Reviewerly/exHarmony huggingface dataset:
Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance

Recently, Information Retrieval community has witnessed fast-paced advances in Dense Retrieval (DR), which performs first-stage retrieval with embedding-based search. Despite the impressive ranking performance, previous studies usually adopt brute-force search to acquire candidates, which is prohibitive in practical Web search scenarios due to its tremendous memory usage and time cost. To overcome these problems, vector compression methods have been adopted in many practical embedding-based retrieval applications. One of the most popular methods is Product Quantization (PQ). However, although existing vector compression methods including PQ can help improve the efficiency of DR, they incur severely decayed retrieval performance due to the separation between encoding and compression. To tackle this problem, we present JPQ, which stands for Joint optimization of query encoding and Product Quantization. It trains the query encoder and PQ index jointly in an end-to-end manner based on three optimization strategies, namely ranking-oriented loss, PQ centroid optimization, and end-to-end negative sampling. We evaluate JPQ on two publicly available retrieval benchmarks. Experimental results show that JPQ significantly outperforms popular vector compression methods. Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size. The compressed index further brings 10x speedup on CPU and 2x speedup on GPU in query latency.
Llama

The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, represented as DAGs of operations, to transform videos, process extracted metadata, and answer questions like, "Is this intersection congested?" The latency and resource efficiency of pipelines can be optimized using configurable knobs for each operation (e.g., sampling rate, batch size, or type of hardware used). However, determining efficient configurations is challenging because (a) the configuration search space is exponentially large, and (b) the optimal configuration depends on users' desired latency and cost targets, (c) input video contents may exercise different paths in the DAG and produce a variable amount intermediate results. Existing video analytics and processing systems leave it to the users to manually configure operations and select hardware resources.
How Powerful is Graph Convolution for Recommendation?

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical successes remain elusive. In this paper, we endeavor to obtain a better understanding of GCN-based CF methods via the lens of graph signal processing. By identifying the critical role of smoothness, a key concept in graph signal processing, we develop a unified graph convolution-based framework for CF. We prove that many existing CF methods are special cases of this framework, including the neighborhood-based methods, low-rank matrix factorization, linear auto-encoders, and LightGCN, corresponding to different low-pass filters. Based on our framework, we then present a simple and computationally efficient CF baseline, which we shall refer to as Graph Filter based Collaborative Filtering (GF-CF). Given an implicit feedback matrix, GF-CF can be obtained in a closed form instead of expensive training with back-propagation. Experiments will show that GF-CF achieves competitive or better performance against deep learning-based methods on three well-known datasets, notably with a 70% performance gain over LightGCN on the Amazon-book dataset.
Academic Success of Online Learning in Undergraduate Nursing Education Programs in the COVID-19 Pandemic Era

This study aimed to investigate predictors for academic success, including satisfaction with online class and academic achievement, in the coronavirus disease 19 (COVID-19) pandemic era.To obtain basic data needed to improve the quality and outcomes of online learning in lectures for nursing students.A cross-sectional, descriptive, nationwide online survey in South Korea was performed using structured questionnaires. Participants were 200 nursing students taking online-based learning at universities in 2020. Data were analyzed using descriptive statistics and hierarchical multiple regression with SPSS WIN 26.0 program.Cyber-class flow (β = 0.65, p < 0.001) was a significant predictor of satisfaction with online class. Self-directed learning (β = 0.18, p = 0.014) and satisfaction with online class (β = 0.19, p = 0.035) were significant predictors of academic achievement.To achieve academic success from online learning, self-directed learning should be prioritized and satisfaction with online class needs to be managed by nursing educators. To improve satisfaction with online class, cyber-class flow should be considered when designing teaching and learning methods for undergraduate nursing education programs.
Ceasing hate with MoH: Hate Speech Detection in Hindi–English code-switched language

Warning: This manuscript may contain upsetting language. Social media has become a bedrock for people to voice their opinions worldwide. Due to the greater sense of freedom with the anonymity feature, it is possible to disregard social etiquette online and attack others without facing severe consequences, inevitably propagating hate speech. The current measures to sift the online content and offset the hatred spread do not go far enough. One factor contributing to this is the prevalence of regional languages in social media and the paucity of language flexible hate speech detectors. The proposed work focuses on analyzing hate speech in Hindi–English code-switched language. Our method explores transformation techniques to capture precise text representation. To contain the structure of data and yet use it with existing algorithms, we developed ‘MoH’ or (Map Only Hindi), which means ‘Love’ in Hindi. ‘MoH’ pipeline which consists of language identification, Roman to Devanagari Hindi transliteration using a knowledge base of Roman Hindi words, and finally employs the fine-tuned Multilingual Bert, and MuRIL language models. We conducted several quantitative experiment studies on three datasets, and evaluated performance using Precision, Recall and F1 metrics. The first experiment studies ‘MoH’ mapped text’s performance with classical machine learning models and shows an average increase of 13% in F1 scores. The second compares the proposed work’s scores with those of the baseline models and shows a rise in performance by 6%. Finally, the third compares the proposed ‘MoH’ technique with various data simulations using the existing transliteration library. Here, ‘MoH’ outperforms the rest by 15%. Our results demonstrate a significant improvement in the state-of-the-art scores on all three datasets.
Differential evolution with orthogonal array‐based initialization and a novel selection strategy

Differential evolution (DE) has been a simple yet effective algorithm for global optimization problems. The performance of DE highly depends on its operators and parameter settings. In the last couple of decades, many advanced variants of DE have been proposed by modifying the operators and introducing new parameter tuning methods. However, the majority of the works on advanced DE have been concentrated upon the mutation and crossover operators. The initialization and selection operators are less explored in the literature. In this work, we implement the orthogonal array-based initialization of the population and propose a neighborhood search strategy to construct the initial population for the DE-based algorithms. We also introduce a conservative selection scheme to improve the performance of the algorithm. We analyze the influence of the proposed initialization and selection schemes on several variants of DE. Results suggest that the proposed methods highly improve the performance of DE algorithm and its variants. Furthermore, we introduce an ensemble strategy for parameter adaptation techniques in DE. Incorporating all the proposed initialization, selection, and parameter adaptation strategies, we develop a new variant of DE, named OLSHADE-CS. The performance of OLSHADE-CS is found to be highly competitive and significantly better in many cases when compared with the performance of the state-of-the-art algorithms on CEC benchmark problems.
Immersive 360° videos in health and social care education: a scoping review

Abstract Background Research on the pedagogical use of immersive 360° videos is a rapidly expanding area within health and social care education. Despite this interest, there is a paucity of empirical data on its application. Method A scoping review methodology framework was used to search for relevant articles published between 1970 and July 2021. Six databases were used to identify studies using immersive 360° videos for training and education purposes within health and social care: PubMed, Ovid Medline, Psych Info, Psych Articles, Cochrane Database and Embase. Research questions included: Is there any evidence that immersive 360° videos increase learning outcomes and motivation to learn in health and social care education? What are the key pedagogical concepts and theories that inform this area of research? What are the limitations of using immersive 360° videos within health and social education? The four dimensions contained within Keller’s ARCS model (attention, relevance, confidence and satisfaction) frame the results section. Results Fourteen studies met our inclusion criteria. Learning outcomes confirm that immersive 360° videos as a pedagogical tool: increases attention, has relevance in skill enhancement, confidence in usability and user satisfaction. In particular, immersive 360° videos has a positive effect on the user’s emotional response to the learning climate, which has a significant effect on users’ motivation to learn. There was a notable lack of pedagogical theory within the studies retrieved and a general lack of clarity on learning outcomes. Conclusion Studies examining the effectiveness of such interventions remains weak due to smaller sample sizes, lack of randomised control trials, and a gap in reporting intervention qualities and outcomes. Nevertheless, 360° immersive video is a viable alternative to VR and regular video, it is cost-effective, and although more robust research is necessary, learning outcomes are promising. Future directions Future research would do well to focus on interactivity and application of pedagogical theory within immersive 360° videos experiences. We argue that more and higher quality research studies, beyond the scope of medical education, are needed to explore the acceptability and effective implementation of this technology.
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey

Graph convolutional networks (GCNs) have received significant attention from various research fields due to the excellent performance in learning graph representations. Although GCN performs well compared with other methods, it still faces challenges. Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs. Therefore, motivated by an urgent need in terms of efficiency and scalability in training GCN, sampling methods have been proposed and achieved a significant effect. In this paper, we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN. To highlight the characteristics and differences of sampling methods, we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories. Finally, we discuss some challenges and future research directions of the sampling methods.
Trojaning Language Models for Fun and Profit

Recent years have witnessed the emergence of a new paradigm of building natural language processing (NLP) systems: general-purpose, pre-trained language models (LMs) are composed with simple downstream models and fine-tuned for a variety of NLP tasks. This paradigm shift significantly simplifies the system development cycles. However, as many LMs are provided by untrusted third parties, their lack of standardization or regulation entails profound security implications, which are largely unexplored. To bridge this gap, this work studies the security threats posed by malicious LMs to NLP systems. Specifically, we present Trojan <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LM</sup> , a new class of trojaning attacks in which maliciously crafted LMs trigger host NLP systems to malfunction in a highly predictable manner. By empirically studying three state-of-the-art LMs (BERT, GPT-2, XLNet) in a range of security-critical NLP tasks (toxic comment detection, question answering, text completion) as well as user studies on crowdsourcing platforms, we demonstrate that Trojan <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LM</sup> possesses the following properties: (i) flexibility - the adversary is able to flexibly define logical combinations (e.g., ‘and’, ‘or’, ‘xor’) of arbitrary words as triggers, (ii) efficacy - the host systems misbehave as desired by the adversary with high probability when “trigger” -embedded inputs are present, (iii) specificity - the trojan LMs function indistinguishably from their benign counterparts on clean inputs, and (iv) fluency - the trigger-embedded inputs appear as fluent natural language and highly relevant to their surrounding contexts. We provide analytical justification for the practicality of Trojan <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LM</sup> , and further discuss potential countermeasures and their challenges, which lead to several promising research directions.
Interpretable Machine Learning: A brief survey from the predictive maintenance perspective

In the field of predictive maintenance (PdM), machine learning (ML) has gained importance over the last years. Accompanying this development, an increasing number of papers use non-interpretable ML to address PdM problems. While ML has achieved unprecedented performance in recent years, the lack of model explainability or interpretability may manifest itself in a lack of trust. The interpretability of ML models is researched under the terms explainable AI (XAI) and interpretable ML. In this paper, we review publications addressing PdM problems which are motivated by model interpretability. This comprises intrinsically interpretable models and post-hoc explanations. We identify challenges of interpretable ML for PdM, including (1) evaluation of interpretability, (2) the observation that explanation methods explaining black box models may show black box behavior themselves, (3) non-consistent use of terminology, (4) a lack of research for time series data, (5) coverage of explanations, and finally (6) the inclusion of domain knowledge,
A Blockchain-Based Federated Learning Method for Smart Healthcare

The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain-based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification-based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real-world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.
Prophetic Analysis of Bitcoin price using Machine Learning Approaches

The most significant disturbance now affecting all economies and financial institutions is the digital transformation of economies. The world’s economy and financial institutions are digitizing at an unprecedented rate. Bitcoin is a devolved crypto-currency, or digital asset, that uses blockchain technology to expedite peer-to-peer financial transactions. Price volatility is one of the primary issues with decentralized cryptocurrencies, highlighting the need of examining the underlying price mechanism. Additionally, Bitcoin prices display non-stationary behaviour, meaning that their statistical distribution fluctuates over time. Bitcoin prices are stochastic, and no one set of characteristics can be used to forecast them completely. Nonetheless, academics have demonstrated varying degrees of effectiveness in estimating Bitcoin values using various feature sets. This article explains how to forecast Bitcoin price movements and prices using machine learning approaches. We intend to apply ARIMA, Facebook Prophet and XGBoost techniques for price prediction.
Image‐based monitoring of bolt loosening through deep‐learning‐based integrated detection and tracking

Abstract Structural bolts are critical components used in different structural elements, such as beam‐column connections and friction damping devices. The clamping force in structural bolts is highly influenced by the bolt rotation. Much of the existing vision‐based research about bolt rotation estimation relies on traditional computer vision algorithms such as Hough transform to assess static images of bolts. This requires careful image preprocessing, and it may not perform well in the situation of complicated bolt assemblies, or in the presence of surrounding objects and background noise, thus hindering their real‐world applications. In this study, an integrated real‐time detect‐track method, namely, RTDT‐bolt, is proposed to monitor the bolt rotation angle. First, a real‐time convolutional‐neural‐networks‐based object detector, named YOLOv3‐tiny, is established and trained to localize structural bolts. Then, the target‐free object tracking algorithm based on optical flow is implemented to continuously monitor and quantify the rotation of structural bolts. In order to enhance the tracking performance against background noise and potential illumination changes during tracking, the YOLOv3‐tiny is integrated with the optical flow tracking algorithm to re‐detect the bolts when the tracking gets lost. Extensive parameter studies were conducted to identify optimal tracking performance and examine the potential limitations. The results indicate that the RTDT‐bolt method can greatly enhance the tracking performance of bolt rotation, which can achieve over 90% accuracy using the recommended range for the parameters.
LE-GAN: Unsupervised low-light image enhancement network using attention module and identity invariant loss

Low-light image enhancement aims to recover normal-light images from the images captured under very dim environments. Existing methods cannot well handle the noise, color bias and over-exposure problem, and fail to ensure visual quality when lacking paired training data. To address these problems, we propose a novel unsupervised low-light image enhancement network named LE-GAN, which is based on generative adversarial networks and is trained with unpaired low/normal-light images. Specifically, we design an illumination-aware attention module that enhances the feature extraction of the network to address the problems of noise and color bias, as well as improve the visual quality. We further propose a novel identity invariant loss to address the over-exposure problem to make the network learn to enhance low-light images adaptively. Extensive experiments show that the proposed method can achieve promising results. Furthermore, we collect a large-scale low-light dataset named Paired Normal/Lowlight Images (PNLI). It consists of 2,000 pairs of low/normal-light images captured in various real-world scenes, which can provide the research community with a high-quality dataset to advance the development of this field.
Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants

Photovoltaic power generation (PVPG) forecasting has attracted increasing research and industry attention due to its significance for energy management, infrastructure planning, and budgeting. Emerging deep learning (DL) models based on historical data have provided effective solutions for PVPG forecasting with great success. However, newly-constructed photovoltaic (NCPV) plants often lack collections of historical data, and thus it is difficult to forecast their future generation accurately. In this work, combining transfer learning (TL) and DL models, we initially propose two parameter-transferring strategies and a constrained long short-term memory (C-LSTM) model, to address the hourly day-ahead PVPG forecasting problem of NCPV plants. The K-nearest neighbors (KNN) algorithm is utilized to extract prior knowledge as physical constraints, which can guide the training process of C-LSTM. The performances of different TL methods combined with C-LSTM are evaluated specifically, and appropriate ones are determined accordingly. The proposed models are evaluated based on real-life datasets collected from actual PV plants in Australia. The results demonstrate that the proposed C-LSTM model outperforms the standard LSTM model with higher forecasting accuracy. In addition, the results also indicate that significant improvements in forecasting accuracy and stability can be obtained by the proposed TL strategies combined with C-LSTM, regardless of different sky conditions (i.e., clear sky, partly cloudy sky, and overcast sky), compared to the conventional machine learning and statistical models in the literature. The forecasting skill of the combined model has improved up to 68.4% compared with the reference persistence model.