Reviewerly/exHarmony huggingface dataset:
Deep and dense convolutional neural network for multi category classification of magnification specific and magnification independent breast cancer histopathological images

Nowadays, breast cancer is the leading cause of mortality among all other cancers affecting women. Histopathological images serve as an appropriate template for the pathological investigation of this disease. However, a proper and precise classification of these images is indispensable for an accurate prognosis. Over the time numerous attempts have been made for classification of histopathological images. However, most of the research works produced on this subject deal with identifying benign and malignant categories only. Since specific treatments and therapeutic schedules exist for different sub-categories of this disease, identifying the actual sub-class on a sample image is highly desirable. The present study proposes a deep learning based framework for automatic classification of breast cancer images into sub-classes of benign and malignant categories performed on magnification dependent and magnification independent samples of the BreakHis dataset. The proposed framework based on DenseNet architecture achieved respective mean patient recognition scores of 96.55% and 91.82% for binary and multi-classifications of H&E stained images of the BreakHis dataset. Likewise, at the image level mean recognition scores of 91.72% and 96.72% achieved by the proposed framework outperform the results reported in various latest available studies employing deep convolutional neural networks and state-of-the-art architectures. Further, the generalization ability of the proposed framework is tested on a different histopathological dataset - ICIAR 2018, yielding competitive accuracy scores of 93.25% and 92.3% at patient and image levels respectively.
Adversarial Multi-Grained Embedding Network for Cross-Modal Text-Video Retrieval

Cross-modal retrieval between texts and videos has received consistent research interest in the multimedia community. Existing studies follow a trend of learning a joint embedding space to measure the distance between text and video representations. In common practice, video representation is constructed by feeding clips into 3D convolutional neural networks for a coarse-grained global visual feature extraction. In addition, several studies have attempted to align the local objects of video with the text. However, these representations share a drawback of neglecting rich fine-grained relation features capturing spatial-temporal object interactions that benefits mapping textual entities in the real-world retrieval system. To tackle this problem, we propose an adversarial multi-grained embedding network (AME-Net), a novel cross-modal retrieval framework that adopts both fine-grained local relation and coarse-grained global features in bridging text-video modalities. Additionally, with the newly proposed visual representation, we also integrate an adversarial learning strategy into AME-Net, to further narrow the domain gap between text and video representations. In summary, we contribute AME-Net with an adversarial learning strategy for learning a better joint embedding space, and experimental results on MSR-VTT and YouCook2 datasets demonstrate that our proposed framework consistently outperforms the state-of-the-art method.
A maximal ordered ego-clique based approach for prevalent co-location pattern mining

Spatial data often exhibit a tendency highly similar to spatial objects located close to each other. Thus, prevalent co-location pattern (PCP) mining has been studied extensively to discover this tendency. The organization of neighboring relationships on spatial data, called neighborhood materialization (NM), is critical to the PCP problem. However, the previous NM methods suffer from poor efficiency and a large set of results. To this end, a new NM model based on maximal cliques with ego-centric points is proposed in this study, called the maximal ordered ego-clique (MOEC). Here, the correctness of the materialized neighboring relationships of spatial data is proven, and the complexity is further analyzed. In addition, a generalized algorithm GMOEC is designed to effectively transform the neighboring relationships of a spatial data set into MOECs. The geometry of the spatial data set is fully exploited to develop several optimization strategies to enhance efficiency. Furthermore, a novel generalized PCP mining method, GPCP, is proposed to avoid multiple scans of the materialized neighborhood. The GPCP method discovers all PCPs based on the materialized neighborhood using the vertical data format. Finally, extensive experiments on both synthetic and real data sets demonstrate that the proposed solution is highly effective and efficient.
Approaches to simplifying academic texts in English: English teachers’ views and practices

Reading academic and specialised texts in a foreign language is a difficult process, and support for making materials more linguistically accessible is scarce. Approaches used in English as a Foreign Language (EFL) teaching could inform responses to this, as in EFL academic texts are routinely simplified to make them more comprehensible. However, there is a lack of research on the most used method of making texts more accessible – intuitive text simplification. In this study, 24 experienced EFL teachers with Spanish, Chinese and Russian language backgrounds were asked to simplify two academic texts in an online task and then to explain their rationale for each change in a follow-up stimulated recall interview. The study showed that despite the reliance on subjective approximations of comprehensibility in intuitive simplification, there is a shared understanding among teachers as to what constitutes a more accessible academic text in English. The study creates a clearer conceptualisation of the processes involved in intuitive text simplification and suggests a set of pedagogical guidelines that can be used in language teacher training for both general and specific English teaching purposes, as well as in training and support of English-medium instruction (EMI) teachers.
On the use of Machine Learning methods in rock art research with application to automatic painted rock art identification

Rock art is globally recognized as significant, yet the resources allocated to the study and exploration of this important form of cultural heritage are often scarce. In areas where numerous rock art sites exist, much of the rock art is unidentified and therefore remains, unrecorded and unresearched. Manually identifying rock art is time-consuming, tedious, and expensive. Therefore, it is necessary to automate many processes in rock art research, which can be accomplished by Machine Learning. Artificial Intelligence (AI) and Machine Learning (ML) can greatly facilitate rock art research in many ways, such as through Object Recognition and Detection, Motif Extraction, Object Reconstruction, Image Knowledge Graphs, and Representations. This article is a reflective work on the future of ML for rock art research. As a proof-of-concept, it presents a machine learning method based on recent advances in deep learning to train a model to identify images with painted rock art (pictograms). The efficacy of the proposed method is shown using data collected from fieldwork in Australia. Furthermore, our proposed method can be used to train models that are specific to the rock art found in different regions. We provide the code and the trained models in the supplementary section.
Symmetric Network with Spatial Relationship Modeling for Natural Language-based Vehicle Retrieval

Natural language (NL) based vehicle retrieval aims to search specific vehicle given text description. Different from the image-based vehicle retrieval, NL-based vehicle retrieval requires considering not only vehicle appearance, but also surrounding environment and temporal relations. In this paper, we propose a Symmetric Network with Spatial Relationship Modeling (SSM) method for NL-based vehicle retrieval. Specifically, we design a symmetric network to learn the unified cross-modal representations between text descriptions and vehicle images, where vehicle appearance details and vehicle trajectory global information are pre-served. Besides, to make better use of location information, we propose a spatial relationship modeling methods to take surrounding environment and mutual relationship between vehicles into consideration. The qualitative and quantitative experiments verify the effectiveness of the proposed method. We achieve 43.92% MRR accuracy on the test set of the 6th AI City Challenge on natural language-based vehicle retrieval track, yielding the 4th place on the public leaderboard. The code will be available at https://github.com/hbchen121/AICITY2022_Track2_SSM.
Active Stereo Vision System with Rotated Structured Light Patterns and Two-Step Denoising Process for Improved Spatial Resolution

Three-dimensional (3D) scanning systems are widely used nowadays for multiple applications. If the object is too large, it is a challenge to provide the detailed characteristics of the scanned object. Improving the spatial resolution of the scanning process is essential in improving the quality of the reconstruction results. Accordingly, this study has proposed an active stereo vision system for improving the spatial resolution. It is well known that the reconstruction quality of stereo vision systems is severely impaired for objects with limited features on their surfaces, thereby, in the present study, an active stereo vision system is proposed in which stripe patterns are projected onto the object surface with various rotation angles in order to facilitate a more accurate and complete detection of the matching points. Notably, the point clouds obtained by the projected patterns at each rotation angle are formed within the same world coordinate system. As a result, the final reconstructed 3D object can be obtained by merging sets of point clouds directly without the need for a prior registration process. In practice, the quality of the reconstruction results obtained by stereo vision systems is commonly degraded by the effects of noise. Therefore, this study also proposes a two-step (multi-decoding pattern and Epipolar line) method for removing the incorrect matching points and improving the reconstruction results accordingly. The performance of the proposed reconstruction system is evaluated experimentally and compared with that of a general active stereo vision system. The experimental results show that the active stereo vision system presented in the present study provides a low-cost, convenient and effective approach for performing the reconstruction of 3D objects with high spatial resolution.
Safety-constrained reinforcement learning with a distributional safety critic

Abstract Safety is critical to broadening the real-world use of reinforcement learning. Modeling the safety aspects using a safety-cost signal separate from the reward and bounding the expected safety-cost is becoming standard practice, since it avoids the problem of finding a good balance between safety and performance. However, it can be risky to set constraints only on the expectation neglecting the tail of the distribution, which might have prohibitively large values. In this paper, we propose a method called Worst-Case Soft Actor Critic for safe RL that approximates the distribution of accumulated safety-costs to achieve risk control. More specifically, a certain level of conditional Value-at-Risk from the distribution is regarded as a safety constraint, which guides the change of adaptive safety weights to achieve a trade-off between reward and safety. As a result, we can compute policies whose worst-case performance satisfies the constraints. We investigate two ways to estimate the safety-cost distribution, namely a Gaussian approximation and a quantile regression algorithm. On the one hand, the Gaussian approximation is simple and easy to implement, but may underestimate the safety cost, on the other hand, the quantile regression leads to a more conservative behavior. The empirical analysis shows that the quantile regression method achieves excellent results in complex safety-constrained environments, showing good risk control.
Improving automated Turkish text classification with learning‐based algorithms

Abstract Text classification is the process of determining categories or tags of a document depending on its content. Although text classification is a well‐known process, it has many steps that require tuning to improve mathematical models. This article provides a novel methodology and expresses key points to improve text classification performance using learning‐based algorithms and techniques. First, to check the effectiveness of the proposed methodology, we selected two public Turkish news benchmarking datasets. Then, we performed extensive testing using both supervised machine learning algorithms and state‐of‐art pre‐trained language models. The experimental results show that our methodology outperforms previous news classification studies on these benchmarking datasets improving categorization results based on F1‐score. Therefore, we conclude that the presented methodology efficiently improves the classification results and selects the feasible classifier for a given dataset.
XSI—a genotype compression tool for compressive genomics in large biobanks

Generation of genotype data has been growing exponentially over the last decade. With the large size of recent datasets comes a storage and computational burden with ever increasing costs. To reduce this burden, we propose XSI, a file format with reduced storage footprint that also allows computation on the compressed data and we show how this can improve future analyses.We show that xSqueezeIt (XSI) allows for a file size reduction of 4-20× compared with compressed BCF and demonstrate its potential for 'compressive genomics' on the UK Biobank whole-genome sequencing genotypes with 8× faster loading times, 5× faster run of homozygozity computation, 30× faster dot products computation and 280× faster allele counts.The XSI file format specifications, API and command line tool are released under open-source (MIT) license and are available at https://github.com/rwk-unil/xSqueezeIt.Supplementary data are available at Bioinformatics online.
Scene Text Detection Using Attention with Depthwise Separable Convolutions

In spite of significant research efforts, the existing scene text detection methods fall short of the challenges and requirements posed in real-life applications. In natural scenes, text segments exhibit a wide range of shape complexities, scale, and font property variations, and they appear mostly incidental. Furthermore, the computational requirement of the detector is an important factor for real-time operation. To address the aforementioned issues, the paper presents a novel scene text detector using a deep convolutional network which efficiently detects arbitrary oriented and complex-shaped text segments from natural scenes and predicts quadrilateral bounding boxes around text segments. The proposed network is designed in a U-shape architecture with the careful incorporation of skip connections to capture complex text attributes at multiple scales. For addressing the computational requirement of the input processing, the proposed scene text detector uses the MobileNet model as the backbone that is designed on depthwise separable convolutions. The network design is integrated with text attention blocks to enhance the learning ability of our detector, where the attention blocks are based on efficient channel attention. The network is trained in a multi-objective formulation supported by a novel text-aware non-maximal procedure to generate final text bounding box predictions. On extensive evaluations on ICDAR2013, ICDAR2015, MSRA-TD500, and COCOText datasets, the paper reports detection F-scores of 0.910, 0.879, 0.830, and 0.617, respectively.
ACUX Typology: A Harmonisation of Cultural-Visitor Typologies for Multi-Profile Classification

The modern cultural industry and the related academic sectors have shown increased interest in Cultural User eXperience (CUX) research, since it constitutes a critical factor to examine and apply when presenting cultural content. Recent CUX studies show that visitors tend to carry their own cultural characteristics and preferences when visiting destinations of cultural interest, thus obtaining a virtually unique experience. To cope with this tendency, various research efforts have been made to identify different profiles of cultural visitors based on their background and preferences and classify them into distinct visitor types. In this paper, we proposed the ACUX (Augmented Cultural User eXperience) typology for classifying visitors of cultural destinations. The proposed typology aims to provide the multi-profile classification of cultural visitors based on their visiting preferences. Methodology-wise, the ACUX typology was the output of a harmonisation process of existing cultural-visitor typologies that base their classification on visiting preferences. The proposed typology was evaluated in juxtaposition with the harmonised typologies from which it was derived through an experiment conducted using a recommender and a dataset of TripAdvisor user responses. The evaluation showed that the ACUX typology achieved a more accurate profiling of cultural visitors, enabling them to reduce information overload by directly suggesting content that is more likely to meet their diverse preferences and needs.
The Dichotomy of Neural Networks and Cryptography: War and Peace

In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.
dMOBAs: A data marketplace on blockchain with arbitration using side-contracts mechanism

The emergence of a data-driven society has sparked interest in data trading marketplaces. Traditional centralized marketplaces are unable to meet the needs of a transparent, safe, and privacy-protected data marketplace. To address the above challenges, we propose a data marketplace on blockchain with arbitration using side-contracts mechanisms (dMOBAs), in which a consortium of data owners, data buyers, and arbitrators collaborate to create an ecosystem in the blockchain's witnesses. Our marketplace's goals are to provide a decentralized platform for trading personal data for both service providers and data owners. In the meantime, the transaction method can be arbitrated. The arbitrators alliance, which is responsible for resolving data disputes, is formed by a service provider jointly nominated by the buyer and seller. The integrity and value of transacted data can be validated thanks to the arbitrators' consortium. The side-contracts concept decreases the public chain's overhead and enhances the marketplace's operating efficiency. The marketplace's orderly functioning is ensured by explicit incentives and rigorous arbitration methods. To demonstrate the utility and efficiency of the mechanisms, we implement dMOBAs on Ethereum, Fabric and IPFS platforms and analyze its performance. According to the testing results, our arbitration method is 2 times faster than full public chain-based arbitration.
On pushing DeepFake Tweet Detection capabilities to the limits

The recent advances in natural language generation provide an additional tool to manipulate public opinion on social media. Even though there has not been any report of malicious exploit of the newest generative techniques so far, disturbing human-like scholarly examples of GPT-2 and GPT-3 can be found on social media. Therefore, our paper investigates how the state-of-the-art deepfake social media text detectors perform at recognizing GPT-2 tweets as machine-written, also trying to improve the state-of-the-art by hyper-parameter tuning and ensembling the most promising detectors; finally, our work concentrates on studying the detectors’ capabilities to generalize over tweets generated by the more sophisticated and complex evolution of GPT-2, that is GPT-3. Results demonstrate that hyper-parameter optimization and ensembling advance the state-of-the-art, especially on the detection of GPT-2 tweets. However, all tested detectors dramatically decreased their accuracy on GPT-3 tweets. Despite this, we found out that even though GPT-3 tweets are much closer to human-written tweets than the ones produced by GPT-2, they still have latent features in common share with other generative techniques like GPT-2, RNN and other older methods. All things considered, the research community should quickly devise methods to detect GPT-3 social media texts, as well as older generative methods.
Medical Image Segmentation with Learning Semantic and Global Contextual Representation

Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area. The U-Net model has become the standard design choice. Although the symmetrical structure of the U-Net model enables this network to encode rich semantic representation, the intrinsic locality of the CNN layers limits this network's capability in modeling long-range contextual dependency. On the other hand, sequence to sequence Transformer models with a multi-head attention mechanism can enable them to effectively model global contextual dependency. However, the lack of low-level information stemming from the Transformer architecture limits its performance for capturing local representation. In this paper, we propose a two parallel encoder model, where in the first path the CNN module captures the local semantic representation whereas the second path deploys a Transformer module to extract the long-range contextual representation. Next, by adaptively fusing these two feature maps, we encode both representations into a single representative tensor to be further processed by the decoder block. An experimental study demonstrates that our design can provide rich and generic representation features which are highly efficient for a fine-grained semantic segmentation task.
Multimodal Cheapfakes Detection by Utilizing Image Captioning for Global Context

The rapid development of technology in social media platforms has led to abundant misinformation and fake news spreading in the community. One of the most prevalent ways to misleading information on social media is cheapfakes, which are more accessible and affordable than deepfakes. Most existing approaches extract features from text or concatenate visual and textual features and train with multimodal to classify news. This paper proposed several strategies to leverage object, textual, image captioning features. These strategies focus on utilizing image captioning to extract the correlation between images and captions. We also propose some boosting techniques to enhance the result. Our methods are evaluated on the "MMSys'21 Grand Challenge" dataset and have 86.75% accuracy.