Reviewerly/exHarmony huggingface dataset:
Transfer Driven Ensemble Learning Approach using ROI Pooling CNN For Enhanced Breast Cancer Diagnosis

Cancer is a major cause of death that is brought on by the body's abnormal cell proliferation, including breast cancer. It poses a significant threat to the safety and health of people globally. Several imaging methods, such as mammography, CT scans, MRI, ultrasound, and biopsies, can help detect breast cancer. A biopsy is commonly done in histopathology to examine an image and assist in diagnosing breast cancer. However, accurately identifying the appropriate Region of Interest (ROI) remains challenging due to the complex nature of pre-processing phases, feature extracting regions, segmenting process and other conventional machine learning phases. This reduces the system's efficiency and accuracy. In order to reduce the variance that exists among viewers, the aim of this work is to build superior deep-learning phases algorithms. This research introduces a classifier that can detect and classify images simultaneously, without any human involvement. It employs a transfer-driven ensemble learning approach, where the framework comprises two main phases: production and detection of pseudo-color images and segmentation based on ROI Pooling CNN, which then feeds its output to ensemble models such as Efficientnet, ResNet101, and VGG19. Before the feature extraction process, data augmentation is necessary, involving minor adjustments like random cropping, horizontal flipping, and color space augmentations. Implementing and simulating the proposed segmentation and classification algorithms for any decision-making framework suggested could decrease the frequency of incorrect diagnoses and enhance classification accuracy. This could aid pathologists in obtaining a second opinion and facilitate the early identification of diseases. With a prediction accuracy of 98.3%, the proposed method outperforms the individual pre-trained models, namely Efficientnet, ResNet101, VGG16, and VGG19, by 2.3%, 1.71%, 2.01%, and 1.47%, respectively.
Blockchain: The Economic and Financial Institution for Autonomous AI?

This paper examines how the combination of Artificial Intelligence (AI) and Blockchain technology can enable autonomous AI agents to engage and execute economic and financial transactions. We critically examine the constraints of AI agents in achieving predefined objectives independently, especially due to their limited access to economic and financial institutions. We argue that AI's access to these institutions is vital in enhancing its capabilities to augment human productivity. Drawing on the theory of institutional economics, we propose that Blockchain provides a solution for creating digital economic and financial institutions, permitting AI to engage with these institutions through the management of private keys. This extends AI's capabilities to form and execute contracts, participate in marketplaces, and utilize financial services autonomously. The paper encourages further research on AI as a general-purpose technology and Blockchain as an institutional technology that can unlock the full capabilities of autonomous AI agents.
Federated Learning for Wearable Sensor-Based Human Activity Recognition

Computing devices that can identify various human behaviors or motions may be used to aid individuals in multiple contexts, including sports, healthcare, and interactions between humans and robots. Data readily accessible for this purpose may be collected from smartphones and wearable devices used daily. Therefore, efforts are made to classify real-time activity data effectively utilizing various machine learning models. However, current methods for human activity recognition do not sufficiently consider user data privacy. To mitigate privacy issues, federated learning can be employed to build generic activity classification model by aggregating a locally trained model at a user-edge device. This paper adopted a deep-learning neural network model called the transformer for motion signal time-series analysis. It uses the attention mechanism to provide context for each point in the time series. It also compares federated learning’s performance to centralized learning. Experimental results show that federated learning outperformed centralized training without com- promising user data privacy, with an accuracy of 96.87%.
Face Super Resolution Through Face Semantic and Structural Prior

Face super-resolution (SR) is one of the domains of image SR that aims to recover a high-resolution (HR) image from low-resolution (LR) input. Existing deep-learning-based methods have significantly improved the performance, but most of them failed to preserve the semantics and structure in the output face images. The facial semantic and structural information determines the human face’s characteristics that need to be preserved during the SR process. Therefore, using facial semantics and structural priors, this research proposes a two-stage face SR framework. The first stage upsamples the LR image and utilizes the facial semantics (attribute information) prior which helps to generate the intermediate image with useful attribute information. After that, the second stage takes the intermediate image as input and extracts the facial features. The heatmap estimation unit is applied to estimate the landmark-based heatmap and fuses with extracted features that assist the proposed framework in preserving the structure in output face images. Numerous experiments shows that when reconstructing HR images from LR inputs, the suggested framework outperforms the state-of-the-art methods.
ChatGPT and Language Learning

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A Deep-Neural-Network-Based Decoding Scheme in Wireless Communication Systems

With the flourishing development of wireless communication, further challenges will be introduced by the future demands of emerging applications. However, in the face of more complex communication scenarios, favorable decoding results may not be yielded by conventional channel decoding schemes based on mathematical models. The remarkable contributions of deep neural networks (DNNs) in various fields have garnered widespread recognition, which has ignited our enthusiasm for their application in wireless communication systems. Therefore, a reliable DNN-based decoding scheme designed for wireless communication systems is proposed. This scheme comprises efficient local decoding using linear and nonlinear operations. To be specific, linear operations are carried out on the edges connecting neurons, while nonlinear operations are performed on each neuron. After forward propagation through the DNN, the loss value is estimated based on the output, and backward propagation is employed to update the weights and biases. This process is performed iteratively until a near-optimal message sequence is recovered. Various factors within the DNN are considered in the simulation and the potential impacts of each factor are analyzed. Simulation results indicate that our proposed DNN-based decoding scheme is superior to the conventional hard decision.
Does the sun rise for ChatGPT? Scientific discovery in the age of generative AI

In the current hype-laden climate surrounding the rapid proliferation of foundation models and generative AI systems like ChatGPT, it is becoming increasingly important for societal stakeholders to reach sound understandings of their limitations and potential transformative effects. This is especially true in the natural and applied sciences, where magical thinking among some scientists about the take-off of “artificial general intelligence” has arisen simultaneously as the growing use of these technologies is putting longstanding norms, policies, and standards of good research practice under pressure. In this analysis, I argue that a deflationary understanding of foundation models and generative AI systems can help us sense check our expectations of what role they can play in processes of scientific exploration, sense-making, and discovery. I claim that a more sober, tool-based understanding of generative AI systems as computational instruments embedded in warm-blooded research processes can serve several salutary functions. It can play a crucial bubble-bursting role that mitigates some of the most serious threats to the ethos of modern science posed by an unreflective overreliance on these technologies. It can also strengthen the epistemic and normative footing of contemporary science by helping researchers circumscribe the part to be played by machine-led prediction in communicative contexts of scientific discovery while concurrently prodding them to recognise that such contexts are principal sites for human empowerment, democratic agency, and creativity. Finally, it can help spur ever richer approaches to collaborative experimental design, theory-construction, and scientific world-making by encouraging researchers to deploy these kinds of computational tools to heuristically probe unbounded search spaces and patterns in high-dimensional biophysical data that would otherwise be inaccessible to human-scale examination and inference.
Revisiting model fairness via adversarial examples

Existing research literally evaluates model fairness over limited observed data. In practice, however, factors such as maliciously crafted examples and naturally corrupted examples often appear in real-world data collection. This severely limits the reliability of bias removal methods, inhibits fairness improvement in long-term learning systems, and probes to study accuracy-related robustness. Therefore, we ask: How adversarial examples will skew model fairness? In this paper, we investigate the vulnerability of individual fairness and group fairness to adversarial attacks. We further propose a general adversarial fairness attack framework capable of twisting model bias through a small subset of adversarial examples. We formulate this problem as an optimization problem: maximizing the model bias with the constraint of the number of adversarial examples and the perturbation scale. Our approach finds the most vulnerable examples to model fairness based on the estimated distance from examples to the decision boundary and demographic information. The experimental results 1 show that model fairness is easily skewed by a small number of adversarial examples.
Estimating the ordering of variables in a VAR using a Plackett–Luce prior

Estimating Bayesian Vector Autoregressions (VARs) involving the Cholesky decomposition is sensitive to the ordering of variables. We treat the ordering as unknown, develop a prior over variable orderings and Markov Chain Monte Carlo (MCMC) methods for posterior sampling over orderings.
Incremental specialized and specialized-generalized matrix factorization models based on adaptive learning rate optimizers

Recommender systems suggest items that are likely to be preferred by a particular user based on historical behavior, actions, and feedback. In real-world applications, data on users and items are continuously generated at a fast pace, such as in e-commerce, social media, digital marketing, and content consumption applications. Since interactions occur over time, these scenarios can be formulated as a data stream where users’ interests are potentially dynamic, i.e., they change over time. Given that changes are expected to occur, one of the current research challenges in streaming recommender systems is that models must adapt their parameters when changes occur to maintain performance. As such changes do not occur for all users and items in the stream at the same time, we consider adapting learning schemes to account for user or item identifiers and model individual parameters. Therefore, we used specialized parameters to adjust the step size for each dataset user or item. More specifically, this study proposes four specialized and specialized-generalized variants of four well-known adaptive learning rate optimizers and shows how they are combined with incremental matrix factorization methods. We tested our proposed optimization strategies on different datasets and showed that one of the proposed specialized variants, that is, InAMSGradUser, improves the RECALL and NDCG rates by up to 11.1 and 7.5 percentage points, respectively, compared to the traditional stochastic gradient descent (SGD) optimizer.
A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs)

Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process. An alternative to anonymization is construction of a synthetic dataset that behaves similar to the real clinical data but preserves patient privacy. As part of a collaboration between Novartis and the Oxford Big Data Institute, a synthetic dataset was generated based on images from COSENTYX® (secukinumab) ankylosing spondylitis (AS) clinical studies. An auxiliary classifier Generative Adversarial Network (ac-GAN) was trained to generate synthetic magnetic resonance images (MRIs) of vertebral units (VUs), conditioned on the VU location (cervical, thoracic and lumbar). Here, we present a method for generating a synthetic dataset and conduct an in-depth analysis on its properties along three key metrics: image fidelity, sample diversity and dataset privacy.
Lightweight and flexible hardware implementation of authenticated encryption algorithm SIMON‐Galois/Counter Mode

Summary This brief proposes a new lightweight authenticated encryption algorithm SIMON‐GCM for Internet of Things (IoT) security, which realizes the combination of SIMON block cipher and Galois/Counter Mode (GCM). The designed SIMON circuit supports 128/192/256‐bit key size, which improves the flexibility and enlarges the range of applications. Moreover, the scheme of 32‐cycle Galois field (GF) multiplier in GF(2 128 ) is adopted to effectively reduce the hardware cost of the Galois Hash (GHASH) function in GCM. At the same time, a finite state machine (FSM) is used to run the SIMON and GHASH modules in parallel, thus shortening the authenticated encryption time. The whole circuit is designed and implemented in field programmable gate array (FPGA) platforms. It is measured to yield a throughput of 32.4 Gbps when consuming 331 slices in Artix‐7. Compared with the existing authenticated encryption algorithms, the proposed algorithm achieves lower resource consumption and better flexibility.
Paired contrastive feature for highly reliable offline signature verification

Signature verification requires high reliability. Especially in the writer-independent scenario with the skilled-forgery-only condition, achieving high reliability is challenging but very important. In this paper, we propose to apply two machine learning frameworks, learning with rejection and top-rank learning, to this task because they can suppress ambiguous results and thus give only reliable verification results. Since those frameworks accept a single input, we transform a pair of genuine and query signatures into a single feature vector, called Paired Contrastive Feature (PCF). PCF internally represents similarity (or discrepancy) between the two paired signatures; thus, reliable machine learning frameworks can make reliable decisions using PCF. Through experiments on three public signature datasets in the offline skilled-forgery-only writer-independent scenario, we evaluate and validate the effectiveness and reliability of the proposed models by comparing their performance with a state-of-the-art model.
MBSNN: A multi-branch scalable neural network for resource-constrained IoT devices

Recent breakthroughs in artificial intelligence promote the development of deep neural networks (DNNs)-based intelligent applications in the Internet of Things (IoT). However, these powerful DNNs have considerable model parameters, computational complexity, and storage usage, which impose heavy costs on resource-constrained IoT devices and incur unendurable latency in smart applications. To overcome these challenges, numerous works aim to design lightweight DNNs, but existing works encounter two severe bottlenecks: one is the acute accuracy degradation when reducing model parameters, and the other is the failure to balance the accuracy and resource usage under dynamic workloads. In this paper, we investigate the problems of designing and deploying DNNs on resource-constrained IoT devices. Specifically, we first design a novel multi-branch scalable neural network (MBSNN) architecture, which features multiple subnets to provide optional intelligent services of different accuracy. Then, we propose a stepwise incremental training technique and a learning rate dual decay strategy that can efficiently train MBSNN and increase accuracy. Afterwards, we develop a threshold selection-based adaptive inference mechanism, which can wisely select thresholds for MBSNN to achieve optimal accuracy under given timing constraints. Extensive experiments show that compared to benchmarking methods, our MBSNN improves accuracy by up to 1.63%, reduces computational complexity by up to 32.7%, and decreases model parameters by up to 41.9%.
EatSense: Human centric, action recognition and localization dataset for understanding eating behaviors and quality of motion assessment

Current datasets for computer vision-based action recognition and localization cover a wide range of classes and challenging scenarios. However, these datasets don't cater to healthcare applications that involve long-term monitoring, tracking minor changes in movements over time for healthcare purposes, or completely modeling a specific human behavior that includes multiple sub-actions. Specifically, there are no existing datasets for research on either health monitoring on atomic-action-based eating behavior or for a full range of eating sub-actions that fully segment the main action. Addressing these gaps is valuable for extending research on the health monitoring of elderly people and is needed for creating richer and more complete descriptions of actions. This paper introduces a new benchmark dataset named EatSense that targets both the computer vision and healthcare communities and fills in the aforementioned gaps. EatSense is recorded while a person eats in an uncontrolled dining setting. The key features of EatSense are the introduction of challenging atomic actions for action recognition, the significantly diverse durations of actions that make it difficult for current temporal action localization frameworks to localize, the capability to model comprehensive eating behavior in terms of a sequence of action-based behaviors, and the simulation of minor variations in motion or performance. We conduct extensive experiments on EatSense with baseline deep learning-based approaches for benchmarking and hand-crafted feature-based approaches for explainable applications. We believe this dataset will benefit future researchers in building robust temporal action localization networks, behavior recognition, and performance assessment models for eating.
IceDCST: A Dilated Convolutional Segmentation Transformer for Ice Crystals

Ice crystals are one of the key factors limiting the quality and efficiency of preserved biological materials. However, the current quantitative analysis of ice crystals is often performed by the subjective judgment of the experimental operator, which leads to subjective and error-prone experimental results. Therefore, in this paper, IceDCST is proposed for the semantic segmentation of ice crystals images for objective and accurate quantitative analysis of the ice crystal growth process.IceDCST designed Crossed Patch Merging to enable multi-scale features to be obtained while maintaining local information. Also, we designed Learable Location Information Module (LLI) to reduce the computational effort and introduce learnable location information. In addition, we design the Dilated Convolutional Block (DCB) to increase the perception of contextual spatial information. On the homemade ice crystals dataset, IceDCST performs best with 94.51% accuracy and 87.76 mIoU, which is 2.1% and 1.89 better than the accuracy and mIoU of the second-performing network, respectively. In this paper, the visualization results are analyzed and verified by quantitative analysis of ice crystals growth using IceDCST. The experimental results show that the quantitative analysis of ice crystals using IceDCST is feasible.
Design and Implementation of Manufacturing Enterprise Innovation Management System Based on Data Mining Technology

With the continuous development of modern science and technology in China, various types of big data emerge endlessly. Currently, the manufacturing industry is facing the development task of high-quality development, and the use of big data is particularly critical. Manufacturing enterprises’ innovation activities generate a large amount of data, but the information contained in these data has not been effectively utilized. Therefore, in order to effectively analyze various types of innovation data generated by manufacturing enterprises, this paper designs a manufacturing enterprise innovation management system based on data mining technology, mainly using association rules, K-means clustering analysis, and other technologies to effectively improve the use value of innovation data from various enterprises. And by using the innovation data of listed manufacturing enterprises for testing, it can provide certain guidance for manufacturing enterprises in decision-making innovation projects.
TBNet: Three branch network for image dehazing via subspace projection

The advancement of modern society is always followed by environmental pollution, and the haze phenomenon is becoming more common, having a significant impact on our lives. Haze is a typical atmospheric phenomena caused by small floating particles in the air such as dust and smoke. These floating particles absorb and scatter light significantly, lowering the quality of acquired photos. Many practical applications, such as video surveillance, remote sensing, and autonomous driving, are jeopardized by haze, and advanced computer vision tasks, such as detection and recognition, are difficult to complete. As a result, image defogging has become an increasingly relevant technique with great research value while also being a tough topic. In this paper, we take this as a starting point and design the TBNet: Three branch network with subspace projection as an introduction point to implement image defogging, and achieve satisfactory results on the Fog-Cityscpace dataset.
ColorGAN: Automatic Image Colorization with GAN

Colorization has attracted increasing interest in recent years. However, image colorization is an ill-posed problem with multi-modal correct solutions and they still suffer problems of context confusion and object-edge color bleeding. In this paper, we proposed the Color-GAN, a novel auto adversarial learning colorization methods coupled with channel and spatial attention based on residual structure enhanced by feature extractor and skip-connection. Our network learns colorizing in the method of combining perceptual and semantic understanding of color with class distributions. Experimental results show that our network outperformers existing methods on different quality metrics, meanwhile generates state-of-the-art performance on auto image colorization.