Miracle-dz/UniFAR-FTUs huggingface dataset:
New information triggers prospective codes to adapt for flexible navigation

Navigating a dynamic world requires rapidly updating choices by integrating past experiences with new information. In hippocampus and prefrontal cortex, neural activity representing future goals is theorized to support planning. However, it remains unknown how prospective goal representations incorporate new, pivotal information. Accordingly, we designed a novel task that precisely introduces new information using virtual reality, and we recorded neural activity as mice flexibly adapted their planned destinations. We found that new information triggered increased hippocampal prospective representations of both possible goals; while in prefrontal cortex, new information caused prospective representations of choices to rapidly shift to the new choice. When mice did not flexibly adapt, prefrontal choice codes failed to switch, despite relatively intact hippocampal goal representations. Prospective code updating depended on the commitment to the initial choice and degree of adaptation needed. Thus, we show how prospective codes update with new information to flexibly adapt ongoing navigational plans.
Minimum Gain Requirements for Trajectory Tracking of Compliant Robots in Divergent Force Fields

The future of robotics involves a growing need for robots to perform everyday tasks in dynamic and unstructured environments. This often requires them to handle complex and unstable interactions with various objects, including mechanical tools, flexible objects such as food items and fruits, and more. Robots must be able to interact with their environment like humans, who rely on the compliance of their musculoskeletal system to achieve safe and effective interaction. Previous research has analyzed the benefits of incorporating mechanical compliance in parallel and series configurations to address this requirement. In this work, we introduce a novel approach to parallel compliance, where the mean position is adjusted based on desired trajectories and study its advantages. The unstable interactions are modeled as a position-based divergent force field. A generalized theory is presented that determines the minimum gains required for trajectory tracking in all types of compliance configurations in the presence of divergent force fields. The results are demonstrated through simulations of 1-DOF and 2-DOF robots and provides insight into the associated control effort.
Automated Modeling Verification Complex of the Intelligent Instrument System

Automated Model Verification Complexes are an important tool for research, testing, and verification of information and measuring instrument control systems, navigation, mechanical engineering, and other branches of technology. The model of the automated verification and simulation complex of the instrument system was developed based on the method of calibrated signals using a software-controlled measure. The calculation of the mathematical methodical error and the mean square deviation of the methodical error during quantization of the sum of input influences and additive interference are given
Localization of a Mobile Fastening Robot Based on a Single Camera and two Ultrasonic Sensors

Presented in this paper is a localization system for quickly positioning a mobile fastening robot from station to station to perform fastening tasks for large and fixed parts such as wingbox. This system is developed by combining a camera with two ultrasonic sensors. The first part of the methodology is to use the camera to detect the fastener locations and express them in the camera frame in a length unit. The second part is to use the ultrasonic sensors to determine the camera's pose relative to the wingbox. The developed localization system consists of three phases: 1) Ultrasonic Sensor Phase for determination of the camera's distance and skew angle; 2) Camera Phase for image capture, correction, and binary image conversion as well as extraction of the fastener head coordinates; 3) Alignment and Mapping Phase for alignment of the camera frame and transformation to the robot frame. The tests show that the resulting system can implement localization in less than 30s with ±0.04 in accuracy.
Exploring the role of aggregators in the ecosystem of energy flexibility

This paper aims to explore the role of aggregators in the energy flexibility ecosystem through the lens of an employed framework built on two theories: Information ecology theory and the architectural theory of digital innovation. The framework is initially tested using descriptive data from an organization working as an aggregator in the energy market. The potential of the framework is shown from a combination of these theories and an initial test using descriptions from the aggregator case and its products and services in the context of energy flexibility. By highlighting four integration tasks of sharing, combining, standardizing, and multi-homing, the framework has the potential to explore the role of aggregator within the flexibility ecosystem. This study concludes with three propositions regarding the interaction of the ecosystem actors and the role of aggregators in the creation of flexibility ecosystem that opens up avenues for future studies.
Towards Trajectory Tracking Control of a Flexible Tube Manipulator

The present work discusses the development and control of an antagonistic shape memory alloy (SMA) springs actuated flexible tube manipulator (FTM) which demonstrated its capabilities in trajectory tracking applications. A detailed design of FTM is followed by its experimental validation on two basic 2-D trajectories namely square and pocket square. The experimental results demonstrate the satisfactory performance of the proposed manipulator in tracking drawn trajectories. It has aspired that the proposed manipulator can be utilized for medical applications if it is able to track more 2-D trajectories like circle and ellipse that are frequently encountered in medical applications.
Intelligent System for Adhesion Control of Universal Robotic Platform

The paper is devoted to modern aspects of mobile robotics, namely to the universal robotic platforms that can be used in various technological environments and conditions to perform different technical tasks at industrial facilities and enterprises. These platforms are capable of moving on horizontal surfaces with complex terrain as well as climbing vertically on sheer walls and ceilings and are the universal autonomous means of carrying out complex operations in hard-to-reach and dangerous places for humans. One of the biggest challenges in operating these robotic platforms is proper adhesion control when moving on inclined planes. In this study, the authors performed the design and study of the intelligent adhesion control system that provides efficient and reliable fastening and movement of the platform on inclined surfaces of various types. This system is based on fuzzy control principles that allows determining the necessary adhesion force of the robotic platform for its safe and efficient use at various angles of the surface inclination. The system’s performance is verified by computer simulation.
Development of a QDD Actuator based Robotic Leg

There has been a lot of work going on in the area of legged robotics, which involves design, dynamics, control and learning. Still, these robots are not comparable to human-like locomotion. Bionic legged robots have been developed to increase robots’ adaptation to different environments. The level of performance demonstrated by biological systems has yet to be fully realized in a legged robot. This work focuses on development of a 2 degrees of freedom Quadruped robot leg based on quasi direct drive actuator. The leg is attached with the linear slider on a structure to actuate the jumping in a vertical direction. The trajectories of the two motors are generated for achieving a jumping motion. We have successfully executed the jumping motion in the vertical direction with the developed prototype of the leg model.
Gait Generation of 6-DOF Biped Robot on Inclined Deformable Terrain Using Nonlinear Inverted Pendulum

In this paper, a 6-DOF biped’s gait is generated based on the motion of the center of mass and the contact point of a nonlinear inverted pendulum (NIP). For walking on deformable terrain, the foot-terrain interaction is modeled using terrain stiffness and damping. The impact dynamics of the NIP model is used to obtain the curve of capture to stop the motion and the curve of equal local energy for walking with equal minimum velocity. The step length obtained from the intersection of the energy curve of the NIP and the terrain is used for the swing leg ankle trajectory. The center of mass and contact point trajectories of the NIP are used in the trajectory generation of the hip-link and the stance leg’s ankle, and the joint angles of the biped are obtained from the inverse kinematics. The floating-base dynamics is used to compare the actual and desired center of mass trajectories of the biped robot. The energy analysis of the biped validates the footstep planning on deformable terrain. The joint torques of the actuated joints are obtained from the inverse dynamics, and the actuation energy is shown to be negligible for energy-efficient walking. The dynamic stability of the biped’s gait is also analyzed based on the rotation of the foot on the deformable terrain.
An Ensemble Learning Approach for Exercise Detection in Patients with Type 1 Diabetes

Type 1 diabetes is a serious disease in which individuals are unable to regulate their blood glucose levels, leading to various medical complications. Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels. However, current AP systems lack detection capabilities for exercise-induced glucose intake, which can last up to 4 to 8 hours. This incapability can lead to hypoglycemia, which if left untreated, could have serious consequences, including death. Existing exercise detection methods are either limited to single sensor data or use inaccurate models for exercise detection, making them less effective in practice. In this work, we propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection with high accuracy. To evaluate the effectiveness of our proposed approach, we utilized a public dataset of multiple diabetic patients collected from an 8-week clinical trial. Our approach achieves a true positive rate for exercise detection of and a true negative rate of , outperforming state-of-the-art solutions.
Auction theory in computational settings (תורת המכרזים עבור מערכות ממוחשבות.)

This thesis studies distributed systems and distributed agent systems that are characterized by interactions among computers with different ownerships and incentives. We ask what happens when the input of an algorithm is kept by independent agents, acting selfishly to maximize their own utility. In such an environment, an agent might not fully cooperate with the algorithm, as a slight deviation from the behavior required by the protocol might increase the agent’s own utility. The challenge is to design algorithms that have guaranteed performance with respect to the true input of the agents, despite the non-cooperative behavior. One approach to this is to design a mechanism that is able to collect payments from the participants. The payments should be designed carefully, so that agents will indeed find it in their best interest to behave as required by the algorithm, according to their true input. Such algorithms are termed incentive compatible, or truthful. A broad model that captures such questions is auctions. In an auction, a seller sells one, or several, items to a set of buyers. This is not just a pure economic model it actually captures many traditional computational settings, like job scheduling, routing and bandwidth allocation, and much more. In this thesis I focus on the implications of the computational and algorithmic theory to the design of computerized auctions. I study possibilities and impossibilities that arise from the integration of new models and considerations from classic algorithmic theory with classic auction theory. Three groups of results are presented: Competitive Analysis of Incentive Compatible Online Auctions. We study auctions in a setting where the different bidders arrive at different times, and the auction mechanism is required to make decisions about each bid as it is received. We call such auctions, on-line auctions. While, in the economic literature, most auctions are analyzed using average-case analysis, we apply CS-related tools and give a competitive worst-case analysis of incentive compatible on-line auctions. We first characterize exactly those on-line auctions that are incentive compatible, i.e. where rational bidders are always motivated to bid their true valuation. We then embark on a competitive worst-case analysis of incentive compatible on-line auctions. We obtain several results, the cleanest of which is an incentive compatible on-line auction for a large number of identical items. We prove that this auction has the optimal competitive ratio, both in
Comparison of general-purpose and domain-specific modelling languages in the IoT domain: A case study from the OMiLAB community

The Internet of Things (IoT) is a revolutionary concept that has rapidly transformed how we interact with technology and the world around us. In response to the inherent complexity and heterogeneity of the IoT domain, there has been a surge in the development of modeling languages and supporting platforms for developing IoT applications. Among the many modeling options available, one can distinguish between General-Purpose Modeling Languages (GPML) and Domain-Specific Modeling Languages (DSML). Each language has unique characteristics, offering distinct levels of abstraction and expressiveness crucial for effective IoT solution modeling. The challenge of selecting the most suitable language remains, with developers needing to weigh the benefits and drawbacks of each option carefully. This paper compares GPML and DSML regarding their characteristics, benefits, and drawbacks. By identifying key factors to consider when choosing a modeling language for IoT solutions, this research aims to provide valuable insights for a decision-making framework to help practitioners with this choice. To validate the findings and practical implications, a practical workshop was conducted. After creating a smart room scenario using the X-IoT DSML, the participants confirmed the advantages of DSML regarding user-friendliness, higher abstraction, improved communication, faster development, and the ability for non-experts to participate in the IoT application development process.
Fall Detection with LSTM and Attention Mechanism

Falls in people are frequent and dangerous problems that can lead to death or infirmity, especially as people age. Therefore, fall detection and immediate intervention is essential to prevent more severe consequences. In this paper, two Fall Detection systems have been proposed. The proposed systems use 2D and 3D pose estimation of subjects respectively and a LSTM deep neural network architecture. An Attention Mechanism has been implemented in the neural network architecture to improve the performance of fall detection. The conducted experiments show that the model created with attention can generalize and does not suffer from scene bias. The 3D system using the Attention Mechanism has achieved an accuracy of 82% compared to the accuracy of 70% obtained without attention. With the 2D system using the neural network with Attention an accuracy of 72% has been obtained, instead, without attention, an accuracy of 52% has been obtained.
Fabrication and characterization of wearable sensing system using mechanical micromachining process.

The present work reports a flexible and stretchable wearable sensing system based on a piezoresistive method inspired by human skin mechanoreceptors and strain gauges. The tactile sensor consists of Polydimethylsiloxane (PDMS) as a flexible and stretchable body and liquid metal Eutectic Gallium Indium (EGaIn) as the sensing material. Microchannel fabrication on a PDMS elastomer is an essential step, and we are achieving this by preparing the mold of stainless steel using mechanical micromachining and casting out the PDMS. Microchannel fabrication using a micromachining facility on stretchable and flexible material is a green and economical alternative to commonly known chemical itch and photolithography techniques. In addition, it has advantages over 3D printing techniques in terms of the size limitation fabricating the smallest feature and surface finish. This work focuses on fabricating microchannels using a multipurpose micromachining setup, which can prepare microchannel patterns of different shapes like serpentine and coil and sizes such as 200x200μm2 cross-section. The overall size of the sensor is just 15mm x15mm x1mm with a sensing area of 10mm x 10 mm, which is suitable to wear on human fingers. With this pattern, we have cast out the outer layer of elastomer for the development of the sensor. A strain sensor design has been developed to fabricate these sensors to sense directional forces and bending. Most importantly, creating microchannels on the PDMS is the point of focus where a micromachining facility is being used. Up to 25% strain, we have achieved a gauge factor of 0.98. It has many applications, such as healthcare, human motion detection, virtual reality gaming, human-robot interaction, etc.
Three-dimensional point cloud indoor reconstruction based on plane calibration for smart city

The 3D reconstruction technology based on point cloud can accurately present various elements and facilities in the environment in the digital city model, and realize the visual management and operation of smart city.In this paper, a monochromatic rhomboid calibration object is proposed to reconstruct the 3D point cloud scene. Firstly, the comprehensive environmental point cloud information is obtained by rotating scanning. Then the point cloud of the calibration object is extracted based on the threshold segmentation of coordinate axes and Random Sample Consensus (RANSAC) algorithm. In order to accurately extract the feature points of the calibration object, the point clouds scanned by different laser beam were clustered, and then the boundary points were calculated to generate the scene feature points. Finally, Iterative Closest Point (ICP) algorithm was used to register feature points of different scenes to obtain the inter-frame transformation matrix, so as to complete the high-precision point cloud scene reconstruction and realize the high-precision digital presentation of the real world scene.
Semantic Similarity and Correspondence of 3D Shapes and Images

In this work we investigate the concept of semantic similarity between shapes and images. Similarities between elements can be used for numerous applications, such as clustering, categorization, visualization, retrieval, and browsing. For shape and image browsing, we present a novel exploration method which relies on the similarities between elements to produce a seemingly endless grid, which can be navigated in any direction like a regular map. This provides a smooth and intuitive browsing experience. Our method is efficient and highly scalable, and can be used for datasets that contain millions of elements. For all of these applications, the quality of the solution depends strongly on the quality of the similarity measure. Similarities between elements must reflect the perceived similarity by humans, or the semantic similarity. This motivates further research to improve existing similarity measures, which often lack semantic context. First, we show how crowd sourced data can be used to deduce complex semantic similarities between shapes or images which are not possible to compute automatically without external knowledge and context. Such methods can be used to complement automatic methods and provide additional external context. Next, we focus on the shapes domain. Shape segmentation, correspondence between shapes and semantic similarity are some of the pillars of shape analysis, and each of these problems enjoys extensive research of its own. However, these problems are linked together, and the output of one can be the input of another. Similarity of parts can be used to discover shape segmentation, segmentation can be used to compute shape correspondence, and correspondence can be used to compute semantic similarity between shapes. We present a similarity measure between shapes which is computed by segmenting and finding a correspondence between the segments. This similarity measure captures semantic relations such as shapes that belong to the same style, or have a similar function. We further investigate the link between correspondence and segmentation of shapes, showing how segmentation of the shape can improve point-to-point correspondence. When matching shapes which contain symmetries, there are multiple contradicting solutions to the correspondence problem, which often cause instability of the final solution. To alleviate this problem we propose a symmetry aware correspondence, in which each segment can match several segments in the other shape. This enables us to find a matching between symmetric segments in a very efficient manner. The symmetric matching between segments is less detailed but more accurate than non-symmetric methods, and it can be used to improve point-to-point correspondence even without providing a one-to-one mapping between the segments. Furthermore, resolving the symmetry can potentially be done as a post-process which is decoupled from the matching and thus less complicated.
Overview of LiLAS 2020 - Living Labs for Academic Search

Academic Search is a timeless challenge that the field of Information Retrieval has been dealing with for many years. Even today, the search for academic material is a broad field of research that recently started working on problems like the COVID-19 pandemic. However, test collections and specialized data sets like CORD-19 only allow for system-oriented experiments, while the evaluation of algorithms in real-world environments is only available to researchers from industry. In LiLAS, we open up two academic search platforms to allow participating research to evaluate their systems in a Docker-based research environment. This overview paper describes the motivation, infrastructure, and two systems LIVIVO and GESIS Search that are part of this CLEF lab.
Multi-Robot Motion Planning

—Multi-robot Motion Planning (MRMP) is an active research field which has gained attention over the years. MRMP has significant roles to improve efficiency and reliability of multi-robot system in a wide range of applications from delivery robots to collaborative assembly lines. This survey provides an overview of MRMP taxonomy, state-of-the-art algorithms, and approaches which have been developed for multi-robot systems. Moreover, it discusses strengths and limitations, and applications of the algorithms in various scenarios. From the discussion, we can draw out open problems which can be extended in our future research.