Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation of Medical Images Based on Runge–Kutta Methods

In recent years, deep learning has achieved good results in the semantic segmentation of medical images. A typical architecture for segmentation networks is an encoder–decoder structure. However, the design of the segmentation networks is fragmented and lacks a mathematical explanation. Consequently, segmentation networks are inefficient and less generalizable across different organs. To solve these problems, we reconstructed the segmentation network based on mathematical methods. We introduced the dynamical systems view into semantic segmentation and proposed a novel segmentation network based on Runge–Kutta methods, referred to hereafter as the Runge–Kutta segmentation network (RKSeg). RKSegs were evaluated on ten organ image datasets from the Medical Segmentation Decathlon. The experimental results show that RKSegs far outperform other segmentation networks. RKSegs use few parameters and short inference time, yet they can achieve competitive or even better segmentation results compared to other models. RKSegs pioneer a new architectural design pattern for segmentation networks.
Long short-term memory with activation on gradient

As the number of long short-term memory (LSTM) layers increases, vanishing/exploding gradient problems exacerbate and have a negative impact on the performance of the LSTM. In addition, the ill-conditioned problem occurs in the training process of LSTM and adversely affects its convergence. In this work, a simple and effective method of the gradient activation is applied to the LSTM, while empirical criteria for choosing gradient activation hyperparameters are found. Activating the gradient refers to modifying the gradient with a specific function named the gradient activation function. Moreover, different activation functions and different gradient operations are compared to prove that the gradient activation is effective on LSTM. Furthermore, comparative experiments are conducted, and their results show that the gradient activation alleviates the above problems and accelerates the convergence of the LSTM. The source code is publicly available at https://github.com/LongJin-lab/ACT-In-NLP.
Optimization of convolutional neural network hyperparameters using improved competitive gray wolf optimizer for recognition of static signs of Indian Sign Language

In the last decade, the success achieved by convolutional neural networks (CNN) has changed the trend of research in the computer vision field from manual features to learned features for various applications. However, designing the architecture of CNN and selecting optimum values of its hyperparameters poses a critical challenge because of their massive and complex search space. Automatically picking up the optimal values of hyperparameters may facilitate the designers of CNN-based deep learning models. Swarm intelligence (SI)-based improved competitive gray wolf optimizer (ICGWO) in continuous search space is proposed for optimizing hyperparameters of custom-designed CNN architecture. To evaluate the performance of the ICGWO, we employ 16 benchmark functions. We also assess the effectiveness of the ICGWO by evaluating it with six state-of-art meta-heuristic algorithms. The experiment results show that the ICGWO can obtain the best accuracy on eleven benchmark functions and second best on two benchmark functions. The optimized CNN recognizes static signs of English alphabets in Indian Sign Language (ISL). We also evaluated the optimized CNN on the publicly available ISL dataset for the English alphabet, where it attains 99.93% validation accuracy. In order to evaluate the efficacy of the proposed variant of CGWO, we optimized the hyperparameters of CNN using the existing SI-based search optimization algorithms namely particle swarm optimization, whale optimization algorithm (WOA), differential evolution, and gray wolf optimizer as well.
Dual-focus transfer network for zero-shot learning

Zero-shot learning aims to recognize image categories which are "unseen" in the training phase of image classification models. The key to this task is to transfer the learned knowledge from "seen" classes to "unseen" classes. In order to make the knowledge transfer process more effective, we propose to exploit both the visual and semantic attention mechanisms simultaneously in zero-shot learning tasks. Specifically, a dual-focus transfer network (DFTN) model is proposed to implement attention mechanisms from both the visual and semantic ends in a mapping based zero-shot learning framework with a visual focus transfer (VFT) module and a semantic focus transfer (SFT) module. The VFT module is composed by multi-head self-attention networks, which endows salient parts of images with greater weights at different resolutions of the feature maps. The SFT module generates semantic weights to re-weight semantic attribute features with the guidance of visual representations, where the semantic attributes corresponding to more visual discrimination capability will obtain greater weights. Extensive experiments of zero-shot learning and generalized zero-shot learning on five representative benchmarks demonstrate the superiority of the proposed DFTN model, compared to other state-of-the-art methods.
Library book recommendation with CNN-FM deep learning approach

Purpose Traditional library book recommendation methods are mainly based on association rules and user profiles. They may help to learn about students' interest in different types of books, e.g., students majoring in science and engineering tend to pay more attention to computer books. Nevertheless, most of them still need to identify users' interests accurately. To solve the problem, the authors propose a novel embedding-driven model called InFo, which refers to users' intrinsic interests and academic preferences to provide personalized library book recommendations. Design/methodology/approach The authors analyze the characteristics and challenges in real library book recommendations and then propose a method considering feature interactions. Specifically, the authors leverage the attention unit to extract students' preferences for different categories of books from their borrowing history, after which we feed the unit into the Factorization Machine with other context-aware features to learn students' hybrid interests. The authors employ a convolution neural network to extract high-order correlations among feature maps which are obtained by the outer product between feature embeddings. Findings The authors evaluate the model by conducting experiments on a real-world dataset in one university. The results show that the model outperforms other state-of-the-art methods in terms of two metrics called Recall and NDCG. Research limitations/implications It requires a specific data size to prevent overfitting during model training, and the proposed method may face the user/item cold-start challenge. Practical implications The embedding-driven book recommendation model could be applied in real libraries to provide valuable recommendations based on readers' preferences. Originality/value The proposed method is a practical embedding-driven model that accurately captures diverse user preferences.
Blockchain-Enabled Technique for Privacy- Preserved Medical Recommender System

With the proliferation of privacy issues surrounding the Internet of Medical (IoMT) recommender system data, this study presents a Secure Recommendation and Training Technique (SERTT) which is contingent on a combination of both federated learning and blockchain approaches.Firstly, the study presents a new framework for recording, sorting, and transmission of IoMT data while incorporating blockchain to ensure that the IoMT data transmitted to cloud servers is not made vulnerable by the sharing of the original data.Secondly, by utilizing medical data, the study designs a Recommender Data Management Neural Architecture (REDMANA) which is based on federated learning and model searching training framework.The proposed technique guarantees that the model gradients which are trained by each node are not disclosed all through the universal training and modeling procedure.This makes the raw data inaccessible to either the IoMT data provider or the user.Considering that the model ensures that users can only obtain their necessary inquiries, neither medical data suppliers nor users can obtain access to raw data.Thus, it reduces the issues of safeguarding medical data sets to the issues of securing data processing.Using numerical analysis and experiments the proposed technique is compared with other existing techniques, the result shows that the proposed SERTT system is efficient and secures recommender data management training and modeling technique and that it performs previously designed techniques as compared.
Artificial Intelligence (AI) and Its Application on Banking and Financial Services Sector in India – A Conceptual Study

Adoption of Artificial Intelligence has accelerated significantly in the last five years. To assess the most recent applications and advancements the current study focuses on understanding various applications of this trending technology on Indian banking and financial services sector with real-time examples through various literature reviews, articles, reports, websites, and newspapers.
Metaverse for Public Good: Embracing the Societal Impact of Metaverse Economies

The metaverse is internet’s next tectonic shift. Currently, there are approximately 400 million monthly users of the collection of worlds that make up the metaverse, with projected growth to 5 billion users worldwide by the end of the decade This is expected to drive a total addressable metaverse market of $8–13 trillion by 2030. According to Statista, 15% of the world’s digital economy has already shifted to the metaverse and many “world’s first” metaverse initiatives have been launched over the past two years. The potential applications of a metaverse for public services are vast, including remote governance, service delivery, virtual public spaces, enhanced transparency and accountability, increased social connectedness, improved access to information and education, new job opportunities, innovative forms of entertainment and creativity, and improved accessibility, to name a few. This article explores the practical applications of the metaverse for citizens and some multidisciplinary use cases from around the world. We also examine the national metaverse strategies and opportunities as well as challenges of the metaverse ecosystems, including its potential impact on society and public services. Finally, we propose some recommendations for policymakers and governments to construct forward-thinking metaverse economies.
A Collaborative Compression Scheme for Fast Activity Recognition on Mobile Devices Via Global Compression Ratio Decision

Despite strong representation ability, deep convolutional neural networks (CNNs) are largely hindered in practical human activity recognition (HAR) deployment due to high computational cost, which is often unaffordable on resource-limited wearable devices. In this paper, to bridge the gap between on-device HAR and deep learning, we present a collaborative compression scheme to reduce the runtime of HAR with an acceptable performance degradation, which combines channel pruning and tensor decomposition to simultaneously handle sparsity and low-rankness when fully considering mutual interference in one network consisting of efficient 1-dimensional convolutional kernels. Our method includes two main stages. Concretely, given a target compression ratio, a global compression ratio decision optimization is first performed to automatically decide per-layer compression ratio by measuring compression sensitivity, without requiring labor-exhaustive human intervention. Then a multi-step collaborative compression is iteratively implemented to remove the least important compression unit based on an improved importance metric until the per-layer target compression ratio is attained. Extensive experiments on multiple HAR benchmarks show that our approach considerably outperforms previous compression strategies. For example, it can achieve around 50% FLOPs reduction with only an accuracy drop of 0.25% and 0.15% on UCI-HAR and PAMAP2, respectively. Actual implementation is evaluated on an embedded platform.
On the Generalization of Deep Learning Models in Video Deepfake Detection

The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as “deepfakes”, is making it more challenging to differentiate between real and fake content, while various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set. In this study, we carry out an analysis of different deep learning architectures in an attempt to understand which is more capable of better generalizing the concept of deepfake. According to our results, it appears that Convolutional Neural Networks (CNNs) seem to be more capable of storing specific anomalies and thus excel in cases of datasets with a limited number of elements and manipulation methodologies. The Vision Transformer, conversely, is more effective when trained with more varied datasets, achieving more outstanding generalization capabilities than the other methods analysed. Finally, the Swin Transformer appears to be a good alternative for using an attention-based method in a more limited data regime and performs very well in cross-dataset scenarios. All the analysed architectures seem to have a different way to look at deepfakes, but since in a real-world environment the generalization capability is essential, based on the experiments carried out, the attention-based architectures seem to provide superior performances.
Workshop: Mixing Realities: Cross-Reality Visualization, Interaction, and Collaboration

Cross-reality (CR) systems offer different levels of virtuality/physicality to users and enable them to move back and forth between the reality-virtuality continuums in a seamless way. Immersive augmented, virtual, and mixed reality (AR/VR/MR) head-mounted displays (HMDs) have become the main tools that enable cross-reality interaction. However, working with high-dimensional data is challenging due to complex data structures and their dimensionality. The goal of the workshop is to provide an opportunity for researchers from immersive technologies, HCI, and Visualization fields to share their original ideas and work-in-progress thoughts on the design and evaluation of interactive techniques and systems for effective cross-reality visualization, interaction, and collaboration.
Streamlining Physiological Observations in Immersive Virtual Reality Studies with the Virtual Reality Scientific Toolkit

The open-source Virtual Reality Scientific Toolkit (VRSTK) facil-itates designing and conducting user studies in immersive virtual reality (VR) environments within the Unity 3D real-time development platform. Features include recording and reviewing scenes, collecting and evaluating user-specific data from sensors or in-Vr questionnaires, and remote and multi-user setups. In this paper, we describe the core features of the VRSTK by guiding the reader through a hypothetical user study.
Bringing Instant Neural Graphics Primitives to Immersive Virtual Reality

Neural radiance field (NeRF), in particular, its extension by instant neural graphics primitives is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its enormous potential for virtual reality (VR) applications, there is currently little robust integration of NeRF into typical VR systems available for research and benchmarking in the VR community. In this poster paper, we present an extension to instant neural graphics primitives and bring stereoscopic, high-resolution, low-latency, 6-DoF NeRF rendering to the Unity game engine for immersive VR applications. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Link to the repository: https://github.com/uhhhci/immersive-ngp
Trial through Communication Games for Teamwork Skill Enhancement at First-year Students in Science and Technology Course

The Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan had been promoted a “program of student employability” to cultivate a sense of sociality and independence in university education. Reflecting these trends, educational programs to improve the teamwork skills are in demand. At our university, first-year students are trained in group work skills and cooperative education. For this purpose, Career Planning and Career Design coursesare offered during first and second semesters of first-year education. Enhancement of teamwork skills at an early stage of education is deemed extremely important. In this paper, we report on a training program to enhance teamwork skills for first-year students of science and technology. Communication games were used to evaluate the importance of communication and teamwork. The effect of this trial was analyzed through a student questionnaire. As a result, weconfirmed that students experienced the importance of teamwork for the solving of problems through the trial of teamwork ability using communication games.
Generating an M<sup>2</sup> × N<sup>2</sup> spot array with a dual-period hybrid Dammann grating fabricated using maskless projection lithography

The Dammann grating (DG), which redistributes a collimated laser beam into a spot array with a uniform intensity, is a widely adopted approach for profile measurement. Conventional DGs for dense spot projection are binary phase gratings with precisely designed groove structures, which suffer from low efficiency, poor uniformity, and a hard-to-fabricate fine feature size when utilized for a large field of view (FOV). Here, we propose a new, to the best of our knowledge, hybrid DG architecture consisting of two different grating periods which effectively generates an engineering M 2 × N 2 spot array with a non-complex structural design. As a proof-of-concept, a dual-period hybrid DG with a two-scale grating period ratio of 11.88 μm/95.04 μm (∼1/8) is designed and fabricated as a means to generate a dense 7 2 × 7 2 diffraction spot array with a FOV of 17° × 17°. In addition, the DG exhibits superior performance, with a high efficiency (&gt;60%) and a low non-uniformity (&lt;18%) at a wavelength of 532 nm. This kind of hybrid DG constructed from photoresist patterns with a minimum feature size of ∼1.2 μm can be perfectly fabricated by maskless projection lithography for large-scale and low-cost production. The proposed dual-period hybrid DG can pave the way for depth-perception-related applications such as face unlocking and motion sensing.
A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

In this research paper, we presented a novel approach to detect impulsive sounds in real-time using a combination of Deep CNN and RNN architectures.The proposed approach was evaluated using our collected dataset of impulsive sounds, and the results showed that it outperformed traditional audio signal processing methods in terms of accuracy and F1score.The proposed approach has several advantages over traditional methods, including the ability to handle complex audio patterns, detect impulsive sounds in real-time, and improve its performance with a large dataset of labeled impulsive sounds.However, there are some limitations to the proposed approach, including the requirement for a large amount of labeled data to train effectively, environmental factors that may impact the accuracy of the detection, and high computational requirements.Overall, the proposed approach demonstrates the effectiveness of using a combination of Deep CNN and RNN architectures for impulsive sound detection, with potential applications in various fields such as public safety, industrial settings, and home security systems.The proposed approach is a significant step towards developing automated systems for detecting dangerous events and improving public safety.
Sample-efficient verification of continuously-parameterized quantum gates for small quantum processors

Most near-term quantum information processing devices will not be capable of implementing quantum error correction and the associated logical quantum gate set. Instead, quantum circuits will be implemented directly using the physical native gate set of the device. These native gates often have a parameterization (e.g., rotation angles) which provide the ability to perform a continuous range of operations. Verification of the correct operation of these gates across the allowable range of parameters is important for gaining confidence in the reliability of these devices. In this work, we demonstrate a procedure for sample-efficient verification of continuously-parameterized quantum gates for small quantum processors of up to approximately 10 qubits. This procedure involves generating random sequences of randomly-parameterized layers of gates chosen from the native gate set of the device, and then stochastically compiling an approximate inverse to this sequence such that executing the full sequence on the device should leave the system near its initial state. We show that fidelity estimates made via this technique have a lower variance than fidelity estimates made via cross-entropy benchmarking. This provides an experimentally-relevant advantage in sample efficiency when estimating the fidelity loss to some desired precision. We describe the experimental realization of this technique using continuously-parameterized quantum gate sets on a trapped-ion quantum processor from Sandia QSCOUT and a superconducting quantum processor from IBM Q, and we demonstrate the sample efficiency advantage of this technique both numerically and experimentally.
A Weighted Stacking Ensemble Model With Sampling for Fake Reviews Detection

Customers use reviews as a primary source of information to judge a product or service. Positive reviews help boost companies’ reputations, increasing their revenue by attracting new clients, and increasing the purchasing order size. On the other hand, negative reviews significantly reduce sales, which might be the case due to competitive advantage. Organizations can use fake (i.e., misleading or fraudulent) reviews to generate fast profits by deceiving customers into buying their products. Recently, various methods to assess the legitimacy of reviews have been introduced using advances in machine learning. However, existing methods fall short of achieving highly accurate detection results for unbalanced classes. We aimed to create a spam review identification model using ensemble-based learning while balancing classes using sampling techniques. This article proposes a weighted stacking ensemble model with sampling (WSEM-S) for efficient fake reviews detection. We used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram models to effectively model language data for feature retrieval. The experimental results on three customer reviews datasets: YELPNYC, Deceptive Opinion Spam Corpus (DOSC) v1.4, and Deception datasets show that the proposed model outperforms the conventional machine learning techniques Naïve Bayes, logistic regression, K-nearest neighbor (KNN), random forest, extreme gradient boosting (XGBoost), and convolutional neural network (CNN) as well as the state-of-the-art ensemble models.