Reviewerly/exHarmony huggingface dataset:
Medical lesion segmentation by combining multimodal images with modality weighted UNet

Automatic segmentation of medical lesions is a prerequisite for efficient clinic analysis. Segmentation algorithms for multimodal medical images have received much attention in recent years. Different strategies for multimodal combination (or fusion), such as probability theory, fuzzy models, belief functions, and deep neural networks, have also been developed. In this paper, we propose the modality weighted UNet (MW-UNet) and attention-based fusion method to combine multimodal images for medical lesion segmentation.MW-UNet is a multimodal fusion method which is based on UNet, but we use a shallower layer and fewer feature map channels to reduce the amount of network parameters, and our method uses the new multimodal fusion method called fusion attention. It uses weighted sum rule and fusion attention to combine feature maps in intermediate layers. During training, all the weight parameters are updated through backpropagation like other parameters in the network. We also incorporate residual blocks into MW-UNet to further improve segmentation performance. The comparison between the automatic multimodal lesion segmentations and the manual contours was quantified by (1) five metrics including Dice, 95% Hausdorff Distance (HD95), volumetric overlap error (VOE), relative volume difference (RVD), and mean-Intersection-over-Union (mIoU); (2) Number of parameters and flops to calculate the complexity of the network.The proposed method is verified on ZJCHD, which is the data set of contrast-enhanced computed tomography (CECT) for Liver Lesion Segmentation taken from Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China. For accuracy evaluation, we use 120 patients with liver lesions from ZJCHD, of which 100 are used for fourfold cross-validation (CV) and 20 are used for hold-out (HO) test. The mean Dice was 90.55±14.44%$90.55 \pm 14.44\%$ and 89.31±19.07%$89.31 \pm 19.07\%$ for HO and CV tests, respectively. The corresponding HD95, VOE, RVD, and mIoU of the two tests are 1.95 ± 1.83 and 2.67 ± 3.35 mm, 13.11 ± 15.83 and 13.13±18.52%$13.13 \pm 18.52 \%$ , 12.20 ± 18.20 and 13.00±21.82%$13.00 \pm 21.82 \%$ , and 83.79 ± 15.83 and 82.35±20.03%$82.35 \pm 20.03 \%$ . The parameters and flops of our method is 4.04 M and 18.36 G, respectively.The results show that our method performs well on multimodal liver lesion segmentation. It can be easily extended to other multimodal data sets and other networks for multimodal fusion. Our method is the potential to provide doctors with multimodal annotations and assist them with clinical diagnosis.
Dedicated Farm-Haystack Question Answering System for Pregnant Women and Neonates Using Corona Virus Literature

The global pandemic, COVID-19 has made it more important to quickly and precisely retrieve critical information for effective use by specialists in a wide range of fields. Domain question answering system will work or produce good results to certain extent but still favour more to the pretrained dataset. In this work we target developing a customized question answering framework that can assist the medical network with retrieval of answers to important logical questions like risk factors, effective modes of communication, various treatment options for target high-risk populaces like pregnant women and neonates.The proposed framework uses a customized Farm-Haystack question answering system and introduces a novel pipeline architecture using latent dirichlet allocation and bidirectional encoder representation from transformers for embedding the information. The system is modeled to produce the best and reliable answers for the delicate population, which requires more efficient answers rather than generic population, which can be answered using pretrained systems. In this context, the system has showed the accurate and compact answers for different inquiries related to the sensitive population.
Semi-supervised Latent Block Model with pairwise constraints

Co-clustering aims at simultaneously partitioning both dimensions of a data matrix. It has demonstrated better performances than one-sided clustering for high-dimensional data. The Latent Block Model (LBM) is a probabilistic model for co-clustering based on mixture models that has proven useful for a broad class of data. In this paper, we propose to leverage prior knowledge in the form of pairwise semi-supervision in both row and column space to improve the clustering performances of the algorithms derived from this model. We present a general probabilistic framework for incorporating must link and cannot link relationships in the LBM based on Hidden Markov Random Fields. We instantiate this framework on a model for count data and present two inference algorithms based on Variational and Classification EM. Extensive experiments on simulated data and on real-world attributed networks confirm the interest of our approach and demonstrate the effectiveness of our algorithms.
Application of Artificial Intelligence Computing in the Universal Design of Aging and Healthy Housing

Intelligent control technology is not only the use of the so-called highly sophisticated technology in the daily life of the elderly but also control services according to the individual needs of the elderly. This paper combines research in psychology and ergonomics to explore how to use the living space to build indoor scenarios that influence the behavioural and psychological changes of the elderly based on satisfying functionality. The external environment influences the user's perception, and the perception determines the user's behaviour. Through the construction of scenarios, objects and people can interact with each other, thus achieving the objective of "solitude but not loneliness" for the elderly living alone and providing a modern ageing environment with high safety, convenience, quality, and comfort for the elderly.
A Survey of Security Aggregation

Machine learning (ML) requires the collection of large amounts of data to train robust predictive models. Federated learning (FL) allows sensitive data to be kept on the client side to train a shared model, but shared models still pose privacy concerns. Secure aggregation is an important algorithm for securing federation learning by being able to compute the sum of client-side model updates without revealing information about individual client updates. In this study, we investigate the results of secure aggregation protocols in recent years and review the research results in the field of secure aggregation according to secret sharing, differential privacy, and homomorphic encryption mechanisms. On this basis, we compare and analyze the advantages and disadvantages of different mechanisms and then evaluate the security aggregation protocols in terms of security, dynamic user robustness, computational cost, and communication cost. Finally, we provide an outlook on the future development of secure aggregation protocols and give possible future research directions.
Onion Encrypted Multilevel Security Framework for Public Cloud

Cloud computing has evolved as a renowned technology in the twenty-first century for delivering data service requirements at a low cost, with minimal effort, and with a high level of scalability. Because the Public Cloud has some vulnerability issues, hackers can easily gain unauthorized access to data, launch fabrication attacks, and conduct a variety of other internal and external attacks. In this paper, a multilevel level security framework based on Onion encryption and Needham-Schroeder Symmetric Key Protocol was proposed. This framework generates session keys to maintain data confidentiality, authentication and mitigates interception, fabrication attacks, Onion encryption was used to mitigate any attempt to modify or manipulate paths for gaining access, and a Blockchain-based decentralized framework to maintain data integrity.
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The critical behavior of the light-matter interactions in controllable systems provides an attractive direction for quantum manipulation. Inspired by “the broken symmetry will give rise to the quantum phase transitions (QPTs)”, the open-system QPTs of the Tavis–Cummings (TC) interactions with an extra second-order nonlinearity (SONL) are explored, where the systemic symmetry is reduced from U(1) to Z2. The critical behavior of a generic spins-microwave hybrid system is investigated using the standard mean-field approach in conjunction with different factors such as the SONL phase, the spins’ collective decay and dephasing, cavity dissipation, and detunings. The results show that all of the three decoherence factors and the detunings will affect its critical points, and the phase of SONL can especially exhibit a protective effect on QPTs. This study provides an encouraging example for mimicking the interesting light-matter interactions, and may further evoke some potential applications in quantum manipulations.
Discrete-time ZNN-based noise-handling ten-instant algorithm solving Yang-Baxter-like matrix equation with disturbances

Time-variant Yang-Baxter-like matrix equation (YBLME) with the disturbances of noises is the hotspot in various scientific disciplines. The existing research, either can not handle the noise, or rely on the build-in numerical algorithm provided by MATLAB. Therefore, it is necessary to further study the digital-computer discrete solution of this problem. In this paper, the authors propose a noise-handling ten-instant discrete solution for the time-variant YBLME. By defining the matrix-form error function, Zhang neural network (ZNN) is exploited to design the continuous-time noise-handling ZNN model. For potential digital hardware (i.e., digital computer) realization, a discrete-time ZNN-based noise-handling ten-instant (DTZNH10i) algorithm, which is capable of handling three types of noises, is proposed on the basis of a Zhang time discretization (ZTD) formula. The convergence and precision of the proposed DTZNH10i algorithm are investigated and discussed. Theoretical analyses show the correctness and effectiveness of the proposed algorithm. The paper offers three examples of time-variant YBLME with disturbances of constant, linear, and bounded random noises to illustrate the efficacy and convergence of the proposed algorithm.
Web monetisation

Web monetisation is the conversion of user traffic into revenue.Initially referring to websites, in more recent years, the meaning of the term has been expanded to refer to non-website traffic, such as social media applications, which this glossary entry gives more attention to.Within social media, the concept of content monetisation has developed as a way to denote the various approaches content creators have in creating online revenue out of the content they produce.This glossary entry provides an overview of the concepts of web and content monetisation, discusses aspects arising out of their interaction, and addresses three main issues currently associated with the term: the interoperability of social media infrastructures, the interoperability of content and web monetisation, and the moderation of content monetised decentrally.
Exploring Metrics to Establish an Optimal Model for Image Aesthetic Assessment and Analysis

To establish an optimal model for photo aesthetic assessment, in this paper, an internal metric called the disentanglement-measure (D-measure) is introduced, which reflects the disentanglement degree of the final layer FC (full connection) nodes of convolutional neural network (CNN). By combining the F-measure with the D-measure to obtain an FD measure, an algorithm of determining the optimal model from many photo score prediction models generated by CNN-based repetitively self-revised learning (RSRL) is proposed. Furthermore, the aesthetics features of the model regarding the first fixation perspective (FFP) and the assessment interest region (AIR) are defined by means of the feature maps so as to analyze the consistency with human aesthetics. The experimental results show that the proposed method is helpful in improving the efficiency of determining the optimal model. Moreover, extracting the FFP and AIR of the models to the image is useful in understanding the internal properties of these models related to the human aesthetics and validating the external performances of the aesthetic assessment.
A Pruning Method for Deep Convolutional Network Based on Heat Map Generation Metrics

With the development of deep learning, researchers design deep network structures in order to extract rich high-level semantic information. Nowadays, most popular algorithms are designed based on the complexity of visible image features. However, compared with visible image features, infrared image features are more homogeneous, and the application of deep networks is prone to extracting redundant features. Therefore, it is important to prune the network layers where redundant features are extracted. Therefore, this paper proposes a pruning method for deep convolutional network based on heat map generation metrics. The 'network layer performance evaluation metrics' are obtained from the number of pixel activations in the heat map. The network layer with the lowest 'network layer performance evaluation metrics' is pruned. To address the problem that the simultaneous deletion of multiple structures may result in incorrect pruning, the Alternating training and self-pruning strategy is proposed. Using a cyclic process of pruning each model once and retraining the pruned model to reduce the incorrect pruning of network layers. The experimental results show that proposed method in this paper improved the performance of CSPDarknet, Darknet and Resnet.
Trends and Challenges Regarding Cyber Risk Mitigation by CISOs—A Systematic Literature and Experts’ Opinion Review Based on Text Analytics

Background: Cyber security has turned out to be one of the main challenges of recent years. As the variety of system and application vulnerabilities has increased dramatically in recent years, cyber attackers have managed to penetrate the networks and infrastructures of larger numbers of companies, thus increasing the latter’s exposure to cyber threats. To mitigate this exposure, it is crucial for CISOs to have sufficient training and skills to help them identify how well security controls are managed and whether these controls offer the company sufficient protection against cyber threats, as expected. However, recent literature shows a lack of clarity regarding the manner in which the CISOs’ role and the companies’ investment in their skills should change in view of these developments. Therefore, the aim of this study is to investigate the relationship between the CISOs’ level of cyber security-related preparation to mitigate cyber threats (and specifically, the companies’ attitudes toward investing in such preparation) and the recent evolution of cyber threats. Methods: The study data are based on the following public resources: (1) recent scientific literature; (2) cyber threat-related opinion news articles; and (3) OWASP’s reported list of vulnerabilities. Data analysis was performed using various text mining methods and tools. Results: The study’s findings show that although the implementation of cyber defense tools has gained more serious attention in recent years, CISOs still lack sufficient support from management and sufficient knowledge and skills to mitigate current and new cyber threats. Conclusions: The research outcomes may allow practitioners to examine whether the companies’ level of cyber security controls matches the CISOs’ skills, and whether a comprehensive security education program is required. The present article discusses these findings and their implications.
Narrative Integrated Career Exploration Platform

Career and technical education play a significant role in reducing high school and college dropouts as well providing necessary skills and opportunities to make suitable career decisions. The recent technological advances have benefited the education sector tremendously with the introduction of exciting innovations including virtual and augmented reality. The benefits of NL and game-based learning are well-established in the literature. However, their implementation has been limited to the education sector. In this research, the design and implementation of a Narrative Integrated Career Exploration (NICE) platform is discussed. The platform contains four playable tracks allowing students to explore careers in artificial intelligence, cybersecurity, internet of things, and electronics. The tracks are carefully designed with narrative problem-solving reflecting contemporary real-world challenges. To evaluate the perceived usefulness of the platform, a case study involving university students was performed. The results clearly reflect students’ interest in narrative and game-based career exploration approaches.
Silent speech command word recognition using stepped frequency continuous wave radar

Abstract Recovering speech in the absence of the acoustic speech signal itself, i.e., silent speech , holds great potential for restoring or enhancing oral communication in those who lost it. Radar is a relatively unexplored silent speech sensing modality, even though it has the advantage of being fully non-invasive. We therefore built a custom stepped frequency continuous wave radar hardware to measure the changes in the transmission spectra during speech between three antennas, located on both cheeks and the chin with a measurement update rate of 100 Hz. We then recorded a command word corpus of 40 phonetically balanced, two-syllable German words and the German digits zero to nine for two individual speakers and evaluated both the speaker-dependent multi-session and inter-session recognition accuracies on this 50-word corpus using a bidirectional long-short term memory network. We obtained recognition accuracies of 99.17% and 88.87% for the speaker-dependent multi-session and inter-session accuracy, respectively. These results show that the transmission spectra are very well suited to discriminate individual words from one another, even across different sessions, which is one of the key challenges for fully non-invasive silent speech interfaces.
Using Deep Siamese networks for trajectory analysis to extract motion patterns in videos

This paper investigates the use of Siamese networks for trajectory similarity analysis in surveillance tasks. Specifically, the proposed approach uses an auto-encoder as a part of training a discriminative twin (Siamese) network to perform trajectory similarity analysis, thus presenting an end-to-end framework to perform an online motion pattern extraction in the scene with an ability to incorporate new incoming trajectory(ies) incrementally. The effectiveness of the proposed method is evaluated on four challenging public real-world datasets containing both vehicle and person targets, and compared with five existing methods. The proposed method consistently shows better or comparable performance than the existing methods on all datasets.
Universal photonic processors fabricated by femtosecond laser writing

Universal photonic processors (UPPs) are reconfigurable photonic integrated circuits able to implement arbitrary unitary transformations on an input photonic state. Femtosecond laser writing (FLW) allows for rapid and cost-effective fabrication of circuits with low propagation losses. A FLW process featuring thermal isolation allows for a dramatic reduction in dissipated power and crosstalk in integrated thermally-reconfigurable Mach-Zehnder interferometers (MZIs), especially when operated in vacuum, with 0.9 mW dissipation for full reconfiguration and 0.5% crosstalk at 785 nm wavelength. To demonstrate the potential of this technology we fabricated and characterized a 6-mode FLW-UPP in a rectangular MZI mesh with 30 thermal shifters.
Adaptive Video Streaming With Automatic Quality-of-Experience Optimization

Video streaming has grown tremendously in recent years and it is now one of the main applications on the Internet. Due to the networks' inherent bandwidth fluctuations, various rate-adaptive streaming algorithms have been developed to compensate for such fluctuations to improve Quality-of-Experience (QoE). However, in practice, the preference for QoE typically differs significantly across different viewers and there is no systematic way so far to comprehensively incorporate different sets of conflicting QoE objectives into the algorithm design. Thus, it is not surprising that the QoE performance achieved by the existing algorithms is in fact far from optimal. This work aims at attacking the heart of the problem by developing a novel framework called Post Streaming Quality Analysis (PSQA) that can maximize the QoE under any preference through automatically tuning the adaptation logic of the streaming algorithms. Evaluation results show that the QoE achieved by PSQA is substantially better than the existing approaches and in some scenarios even close to optimal. Moreover, PSQA can be readily implemented into real streaming platforms, offering a practical and reliable solution for high-performance streaming services.
Feature aligned ship detection based on improved RPDet in SAR images

Ship detection in Synthetic aperture radar (SAR) images is an important and challenging task. A number of deep learning-based methods have been proposed for the SAR ship detection task and achieved remarkable performance. However, existing methods still suffer from some problems, such as feature misalignment, multi-scale, and small target missing. To address these problems, we first introduce RPDet into the SAR ship detection task to address the feature misalignment problem, then propose an improved RPDet to address the multi-scale and small target missing problems. Specifically, the Path Aggregation Feature Pyramid Network (PAFPN) is utilized in our method to fuse multi-scale features, area distribution of ships in SAR images is analyzed for better feature-levels selection, and Complete-IoU (CIoU) loss is adopted for more accurate and stable bounding box regression. Extensive results on a publicly available SAR ship detection dataset SSDD show that the improved RPDet achieves 3.70% higher average precision (AP) than the original RPDet, and obtains SOTA results compared to other methods.
The integrated information theory of consciousness: Unmasked and identified

In our response to a truly diverse set of commentaries, we first summarize the principal topical themes around which they cluster, then address two "outlier" positions (the problem of consciousness has been solved vs. is intractable). Next, we address ways in which commentaries by non-integrated information theory (IIT) authors engage with the specifics of our IIT critique, turning finally to the four commentaries by IIT authors.