Reviewerly/exHarmony huggingface dataset:
Survey of Supervised Learning for Medical Image Processing

Medical image interpretation is an essential task for the correct diagnosis of many diseases. Pathologists, radiologists, physicians, and researchers rely heavily on medical images to perform diagnoses and develop new treatments. However, manual medical image analysis is tedious and time consuming, making it necessary to identify accurate automated methods. Deep learning-especially supervised deep learning-shows impressive performance in the classification, detection, and segmentation of medical images and has proven comparable in ability to humans. This survey aims to help researchers and practitioners of medical image analysis understand the key concepts and algorithms of supervised learning techniques. Specifically, this survey explains the performance metrics of supervised learning methods; summarizes the available medical datasets; studies the state-of-the-art supervised learning architectures for medical imaging processing, including convolutional neural networks (CNNs) and their corresponding algorithms, region-based CNNs and their variants, fully convolutional networks (FCN) and U-Net architecture; and discusses the trends and challenges in the application of supervised learning methods to medical image analysis. Supervised learning requires large labeled datasets to learn and achieve good performance, and data augmentation, transfer learning, and dropout techniques have widely been employed in medical image processing to overcome the lack of such datasets.
Fibonacci Multichaos Algorithm for Medical Image Encryption for Transmission Through Wavelet Transform Based OFDM System and Its VLSI Realization

At every stage of the digital media transfer, storage, and retrieval process, sensitive images are rendered indecipherable through the use of image encryption. Germany's hospitals have relied on image encryption for years to keep patient data from being accessed by the IT personnel, stolen or left recorded into the network system that could be compromised remotely.&#x0D; In order to encrypt a file manually, the process is extremely time- and effort-consuming. Automating this process while retaining high security is desirable. In this research, we present an approach for encrypting medical images that combines scientific computing with cryptography.&#x0D; This algorithm uses the Fibonacci Multi Chaos Algorithm to encrypt medical images, making them more secure. Distinct wavelet transform orthogonal frequency division multiplexing is used to convey picture data in this work, which is a reliable and secure method.
Controlling long ion strings for quantum simulation and precision measurements

In order to better characterize the performance of trapped ion systems and ease the experimental difficulties of addressing individual ions, the authors apply carefully-timed pulse sequences to ion chains up to 51 ions long. These techniques allow them to generate entanglement between all neighboring ion pairs and to quantify and mitigate sources of decoherence in the chains.
An Empirical Study on Ensemble of Segmentation Approaches

Recognizing objects in images requires complex skills that involve knowledge about the context and the ability to identify the borders of the objects. In computer vision, this task is called semantic segmentation and it pertains to the classification of each pixel in an image. The task is of main importance in many real-life scenarios: in autonomous vehicles, it allows the identification of objects surrounding the vehicle; in medical diagnosis, it improves the ability of early detecting of dangerous pathologies and thus mitigates the risk of serious consequences. In this work, we propose a new ensemble method able to solve the semantic segmentation task. The model is based on convolutional neural networks (CNNs) and transformers. An ensemble uses many different models whose predictions are aggregated to form the output of the ensemble system. The performance and quality of the ensemble prediction are strongly connected with some factors; one of the most important is the diversity among individual models. In our approach, this is enforced by adopting different loss functions and testing different data augmentations. We developed the proposed method by combining DeepLabV3+, HarDNet-MSEG, and Pyramid Vision Transformers. The developed solution was then assessed through an extensive empirical evaluation in five different scenarios: polyp detection, skin detection, leukocytes recognition, environmental microorganism detection, and butterfly recognition. The model provides state-of-the-art results.
Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems

In MIMO-OFDM systems, pilot design and estimation algorithm jointly determine the reliability and effectiveness of pilot-based channel estimation methods. In order to improve the channel estimation accuracy with less pilot overhead, a deep learning scheme for joint pilot design and channel estimation is proposed. This new hybrid network structure is named CAGAN, which is composed of a concrete autoencoder (concrete AE) and a conditional generative adversarial network (cGAN). We first use concrete AE to find and select the most informative position in the time-frequency grid to achieve pilot optimization design and then input the optimized pilots to cGAN to complete channel estimation. Simulation experiments show that the CAGAN scheme outperforms the traditional LS and MMSE estimation methods with fewer pilots, and has good robustness to environmental noise.
Factors influencing data saturation in qualitative studies

This study aims to evaluate the factors that influence data saturation in qualitative studies. The study adopted a systematic literature review of 24 journal articles published between 2018 and 2022. The articles were obtained from a Google scholar database. The findings point to five factors that affect data saturation namely, pre-determined codes and themes, sample size, relevancy of research subjects (respondents), number of research methods, and length of data collection sessions. It is argued in this paper, therefore, that in order to attain data saturation, researchers should consider multiple factors that may affect data saturation in order to increase the validity of qualitative studies.
Legal Conundrums of the Metaverse

The metaverse gained its momentum after a CEO of the biggest social media made a statement that it would be the next big thing after the Internet. Although there is no single, agreed definition of the metaverse, the common understanding of the metaverse is that the concept combines IoT, AR, VR, XR, and 3D technologies. It is also called the Web 3.0. The market capital and the economic potential of the metaverse are enormous. The market cap was calculated around USD14.8 trillion in October 2021 while the economic potential ranged from USD3,75 trillion to USD12.5 trillion. Hence, it is of importance to discuss the legal aspects of the metaverse. This article is the first to elaborate the legal conundrums of the metaverse in a more proper manner. It includes discussion on the property law and intellectual property law, and whether the time has come to have “a virtual property law”. It also discusses some other legal aspects such as privacy and data protection, contract law and smart contracts, cybersecurity and cyberattacks, monetary and payment systems laws, and regulation of virtual assets (including securities and commodities laws), tax law, anti-money laundering and KYC, and criminal law. To give a more comprehensive view, some governance and ethical issues of the metaverse are also touched upon.
Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.
Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

Because of their superior problem-solving ability, nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems. Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges. Among the state-of-the-art algorithms, Differential Evolution (DE) is one of the most successful algorithms and is frequently used to solve various industrial problems. Over the previous 2 decades, DE has been heavily modified to improve its capabilities. Several DE variations secured positions in IEEE CEC competitions, establishing their efficacy. However, to our knowledge, there has never been a comparison of performance across various CEC-winning DE versions, which could aid in determining which is the most successful. In this study, the performance of DE and its eight other IEEE CEC competition-winning variants are compared. First, the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions, and the performances have been compared. One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems, out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite, have been solved to compare the performances. Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm's ability statistically. Performance analysis exposes that none of the DE variants can solve all the problems efficiently. Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.
The role of cryptocurrency in the dynamics of blockchain-based social networks: The case of Steemit

A shift of paradigm is running over online social platforms: the over-centralization of these platforms is leaving room for decentralized solutions based on blockchain technologies, such as blockchain-based online social networks-BOSNs. Among the many unknown aspects of these techno-social systems, the objective of this study is to propose an analytical framework to assess the impact of the cryptocurrencies linked to a BOSN platform on the evolution of its social network and on the behavior of their users, in terms of production of content and/or its promotion through a voting and rewarding system. The framework has been applied to Steemit, one of the most widespread BOSNs, from which we collected three-year-long high-resolution data on its evolution along with the price of its main cryptocurrencies. On users' activities extracted from these longitudinal data, we applied a time-series correlation analysis and a correlation analysis between the action allocation strategies and the obtained rewards, in the case of most central accounts. The analysis has highlighted pieces of evidence of the influence of the cryptocurrency price on users' actions, particularly on actions that shape the structure of the social networks. Second, we also found highly rewarded users prefer actions related to the promotion of content rather than the creation of high-quality content, exploiting the reward distribution mechanisms implemented by the platform. These findings highlight that the shift of paradigm towards blockchain and cryptocurrency technologies might strengthen the influence of financial and economic factors rather than relational/social aspects on the evolution of these new complex techno-social systems.
Hybrid Graph Neural Networks for Few-Shot Learning

Graph neural networks (GNNs) have been used to tackle the few-shot learning (FSL) problem and shown great potentials under the transductive setting. However under the inductive setting, existing GNN based methods are less competitive. This is because they use an instance GNN as a label propagation/classification module, which is jointly meta-learned with a feature embedding network. This design is problematic because the classifier needs to adapt quickly to new tasks while the embedding does not. To overcome this problem, in this paper we propose a novel hybrid GNN (HGNN) model consisting of two GNNs, an instance GNN and a prototype GNN. Instead of label propagation, they act as feature embedding adaptation modules for quick adaptation of the meta-learned feature embedding to new tasks. Importantly they are designed to deal with a fundamental yet often neglected challenge in FSL, that is, with only a handful of shots per class, any few-shot classifier would be sensitive to badly sampled shots which are either outliers or can cause inter-class distribution overlapping. Extensive experiments show that our HGNN obtains new state-of-the-art on three FSL benchmarks. The code and models are available at https://github.com/TianyuanYu/HGNN.
Model-Based Image Signal Processors via Learnable Dictionaries

Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due to the inherent hardware design, but also due to the appealing simplicity of noise statistics that result from the direct sensor readings. Despite this, the availability of RAW images is limited in comparison with the abundance and diversity of available RGB data. Recent approaches have attempted to bridge this gap by estimating the RGB to RAW mapping: handcrafted model-based methods that are interpretable and controllable usually require manual parameter fine-tuning, while end-to-end learnable neural networks require large amounts of training data, at times with complex training procedures, and generally lack interpretability and parametric control. Towards addressing these existing limitations, we present a novel hybrid model-based and data-driven ISP that builds on canonical ISP operations and is both learnable and interpretable. Our proposed invertible model, capable of bidirectional mapping between RAW and RGB domains, employs end-to-end learning of rich parameter representations, i.e. dictionaries, that are free from direct parametric supervision and additionally enable simple and plausible data augmentation. We evidence the value of our data generation process by extensive experiments under both RAW image reconstruction and RAW image denoising tasks, obtaining state-of-the-art performance in both. Additionally, we show that our ISP can learn meaningful mappings from few data samples, and that denoising models trained with our dictionary-based data augmentation are competitive despite having only few or zero ground-truth labels.
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples). However, beyond these neighbouring nodes, graphs have a large, dark, all-but forgotten world in which we find the non-neighbouring nodes (negative samples). In this paper, we show that this great dark world holds a substantial amount of information that might be useful for representation learning. Most specifically, it can provide negative information about the node representations. Our overall idea is to select appropriate negative samples for each node and incorporate the negative information contained in these samples into the representation updates. Moreover, we show that the process of selecting the negative samples is not trivial. Our theme therefore begins by describing the criteria for a good negative sample, followed by a determinantal point process algorithm for efficiently obtaining such samples. A GCN, boosted by diverse negative samples, then jointly considers the positive and negative information when passing messages. Experimental evaluations show that this idea not only improves the overall performance of standard representation learning but also significantly alleviates over-smoothing problems.
One Model is Not Enough: Ensembles for Isolated Sign Language Recognition

In this paper, we dive into sign language recognition, focusing on the recognition of isolated signs. The task is defined as a classification problem, where a sequence of frames (i.e., images) is recognized as one of the given sign language glosses. We analyze two appearance-based approaches, I3D and TimeSformer, and one pose-based approach, SPOTER. The appearance-based approaches are trained on a few different data modalities, whereas the performance of SPOTER is evaluated on different types of preprocessing. All the methods are tested on two publicly available datasets: AUTSL and WLASL300. We experiment with ensemble techniques to achieve new state-of-the-art results of 73.84% accuracy on the WLASL300 dataset by using the CMA-ES optimization method to find the best ensemble weight parameters. Furthermore, we present an ensembling technique based on the Transformer model, which we call Neural Ensembler.
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

Feature selection is a common step in many ranking, classification, or prediction tasks and serves many purposes. By removing redundant or noisy features, the accuracy of ranking or classification can be improved and the computational cost of the subsequent learning steps can be reduced. However, feature selection can be itself a computationally expensive process. While for decades confined to theoretical algorithmic papers, quantum computing is now becoming a viable tool to tackle realistic problems, in particular special-purpose solvers based on the Quantum Annealing paradigm. This paper aims to explore the feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification.
COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by Silhouette Masks and Fashion Styles

How to recommend outfits has gained considerable attention in both academia and industry in recent years. Many studies have been carried out regarding fashion compatibility learning, to determine whether the fashion items in an outfit are compatible or not. These methods mainly focus on evaluating the compatibility of existing outfits and rarely consider applying such knowledge to ‘design’ new fashion items. We propose the new task of generating complementary and compatible fashion items based on an arbitrary number of given fashion items. In particular, given some fashion items that can make up an outfit, the aim of this paper is to synthesize photo-realistic images of other, complementary, fashion items that are compatible with the given ones. To achieve this, we propose an outfit generation framework, referred to as COutfitGAN, which includes a pyramid style extractor, an outfit generator, a UNet-based real/fake discriminator, and a collocation discriminator. To train and evaluate this framework, we collected a large-scale fashion outfit dataset with over 200 K outfits and 800 K fashion items from the Internet. Extensive experiments show that COutfitGAN outperforms other baselines in terms of similarity, authenticity, and compatibility measurements.
ExtEnD: Extractive Entity Disambiguation

Local models for Entity Disambiguation (ED) have today become extremely powerful, in most part thanks to the advent of large pretrained language models.However, despite their significant performance achievements, most of these approaches frame ED through classification formulations that have intrinsic limitations, both computationally and from a modeling perspective.In contrast with this trend, here we propose EXTEND, a novel local formulation for ED where we frame this task as a text extraction problem, and present two Transformer-based architectures that implement it.Based on experiments in and out of domain, and training over two different data regimes, we find our approach surpasses all its competitors in terms of both data efficiency and raw performance.EXTEND outperforms its alternatives by as few as 6 F 1 points on the more constrained of the two data regimes and, when moving to the other higher-resourced regime, sets a new state of the art on 4 out of 6 benchmarks under consideration, with average improvements of 0.7 F 1 points overall and 1.1 F 1 points out of domain.In addition, to gain better insights from our results, we also perform a fine-grained evaluation of our performances on different classes of label frequency, along with an ablation study of our architectural choices and an error analysis.We release our code and models for research purposes at https:// github.com/SapienzaNLP/extend.
MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, which is naturally sensitive to the order of items in a sequence. To be specific, we develop a tri-directional fusion scheme to coherently capture sequential, cross-channel and cross-feature correlations. Extensive experiments demonstrate the effectiveness of MLP4Rec over various representative baselines upon two benchmark datasets. The simple architecture of MLP4Rec also leads to the linear computational complexity as well as much fewer model parameters than existing self-attention methods.