Man-machine cooperation in advanced teleoperation

Teleoperation experiments at JPL have shown that advanced features in a telerobotic system are a necessary condition for good results, but that they are not sufficient to assure consistently good performance by the operators. Two or three operators are normally used during training and experiments to maintain the desired performance. An alternative to this multi-operator control station is a man-machine interface embedding computer programs that can perform some of the operator's functions. In this paper we present our first experiments with these concepts, in which we focused on the areas of real-time task monitoring and interactive path planning. In the first case, when performing a known task, the operator has an automatic aid for setting control parameters and camera views. In the second case, an interactive path planner will rank different path alternatives so that the operator will make the correct control decision. The monitoring function has been implemented with a neural network doing the real-time task segmentation. The interactive path planner was implemented for redundant manipulators to specify arm configurations across the desired path and satisfy geometric, task, and performance constraints

Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation

The rapid proliferation of new users and items on the social web has aggravated the gray-sheep user/long-tail item challenge in recommender systems. Historically, cross-domain co-clustering methods have successfully leveraged shared users and items across dense and sparse domains to improve inference quality. However, they rely on shared rating data and cannot scale to multiple sparse target domains (i.e., the one-to-many transfer setting). This, combined with the increasing adoption of neural recommender architectures, motivates us to develop scalable neural layer-transfer approaches for cross-domain learning. Our key intuition is to guide neural collaborative filtering with domain-invariant components shared across the dense and sparse domains, improving the user and item representations learned in the sparse domains. We leverage contextual invariances across domains to develop these shared modules, and demonstrate that with user-item interaction context, we can learn-to-learn informative representation spaces even with sparse interaction data. We show the effectiveness and scalability of our approach on two public datasets and a massive transaction dataset from Visa, a global payments technology company (19% Item Recall, 3x faster vs. training separate models for each domain). Our approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202

Compliant Gripper for a Robotic Manipulator

A figure depicts a prototype of a robotic-manipulator gripping device that includes two passive compliant fingers, suitable for picking up and manipulating objects that have irregular shapes and/or that are, themselves, compliant. The main advantage offered by this device over other robotic-manipulator gripping devices is simplicity: Because of the compliance of the fingers, force-feedback control of the fingers is not necessary for gripping objects of a variety of sizes, shapes, textures, and degrees of compliance. Examples of objects that can be manipulated include small stones, articles of clothing, and parts of plants

Pencemaran Lingkungan oleh Adanya Proses B10metilasi Logam Berat

Air limbah industri yang dikeluarkan oleh proses-proses industriberbeda-heda dalam jumlah maupun kekuatan pencemaranoya,sesuai dengan satuan operasi atau proses yang merupakansumbernya. Dalam Ungkup sempit. air limbah buanganyang dapat dapat mengakibatkan pencemaran antara lain:buangan dari laboratorium kimia, yang di dalamnya terdapatlogam-Iogam herat, seperti air raksa, arsen. .Logam herat tersebut dapat berubah menjadi suatusenyawa metil dari logam herat yang sangat berbahaya bagikesehatan, baik dalam bentuk gas maupun cab". Proses 1nlterjadi dengan bantuan bakteri dalam kondisi anaerob karenadi dalam bakteri ada suatu koenzim metilkobalamin. Mekanismeperpindahan metil menjadi suatu senyawa metiI darilogam berat yang utama melalui: serangan elektrofilH: Fadaikatan Co-C (metilkobalamin) atau perpindahan CH3-, danserangan radikal bebas (CH3.).Agar hasil dari reaksi' di a tas tidak terjadi (da pa t dikurangi)diperlukan suatu teknik pembuangan, proses pengolahansedemikian rupa sehingga proses metilasi oleh bakteritidak dapat berlangsung. Misalnya, penghilangan ion-ionlogam berat sebelum dibuang ke lingkungan

Use of graphics in decision aids for telerobotic control: (Parts 5-8 of an 8-part MIT progress report)

Four separate projects recently completed or in progress at the MIT Man-Machine Systems Laboratory are summarized. They are: a decision aid for retrieving a tumbling satellite in space; kinematic control and graphic display of redundant teleoperators; real time terrain/object generation: a quad-tree approach; and two dimensional control for three dimensional obstacle avoidance

Gender-roles, learning styles and student perceptions of "best" professors: some preliminary research findings

343 students enrolled in business courses in two urban universities in Atlantic Canada rated their best instructors and themselves using Bem's Sex Role Inventory. Findings indicate that a student's gender role is signficantly related to perceived gender-role of best instructor chosen. Further, the present study indicates that a student's learning style and gender-role are significantly related. Implications of the present findings are discussed

FedAR+: A Federated Learning Approach To Appliance Recognition With Mislabeled Data In Residential Environments

With the enhancement of people\u27s living standards and the rapid evolution of cyber-physical systems, residential environments are becoming smart and well-connected, causing a significant raise in overall energy consumption. As household appliances are major energy consumers, their accurate recognition becomes crucial to avoid unattended usage and minimize peak-time load on the smart grids, thereby conserving energy and making smart environments more sustainable. Traditionally, an appliance recognition model is trained at a central server (service provider) by collecting electricity consumption data via smart plugs from the clients (consumers), causing a privacy breach. Besides that, the data are susceptible to noisy labels that may appear when an appliance gets connected to a non-designated smart plug. While addressing these issues jointly, we propose a novel federated learning approach to appliance recognition, called FedAR+, enabling decentralized model training across clients in a privacy-preserving way even with mislabeled training data. FedAR+ introduces an adaptive noise handling method, essentially a joint loss function incorporating weights and label distribution, to empower the appliance recognition model against noisy labels. By deploying smart plugs in an apartment complex, we collect a labeled dataset that, along with two existing datasets, are utilized to evaluate the performance of FedAR+. Experimental results show that our approach can effectively handle up to 30% concentration of noisy labels while outperforming the prior solutions by a large margin on accuracy