Reviewerly/exHarmony huggingface dataset:
Unsupervised Outlier Detection in IOT Using Deep VAE

The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT's data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data's latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%.
Virtual Reality in Tourism Promotion: A Research Agenda Based on A Bibliometric Approach

Virtual Reality (VR) has the capacity to increase tourists’ responses, compared with other marketing tools. In tourism, it can play a decisive role in its promotion, since it can generate impactful information that will increase the visit intention. However, there are few reviews that focus on VR as a promotional tool in tourism. To overcome this limitation, this work provides a bibliometric analysis of papers from the Web of Science and Scopus databases. The analysis allows us to conclude that although its potential is recognized, the use of VR is infrequent in tourism. We also identified three main avenues for future research: presence and devices, promotional strategies, and segments to explore.
How can we detect Homophobia and Transphobia? Experiments in a multilingual code-mixed setting for social media governance

Homophobia or Transphobia can be defined as the hatred, discomfort, or dislike of lesbian, gay, transgender or bisexual people. Studies have shown that these individuals were more likely to develop mental health issues, likely due to being subjected to more forms of abuse on social media. Hence there is an ardent need to develop automated abusive speech detection systems to tackle the abusive content on social media. There has been an elevation in hate speech or abuse and this paper focuses on the LGBTQIA+ community. Due to the shortage of resources in the said study area, we hypothesize that data augmentation via Pseudolabeling by transliterating the code-mixed text to the parent language will improve the models’ performances on the newly constructed dataset. We put our hypothesis into testing, and studied the performances of several multilingual language models for our cause.
DPCFN: Dual path cross fusion network for medical image segmentation

Thanks to the better performance of U-Net in medical segmentation, many U-Net variants have emerged one after another, but U-Net has the non-negligible drawback that it cannot accurately segment low-level features such as edge regions of images, and in addition this simple skip connection of U-Net itself is still a challenge for global information modeling. To solve the above problems, Channel-wise Cross Fusion Transformer (CCT) and Channel-wise Cross Attention (CCA) are introduced on the basis of U-Net, where CCT is used for cross fusion of U-Net encoders and Transformer, and CCA interacts the fused features with the decoder features to eliminate semantic gaps, naming the network Trans-Net. Another branch network SeU-Net is built to capture details and edge regions, and SE-Attention is added at the skip joints of the network to reinforce important features. The two branches interact through a Cross Residual Feature Block (CRFB). By testing on five datasets, it was experimentally demonstrated that the method proposed in this paper has more accurate segmentation performance.
Sentiment Analysis of Tunisian Users on Social Networks: Overcoming the Challenge of Multilingual Comments in the Tunisian Dialect

The presence of the dialect in the Arabic texts made Arabic sentiment analysis (ASA) a challenging issue, owing to it usually does not follow specific rules in writing systems, especially Tunisian Dialectical (TD) which presents an undertaking challenge due to its complexity, ambiguity, the morphological richness of the language, the absence of contextual information, the code-switching (CS) and mostly the multilingualism phenomena in textual productions. Recently, deep learning models have clearly demonstrated a great success in the field of sentiment analysis (SA). Although, the state-of-the-art accuracy for dialectical sentiment analysis (DSA) still needs improvements regarding contextual information and implicit sentiment expressed in different real cases. To address this challenge, we propose, an efficient Bidirectional LSTM network preceded by a preprocessing stage in order to enhance Tunisian SA, by applying Forward-Backward encapsulate contextual information from multilingual feature sequences. To evaluate our model, and due to the lack of publicly available multilingual resources associated with the TD, we collect different datasets available with different variants of TD to create our own multilingual corpus for sentiment classification. The experimental results based on the evaluation standards “Accuracy”, “Recall” and “F1-score” demonstrate that our model achieves significant improvements over the state-of-art deep learning models and the baseline traditional machine learning methods.
Weakly Flux-Tunable Superconducting Qubit

Superconducting qubits are building blocks for quantum processors, but they suffer from frequency collisions, which hinder the construction of large qubit lattices. This study introduces weakly tunable qubits, whose frequency can be tuned over a small range via external magnetic flux. Through theoretical derivation and experimental demonstration, the authors show that these transmonlike qubits---without losing the desired key properties---can be used to avoid frequency collisions in large qubit systems while minimizing the sensitivity to flux noise. They can also be applied to operate flux-tuned qubit gates.
Use of Digital Resources in Higher Education during COVID-19: A Literature Review

The sudden transition from face-to-face education to remote education under the international-level restrictions imposed as a result of the COVID-19 pandemic—a transition made in the spirit of achieving and developing accessible education—involved the application of methodologically diverse resources and strategies. The objective of this article is to investigate the digital resources that have been applied in higher education institutions, paying special attention to the type and frequency of use of resources. A literature review was carried out on a total of 44 articles. The main results show that the primary resources applied in higher education institutions were videoconferencing tools, educational videos, and virtual platforms. Most higher education institutions made use of free and open access resources. Our primary conclusions posit our observation that the use of digital resources for teaching in an emergency context has not enabled reflection on their use. Such reflection would equip institutions for the optimization of these resources toward their efficient pedagogical application in teaching–learning processes.
Evaluating the Performance of StyleGAN2-ADA on Medical Images

Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impede their utility: computational cost, data requirements, reliable evaluation measures, and training complexity. Our work investigates each of these obstacles in a novel application of StyleGAN2-ADA to high-resolution medical imaging datasets. Our dataset is comprised of liver-containing axial slices from non-contrast and contrast-enhanced computed tomography (CT) scans. Additionally, we utilized four public datasets composed of various imaging modalities. We trained a StyleGAN2 network with transfer learning (from the Flickr-Faces-HQ dataset) and data augmentation (horizontal flipping and adaptive discriminator augmentation). The network's generative quality was measured quantitatively with the Fréchet Inception Distance (FID) and qualitatively with a visual Turing test given to seven radiologists and radiation oncologists. The StyleGAN2-ADA network achieved a FID of 5.22 (±0.17) on our liver CT dataset. It also set new record FIDs of 10.78, 3.52, 21.17, and 5.39 on the publicly available SLIVER07, ChestX-ray14, ACDC, and Medical Segmentation Decathlon (brain tumors) datasets. In the visual Turing test, the clinicians rated generated images as real 42% of the time, approaching random guessing. Our computational ablation study revealed that transfer learning and data augmentation stabilize training and improve the perceptual quality of the generated images. We observed the FID to be consistent with human perceptual evaluation of medical images. Finally, our work found that StyleGAN2-ADA consistently produces high-quality results without hyperparameter searches or retraining.
Multi-view representation learning for data stream clustering

Data stream clustering provides valuable insights into the evolving patterns of long sequences of continuously generated data objects. Most existing clustering methods focus on single-view data streams. In this paper, we propose a multi-view representation learning (MVRL) method for multi-view clustering of data streams. We first introduce an integrated representation learning model to learn a fused sparse affinity matrix across multiple views for spectral clustering. Motivated by the optimization procedure of the integrated representation learning model, we propose three consecutive stages: collaborative representation, the construction of individual global affinity matrices using a mapping function, and the calculation of a fused sparse affinity matrix using Euclidean projection. These stages allow the effective capture of the global and local structures of high-dimensional data objects. Moreover, each stage has a closed-form solution, which determines the upper bound of the computational cost and memory consumption. We then employ the construction residuals of the collaborative representation to adaptively update a dynamic set, which is used to preserve the representative data objects. The dynamic set efficiently transfers previously learned useful knowledge to the arriving data objects. Extensive experimental results on multi-view data stream datasets demonstrate the effectiveness of the proposed MVRL method.
Quadrotor Model for Energy Consumption Analysis

In this paper, a quadrotor dynamic model’s energy efficiency was investigated. A method for the design of the dynamic model which assures energy consumption estimation was presented. This model was developed to analyze the energy efficiency of the quadrotor during each maneuver. A medium-class quadrotor (4.689 kg) was used as a test platform. Thrust force correction factors obtained with FLIGHTLAB software were used to predict object behavior in forward flight. Model validation and long-duration flight tests in outdoor windy conditions are also presented. Monte-Carlo simulation was used to study the influence of uncertainties in model parameters on the simulation reliability. The developed model might be used for practical purposes (for example, energy-efficient coverage path planning).
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis

Speech synthesis has come a long way as current text-to-speech (TTS) models can now generate natural human-sounding speech. However, most of the TTS research focuses on using adult speech data and there has been very limited work done on child speech synthesis. This study developed and validated a training pipeline for fine-tuning state-of-the-art (SOTA) neural TTS models using child speech datasets. This approach adopts a multi-speaker TTS retuning workflow to provide a transfer-learning pipeline. A publicly available child speech dataset was cleaned to provide a smaller subset of approximately 19 hours, which formed the basis of our fine-tuning experiments. Both subjective and objective evaluations were performed using a pretrained MOSNet for objective evaluation and a novel subjective framework for mean opinion score (MOS) evaluations. Subjective evaluations achieved the MOS of 3.95 for speech intelligibility, 3.89 for voice naturalness, and 3.96 for voice consistency. Objective evaluation using a pretrained MOSNet showed a strong correlation between real and synthetic child voices. Speaker similarity was also verified by calculating the cosine similarity between the embeddings of utterances. An automatic speech recognition (ASR) model is also used to provide a word error rate (WER) comparison between the real and synthetic child voices. The final trained TTS model was able to synthesize child-like speech from reference audio samples as short as 5 seconds.
Interactive Contrastive Learning for Self-Supervised Entity Alignment

Self-supervised entity alignment (EA) aims to link equivalent entities across different knowledge graphs (KGs) without the use of pre-aligned entity pairs. The current state-of-the-art (SOTA) self-supervised EA approach draws inspiration from contrastive learning, originally designed in computer vision based on instance discrimination and contrastive loss, and suffers from two shortcomings. Firstly, it puts unidirectional emphasis on pushing sampled negative entities far away rather than pulling positively aligned pairs close, as is done in the well-established supervised EA. Secondly, it advocates the minimum information requirement for self-supervised EA, while we argue that self-described KG's side information (e.g., entity name, relation name, entity description) shall preferably be explored to the maximum extent for the self-supervised EA task. In this work, we propose an interactive contrastive learning model for self-supervised EA. It conducts bidirectional contrastive learning via building pseudo-aligned entity pairs as pivots to achieve direct cross-KG information interaction. It further exploits the integration of entity textual and structural information and elaborately designs encoders for better utilization in the self-supervised setting. Experimental results show that our approach outperforms the previous best self-supervised method by a large margin (over 9% [email protected] absolute improvement on average) and performs on par with previous SOTA supervised counterparts, demonstrating the effectiveness of the interactive contrastive learning for self-supervised EA. The code and data are available at https://github.com/THU-KEG/ICLEA.
Field-Deployable Quantum Memory for Quantum Networking

The quantum Internet will support paradigm shifts in cybersecurity, distributed quantum computing, and sensing. Its realization, however, has been held back by the lack of practical technologies to enable deployment and scalability. To date, most demonstrations have been limited to laboratories with expensive resources such as cryogenic and vacuum systems. The authors overcome this challenge by designing a field-deployable quantum memory, based on warm atomic vapor, that demonstrates high-fidelity performance and robustness to environmental noise. This represents a key technology for distributing quantum entanglement across large-scale quantum networks.
Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images

Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack. However, existing research has paid less attention to the physical attack of object detection in UAV remote sensing images (RSIs). In this work, we carefully analyze the universal adversarial patch attack for multi-scale objects in the field of remote sensing. There are two challenges faced by an adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for an adversarial patch to show an adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide height range of the photography platform causes the size of objects to vary a great deal, which presents challenges for the generation of universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method of object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of the adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Furthermore, we raise the scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures that the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.
Cryptanalysis of an Image Encryption Algorithm Based on a 2D Hyperchaotic Map

Recently, an image encryption scheme based on a 2D hyperchaotic map is proposed. It adopts the permutation-diffusion architecture and consists of three steps, which are permutation, forward diffusion, and backward diffusion. In this paper, we break this cipher with both the chosen-plaintext attack (CPA) and the chosen-ciphertext attack (CCA). According to our analysis, we found the two complex diffusion processes could be simplified into two simple diffusions and a modular addition operation. Based on this, the equivalent key can be obtained with CPA and CCA. Detailed theoretical derivations and the results of experiments confirmed the feasibility of our attack methods. When the image size was 256×256, the running time of the attacks was less than 2 hours on a laptop with a 2.59 GHz Intel Core i7 and 16 GB DDR3 memory. Other sizes of images were also tested, and some rules were found. In addition, the probability of other attacks has also been discussed, and some suggestions for improvements are given. The source codes are publicly available and can be found online.
How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda

Recommender Systems (RS) help the user in the decision-making process when there is a problem of plenty or lack of information. The context-aware recommender systems (CARS) incorporate contextual information to improve the efficiency of RS. Prior works on CARS have compared algorithms and reported application domains. However, the practical challenges involved in CARS implementation with technological advances have not been adequately examined. We chose fitness as the application domain as there is increased user adoption, and the complexity is high. It involves the dynamic interplay of multi-dimensional contexts and technological advances in contextual attributes and incorporates machine learning algorithms in the process of context detection. We conduct textual analysis and conduct a systematic review to identify developments. The concept map demonstrates the components of contextuality, the methods adopted, and the role of AI techniques in amplifying contextuality in the algorithm. The paper identifies the challenges and presents the research opportunities.
Interpretable classification of pathology whole-slide images using attention based context-aware graph convolutional neural network

Whole slide image (WSI) classification and lesion localization within giga-pixel slide are challenging tasks in computational pathology that requires context-aware representations of histological features to adequately infer nidus. The existing weakly supervised learning methods mainly treat different locations in the slide as independent regions and cannot learn potential nonlinear interactions between instances based on i.i.d assumption, resulting in the model unable to effectively utilize context-ware information to predict the labels of WSIs and locate the region of interest (ROI). Here, we propose an interpretable classification model named bidirectional Attention-based Multiple Instance Learning Graph Convolutional Network (ABMIL-GCN), which hierarchically aggregates context-aware features of instances into a global representation in a topology fashion to predict the slide labels and localize the region of lymph node metastasis in WSIs. We verified the superiority of this method on the Camelyon16 dataset, and the results show that the average predicted ACC and AUC of the proposed model after flooding optimization can reach 90.89% and 0.9149, respectively. The average accuracy and ACC score are improved by more than 7% and 4% compared with the existing state-of-the-art algorithms. The results demonstrate that context-aware GCN outperforms existing weakly supervised learning methods by introducing spatial correlations between the neighbor image patches, which also addresses the ‘accuracy-interpretability trade-off’ problem. The framework provides a novel paradigm for the clinical application of computer-aided diagnosis and intelligent systems.
Self-calibrating microring synapse with dual-wavelength synchronization

As a resonator-based optical hardware in analog optical computing, a microring synapse can be straightforwardly configured to simulate the connection weights between neurons, but it faces challenges in precision and stability due to cross talk and environmental perturbations. Here, we propose and demonstrate a self-calibration scheme with dual-wavelength synchronization to monitor and calibrate the synaptic weights without interrupting the computation tasks. We design and fabricate an integrated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mn>4</mml:mn> <mml:mo>×</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> microring synapse and deploy our self-calibration scheme to validate its effectiveness. The precision and robustness are evaluated in the experiments with favorable performance, achieving 2-bit precision improvement and excellent robustness to environmental temperature fluctuations (the weights can be corrected within 1 s after temperature changes 0.5°C). Moreover, we demonstrate matrix inversion tasks based on Newton iterations beyond 7-bit precision using this microring synapse. Our scheme provides an accurate and real-time weight calibration independently parallel from computations and opens up new perspectives for precision boost solutions to large-scale analog optical computing.