Reviewerly/exHarmony huggingface dataset:
Distributed Neural Networks Training for Robotic Manipulation With Consensus Algorithm

Authors:
raw_author_name: Hanlin Niu
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.
raw_author_name: Inmo Jang
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.
raw_author_name: Guido Herrmann
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.
raw_author_name: Joaquin Carrasco
raw_affiliation_string: Department of Electrical and Electronic Engineering, The University of Manchester, Manchester, U.K.

Abstract: In this article, we propose an algorithm that combines actor-critic-based off-policy method with consensus-based distributed training to deal with multiagent deep reinforcement learning problems. Specifically, convergence analysis of a consensus algorithm for a type of nonlinear system with a Lyapunov method is developed, and we use this result to analyze the convergence properties of the actor training parameters and the critic training parameters in our algorithm. Through the convergence analysis, it can be verified that all agents will converge to the same optimal model as the training time goes to infinity. To validate the implementation of our algorithm, a multiagent training framework is proposed to train each Universal Robot 5 (UR5) robot arm to reach the random target position. Finally, experiments are provided to demonstrate the effectiveness and feasibility of the proposed algorithm.
Penguin coloration affects skin friction drag

Authors:
raw_author_name: Mostafa Hassanalian
raw_affiliation_string: Mechanical Engineering Department New Mexico Tech

Abstract: Why do penguins have black and white patterns?Some theories claim that it is camouflage against predators.Others believe that this helps with thermal regulation: black absorbs more heat than white does.There are even theories that suggest this coloration protects from abrasion and helps with energy balance.This study suggests that the specific color of penguins results in temperature differences on top and bottom surfaces reducing their skin drag.To prove this hypothesis, we conducted several experimental and computational studies.In experiments, an infrared camera was used to measure the penguin's temperature of the penguin's back and front.These temperatures were used to calculate energy balance and friction drag.The results show that penguin coloration affects the drag.
Using Chatbots as Smart Teaching Assistants for First-Year Engineering Students

Authors:
raw_author_name: Andrew Katz
raw_affiliation_string: Center for Open Science, Virginia, USA.; Computer Science at the College of Comput-ing and Information Technology (CCIT).; Virginia Polytechnic Institute and State University; for Engineering Education, 2020

Abstract: As the artificial intelligence (AI) field advances, more research areas within AI are emerging including machine learning, neural networks, fuzzy systems, and much more.In the last decade, chatbot technology has emerged as a new area of AI.Chatbot use-cases are surfacing across various sectors including banking, customer services, medicine, and e-commerce.Recently, chatbots have begun being used in education, and the potential advantages are notable.In this paper, we are reviewing related literature and presenting a mobile application system, named Alpha, that utilizes chatbots as smart teaching assistants.The system consists of an artificially intelligent (smart) chatbot, cloud-based database, speech recognition, and web services.We conducted a student survey and a usability assessment of the implemented system.We have integrated our system into a first-year engineering course, and more courses will be added in the future.The system aims to support students' learning processes and engagement in addition to providing real-time 24/7 assistance to students.
A Writing Tool that Provides Real-Time Feedback to Students on their Grammar Using Deep Learning

Authors:
raw_author_name: Ashwin Satyanarayana
raw_affiliation_string: New York City College of Technology

Abstract: Abstract Teaching English writing skills to students from non-English backgrounds is a challenge in undergraduate colleges. Grammar teaching is centered on accuracy of form and rule learning. Grammar pedagogy has received a lot of attention in recent years by researchers. One of the main challenges for first-language English teacher’s approach to teaching writing is introducing formulaic approaches to grammar. Another challenge is the amount of time it takes for a teacher to provide feedback to students on their written work. Exceptional rules in the language, cultural and social differences cause a lot of language issues. Applying correct grammar, syntax, spelling, and vocabulary into written work might be challenging especially for international students. In our work, we address both the challenges by proposing a writing tool for students. In our work we use a state- of-the-art deep learning model which helps students identify grammatically incorrect sentences in real time. Our tool not only identifies grammatically incorrect sentences but also gives students an opportunity to correct the sentence themselves. The tool does not correct the sentence for the student. Using this active approach with feedback, students learn by reflecting on their writing and correcting sentences on their own. Our tool identifies incorrect sentences with an accuracy of 80%. We have tried our tool on student essays and have shown that it helps them better understanding grammatical rules of English.
An Interactive Learning System for Cyber Security Education

Authors:
raw_author_name: Te-Shun Chou
raw_affiliation_string: Department of Technology Systems College of Engineering and Technology East Carolina University

Abstract: This paper describes a learning system that uses virtualization technology to build a multiplayer cyber-attack and defense learning system infrastructure.The infrastructure emulated a realistic network that included numerous student network environments.Virtual machines were implemented in each student's network environment and they served as both attack and defense.In addition, the system infrastructure allowed interaction among students' network environments, therefore making it more similar to a realistic world network.The learning system functioned as a competition environment to encourage students to interact with each other.Each student acted as both attacker and defender.From the perspective of the attacker, students were able to perform hacking activities to other class members.Students were required to identify other students' system vulnerabilities and model the actions of the attacks.From the defender's point of view, students needed to apply a certain technique to prevent the corresponding attacks.The system allowed students access to the infrastructure anytime and anywhere.The system provided students with opportunities to learn cyberattack and prevention techniques in a simulation environment.
Applied Classroom Use of a Mobile Multifunctional Weather Station

Authors:
raw_author_name: Kumar Yelamarthi
raw_affiliation_string: Central Michigan University

Abstract: Abstract This project is rooted in the need for a mobile device to use outside of my future science classroom to measure, collect, and compile weather and water data. Incorporating this device into a secondary school classroom will not only allow students to use the weather station, they will also integrate engineering practices into their science education by building the weather station in small teams. Intelligent weather stations have been around for many years, often functioning on a large scale to collect weather, wind, and solar data to be used for forecasts, energy management, agriculture, and in the planning of clean energy projects. These tools have also been named automatic weather stations, they have been developed to function wirelessly and to remotely send their collected data to be stored or displayed for research and development purposes. Weather stations typically consist of an array of sensors to measure temperature, humidity, wind speed and direction, solar radiation, and atmospheric pressure. They also include a battery to power the station and a base that processes collected data and has the capabilities to send and/or store the data. Currently, the Python 3 code to implement individual sensors is complete. The device is able to upload data to the cloud using ThingSpeak. In addition, I have begun prototyping and testing possible circuit designs for the final model. Due to limitations of the Raspberry Pi's ability to supply power to multiple sensors in parallel, device calls must be done sequentially and with regard to time. The design, development, and use of a wireless weather station device can be used in science, technology, engineering, and math (STEM) experiences to introduce secondary students to the science and engineering practices laid out in the Next Generation Science Standards (NGSS). After the completion of this project in my future science class, students will have experience using engineering practices and problem-solving skills to assemble a simple IoT device. Thus far, I have encountered obstacles that students will likely face, such as limitations in the required power of all sensors, and I am now better equipped to assist them when these problems arise.
Blockchain Database for a Cyber Security Learning System

Authors:
raw_author_name: Te-Shun Chou

Abstract: Our cyber security learning system involves an interactive environment for students to practice executing different attack and defense techniques relating to cyber security concepts.We intend to use a blockchain database to secure data from this learning system.The data being secured are students' scores accumulated by successful attacks or defends from the other students' implementations.As more professionals are departing from traditional relational databases, the enthusiasm around distributed ledger databases is growing, specifically blockchain.With many available platforms applying blockchain structures, it is important to understand how this emerging technology is being used, with the goal of utilizing this technology for our learning system.In order to successfully secure the data and ensure it is tamper resistant, an investigation of blockchain technology use cases must be conducted.In addition, this paper defined the primary characteristics of the emerging distributed ledgers or blockchain technology, to ensure we effectively harness this technology to secure our data.Moreover, we explored using a blockchain database for our data.
Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution

Authors:
raw_author_name: Liu, Yuqing
raw_affiliation_string: School of Software, Dalian University of Technology, Dalian, China
raw_author_name: Jia, Qi
raw_affiliation_string: International School of Information Science and Engineering, Dalian University of Technology, Dalian, China
raw_author_name: Zhang, Jian
raw_affiliation_string: School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China
raw_author_name: Fan, Xin
raw_affiliation_string: International School of Information Science and Engineering, Dalian University of Technology, Dalian, China
raw_author_name: Wang, Shanshe
raw_affiliation_string: School of Electronics Engineering and Computer Science, Institute of Digital Media, Peking University, Beijing, China
raw_author_name: Ma, Siwei
raw_affiliation_string: School of Electronics Engineering and Computer Science, Institute of Digital Media, Peking University, Beijing, China
raw_author_name: Gao, Wen
raw_affiliation_string: School of Electronics Engineering and Computer Science, Institute of Digital Media, Peking University, Beijing, China

Abstract: As a highly ill-posed issue, single-image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by the degradation procedure. According to the Nyquist sampling theory, the degradation leads to the aliasing effect and makes it hard to restore the correct textures from low-resolution (LR) images. In practice, there are correlations and self-similarities among the adjacent patches in the natural images. This article considers the self-similarity and proposes a hierarchical image super-resolution network (HSRNet) to suppress the influence of aliasing. We consider the SISR issue in the optimization perspective and propose an iterative solution pattern based on the half-quadratic splitting (HQS) method. To explore the texture with local image prior, we design a hierarchical exploration block (HEB) and progressive increase the receptive field. Furthermore, multilevel spatial attention (MSA) is devised to obtain the relations of adjacent feature and enhance the high-frequency information, which acts as a crucial role for visual experience. The experimental result shows that HSRNet achieves better quantitative and visual performance than other works and remits the aliasing more effectively.
Embodied Footprints: A Safety-Guaranteed Collision-Avoidance Model for Numerical Optimization-Based Trajectory Planning

Authors:
raw_author_name: Li, Bai
raw_affiliation_string: College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
raw_author_name: Zhang, Youmin
raw_affiliation_string: Department of Mechanical, Industrial, and Aerospace Engineering, Concordia University, Montreal, Canada
raw_author_name: Zhang, Tantan
raw_affiliation_string: College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
raw_author_name: Acarman, Tankut
raw_affiliation_string: Department of Computer Engineering, Galatasaray University, Istanbul, Turkey
raw_author_name: Ouyang, Yakun
raw_affiliation_string: College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
raw_author_name: Li, Li
raw_affiliation_string: Department of Automation, BNRist, Tsinghua University, Beijing, China
raw_author_name: Cao, Dongpu
raw_affiliation_string: School of Vehicle and Mobility, Tsinghua University, Beijing, China

Abstract: Optimization-based methods are commonly applied in autonomous driving trajectory planners, which transform the continuous-time trajectory planning problem into a finite nonlinear program with constraints imposed at finite collocation points. However, potential violations between adjacent collocation points can occur. To address this issue thoroughly, we propose a safety-guaranteed collision-avoidance model to mitigate collision risks within optimization-based trajectory planners. This model introduces an embodied footprint, an enlarged representation of the vehicle's nominal footprint. If the embodied footprints do not collide with obstacles at finite collocation points, then the ego vehicle's nominal footprint is guaranteed to be collision-free at any of the infinite moments between adjacent collocation points. According to our theoretical analysis, we define the geometric size of an embodied footprint as a simple function of vehicle velocity and curvature. Particularly, we propose a trajectory optimizer with the embodied footprints that can theoretically set an appropriate number of collocation points prior to the optimization process. We conduct this research to enhance the foundation of optimization-based planners in robotics. Comparative simulations and field tests validate the completeness, solution speed, and solution quality of our proposal.
Minimax estimation of low-rank quantum states and their linear functionals

Authors:
raw_author_name: Samriddha Lahiry
raw_affiliation_string: Department of Statistics, Science Center, Harvard University, Cambridge, MA 02138, USA
raw_author_name: Michael Nussbaum
raw_affiliation_string: Department of Mathematics, Malott Hall, Cornell University, Ithaca, NY 14853, USA

Abstract: In classical statistics, a well known paradigm consists in establishing asymptotic equivalence between an experiment of i.i.d. observations and a Gaussian shift experiment, with the aim of obtaining optimal estimators in the former complicated model from the latter simpler model. In particular, a statistical experiment consisting of n i.i.d. observations from d-dimensional multinomial distributions can be well approximated by an experiment consisting of d−1 dimensional Gaussian distributions. In a quantum version of the result, it has been shown that a collection of n qudits (d-dimensional quantum states) of full rank can be well approximated by a quantum system containing a classical part, which is a d−1 dimensional Gaussian distribution, and a quantum part containing an ensemble of d(d−1)∕2 shifted thermal states. In this paper, we obtain a generalization of this result when the qudits are not of full rank. We show that when the rank of the qudits is r, then the limiting experiment consists of an r−1 dimensional Gaussian distribution and an ensemble of both shifted pure and shifted thermal states. For estimation purposes, we establish an asymptotic minimax result in the limiting Gaussian model. Analogous results are then obtained for estimation of a low rank qudit from an ensemble of identically prepared, independent quantum systems, using the local asymptotic equivalence result. We also consider the problem of estimation of a linear functional of the quantum state. We construct an estimator for the functional, analyze the risk and use quantum local asymptotic equivalence to show that our estimator is also optimal in the minimax sense.
Integrated photonic circuits for contextuality tests via sequential measurements in three-level quantum systems

Authors:
raw_author_name: Wilder Cardoso
raw_author_name: Sebastião Pádua
raw_affiliation_string: Universidade Federal de Minas Gerais

Abstract: In this paper, we present a protocol to obtain photonic circuits that can be used in the implementation of contextuality tests on qutrit systems. The use of photonic integrated circuits offers several advantages for performing this type of task. These include scalability, accuracy, robustness, high-speed and efficient quantum measurements, precise control over the phase properties of photons by using electrically driven heaters to induce a thermo-optic phase shift and resistance to noise. We relate the average values that appear in the inequalities with the probability of photon counting in the circuit outputs and present a realizable configuration for the desired device, taking into account state-dependent and state-independent contextuality tests.
Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium

Authors:
raw_author_name: Ari Z Klein
raw_affiliation_string: Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania , Philadelphia, PA 19104, United States
raw_author_name: Juan M Banda
raw_affiliation_string: Department of Computer Science, Georgia State University , Atlanta, GA 30302, United States
raw_author_name: Yuting Guo
raw_affiliation_string: Department of Biomedical Informatics, Emory University , Atlanta, GA 30322, United States
raw_author_name: Dongfang Xu
raw_affiliation_string: Department of Computational Biomedicine, Cedars-Sinai Medical Center , Los Angeles, CA 90048, United States
raw_author_name: Raul Rodriguez-Esteban
raw_affiliation_string: Roche Innovation Center , 4070 Basel, Switzerland
raw_author_name: Abeed Sarker
raw_affiliation_string: Department of Biomedical Informatics, Emory University , Atlanta, GA 30322, United States
raw_author_name: Graciela Gonzalez-Hernandez
raw_affiliation_string: Department of Computational Biomedicine, Cedars-Sinai Medical Center , Los Angeles, CA 90048, United States

Abstract: The aim of the Social Media Mining for Health Applications (#SMM4H) shared tasks is to take a community-driven approach to address the natural language processing and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.The eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events).In total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora.To facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.
Observations of how boundary conditions affect estimates of Poisson's ratio from simulated mode shapes

Authors:
raw_author_name: Matthew Luceadams
raw_affiliation_string: Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA; Los Alamos National Laboratory, Los Alamos, NM 87545, USA
raw_author_name: Abdessattar Abdelkefi
raw_affiliation_string: Department of Mechanical and Aerospace Engineering, New Mexico State University, Las Cruces, NM 88003, USA

Abstract: This research extends a method for direct estimation of Poisson's ratio from mode shapes of plates with hinged-hinged boundary conditions to include combinations of clamped and free boundary conditions. Using finite element simulations, we observe the effects of boundary conditions on the estimate of Poisson's ratio using the combination of Time-Average Scanning Digital Holography and Cornu's method. First, we introduce the abstraction of the span-to-width ratio to an effective antinode region. This abstraction enables us to extend the direct estimation technique to plates with various boundary conditions. Second, for long and thin enough plates, the effects of boundary conditions become negligible and Cornu's method is sufficient for direct estimation of the true value of Poisson's ratio from an out-of-plane bending mode shape. We note that for many situations, the necessary plate dimensions could be a limitation. Therefore, as a third concept, we discuss the behavior of the direct estimate of Poisson's ratio with respect to each type of boundary condition. Ultimately, this research initiates a path forward for a common processing technique for making a direct estimate of Poisson's ratio across different types of opposing boundary conditions for thin plates.