Reviewerly/exHarmony huggingface dataset:
Word Embedding Methods of Text Processing in Big Data: A Comparative Study

One of the biggest challenges any NLP data scientist faces is choosing the best numeric/vector representation of a text string for running a machine learning model. This research paper provides a comprehensive study of big data and its impact on improving the performance of word embedding techniques for text processing. Therefore, we propose a method for text processing using word embedding techniques and machine learning (ML) algorithms to improve the performance of analyzing data and decision-making. For this reason, it is possible to use several word embedding methods for text processing, especially the most popular ones like CountVectoriser, TF-IDF, and HashingVectorizer, and combine them with ML algorithms like decision trees, random forest classifiers, and logistic regression for implemented text Regression and other supervised machine learning algorithms are combined with classification. Compared with recent work, our comparative study shows the impact of dataset size on the performance of text classification algorithms and gives good results.
Temporal Blockchains for Intelligent Transportation and Autonomous Vehicles Management

Nowadays, blockchain technology is being used in several domains, and in particular for managing intelligent transportation systems and autonomous vehicles. However, since block timestamps, put by miners, are inaccurate (with about one hour of delay), they could not be useful for many real-time applications that require to deal with exact timing of events, like traffic laws enforcement and insurance accident investigation. Hence we propose to add new timestamps, put by data originators, with the meaning of valid time as defined in temporal databases and which can represent the accurate time of data recording within a transaction. Consequently, the new enhanced blockchain data model, named BiTchain, can be considered, from a temporal database perspective, as a bitemporal database. In order to let users and applications enjoy the potentialities of BiTchain, we also propose an expressive temporal query language, named BiTEQL and defined as a TSQL2-like temporal extension of the EQL blockchain query language.
Implementation of Artificial Intelligence Methods for Solar Energy Prediction

In order to satisfy the growing world energy demand and decreasing the emission of greenhouse gases the requirement for more green energy has led to an increased focus on research related to forecasting solar energy recently. In this study we aim to develop forecast models, based on Artificial Neural Network and Random Forrest algorithms to predict daily solar energy based on daily historical meteorological data measured between 2019 and 2021. The accuracy and the performance of each model are compared using mean squared error, mean absolute percentage error, mean absolute error, max error and R-squared for evaluation. The prediction of daily solar energy from the daily maximum, minimum and average values of the metrological variables using Artificial Neural Network and Random Forest was carried out. The results obtained indicate that both models can predict daily solar energy with good accuracy (MAPE = 13%). On the one hand, the RF model showed excellent accuracy during the training phase (MAPE = 8%, R2 = 0.97), but it failed to show same results during the testing phase (MAPE = 13%, R2 = 0.79). On the other hand, the ANN was able to maintain the same results during training and testing (MAPE = 13%, R2 = 0.81).
FishAI: The Lodestar fishing platform

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Comparison of R Packages for Automated Test Assembly with Mixed-Integer Linear Programming

Many organizations utilize some form of automation in the test assembly process; either fully algorithmic or heuristically constructed. However, one issue with heuristic models is that when the test assembly problem changes the entire model may need to be re-conceptualized and recoded. In contrast, mixed-integer programming (MIP) is a mathematical representation of the test assembly problem that looks for the statistically optimal solution. Because MIP is a mathematical representation, changes to the test assembly problem typically involve only minor changes to the programming. This review focuses on comparing two free and open-source R packages for mixed integer linear programming: inlinelpSolveAPI and inlineompr. Programming style (with code provided), ease of use, run time, and other considerations will be examined. Solvers from other open-source platforms (e.g. Python, Julia) will also be discussed. Code and sample data are also provided.
Free-HeadGAN: Neural Talking Head Synthesis With Explicit Gaze Control

We present Free-HeadGAN, a person-generic neural talking head synthesis system. We show that modeling faces with sparse 3D facial landmarks is sufficient for achieving state-of-the-art generative performance, without relying on strong statistical priors of the face, such as 3D Morphable Models. Apart from 3D pose and facial expressions, our method is capable of fully transferring the eye gaze, from a driving actor to a source identity. Our complete pipeline consists of three components: a canonical 3D key-point estimator that regresses 3D pose and expression-related deformations, a gaze estimation network and a generator that is built upon the architecture of HeadGAN. We further experiment with an extension of our generator to accommodate few-shot learning using an attention mechanism, in case multiple source images are available. Compared to recent methods for reenactment and motion transfer, our system achieves higher photo-realism combined with superior identity preservation, while offering explicit gaze control.
TYPES OF RESEARCH ACTIVITIES OF APPLICANTS OF THE FIRST AND SECOND LEVELS OF HIGHER PEDAGOGICAL EDUCATION IN THE PROCESS OF MASTERING EDUCATIONAL COMPONENTS

The formation of research competence of students of the first and second levels of higher pedagogical research competence, education is carried out in compliance with the principles of continuity and consistency: from familiarization with the principles o
EFFECTIVE ORGANIZATION OF WORK WITH GIFTED YOUTH AS A MEANS OF IMPROVING PROFESSIONAL TRAINING OF FUTURE SPECIALISTS

Limitations dictated by educational programs and deadlines for their implementation in the educational process of vocational education institutions slow down the possibility of full familiarization and acquisition of relevant skills for working with 3D mo
Differences in attitude toward <scp>COVID</scp>‐19 based on internet and social media usage among <scp>community‐dwelling</scp> older adults during the first state of emergency

In order to understand the digital divide among older adults during the coronavirus disease 2019 (COVID-19) pandemic, we investigated the association between internet use and compliance with COVID-19 preventive behaviors during the first state of emergency in Japan.A total of 8952 community-dwelling citizens aged 75 years and above were asked about their preventive behaviors during the first state of emergency using a paper-based questionnaire. Among them, 51% responded and were divided into internet users and non-users. We used multivariable logistic regression models to estimate the adjusted odds ratios and 95% confidence intervals of internet use for compliance with preventive behaviors.Approximately 40% of the respondents used the internet, and 9.29% used social media to collect COVID-19-related information. Internet usage was independently associated with compliance with using hand sanitizers, avoiding going out, avoiding eating out, avoiding traveling, getting vaccinated, and getting tested for COVID-19; the adjusted odds ratios (95% confidence intervals) were 1.21 (1.05-1.38), 1.19 (1.04-1.37), 1.20 (1.05-1.38), 1.32 (1.15-1.52), 1.30 (1.11-1.53), and 1.23 (1.07-1.41), respectively. Exploratory subgroup analyses demonstrated that social media users might have shown early adaptation to newly recommended preventive behaviors during the first state of emergency.Results suggest that a digital divide exists, as evidenced by the varied compliance with preventive behaviors depending on internet use. Additionally, social media use may be associated with early adaptation to newly recommended preventive behaviors. Therefore, future studies regarding the digital divide among older adults should investigate differences depending on the types and content of internet resources. Geriatr Gerontol Int 2023; 23: 289-296.
Providing an Approach for Early Prediction of Fall in Human Activities Based on Wearable Sensor Data and the Use of Deep Learning Algorithms

Abstract Falling is one of the major health concerns, and its early detection is very important. The goal of this study is an early prediction of impending falls using wearable sensors data. The SisFall data set has been used along with two deep learning models (CNN and a combination model named Conv_Lstm). Also, a dynamic sampling method is offered to improve the accuracy of the models by increasing the equilibrium rate between the samples of the majority and minority classes. To fulfill the main idea of this paper, we present a future prediction strategy. Then, by defining a time variable ‘T’, the system replaces and labels the state of the next T s instead of considering the current state only. This leads to predicting falling states at the beginning moments of balance disturbance. The results of the experiments show that the Conv_Lstm model was able to predict the fall in 78% of cases and an average of 340 ms before the accident. Also, for the Sensitivity criterion, a value of 95.18% has been obtained. A post-processing module based on the median filter was implemented, which could increase the accuracy of predictions to 95%.
Path Generation for a Given Performance Evaluation Value Interval by Modifying Bat Algorithm with Heuristic

Path generation means generating a path or a set of paths so that the generated path meets specified properties or constraints. To our knowledge, generating a path with the performance evaluation value of the path within a given value interval has received scant attention. This paper subtly formulates the path generation problem as an optimization problem by designing a reasonable fitness function, adapts the Markov decision process with reward model into a weighted digraph by eliminating multiple edges and non-goal dead nodes, constructs the path by using a priority-based indirect coding scheme, and finally modifies the bat algorithm with heuristic to solve the optimization problem. Simulation experiments were carried out for different objective functions, population size, number of nodes, and interval ranges. Experimental results demonstrate the effectiveness and superiority of the proposed algorithm.
Anomaly Detection for Dynamic Human-Robot Assembly

Human-Robot Collaboration (HRC) requires humans and robots to work on the same product in the same work environment at the same time. Therefore, the robotic system needs to understand human behavior so it can assist the human appropriately. Since the human is an uncertain variable in this system, human action recognition is one of the key challenges when it comes to HRC. To address this problem, we developed an anomaly detection framework for the dynamic assembly of complex products. We used an Long-Short-Term-Memory (LSTM)-based autoencoder to detect anomalies in human behavior and post-process the output to categorize it as a green or red anomaly. A green anomaly represents a deviation from the intended order but a valid assembly sequence. A red anomaly represents an invalid sequence. In both cases, the worker is guided to complete the assembly process. We demonstrate our proposed framework using an appropriate industrial use case.
Can a Robot's Hand Bias Human Attention?

Previous studies have revealed that humans prioritize attention to the space near their hands (the so-called near-hand effect). This effect may also occur towards a human partner's hand, but only after sharing a physical joint action. Hence, in human dyads, interaction leads to a shared body representation that may influence basic attentional mechanisms. Our project investigates whether a collaborative interaction with a robot might similarly influence attention. To this aim, we designed an experiment to assess whether the mere presence of a robot with an anthropomorphic hand could bias the human partner's attention. We replicated a classical psychological paradigm to measure this attentional bias (i.e., the near-hand effect) by adding a robotic condition. Preliminary results found the near-hand effect when performing the task with the self-hand near the screen, leading to shorter reaction times on the same side of the hand. On the contrary, we found no effect on the robot's hand in the absence of previous collaborative interaction with the robot, in line with studies involving human partners.
Haru He's Here to Help!

Current robot dialog systems are predominantly implemented using a sequential, utterance based, two-party, speak-wait/speak-wait approach. This is inadequate for any interaction where rapid timed turn-taking is required. In this demo we show how a conversational listener can support a comedy dialogue with the social robot 'Haru'. The system depends on fast local word-spotting and can be extended to support scripted and semi-scripted interactions where conversational timing is important such as fictive dialogues for educational purposes, or to support acting within a story telling context. The system could also be integrated with a full dialogue manager for non-scripted open dialogue applications.
Utilizing Prior Knowledge to Improve Automatic Speech Recognition in Human-Robot Interactive Scenarios

The prolificacy of human-robot interaction not only depends on a robot's ability to understand the intent and content of the human utterance but also gets impacted by the automatic speech recognition (ASR) system. Modern ASR can provide highly accurate (grammatically and syntactically) translation. Yet, the general purpose ASR often misses out on the semantics of the translation by incorrect word prediction due to open-vocabulary modeling. ASR inaccuracy can have significant repercussions as this can lead to a completely different action by the robot in the real world. Can any prior knowledge be helpful in such a scenario? In this work, we explore how prior knowledge can be utilized in ASR decoding. Using our experiments, we demonstrate how our system can significantly improve ASR translation for robotic task instruction.
Stretch to the Client; Re-imagining Interfaces

This paper presents the efforts made towards the creation of a client interface to be used with Hello-Robot Stretch. The goal is to create an interface that is accessible to allow for the best user experience. This interface enables users to control Stretch with basic commands through several modalities. To make this interface accessible, a simple and clear web interface was crafted so users of differing abilities can successfully interact with Stretch. A voice activated option was also added to further increase the range of possible interactions.
Anomaly Detection for Dynamic Human-Robot Assembly

Human action recognition is one of the key challenges in human-robot collaboration (HRC), especially when the process has multiple valid ways to assemble a product. To address this problem, we developed an anomaly detection framework for the assembly of complex products. We used an Long-Short-Term-Memory (LSTM)-based autoencoder to detect anomalies in human behavior and post-process the output to categorize it as a green or red anomaly. A green anomaly represents a deviation from the intended order but a valid assembly sequence. A red anomaly represents an invalid sequence. In both cases, the worker is guided to complete the assembly process.
Making Music More Inclusive with Hospiano

Music brings people together; it is a universal language that can help us be more expressive and help us understand our feelings and emotions in a better manner. The "Hospiano" robot is a prototype developed with the goal of making music accessible to all, regardless of physical ability. The robot acts as a pianist and can be placed in hospital lobbies and wards, playing the piano in response to the gestures and facial expressions of patients (i.e. head movement, eye and mouth movement, and proximity). It has three main modes of operation: "Robot Pianist mode", in which it plays pre-existing songs; "Play Along mode", which allows anyone to interact with the music; and "Composer mode", which allows patients to create their own music. The software that controls the prototype's actions runs on the Robot Operating System (ROS). It has been proven that humans and robots can interact fluently via a robot's vision, which opens up a wide range of possibilities for further interactions between these logical machines and more emotive beings like humans, resulting in an improvement in the quality of life of people who use it, increased inclusivity, and a better world for future generations to live in.
Towards Online Adaptation for Autonomous Household Assistants

Many assistive home robotics applications assume open-loop interactions: robots incorporate little feedback from people while autonomously completing tasks. This places undue burden on people to condition their actions and environment to maximize the likelihood of their desired outcomes. We formalize assistive household rearrangement as collaborative online inverse reinforcement learning (IRL). Since online IRL can lead to sample inefficient interactions and overfit to specific user objectives, we compare sample efficiency and generalizability of two initial choices of action representations in a simulated household rearrangement task. We show, under certain assumptions, that representing objects by their material properties can increase sample efficiency and generalizability to out of domain objects.
An Expressive Robotic Table to Enhance Social Interactions

We take initial steps into prototyping an expressive robotic table that can serve as a social mediator. The work is constructed through a rapid prototyping process consisting of five workshop-based phases with five interaction design participants. We report on the various prototyping techniques that led to the generated concept of an expressive robotic table. Our design process explores how expressive motion cues such as respiratory movements can be leveraged to mediate social interactions between people in cold outdoor environments. We conclude by discussing the implications of the different prototyping methods applied and the envisioned future directions of the work within the scope of expressive robotics.
Uncertainty-Resolving Questions for Social Robots

Social robots should deal with uncertainties in unseen environments and situations in an interactive setting. For humans, question-answering is one of the most typical activities for resolving or reducing uncertainty by acquiring additional information, which is also desirable for social robots. In this study, we propose a framework for leveraging the research on learning-by-asking techniques for social robots. This framework is inspired by human inquiries. Information seeking by asking should be considered at the multi-dimensional level, including required knowledge, cognitive processes, and question types. These dimensions offer a framework to embed generated questions into the three-dimensional question space, which is expected to provide a reasonable benchmark for the active learning approach and evaluation methodologies of uncertainty-resolving question generation for social robots.
CHIBO

Parents may inadvertently promote excess weight gain in childhood by using inappropriate child-feeding behaviors. This study presents the design of a home robot in a parent-child feeding scenario. Designed for parents who have difficulty quantifying the amount of food their children, it helps them quantify calorie intake through pneumatic feedback and gives real-time feedback and intervention by recognizing feeding behaviors. It aims to create a more intuitive link between scientific quantification standards and actual eating scenarios by a pneumatic device in the form of a peripheral interaction. Based on our current prototype development, we have sought to explore the effectiveness of peripheral interactions with physical robotic interventions in user behavior.
Aligning Robot Behaviors with Human Intents by Exposing Learned Behaviors and Resolving Misspecifications

Human-robot interaction is limited in large part by the challenge of writing correct specifications for robots. The research community wants alignment between humans' goals and robot behaviors, but this alignment is very hard to achieve. My research tackles this problem. I view alignment as the consequence of iterative design and ample testing, and I design methods in service of these processes. I first study how humans currently write reward functions, and I profile some of the typical errors they make when doing so. I then study how humans can inspect the behaviors robots learn from any given specification. A typical approach to this mandates unstructured or hand-designed test cases; I instead introduce a Bayesian inference method for finding behavior examples which cover information-rich test cases. Alongside finding these behavior examples, I study how these examples should be presented to the human through applying cognitive theories of human concept learning. For the remainder of my thesis, I am pursuing two open questions. My first question concerns how these components can be combined such that humans are able to iteratively design better behavioral specifications. My second question concerns how robots can better interpret humans' erroneous specifications and attempt to infer their true intent, in spite of the errors.
RoPi

Injury burns are traumatic events, especially for children. The use of combined pharmacological and non-pharmacological strategies in hospitals favor emotional experiences. However, in Latin American hospitals, which have low budgets, it is not possible to hire therapeutic support personnel, such as clowns, due to the lack of available human resources. RoPi is a social robot created to assist hospitalized children's emotional support through its multicolor interchangeable pieces and its interactive functions.
Development of a Wearable Robot that Moves on the Arm to Support the Daily Life of the User

Wearable robots can maintain physical contact with the user and interact with them to assist in daily life. However, since most wearable robots operate at a single point on the user's body, the user must be constantly aware of their presence. This imposes a burden on the user, both physically and mentally, and prevents them from wearing the robot daily. One solution to this problem is for the robot to move around the user's body. When the user does not interact with the robot, it can move to an unobtrusive position and attract less attention from the user. This research aims to develop a wearable robot that reduces the burden by developing an arm movement mechanism for wearable robots and a self-localization method for autonomous movement and helps the user's daily life by providing supportive interactions.