulator, which can be applied for estimating accurate 6 degrees of freedom pose of the acoustic cameras. We calculate the derivatives of synthetic acoustic images with respect to camera pose, and further integrated them into a gradient-based optimization pipeline to refine the pose. To mitigate the domain gap between real and synthetic images, an unpaired image translation method is used to transfer the real image to synthetic domain. Experiments prove the feasibility of the proposed method. It outperforms methods of previous research for higher efficiency and accuracy.
Applications of Distributed Ledger Technologies in Robotics

Increasing convergence of virtual space and physical space driven by society 5.0 has propelled research in distributed ledgers to identify how trust in autonomous systems and robotics can be enabled through decentralised frameworks for decision making and consensus building. Industrial, multi-robot systems, embedded systems, and more have seen growth in applications using distributed ledgers. Although progressing, implementations are currently ad-hoc with middleware systems, such as ROS, not yet adopting a standard to permit easy integration of these technologies. Nor is the technology readiness level of distributed ledgers easily identifiable. This creates challenges for adoption and system integration.
A Hybrid Hierarchical Navigation Architecture for Highly Dynamic Environments Using Time-Space Optimization

Navigation of mobile robots within crowded environments is an essential task in various use cases, such as delivery, health care, or logistics. Common navigation approaches have weaknesses when deployed as a standalone system. For instance, global planners excel in planning collision-free paths in static environments when the map is perfectly known but can not consider dynamic or unknown obstacles. Learning-based local planners have shown superior performance in dynamic obstacle avoidance but can not handle long planning horizons due to their myopic nature. To address these issues, we adopt a hierarchical motion planning framework to handle complex long-range navigation problems. Three modules are designed for different planning horizons leveraging different observations. First, an extended hybrid A-Star approach is proposed to efficiently search for an optimal solution in the time-state space and produce reasonable landmarks for the subsequent modules. Second, an intermediate planner is proposed, which utilizes Delaunay Triangulation to encode obstacles and provides safer and more robust subgoals for the third module, the learning-based local planner trained using Deep Reinforcement Learning. The proposed approach is compared to two baseline navigation systems and outperforms them in terms of safety, efficiency, and robustness.
Multi-Zone Transformer Based on Self-Distillation for Facial Attribute Recognition

Recently, transformers have shown great promising performance in various computer vision tasks. However, the current transformer based methods ignore the information exchanges between transformer blocks, and they have not been applied in the facial attribute recognition task. In this paper, we propose a multi-zone transformer based on self-distillation for FAR, termed MZTS, to predict the facial attributes. A multi-zone transformer encoder is firstly presented to achieve the interactions of the different transformer encoder blocks, thus avoiding forgetting the effective information between the transformer encoder block groups during the iteration process. Furthermore, we introduce a new self-distillation mechanism based on class tokens, which distills the class tokens obtained from the last transformer encoder block group to the other shallow groups by interacting with the significant information between the different transformer blocks through attention. Extensive experiments on the challenging CelebA and LFWA datasets have demonstrated the excellent performance of the proposed method for FAR.
Learning to focus on region-of-interests for pain intensity estimation

The breakthrough success of many deep learning approaches is mainly due to the availability of large-scale labeled datasets. However, large-scale labeled datasets are not always available in some domains. Pain intensity estimation is unsurprisingly one those domains that suffer from lacking of labeled training data. In this work, we proposed a learning approach that is able to learn to focus on region-of-interests in face image for better feature extraction, thus improving overall performance of the network when training on a limited amount of data. Our extensive experiments demonstrate that our learning to focus on region-of-interests approach performs better in overall compared to state-of-the-art approaches in pain intensity estimation. From the experimental results, we emphasise the importance of learning to focus on region-of-interests for better extracting feature representations and reducing the effect of overfitting when training on a limited amount of data.
RavenGaze: A Dataset for Gaze Estimation Leveraging Psychological Experiment Through Eye Tracker

One major challenge in appearance-based gaze estimation is the lack of high-quality labeled data. Establishing databases or datasets is a way to obtain accurate gaze data and test methods or tools. However, the methods of collecting data in existing databases are designed on artificial chasing target tasks or unintentional free-looking tasks, which are not natural and real eye interactions and cannot reflect the inner cognitive processes of humans. To fill this gap, we propose the first gaze estimation dataset collected from an actual psychological experiment by the eye tracker, called the RavenGaze dataset. We design an experiment employing Raven's Matrices as visual stimuli and collecting gaze data, facial videos as well as screen content videos simultaneously. Thirty-four participants were recruited. The results show that the existing algorithms perform well on our RavenGaze dataset in the 3D and 2D gaze estimation task, and demonstrate good generalization ability according to cross-dataset evaluation task. RavenGaze and the establishment of the benchmark lay the foundation for other researchers to do further in-depth research and test their methods or tools. Our dataset is available at https://intelligentinteractivelab.github.io/datasets/RavenGaze/index.html.