Reviewerly/exHarmony huggingface dataset:
Mixed-UNet: Refined class activation mapping for weakly-supervised semantic segmentation with multi-scale inference

Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions. However, training these segmentation models requires a large number of manually annotated pixel-level labels, which are time-consuming and labor-intensive, in contrast to image-level labels that are easier to obtain. It is imperative to resolve this problem through weakly-supervised semantic segmentation models using image-level labels as supervision since it can significantly reduce human annotation efforts. Most of the advanced solutions exploit class activation mapping (CAM). However, the original CAMs rarely capture the precise boundaries of lesions. In this study, we propose the strategy of multi-scale inference to refine CAMs by reducing the detail loss in single-scale reasoning. For segmentation, we develop a novel model named Mixed-UNet, which has two parallel branches in the decoding phase. The results can be obtained after fusing the extracted features from two branches. We evaluate the designed Mixed-UNet against several prevalent deep learning-based segmentation approaches on our dataset collected from the local hospital and public datasets. The validation results demonstrate that our model surpasses available methods under the same supervision level in the segmentation of various lesions from brain imaging.
Byzantine-robust variance-reduced federated learning over distributed non-i.i.d. data

We consider the federated learning problem where data on workers are not independent and identically distributed (i.i.d.). During the learning process, an unknown number of Byzantine workers may send malicious messages to the central node, leading to remarkable learning error. Most of the Byzantine-robust methods address this issue by using robust aggregation rules to aggregate the received messages, but rely on the assumption that all the regular workers have i.i.d. data, which is not the case in many federated learning applications. In light of the significance of reducing stochastic gradient noise for mitigating the effect of Byzantine attacks, we use a resampling strategy to reduce the impact of both inner variation (that describes the sample heterogeneity on every regular worker) and outer variation (that describes the sample heterogeneity among the regular workers), along with a stochastic average gradient algorithm to gradually eliminate the inner variation. The variance-reduced messages are then aggregated with a robust geometric median operator. We prove that the proposed method reaches a neighborhood of the optimal solution at a linear convergence rate and the learning error is determined by the number of Byzantine workers. Numerical experiments corroborate the theoretical results and show that the proposed method outperforms the state-of-the-arts in the non-i.i.d. setting.
Hybrid Technique for Human Activities and Actions Recognition Using PCA, Voting, and K-means

Because of the proclivity for providing information about a group’s character and mental state, human activities are considered to be important in people-to-people communication and social relations. It is really tough to abstract this kind of complex data. The technical fields of computer vision and machine learning are built on a person’s capacity to track the behaviors of others, and the term “activity” refers to a set of acts performed by the human body that engage multiple portions of the body at the same time. Any form of observation is compared to a pre-defined pattern in computer vision, and the action is then detected and labeled for further identification. In this study, a hybrid technique for recognizing human activities is proposed. Principal Component Analysis, K-means, and voting categorization have all been combined in this hybrid technique. It has been observed that in terms of precision and recall, the voting classification outperforms the present logistic regression classification. The average results shown the proficiency level of about 96% for the identification of different human activities.
Deep Learning

Deep learning has become one of the hottest research topics in the machine-learning world, with tremendous success in several sectors. The summary and inductive reasoning procedures of deep learning are mostly used in this study. It begins by outlining the history and present state of deep learning globally. The second part of the chapter explains the fundamental structure, the traits, and a few types of traditional deep learning techniques, including the stacked auto encoder, deep belief network, deep Boltzmann machine, and convolutional neural network. Thirdly, it discusses the most recent advancements and uses of deep learning in a variety of industries, including speech recognition, machine learning, computational linguistics, and healthcare. Finally, it outlines the issues and potential possibilities for deep learning studies in the future.
Reversible data hiding in encrypted image with local-correlation-based classification and adaptive encoding strategy

In recent years, reversible data hiding in encrypted images has attracted more and more attention due to its wide application in secret information transmission and image content protection. In this paper, we propose a reversible data hiding scheme for encrypted image based on classification and multi-layer processing. The image owner utilizes a block-level encryption method, including block-level bitwise exclusive-or operation and block permutation, to obtain encrypted image such that the correlation of pixels in most blocks is preserved. Therefore, there provably exist identical consecutive high-order bit planes (ICHOBPs) from the most significant bit (MSB) where the number of ICHOBPs is defined as local correlation. Further, a local-correlation-based classification mechanism is proposed such that these blocks can be classified into different types. The data hider adopts multi-layer processing to fully exploit the local correlation in each block. First, ICHOBPs of each block are utilized as redundant space for data embedding. For those blocks with a small number of ICHOBPs, an adaptive coding strategy is proposed to improve embedding capacity. Especially for those blocks whose correlation is not well preserved, we propose a special processing method to exploit redundant space. Experimental results show that our method can effectively improve the embedding capacity than other similar ones with keeping high quality in security analysis.
Continuous Variable Quantum Teleportation Network

Abstract Quantum network scales the advantages of quantum communication protocols to more than two detached users, which offers great potential to realize the quantum internet. Here, a fully connected continuous‐variable (CV) quantum teleportation network architecture is presented, in which a squeezed state of light distributes each pair of entangled sideband modes to each communication link that bridges any pair of users. The quantum teleportation scheme is similar to standard two‐party ones for each communication link, without sacrificing communication capability and reliability. The demonstration based on CV‐entangled sideband modes opens an innovative possibility to implement many tasks of deterministic quantum information processing.
Efficient SfM for Large-Scale UAV Images Based on Graph-Indexed BoW and Parallel-Constructed BA Optimization

Structure from Motion (SfM) for large-scale UAV (Unmanned Aerial Vehicle) images has been widely used in the fields of photogrammetry and computer vision. Its efficiency, however, decreases dramatically as well as with the memory occupation rising steeply due to the explosion of data volume and the iterative BA (bundle adjustment) optimization. In this paper, an efficient SfM solution is proposed to solve the low-efficiency and high memory consumption problems. First, an algorithm is designed to find UAV image match pairs based on a graph-indexed bag-of-words (BoW) model (GIBoW), which treats visual words as vertices and link relations as edges to build a small-world graph structure. The small-world graph structure can be used to search the nearest-neighbor visual word for query features with extremely high efficiency. Reliable UAV image match pairs can effectively improve feature matching efficiency. Second, a central bundle adjustment with object point-wise parallel construction of the Schur complement (PSCBA) is proposed, which is designed as the combination of the LM (Levenberg–Marquardt) algorithm with the preconditioned conjugate gradients (PCG). The PSCBA method can dramatically reduce the memory consumption in both error and normal equations, as well as improve efficiency. Finally, by using four UAV datasets, the effectiveness of the proposed SfM solution is verified through comprehensive analysis and comparison. The experimental results show that compared with Colmap-Bow and Dbow2, the proposed graph index BoW retrieval algorithm improves the efficiency of image match pair selection with an acceleration ratio ranging from 3 to 7. Meanwhile, the parallel-constructed BA optimization algorithm can achieve accurate bundle adjustment results with an acceleration ratio by 2 to 7 times and reduce memory occupation by 2 to 3 times compared with the BA optimization using Ceres solver. For large-scale UAV images, the proposed method is an effective and reliable solution.
Semiconductor superlattice physical unclonable function based two-dimensional compressive sensing cryptosystem and its application to image encryption

• A key generation and distribution scheme based on SSL-PUF is proposed in 2DCS based cryptosystem, which achieves key distribution in public channel. • A key expansion method based on spatiotemporal system pseudo-random coupled map lattices (PRCML) model is used to generate the original measurement matrix. • For decreasing the coherence of the measurement matrix, an adaptive coherent gradient descent (ACGD) method based on iterative shrinkage of the Gram matrix is proposed. • The TV regularization reconstruction method are optimized based on the Adam algorithm. Most image encryption methods are symmetric cryptosystems, in which the encryptor and decryptor are required to share the same key. To reduce the risk of key generation and key distribution for image cipher scheme, in this paper, we present a novel scheme based on semiconductor superlattice physical unclonable function (SSL-PUF) and two-dimensional compressive sensing (2DCS). Distinguished from other existing CS-based cryptosystems, SSL-PUF with quantum tunneling induced chaos is utilized, in which the key distribution for the public channel can be achieved by a pair of matched SSL-PUF. First, the seed key generated from SSL-PUF is extended with the pseudo-random coupled map lattices (PRCML) module. Next, gradient-type alternating minimization approach is employed to solve the low coherence measurement matrix. Finally, compressing and encrypting can be implemented simultaneously by measuring the plain image bi-directionally. Moreover, the total variation (TV) regularization reconstruction method is optimized based on the Adam algorithm. With low compression ratios and computationally constrained environments, the proposed optimization method can reconstruct images with high quality. Numerous experiments were simulated and analyzed to validate that the proposed 2DCS-based cryptosystems possess well performance, such as image reconstruction effect, security against various attacks, and good robustness.
Equivalence of machine learning models in modeling chaos

Recent advances have demonstrated that machine learning models are effective methods for predicting chaotic systems. Although short-term chaos prediction can be successfully realized by seemingly different machine learning models, an intriguing question of their correlation is still unknown. Here, we focus on three commonly used machine learning models that are reservoir computing, long-short term memory networks, and deep belief networks, respectively. We find that these selected models present almost identical long-term statistical properties as that of a learned chaotic system. Specifically, we show that these machine learning models have the same correlation dimension and recurrence time. Furthermore, by sharing a common signal, we realize synchronization, cascading synchronization, and coupled synchronization among machine learning models. Our findings reveal the equivalence of machine learning models in characterizing and modeling chaotic systems. • We investigate what are the common features among distinct machine learning models. • We show that long-term behaviors of machine learning models are almost the same. • We realize several types of synchronization among distinct machine learning models. • Our findings reveal their equivalence in characterizing and modeling chaotic systems.
An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain

Deep neural networks achieve state-of-the-art performance on many tasks, but require increasingly complex architectures and costly training procedures. Engineers can reduce costs by reusing a pre-trained model (PTM) and fine-tuning it for their own tasks. To facilitate software reuse, engineers collaborate around model hubs, collections of PTMs and datasets organized by problem domain. Although model hubs are now comparable in popularity and size to other software ecosystems, the associated PTM supply chain has not yet been examined from a software engineering perspective. We present an empirical study of artifacts and security features in 8 model hubs. We indicate the potential threat models and show that the existing defenses are insufficient for ensuring the security of PTMs. We compare PTM and traditional supply chains, and propose directions for further measurements and tools to increase the reliability of the PTM supply chain.
Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

Summary Lung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.
Gradient Backpropagation based Feature Attribution to Enable Explainable-AI on the Edge

There has been a recent surge in the field of Explainable AI (XAI) which tackles the problem of providing insights into the behavior of black-box machine learning models. Within this field, feature attribution encompasses methods which assign relevance scores to input features and visualize them as a heatmap. Designing flexible accelerators for multiple such algorithms is challenging since the hardware mapping of these algorithms has not been studied yet. In this work, we first analyze the dataflow of gradient backpropagation based feature attribution algorithms to determine the resource overhead required over inference. The gradient computation is optimized to minimize the memory overhead. Second, we develop a High-Level Synthesis (HLS) based configurable FPGA design that is targeted for edge devices and supports three feature attribution algorithms. Tile based computation is employed to maximally use on-chip resources while adhering to the resource constraints. Representative CNNs are trained on CIFAR-10 dataset and implemented on multiple Xilinx FPGAs using 16-bit fixed-point precision demonstrating flexibility of our library. Finally, through efficient reuse of allocated hardware resources, our design methodology demonstrates a pathway to repurpose inference accelerators to support feature attribution with minimal overhead, thereby enabling real-time XAI on the edge.
Transmedia learning: fact or fiction? A systematic review (<i>Aprendizaje transmedia: ¿realidad o ficción? Una revisión sistemática</i>)

This article deals with the concept of transmedia learning, which has emerged in recent years from Jenkins' approaches, with an important echo not only on cultural approaches and research, but also on education.Transmedia educational experiences cause interest in the scientific community; however, it is difficult to find a univocal understanding of transmedia learning or a unique definition of it.Therefore, this research aims to address the conceptual background of transmedia educational practices through a research design typical of a systematic literature review.To do so, the research process starts with the searching of keywords referring to transmedia learning and without time limitation, with 38 documents yielded in the initial search.After following the guidelines of the PRISMA statement, the process ends with a total of 24 documents that have been qualitatively analysed in depth using NVivo 12.Among the main results obtained, it is highlighted that transmedia learning, from a narrative, allows us to concretize the pedagogical principles of constructivism or connectivism in proposals that are motivating for students and in a relatively personalized way that is close to their daily lives.However, its integration into teaching-learning processes is complex due to the large number of factors that must be considered.
Contrastive language and vision learning of general fashion concepts

The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry. We demonstrate the effectiveness of the representations learned by FashionCLIP with extensive tests across a variety of tasks, datasets and generalization probes. We argue that adaptations of large pre-trained models such as CLIP offer new perspectives in terms of scalability and sustainability for certain types of players in the industry. Finally, we detail the costs and environmental impact of training, and release the model weights and code as open source contribution to the community.
Knowledge and data in cooperative modeling: Case studies on ship trajectory prediction

A ship automation will be a key to the future maritime. In particular, ship dynamic models play an integral role. However, it is challenging to develop an accurate model readily. Recent studies proposed a physics-data cooperative model that predicts a future trajectory by compensating for position errors made by the physics-based model by using a machine learning model, which learns such a multiple-step-ahead compensation based on onboard sensor measurements. It seems to be promising to reduce effort in model development by exploiting observation data while having physics knowledge and a stable foundation in the model. However, it has been an open question “how much does the cooperative model benefit from physics knowledge and observation data?”. We tackled this key question experimentally. To investigate the benefit of the physics-based model and the data amount, by changing the accuracy of the physics-based model and the size of observation dataset, simulation and full-scale experiments were conducted. Results show that the accuracy of the physics-based model and the data amount were complementary to each other to some extent. A wide range of physics-based models worked as prior knowledge, however, too inaccurate models disturbed the training.
Embracing iterations in Quantum software: a vision

In today's software engineering, iterations, affordable en masse, form an important part of just about any system. However, not all computing resources are cheap to consume. In High-Performance (HPC) and Quantum Computing (QC), executions can consume considerable amounts of energy and time, which is reserved and used even if the very first steps in the process fail. This means that developers must assume a different attitude towards programming, and aim at error-free software before its execution. This is commonly facilitated using simulators, which are commonplace for both HPC and QC. However the fashion developers advance from one tool to another is ad-hoc, with no established software engineering guidelines, and the final step from simulators to HPC/QC is still a leap of faith, comparable to releasing software. In this paper, we propose a vision where developers can iterate in an agile fashion when developing quantum software. The iterations are defined such that when the solution is still vague in the beginning, computations are interactive and provide instant feedback, thus supporting conceptualization of the software and experimenting with new ideas. When the solution becomes more precise, more expensive computations such as quantum algorithm and hyperparameter optimization are executed in batches.
VeRSA

Verifiable registries allow clients to securely access a key-value mapping maintained by an untrusted server. Registries must be audited to ensure global invariants are preserved, which, in turn, allows for efficient monitoring of individual registry entries by their owners. To this end, existing proposals either assume trusted third-party auditors or rely on incrementally verifiable computation (IVC) via expensive recursive SNARKs to make registries client-auditable.