Reviewerly/exHarmony huggingface dataset:
Classification of Multiclass Histopathological Breast Images Using Residual Deep Learning

Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.
Unsupervised Anomaly Detection in RS-485 Traffic using Autoencoders with Unobtrusive Measurement

Remotely connected devices have been adopted in several industrial control systems (ICS) recently due to the advancement in the Industrial Internet of Things (IIoT). This led to new security vulnerabilities because of the expansion of the attack surface. Moreover, cybersecurity incidents in critical infrastructures are increasing. In the ICS, RS-485 cables are widely used in its network for serial communication between each component. However, almost 30 years ago, most of the industrial network protocols implemented over RS-485 such as Modbus were designed without security features. Therefore, anomaly detection is required in industrial control networks to secure communication in the systems. The goal of this paper is to study unsupervised anomaly detection in RS-485 traffic using autoencoders. Five threat scenarios in the physical layer of the industrial control network are proposed. The novelty of our method is that RS-485 traffic is collected indirectly by an analog-to-digital converter. In the experiments, multilayer perceptron (MLP), 1D convolutional, Long Short-Term Memory (LSTM) autoencoders are trained to detect anomalies. The results show that three autoencoders effectively detect anomalous traffic with F1-scores of 0.963, 0.949, and 0.928 respectively. Due to the indirect traffic collection, our method can be practically applied in the industrial control network.
Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing

Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervised learning approaches based on graph signal processing emerged in computer vision due to their ability to avail the geometrical structure of data. Video object segmentation can be considered as a node classification problem. In this paper, we propose a new approach called GraphECV based on the use of graph signal processing for semi-supervised learning of video object segmentation applied for the segmentation of the left ventricle in echordiography videos. GraphECV includes instance segmentation, extraction of temporal, texture and statistical features to represent the nodes, construction of a graph using K-nearest neighbors, graph sampling to embed the graph with small amount of labeled nodes or graph signals, and finally a semi-supervised learning approach based on the minimization of the Sobolov norm of graph signals. The new algorithm is evaluated using two publicly available echocardiography videos, EchoNet-Dynamic and CAMUS datasets. The proposed approach outperforms other state-of-the-art methods under challenging background conditions.
3DHD CityScenes: High-Definition Maps in High-Density Point Clouds

In this paper, we present 3DHD CityScenes - a new dataset with the most comprehensive, large-scale high-definition (HD) map to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. The HD map covers a wide variety of map element types, for instance traffic signs and lights, construction site elements such as cones and fences, markings, lanes, and relations between map elements. Our pre-sented dataset is suitable for numerous perception tasks, such as 3D object detection or map deviation detection. Furthermore, we address the example task of detecting traffic signs in LiDAR point clouds, proposing a novel method based on a deep neural network. Our architecture, named 3DHDNet, specifically allows for the individual detection of vertically stacked signs. 3DHDNet significantly outperforms two state-of-the-art architectures that we selected for comparison. Our method achieves an F 1 score, recall, and precision, of 0.83, 0.76, and 0.90, respectively, and may serve as a baseline for future approaches. The dataset is available at https://www.hi-drive.eu/Data for both commercial and non-commercial use.
Adaptive Local Implicit Image Function for Arbitrary-Scale Super-Resolution

Image representation is critical for many visual tasks. Instead of representing images discretely with 2D arrays of pixels, a recent study, namely local implicit image function (LIIF), denotes images as a continuous function where pixel values are expansion by using the corresponding coordinates as inputs. Due to its continuous nature, LIIF can be adopted for arbitrary-scale image super-resolution tasks, resulting in a single effective and efficient model for various up-scaling factors. However, LIIF often suffers from structural distortions and ringing artifacts around edges, mostly because all pixels share the same model, thus ignoring the local properties of the image. In this paper, we propose a novel adaptive local image function (A-LIIF) to alleviate this problem. Specifically, our A-LIIF consists of two main components: an encoder and a expansion network. The former captures cross-scale image features, while the latter models the continuous up-scaling function by a weighted combination of multiple local implicit image functions. Accordingly, our A-LIIF can reconstruct the high-frequency textures and structures more accurately. Experiments on multiple benchmark datasets verify the effectiveness of our method. Our codes are available at https://github.com/LeeHW-THU/A-LIIF.
In the shadow of privacy: Overlooked ethical concerns in COVID-19 digital epidemiology

The COVID-19 pandemic witnessed a surge in the use of health data to combat the public health threat. As a result, the use of digital technologies for epidemic surveillance showed great potential to collect vast volumes of data, and thereby respond more effectively to the healthcare challenges. However, the deployment of these technologies raised legitimate concerns over risks to individual privacy. While the ethical and governance debate focused primarily on these concerns, other relevant issues remained in the shadows. Leveraging examples from the COVID-19 pandemic, this perspective article aims to investigate these overlooked issues and their ethical implications. Accordingly, we explore the problem of the digital divide, the role played by tech companies in the public health domain and their power dynamics with the government and public research sector, and the re-use of personal data, especially in the absence of adequate public involvement. Even if individual privacy is ensured, failure to properly engage with these other issues will result in digital epidemiology tools that undermine equity, fairness, public trust, just distribution of benefits, autonomy, and minimization of group harm. On the contrary, a better understanding of these issues, a broader ethical and data governance approach, and meaningful public engagement will encourage adoption of these technologies and the use of personal data for public health research, thus increasing their power to tackle epidemics.
An efficient and secure technique for image steganography using a hash function

Steganography is a technique in which a person hides information in digital media. The message sent by this technique is so secret that other people cannot even imagine the information's existence. This article entails developing a mechanism for communicating one-on-one with individuals by concealing information from the rest of the group. Based on their availability, digital images are the most suited components for use as transmitters when compared to other objects available on the internet. The proposed technique encrypts a message within an image. There are several steganographic techniques for hiding hidden information in photographs, some of which are more difficult than others, and each has its strengths and weaknesses. The encryption mechanism employed may have different requirements depending on the application. For example, certain applications may require complete invisibility of the key information, while others may require the concealment of a larger secret message. In this research, we proposed a technique that converts plain text to ciphertext and encodes it in a picture using up to the four least significant bit (LSB) based on a hash function. The LSBs of the image pixel values are used to substitute pieces of text. Human eyes cannot predict the variation between the initial Image and the resulting image since only the LSBs are modified. The proposed technique is compared with state-of-the-art techniques. The results reveal that the proposed technique outperforms the existing techniques concerning security and efficiency with adequate MSE and PSNR.
Asking Clarifying Questions: To benefit or to disturb users in Web search?

Modern information-seeking systems are becoming more interactive, mainly through asking Clarifying Questions (CQs) to refine users' information needs. System-generated CQs may be of different qualities. However, the impact of asking multiple CQs of different qualities in a search session remains underexplored. Given the multi-turn nature of conversational information-seeking sessions, it is critical to understand and measure the impact of CQs of different qualities, when they are posed in various orders. In this paper, we conduct a user study on CQ quality trajectories, i.e., asking CQs of different qualities in chronological order. We aim to investigate to what extent the trajectory of CQs of different qualities affects user search behavior and satisfaction, on both query-level and session-level. Our user study is conducted with 89 participants as search engine users. Participants are asked to complete a set of Web search tasks. We find that the trajectory of CQs does affect the way users interact with Search Engine Result Pages (SERPs), e.g., a preceding high-quality CQ prompts the depth users to interact with SERPs, while a preceding low-quality CQ prevents such interaction. Our study also demonstrates that asking follow-up high-quality CQs improves the low search performance and user satisfaction caused by earlier low-quality CQs. In addition, only showing high-quality CQs while hiding other CQs receives better gains with less effort. That is, always showing all CQs may be risky and low-quality CQs do disturb users. Based on observations from our user study, we further propose a transformer-based model to predict which CQs to ask, to avoid disturbing users. In short, our study provides insights into the effects of trajectory of asking CQs, and our results will be helpful in designing more effective and enjoyable search clarification systems.
The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps

The coordinated scheduling of the cascade hydro-photovoltaic hybrid system can significantly alleviate the adverse impact from the intermittence of solar energy resources and hence improve the overall stability of bundled power output. The traditional short-term scheduling strategy of cascade hydro-photovoltaic systems is generally based on a fixed time step, which cannot efficiently adapt to the ever-changing photovoltaic output. In this paper, a novel artificial intelligence-assisted short-term scheduling model with hybrid time steps is proposed, where the time-frequency characteristics of photovoltaic output are taken into consideration. The photovoltaic output is estimated by a data-driven long short-term memory network. The Hilbert-Huang transform is used to analyze the time-frequency characteristics of the photovoltaic power. The hybrid scheduling time steps are determined according to the analyzed instantaneous frequency of photovoltaic power. A short-term optimal scheduling model based on data-driven photovoltaic output evaluation and hybrid time intervals is established to minimize the operational fluctuation of hydropower units and to track the planned output curve simultaneously. Finally, a comprehensive index involving the safety, economy, and operational efficiency is established to compare the overall performance of the hybrid time step model and that of the fixed time step models. The results demonstrate that the proposed hybrid time step model yields the lowest comprehensive ranking score of 1.00, obviously better than the figures for the fixed 60-minuite step model (2.86) and the fixed 15-min step model (2.20). It indicates that the proposed model can achieve the optimal compromise of overall performance via adaptive scheduling time steps.
A certifying extraction with time bounds from Coq to call-by-value  $\lambda$-calculus

We provide a plugin extracting Coq functions of simple polymorphic types to the (untyped) call-by-value $\lambda$-calculus L. The plugin is implemented in the MetaCoq framework and entirely written in Coq. We provide Ltac tactics to automatically verify the extracted terms w.r.t a logical relation connecting Coq functions with correct extractions and time bounds, essentially performing a certifying translation and running time validation. We provide three case studies: A universal L-term obtained as extraction from the Coq definition of a step-indexed self-interpreter for \L, a many-reduction from solvability of Diophantine equations to the halting problem of L, and a polynomial-time simulation of Turing machines in L.
A<sup>2</sup>S<sup>2</sup>-GNN: Rigging GNN-Based Social Status by Adversarial Attacks in Signed Social Networks

Social status, the social influence of a user, plays an important role in many real-world applications, e.g., trust relations and information propagation in a social network. In this paper, we reveal the possibility of falsifying social status through adversarial attacks in graph neural networks (GNNs). Different from neural networks in the visual or speech domain, GNNs take the attributes of nodes and edges in a graph as features. To cater to the characteristics of GNNs, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\vphantom {_{\int }}$ </tex-math></inline-formula> we design a new paradigm of adversarial example attack, named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A^{2} S^{2}$ </tex-math></inline-formula> - GNN ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {GNN}$ </tex-math></inline-formula> -based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> dversarial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> ttacks on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {S}$ </tex-math></inline-formula> ocial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {S}$ </tex-math></inline-formula> tatus), aiming at manipulating the social status of a target node in social networks. The key idea is to establish relationships or break relationships between a set of compromised nodes and the target node. More specifically, we consider a signed directed graph representing complicated positive/negative asymmetric relationships between nodes. We design an efficient adversarial attack algorithm to determine the minimum set of signed links that should be created or deleted to reach the attack objective. We conduct extensive experiments on baseline datasets. Compared with the benchmark algorithms, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$A^{2} S^{2}$ </tex-math></inline-formula> - GNN can effectively promote or vilify the social status of the target node up to 89.36% and 192.38%, respectively, while keeping the modification to the social network to the minimum. Furthermore, the experimental results on six status evaluating algorithms verify the transferability of our proposed attack algorithm.
Comparison of Methods for Estimating Temporal Topic Models From Primary Care Clinical Text Data: Retrospective Closed Cohort Study

Health care organizations are collecting increasing volumes of clinical text data. Topic models are a class of unsupervised machine learning algorithms for discovering latent thematic patterns in these large unstructured document collections.We aimed to comparatively evaluate several methods for estimating temporal topic models using clinical notes obtained from primary care electronic medical records from Ontario, Canada.We used a retrospective closed cohort design. The study spanned from January 01, 2011, through December 31, 2015, discretized into 20 quarterly periods. Patients were included in the study if they generated at least 1 primary care clinical note in each of the 20 quarterly periods. These patients represented a unique cohort of individuals engaging in high-frequency use of the primary care system. The following temporal topic modeling algorithms were fitted to the clinical note corpus: nonnegative matrix factorization, latent Dirichlet allocation, the structural topic model, and the BERTopic model.Temporal topic models consistently identified latent topical patterns in the clinical note corpus. The learned topical bases identified meaningful activities conducted by the primary health care system. Latent topics displaying near-constant temporal dynamics were consistently estimated across models (eg, pain, hypertension, diabetes, sleep, mood, anxiety, and depression). Several topics displayed predictable seasonal patterns over the study period (eg, respiratory disease and influenza immunization programs).Nonnegative matrix factorization, latent Dirichlet allocation, structural topic model, and BERTopic are based on different underlying statistical frameworks (eg, linear algebra and optimization, Bayesian graphical models, and neural embeddings), require tuning unique hyperparameters (optimizers, priors, etc), and have distinct computational requirements (data structures, computational hardware, etc). Despite the heterogeneity in statistical methodology, the learned latent topical summarizations and their temporal evolution over the study period were consistently estimated. Temporal topic models represent an interesting class of models for characterizing and monitoring the primary health care system.
A verifiable and privacy-preserving cloud mining pool selection scheme in blockchain of things

Blockchain of Things (BCoT), as a new paradigm that incorporates blockchain with the Internet of Things (IoT), can address the privacy and extensibility issues in IoT systems and effectively enhance the data management of IoT devices. Asmost of theIoT devices are resource-constrained in terms of computing power, storage capacity, and energy consumption. Meanwhile,the enormous computational consumption in the consensus process of the blockchain makes it difficult to integrate with IoT closely and exert the great potential of BCoT. latterly, the ”cloud mining” mechanism has been proposed to address these issues, however,some potential problems still exist, such as data privacy and dishonest cloud mining pools, etc. In this paper, we propose a verifiable and privacy-preserving cloud mining pool selection scheme in BCoT (namely, VPP-CMPS). To obtain a better solution for cloud mining pool selection, we implement an improved time-locked puzzles technique to identify dishonest mining pools and perform the authenticity for mining pool selection.Afterwards,we obtain the privacy protection of the data during the entire selection process by adopting additively homomorphic ElGamal cryptosystem. Meanwhile, considering the resource-constrained case in IoT devices, we use a semi-honest cloud server to cooperate in dealing with the selection procedure, thereby reducing the computational overhead of IoT devices. Experimental results indicate that our scheme is more efficient and provides less computational cost.
Privacy-preserved learning from non-iid data in fog-assisted IoT: A federated learning approach

With the prevalence of the Internet of Things (IoT) systems, smart cities comprise complex networks, including sensors, actuators, appliances, and cyber services. The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks, especially privacy-related attacks such as inference and data poisoning ones. Federated Learning (FL) has been regarded as a hopeful method to enable distributed learning with privacy-preserved intelligence in IoT applications. Even though the significance of developing privacy-preserving FL has drawn as a great research interest, the current research only concentrates on FL with independent identically distributed (i.i.d) data and few studies have addressed the non-i. i.d. setting. FL is known to be vulnerable to Generative Adversarial Network (GAN) attacks, where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors. This paper proposes an innovative Privacy Protection-based Federated Deep Learning (PP-FDL) framework, which accomplishes data protection against privacy-related GAN attacks, along with high classification rates from non-i. i.d data. PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other, where class probabilities are protected utilizing a private identifier generated for each class. The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets. The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8% as accuracy improvements.