Reviewerly/exHarmony huggingface dataset:
BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters' initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets-CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.
Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep learning can predict genetic alterations from pathology slides, but it is unclear how well these predictions generalize to external datasets. We performed a systematic study on Deep-Learning-based prediction of genetic alterations from histology, using two large datasets of multiple tumor types. We show that an analysis pipeline that integrates self-supervised feature extraction and attention-based multiple instance learning achieves a robust predictability and generalizability.
A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research

Sentiment analysis is a critical subfield of natural language processing that focuses on categorizing text into three primary sentiments: positive, negative, and neutral. With the proliferation of online platforms where individuals can openly express their opinions and perspectives, it has become increasingly crucial for organizations to comprehend the underlying sentiments behind these opinions to make informed decisions. By comprehending the sentiments behind customers’ opinions and attitudes towards products and services, companies can improve customer satisfaction, increase brand reputation, and ultimately increase revenue. Additionally, sentiment analysis can be applied to political analysis to understand public opinion toward political parties, candidates, and policies. Sentiment analysis can also be used in the financial industry to analyze news articles and social media posts to predict stock prices and identify potential investment opportunities. This paper offers an overview of the latest advancements in sentiment analysis, including preprocessing techniques, feature extraction methods, classification techniques, widely used datasets, and experimental results. Furthermore, this paper delves into the challenges posed by sentiment analysis datasets and discusses some limitations and future research prospects of sentiment analysis. Given the importance of sentiment analysis, this paper provides valuable insights into the current state of the field and serves as a valuable resource for both researchers and practitioners. The information presented in this paper can inform stakeholders about the latest advancements in sentiment analysis and guide future research in the field.
An improved heuristic mechanism ant colony optimization algorithm for solving path planning

With the development of artificial intelligence algorithms, researchers are attracted to intelligent path planning due to its broad applications and potential development. The ant colony optimization (ACO) algorithm is one of the most widely used methods to solve path planning. However, the traditional ACO has some shortcomings such as low search efficiency, easy stagnation, etc. In this study, a novel variant of ACO named improved heuristic mechanism ACO (IHMACO) is proposed. The IHMACO contains four improved mechanisms including adaptive pheromone concentration setting, heuristic mechanism with directional judgment, improved pseudo-random transfer strategy, and dynamic adjustment of the pheromone evaporation rate. In detail, the adaptive pheromone concentration setting and heuristic mechanism with directional judgment are presented to enhance the purposiveness and reduce turn times of planned path. The improved pseudo-random transfer strategy and dynamic adjustment of the pheromone evaporation rate are introduced to enhance search efficiency and global search ability, further avoiding falling into local optimum. Subsequently, a series of experiments are conducted to test effectiveness of the four mechanisms and verify the performance of the presented IHMACO. Compared with 15 existing approaches for solving path planning, including nine variants of ACO and six commonly used deterministic search algorithms. The experimental results indicate that the relative improvement percentages of the proposed IHMACO in terms of the path turn times are 33.33%, 83.33%, 35.29%, 38.46%, and 38.46% respectively, demonstrating the superiority of IHMACO in terms of the availability and high-efficiency.
Gender equality and central bank independence

Central bank independence (CBI) is vital for maintaining stable economic expectations. Our work employs a panel of 161 economies during the period 1970–2019 to investigate the relationship between gender equality and CBI. By estimating a two-way fixed-effect model, we find that gender equality improvement promotes CBI. This baseline conclusion is confirmed to be robust to fungible indicators, cross-sectional dependence, and endogenous concern. One of these checks, which has a dynamic model specification, shows that CBI’s effect could last for more than three years. Moreover, a set of extended investigations reveals heterogeneity, whereby the positive effect of gender equality on CBI is more profound in economies where there is less political segmentation of the product market, more existing educational resources, and fewer restrictions on accessing information from freely-expressed media. Based on our findings, relevant suggestions are provided for global investors and development banks.
Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems

Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends requests to gather private data from an edge-computing-based IoT cloud storage system across the edge nodes. To solve this problem, a privacy-preservation signaling game for edge-computing-based IoT networks is proposed to characterize the interactions between an IoT node and its corresponding edge node when managing an IoT resource-grant system. Optimal privacy preservation strategies for edge nodes are then theoretically derived. A signaling Q-learning algorithm is then designed to address the problem of achieving convergent equilibrium and game parameters from a practical perspective. The theoretical results are validated using simulations that focus on two statistical points (i.e., the optimal probability of an IoT node requesting maliciously and the posterior probability of an IoT node being malicious). By comparing the proposed signaling Q-learning algorithm with the greedy algorithm benchmark, the proposed algorithm is shown to more effectively decrease the optimal probability of an IoT node sending malicious requests. Thus, privacy preservation for edge-computing-based IoT cloud storage systems can be strengthened.
Mid-circuit correction of correlated phase errors using an array of spectator qubits

Scaling up invariably error-prone quantum processors is a formidable challenge. Although quantum error correction ultimately promises fault-tolerant operation, the required qubit overhead and error thresholds are daunting. In a complementary proposal, colocated, auxiliary "spectator" qubits act as in situ probes of noise and enable real-time, coherent corrections of data qubit errors. We used an array of cesium spectator qubits to correct correlated phase errors on an array of rubidium data qubits. By combining in-sequence readout, data processing, and feedforward operations, these correlated errors were suppressed within the execution of the quantum circuit. The protocol is broadly applicable to quantum information platforms and establishes key tools for scaling neutral-atom quantum processors: mid-circuit readout of atom arrays, real-time processing and feedforward, and coherent mid-circuit reloading of atomic qubits.
Dual-interactive fusion for code-mixed deep representation learning in tag recommendation

Automatic tagging on software information sites is a tag recommendation service. It aims to recommend content-based tags for a software object to help developers make distinctions among software objects. Due to deep correlations between software objects and tags, it is challenging to simultaneously consider the code snippet and text description of a software object. Towards automatic tagging service, we propose a novel CDR4Tag method, Code-mixed Deep Representation learning via dual-interactive fusion for Tag recommendation on software information sites. In CDR4Tag, a code-mixed dual interaction strategy is designed to fuse the deep semantic correlations between software objects and tags into a joint representation space. On the basis of it, the matching probability is predicted to complete our tag recommendation. Comprehensive experimental results on four software information site datasets have demonstrated the effectiveness of our proposed CDR4Tag in tag recommendation compared with the state-of-the-art methods.
Dual color images watermarking scheme with geometric correction based on quaternion FrOOFMMs and LS-SVR

To better describe the features of a color image, the quaternion fractional-order orthogonal Fourier-Mellin moments (QFrOOFMMs) are defined. By combining QFrOOFMMs with least square support vector regression, a dual color images watermarking scheme is investigated based on geometric correction. The original color watermark is encrypted by 3D sine map to enhance watermark security. A pure quaternion matrix can be obtained by performing the lifting wavelet transform, block division and the discrete cosine transform on the original color cover image. The robustness of the watermarking scheme is enhanced by executing the quaternion discrete Fourier transform on the quaternion matrix. Subsequently, the singular value matrix of the watermarked image with embedding operation is yielded by performing the quaternion singular value decomposition on the encryption watermark and the processed cover image. Before extracting the watermark, geometric distortion parameters to realize geometric correction are estimated by the least square support vector regression with the low-order QFrOOFMMs. Experimental results illustrate that the designed dual color images watermarking algorithm performs well in terms of imperceptibility and robustness, where the average PSNR value of the watermarked cover images is 61.6929 dB, and most of the NC values of the extracted watermarks are above 0.8500.
Autonomous travel decision-making: An early glimpse into ChatGPT and generative AI

Generative AI technologies, including large language models (LLMs), have the potential to bring significant advancements to the tourism and hospitality industry through an array of ingenious features. ChatGPT, as a type of generative AI, is a state-of-the-art LLM that is bundled with extensive capabilities. This commentary provides an early snapshot of ChatGPT's ability to enhance the tourist decision-making process in the pre-trip, en-route, and post-trip stages. Particularly, ChatGPT greatly differentiates itself from the traditional travel decision-making process by putting the tourist in the driver's seat to actively probe for highly relevant information through a question-and-answer mode. In this research note, we present different scenarios with cases demonstrating how ChatGPT can improve tourists' experience in these three travel stages, with trip planning efficiency, customized recommendations, 24/7 personal assistant, AI-mediated communication fluency, autonomous guided tours, enriched sharing experience, and AI-induced prolonged satisfaction. In summary, these value-added autonomous services provide tourists with travel solutions that are highly cost-effective, offering customized information that allows them to travel with ease.
What is novel about the Metaverse?

The Metaverse is not a radical departure. It is an incremental evolution. Previous virtual worlds, like Second Life, have laid the groundwork for the development of the Metaverse by providing valuable insights into virtual environments and social interactions. Studies of these earlier platforms contribute to a more nuanced understanding of what the Metaverse is, and what it is not. Based on studies of virtual worlds and by applying inductive processes of reflection and abstraction, we propose a framework that supports the identification of attributes that help differentiate the Metaverse from previous virtual worlds from a user experience perspective. We demonstrate the value of the framework by comparing the Metaverse to Second Life. Our framework and comparative analysis ground the current discussions about the Metaverse deeper in the established discourse on virtual worlds. They also shed light on the potential impact of the Metaverse, the special role of its immersiveness and help us to identify lessons learned that can inform the Metaverse's further development.
Are users willing to embrace ChatGPT? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework

As a rapidly emerging generative AI chatbot, ChatGPT has garnered unprecedented global attention for its advanced AI-based text generation capabilities. However, the issue of ChatGPT acceptance requires further investigation. Prior studies on chatbot acceptance primarily focused on traditional technology acceptance models (TAMs) and did not consider the intelligence features of AI technology. Based on the AI device use acceptance (AIDUA) model and cognitive appraisal theory (CAT), this study proposed a research model to investigate the acceptance of ChatGPT. Participants with experience using ChatGPT were invited to take part in the survey. A total of 500 valid questionnaires were collected through the Credamo survey platform. Our findings reveal compelling associations: social influence, novelty value, and humanness positively correlate with performance expectations, while hedonic motivation, novelty value, and humanness negatively correlate with effort expectations. Both performance and effort expectations contribute to cognitive attitudes. Age, as a control variable, exhibits a significant negative impact on the willingness to reject ChatGPT. Notably, this study expands the current AIDUA framework within chatbot contexts by incorporating perspectives on novelty value, perceived humanness, and cognitive attitudes to examine chatbot acceptance. These insights offer practical implications for the design and development of AI-based chatbots, contributing to the evolving landscape of AI technology acceptance.
Learning and managing context enriched behavior patterns in smart homes

In a society with a growing population of elders, providing efficient and cost-effective long-term care has become central to reducing the economic and societal impact of this demographic shift. Part of the solution to improving the well-being of the elderly at home are smart homes. We examine the role of smart homes in monitoring the activities of the elderly, identifying safety hazards in the home, and understanding environmental changes that may correlate with the deterioration of cognitive and physical health. In this paper, we present LaPlace, a system used to manage context enriched behavior patterns learned in a smart home with sensing devices. LaPlace is based on a formal model to represent intelligible, context enriched behavior patterns, an online adaptive learning algorithm called TIMe which was created for learning such patterns. Context-awareness provides insights about behavior that may otherwise go unnoticed. TIMe is a one-pass algorithm that uses a stream processing model. The TIMe algorithm is presented in this paper along with an extensive evaluation of it using real life datasets.
Multi-Language Handwritten Digits Recognition based on Novel Structural Features

Automated handwritten script recognition is an important task for several applications. In this article, a multi-language handwritten numeral recognition system is proposed using novel structural features. A total of 65 local structural features are extracted and several classifiers are used for testing numeral recognition. Random Forest was found to achieve the best results with an average recognition of 96.73%. The proposed method is tested on six different popular languages, including Arabic Western, Arabic Eastern, Persian, Urdu, Devanagari, and Bangla. In recent studies, single language digits or multiple languages with digits that resemble each other are targeted. In this study, the digits in the languages chosen do not resemble each other. Yet using the novel feature extraction method a high recognition accuracy rate is achieved. Experiments are performed on well-known available datasets of each language. A dataset for Urdu language is also developed in this study and introduced as PMU-UD. Results indicate that the proposed method gives high recognition accuracy as compared to other methods. Low error rates and low confusion rates were also observed using the novel method proposed in this study.
Efficient Lazy Theta* Path Planning over a Sparse Grid to Explore Large 3D Volumes with a Multirotor UAV

Exploring large, unknown, and unstructured environments is challenging for Unmanned Aerial Vehicles (UAVs), but they are valuable tools to inspect large structures safely and efficiently. The Lazy Theta* path-planning algorithm is revisited and adapted to generate paths fast enough to be used in real time and outdoors in large 3D scenarios. In real unknown scenarios, a given minimum safety distance to the nearest obstacle or unknown space should be observed, increasing the associated obstacle detection queries, and creating a bottleneck in the path-planning algorithm. We have reduced the dimension of the problem by considering geometrical properties to speed up these computations. On the other hand, we have also applied a non-regular grid representation of the world to increase the performance of the path-planning algorithm. In particular, a sparse resolution grid in the form of an octree is used, organizing the measurements spatially, merging voxels when they are of the same state. Additionally, the number of neighbors is trimmed to match the sparse tree to reduce the number of obstacle detection queries. The development methodology adopted was Test-Driven Development (TDD) and the outcome was evaluated in real outdoors flights with a multirotor UAV. In the results, the performance shows over 90 percent decrease in overall path generation computation time. Furthermore, our approach scales well with the safety distance increases.
Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models

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Wireless IoT Motion-Recognition Rings and a Paper Keyboard

In this paper, we present a new scheme for implementing virtual keyboards, which uses only two to four motion-recognition rings per hand and a two-dimensional keyboard template (e.g., an A4 size paper with printed key positions). It has the benefit of portability, customizability, and low-cost when compared with existing approaches. Essentially, we have shown that wearing two wireless IoT rings on the middle phalanges of two fingers of each hand, users can input the alphabetic characters into a computing device by typing on a flat paper on a desk, and potentially in mid-air. We have demonstrated that two rings are sufficient in capturing the gestures and motions of all fingers in a typing hand for keystrokes recognition. A single wireless IoT ring, which weighs 7.8 grams, consists of a Bluetooth low energy (BLE) unit, a micro inertial measurement unit (mIMU), and a cell battery. The 3-axes attitude angles and the Z-axis acceleration of each ring are adopted as the features for keystroke recognition. The overall keystroke recognition accuracy rate can reach as high as 94.8% when two IoT rings are worn by a user on each hand; this accuracy rate increases to 98.6%, when four rings are worn on each typing hand.
Real-Time Multi-Scale Face Detector on Embedded Devices

Face detection is the basic step in video face analysis and has been studied for many years. However, achieving real-time performance on computation-resource-limited embedded devices still remains an open challenge. To address this problem, in this paper we propose a face detector, EagleEye, which shows a good trade-off between high accuracy and fast speed on the popular embedded device with low computation power (e.g., the Raspberry Pi 3b+). The EagleEye is designed to have low floating-point operations per second (FLOPS) as well as enough capacity, and its accuracy is further improved without adding too much FLOPS. Specifically, we design five strategies for building efficient face detectors with a good balance of accuracy and running speed. The first two strategies help to build a detector with low computation complexity and enough capacity. We use convolution factorization to change traditional convolutions into more sparse depth-wise convolutions to save computation costs and we use successive downsampling convolutions at the beginning of the face detection network. The latter three strategies significantly improve the accuracy of the light-weight detector without adding too much computation costs. We design an efficient context module to utilize context information to benefit the face detection. We also adopt information preserving activation function to increase the network capacity. Finally, we use focal loss to further improve the accuracy by handling the class imbalance problem better. Experiments show that the EagleEye outperforms the other face detectors with the same order of computation costs, on both runtime efficiency and accuracy.