Reviewerly/exHarmony huggingface dataset:
Masked Joint Bilateral Filtering via Deep Image Prior for Digital X-Ray Image Denoising

Medical image denoising faces great challenges. Although deep learning methods have shown great potential, their efficiency is severely affected by millions of trainable parameters. The non-linearity of neural networks also makes them difficult to be understood. Therefore, existing deep learning methods have been sparingly applied to clinical tasks. To this end, we integrate known filtering operators into deep learning and propose a novel Masked Joint Bilateral Filtering (MJBF) via deep image prior for digital X-ray image denoising. Specifically, MJBF consists of a deep image prior generator and an iterative filtering block. The deep image prior generator produces plentiful image priors by a multi-scale fusion network. The generated image priors serve as the guidance for the iterative filtering block, which is utilized for the actual edge-preserving denoising. The iterative filtering block contains three trainable Joint Bilateral Filters (JBFs), each with only 18 trainable parameters. Moreover, a masking strategy is introduced to reduce redundancy and improve the understanding of the proposed network. Experimental results on the ChestX-ray14 dataset and real data show that the proposed MJBF has achieved superior performance in terms of noise suppression and edge preservation. Tests on the portability of the proposed method demonstrate that this denoising modality is simple yet effective, and could have a clinical impact on medical imaging in the future.
Artificial Intelligence Risks and Algorithmic Regulation

Abstract In this editorial article, we aim to map out the central features of algorithmic regulation and its conceptual basis – seeking to bring together different strands of the literature relating to the topic that have often remained apart. We then reflect on the ways through which algorithmic law could evolve to address the challenges of artificial intelligence in the legal domain, particularly by examining the potential of applying a “prudential” test in order to determine whether automated decision-making systems are suitable to adequately support legal decision-making.
Artificial intelligence to complement rather than replace radiologists in breast screening

In The Lancet Digital Health, Christian Leibig and colleagues1 evaluated an artificial intelligence (AI) system designed to detect cancer on mammograms using a retrospective external validation test set of mammograms from 82 851 women attending breast cancer screening in Germany. Current practice in breast cancer screening is for one or two radiologists to examine each women's mammograms to determine whether there is sufficient suspicion of cancer to recall her for further tests. This study examined the potential for the same AI system to contribute in different ways to the testing pathway.
Mass Extinctions, Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544

Book Review| June 24, 2022 Mass Extinctions, Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544 Robert Anderson Robert Anderson 2552 Humphrey Road, Loomis, CA 95650 Search for other works by this author on: GSW Google Scholar Environmental & Engineering Geoscience (2022) 28 (3): 333. https://doi.org/10.2113/eegs-28-03-06 Article history first online: 24 Jun 2022 Cite View This Citation Add to Citation Manager Share Icon Share MailTo Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation Robert Anderson; Mass Extinctions, Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544. Environmental & Engineering Geoscience 2022;; 28 (3): 333. doi: https://doi.org/10.2113/eegs-28-03-06 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyEnvironmental & Engineering Geoscience Search Advanced Search Mass Extinctions,Volcanism, and Impacts: New Developments: Geological Society of America Special Paper 544 (Adatte et al., 2020),the second of two special papers on mass extinctions, volcanism, and impacts, contains nine chapters. (Note: This review was written without the benefit of having read the first special paper.) The majority of this collection is focused on interpreting models regarding the acidification of water and the impact on organisms that depended on carbonates to grow. The first chapter, Volcanism as a Prime Cause of Mass Extinctions: Retrospectives and Perspective, by Grzegorz Racki, provides the reader with a general... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
A Chronicle of the Application of Differential Privacy to the 2020 Census

In this article, we chronicle the U.S. Census Bureau's development of the Disclosure Avoidance System (DAS) for the publicly released products of the 2020 Census of Population.We provide a brief history of the Census Bureau's fulfillment of its dual mission of conducting and disseminating the constitutionally mandated decennial information on the U.S. population and its promise of safeguarding the confidentiality of that information.We discuss the basis for and development of a new DAS for released data products from the 2020 Census and the evidence that emerged from various user communities on the accuracy and usability of data produced under this new DAS.We offer some assessments of this experience, the dilemmas and challenges that the Census Bureau faces for producing usable data while safeguarding the confidentiality of the information it collects, and some recommendations for addressing these challenges in the future.
Self-Supervised Object Localization with Joint Graph Partition

Object localization aims to generate a tight bounding box for the target object, which is a challenging problem that has been deeply studied in recent years. Since collecting bounding-box labels is time-consuming and laborious, many researchers focus on weakly supervised object localization (WSOL). As the recent appealing self-supervised learning technique shows its powerful function in visual tasks, in this paper, we take the early attempt to explore unsupervised object localization by self-supervision. Specifically, we adopt different geometric transformations to image and utilize their parameters as pseudo labels for self-supervised learning. Then, the class-agnostic activation map (CAAM) is used to highlight the target object potential regions. However, such attention maps merely focus on the most discriminative part of the objects, which will affect the quality of the predicted bounding box. Based on the motivation that the activation maps of different transformations of the same image should be equivariant, we further design a siamese network that encodes the paired images and propose a joint graph cluster partition mechanism in an unsupervised manner to enhance the object co-occurrent regions. To validate the effectiveness of the proposed method, extensive experiments are conducted on CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets. Experimental results show that our method outperforms state-of-the-art methods using the same level of supervision, even outperforms some weakly-supervised methods.
Interpretable Neural Subgraph Matching for Graph Retrieval

Given a query graph and a database of corpus graphs, a graph retrieval system aims to deliver the most relevant corpus graphs. Graph retrieval based on subgraph matching has a wide variety of applications, e.g., molecular fingerprint detection, circuit design, software analysis, and question answering. In such applications, a corpus graph is relevant to a query graph, if the query graph is (perfectly or approximately) a subgraph of the corpus graph. Existing neural graph retrieval models compare the node or graph embeddings of the query-corpus pairs, to compute the relevance scores between them. However, such models may not provide edge consistency between the query and corpus graphs. Moreover, they predominantly use symmetric relevance scores, which are not appropriate in the context of subgraph matching, since the underlying relevance score in subgraph search should be measured using the partial order induced by subgraph-supergraph relationship. Consequently, they show poor retrieval performance in the context of subgraph matching. In response, we propose ISONET, a novel interpretable neural edge alignment formulation, which is better able to learn the edge-consistent mapping necessary for subgraph matching. ISONET incorporates a new scoring mechanism which enforces an asymmetric relevance score, specifically tailored to subgraph matching. ISONET’s design enables it to directly identify the underlying subgraph in a corpus graph, which is relevant to the given query graph. Our experiments on diverse datasets show that ISONET outperforms recent graph retrieval formulations and systems. Additionally, ISONET can provide interpretable alignments between query-corpus graph pairs during inference, despite being trained only using binary relevance labels of whole graphs during training, without any fine-grained ground truth information about node or edge alignments.
Path-Following and Obstacle Avoidance Control of Nonholonomic Wheeled Mobile Robot Based on Deep Reinforcement Learning

In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified.
Text to Image Synthesis Using Stacked Conditional Variational Autoencoders and Conditional Generative Adversarial Networks

Synthesizing a realistic image from textual description is a major challenge in computer vision. Current text to image synthesis approaches falls short of producing a highresolution image that represent a text descriptor. Most existing studies rely either on Generative Adversarial Networks (GANs) or Variational Auto Encoders (VAEs). GANs has the capability to produce sharper images but lacks the diversity of outputs, whereas VAEs are good at producing a diverse range of outputs, but the images generated are often blurred. Taking into account the relative advantages of both GANs and VAEs, we proposed a new stacked Conditional VAE (CVAE) and Conditional GAN (CGAN) network architecture for synthesizing images conditioned on a text description. This study uses Conditional VAEs as an initial generator to produce a high-level sketch of the text descriptor. This high-level sketch output from first stage and a text descriptor is used as an input to the conditional GAN network. The second stage GAN produces a 256x256 high resolution image. The proposed architecture benefits from a conditioning augmentation and a residual block on the Conditional GAN network to achieve the results. Multiple experiments were conducted using CUB and Oxford-102 dataset and the result of the proposed approach is compared against state-ofthe-art techniques such as StackGAN. The experiments illustrate that the proposed method generates a high-resolution image conditioned on text descriptions and yield competitive results based on Inception and Frechet Inception Score using both datasets
Rethinking Document-level Neural Machine Translation

This paper does not aim at introducing a novel model for document-level neural machine translation.Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for documentlevel translation?Interestingly, we observe that the original Transformer with appropriate training techniques can achieve strong results for document translation, even with a length of 2000 words.We evaluate this model and several recent approaches on nine documentlevel datasets and two sentence-level datasets across six languages.Experiments show that document-level Transformer models outperforms sentence-level ones and many previous methods in a comprehensive set of metrics, including BLEU, four lexical indices, three newly proposed assistant linguistic indicators, and human evaluation.Our new datasets and evaluation scripts are in https://github.com/sunzewei2715/Doc2Doc_NMT.
Traffic Anomaly Detection Using Deep Semi-Supervised Learning at the Mobile Edge

In this paper, we design an Anomaly Detection (AD) framework for mobile data traffic, capable of identifying different types of anomalous events generated by flash crowds in metropolitan areas. We state the problem using a semi-supervised approach and exploit the great performance of different Recurrent Neural Network (RNN) models to learn the temporal context of input sequences. Our proposal processes real traffic traces from the unencrypted LTE Physical Downlink Control Channel (PDCCH) of an operative network, gathered during an extensive measurement campaign in two major cities in Spain. The AD framework is designed to perform: i) a-posteriori analysis to understand users’ behavior and urban environment variations; ii) real-time analysis to automatically and on-the-fly alert urban anomalies; and iii) estimation of the duration of the periods identified as anomalous. Numerical results show the higher performance of our AD framework compared to classic AD algorithms and confirm that the proposed framework predicts anomalous behaviours with high accuracy and regardless of their cause.
Post-Quantum Security of the Even-Mansour Cipher

The Even-Mansour cipher is a simple method for constructing a (keyed) pseudorandom permutation E from a public random permutation $$P:\{0,1\}^n \rightarrow \{0,1\}^n$$ . It is secure against classical attacks, with optimal attacks requiring $$q_E$$ queries to E and $$q_P$$ queries to P such that $$q_E \cdot q_P \approx 2^n$$ . If the attacker is given quantum access to both E and P, however, the cipher is completely insecure, with attacks using $$q_E, q_P = O(n)$$ queries known. In any plausible real-world setting, however, a quantum attacker would have only classical access to the keyed permutation E implemented by honest parties, while retaining quantum access to P. Attacks in this setting with $$q_E \cdot q_P^2 \approx 2^n$$ are known, showing that security degrades as compared to the purely classical case, but leaving open the question as to whether the Even-Mansour cipher can still be proven secure in that natural, "post-quantum" setting. We resolve this question, showing that any attack in that setting requires $$q_E \cdot q^2_P + q_P \cdot q_E^2 \approx 2^n$$ . Our results apply to both the two-key and single-key variants of Even-Mansour. Along the way, we establish several generalizations of results from prior work on quantum-query lower bounds that may be of independent interest.
LinkBreaker: Breaking the Backdoor-Trigger Link in DNNs via Neurons Consistency Check

Backdoor attacks cause model misbehaving by first implanting backdoors in deep neural networks (DNNs) during training and then activating the backdoor via samples with triggers during inference. The compromised models could pose serious security risks to artificial intelligence systems, such as misidentifying ‘stop’ traffic sign into ‘80km/h’. In this paper, we investigate the connection characteristic between the backdoor and the trigger in DNNs and observe the fact that the backdoor is implanted via establishing a link between a cluster of neurons, representing the backdoor, and the triggers. Based on this observation, we design LinkBreaker, a new generic scheme for defending against backdoor attacks. In particular, LinkBreaker deploys a neuron consistency check mechanism for identifying compromised neuron set related to the trigger. Then, the LinkBreaker regulates the model to make predictions based on benign neuron set only and thus breaks the link between the backdoor and the trigger. Compared to previous defenses, LinkBreaker offers a more general backdoor countermeasure that is not only effective against input-agnostic backdoors but also source-specific backdoors, which the later can not be defeated by majority of state-of-the-arts. Besides, LinkBreaker is robust against adversarial examples, which, to a large extent, provides a holistic defense against adversarial example attacks on DNNs, while almost all current backdoor defenses do not have such consideration and capability. Extensive experimental evaluations on real datasets demonstrate that LinkBreaker is with high efficacy of suppressing trigger inputs while incurring no noticeable accuracy deterioration on benign inputs.
A semi-automatic toolbox for markerless effective semantic feature extraction

Abstract VisionTool is an open-source python toolbox for semantic features extraction, capable to provide accurate features detectors for different applications, including motion analysis, markerless pose estimation, face recognition and biological cell tracking. VisionTool leverages transfer-learning with a large variety of deep neural networks allowing high-accuracy features detection with few training data. The toolbox offers a friendly graphical user interface, efficiently guiding the user through the entire process of features extraction. To facilitate broad usage and scientific community contribution, the code and a user guide are available at https://github.com/Malga-Vision/VisionTool.git .
Defining a continuum from individual, to swarm, to collective intelligence, and to general collective intelligence

The concept of collective intelligence (CI) is distinguished from the concept of a general collective intelligence or GCI in that CI addresses a specific range of problems and therefore has narrow problem-solving ability, while GCI can potentially address any problem and therefore has general problem-solving ability. General problem-solving ability in groups has been represented by a GCI factor c defined in analogy with the individual intelligence factor g which measures IQ. While groups might have an innate GCI factor, GCI is a hypothetical platform that combines groups into a virtual collective cognition with a single well-defined thread of collective reasoning having general problem-solving ability, thereby creating an artificial c factor separate from the innate c factor. GCI also creates the opportunity to exponentially increase this c factor. This paper explores the differences between various forms of innate or artificial, as well as individual and group cognition.
TiRGN: Time-Guided Recurrent Graph Network with Local-Global Historical Patterns for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) have been widely used in various fields that model the dynamics of facts along the timeline. In the extrapolation setting of TKG reasoning, since facts happening in the future are entirely unknowable, insight into history is the key to predicting future facts. However, it is still a great challenge for existing models as they hardly learn the characteristics of historical events adequately. From the perspective of historical development laws, comprehensively considering the sequential, repetitive, and cyclical patterns of historical facts is conducive to predicting future facts. To this end, we propose a novel representation learning model for TKG reasoning, namely TiRGN, a time-guided recurrent graph network with local-global historical patterns. Specifically, TiRGN uses a local recurrent graph encoder network to model the historical dependency of events at adjacent timestamps and uses the global history encoder network to collect repeated historical facts. After the trade-off between the two encoders, the final inference is performed by a decoder with periodicity. We use six benchmark datasets to evaluate the proposed method. The experimental results show that TiRGN outperforms the state-of-the-art TKG reasoning methods in most cases.
Position Control of a Mobile Robot through Deep Reinforcement Learning

This article proposes the use of reinforcement learning (RL) algorithms to control the position of a simulated Kephera IV mobile robot in a virtual environment. The simulated environment uses the OpenAI Gym library in conjunction with CoppeliaSim, a 3D simulation platform, to perform the experiments and control the position of the robot. The RL agents used correspond to the deep deterministic policy gradient (DDPG) and deep Q network (DQN), and their results are compared with two control algorithms called Villela and IPC. The results obtained from the experiments in environments with and without obstacles show that DDPG and DQN manage to learn and infer the best actions in the environment, allowing us to effectively perform the position control of different target points and obtain the best results based on different metrics and indices.
Deep Sentiment Analysis of Twitter Data Using a Hybrid Ghost Convolution Neural Network Model

Several problems remain, despite the evident advantages of sentiment analysis of public opinion represented on Twitter and Facebook. On complicated training data, hybrid approaches may reduce sentiment mistakes. This research assesses the dependability of numerous hybrid approaches on a variety of datasets. Across domains and datasets, we compare hybrid models to singles. Text tweets and reviews are included in our deep sentiment analysis learning systems. The support vector machine (SVM), Long Short-Term Memory (LSTM), and ghost model convolution neural network (CNN) are combined to get the hybrid model. The dependability and computation time of each approach were evaluated. On all datasets, hybrid models outperform single models when deep learning and SVM are combined. The traditional models were less trustworthy, and deep learning algorithms have recently shown their enormous promise in sentiment analysis. Linear transformations are used in feature maps to eliminate duplicate or related features. The ghost unit makes ghost features by taking away attributes that are both similar and duplicated from each intrinsic feature. LSTM produces higher results but takes longer to process, while CNN needs less hyperparameter adjusting and monitoring. The effectiveness of the integrated model varies depending on the work, and all performed better than the others. For hybrid deep sentiment analysis learning models, LSTM networks, CNNs, and SVMs are needed. Hybrid models are used to compare SVM, LSTM, and CNN, and we tested each method's accuracy and errors. Deep learning-SVM hybrid models improve sentiment analysis accuracy. Experimental results have shown the accuracy of the proposed model shown 91.3 percent and 91.5 percent for datasets type 1 and 8, respectively.