Reviewerly/exHarmony huggingface dataset:
COMPARATIVE STUDY OF AUTOMATED CELL COUNTER HISTOGRAM AND PERIPHERAL BLOOD SMEAR IN DIAGNOSIS OF ANEMIA

Introduction: Over the past few years, complete blood count, RBC histogram and peripheral blood smear have become the important diagnostic tools to diagnose various haematological conditions. Major public health burden worldwide is anemia with high prevalence in developing countries like India1. Red blood cell and histogram are indispensable for diagnosis and management of anemia2. The major diagnostic tool for work up of most commercial laboratories has been analysing the blood lm routinely. The aim AIM: of the study is to compare between automated cell counter histogram and peripheral smear nding in diagnosis of anemia. Material and method: A prospective comparative study of RBC histogram and peripheral blood smear in diagnosis of anemia was done on 100 patients of HB&lt;14gm%, over six month time span (June2022-Nov2022) in the central laboratory of Saraswathi Institute of Medical Sciences Hapur, (UP) India. This study included all the age groups. All cases of anemia that have undergone blood transfusion is excluded from this study. In Result: our study it was observed that on peripheral blood smear, the most common type of anemia was microcytic hypochromic anemia followed by normocytic normochromic anemia, when we compared with automated cell counter generated histogram most common type of anemia was normocytic normochromic followed by microcytic hypochromic anemia. In our study female population were more than males. The mean age was(32.4yr). Automated cell counters generated CBC histograms and peripheral blood smears Conclusion: plays a major role in diagnosis and management of red cell disorder. Our study observed that histogram patterns and their conrmation by peripheral blood smear along with clinical history gives accurate and conrmed diagnosis of various haematogical conditions. There is much improvement in accuracy and precision 3 reducing the subjective errors .
Retinex model with learned prior for partly-paired low-light image enhancement

Low-light image enhancement is a necessary preprocessing step for target detection and recognition in low-light environment and urgently needed in night vision monitoring, medical imaging, remote sensing imaging and other fields. With the rapid development of machine learning research, machine learning based low-light image enhancement has attracted extensive attention and achieved good results. However, most of the existing machine learning based low-light image enhancement methods rely on the "bright-dark" paired datasets. On the one hand, the construction of the paired dataset has a high cost, which is not conducive to the promotion and practical application. On the other hand, in practical problems, we can usually get partly-paired images with similar background, and there are a lot of shared information between these images. Taking full advantage of this shared information is also conducive to further improve the efficiency of learning methods. This paper focuses on the low-light image enhancement model based on Retinex theory. By mining and modeling the shared prior between partly-paired images of the same scene, and coupling with the existing machine learning methods based on paired dataset training, a Retinex model for partly-paired low-light image enhancement method with learned prior is proposed. Experiments demonstrate that the proposed method can recover more details and richer colors in visual effects, and can improve the numerical results by up to 20%.
The impact of CSR on rural women custodians of seed, food and climate change resilience in Nigeria’s Niger Delta region

Purpose The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on development of enterprising rural women as custodians of seed, food and traditional knowledge for climate change resilience in the Niger Delta region of Nigeria. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 768 rural women respondents were sampled across the rural areas of the Niger Delta region in Nigeria. Findings The results from the use of a combined propensity score matching and logit model indicated that the meagre interventions of MOCs’ CSR targeted at the empowerment of rural women as custodians of seed, food and traditional knowledge for climate change resilience recorded significant success in improving the role of women in agricultural production, especially in women’s involvement across value chains. Practical implications This suggests that any increase in the MOCs’ CSR targeted at increasing rural women’s access to seed preservation facilities, food processing facilities and extension systems that impact a strong body of knowledge and expertise that can be used in climate change mitigation, disaster reduction and adaptation strategies will enhance women’s responsibilities in households and communities as stewards of natural and household resources and will position them well to contribute to livelihood strategies adapted to changing environmental realities. Social implications This implies that MOCs’ GMoUs’ policies and practices should enhance women’s participation, value and recognize women’s knowledge and enable women as well as men farmers to participate in the decision-making process in agriculture, food production and land governance, as women need to be acknowledged and supported as the primary producers of food in the region, able to both cultivate healthy food and climate change resilience through small-scale agro-ecological farming system. Originality/value This research contributes to gender debate in agriculture from a CSR perspective in developing countries and explains the rational for demands for social projects by host communities. It concludes that business has an obligation to help solve problems of public concern.
SGFusion: Camera-LiDAR Semantic and Geometric Fusion for 3D Object Detection

Camera and lidar are considered as important sensors to achieve higher-level autonomous driving. And the complementary information provided by these sensors offer more opportunities for improving performance. However, it is difficulty to fuse them because of different representation of both. In this work, we propose a novel fusion framework SGFusion, which fuse the image and point clouds at semantic and geometric level. The SGFusion framework is divided in sequence into two stages: semantic fusion stage and geometric fusion stage. First, the point clouds are painted with the object-level semantic information obtained from an 2D object detector on the semantic fusion stage. At the same time, the output candidates of 2D object detector will be saved. Then, the painted point clouds fed to one LiDAR-based detector for obtaining high-quality 3D detection candidates. Finally, on the geometric fusion stage, these 3D detection candidates and the 2D detection candidates just saved are combined, and the more accurate detection results can be obtained by using their geometric consistence. The experimental results on KITTI detection benchmark show that our SGFusion achieve up to 3.64% AP improvement on car class compared to three different baselines. Furthermore, our method outperforms prior state-of-the-art works on 3D detection of car class on KITTI testing benchmark.
Feature Enhanced Zero-Shot Stance Detection via Contrastive Learning

Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-shot stance detection. In this paper, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge. Specifically, we first augment the data by masking the topic words of sentences, and then feed the augmented data to an unsupervised contrastive learning module to capture transferable features. Besides, to fit a specific target, we encode the raw text as target-specific features. Finally, we adopt an attention mechanism, which combines syntactic expression patterns with target-specific features to obtain enhanced features for predicting previously unseen targets. Experiments demonstrate that our model outperforms competitive baselines on four benchmark datasets.
NAS-StegNet: Lightweight Image Steganography Networks via Neural Architecture Search

Deep steganography describes the task of hiding a full image in another for secret communication, and such a model usually consists of hide (H) network for secret hiding followed by revealing (R) network for secret revealing. To guarantee the hiding effect for the secret communication applications (e.g., watermarking and light field messaging), most of existing deep steganography models design complex network architecture for H and R, increasing the challenge for model deployment. To achieve a better trade-off between steganography effect and model complexity, in this paper, we explore the idea of neural architecture search to learn a more practical deep steganography network, which is able to produce powerful steganography results but with much less parameters. Specifically, our automatically-learned network, termed as NAS-StegaNet, has 26 $$\times $$ fewer parameters and requires 2 $$\times $$ fewer GFLOPs when compared with the most powerful model. Codes are available at https://github.com/wang-MIG-CFM-UESTC/nas_stegan.git .
Impact of the Composition of Feature Extraction and Class Sampling in Medicare Fraud Detection

With healthcare being critical aspect, health insurance has become an important scheme in minimizing medical expenses. Medicare is an example of such a healthcare insurance initiative in the United States. Following this, the healthcare industry has seen a significant increase in fraudulent activities owing to increased insurance, and fraud has become a significant contributor to rising medical care expenses, although its impact can be mitigated using fraud detection techniques. To detect fraud, machine learning techniques are used. The Centers for Medicaid and Medicare Services (CMS) of the United States federal government released “Medicare Part D” insurance claims is utilized in this study to develop fraud detection system. Employing machine learning algorithms on a class-imbalanced and high dimensional medicare dataset is a challenging task. To compact such challenges, the present work aims to perform feature extraction following data sampling, afterward applying various classification algorithms, to get better performance. Feature extraction is a dimensionality reduction approach that converts attributes into linear or non-linear combinations of the actual attributes, generating a smaller and more diversified set of attributes and thus reducing the dimensions. Data sampling is commonly used to address the class imbalance either by expanding the frequency of minority class or reducing the frequency of majority class to obtain approximately equal numbers of occurrences for both classes. The proposed approach is evaluated through standard performance metrics such as F-measure and AUC score. Thus, to detect fraud efficiently, this study applies autoencoder as a feature extraction technique, synthetic minority oversampling technique (SMOTE) as a data sampling technique, and various gradient boosted decision tree-based classifiers as a classification algorithm. The experimental results show the combination of autoencoders followed by SMOTE on the LightGBM (short for, Light Gradient Boosting Machine) classifier achieved best results.
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering

In recent years, benefiting from the expressive power of Graph Convolutional Networks (GCNs), significant breakthroughs have been made in face clustering area. However, rare attention has been paid to GCN-based clustering on imbalanced data. Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN- based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations. The former is similar to that in classic image classification task, but the latter is a particular problem in GCN-based clustering via linkage prediction. Significantly biased graph representations in training can cause catastrophic over-fitting of a GCN model. To tackle these challenges, we propose a linkage-based doubly imbalanced graph learning framework for face clustering. In this framework, we evaluate the feasibility of those existing methods for imbalanced image classification problem on GCNs, and present a new method to alleviate the imbal- anced labels and also augment graph representations using a Reverse-Imbalance Weighted Sampling (RIWS) strategy. With the RIWS strategy, probability-based class balancing weights could ensure the overall distribution of positive and negative samples; In addition, weighted random sampling provides diverse subgraph structures, which effectively alleviates the over-fitting problem and improves the representation ability of GCNs. Extensive experiments on series of imbalanced benchmark datasets synthesized from MS-Celeb-1M and DeepFashion demonstrate the effectiveness and generality of our proposed method. Our implementation and the synthesized datasets will be openly available on https://github.com/espectre/GCNs_on_imbalanced_datasets.KeywordsCluster AnalysisImbalance LearningGraph Convolutional Networks
Estimating Propensity Scores with Deep Adaptive Variable Selection

The proliferation of observational data demands the development of statistical methods for causal inference. Many widely used causal inference methods are based on the propensity score. When estimating the propensity score, one essential question is which covariates should be included in the model. In this paper, we propose a deep adaptive variable selection based propensity score method (DAVSPS) by using representation learning and adaptive group LASSO. The key idea of DAFSPS is to combine the data-driven learning capability of representation learning and variable selection consistency of adaptive group LASSO to improve the estimation of the propensity score by selecting confounders and adjustment variables while removing instrumental and spurious variables. We also provide a detailed theoretical analysis to prove the variable selection consistency of DAVSPS. We evaluate the performance of our method on simulated data to demonstrate its superiority over state-of-the-art methods and apply it to real data.Keywordscausal inferencepropensity scorevariable selectionadaptive LASSOconsistency
Support Vector Machine Based Models with Sparse Auto-encoder Based Features for Classification Problem

Auto-encoder is a special type of artificial neural network (ANN) that is used to learn informative features from data. In the literature, the generalization performance of several machine learning models have been improved either using auto-encoder based features or high dimensional features (original + auto-encoder based features). Random vector functional link (RVFL) network also uses two type of features, i.e., original features and randomized features, that makes it a special randomized neural network. These hybrid features improve the generalization performance of the RVFL network. In this paper, we introduce the idea of using additional features into robust energy-based least squares twin support vector machines (RELS-TSVM) and least squares twin support vector machines (LSTSVM). We used sparse auto-encoder with $$L_{1}$$ norm regularization to learn the auxiliary feature representation from original feature space. These new additional features are concatenated with the original features to get the extended feature space. The conventional RELS-TSVM and LSTSVM are trained over new extended feature space. Experiments demonstrate that auto-encoder based features improve the generalization capability of the conventional RELS-TSVM and LSTSVM models. To examine the performance of the proposed classifiers, i.e., extended-RELS-TSVM (ext-RELS-TSVM) and extended LSTSVM (ext-LSTSVM), experiments have been conducted over 15 UCI binary datasets and the results show that the proposed classifiers have better generalization performance than the baseline classifiers.
VeriTrain: Validating MLaaS Training Efforts via Anomaly Detection

Machine learning as a service (MLaaS) offers users the benefit of training state-of-the-art neural network models on fast hardware with low costs. However, it also brings security concerns since the user does not fully trust the cloud. To prove to the user that the ML training results are legitimate, existing approaches mainly adopt cryptographic techniques such as secure multi-party computation, which incur large overheads. In this paper, we model the problem of verifying ML training efforts as an anomaly detection problem. We design a verification system, dubbed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> , which combines unsupervised anomaly detection approaches and hypothesis testing techniques to verify the legitimacy of training efforts on the MLaaS cloud. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> is run inside trusted execution environments (TEEs) on the same cloud machine to ensure the integrity of its execution. We consider a threat model where the cloud model trainer is a lazy attacker and tries to fool <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> with minimum training effort. We perform extensive evaluations on multiple neural network models and datasets, which shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> performs well in detecting parameter updates crafted by the attacker. We also implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VeriTrain</small> with Intel SGX and show that it only incurs moderate overheads.
Suggestions for a Revision of the European Smart Robot Liability Regime

In recent years, the need for regulation of robots and Artificial Intelligence, together with the urgency of reshaping the civil liability framework, has become apparent in Europe. Although the matter of civil liability has been the subject of many studies and resolutions, multiple attempts to harmonize EU tort law have been unsuccessful so far, and only the liability of producers for defective products has been harmonized so far. In 2021, by publishing the AI Act proposal, the European Commission has reached the goal to regulate AI at the European level, classifying smart robots as ”high risk systems”. This new piece of legislation, albeit tackling important issues, does not focus on liability rules. However, regulating the responsibility of developers and manufacturers of robots and AI systems, in order to avoid a fragmented legal framework across the EU and an uneven application of liability rules in each Member State, is still an important issue that raises many concerns in the industry sector. In particular, deep learning techniques need to be carefully regulated, as they challenge the traditional liability paradigm: it is often not possible to know the reason underneath the output given by those models, and nor the programmer nor the manufacturer are able to predict the AI behaviour. For this reason, some authors have argued that we need to take liability away from producers and programmers when robots are capable of acting autonomously from their original design, while others have proposed a strict liability regime. This article explores liability issues about AI and robots with regards to users, producers, and programmers, especially when the use of machine learning techniques is involved, and suggests some regulatory solutions for European lawmakers.
Patch Mix Augmentation with Dual Encoders for Meta-Learning

Meta-learning aims to learn models that can make quick adaptations to new tasks. However, due to the lack of data, the further improvement of meta-learning can be severely constrained. Since, data augmentation has been a commonly used method to help models reach state-of-art performance in various image classification tasks. It is wise to use data augmentation methods in meta-learning. Different strategies for applying data augmentation to meta-learning have emerged. One common combination of data augmentation and meta-learning is performing different transformations on images. Other methods use generative models, such as GAN, VAE, or AE, to generate samples and expand the data set. In this paper, we proposed a novel data augmentation method aiming to enlarge the number of samples in the support sets. Our approach uses wavelet transform, a widely used method in signal analysis and processing and style mix from AdaIn. Furthermore, we use both ResNet and ViT as our feature encoder. Combining with the idea of contrastive learning, we train our ViT in an unsupervised way. Experimental results show that we achieve a decent performance improvement.
Double Regularization-Based RVFL and edRVFL Networks for Sparse-Dataset Classification

In our previous work, the random vector functional link network (RVFL) and the ensemble deep RVFL network (edRVFL) have been proven to be competitive for tabular-dataset classification, and their sparse pre-trained versions (SP-RVFL and SP-edRVFL) perform well for sparse-dataset (datasets with a large number of features) classification. However, the sparse auto-encoder-based versions suffer from a time-consuming problem. Therefore, we need to find an alternative way to have similar performance and faster training time. In this paper, we propose the double regularization-based RVFL (2R-RVFL) and edRVFL networks (2R-edRVFL). Two different regularization parameters are assigned to the input and hidden features, respectively. The experiments on 12 sparse datasets show that the 2R-RVFL and 2R-edRVFL networks have similar performance as the SP-RVFL and SP-edRVFL networks, and the double regularized variants have a huge training time advantage. Thus, we believe the newly proposed 2R-RVFL and 2R-edRVFL networks are more suitable for sparse dataset classification.
Heterogeneous Graph Representation for Knowledge Tracing

Knowledge tracing (KT) is a fundamental task of intelligent education, which traces students’ knowledge states by their historical interactions. In KT, students, questions, concepts, and answers are four main types of entities, and they contain various relations, including student-question interactive relations, question-concept relations, and question-answer relations. Such rich knowledge in these heterogeneous relations could potentially improve the prediction of KT. However, it has not been sufficiently utilized in existing KT methods. In this paper, we propose a novel method, called Heterogeneous Graph Representation for Knowledge Tracing, to leverage these useful relations. Our method first models all the complex entities and relations in KT as a heterogeneous graph, and then uses a heterogeneous graph neural network to obtain entities’ feature representations. After that, we feed the feature embeddings to a KT model in an end-to-end training manner. Due to the heterogeneous graph’s high representational capacity, our method exploits the relations among students, questions, concepts, and answers in a concise and unified way. Experiments on four KT datasets show that our method achieves state-of-the-art performance.