Reviewerly/exHarmony huggingface dataset:
Survival Prediction for Gastric Cancer via Multimodal Learning of Whole Slide Images and Gene Expression

Gastric cancer (GC) is one of the most common malignancies worldwide. As histopathology tissue analysis is considered as the gold standard in cancer studies, whole slide images (WSIs) have been widely used for GC diagnosis and prognosis, while multimodal studies for GC patients have been very few. Especially, WSIs and gene expression are complementary modalities of data, thus fusion of these two modalities has great potential in the prediction of survival outcomes and other computer-aided tasks, like the mechanism study and clinical treatment for GC patients. However, multimodal learning requires good data fusion strategies and also suffers from the missing data issue. To address these issues, we propose GC-SPLeM, to predict risk scores for patients, which consists of three parts, WSI feature extraction, modal-fusing network, and GNN-based predictor. We conduct experiments on a GC dataset built by ourselves and a public dataset for survival prediction. For both datasets, GC-SPLeM outperforms the state-of-the-art single-modality learning method and multimodal learning method by large margins (over 5% on C-index). We find that the GNN plays an important role in performance enhancement. Through learning the graph of patients, topological structure and neighborhood clinical information are encoded into feature representations of patients. GC-SPLeM not only improves the survival prediction results but also has advantages in dealing with incomplete data over other methods. The Source code, sample data, and gene list of this study are available at https://github.com/constantjxyz/GC-SPLeM.
TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

Telecom fraud detection is of great significance in online social networks. Yet the massive, redundant, incomplete, and uncertain network information makes it a challenging task to handle. Hence, this paper mainly uses the correlation of attributes by entropy function to optimize the data quality and then solves the problem of telecommunication fraud detection with incomplete information. First, to filter out redundancy and noise, we propose an attribute reduction algorithm based on max-correlation and max-independence rate (MCIR) to improve data quality. Then, we design a rough-gain anomaly detection algorithm (MCIR-RGAD) using the idea of maximal consistent blocks to deal with missing incomplete data. Finally, the experimental results on authentic telecommunication fraud data and UCI data show that the MCIR-RGAD algorithm provides an effective solution for reducing the computation time, improving the data quality, and processing incomplete data.
Input–Output-Improved Reservoir Computing Based on Duffing Resonator Processing Dynamic Temperature Compensation for MEMS Resonant Accelerometer

An MEMS resonant accelerometer is a temperature-sensitive device because temperature change affects the intrinsic resonant frequency of the inner silicon beam. Most classic temperature compensation methods, such as algorithm modeling and structure design, have large errors under rapid temperature changing due to the hysteresis of the temperature response of the accelerometer. To address this issue, we propose a novel reservoir computing (RC) structure based on a nonlinear silicon resonator, which is specifically improved for predicting dynamic information that is referred to as the input-output-improved reservoir computing (IOI-RC) algorithm. It combines the polynomial fitting with the RC on the input data mapping ensuring that the system always resides in the rich nonlinear state. Meanwhile, the output layer is also optimized by vector concatenation operation for higher memory capacity. Therefore, the new system has better performance in dynamic temperature compensation. In addition, the method is real-time, with easy hardware implementation that can be integrated with MEMS sensors. The experiment's result showed a 93% improvement in IOI-RC compared to raw data in a temperature range of -20-60 °C. The study confirmed the feasibility of RC in realizing dynamic temperature compensation precisely, which provides a potential real-time online temperature compensation method and a sensor system with edge computing.
FedRFID: Federated Learning for Radio Frequency Fingerprint Identification of WiFi Signals

With the rapid development of the cognitive radio networks, the number of terminal devices has exploded. Massive devices generate a large amount of privacy-sensitive data, typically WiFi signals. This paper proposes a method for Radio frequency (RF) fingerprinting identification of WiFi signals based on federated learning, which trains a cooperative model to complete RF fingerprinting identification without transmitting privacy-sensitive data. The experimental findings on a real-world dataset validate that the strategy described in this study increases the RF fingerprinting identification accuracy in a variety of size circumstances, and ensures that data privacy will not be compromised.
Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. This work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.
Urban solar harvesting: The importance of diffuse shadows in complex environments

Cities are arguably the epicenters of human and technological development, but as the world’s population increasingly concentrates in urban areas, meeting the intense energy demand sustainably and efficiently is becoming critical. Thanks to improving cost-effectiveness and versatility, solar harvesting technologies can be key in transforming cities into energy producers rather than just recipients. Every surface in the cities of the future may have the potential to harvest solar energy. But urban centers are complex environments with constrained areas, dynamic conditions, and many shadow-casting objects. To make the most of available resources, a decisive challenge is building modeling frameworks that are more geometrically flexible and balance realism with efficiency and practicality. In particular, some common simplifications like not modeling Diffuse shadows or not considering their anisotropic nature can be limiting in complex environments. Here, three shadow modeling approaches that consider all anisotropic components of solar radiation are developed and demonstrated in a variety of urban scenes. They are characterized and compared in terms of accuracy, precision, energy implications, and computational cost as a function of resolution and scene complexity. Not modeling Diffuse shadows leads to 3.9–30.9% RMSE in power estimates, and considering Diffuse shadows, but as isotropic-only, leads to 0.94–7.45% RMSE. All approaches converge within 5% deviations and the most accurate achieves <1% deviations. The rasterization-based approach is 7–30 times faster than the iterative analytical approach and 4–20 times faster than the ray-based approach. This work enables new technological capabilities for more diverse and widespread exploration of urban solar harvesting applications.
Using Multilingual Bidirectional Encoder Representations from Transformers on Medical Corpus for Kurdish Text Classification

Technology has dominated a huge part of human life. Furthermore, technology users use language continuously to express feelings and sentiments about things. The science behind identifying human attitudes toward a particular product, service,or topic is one of the most active fields of research, and it is called sentiment analysis. While the English language is making real progress in sentiment analysis daily, other less-resourced languages, such as Kurdish, still suffer from fundamental issues and challenges in Natural Language Processing (NLP). This paper experimentswith the recently published medical corpus using the classical machine learning method and the latest deep learning tool in NLP and Bidirectional Encoder Representations from Transformers (BERT). We evaluated the findings of both machine learning and deep learning. The outcome indicates that BERT outperforms all the machine learning classifiers by scoring (92%) in accuracy, which is by two points higher than machine learning classifiers.
Long-Range Dependence Involutional Network for Logo Detection

Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach.
Should schools ban AI chatbots?

Many educators want to limit access to these powerful tools, but some say it would be better to make them part of the curriculum, finds Jeremy Hsu
Cryptocurrency technology revolution: are Bitcoin prices and terrorist attacks related?

As a financial innovation of the information age, cryptocurrency is a complex concept with clear advantages and disadvantages and is worthy of discussion. Exploring from a terrorism perspective, this study uses the time-varying parameter/stochastic volatility vector autoregression model to explore the risk hedging and terrorist financing capabilities of Bitcoin. Empirical results show that both terrorist incidents and brutality may explain Bitcoin price, but their effects are slightly different. Compared to terrorist brutality, terrorist incidents have a weaker impact on Bitcoin price, showing that Bitcoin investors are more concerned about the number of deaths than the frequency of terrorist attacks. In turn, the impact of Bitcoin price on terrorist attacks is negligible. Bitcoin is a potential means of financing terrorism, but it does not currently play an important role. Our research findings can help investors analyze and predict Bitcoin prices and help improve the theoretical system of anti-terrorist financing, helping to maintain world peace and security.
Combining Phase Congruency and Self-Similarity Features for Multimodal Remote Sensing Image Matching

The structural features using self-similarity have become more popular for multimodal remote sensing image matching. However, mostly because of significant geometric distortions and nonlinear intensity differences between images, these methods produce a limited matching performance when directly applied to multimodal remote sensing images. To address that, we propose a novel feature descriptor named pyramid features of orientated self-similarity (POSS) for multimodal remote sensing image matching, which integrates phase congruency (PC) into the self-similarity model for better encoding structural information. Unlike these conventional self-similarity-based descriptors, the POSS is constructed by using PC instead of image intensity in a pyramid manner and thus has better discriminability and robustness to modality variations. In addition, we design a uniform multimoment of PC (UMMPC) detector for feature detection, which can improve the number, distribution, and repeatability of feature points. Experimental results demonstrate its superior applicability of the proposed method over the state-of-the-art methods, showing much better matching performance.
Improving the Accuracy of Variational Quantum Eigensolvers with Fewer Qubits Using Orbital Optimization

Near-term quantum computers will be limited in the number of qubits on which they can process information as well as the depth of the circuits that they can coherently carry out. To date, experimental demonstrations of algorithms such as the Variational Quantum Eigensolver (VQE) have been limited to small molecules using minimal basis sets for this reason. In this work we propose incorporating an orbital optimization scheme into quantum eigensolvers wherein a parametrized partial unitary transformation is applied to the basis functions set in order to reduce the number of qubits required for a given problem. The optimal transformation is found by minimizing the ground state energy with respect to this partial unitary matrix. Through numerical simulations of small molecules up to 16 spin orbitals, we demonstrate that this method has the ability to greatly extend the capabilities of near-term quantum computers with regard to the electronic structure problem. We find that VQE paired with orbital optimization consistently achieves lower ground state energies than traditional VQE when using the same number of qubits and even frequently achieves lower ground state energies than VQE methods using more qubits.
Mapping the Access of Internet Facility to Schedule Tribe Population—An Approach Towards Digital Education

The impact of COVID-19 remains a major reason behind the incoming of digital education. The schools and higher education institutes have to impart education using digital tools despite the availability of a continuous supply of Internet. The disparity of Internet facility lies everywhere in the India with respect to geographical and profit disparity dependent on income strata. The study analyzes the socioeconomic background of students in national institute of technology in Madhya Pradesh and identify the availability of Internet facilities in ST dominated districts of M.P. The result depicts that the association of ST population with Internet facilities is negative and strongly correlated. The results are helpful planners, policymakers to understand and prioritize the areas that require special attention to provide the ease of digital education.
All-photonic quantum repeater for multipartite entanglement generation

Quantum network applications such as distributed quantum computing and quantum secret sharing represent a promising future network equipped with quantum resources. Entanglement generation and distribution over long distances are critical and unavoidable when utilizing quantum technology in a fully connected network. The distribution of bipartite entanglement over long distances has seen some progress, while the distribution of multipartite entanglement over long distances remains unsolved. Here we report a two-dimensional quantum repeater protocol for the generation of multipartite entanglement over long distances with an all-photonic framework to fill this gap. The entanglement generation yield remains proportional to the transmission efficiency regardless of the number of network users and shows long transmission distance under various numbers of network users. With the improved efficiency and flexibility of extending the number of users, we anticipate that our protocol can work as a significant building block for quantum networks in the future.
Understanding faculty acceptance of online teaching during the COVID-19 pandemic: a Saudi Arabian case study

Purpose This study aims to leverage the Unified Theory of Acceptance and Use of Technology framework developed by Venkatesh et al. (2003) to explore the factors influencing faculty willingness and acceptance of online teaching at a major Saudi Arabian university as we move to a post-COVID-19 new normal. Design/methodology/approach We surveyed business school faculty from a major Saudi Arabian university that transitioned to online learning because of the COVID-19 lockdown. We used partial least square structural equation modeling to examine the factors that impact faculty satisfaction and behavioral intention to continue using online teaching in the future. Findings The results of the study indicated that when faculty perceive that e-learning improves their teaching performance and effectiveness (performance expectancy) and find that online teaching tools are relatively easy to use (effort expectancy), then they are more open to considering online teaching and using digital tools even after the pandemic. Research limitations/implications The study uses a Saudi Arabian sample, so the results of the study may not be generalizable to other countries. The study was cross-sectional in nature; a longitudinal design would help in uncovering more stable relationships and enabling us to draw stronger conclusions. Lastly, the sample size for the study was relatively small, resulting in a loss of power in statistical testing. Notwithstanding these limitations, our study contributes to a greater understanding and appreciation of faculty acceptance of online teaching as we progress to a post-COVID-19 new normal. As such, it should be useful to educators, institutions and policymakers as they seek to reimagine business education going forward. Originality/value The present study is one of the first scholarly studies to focus on exploring e-learning acceptance in a business school from a faculty perspective, considering the natural experiment that forced institutions to move to online teaching irrespective of their prior acceptance or experience with this teaching modality.
Reversible information hiding scheme based on interpolation and histogram shift for medical images

Medical imaging and information management systems require transmission and storage of medical images over the Internet. Many reversible information hiding schemes for image have been proposed to ensure security and availability. In order to avoid the risk of medical information leakage and the medical image distortion, a reversible information hiding scheme based on interpolation and histogram shift for medical images has been proposed in this paper. The proposed adaptive interpolation between neighbor pixels (AIA) technique is used to obtain seed and non-seed pixels, which ensures the reversibility of the scheme while balancing the embedding capacity and the quality of marked image. Then, the image is divided into the region of interest (ROI) and the region of non-interest (NROI). Sensitive information such as electronic patient records (EPR) and electronic signatures of medical images are embedded as secret information. In the ROI, the corresponding bit histogram shift repeated embedding method (CBHSR) is adopted for embedding information to effectively avoid the problem of image distortion caused by histogram stretching. Experimental results show that algorithm not only has high embedding capacity, but also keeps the peak signal-to-noise ratio above 50dB, visual information fidelity and structural similarity above 0.99, which has good subjective and objective image quality.
An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based learning

The aquila optimization algorithm (AO) is an efficient swarm intelligence algorithm proposed recently. However, considering that AO has better performance and slower late convergence speed in the optimization process. For solving this effect of AO and improving its performance, this paper proposes an enhanced aquila optimization algorithm with a velocity-aided global search mechanism and adaptive opposition-based learning (VAIAO) which is based on AO and simplified Aquila optimization algorithm (IAO). In VAIAO, the velocity and acceleration terms are set and included in the update formula. Furthermore, an adaptive opposition-based learning strategy is introduced to improve local optima. To verify the performance of the proposed VAIAO, 27 classical benchmark functions, the Wilcoxon statistical sign-rank experiment, the Friedman test and five engineering optimization problems are tested. The results of the experiment show that the proposed VAIAO has better performance than AO, IAO and other comparison algorithms. This also means the introduction of these two strategies enhances the global exploration ability and convergence speed of the algorithm.
A fusion multi-criteria collaborative filtering algorithm for hotel recommendations

Recommender systems employ information filtering techniques to mitigate the problem of generating personalised recommendations in a digital world that is heavily overloaded with information. Recently, the tourism industry becomes more and more popular and the number of online hotel booking sites with search engines has been growing. However, using such sites can be time consuming and burdensome for potential travellers. Accordingly, this paper proposes a fusion multi-criteria user-item collaborative filtering (MC-UICF) recommendation algorithm that exploits the multi-criteria ratings of users and integrates MC user-based CF and MC item-based CF techniques to produce personalised hotel recommendations. Experimental results on two real-world multi-criteria datasets show the effectiveness of the proposed algorithm by outperforming other baseline single-criteria and multi-criteria CF recommendation approaches in terms of recommendation accuracy and coverage, in particular, when dealing with sparse datasets.