Reviewerly/exHarmony huggingface dataset:
Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem

Inspired by the bamboo growth process, Chu et al. proposed the Bamboo Forest Growth Optimization (BFGO) algorithm. It incorporates bamboo whip extension and bamboo shoot growth into the optimization process. It can be applied very well to classical engineering problems. However, binary values can only take 0 or 1, and for some binary optimization problems, the standard BFGO is not applicable. This paper firstly proposes a binary version of BFGO, called BBFGO. By analyzing the search space of BFGO under binary conditions, the new curve V-shaped and Taper-shaped transfer function for converting continuous values into binary BFGO is proposed for the first time. A long-mutation strategy with a new mutation approach is presented to solve the algorithmic stagnation problem. Binary BFGO and the long-mutation strategy with a new mutation are tested on 23 benchmark test functions. The experimental results show that binary BFGO achieves better results in solving the optimal values and convergence speed, and the variation strategy can significantly enhance the algorithm's performance. In terms of application, 12 data sets derived from the UCI machine learning repository are selected for feature-selection implementation and compared with the transfer functions used by BGWO-a, BPSO-TVMS and BQUATRE, which demonstrates binary BFGO algorithm's potential to explore the attribute space and choose the most significant features for classification issues.
Global Path Planning Method Based on a Modification of the Wavefront Algorithm for Ground Mobile Robots

This article is focused on the problematics of path planning, which means finding the optimal path between two points in a known environment with obstacles. The proposed path-planning method uses the wavefront algorithm, and two modifications are implemented and verified. The first modification is the removal of redundant waypoints. The first modification is applied because the wavefront algorithm generates redundant waypoints. These waypoints cause unnecessary changes in the direction of movement. The second one is smoothing the generated trajectory using B-spline curves. The reason for applying the second modification is that trajectory generated by the wavefront algorithm is in the form of the polyline, which is inadequate in terms of the smoothness of the robot’s motion. The verification of the proposed method is performed in environments with different densities of obstacles compared with standard Dijkstra’s and A* algorithms.
Spatially Structured-Mode Multiplexing Holography for High-Capacity Security Encryption

Holography has been widely used in optical displays, high-security optical encryption, and optical artificial intelligence. Optical multiplexing technologies by utilizing various dimensions of light effectively expand the information capacity and density for holography. In this work, we propose and experimentally demonstrate a novel spatially structured-mode multiplexing holography with the assistance of deep learning algorithms. In the experiment, we utilize Hermite–Gaussian (HG) and Laguerre–Gaussian (LG) modes for example as decoding channels of various holographic images. The results prove that these spatial modes work well as a multiplexing dimension in addition to wavelength, polarization, and orbital angular momentum (OAM) of light. In addition, by designing a specifically computed hologram, multiple spatial modes can be superposed together to compose a single decoding channel, which can significantly enhance the capacity and security for holographic encryption. Our work provides a promising scheme for high-capacity computational holography and information encryption.
Affective Out-World Experience via Virtual Reality for Older Adults Living with Mild Cognitive Impairments or Mild Dementia

Older adults with cognitive impairments may face barriers to accessing experiences beyond their physical premises. Previous research has suggested that missing out on emotional experiences may affect mental health and impact cognitive abilities. In recent years, there has been growing research interest in designing non-pharmacological interventions to improve the health-related quality of life of older adults. With virtual reality offering endless opportunities for health support, we must consider how virtual reality can be sensitively designed to provide comfortable, enriching out-world experiences to older adults to enhance their emotional regulation. Thirty older adults living with mild cognitive impairment or mild dementia participated in the study. Affect and emotional behavior were measured. The usability and the sense of presence were also assessed. Finally, we assessed the virtual reality experiences based on physiological responses and eye-tracking data. The results indicated that virtual reality can positively enhance the mental health of this population by eliciting a positive affective state and enhancing their emotional regulation. Overall, this paper raises awareness of the role of virtual reality in emotion elicitation, regulation, and expression and enhances our understanding of the use of virtual reality by older adults living with mild cognitive impairments or mild dementia.
Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm

With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent-Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham's line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.
Novel Speech Recognition Systems Applied to Forensics within Child Exploitation: Wav2vec2.0 vs. Whisper

The growth in online child exploitation material is a significant challenge for European Law Enforcement Agencies (LEAs). One of the most important sources of such online information corresponds to audio material that needs to be analyzed to find evidence in a timely and practical manner. That is why LEAs require a next-generation AI-powered platform to process audio data from online sources. We propose the use of speech recognition and keyword spotting to transcribe audiovisual data and to detect the presence of keywords related to child abuse. The considered models are based on two of the most accurate neural-based architectures to date: Wav2vec2.0 and Whisper. The systems were tested under an extensive set of scenarios in different languages. Additionally, keeping in mind that obtaining data from LEAs are very sensitive, we explore the use of federated learning to provide more robust systems for the addressed application, while maintaining the privacy of the data from LEAs. The considered models achieved a word error rate between 11% and 25%, depending on the language. In addition, the systems are able to recognize a set of spotted words with true-positive rates between 82% and 98%, depending on the language. Finally, federated learning strategies show that they can maintain and even improve the performance of the systems when compared to centralized trained models. The proposed systems set the basis for an AI-powered platform for automatic analysis of audio in the context of forensic applications of child abuse. The use of federated learning is also promising for the addressed scenario, where data privacy is an important issue to be managed.
Liminal design: A conceptual framework and three-step approach for developing technology that delivers transcendence and deeper experiences

As ubiquitous technology is increasingly mediating our relationships with the world and others, we argue that the sublime is struggling to find room in product design primarily aimed at commercial and transactional goals such as speed and efficiency. We suggest a new category of products to promote deeper and more meaningful experiences, specifically those offering liminality, transcendence, and personal transformation. This paper introduces a conceptual framework and three-step design approach looking at narrative participation in design through abstractions to promote, hold and deepen more complex emotions. We explore implications from a theoretical point of view and suggest product examples for how the model might be applied in practice.
A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary.A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates.
MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person's tissues and plasma.This condition may be fatal when if it is not diagnosed and recognized at an early stage.The physical technique and lab procedures for Leukaemia identification are considered time-consuming.It is crucial to use a quick and unexpected way to identify different forms of Leukaemia.Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment.Various deep-learning (DL) model-based segmentation and categorization techniques have already been introduced, although they still have certain drawbacks.In order to enhance feature extraction and classification in such a practical way, Mayfly optimization with Generative Adversarial Network (MayGAN) is introduced in this research.Furthermore, Generative Adversarial System (GAS) is integrated with Principal Component Analysis (PCA) in the feature-extracted model to classify the type of blood cancer in the data.The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia.Acute lymphocytic Leukaemia (ALL), acute myelogenous Leukaemia (AML), chronic lymphocytic Leukaemia (CLL), chronic myelogenous Leukaemia (CML), and aberrant White Blood Cancers (WBCs) are all successfully classified by the proposed MayGAN model.The proposed MayGAN identifies the abnormal activity in the WBC, considering the geometric features.Compared with the state-of-the-art methods, the proposed MayGAN achieves 99.8% accuracy, 98.5% precision, 99.7% recall, 97.4% F1-score, and 98.5% Dice similarity coefficient (DSC).
EDMH: Efficient discrete matrix factorization hashing for multi-modal similarity retrieval

Hashing has been an emerging topic and has recently attracted widespread attention in multi-modal similarity search applications. However, most existing approaches rely on relaxation schemes to generate binary codes, leading to large quantization errors. In addition, amounts of existing approaches embed labels into the pairwise similarity matrix, leading to expensive time and space costs and losing category information. To address these issues, we propose an Efficient Discrete Matrix factorization Hashing (EDMH). Specifically, EDMH first learns the latent subspaces for individual modality through matrix factorization strategy, which preserves the semantic structure representation information of each modality. In particular, we develop a semantic label offset embedding learning strategy, improving the stability of label embedding regression. Furthermore, we design an efficient discrete optimization scheme to generate compact binary codes discretely. Eventually, we present two efficient learning strategies EDMH-L and EDMH-S to pursue high-quality hash functions. Extensive experiments on various widely-used databases verify that the proposed algorithms produce significant performance and outperform some state-of-the-art approaches, with an average improvement of 2.50% (for Wiki), 2.66% (for MIRFlickr) and 2.25% (for NUS-WIDE) over the best available results, respectively.
Automated Identification of Toxic Code Reviews Using ToxiCR

Toxic conversations during software development interactions may have serious repercussions on a Free and Open Source Software (FOSS) development project. For example, victims of toxic conversations may become afraid to express themselves, therefore get demotivated, and may eventually leave the project. Automated filtering of toxic conversations may help a FOSS community maintain healthy interactions among its members. However, off-the-shelf toxicity detectors perform poorly on a software engineering dataset, such as one curated from code review comments. To counter this challenge, we present ToxiCR , a supervised learning based toxicity identification tool for code review interactions. ToxiCR includes a choice to select one of the 10 supervised learning algorithms, an option to select text vectorization techniques, eight preprocessing steps, and a large-scale labeled dataset of 19,651 code review comments. Two out of those eight preprocessing steps are software engineering domain specific. With our rigorous evaluation of the models with various combinations of preprocessing steps and vectorization techniques, we have identified the best combination for our dataset that boosts 95.8% accuracy and an 88.9% F1-score in identifying toxic texts. ToxiCR significantly outperforms existing toxicity detectors on our dataset. We have released our dataset, pre-trained models, evaluation results, and source code publicly, which is available at https://github.com/WSU-SEAL/ToxiCR .
A Systematic Literature Review (SLR) on Autonomous Path Planning of Unmanned Aerial Vehicles

UAVs have been contributing substantially to multi-disciplinary research and around 70% of the articles have been published in just about the last five years, with an exponential increase. Primarily, while exploring the literature from the scientific databases for various aspects within the autonomous UAV path planning, such as type and configuration of UAVs, the complexity of their environments or workspaces, choices of path generating algorithms, nature of solutions and efficacy of the generated paths, necessitates an increased number of search keywords as a prerequisite. However, the addition of more and more keywords might as well curtail some conducive and worthwhile search results in the same pursuit. This article presents a Systematic Literature Review (SLR) for 20 useful parameters, organized into six distinct categories that researchers and industry practitioners usually consider. In this work, Web of Science (WOS) was selected to search the primary studies based on three keywords: “Autonomous” + “Path Planning” + “UAV” and following the exclusion and inclusion criteria defined within the SLR methodology, 90 primary studies were considered. Through literature synthesis, a unique perspective to see through the literature is established in terms of characteristic research sectors for UAVs. Moreover, open research challenges from recent studies and state-of-the-art contributions to address them were highlighted. It was also discovered that the autonomy of UAVs and the extent of their mission complexities go hand-in-hand, and the benchmark to define a fully autonomous UAV is an arbitral goal yet to be achieved. To further this quest, the study cites two key models to measure a drone’s autonomy and offers a novel complexity matrix to measure the extent of a drone’s autonomy. Additionally, since preliminary-level researchers often look for technical means to assess their ideas, the technologies used in academic research are also tabulated with references.
Deep reinforcement learning for application scheduling in resource-constrained, multi-tenant serverless computing environments

Serverless computing has sparked a massive interest in both the cloud service providers and their clientele in recent years. This model entails the shift of the entire matter of resource management of user applications to the service provider. In serverless systems, the provider is highly motivated to attain cost efficient usage of their infrastructure, given the granular billing modules involved. However, due to the dynamic and multi-tenant nature of the serverless workloads and systems, achieving efficient resource management while maintaining function performance is a challenging task. Rapid changes in demand levels for applications cause variations in actual resource usage patterns of function instances. This leads to performance variations in co-located functions which compete for similar resources, due to resource contentions. Most existing serverless scheduling works offer heuristic techniques for function scheduling, which are unable to capture the true dynamism in these systems caused by multi-tenancy and varying user request patterns. Further, they rarely consider the often contradicting dual objectives of achieving provider resource efficiency along with application performance. In this article, we propose a novel technique incorporating Deep Reinforcement Learning (DRL) to overcome the aforementioned challenges for function scheduling in a highly dynamic serverless computing environment with heterogeneous computing resources. We train and evaluate our model in a practical setting incorporating Kubeless, an open-source serverless framework, deployed on a 23-node Kubernetes cluster setup. Extensive experiments done on this testbed environment show promising results with improvements of up to 24% and 34% in terms of application response time and resource usage cost respectively, compared to baseline techniques.
Image-to-Character-to-Word Transformers for Accurate Scene Text Recognition

Leveraging the advances of natural language processing, most recent scene text recognizers adopt an encoder-decoder architecture where text images are first converted to representative features and then a sequence of characters via 'sequential decoding'. However, scene text images suffer from rich noises of different sources such as complex background and geometric distortions which often confuse the decoder and lead to incorrect alignment of visual features at noisy decoding time steps. This paper presents I2C2W, a novel scene text recognition technique that is tolerant to geometric and photometric degradation by decomposing scene text recognition into two inter-connected tasks. The first task focuses on image-to-character (I2C) mapping which detects a set of character candidates from images based on different alignments of visual features in an non-sequential way. The second task tackles character-to-word (C2W) mapping which recognizes scene text by decoding words from the detected character candidates. The direct learning from character semantics (instead of noisy image features) corrects falsely detected character candidates effectively which improves the final text recognition accuracy greatly. Extensive experiments over nine public datasets show that the proposed I2C2W outperforms the state-of-the-art by large margins for challenging scene text datasets with various curvature and perspective distortions. It also achieves very competitive recognition performance over multiple normal scene text datasets.