Reviewerly/exHarmony huggingface dataset:
Secure and Efficient Smart Healthcare System Based on Federated Learning

The rapid development of smart healthcare system in the Internet of Things (IoT) has made the early detection of many chronic diseases more convenient, quick, and economical. However, when healthcare organizations collect users’ health data through deployed IoT devices, there are issues of compromising users’ privacy. In view of this situation, this paper introduces federated learning technology to solve the problem of data security. In this paper, we consider the two main problems of federated learning applications in IoT smart healthcare system: (1) how to reduce the time overhead of system running and (2) how to authenticate that the user device uploading data is deployed by the system itself. To solve the above problems, we propose the first federated learning scheme based on full dynamic secret sharing. First, we use a two-mask protocol to keep the user’s local model parameters confidential during federated learning. Then, based on homogeneous linear recursive equation, homomorphic hash function, and elliptic curve cryptosystem, the full dynamic secret sharing and user identity authentication are realized. In addition, our scheme allows users to join or quit during training. Finally, we have carried out simulation test on this scheme. The experimental results show that the efficiency of our scheme is improved by about 60% on average in the case of no user dropping and by about 30% in the case of some users dropping.
A collaborative cuckoo search algorithm with modified operation mode

Cuckoo search (CS) is a nature-inspired algorithm that has shown its favorable potential for solving complex optimization problems. Nevertheless, there is a lack of effective information sharing between individuals in CS, which would doubtless limit its achievable performance. While several CS variants have considered this issue, they commonly strengthen the information sharing in just one of the two search parts (i.e., global and local search parts). In this paper, to further address the above issue and to get a more rational allocation of the workloads of global search and local search, a new CS variant called collaborative CS with modified operation mode (CCSMO) is proposed. One novelty is that a collaborative mechanism is presented to strengthen the information sharing and collaboration between individuals in both search parts, and correspondingly, two new iterative strategies are introduced respectively for global search and local search. Another novelty is that the conventional operation mode adopted by almost all existing CS-based algorithms is modified for more rationally allocating the workloads of global search and local search. To validate the performance of CCSMO, extensive experiments and comparisons between CCSMO and 17 state-of-the-art algorithms are made on two popular test suites from IEEE Conference on Evolutionary Computation (CEC). Besides, the algorithm is also applied to solve three engineering design problems and one large-scale combined heat and power economic dispatch problem. The results demonstrate that CCSMO can offer highly competitive performance. Additionally, the time complexity, search behavior, modification effectiveness, and parameter sensitivity of CCSMO are also evaluated.
Understanding the Basis of Graph Convolutional Neural Networks via an Intuitive Matched Filtering Approach [Lecture Notes]

Graph convolutional neural networks (GCNNs) are becoming a model of choice for learning on irregular domains. However, due to the black-box nature of NNs, their underlying principles are rarely examined in depth.
Three-dimensional continuous picking path planning based on ant colony optimization algorithm

Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picking robots. And a good fruit-picking path determines the efficiency of fruit-picking. Currently, most picking path planning is a point-to-point approach, which means that the path needs to be re-planned after each completed path planning. If the picking path planning method of the fruit-picking robot is changed from a point-to-point approach to a continuous picking method, it will significantly improve its picking efficiency. The optimal sequential ant colony optimization algorithm(OSACO) is proposed for the path planning problem of continuous fruit-picking. The algorithm adopts a new pheromone update method. It introduces a reward and punishment mechanism and a pheromone volatility factor adaptive adjustment mechanism to ensure the global search capability of the algorithm, while solving the premature and local convergence problems in the solution process. And the multi-variable bit adaptive genetic algorithm is used to optimize its initial parameters so that the parameter selection does not depend on empirical and the combination of parameters can be intelligently adjusted according to different scales, thus bringing out the best performance of the ant colony algorithm. The results show that OSACO algorithms have better global search capability, higher quality of convergence to the optimal solution, shorter generated path lengths, and greater robustness than other variants of the ant colony algorithm.
Hand Gesture Recognition Used for Functioning System Using OpenCV

Recently much attention has been paid to the design of intelligent and natural user-computer interfaces. Hand Gesture Recognition systems has been developed continuously as its ability to interact with the machines. Now-a-days the news of metaverse ecosystem has increased the number of system in gesture recognition. Gestures are used to communicate with the PCs in a virtual environment. In this project Hand gestures are used to communicate information non-verbally which are free of expression to do a particular task. Here the hand gestures are recognized by using hand skeleton recognition using mediapipe library in python. In this project, the PC camera will record live video and recognizes the hand gestures based on which a particular functionality will take place. This project will present virtual keyboard, calculator and control system’s volume using hand gestures recognition technique by coding in Python using the OpenCV library.
Adopting a metaverse-based workspace to support research team collaboration: a pilot study from an academic health informatics laboratory

This study aimed to understand how a metaverse-based (virtual) workspace can be used to support the communication and collaboration in an academic health informatics lab.A survey of lab members (n = 14) was analyzed according to a concurrent triangulation mixed methods design. The qualitative survey data were organized according to the Capability, Opportunity, Motivation, Behavior (COM-B) model and combined to generate personas that represent the overall types of lab members. Additionally, scheduled work hours were analyzed quantitatively to complement the findings of the survey feedback.Four personas, representative of different types of virtual workers, were developed using the survey responses. These personas reflected the wide variety of opinions about virtual work among the participants and helped to categorize the most common feedback. The Work Hours Schedule Sheet analysis showed the low number of possible collaboration opportunities that were utilized compared to the number available.We found that informal communication and co-location were not supported by the virtual workplace as we had originally planned. To solve this issue, we offer 3 design recommendations for those looking to implement their own virtual informatics lab. First, labs should establish common goals and norms for virtual workplace interactions. Second, labs should carefully plan the virtual space layout to maximize communication opportunities. Finally, labs should work with their platform of choice to address technical limitations for their lab members to improve user experience. Future work includes a formal, theory-guided experiment with consideration on ethical and behavioral impact.
Brain Tumor Segmentation Using Modified Double U-Net Architecture

Children and the elderly are most susceptible to brain tumors. It's deadly cancer caused by uncontrollable brain cell proliferation inside the skull. The heterogeneity of tumor cells makes classification extremely difficult. Image segmentation has been revolutionized because of the Convolution Neural Network (CNN), which is especially useful for medical images. Not only does the U-Net succeed in segmenting a wide range of medical pictures in general, but also in some particularly difficult instances. However, we uncovered severe problems in the standard models that have been used for medical image segmentation. As a result, we applied modification and created an efficient U-net-based deep learning architecture, which was examined on the Brain Tumor dataset from the Kaggle repository, which consists of over 1500 images of brain tumors together with their ground truth. After comparing our model to comparable cutting-edge approaches, we determined that our design resulted in at least a 10% improvement, showing that it generates more efficient, better, and robust results.
Clinical capability of modern brain tumor segmentation models

State-of-the-art automated segmentation methods achieve exceptionally high performance on the Brain Tumor Segmentation (BraTS) challenge, a dataset of uniformly processed and standardized magnetic resonance generated images (MRIs) of gliomas. However, a reasonable concern is that these models may not fare well on clinical MRIs that do not belong to the specially curated BraTS dataset. Research using the previous generation of deep learning models indicates significant performance loss on cross-institutional predictions. Here, we evaluate the cross-institutional applicability and generalzsability of state-of-the-art deep learning models on new clinical data.We train a state-of-the-art 3D U-Net model on the conventional BraTS dataset comprising low- and high-grade gliomas. We then evaluate the performance of this model for automatic tumor segmentation of brain tumors on in-house clinical data. This dataset contains MRIs of different tumor types, resolutions, and standardization than those found in the BraTS dataset. Ground truth segmentations to validate the automated segmentation for in-house clinical data were obtained from expert radiation oncologists.We report average Dice scores of 0.764, 0.648, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively, in the clinical MRIs. These means are higher than numbers reported previously on same institution and cross-institution datasets of different origin using different methods. There is no statistically significant difference when comparing the dice scores to the inter-annotation variability between two expert clinical radiation oncologists. Although performance on the clinical data is lower than on the BraTS data, these numbers indicate that models trained on the BraTS dataset have impressive segmentation performance on previously unseen images obtained at a separate clinical institution. These images differ in the imaging resolutions, standardization pipelines, and tumor types from the BraTS data.State-of-the-art deep learning models demonstrate promising performance on cross-institutional predictions. They considerably improve on previous models and can transfer knowledge to new types of brain tumors without additional modeling.
The Generalization of Non-Negative Matrix Factorization Based on Algorithmic Stability

The Non-negative Matrix Factorization (NMF) is a popular technique for intelligent systems, which can be widely used to decompose a nonnegative matrix into two factor matrices: a basis matrix and a coefficient one, respectively. The main objective of NMF is to ensure that the operation results of the two matrices are as close to the original matrix as possible. Meanwhile, the stability and generalization ability of the algorithm should be ensured. Therefore, the generalization performance of NMF algorithms is analyzed from the perspective of algorithm stability and the generalization error bounds are given, which is named AS-NMF. Firstly, a general NMF prediction algorithm is proposed, which can predict the labels for new samples, and then the corresponding loss function is defined further. Secondly, the stability of the NMF algorithm is defined according to the loss function, and two generalization error bounds can be obtained by employing uniform stability in the case where U is fixed and it is not fixed under the multiplicative update rule. The bounds numerically show that its stability parameter depends on the upper bound on the module length of the input data, dimension of hidden matrix and Frobenius norm of the basis matrix. Finally, a general and stable framework is established, which can analyze and measure generalization error bounds for the NMF algorithm. The experimental results demonstrate the advantages of new methods on three widely used benchmark datasets, which indicate that our AS-NMF can not only achieve efficient performance, but also outperform the state-of-the-art of recommending tasks in terms of model stability.
Blockchain Operations in the Presence of Security Concerns

Problem definition: A blockchain payment system, such as Bitcoin or Ethereum, validates electronic transactions and stores them in a chain of blocks without a central authority. Miners with computing power compete for the rights to create blocks according to a preset protocol, referred to as hashing or mining, and, in return, earn fees paid by users who submit transactions. Because of security concerns caused by decentralization, a transaction is confirmed after a number of additional blocks are subsequently extended to the block containing it. This confirmation latency introduces an intricate interplay between miners and users. This paper provides approximate system equilibria and studies optimal designs of a blockchain. Methodology/results: The hashing process is essentially a single-server queue with batch services based on a fee-based priority discipline, and confirmation latency adds complexity to the equilibrium behavior and optimal design. We analyze how miners’ participation decisions interact with users’ participation and fee decisions and identify optimal designs when the goal is to maximize the throughput or social welfare. We validate our model and conduct numerical studies using data from Bitcoin. Managerial implications: By incorporating security issues, we uncover the interdependence of the decisions between users and miners and the driver for nonzero entrance fees in practice. We show that miners and users may end up in either a vicious or virtuous cycle, depending on the initial system state. By allowing the entrance fee to be a design parameter, we are able to establish that it is optimal to simply run a blockchain system at its full capacity and a block size as small as possible. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 16200019, 16200617, 16200821, 16208120, and 16214121]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2023.1197 .
Efficient Layer-Wise N:M Sparse CNN Accelerator with Flexible SPEC: Sparse Processing Element Clusters

Recently, the layer-wise N:M fine-grained sparse neural network algorithm (i.e., every M-weights contains N non-zero values) has attracted tremendous attention, as it can effectively reduce the computational complexity with negligible accuracy loss. However, the speed-up potential of this algorithm will not be fully exploited if the right hardware support is lacking. In this work, we design an efficient accelerator for the N:M sparse convolutional neural networks (CNNs) with layer-wise sparse patterns. First, we analyze the performances of different processing element (PE) structures and extensions to construct the flexible PE architecture. Second, the variable sparse convolutional dimensions and sparse ratios are involved in the hardware design. With a sparse PE cluster (SPEC) design, the hardware can efficiently accelerate CNNs with the layer-wise N:M pattern. Finally, we employ the proposed SPEC into the CNN accelerator with flexible network-on-chip and specially designed dataflow. We implement hardware accelerators on Xilinx ZCU102 FPGA and Xilinx VCU118 FPGA and evaluate them with classical CNNs such as Alexnet, VGG-16, and ResNet-50. Compared with existing accelerators designed for structured and unstructured pruned networks, our design achieves the best performance in terms of power efficiency.
Can AI-Oriented Requirements Enhance Human-Centered Design of Intelligent Interactive Systems? Results from a Workshop with Young HCI Designers

In this paper, we show that the evolution of artificial intelligence (AI) and its increased presence within an interactive system pushes designers to rethink the way in which AI and its users interact and to highlight users’ feelings towards AI. For novice designers, it is crucial to acknowledge that both the user and artificial intelligence possess decision-making capabilities. Such a process may involve mediation between humans and artificial intelligence. This process should also consider the mutual learning that can occur between the two entities over time. Therefore, we explain how to adapt the Human-Centered Design (HCD) process to give centrality to AI as the user, further empowering the interactive system, and to adapt the interaction design to the actual capabilities, limitations, and potentialities of AI. This is to encourage designers to explore the interactions between AI and humans and focus on the potential user experience. We achieve such centrality by extracting and formalizing a new category of AI requirements. We have provocatively named this extension: “Intelligence-Centered”. A design workshop with MsC HCI students was carried out as a case study supporting this change of perspective in design.
Differential evolution-based transfer rough clustering algorithm

Abstract Due to well processing the uncertainty in data, rough clustering methods have been successfully applied in many fields. However, when the capacity of the available data is limited or the data are disturbed by noise, the rough clustering algorithms always cannot effectively explore the structure of the data. Furthermore, rough clustering algorithms are usually sensitive to the initialized cluster centers and easy to fall into local optimum. To resolve the problems mentioned above, a novel differential evolution-based transfer rough clustering (DE-TRC) algorithm is proposed in this paper. First, transfer learning mechanism is introduced into rough clustering and a transfer rough clustering framework is designed, which utilizes the knowledge from the related domain to assist the clustering task. Then, the objective function of the transfer rough clustering algorithm is optimized by using the differential evolution algorithm to enhance the robustness of the algorithm. It can overcome the sensitivity to initialized cluster centers and meanwhile achieve the global optimal clustering. The proposed algorithm is validated on different synthetic and real-world datasets. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with both traditional rough clustering algorithms and other state-of-the-art clustering algorithms.
Delineation of hydrothermal alteration zones associated with mineral deposits, using remote sensing and airborne geophysics data. A case study: El-Bakriya area, Central Eastern Desert, Egypt

Abstract El-Bakriya area is situated in the Central Eastern Desert of Egypt. It includes several mineral occurrences and/or deposits. Data from remote sensing and airborne geophysics (gamma-ray and magnetic) were combined to identify the hydrothermal alteration zones and structures related to these occurrences. Various processing of ASTER satellite images revealed fifteen zones of high probability for metallic mineralizations. Airborne gamma-ray spectrometric data clarify K-enrichment zones, which are correlated geologically with the distribution of calc-alkaline, alkali feldspar granites (Younger granites), and calc-alkaline quartzdiorites to granodiorites (Older granites), as well as basement metavolcanics and metasediments. An integrated hydrothermal alteration map was constructed, based on both ASTER remote sensing and airborne gamma-ray spectrometric data. This map delineates some metallic mineral occurrences and/or deposits, which are located in the detected hydrothermal alteration zones. Besides, it displays new potential zones for mineralization, such as: Gabal (G.) El-Shalul, G. El-Bakriya, G. Siwat El-Arsha and G. Umm Bisilla. The geologic and airborne magnetic maps demonstrate five structural lineament trends of E–W, ENE–WSW, NE–SW, NW–SE, and NNW–SSE directions, which might act as pathways to transport the hydrothermal solutions in the study area.
Intelligent Recognition of Key Earthquake Emergency Chinese Information Based on the Optimized BERT-BiLSTM-CRF Algorithm

To address the problems of the text of earthquake emergency information keeps changing incrementally with the time of an earthquake’s occurrence and there being more and more information categories, thus making it difficult to identify earthquake emergency key information, this paper proposes an intelligent recognition algorithm of earthquake emergency information based on the optimized BERT-BiLSTM-CRF algorithm. Based on the historical seismic emergency information dataset of the past 10 years, first, the BIO sequence labeling method is used to classify the seismic entities, and the BERT pretraining model is constructed to represent the seismic emergency text with sentence-level feature vectors. The BiLSTM algorithm is used to obtain the contextual information of the bidirectional seismic emergency text, and we introduce the attention mechanism to enhance the recognition effect of the seismic emergency key information in the statements. Finally, we use conditional randomization to enhance the recognition of earthquake emergency key information in the utterance. The conditional randomization algorithm is applied to extract the dependency relationship between adjacent vectors and improve the accuracy identification to realize the intelligent recognition of earthquake emergency information. The experimental results show that our model can extract earthquake emergency information from online media efficiently and accurately, with better performance than other baseline models.