Reviewerly/exHarmony huggingface dataset:
Resources for Multilingual Hate Speech Detection

Most of the published approaches and resources for offensive language and hate speech detection are tailored for the English language.In consequence, cross-lingual and cross-cultural perspectives lack some essential resources.The lack of diversity of the datasets in Spanish is notable.Variations throughout Spanish-speaking countries make existing datasets not enough to encompass the task in the different Spanish variants.We manually annotated 9834 tweets from Chile to enrich the existing Spanish resources with different words and new targets of hate that have not been considered in previous studies.We conducted several cross-dataset evaluation experiments of the models published in the literature using our Chilean dataset and two others in English and Spanish.We propose a comparative framework for quickly conducting comparative experiments using different previously published models.In addition, we set up a Codalab competition for further comparison of new models in a standard scenario, that is, data partitions and evaluation metrics.All resources can be accessed through a centralized repository for researchers to get a complete picture of the progress on the multilingual hate speech and offensive language detection task.
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora

Pretrained language models (PTLMs) are typically learned over a large, static corpus and further fine-tuned for various downstream tasks.However, when deployed in the real world, a PTLM-based model must deal with data distributions that deviate from what the PTLM was initially trained on.In this paper, we study a lifelong language model pretraining challenge where a PTLM is continually updated so as to adapt to emerging data.Over a domain-incremental research paper stream and a chronologically-ordered tweet stream, we incrementally pretrain a PTLM with different continual learning algorithms, and keep track of the downstream task performance (after fine-tuning).We evaluate PTLM's ability to adapt to new corpora while retaining learned knowledge in earlier corpora.Our experiments show distillation-based approaches to be most effective in retaining downstream performance in earlier domains.The algorithms also improve knowledge transfer, allowing models to achieve better downstream performance over the latest data, and improve temporal generalization when distribution gaps exist between training and evaluation because of time.We believe our problem formulation, methods, and analysis will inspire future studies towards continual pretraining of language models.
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold

Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a taskoriented dialog system.A key challenge of OOD detection is the overconfidence of neural models.In this paper, we comprehensively analyze overconfidence and classify it into two perspectives: over-confident OOD and in-domain (IND).Then according to intrinsic reasons, we respectively propose a novel reassigned contrastive learning (RCL) to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents for overconfident IND.Experiments and analyses show the effectiveness of our proposed method for both aspects of overconfidence issues.
A new watermarking scheme for digital videos using DCT

Abstract With the advent of high-speed broadband Internet access, the need to protect digital videos is highly recommended. The main objective of this study is to propose an adaptive algorithm for watermarked digital videos in the frequency domain based on discrete cosine transform (DCT). The watermark signature image is embedded into the whole frame of the video. The green channel of the RGB frame is selected for the embedding process using the DCT algorithm as it shows the recommended quality of the watermarked frames. The experiment results indicate that the proposed algorithm shows robustness and high quality of the watermarked videos by testing various strength values ∆ for different videos. It offers resistance against different types of attacks.
Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan

Abstract Objective . Accurate segmentation of the pancreas from abdomen CT scans is highly desired for diagnosis and treatment follow-up of pancreatic diseases. However, the task is challenged by large anatomical variations, low soft-tissue contrast, and the difficulty in acquiring a large set of annotated volumetric images for training. To overcome these problems, we propose a new segmentation network and a semi-supervised learning framework to alleviate the lack of annotated images and improve the accuracy of segmentation. Approach. In this paper, we propose a novel graph-enhanced pancreas segmentation network (GEPS-Net), and incorporate it into a semi-supervised learning framework based on iterative uncertainty-guided pseudo-label refinement. Our GEPS-Net plugs a graph enhancement module on top of the CNN-based U-Net to focus on the spatial relationship information. For semi-supervised learning, we introduce an iterative uncertainty-guided refinement process to update pseudo labels by removing low-quality and incorrect regions. Main results. Our method was evaluated by a public dataset with four-fold cross-validation and achieved the DC of 84.22%, improving 5.78% compared to the baseline. Further, the overall performance of our proposed method was the best compared with other semi-supervised methods trained with only 6 or 12 labeled volumes. Significance. The proposed method improved the segmentation performance of the pancreas in CT images under the semi-supervised setting. It will assist doctors in early screening and making accurate diagnoses as well as adaptive radiotherapy.
Conversation with Dr. Lawrence Tabak and Dr. Lyric Jorgenson on the NIH Perspective on Data Sharing and Management

In June 2022,Â HDSRÂ guest editors Maryann Martone and Richard Nakamura conducted an online interview with Dr. Lawrence Tabak, Acting Director of the National Institutes of Health (NIH) and Dr. Lyric Jorgenson, Acting Associate Director for Science Policy and the Acting Director of the Office of Science Policy at the NIH. The quartet talked about the importance of the new data management and sharing policy to the NIH and updates on the NIHâs activities and priorities since the April 2021 workshop on data management and sharing.
Optimizing graph layout by t-SNE perplexity estimation

Abstract Perplexity is one of the key parameters of dimensionality reduction algorithm of t-distributed stochastic neighbor embedding (t-SNE). In this paper, we investigated the relationship of t-SNE perplexity and graph layout evaluation metrics including graph stress, preserved neighborhood information and visual inspection. As we found that a small perplexity is correlated with a relative higher normalized stress while preserving neighborhood information with a higher precision but less global structure information, we proposed our method to estimate appropriate perplexity either based on a modified standard t-SNE or the sklearn Barnes–Hut TSNE. Experimental results demonstrate effectiveness and ease of use of our approach when tested on a set of benchmark datasets.
Novel lightweight video encryption method based on ChaCha20 stream cipher and hybrid chaotic map

&lt;span lang="EN-US"&gt;In the recent years, an increasing demand for securing visual resource-constrained devices become a challenging problem due to the characteristics of these devices. Visual resource-constrained devices are suffered from limited storage space and lower power for computation such as wireless sensors, internet protocol (IP) camera and smart cards. Consequently, to support and preserve the video privacy in video surveillance system, lightweight security methods are required instead of the existing traditional encryption methods. In this paper, a new light weight stream cipher method is presented and investigated for video encryption based on hybrid chaotic map and ChaCha20 algorithm. Two chaotic maps are employed for keys generation process in order to achieve permutation and encryption tasks, respectively. The frames sequences are encrypted-decrypted based on symmetric scheme with assist of ChaCha20 algorithm. The proposed lightweight stream cipher method has been tested on several video samples to confirm suitability and validation in term of encryption–decryption procedures. The performance evaluation metrics include visual test, histogram analysis, information entropy, correlation analysis and differential analysis. From the experimental results, the proposed lightweight encryption method exhibited a higher security with lower computation time compared with state-of-the-art encryption methods.&lt;/span&gt;
A Pragmatic Analysis of Criticism Strategies against Government Policies on Social Media in Jordan: A Gender-Based Investigation

This study presents a pragmatic analysis of the speech act of criticism as used on Facebook and Twitter against government policies in Jordan. It examines the effect of gender on the participants’ choice of criticism strategies. The data obtained comprises 300 public posts, comments, and tweets. An adapted version of Nguyen’s (2005) framework of criticism strategies is used to analyse the data. The results reveal that the most frequently used strategies by the writers of the posts, comments, and tweets are sarcasm, identification of a problem, grievance/complaint to God, and negative evaluation, respectively. The results also showcase that men and women use some criticism strategies differently: Men tend to make more use of expression of uncertainty and asking/presupposing while women tend to use grievance/complaint to Allah and emojis/pictures. The study shows how language used on social media is a strong indicator of the distrust in the government-citizen relationship evident in the writers’ expression of uncertainty about the government’s decisions. Keywords: Speech Act of Criticism, Media Discourse, Socio-Pragmatics, Government.
Aerial photography trajectory-tracking controller design for quadrotor UAV

Quad-rotor unmanned aerial vehicles (UAV) are prone to external interference during aerial photography of farmland environments. For example, they are affected by external airflow and load, resulting in route deviation and irregular image overlap, which seriously affects image quality. An aerial trajectory tracking controller is designed for this aerial photography process. To ensure that a drone can fly according to the established route during the aerial photography process and meet the requirements of large-scale topographic map stereo mapping for the flight control accuracy of the drone platform, the system was divided into a full-drive subsystem and an underactuated subsystem. The full-drive subsystem uses a fast terminal sliding mode controller to ensure that the variable ([Formula: see text]) reaches the desired value. The under-actuated subsystem adopts the second-order sliding mode control was used to achieve effective position and attitude tracking of variables ([Formula: see text]). The flight controllers are derived by using Lyapunov theory. Finally, with the aerial trajectory of a farmland taken as an example, the flight path control of the UAV is simulated. Simulation results show that the designed control system can be applied to the aerial photography process of the UAV and has strong anti-system parameter perturbation, robustness and good trajectory tracking.
Automatic thumbnail selection for soccer videos using machine learning

Thumbnail selection is a very important aspect of online sport video presentation, as thumbnails capture the essence of important events, engage viewers, and make video clips attractive to watch. Traditional solutions in the soccer domain for presenting highlight clips of important events such as goals, substitutions, and cards rely on the manual or static selection of thumbnails. However, such approaches can result in the selection of sub-optimal video frames as snapshots, which degrades the overall quality of the video clip as perceived by viewers, and consequently decreases viewership, not to mention that manual processes are expensive and time consuming. In this paper, we present an automatic thumbnail selection system for soccer videos which uses machine learning to deliver representative thumbnails with high relevance to video content and high visual quality in near real-time. Our proposed system combines a software framework which integrates logo detection, close-up shot detection, face detection, and image quality analysis into a modular and customizable pipeline, and a subjective evaluation framework for the evaluation of results. We evaluate our proposed pipeline quantitatively using various soccer datasets, in terms of complexity, runtime, and adherence to a pre-defined rule-set, as well as qualitatively through a user study, in terms of the perception of output thumbnails by end-users. Our results show that an automatic end-to-end system for the selection of thumbnails based on contextual relevance and visual quality can yield attractive highlight clips, and can be used in conjunction with existing soccer broadcast pipelines which require real-time operation.
SOA-based all-optical neuron with reconfigurable nonlinear activation functions

We propose an all-optical neuron based on the self-induced polarization rotation in a semiconductor optical amplifier, which achieves reconfigurable nonlinear activation functions by tuning the initial state of polarization of input signal.
Perioperative Predictions with Interpretable Latent Representation

Given the risks and cost of hospitalization, there has been significant interest in exploiting machine learning models to improve perioperative care. However, due to the high dimensionality and noisiness of perioperative data, it remains a challenge to develop accurate and robust encoding for surgical predictions. Furthermore, it is important for the encoding to be interpretable by perioperative care practitioners to facilitate their decision making process. We proposeclinical variational autoencoder (cVAE), a deep latent variable model that addresses the challenges of surgical applications through two salient features. (1) To overcome performance limitations of traditional VAE, it isprediction-guided with explicit expression of predicted outcome in the latent representation. (2) Itdisentangles the latent space so that it can be interpreted in a clinically meaningful fashion. We apply cVAE to two real-world perioperative datasets to evaluate its efficacy and performance in predicting outcomes that are important to perioperative care, including postoperative complication and surgery duration. To demonstrate the generality and facilitate reproducibility, we also apply cVAE to the open MIMIC-III dataset for predicting ICU duration and mortality. Our results show that the latent representation provided by cVAE leads to superior performance in classification, regression and multi-task predictions. The two features of cVAE are mutually beneficial and eliminate the need of a predictor. We further demonstrate the interpretability of the disentangled representation and its capability to capture intrinsic characteristics of hospitalized patients. While this work is motivated by and evaluated in the context of clinical applications, the proposed approach may be generalized for other fields using high-dimensional and noisy data and valuing interpretable representations.
Amazon Shop the Look: A Visual Search System for Fashion and Home

In this paper, we introduce Shop the Look, a web-scale fashion and home product visual search system deployed at Amazon. Building such a system poses great challenges to both science and engineering practices. We leverage large-scale image data from the Amazon product catalog and adopt effective strategies to reduce the human effort required to annotate data. By employing state-of-the-art computer vision techniques, we train detection, recognition, and feature extraction models to bridge the domain gap between in-the-wild query images and product images which are taken under controlled settings. Our system is designed to achieve a balance between result accuracy and efficiency. The run-time service is optimized to provide retrieval results to users with low-latency. The scalable offline index-building pipeline adapts to the dynamic Amazon catalog that contains billions of products. We present both quantitative and qualitative evaluation results to demonstrate the performance of our system. We believe that the fast-growing Shop the Look service is shaping the way that customers shop on Amazon.
Developing occupational and health susceptibility personas for wastewater personnel in the United States in the age of COVID‐19

Abstract Compared with the public, wastewater personnel, are at an increased risk of infection and illness from wastewater pathogens due to work‐related tasks. Unfortunately, current risk assessment approaches do not consider individual personnel factors (e.g., age and health conditions) that may influence their susceptibility to a health effect. The objective of this study is to establish a baseline level of occupational and health factors among the wastewater personnel population, quantify these factors using a susceptibility evaluation scoring system, and examine relevant susceptibility features using the concept of “Personas.” Using survey data from 246 respondents and public health risk data on COVID‐19 from the CDC, personnel clustered into three persona groups: “low susceptibility,” “high occupational susceptibility,” and “high health susceptibility.” Results highlight the intersectionality between gender, age, underlying health conditions, job tasks, and level of exposure to wastewater and provide context for incorporating individual variables into risk assessment methodologies with the goal of protecting this essential workforce. Practitioner Points A risk assessment framework that combines health and occupational susceptibility factors was developed for wastewater treatment plant personnel. Wastewater personnel clustered into three persona groups: “low susceptibility,” “high occupational susceptibility,” and “high health susceptibility.” The intersectionality between job related activities and individual health provides a holistic approach to risk assessment for wastewater personnel.
Dual supervised sampling networks for real-time segmentation of cervical cell nucleus

The morphology of the cervical cell nucleus is the most important consideration for pathological cell identification. And a precise segmentation of the cervical cell nucleus determines the performance of the final classification for most traditional algorithms and even some deep learning-based algorithms. Many deep learning-based methods can accurately segment cervical cell nuclei but will cost lots of time, especially when dealing with the whole-slide image (WSI) of tens of thousands of cells. To address this challenge, we propose a dual-supervised sampling network structure, in which a supervised-down sampling module uses compressed images instead of original images for cell nucleus segmentation, and a boundary detection network is introduced to supervise the up-sampling process of the decoding layer for accurate segmentation. This strategy dramatically reduces the convolution calculation in image feature extraction and ensures segmentation accuracy. Experimental results on various cervical cell datasets demonstrate that compared with UNet, the inference speed of the proposed network is increased by 5 times without losing segmentation accuracy. The codes and datasets are available at https://github.com/ldrunning/DSSNet
EGM: Enhanced Graph-based Model for Large-scale Video Advertisement Search

Video advertisements may grasp customers' attention instantly and are often adored by advertisers. Since the corpus is vast, achieving an efficient query-to-video search can be challenging. Because traditional approximate nearest neighborhood (ANN) search methods are based simple similarities (e.g., cosine or inner products) on embedding vectors. They are often not sufficient for bridging the modal gap between a text query and video advertisements and typically can only achieve sub-optimal performance in query-to-video search. Tree-based deep model (TDM) overcomes the limited matching capability of embedding-based methods but suffers from the data sparsity problem. Deep retrieval model adopts a graph-based model which overcomes the data sparsity problem in TDM by sharing the nodes. But the shared nodes entangle features of different items, making it difficult to distinguish similar items. In this work, we enhance the graph-based model through sub-path embedding to differentiate similar videos. The added sub-path embedding provides personalized characteristics, beneficial for modeling fine-grain details to discriminate similar items. After launching enhanced graph model (EGM), the click-through rate (CTR) relatively increases by 1.33%, and the conversion rate (CVR) relatively by 1.07%.
White Box: On the Prediction of Collaborative Filtering Recommendation Systems’ Performance

Collaborative Filtering (CF) recommendation algorithms are a popular solution to the information overload problem, aiding users in the item selection process. Relevant research has long focused on refining and improving these models to produce better (more effective) recommendations, and has converged on a methodology to predict their effectiveness on target datasets by evaluating them on random samples of the latter. However, predicting the efficiency of the solutions—especially with regard to their time- and resource-hungry training phase, whose requirements dwarf those of the prediction/recommendation phase—has received little to no attention in the literature. This article addresses this gap for a number of representative and highly popular CF models, including algorithms based on matrix factorization, k-nearest neighbors, co-clustering, and slope one schemes. To this end, we first study the computational complexity of the training phase of said CF models and derive time and space complexity equations. Then, using characteristics of the input and the aforementioned equations, we contribute a methodology for predicting the processing time and memory usage of their training phase. Our contributions further include an adaptive sampling strategy, to address the tradeoff between resource usage costs and prediction accuracy, and a framework that quantifies both the efficiency and effectiveness of CF. Finally, a systematic experimental evaluation demonstrates that our method outperforms state-of-the-art regression schemes by a considerable margin, with an overhead that is a small fraction of the overall requirements of CF training.