Reviewerly/exHarmony huggingface dataset:
Arbitrary-Oriented Ship Detection Based on RetinaNet for Remote Sensing Images

Aiming to address the problems of arbitrary orientations, large aspect ratios, and dense arrangements in ship detection, an arbitrary-oriented ship detection method based on RetinaNet is proposed. Our proposed method includes a rotated RetinaNet, a refined network, a feature alignment module, and an improved loss function. First, the rotated RetinaNet achieves rotation detection by using a feature pyramid network, rotated anchors, the skew intersection-over-union (IoU), and skew nonmaximum suppression. Then, the refined network and feature alignment module are introduced to achieve better detection accuracy. Finally, to address the boundary discontinuity, the loss function is improved by introducing the IoU constant factor. Considering the problems with the HRSC2016 dataset, we establish a new dataset with more accurate labels and more images and object samples. Through an ablation study, we thoroughly analyze the validity of the proposed rotated RetinaNet, feature alignment module, and improved loss function. The experimental results show that our method is superior to other state-of-the-art methods.
What is the state of the art of computer vision-assisted cytology? A Systematic Literature Review

Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Cells are harvested from tissues by aspiration or scraping, and it is still predominantly performed manually by medical or laboratory professionals extensively trained for this purpose. It is a time-consuming and repetitive process where many diagnostic criteria are subjective and vulnerable to human interpretation. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review, searching for approaches for the segmentation, detection, quantification, and classification of cells and organelles using computer vision on cytology slides. We analyzed papers published in the last 4 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.
End-To-End Computer Vision Framework: An Open-Source Platform for Research and Education

Computer Vision is a cross-research field with the main purpose of understanding the surrounding environment as closely as possible to human perception. The image processing systems is continuously growing and expanding into more complex systems, usually tailored to the certain needs or applications it may serve. To better serve this purpose, research on the architecture and design of such systems is also important. We present the End-to-End Computer Vision Framework, an open-source solution that aims to support researchers and teachers within the image processing vast field. The framework has incorporated Computer Vision features and Machine Learning models that researchers can use. In the continuous need to add new Computer Vision algorithms for a day-to-day research activity, our proposed framework has an advantage given by the configurable and scalar architecture. Even if the main focus of the framework is on the Computer Vision processing pipeline, the framework offers solutions to incorporate even more complex activities, such as training Machine Learning models. EECVF aims to become a useful tool for learning activities in the Computer Vision field, as it allows the learner and the teacher to handle only the topics at hand, and not the interconnection necessary for visual processing flow.
A Deep Learning Using DenseNet201 to Detect Masked or Non-masked Face

The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.
Secured Resource Allocation for Authorized Users Using Time Specific Blockchain Methodology

The utilization of energy in blockchain division is high as resource allocation models are using this technology and the rundown of resource utilization cases is continually developing. The communicated and permanent nature of blockchain innovation might be utilized to quicken the progressing change to increasingly decentralized and digitalized vitality frameworks and to address a portion of the difficulties the business is confronting in providing security in identification of authorized users and resource allocation transactions among the authorized users. The allocated resources to the users need to be recorded, otherwise the attackers may use them for malicious operations. In any case, blockchain is a developing innovation and it is viewed as a basic vulnerability by numerous users as the difficulties and chances of execution are still to a great extent. There is in this way an absence of information and shortage of dynamic gadgets for getting why, when and how the innovation can include significant worth. The proposed Resource Allocation for Authorized Users using Time specific Blockchain Methodology (RAAUTBM) performs resource allocation to authorized users to avoid malicious actions among blockchain-based use cases and increase practical information about how blockchain could be actualized. The RAAUTBM model verifies all the users for allotting access to the system. The proposed model allots the resources only to the authorized users and to identify the malicious users and remove them from the framework. The resources once allotted to a user remains for a time interval and then the resource is re-allotted to other authorized users for avoiding delay. Resource exchanges in this segment are known to be dull and wasteful, to a limited extent because of the absence of promoted straightforwardness. This research work centers around the advancement of a blockchain application that can improve the resource exchange procedure among authorized users. The proposed model is compared with the traditional methods and the results demonstrate that the proposed model is effective in allocating resources only to the authorized users.
An analysis of security vulnerabilities in container images for scientific data analysis

Abstract Background Software containers greatly facilitate the deployment and reproducibility of scientific data analyses in various platforms. However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images, widens the attack surface in the container host, and creates substantial security risks for computing infrastructures at large. This article presents a vulnerability analysis of container images for scientific data analysis. We compare results obtained with 4 vulnerability scanners, focusing on the use case of neuroscience data analysis, and quantifying the effect of image update and minification on the number of vulnerabilities. Results We find that container images used for neuroscience data analysis contain hundreds of vulnerabilities, that software updates remove roughly two-thirds of these vulnerabilities, and that removing unused packages is also effective. Conclusions We provide recommendations on how to build container images with fewer vulnerabilities.
Path planning for mobile robot using an enhanced ant colony optimization and path geometric optimization

Path planning of mobile robots in complex environments is the most challenging research. A hybrid approach combining the enhanced ant colony system with the local optimization algorithm based on path geometric features, called EACSPGO, has been presented in this study for mobile robot path planning. Firstly, the simplified model of pheromone diffusion, the pheromone initialization strategy of unequal allocation, and the adaptive pheromone update mechanism have been simultaneously introduced to enhance the classical ant colony algorithm, thus providing a significant improvement in the computation efficiency and the quality of the solutions. A local optimization method based on path geometric features has been designed to further optimize the initial path and achieve a good convergence rate. Finally, the performance and advantages of the proposed approach have been verified by a series of tests in the mobile robot path planning. The simulation results demonstrate that the presented EACSPGO approach provides better solutions, adaptability, stability, and faster convergence rate compared to the other tested optimization algorithms.
Conditional Automated Channel Pruning for Deep Neural Networks

Model compression aims to reduce the redundancy of deep networks to obtain compact models. Recently, channel pruning has become one of the predominant compression methods to deploy deep models on resource-constrained devices. Most channel pruning methods often use a fixed compression rate for all the layers of the model, which, however, may not be optimal. To address this issue, given a target compression rate for the whole model, one can search for the optimal compression rate for each layer. Nevertheless, these methods perform channel pruning for a specific target compression rate. When we consider multiple compression rates, they have to repeat the channel pruning process multiple times, which is very inefficient yet unnecessary. To address this issue, we propose a Conditional Automated Channel Pruning(CACP) method to obtain the compressed models with different compression rates through single channel pruning process. To this end, we develop a conditional model that takes an arbitrary compression rate as input and outputs the corresponding compressed model. In the experiments, the resultant models with different compression rates consistently outperform the models compressed by existing methods with a channel pruning process for each target compression rate.
GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering

Images are more than a collection of objects or attributes -they represent a web of relationships among interconnected objects.Scene Graph has emerged as a new modality as a structured graphical representation of images.Scene Graph encodes objects as nodes connected via pairwise relations as edges.To support question answering on scene graphs, we propose GraphVQA, a language-guided graph neural network framework that translates and executes a natural language question as multiple iterations of message passing among graph nodes.We explore the design space of GraphVQA framework, and discuss the trade-off of different design choices.Our experiments on GQA dataset show that GraphVQA outperforms the state-of-the-art model by a large margin (88.43% vs. 94.78%).
Changes in Online Psychoactive Substance Trade via Telegram during the COVID-19 Pandemic

In this article, we present an evaluation of online psychoactive substance trade via Telegram, a free encrypted social media messenger service. The evaluation took place during the COVID-19 pandemic, which allowed us to monitor the effects of the spring 2020 lockdown in the Netherlands on substance trade via Telegram.The objective of this study was to evaluate whether changes in psychoactive substance trade on Telegram markets in the Netherlands can be observed during the COVID-19 pandemic.Between December 2, 2019, and June 29, 2020, a total of 70,226 posts appeared in two analyzed Telegram groups. A total of 5,643 posts were psychoactive substance related. Based on the analyzed posts, Telegram is mostly a '"sellers" market as only a minority of the posts (6.3%) could be identified as a request for a substance. The proportion of posts related to specific substances varied between the periods before, during, and after the lockdown. The proportion of posts on the stimulants ecstasy, cocaine, and amphetamine was lower during the lockdown than before and after. For psychedelics - ketamine, lysergic acid diethylamide (LSD), and 2,5-dimethoxy-4-bromophenethylamine (2C-B) - and other substances, there was a relative increase in the number of posts during the lockdown, which was maintained after the lockdown.Telegram analysis shows that in the Netherlands, online psychoactive substance trade may have been affected during the COVID-19 pandemic. The direction of this effect was different for different classes of substances.
Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction

Contrastive learning has been used to learn a high-quality representation of the image in computer vision.However, contrastive learning is not widely utilized in natural language processing due to the lack of a general method of data augmentation for text data.In this work, we explore the method of employing contrastive learning to improve the text representation from the BERT model for relation extraction.The key knob of our framework is a unique contrastive pre-training step tailored for the relation extraction tasks by seamlessly integrating linguistic knowledge into the data augmentation.Furthermore, we investigate how large-scale data constructed from the external knowledge bases can enhance the generality of contrastive pre-training of BERT.The experimental results on three relation extraction benchmark datasets demonstrate that our method can improve the BERT model representation and achieve state-of-the-art performance.In addition, we explore the interpretability of models by showing that BERT with contrastive pre-training relies more on rationales for prediction.
PhoNLP: A joint multi-task learning model for Vietnamese part-of-speech tagging, named entity recognition and dependency parsing

We present the first multi-task learning model-named PhoNLP-for joint Vietnamese part-of-speech (POS) tagging, named entity recognition (NER) and dependency parsing.Experiments on Vietnamese benchmark datasets show that PhoNLP produces state-of-the-art results, outperforming a single-task learning approach that fine-tunes the pre-trained Vietnamese language model PhoBERT (Nguyen and Nguyen, 2020) for each task independently.We publicly release PhoNLP as an open-source toolkit under the Apache License 2.0.Although we specify PhoNLP for Vietnamese, our PhoNLP training and evaluation command scripts in fact can directly work for other languages that have a pre-trained BERT-based language model and gold annotated corpora available for the three tasks of POS tagging, NER and dependency parsing.We hope that PhoNLP can serve as a strong baseline and useful toolkit for future NLP research and applications to not only Vietnamese but also the other languages.Our PhoNLP is available at https://github.com/VinAIResearch/PhoNLP.
DirectProbe: Studying Representations without Classifiers

Understanding how linguistic structure is encoded in contextualized embedding could help explain their impressive performance across NLP.Existing approaches for probing them usually call for training classifiers and use the accuracy, mutual information, or complexity as a proxy for the representation's goodness.In this work, we argue that doing so can be unreliable because different representations may need different classifiers.We develop a heuristic, DIRECTPROBE, that directly studies the geometry of a representation by building upon the notion of a version space for a task.Experiments with several linguistic tasks and contextualized embeddings show that, even without training classifiers, DIRECTPROBE can shine light into how an embedding space represents labels, and also anticipate classifier performance for the representation.
Question Answering Infused Pre-training of General-Purpose Contextualized Representations

We propose a pre-training objective based on question answering (QA) for learning generalpurpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode all questions that the phrase can answer in context.To this end, we train a bi-encoder QA model, which independently encodes passages and questions, to match the predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs.By encoding QA-relevant information, the bi-encoder's token-level representations are useful for non-QA downstream tasks without extensive (or in some cases, any) finetuning.We show large improvements over both RoBERTa-large and previous state-of-the-art results on zero-shot and few-shot paraphrase detection on four datasets, few-shot named entity recognition on two datasets, and zero-shot sentiment analysis on three datasets.
Factors Influencing Security Incidents on Personal Computing Devices

Organizations expect their employees to connect securely to the organization's computer systems. Often these employees use their personal computers to access the organization's networks. This research explores whether these same employees apply protective security measures to their personal computers. Perhaps these employees behave riskily based on their optimistic bias. Results indicate that while cyber optimistic bias and perceived vulnerability influence individuals to apply more protective security measures, the users still experienced security incidents. Thus, organization are vulnerable to cyber-attacks if they are allowing employees to use personal computers to access these databases.
Understanding Unintended Memorization in Language Models Under Federated Learning

Recent works have shown that language models (LMs), e.g., for next word prediction (NWP), have a tendency to memorize rare or unique sequences in the training data.Since useful LMs are often trained on sensitive data, it is critical to identify and mitigate such unintended memorization.Federated Learning (FL) has emerged as a novel framework for large-scale distributed learning tasks.It differs in many aspects from the well-studied central learning setting where all the data is stored at the central server, and minibatch stochastic gradient descent is used to conduct training.This work is motivated by our observation that NWP models trained under FL exhibited remarkably less propensity to such memorization compared to the central learning setting.Thus, we initiate a formal study to understand the effect of different components of FL on unintended memorization in trained NWP models.Our results show that several differing components of FL play an important role in reducing unintended memorization.First, we discover that the clustering of data according to users-which happens by design in FLhas the most significant effect in reducing such memorization.Using the Federated Averaging optimizer with larger effective minibatch sizes for training causes a further reduction.We also demonstrate that training in FL with a userlevel differential privacy guarantee results in models that can provide high utility while being resilient to memorizing out-of-distribution phrases with thousands of insertions across over a hundred users in the training set.
Reconsidering Annotator Disagreement about Racist Language: Noise or Signal?

An abundance of methodological work aims to detect hateful and racist language in text.However, these tools are hampered by problems like low annotator agreement and remain largely disconnected from theoretical work on race and racism in the social sciences.Using annotations of 5188 tweets from 291 annotators, we investigate how annotator perceptions of racism in tweets vary by annotator racial identity and two text features of the tweets: relevant keywords and latent topics identified through structural topic modeling.We provide a descriptive summary of our data and estimate a series of linear models to determine if annotator racial identity and our 12 topics, alone or in combination, explain the way racial sentiment was annotated, net of relevant annotator characteristics and tweet features.Our results show that White and non-White annotators exhibit significant differences in ratings when reading tweets with high prevalence of certain racially-charged topics.We conclude by suggesting how future methodological work can draw on our results and further incorporate social science theory into analyses.
Cross-Modal Discrete Representation Learning

In contrast to recent advances focusing on highlevel representation learning across modalities, in this work we present a self-supervised learning framework that is able to learn a representation that captures finer levels of granularity across different modalities such as concepts or events represented by visual objects or spoken words.Our framework relies on a discretized embedding space created via vector quantization that is shared across different modalities.Beyond the shared embedding space, we propose a Cross-Modal Code Matching objective that forces the representations from different views (modalities) to have a similar distribution over the discrete embedding space such that cross-modal objects/actions localization can be performed without direct supervision.We show that the proposed discretized multi-modal finegrained representation (e.g., pixel/word/frame) can complement high-level summary representations (e.g., video/sentence/waveform) for improved performance on cross-modal retrieval tasks.We also observe that the discretized representation uses individual clusters to represent the same semantic concept across modalities.Modality A (e.g.Video) Modality B (e.g.Audio) Encoder # !"#$ % … VQ Encoder # !"#$ & … Shared Discrete Embedding Space Encoder # '"(' & $ ) % $ ) & H ) % H ) & Codebook Projection Projection Encoder # '"(' % & ) & & ) % High-level representation Fine-grained representation ℒ MMS