Reviewerly/exHarmony huggingface dataset:
Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.
Exact rate analysis for quantum repeaters with imperfect memories and entanglement swapping as soon as possible

We present an exact rate analysis for a secret key that can be shared among two parties employing a linear quantum repeater chain. One of our main motivations is to address the question whether simply placing quantum memories along a quantum communication channel can be beneficial in a realistic setting. The underlying model assumes deterministic entanglement swapping of single-spin quantum memories and it excludes probabilistic entanglement distillation, and thus two-way classical communication, on higher nesting levels. Within this framework, we identify the essential properties of any optimal repeater scheme: entanglement distribution in parallel, entanglement swapping as soon and parallel quantum storage as little as possible. While these features are obvious or trivial for the simplest repeater with one middle station, for more stations they cannot always be combined. We propose an optimal scheme including channel loss and memory dephasing, proving its optimality for the case of two stations and conjecturing it for the general case. In an even more realistic setting, we consider additional tools and parameters such as memory cutoffs, multiplexing, initial state and swapping gate fidelities, and finite link coupling efficiencies in order to identify potential regimes in memory-assisted quantum key distribution beyond one middle station that exceed the rates of the smallest quantum repeaters as well as those obtainable in all-optical schemes unassisted by stationary memory qubits and two-way classical communication. Our analytical treatment enables us to determine simultaneous trade-offs between various parameters, their scaling, and their influence on the performance ordering among different types of protocols, comparing two-photon interference after dual-rail qubit transmission with one-photon interference of single-rail qubits or, similarly, optical interference of coherent states. We find that for experimental parameter values that are highly demanding but not impossible (up to 10 s coherence time, about 80% link coupling, and state or gate infidelities in the regime of 1%--2%), one secret bit can be shared per second at a total channel loss budget of 157.6 dB, i.e. a total distance of 800 km for a fiber attenuation length of 22 km with repeater stations placed at every 100 km---a clear improvement over realistic twin-field or, much more pronouncedly, ideal point-to-point quantum key distribution at GHz clock rates.
Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations

Contrastive learning has been proven beneficial for self-supervised skeleton-based action recognition. Most contrastive learning methods utilize carefully designed augmentations to generate different movement patterns of skeletons for the same semantics. However, it is still a pending issue to apply strong augmentations, which distort the images/skeletons’ structures and cause semantic loss, due to their resulting unstable training. In this paper, we investigate the potential of adopting strong augmentations and propose a general hierarchical consistent contrastive learning framework (HiCLR) for skeleton-based action recognition. Specifically, we first design a gradual growing augmentation policy to generate multiple ordered positive pairs, which guide to achieve the consistency of the learned representation from different views. Then, an asymmetric loss is proposed to enforce the hierarchical consistency via a directional clustering operation in the feature space, pulling the representations from strongly augmented views closer to those from weakly augmented views for better generalizability. Meanwhile, we propose and evaluate three kinds of strong augmentations for 3D skeletons to demonstrate the effectiveness of our method. Extensive experiments show that HiCLR outperforms the state-of-the-art methods notably on three large-scale datasets, i.e., NTU60, NTU120, and PKUMMD. Our project is publicly available at: https://jhang2020.github.io/Projects/HiCLR/HiCLR.html.
Head-Free Lightweight Semantic Segmentation with Linear Transformer

Existing semantic segmentation works have been mainly focused on designing effective decoders; however, the computational load introduced by the overall structure has long been ignored, which hinders their applications on resource-constrained hardwares. In this paper, we propose a head-free lightweight architecture specifically for semantic segmentation, named Adaptive Frequency Transformer (AFFormer). AFFormer adopts a parallel architecture to leverage prototype representations as specific learnable local descriptions which replaces the decoder and preserves the rich image semantics on high-resolution features. Although removing the decoder compresses most of the computation, the accuracy of the parallel structure is still limited by low computational resources. Therefore, we employ heterogeneous operators (CNN and vision Transformer) for pixel embedding and prototype representations to further save computational costs. Moreover, it is very difficult to linearize the complexity of the vision Transformer from the perspective of spatial domain. Due to the fact that semantic segmentation is very sensitive to frequency information, we construct a lightweight prototype learning block with adaptive frequency filter of complexity O(n) to replace standard self attention with O(n^2). Extensive experiments on widely adopted datasets demonstrate that AFFormer achieves superior accuracy while retaining only 3M parameters. On the ADE20K dataset, AFFormer achieves 41.8 mIoU and 4.6 GFLOPs, which is 4.4 mIoU higher than Segformer, with 45% less GFLOPs. On the Cityscapes dataset, AFFormer achieves 78.7 mIoU and 34.4 GFLOPs, which is 2.5 mIoU higher than Segformer with 72.5% less GFLOPs. Code is available at https://github.com/dongbo811/AFFormer.
Comparison of organic matter determination methods in soil by loss on ignition and potassium dichromate method

Soil organic carbon (SOC) is an important property in regard to soil and environmental quality.There are many methods to determine SOC.In this study, Walkley Black and Loss on Ignition methods were chosen and sixty sub-samples representing were collected from three different parts of Arsi zone areas of Ethiopia, and then nine a composite samples were used for SOC determination methods.The variables were equipment, accuracy, efficiency, complexity, cost and environmental effect.Walkley-Black and loss on ignition (LOI) were determined on whole soils mesh size of 2 mm.From the two selected methods Loss on Ignition was the accurate and simple method for determining soil organic carbon that do not involve the use of chemicals that damage the environment are required.Walkley-Black method is a wet oxidation procedure which is relatively accurate and popular method for the determination of soil organic matter but it is time-consuming, costly and also has a high potential to cause environmental pollution because of disposal of chromium and strong acids.Therefore, loss-onignition procedure is a simple and cheap method for SOM estimation, which also avoids chromic acid wastes, deserves more attention.The aims of this research were to study the statistical relationships between SOC determined with the LOI and WB methods to compare the spatial variability of SOC in the selected study area.One way ANOVA was used for the significance test between the two methods and there was a significant difference between the values determined by the Walkley Black and Loss on Ignition methods, at a confidence level of 95%.
Digital Examination of LYmph node CYtopathology Using the Sydney system (DELYCYUS): An international, multi‐institutional study

After a series of standardized reporting systems in cytopathology, the Sydney system was recently introduced to address the need for reproducibility and standardization in lymph node cytopathology. Since then, the risk of malignancy for the categories of the Sydney system has been explored by several studies, but no studies have yet examined the interobserver reproducibility of the Sydney system.The authors assessed interobserver reproducibility of the Sydney system on 85 lymph node fine-needle aspiration cytology cases reviewed by 15 cytopathologists from 12 institutions in eight different countries, resulting in 1275 diagnoses. In total, 186 slides stained with Diff-Quik, Papanicolaou, and immunocytochemistry were scanned. A subset of the cases included clinical data and results from ultrasound examinations, flow cytometry immunophenotyping, and fluorescence in situ hybridization analysis. The study participants assessed the cases digitally using whole-slide images.Overall, the authors observed an almost perfect agreement of cytopathologists with the ground truth (median weighted Cohen κ = 0.887; interquartile range, κ = 0.210) and moderate overall interobserver concordance (Fleiss κ = 0.476). There was substantial agreement for the inadequate and malignant categories (κ = 0.794 and κ = 0.729, respectively), moderate agreement for the benign category (κ = 0.490), and very slight agreement for the suspicious (κ = 0.104) and atypical (κ = 0.075) categories.The Sydney system for reporting lymph node cytopathology shows adequate interobserver concordance. Digital microscopy is an adequate means to assess lymph node cytopathology specimens.
Cyberbullying detection for low-resource languages and dialects: Review of the state of the art

The struggle of social media platforms to moderate content in a timely manner, encourages users to abuse such platforms to spread vulgar or abusive language, which, when performed repeatedly becomes cyberbullying a social problem taking place in virtual environments, yet with real-world consequences, such as depression, withdrawal, or even suicide attempts of its victims. Systems for the automatic detection and mitigation of cyberbullying have been developed but, unfortunately, the vast majority of them are for the English language, with only a handful available for low-resource languages. To estimate the present state of research and recognize the needs for further development, in this paper we present a comprehensive systematic survey of studies done so far for automatic cyberbullying detection in low-resource languages. We analyzed all studies on this topic that were available. We investigated more than seventy published studies on automatic detection of cyberbullying or related language in low-resource languages and dialects that were published between around 2017 and January 2023. There are 23 low-resource languages and dialects covered by this paper, including Bangla, Hindi, Dravidian languages and others. In the survey, we identify some of the research gaps of previous studies, which include the lack of reliable definitions of cyberbullying and its relevant subcategories, biases in the acquisition, and annotation of data. Based on recognizing those research gaps, we provide some suggestions for improving the general research conduct in cyberbullying detection, with a primary focus on low-resource languages. Based on those proposed suggestions, we collect and release a cyberbullying dataset in the Chittagonian dialect of Bangla and propose a number of initial ML solutions trained on that dataset. In addition, pre-trained transformer-based the BanglaBERT model was also attempted.
YOLOv7-UAV: An Unmanned Aerial Vehicle Image Object Detection Algorithm Based on Improved YOLOv7

Detecting small objects in aerial images captured by unmanned aerial vehicles (UAVs) is challenging due to their complex backgrounds and the presence of densely arranged yet sparsely distributed small targets. In this paper, we propose a real-time small object detection algorithm called YOLOv7-UAV, which is specifically designed for UAV-captured aerial images. Our approach builds upon the YOLOv7 algorithm and introduces several improvements: (i) removal of the second downsampling layer and the deepest detection head to reduce the model’s receptive field and preserve fine-grained feature information; (ii) introduction of the DpSPPF module, a spatial pyramid network that utilizes concatenated small-sized max-pooling layers and depth-wise separable convolutions to extract feature information across different scales more effectively; (iii) optimization of the K-means algorithm, leading to the development of the binary K-means anchor generation algorithm for anchor allocation; and (iv) utilization of the weighted normalized Gaussian Wasserstein distance (nwd) and intersection over union (IoU) as indicators for positive and negative sample assignments. The experimental results demonstrate that YOLOv7-UAV achieves a real-time detection speed that surpasses YOLOv7 by at least 27% while significantly reducing the number of parameters and GFLOPs to 8.3% and 73.3% of YOLOv7, respectively. Additionally, YOLOv7-UAV outperforms YOLOv7 with improvements in the mean average precision (map (0.5:0.95)) of 2.89% and 4.30% on the VisDrone2019 and TinyPerson datasets, respectively.
Machine vision detection of pointer features in images of analog meter displays

In automatic and accurate reading recognition of analog meters based on machine vision, one of important issues is the detection of pointer features, which includes the meter center and pointer image processing.The current automatic-recognition approaches to reading analog meters often consist in locating the meter center based on the dial region or its border.The located center is not coincident with the rotation center of pointer which leads to inevitable reading errors.In the paper, the centripetalism of annular scale lines is used to calculate the position of the pointer rotation center.First, it uses the region growing method to locate the dial region and uses the eccentricity measure to extract annular scale lines.Second, the parameters of these scale lines are estimated with the Hough transform method.Then, the common intersection of a group of lines, i.e., the meter rotation center, is determined with the maximum probability criterion.Finally, the pointer centerline and direction are detected through the calculated center and the Hough transform results.The simulated and experimental results demonstrate that the proposed method can accurately locate the pointer rotation center and obtain pointer centerline.Moreover, it is applicable to the meter image captured under a slant camera view or with uneven light illumination.
Is GPT-3 a Good Data Annotator?

Data annotation is the process of labeling data that could be used to train machine learning models.Having high-quality annotation is crucial, as it allows the model to learn the relationship between the input data and the desired output.GPT-3, a large-scale language model developed by OpenAI, has demonstrated impressive zero-and few-shot performance on a wide range of NLP tasks.It is therefore natural to wonder whether it can be used to effectively annotate data for NLP tasks.In this paper, we evaluate the performance of GPT-3 as a data annotator by comparing it with traditional data annotation methods and analyzing its output on a range of tasks.Through this analysis, we aim to provide insight into the potential of GPT-3 as a general-purpose data annotator in NLP 1 .
Prompting PaLM for Translation: Assessing Strategies and Performance

David Vilar, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, George Foster. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature

The latest batch of research has equipped language models with the ability to attend over relevant and factual information from nonparametric external sources, drawing a complementary path to architectural scaling.Besides mastering language, exploiting and contextualizing the latent world knowledge is crucial in complex domains like biomedicine.However, most works in the field rely on general-purpose models supported by databases like Wikipedia and Books.We introduce BIOREADER 1 , the first retrieval-enhanced text-to-text model for biomedical natural language processing.Our domain-specific T5-based solution augments the input prompt by fetching and assembling relevant scientific literature chunks from a neural database with ≈60 million tokens centered on PubMed.We fine-tune and evaluate BIORE-ADER on a broad array of downstream tasks, significantly outperforming several state-of-theart methods despite using up to 3x fewer parameters.In tandem with extensive ablation studies, we show that domain knowledge can be easily altered or supplemented to make the model generate correct predictions bypassing the retraining step and thus addressing the literature overload issue.
Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning

Pretrained Language Models (LMs) have demonstrated ability to perform numerical reasoning by extrapolating from a few examples in few-shot settings.However, the extent to which this extrapolation relies on robust reasoning is unclear.In this paper, we investigate how well these models reason with terms that are less frequent in the pretraining data.In particular, we examine the correlations between the model performance on test instances and the frequency of terms from those instances in the pretraining data.We measure the strength of this correlation for a multiple GPT-based language models (pretrained on the Pile dataset) on various numerical deduction tasks (e.g., arithmetic and unit conversion).Our results consistently demonstrate that models are more accurate on instances whose terms are more prevalent, in some cases above 70% (absolute) more accurate on the top 10% frequent terms in comparison to the bottom 10%.Overall, although LMs appear successful at few-shot numerical reasoning, our results raise the question of how much models actually generalize beyond pretraining data, and we encourage researchers to take the pretraining data into account when interpreting evaluation results.
Detection of Fake Accounts on Social Media Using Multimodal Data With Deep Learning

In recent years, the proliferation of fake accounts on social media has become a significant concern for individuals, organizations, and society. Fake accounts play an important role in spreading fake news, rumors, spam, unethical harassment, and other mischievous motives on social media platforms. Detecting such accounts manually is time-consuming and challenging, especially with the increasing sophistication of the methods used to create them. Therefore, there is a need for automated approaches to detect these fake accounts. This article aims to reveal fake accounts and their role on social media platforms. This article has explored various methodologies to detect fake accounts from social platforms, which will help prevent cybercrime. We have also proposed a generalized deep learning (DL) model to detect such accounts on social media using multimodal data. The proposed architecture uses a combination of textual, visual, and network-based features to capture the various characteristics of fake accounts. Specifically, we use a deep neural network that combines convolutional neural networks (CNNs) for visual data, long short-term memory (LSTM) networks for textual data, and convolutional graph networks (GCNs) for network-based data. We evaluated our model on a publicly available dataset of Twitter accounts and achieved state-of-the-art performance in detecting fake accounts, with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$</tex-math> </inline-formula> 1 score of 0.96. We also conducted experiments to show the effectiveness of each feature and the combination of the three features.
In too deep? A systematic literature review of <scp>fully‐immersive</scp> virtual reality and cybersickness among older adults

Abstract Background Virtual reality technology holds great promise in improving the health and well‐being of older adults; however, this technology is associated with potential risks that may outweigh the benefits. The purpose of this systematic review is to synthesize and critique the existing literature on fully‐immersive virtual reality interventions for older adults and the associated risk of cybersickness. Methods We searched eight databases for studies that utilized fully‐immersive virtual reality, stated the population as older adults, provided outcomes related to cybersickness, and were written or translated into the English language. Our search generated 332 articles, 39 of which were selected for inclusion in this systematic synthesis and appraisal. Results We found that the majority of studies utilized a one‐time session. The studies generally had weak sample generalizability and methodological design, but strong data collection and participant retention. The studies nearly all used the Simulator Sickness Questionnaire to measure cybersickness, but the scoring for the Simulator Sickness Questionnaire varied or was not mentioned. Most included studies reported only minor cybersickness among older adult participants. Conclusion Researchers might conclude that the benefits of virtual reality interventions outweigh the risk of cybersickness in this population. Going forward, researchers should concentrate on the efficacy of virtual reality interventions among older adults, using larger samples and randomized controlled study design, along with measuring dosage effects.