Reviewerly/exHarmony huggingface dataset:
Ulysses-RFSQ: A Novel Method to Improve Legal Information Retrieval Based on Relevance Feedback

Obtaining relevant legal documents fast, from very large datasets, is essential for the proper functioning of justice and legislative institutions. Nevertheless, legacy systems currently used by these institutions in Brazil are usually outdated, requiring a large deal of manual work. Legal Information Retrieval focuses on building new methods to deal with the large amount of legal texts, allowing the retrieval of relevant information from them. Relevance Feedback, an important aspect of information retrieval systems, uses the information given by the user to improve the document retrieval for a specific request. However, expanding its use to other queries is a difficult task. A possible approach is to use Relevance Feedback information from past, similar queries. In this paper, we propose Ulysses-RFSQ, a method based on this approach which gives a bonus for the documents marked as relevant for similar queries, and, through this bonus, updates the ranking created by a relevance score based Information Retrieval algorithm, which measures the similarity between the query text and the documents to be retrieved. Due to the lack of available datasets containing relevance information for similar queries, we used a corpus of legislative requests from the Brazilian Chamber of Deputies, which are in most cases redundant, allowing the assessment of the proposed method. According to the experimental results, adding the Relevance Feedback bonus to the documents score improved the Recall@20 of a BM25 algorithm by almost 3% in the legal dataset used.
Sign Language Translation Systems

Sign language, often termed “dactylology,” is a mode of communication for those who are hard of hearing. With over 2.5 billion people projected to have hearing loss by 2050, there are very few efficient real-time sign language translation (SLT) applications present today despite extensive research in the domain. The main purpose of the systematic literature review is to analyze existing research in SLT systems and obtain results that will help in building an efficient and improved SLT system. A total of 125 different research articles within the time frame of 2015–2022 were identified. The study analyzes each paper against nine main research questions. The results obtained show the unique strengths and weaknesses of the different methods used, and while the reviewed papers showed significant results, there is still room for improvement in the implementations. This systematic literature review helps in identifying suitable methods to develop an efficient SLT application, identifies research gaps in this domain, and simultaneously indicates recent trends in the field of SLT systems.
Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of efficiency, memory utilization, and scalability.
Robots That Can Anticipate and Learn in Human-Robot Teams

Robots are moving from working in isolated cham-bers to working in close-proximity with human collaborator(s) as part of human-robot teams. In such situations, robots are increasingly expected to work with multiple humans and ef-fectively model both human-human and human-robot dynamics before taking timely actions. Working toward this goal, we have proposed new algorithms that model human intent and motion while being interpretable and scalable to multiple humans. Our current work builds upon these algorithms to 1) obtain a more holistic representation of the environment and 2) interleave robot perception and control. Our proposed algorithms have attained state-of-the-art performances over various benchmarks and learning scenarios. As part of future work, we aim to enhance our learning algorithms with the capability of acquiring knowledge continually, without overwriting past information.
Evaluation of Visual Acuity and Perceptual Field of View Using the Varjo XR-3 Headset in a Virtual Environment

Understanding the capabilities and limitations of virtual reality head-mounted displays are key to using virtual reality devices appropriately to display complex 3D information. This study sought to replicate and compare tests characterizing visual acuity and perception using the Oculus Rift and HTC Vive Pro head-mounted displays against the Varjo XR-3 headset. 14 participants tested the Varjo XR-3 in a series of visual and perceptual tasks. Performance in the central field of view was assessed using the Snellen and Landolt tests of visual acuity as well as contrast sensitivity circles. Visual performance in the peripheral field was measured by evaluating symbol and color detection at increasing levels of eccentricity. The Varjo XR-3 provides significantly better visual acuity compared to previously tested headsets. Symbol recognition in the peripheral region is similar when viewed through the Varjo XR-3 compared to in the real world and head movements play a significant role in increasing perceptual field of view.
CGLR: Dense Multi-Agent Navigation Using Voronoi Cells and Congestion Metric-based Replanning

We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in scenarios with multiple corridors. To handle dense scenes with narrow passages, our approach computes the initial path for each agent to its assigned goal using a lattice planner. Based on neighbors' information, each agent performs online replanning using a congestion metric that tends to reduce the collisions and improves the navigation performance. Furthermore, we use the Voronoi cells of each agent to plan the local motion as well as a corridor selection strategy to limit the congestion in narrow passages. We evaluate the performance of our approach in complex scenes with tens of agents and narrow passages. We show that our Coupled Global-Local approach and Replanning (CGLR) improves the performance and efficiency over prior decentralized methods. In addition, our approach results in a higher success rate in terms of collision-free navigation to the goals, showing improvement in the range of 3-70% over prior decentralized solutions in certain scenarios.
Analyzing and Overcoming Degradation in Warm-Start Reinforcement Learning

Reinforcement Learning (RL) for robotic applications can benefit from a warm-start where the agent is initialized with a pretrained behavioral policy. However, when transitioning to RL updates, degradation in performance can occur, which may compromise the robot's safety. This degradation, which constitutes an inability to properly utilize the pretrained policy, is attributed to extrapolation error in the value function, a result of high values being assigned to Out-Of-Distribution actions not present in the behavioral policy's data. We investigate why the magnitude of degradation varies across policies and why the policy fails to quickly return to behavioral performance. We present visual confirmation of our analysis and draw comparisons to the Offline RL setting which suffers from similar difficulties. We propose a novel method, Confidence Constrained Learning (CCL) for Warm-Start RL, that reduces degradation by balancing between the policy gradient and constrained learning according to a confidence measure of the Q-values. For the constrained learning component we propose a novel objective, Positive Q-value Distance (CCL-PQD). We investigate a variety of constraint-based methods that aim to overcome the degradation, and find they constitute solutions for a multi-objective optimization problem between maximimal performance and miniminal degradation. Our results demonstrate that hyperparameter tuning for CCL-PQD produces solutions on the Pareto Front of this multi-objective problem, allowing the user to balance between performance and tolerable compromises to the robot's safety.
A New XAI-based Evaluation of Generative Adversarial Networks for IMU Data Augmentation

Data augmentation is a widespread innovative technique in Artificial Intelligence: it aims at creating new synthetic data given an existing real baseline, thus allowing to overcome the issues arising from the lack of labelled data for proper training of classification algorithms. Our paper focuses on how a common data augmentation methodology, the Generative Adversarial Networks (GANs), which is widespread for images and timeseries data, can be also applied to generate multivariate data. We propose a novel scheme for GANs evaluation, based on the performance of an explainable AI (XAI) algorithm and an innovative definition of rule similarity. In particular, we will consider an application dealing with the augmentation of Inertial Movement Units (IMU) data for physical fatigue monitoring in two age subgroups (under and over 40 years old) of the original data. We will show how our innovative rule similarity metric can drive the selection of the best fake dataset among a set of different candidates, corresponding to different GAN training runs.
Human-centered Augmented Reality Guidance for Industrial Maintenance with Digital Twins: A Use-Case Driven Pilot Study

Industrial augmented reality research depends on precise use case descriptions and close communication with specialists from the field. Therefore, user-centered methods should lead this research. This work describes the use case of corrective maintenance in chemical industries. This is done by presenting a use case description as well as extracting and discussing augmented reality functionalities in the context of existing research and emerging digital twin platforms. Additionally, the work provides insights into the first iteration of pro-totyping for the use case. The results offer future research directions and indicate how augmented reality might become a beneficial and accepted technology for maintenance in chemical industries.
Traffic Sign Instances Segmentation Using Aliased Residual Structure and Adaptive Focus Localizer

Traffic sign recognition plays a crucial role in both unmanned vehicles and advanced driver assistance systems. Although many recent deep-learning-based approaches have made some progress on this task, it still suffers from the significant changes in scale and rotation, the presence of objects of the same color appearance, as well as the high similarity between classes. In this paper, we first propose a traffic sign detection network, named AAMNet, by adopting the generally framework of one-stage anchor-free instance segmentation models. For the feature extraction, a novel Aliased residual block is designed to support the encoder to retain more detailed information from the shallow low-level features. For decoder, we introduce a channel attention module into the mask generator to implement an Adaptive focus localizer head, which can filter out the irrelevant prototype masks. A Mask guided center loss is further constructed to improve the localization accuracy. Then, considering the difficulty of text based traffic signs recognition, for the first time, we developed a text-traffic-signs dataset TextTSD covering rich scenes and multiple languages. Extensive experiments both on TT100k and TextTSD show that our AAMNet gains a competitive performance compared with several state-of-the-art methods.
UML-based Live Programming Environment in Virtual Reality

Understanding and developing large applications often leads to a situation where the developer becomes lost. Software visualization aims to help with these problems by providing visual insights and helps to navigate, and, when properly done, also to visually model software. Although the advantages of virtual reality in software visualization have already been demonstrated, relatively limited research has been done in the area of programming.In this paper, we present a virtual reality programming environment, with built-in round-trip engineering support and run-time compilation of source code that allows live programming. The study carried out shows that users are capable of solving complex programming tasks in our VR environment, which performed better in terms of usability and user experience, but the evaluation results also point to still persistent VR related problems.
Multi-layer Echo State Network with Nonlinear Vector Autoregression Reservoir for Time Series Prediction

Reservoir Computing (RC) defines a Recurrent Neural Network (RNN) method that optimally processes transient information that generated based on recurrent tasks utilizing sequential datasets. As a typical actualization of the RC classic, Echo State Network (ESN) can train the transient data by linear optimization using very small training datasets and make the network operations fairly fast. However, classical ESNs use a completely randomized reservoir matrix to delineate the fundamental recursive operations and are accompanied by a large number of hyper-parameters to be optimized, such as input weights, reservoir weights, warm-up time steps, and activation functions. The recently proposed Nonlinear Vector Autoregressive (NVAR) algorithm which does not require random inputs and reservoir weights is equated with the traditional ESN reservoir. As a future direction of RC development, NVAR requires few hyper-parameters to be optimized. In this study, a multi-layer ESN model based on the NVAR (NVAR-MLESN) is constructed and applied to time series prediction. Three sequential benchmarks which are synthetic Mackey-Glass chaotic sequence, Sunspot sequence, and real-world Santa Fe Laser sequential dataset are implemented to show the advantage of NVAR-MLESN. Exhaustive simulated outcomes exhibit that our NVAR-MLESN is efficient to develop the performance of classical MLESN and single layer NVAR-ESN. Meanwhile, NVAR-MLESN has fewer parameters to be optimized, faster computing speed, and better performance comparing with the traditional deep ESNs or MLESNs.
Automated Traffic Management System Using Deep Learning Based Object Detection

The traffic menace in India’s metropolitan cities causes many travelers to suffer daily. In traffic control, simple and old forms of signal controllers, known as electro-mechanical signal controllers, are used till-date which use dial timers that have fixed, signalized intersection time plans. As the time is fixed, the people in the lane with the greatest number of vehicles must wait the most, leading to wastage of time, money, and natural resources such as petrol and diesel. The proposed system is a traffic light system with feedback in real-time. The vehicles present in a specific lane are detected using a camera and then the deep learning algorithm, YOLO (You Only Look Once) detects the total number of vehicles in a lane which is used for feedback control of the lights. The traffic lights controller changes its parameters in response to traffic length in a lane, optimizing the road use and the signal timing of an intersection will benefit from being adapted to the dominant flows changing over the time of the day. The experiment analysis reveals that response time for green light in real-time increases in the lane with a greater number of vehicles and is decreased for the lane with lesser number of vehicles keeping the total time the same, so effective in managing traffic.
ChunkyGAN: Real Image Inversion via Segments

We present ChunkyGAN—a novel paradigm for modeling and editing images using generative adversarial networks. Unlike previous techniques seeking a global latent representation of the input image, our approach subdivides the input image into a set of smaller components (chunks) specified either manually or automatically using a pre-trained segmentation network. For each chunk, the latent code of a generative network is estimated locally with greater accuracy thanks to a smaller number of constraints. Moreover, during the optimization of latent codes, segmentation can further be refined to improve matching quality. This process enables high-quality projection of the original image with spatial disentanglement that previous methods would find challenging to achieve. To demonstrate the advantage of our approach, we evaluated it quantitatively and also qualitatively in various image editing scenarios that benefit from the higher reconstruction quality and local nature of the approach. Our method is flexible enough to manipulate even out-of-domain images that would be hard to reconstruct using global techniques.
Glance-Box: Multi-LOD Glanceable Interfaces for Machine Shop Guidance in Augmented Reality using Blink and Hand Interaction

Glanceable User Interfaces for Augmented Reality (AR) reveal virtual content “at a glance,” providing rapid information retrieval, often based on gaze interaction. They are ideal when the augmented content covers a small proportion of the view space. When the size of virtual content grows, the potential to occlude the real-world increases provoking safety concerns. Compounding this is the Midas Touch Problem, where users unintentionally select virtual elements by simply looking at them. Extending dwell time does not eliminate involuntary selections, impeding interaction time. In this work, we present Glance-Box, a novel interaction system for AR combining Glanceable interfaces and world-based <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> interfaces across three Levels-Of-Detail, including progressively more information and visuals. Glance-box combines eye-gaze and hand interactions, focusing on user safety. A <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2\mathrm{D}$</tex> Glanceable interface facilitates rapid information retrieval at a glance, while extended <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> interfaces provide interaction with denser content and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{D}$</tex> objects. Glance-Box couples blink-based and gaze-based interactions to minimize errors arising from the Midas Touch Problem. While applicable across domains, the Glance-Box interface is designed and optimized for performing manufacturing tasks in the real world. We evaluated the Glance-Box interface using an object selection task of a manufacturing process. Participants completed tasks faster using Glance-Box, employing less dense LOD over time as they gained experience. The perceived accuracy of Glance-Box gaze-based input was high, even when the device's eye tracker accuracy was coarse.
Time Series Concepts and Python

In this chapter, by observing some real-life examples of time series, we will understand the concept of time series and then learn about brief history and objectives of time series analysis. We introduce the programming language Python and its extension packages and demonstrate some useful usages in the field of time series. We also introduce the concept of stationarity and two important time series models: white noise and random walk. At last, we discuss different ways for visualization of time series data with Python so as to further check time series.
Learning Online Multi-sensor Depth Fusion

Many hand-held or mixed reality devices are used with a single sensor for 3D reconstruction, although they often comprise multiple sensors. Multi-sensor depth fusion is able to substantially improve the robustness and accuracy of 3D reconstruction methods, but existing techniques are not robust enough to handle sensors which operate with diverse value ranges as well as noise and outlier statistics. To this end, we introduce SenFuNet,- a depth fusion approach that learns sensor-specific noise and outlier statistics and combines the data streams of depth frames from different sensors in an online fashion. Our method fuses multi-sensor depth streams regardless of time synchronization and calibration and generalizes well with little training data. We conduct experiments with various sensor combinations on the real-world CoRBS and Scene3D datasets, as well as the Replica dataset. Experiments demonstrate that our fusion strategy outperforms traditional and recent online depth fusion approaches. In addition, the combination of multiple sensors yields more robust outlier handling and more precise surface reconstruction than the use of a single sensor. The source code and data are available at https://github.com/tfy14esa/SenFuNet .