Reviewerly/exHarmony huggingface dataset:
Harmonizing marine zooplankton trait data toward a mechanistic understanding of ecosystem functioning

Abstract Compiling trait information promotes discovery and innovation in using trait‐based approaches in ecology. Various zooplankton trait datasets are stored in unlinked data repositories, in diverse data structures, and have varying levels of complexity. These require standardization and harmonization to allow interoperability and to limit the duplication of efforts in the time‐consuming and error‐prone task of trait compilation. This study aggregated and harmonized 33 zooplankton traits datasets and supplemented these with more than 150 references into a single zooplankton trait database with an initial set of 56 traits for 3535 marine zooplankton species. The database has a long data table structure using the entity‐attribute‐value format and includes taxonomic and ancillary metadata, and data source provenance preserving how the data were originally recorded. The database is stored both at the individual level (Level 1) and as species level means (Level 2). The Level 1 database has 57,615 rows of trait records and the Level 2 database has 14,977 unique trait‐taxon records. We evaluated the coverage of trait data, taxonomic representation, and strategies in filling‐in data gaps. Comparison of trait value estimation approaches identified allometric scaling to be more accurate than taxon‐level generalization and imputation. This centralized and harmonized marine zooplankton trait database aims to be extendable and future‐proof and to promote trait data sharing, FAIR (findable, accessible, interoperable, reusable) data practices, and reproducibility.
An Improved Human-Computer Interaction Content Recommendation Method Based on Knowledge Graph

Since human-to-human communication is contentful and coherent, during human-computer interaction (HCI), people naturally hope that the computer’s reply will be contentful and coherent. How to enable computers to recognize, understand and generate quality content and coherent responses like humanshas drawn a great deal of interest from bothindustry and academia.In order to address the current problems of lack of background knowledge of robots and low consistency of responses in open-domain HCI systems, we present a content recommendation method on the basis of knowledge graph ripple network. In order to realize a human-computer interaction system with better content and stronger coherence, and this model simulates the real process of human-human communication. At first, the emotional friendliness of HCI is gotten through calculating the emotional confidence level together with emotional evaluation value of HCI. After that, the external knowledge graph is introduced as the robot’s background knowledge, and the dialog entities are embedded in the ripple network of knowledge graphsto access the content of entities that may be of interest to participants. Lastly, the robot reply is given by comprehensively considering the content and emotional friendliness. The results of the experiments indicate a comparison with comparative modeling approaches like MECs models, robots with emotional measures and background knowledge can effectively enhance their emotional consistency and friendliness when performing HCI.
Vision-Based Object Localization and Classification for Electric Vehicle Driving Assistance

The continuous advances in intelligent systems and cutting-edge technology have greatly influenced the development of intelligent vehicles. Recently, integrating multiple sensors in cars has improved and spread the advanced drive-assistance systems (ADAS) solutions for achieving the goal of total autonomy. Despite current self-driving approaches and systems, autonomous driving is still an open research issue that must guarantee the safety and reliability of drivers. This work employs images from two cameras and Global Positioning System (GPS) data to propose a 3D vision-based object localization and classification method for assisting a car during driving. The experimental platform is a prototype of a two-sitter electric vehicle designed and assembled for navigating the campus under controlled mobility conditions. Simultaneously, color and depth images from the primary camera are combined to extract 2D features, which are reprojected into 3D space. Road detection and depth features isolate point clouds representing the objects to construct the occupancy map of the environment. A convolutional neural network was trained to classify typical urban objects in the color images. Experimental tests validate car and object pose in the occupancy map for different scenarios, reinforcing the car position visually estimated with GPS measurements.
INTEGRATING ENHANCED OPTIMIZATION WITH FINITE ELEMENT ANALYSIS FOR DESIGNING STEEL STRUCTURE WEIGHT UNDER MULTIPLE CONSTRAINTS

Real-world optimization problems are ubiquitous across scientific domains, and many engineering challenges can be reimagined as optimization problems with relative ease. Consequently, researchers have focused on developing optimizers to tackle these challenges. The Snake Optimizer (SO) is an effective tool for solving complex optimization problems, drawing inspiration from snake patterns. However, the original SO requires the specification of six specific parameters to operate efficiently. In response to this, enhanced snake optimizers, namely ESO1 and ESO2, were developed in this study. In contrast to the original SO, ESO1 and ESO2 rely on a single set of parameters determined through sensitivity analysis when solving mathematical functions. This streamlined approach simplifies the application of ESOs for users dealing with optimization problems. ESO1 employs a logistic map to initialize populations, while ESO2 further refines ESO1 by integrating a Lévy flight to simulate snake movements during food searches. These enhanced optimizers were compared against the standard SO and 12 other established optimization methods to assess their performance. ESO1 significantly outperforms other algorithms in 15, 16, 13, 15, 21, 16, 24, 16, 19, 18, 13, 15, and 22 out of 24 mathematical functions. Similarly, ESO2 outperforms them in 16, 17, 18, 22, 23, 23, 24, 20, 19, 20, 17, 22, and 23 functions. Moreover, ESO1 and ESO2 were applied to solve complex structural optimization problems, where they outperformed existing methods. Notably, ESO2 generated solutions that were, on average, 1.16%, 0.70%, 2.34%, 3.68%, and 6.71% lighter than those produced by SO, and 0.79%, 0.54%, 1.28%, 1.70%, and 1.60% lighter than those of ESO1 for respective problems. This study pioneers the mathematical evaluation of ESOs and their integration with the finite element method for structural weight design optimization, establishing ESO2 as an effective tool for solving engineering problems.
Efficient Object Detection Model for Edge Devices

Deep learning-based object detection methods demonstrated promising results. In reality, most methods suffer while running on edge devices due to their extensive network architecture and low inference speed. Additionally, there is a lack of industrial scenarios in the existing person, helmet, and head detection datasets. This research presents an efficient tiny network (ETN) for object detection that can perform on edge devices with high inference speed. We take the YOLOv5s model as our base model. We compress the YOLOv5s object detection model and minimize the computation redundancy, and propose two lightweight C3 modules (MC3 and SC3). Additionally, we construct two novel datasets: H2 (consists of safety helmet and head) and Person104K (consists of person) that fill the gaps in the earlier datasets with various industrial scenarios. We implemented and tested our method on Person104K and H2 datasets and achieved about 50.6% higher inference speed than the original YOLOv5s without compromising the accuracy. On the Nvidia Jetson AGX edge device, ETN achieves 42% higher FPS compared to the original YOLOv5s. Code is available at https://github.com/mdhosen/ETN .
Turkish Sign Language Recognition Using a Fine-Tuned Pretrained Model

Many members of society rely on sign language because it provides them with an alternative means of communication. Hand shape, motion profile, and the relative positioning of the hand, face, and other body components all contribute to the uniqueness of each sign throughout sign languages. Therefore, the field of computer vision dedicated to the study of visual sign language identification is a particularly challenging one. In recent years, many models have been suggested by various researchers, with deep learning approaches greatly improving upon them. In this study, we employ a fine-tuned CNN that has been presented for sign language recognition based on visual input, and it was trained using a dataset that included 2062 images. When it comes to sign language recognition, it might be difficult to achieve the levels of high accuracy that are sought when using systems that are based on machine learning. This is due to the fact that there are not enough datasets that have been annotated. Therefore, the goal of the study is to improve the performance of the model by transferring knowledge. In the dataset that was utilized for the research, there are images of 10 different numbers ranging from 0 to 9, and as a result of the testing, the sign was detected with a level of accuracy that was equal to 98% using the VGG16 pre-trained model.
Image Encryption Using Spined Bit Plane Diffusion and Chaotic Permutation for Color Image Security

With the tremendous rise in multimedia output over the last ten years, image encryption has grown in importance as a component of information security. It is challenging to secure images using conventional encryption methods because of the intrinsic image properties that distinguish image cryptography from text cryptography. This study suggests employing binary key images for lossless encryption of color images. First, chaotic sequences from the 2D Henon map will be used to permute the locations of the pixels in the plaintext image. The key images, in this case, are bit planes created from an additional image. In order to improve the encryption image and make cracking more challenging, the bit plane will be further rotated. It concentrates on two methods for effective image encryption: bit plane slicing and bit plane spinning. In terms of a number of statistical tests, such as key experiments, information entropy experiments, and encryption quality testing, the simulation analysis also shows that the suggested technique is lossless, safe, and effective. Based on the findings, it can be shown that the new method takes less time to conceal the remaining intelligence than it does to encrypt the complete image.
Gesture Me: A Machine Learning Tool for Designers to Train Gesture Classifiers

This paper contributes to the body of work examining how designers can be supported in integrating machine learning (ML) capabilities into their designs for novel applications and services. It presents an online tool enabling designers and other non-specialist audiences to define body gestures, interactively and iteratively train and test a classifier to recognise these gestures, and integrate the trained classifier into a template web application. An empirical evaluation with MSc User Experience Design students and practitioners, all of whom had previous experience in web development but not in ML, found that the tool enables them to define, train and test a gesture recognition classifier with little or no help, and that engagement with the tool advances their understanding of the capabilities, limitations and operational aspects of ML. The evaluation confirmed the value of visualising the ML perspective and encouraging designers to experiment with ML to support their experiential learning. The study led to design recommendations that can inform the development of tools supporting designers to ideate and prototype ML-enhanced applications.
Do Users Tolerate Errors? Effects of Observed Failures on the Subjective Evaluation of a Gesture-Based Virtual Reality Application

Recently, virtual reality (VR) has received increasing attention in science, research, and industry, as well as in consumer electronics. Together with this hardware, innovative interaction modes such as mid-air gestures are being developed and employed. Because these setups are complex and less established than traditional buttons and touch-based interfaces, there is a higher risk that users will perceive errors, failures, and technical malfunctions. This raises the question of how popular, accepted, and trusted such systems are among users and whether observed errors influence this subjective assessment. Previous studies have shown that trust typically increases with usage duration and may decay after system failures. We conducted a study using an HTC Vive headset on which we mounted a Leap Motion device for gesture detection. Participants performed basic tasks with a “blocks” application using a set of gestures. Afterwards, they were asked to rate their levels of trust in and acceptance of the system. We investigated the correlation between the number of observed errors and reported levels of trust and acceptance to determine whether malfunctions directly influence subjective assessment. We found no effect of errors on acceptance but a significant correlation between number of errors and overall trust score.
Multiparty Dialogic Processes of Goal and Strategy Formation in Hybrid Teams

A current trend which is already prevalent in highly structured (e.g. industrial) working environments is the cooperation of people, intelligent physical agents (robots) and, in parts, intelligent information agents (AIs, chatbots) in hybrid teams, mostly in paired settings. This position article discusses a major generalization and extension of this concept: (a) the goals, strategies, and actions are not fully prespecified, but develop in the course of a dialogic process; (b) the agents are not merely tools or assistants, but proactively intervene as peers; (c) the hybrid teams are multiparties with several humans and (situated or remote) intelligent agents, exhibiting and modelling pronounced group behavior. Cognitive, dialogic systems are the technical backbone of such team settings, bringing together techniques of multimodal processing, information retrieval, situated action planning and autonomous action generation, recognizing and anticipating task-related states of the actors.
Towards Gesture Based Assistive Technology for Persons Experiencing Involuntary Muscle Contractions

This research investigates the viability of leveraging Machine Learning (ML) algorithms to develop gesture recognition systems that may benefit people who experience involuntary muscle contractions. This presents distinct challenges, such as the reduced ability to perform gestures accurately and repeatedly (flawed gestures) as well as the ability to provide sufficient data to pre-train models. This investigation revolves around three shortlisted gesture recognition algorithms which were evaluated in a controlled lab environment. The primary objective was to observe specific characteristics such as robustness under different simulated conditions, training requirements, as well as classification latency and accuracy. Results show distinct properties for each shortlisted algorithm. k-Nearest Neighbour (KNN) with Dynamic Time Warping (DTW), or KNN-DTW, is well suited where accurate gesture training is challenging due to frequent involuntary movements. Although this approach works well with one sample, the classification response time is significantly longer than KNN and Support Vector Machine (SVM). However, timing may not always be a priority, depending on the context of use. On the other hand, when real-time responses are necessary, KNN and SVM both offer a good level of performance. These, however, rely on training data to produce accurate classifications, in which case the user must be able to perform gestures in a reasonably repeatable manner. This work also presents a dataset of 1600 samples for four gesture classes, including a corresponding set of flawed gesture samples for each class.
Simplifying the Development of Conversational Speech Interfaces by Non-Expert End-Users Through Dialogue Templates

Conversational speech interface development, maintenance and evolution is challenging for non-experts as it requires linguistic knowledge and proficiency in chatbot design and implementation. To address this issue, this work proposes the use of Dialogue Templates, compact conversational interfaces intended to cater specific interaction capabilities which can be easily adapted to a particular use case by non-expert end-users, just with knowledge of the application domain. Our implementation of Dialogue Templates is presented and detailed for three relevant conversational spoken interaction use cases in the industrial environment: navigating maintenance management systems, recording manufacturing plant activity data and registering warehouse inventory. In addition, a comparative analysis is also conducted to assess the effort required to develop sample conversational assistants in such scenarios using our conventional development platform versus Dialogue Templates. Results show that Dialogue Templates significantly simplify the development of conversational speech interfaces, without demanding linguistic expertise.
Creating Human-Computer Partnerships

How can we can design “human-computer partnerships” that take optimal advantage of human skills and system capabilities? Artificial Intelligence research is usually measured in terms of the effectiveness of an algorithm, whereas Human-Computer Interaction research focuses on enhancing human abilities. I argue that better AI algorithms are neither necessary nor sufficient for creating more effective intelligent systems. Instead, we need to focus on the details of interaction and how to successfully balance the simplicity of the user’s interaction with the expressive power of the system. After describing our approach to “generative theories of interaction”, I illustrate how to create interactive intelligent systems where users can discover relevant functionality, express individual differences and appropriate the system for their own personal use.
Using Audience Avatars to Increase Sense of Presence in Live-Streams

Social interactions and the sense of presence are important for the spectatorship experience in live-streams. In large audiences, communication gets harder and viewers participate less. This paper explores the possibility of representing an audience using animated avatars to increase the sense of presence and potentially move some traffic from the chat window to the avatars. We discuss the motivations for and the challenges in creating an audience avatar interface.
Creating StoryLines: Participatory Design with Power Grid Operators

Designing interactive technology to support safety-critical systems poses multiple challenges with respect to security, access to operators and the proprietary nature of the data. We conducted a two-year participatory design project with French power grid operators to both understand their specific needs and to collaborate on the design of a novel collaborative tool called StoryLines . Our primary objective was to capture detailed, in-context data about operators’ work practices as part of a larger project designed to provide bi-directional assistance between an intelligent agent and human operator. We targeted handovers between shifts to take advantage of the operators’ existing practice of articulating the current status of the grid and expected future events. We use information that would otherwise be lost to gather valuable information about the operator’s decision rationale and decision-making patterns. This paper describes how we combined a bottom-up participatory design approach with a top-down generative theory approach to design StoryLines, an interactive timeline that helps operators collect information from diverse tools, record reminders and share relevant information with the next shift’s operator. We conclude with a discussion of the challenges of working with users in safety-critical environments and directions for future research.
Assessing the effectiveness of educational work in a general education organization: Classification of information to be used in the document-oriented system ‘MongoDB’

The study aims to substantiate the process of obtaining information to assess the effectiveness of educational work in a general education organization based on big data technologies. The paper presents the results of an online survey of specialists in the educational work of education organizations. Within the framework of this survey, the sources of information necessary for assessing the effectiveness of educational work in a general education organization are specified and ranked. The big data technologies that are most suitable for assessing the effectiveness of educational work in a general education organization are analyzed. The scientific novelty of the study lies in the fact that the sources of information and a system of attributes that allow classifying the information necessary to be used when assessing the effectiveness of educational work in a general education organization based on big data technologies are specified. As a result of the study, it has been found that the document-oriented system ‘MongoDB’ can be used to assess the effectiveness of educational work in a general education organization.
Feature redundancy removal for text classification using correlated feature subsets

Abstract The curse of high dimensionality in text classification is a worrisome problem that requires efficient and optimal feature selection (FS) methods to improve classification accuracy and reduce learning time. Existing filter‐based FS methods evaluate features independently of other related ones, which can then lead to selecting a large number of redundant features, especially in high‐dimensional datasets, resulting in more learning time and less classification performance, whereas information theory‐based methods aim to maximize feature dependency with the class variable and minimize its redundancy for all selected features, which gradually becomes impractical when increasing the feature space. To overcome the time complexity issue of information theory‐based methods while taking into account the redundancy issue, in this article, we propose a new feature selection method for text classification termed correlation‐based redundancy removal, which aims to minimize the redundancy using subsets of features having close mutual information scores without sequentially seeking already selected features. The idea is that it is not important to assess the redundancy of a dominant feature having high classification information with another irrelevant feature having low classification information and vice‐versa since they are implicitly weakly correlated. Our method, tested on seven datasets using both traditional classifiers (Naive Bayes and support vector machines) and deep learning models (long short‐term memory and convolutional neural networks), demonstrated strong performance by reducing redundancy and improving classification compared to ten competitive metrics.