Reviewerly/exHarmony huggingface dataset:
Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities

The Internet of Things (IoT) contains many smart devices that collect, store, communicate, and process data. IoT implementation has performed novel opportunities in industries, environments, businesses, and homes. Anomaly detection (AD) is helpful in IoT platforms that can recognize and prevent potential system failures, decrease downtime, enhance the quality of products and services, and improve overall operational efficacy. AD systems for IoT data contain statistical modelling, deep learning (DL), and machine learning (ML) approaches which detect patterns and anomalies in the data. This article introduces an Improved Bacterial Foraging Optimization with optimum deep learning for Anomaly Detection (IBFO-ODLAD) in the IoT network. The presented IBFO-ODLAD technique performs data normalization using Z-score normalization approach. For the feature selection process, the IBFO-ODLAD technique designs the IBFO algorithm to choose an optimal subset of features. In addition, the IBFO-ODLAD technique uses multiplicative long short term memory (MLSTM) model for intrusion detection and classification process. Furthermore, the Bayesian optimization algorithm (BOA) was executed for the optimum hyperparameter selection of the MLSTM model. The experimental outcome of the IBFO-ODLAD method was validated on the UNSW NB-15 dataset and UCI SECOM dataset. The experimental outcomes signified the improved performance of the IBFO-ODLAD algorithm with maximum accuracy of 98.89 % and 98.66 % validated on the UNSW NB-15 dataset and UCI SECOM dataset respectively.
Up-Sampling Active Learning: An Activity Recognition Method for Parkinson’s Disease Patients

Parkinson's Disease (PD) is the second most common neurodegenerative disease. With the advancement of technologies of big data, wearable sensing and artificial intelligence, automatically recognizing PD patients' Physical Activities (PAs), health status and disease progress have become possible. Nevertheless, the PA measures are still facing challenges especially in uncontrolled environments. First, it is difficult for the model to recognize the PA of new PD patients. This is because different PD patients have different symptoms, diseased locations and severity that may cause significant differences in their activities. Second, collecting PA data of new PD patients is time-consuming and laborious, which will inevitably result in only a small amount of data of new patients being available. In this paper, we propose a novel up-sampling active learning (UAL) method, which can reduce the cost of annotation without reducing the accuracy of the model. We evaluated the performance of this method on the 18 PD patient activities data set collected from the local hospital. The experimental results demonstrate that this method can converges to better accuracy using a few labeled samples, and achieve the accuracy from 44.3% to 99.0% after annotating 25% of the samples. It provides the possibility to monitor the condition of PD patients in uncontrolled environments.
Recognizing technique variation in rock engravings: ArchCUT3-D for micromorphological analysis

Abstract Ancient rock engravings evoke the interest of archeologists and art historians as an important remnant of human cultures. Traditionally, engraved images are studied based on iconography, iconology, and stylistic characteristics, with little emphasis on execution technology. In contrast, the research method presented in this study strives to characterize the techniques adopted for making rock engravings in ancient times, with technological variations considered as indicators of the engraver’s production process. 3-D scans of two ancient engravings and contemporary graffiti were obtained from Site 25 in Timna Park, Southern Israel. The models were analyzed with ArchCUT3-D, a software specifically developed to precisely evaluate the 3-D micromorphological characteristics of the incisions making up the engraving. The software analyzes the surface micromorphology by extracting 3-D slices of the incisions using an accurate and repeatable method. Our results indicate that different incisions were executed by remarkably distinct techniques of stroking the rock surface with a sharp tool. The identification of discriminant characteristics enabled us to demonstrate the particularities of the engraving operations, such as ergonomic conditions and the level of consistency of the engraving gesture. ArchCUT3-D thus provides a computational method for incision technique recognition through micromorphology specifications, and the reconstruction of engraving gestures and individual production procedures.
Understanding the core technological features of virtual and augmented reality in tourism: a qualitative and quantitative review

ABSTRACTABSTRACTAs virtual tourism and service automation grow, immersive technologies, including virtual reality (VR) and augmented reality (AR), are widely being used in tourism to attain the aspirational tourism experience. To identify the effectiveness of AR and VR applications in the tourism experience, several technological features have been proposed. However, these features are fragmented and inherently confusing in terms of conceptualizations, internal relationships, and effects on the travel experience. Thus, this qualitative and quantitative review clarifies the similarities and differences among high-frequency technological features retrieved from 118 (with 68 being used for meta-analysis) critical articles. Using presence as an anchor, a qualitative review constructs a web of convergent conceptual models (involving presence, immersion, interaction, vividness, and realms of experience economy), while a quantitative (i.e. meta-analytic) review tests their effect sizes. The findings reveal the overall impact of AR and VR technological features on tourism effectiveness, and provide insights for future research into the profound effects of AR and VR technological features on travellers' experiences.KEYWORDS: Virtual realityaugmented realitytraveller experiencepresencetourism Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was partly supported by the Humanities and Social Sciences Youth Foundation, Ministry of Education of the People’s Republic of China [22YJC630017].
Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning

Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.
GFMRC: A machine reading comprehension model for named entity recognition

Recent advances in natural language representation have enabled the internal state of an upstream trained model to migrate to downstream tasks such as named entity recognition (NER). To better utilize pretrained models to perform NER tasks, the latest approach implements NER using the machine reading comprehension (MRC) framework. However, existing MRC approaches do not consider the limited performance of reading comprehension models due to the absence of contextual information in a single sample. Moreover, only word-level features are employed in the feature extraction phase in existing approaches. In this paper, a novel MRC model named GFMRC is proposed to realize NER. GRMRC enhances the MRC model with contextual information and hybrid features. In the preprocessing stage, the samples of the initial MRC dataset are spliced with N-gram information. In the feature extraction stage, global features are extracted for each token using a CNN, and local features are extracted using LSTM. Experiments are carried out on both Chinese datasets and English datasets, and the results demonstrated the effectiveness of the proposed model. The improvements on the English CoNLL 2003, English OntoNotes 5.0, Chinese MSRA, and Chinese OntoNotes 4.0 datasets are 0.07%, 0.23%, 0.04%, and 0.26%, respectively, compared to BERT-MRC+DSC.
Adversarial Attacks on Large Language Model-Based System and Mitigating Strategies: A Case Study on ChatGPT

Machine learning algorithms are at the forefront of the development of advanced information systems. The rapid progress in machine learning technology has enabled cutting-edge large language models (LLMs), represented by GPT-3 and ChatGPT, to perform a wide range of NLP tasks with a stunning performance. However, research on adversarial machine learning highlights the need for these intelligent systems to be more robust. Adversarial machine learning aims to evaluate attack and defense mechanisms to prevent the malicious exploitation of these systems. In the case of ChatGPT, adversarial induction prompt can cause the model to generate toxic texts that could pose serious security risks or propagate false information. To address this challenge, we first analyze the effectiveness of inducing attacks on ChatGPT. Then, two effective mitigating mechanisms are proposed. The first is a training-free prefix prompt mechanism to detect and prevent the generation of toxic texts. The second is a RoBERTa-based mechanism that identifies manipulative or misleading input text via external detection models. The availability of this method is demonstrated through experiments.
Anomaly Detection of Breast Cancer Using Deep Learning

Cancer is one of the deadliest diseases facing humanity, one of the which is breast cancer, and it can be considered one of the primary causes of death for most women. Early detection and treatment can significantly improve outcomes and reduce the death rate and treatment costs. This article proposes an efficient and accurate deep learning-based anomaly detection framework. The framework aims to recognize breast abnormalities (benign and malignant) by considering normal data. Also, we address the problem of imbalanced data, which can be claimed to be a popular issue in the medical field. The framework consists of two stages: (1) data pre-processing (i.e., image pre-processing); and (2) feature extraction through the adoption of a MobileNetV2 pre-trained model. After that classification step, a single-layer perceptron is used. Two public datasets were used for the evaluation: INbreast and MIAS. The experimental results showed that the proposed framework is efficient and accurate in detecting anomalies (e.g., 81.40% to 97.36% in terms of area under the curve). As per the evaluation results, the proposed framework outperforms recent and relevant works and overcomes their limitations.
Launching Your VR Neuroscience Laboratory

The proliferation and refinement of affordable virtual reality (VR) technologies and wearable sensors have opened new frontiers in cognitive and behavioral neuroscience. This chapter offers a broad overview of VR for anyone interested in leveraging it as a research tool. In the first section, it examines the fundamental functionalities of VR and outlines important considerations that inform the development of immersive content that stimulates the senses. In the second section, the focus of the discussion shifts to the implementation of VR in the context of the neuroscience lab. Practical advice is offered on adapting commercial, off-the-shelf devices to a researcher's specific purposes. Further, methods are explored for recording, synchronizing, and fusing heterogeneous forms of data obtained through the VR system or add-on sensors, as well as for labeling events and capturing game play. The reader should come away with an understanding of fundamental considerations that need to be addressed in order to launch a successful VR neuroscience research program.
MedGAN: An adaptive GAN approach for medical image generation

Generative adversarial networks (GANs) and their variants as an effective method for generating visually appealing images have shown great potential in different medical imaging applications during past decades. However, some issues remain insufficiently investigated: many models still suffer from model collapse, vanishing gradients, and convergence failure. Considering the fact that medical images differ from typical RGB images in terms of complexity and dimensionality, we propose an adaptive generative adversarial network, namely MedGAN, to mitigate these issues. Specifically, we first use Wasserstein loss as a convergence metric to measure the convergence degree of the generator and the discriminator. Then, we adaptively train MedGAN based on this metric. Finally, we generate medical images based on MedGAN and use them to build few-shot medical data learning models for disease classification and lesion localization. On demodicosis, blister, molluscum, and parakeratosis datasets, our experimental results verify the advantages of MedGAN in model convergence, training speed, and visual quality of generated samples. We believe this approach can be generalized to other medical applications and contribute to radiologists' efforts for disease diagnosis. The source code can be downloaded at https://github.com/geyao-c/MedGAN.
SkinLink

Applying customized epidermal electronics closely onto the human skin offers the potential for biometric sensing and unique, always-available on-skin interactions. However, iterating designs of an on-skin interface from schematics to physical circuit wiring can be time-consuming, even with tiny modifications; it is also challenging to preserve skin wearability after repeated alteration. We present SkinLink, a reconfigurable on-skin fabrication approach that allows users to intuitively explore and experiment with the circuitry adjustment on the body. We demonstrate SkinLink with a customized on-skin prototyping toolkit comprising tiny distributed circuit modules and a variety of streamlined trace modules that adapt to diverse body surfaces. To evaluate SkinLink's performance, we conducted a 14-participant usability study to compare and contrast the workflows with a benchmark on-skin construction toolkit. Four case studies targeting a film makeup artist, two beauty makeup artists, and a wearable computing designer further demonstrate different application scenarios and usages.
HateMM: A Multi-Modal Dataset for Hate Video Classification

Hate speech has become one of the most significant issues in modern society, having implications in both the online and the offline world. Due to this, hate speech research has recently gained a lot of traction. However, most of the work has primarily focused on text media with relatively little work on images and even lesser on videos. Thus, early stage automated video moderation techniques are needed to handle the videos that are being uploaded to keep the platform safe and healthy. With a view to detect and remove hateful content from the video sharing platforms, our work focuses on hate video detection using multi-modalities. To this end, we curate ~43 hours of videos from BitChute and manually annotate them as hate or non-hate, along with the frame spans which could explain the labelling decision. To collect the relevant videos we harnessed search keywords from hate lexicons. We observe various cues in images and audio of hateful videos. Further, we build deep learning multi-modal models to classify the hate videos and observe that using all the modalities of the videos improves the overall hate speech detection performance (accuracy=0.798, macro F1-score=0.790) by ~5.7% compared to the best uni-modal model in terms of macro F1 score. In summary, our work takes the first step toward understanding and modeling hateful videos on video hosting platforms such as BitChute.
Efficient Federated Learning Method for Cloud-Edge Network Communication

Traditional federated learning (FL) decomposes the server's training tasks to the client for parallel learning, which increases the computational burden on the client and increases the communication overhead for model exchange between the server and the client. Although split federated learning divides the model and trains it on the client and server respectively, the computational pressure of client training is reduced, but the intermediate features and gradient information between the client and the server introduce additional communication costs. At the same time, if the number of clients is too large, the calculation pressure of the server-side parallel training model will also increase greatly. In this paper, we propose a split federated learning method based on client-side clustering, which reduces the additional communication between the client and the server by clustering the client according to the data distribution and compressing the activation vector of the split layer by vector quantization. It can also reduce the computing overhead of the server. The experimental results show that the method proposed in this paper can reduce the communication cost by 51.2% and the training time by 33.3% while maintaining the same accuracy.
Faster Boundary-aware Transformer for Breast Cancer Segmentation

In this paper, we propose an effective network, termed Faster Boundary-aware Transformer (FBAT), to tackle the critical problem in the breast cancer segmentation task: Imprecise segmentation results owing to ambiguous boundaries in medical images. Our motivation is that dear boundaries facilitate precise medical segmentation, so providing additional boundaries information will help network perform better. To do so, we introduce FBAT to comprehensively address the challenging segmentation task. Specifically, we incorporate a new boundary-wise attention block (BAB) into transformers to allow the entire network to effectively capture local details using boundary-wise prior knowledge. Meanwhile, we improve the transformer decoder’s cross-attention mechanism by generating reference points from decoder embeddings and object queries, thereby narrowing the scope of search objects and accelerating the training. We evaluate the proposed network on Breast Ultrasound Images, Breast Ultrasound Dataset B and our own Breast MRI Images datasets. FBAT achieves convincing segmentation results.
Research on ship target detection based on improved YOLOv5 algorithm

Aiming at the problem that the YOLOv5 algorithm has low detection accuracy for small targets and is prone to missed detection and false detection, this paper proposes an improved YOLOv5 algorithm by introducing the coordinate attention mechanism and the bidirectional feature pyramid network for effective waterborne ship detection. This method aims to improve the detection accuracy of waterborne targets. In this study, we focus on constructing a dataset of waterborne ship ships, modifying the YOLOv5 algorithm, and using the PyTorch framework for experiments to evaluate the performance of the proposed method. The experimental results show that the average accuracy of the improved detection algorithm is 99.1%, which is 3.3% higher than that of the original YOLO v5.
Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review

Rapid growth in the generation of data from various sources has made data visualisation a valuable tool for analysing data. However, visual analysis can be a challenging task, not only due to intricate dashboards but also when dealing with complex and multidimensional data. In this context, advances in Natural Language Processing technologies have led to the development of Visualisation-oriented Natural Language Interfaces (V-NLIs). In this paper, we carry out a scoping review that analyses synergies between the fields of Data Visualisation and Natural Language Interaction. Specifically, we focus on chatbot-based V-NLI approaches and explore and discuss three research questions. The first two research questions focus on studying how chatbot-based V-NLIs contribute to interactions with the Data and Visual Spaces of the visualisation pipeline, while the third seeks to know how chatbot-based V-NLIs enhance users’ interaction with visualisations. Our findings show that the works in the literature put a strong focus on exploring tabular data with basic visualisations, with visual mapping primarily reliant on fixed layouts. Moreover, V-NLIs provide users with restricted guidance strategies, and few of them support high-level and follow-up queries. We identify challenges and possible research opportunities for the V-NLI community such as supporting high-level queries with complex data, integrating V-NLIs with more advanced systems such as Augmented Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods.
Automatic Modulation Classification using amalgam CNN-LSTM

The accurate classification of received wireless signals through modulation is vital for both military and civilian uses. The recent evolution in Deep Learning (DL) have led to a growing interest among wireless researchers to apply DL algorithms to modulation classification. To support this, the RadioML2016.10a dataset has been created using the GNU Radio framework, simulating real-world communication channel imperfections. This work proposes the use of two DL models - standalone RNN and combination of Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM). The amalgam CNN-LSTM model showed the highest accuracy that is 95 % for 18 Signal to Noise Ratio (SNR) value. The models have been trained and tested using the RadioML2016.10b dataset.