Reviewerly/exHarmony huggingface dataset:
Targeting the Source: Selective Data Curation for Debiasing NLP Models

Unjustified social stereotypes have lately been found to taint the predictions of NLP models. Thus, an increasing amount of research focuses on developing methods to mitigate social bias. Most proposed approaches update the parameters of models post-hoc, running the risk of forgetting the predictive task of interest. In this work, we propose a novel way of debiasing NLP models by debiasing and curating their training data. To do so, we propose an unsupervised pipeline to identify which instances in the training data mention stereotypes that tally with the stereotypes encoded in NLP models. Then we either remove or augment these problematic instances, and train NLP models on less biased data. In this pipeline, we propose three methods to excavate stereotypes encoded in models using likelihoods, attention weights and vector representations. Experiments on the tasks of natural language inference, sentiment analysis and question answering suggest that our methods are better at debiasing downstream models than existing techniques.
ARConvL: Adaptive Region-Based Convolutional Learning for Multi-class Imbalance Classification

Real-world image classification usually suffers from the multi-class imbalance issue, probably causing unsatisfactory performance, especially on minority classes. A typical way to address such problem is to adjust the loss function of deep networks by making use of class imbalance ratios. However, such static between-class imbalance ratios cannot monitor the changing latent feature distributions that are continuously learned by the deep network throughout training epochs, potentially failing in helping the loss function adapt to the latest class imbalance status of the current training epoch. To address this issue, we propose an adaptive loss to monitor the evolving learning of latent feature distributions. Specifically, the class-wise feature distribution is derived based on the region loss with the objective of accommodating feature points of this class. The multi-class imbalance issue can then be addressed based on the derived class regions from two perspectives: first, an adaptive distribution loss is proposed to optimize class-wise latent feature distributions where different classes would converge within the regions of a similar size, directly tackling the multi-class imbalance problem; second, an adaptive margin is proposed to incorporate with the cross-entropy loss to enlarge the between-class discrimination, further alleviating the class imbalance issue. An adaptive region-based convolutional learning method is ultimately produced based on the adaptive distribution loss and the adaptive margin cross-entropy loss. Experimental results based on public image sets demonstrate the effectiveness and robustness of our approach in dealing with varying levels of multi-class imbalance issues.
Learning Distinct Features Helps, Provably

We study the diversity of the features learned by a two-layer neural network trained with the least squares loss. We measure the diversity by the average $$L_2$$ -distance between the hidden-layer features and theoretically investigate how learning non-redundant distinct features affects the performance of the network. To do so, we derive novel generalization bounds depending on feature diversity based on Rademacher complexity for such networks. Our analysis proves that more distinct features at the network’s units within the hidden layer lead to better generalization. We also show how to extend our results to deeper networks and different losses.
Continuous Depth Recurrent Neural Differential Equations

Recurrent neural networks (RNNs) have brought a lot of advancements in sequence labeling tasks and sequence data. However, their effectiveness is limited when the observations in the sequence are irregularly sampled, where the observations arrive at irregular time intervals. To address this, continuous time variants of the RNNs were introduced based on neural ordinary differential equations (NODE). They learn a better representation of the data using the continuous transformation of hidden states over time, taking into account the time interval between the observations. However, they are still limited in their capability as they use the discrete transformations and a fixed discrete number of layers (depth) over an input in the sequence to produce the output observation. We intend to address this limitation by proposing RNNs based on differential equations which model continuous transformations over both depth and time to predict an output for a given input in the sequence. Specifically, we propose continuous depth recurrent neural differential equations (CDR-NDE) which generalize RNN models by continuously evolving the hidden states in both the temporal and depth dimensions. CDR-NDE considers two separate differential equations over each of these dimensions and models the evolution in temporal and depth directions alternatively. We also propose the CDR-NDE-heat model based on partial differential equations which treats the computation of hidden states as solving a heat equation over time. We demonstrate the effectiveness of the proposed models by comparing against the state-of-the-art RNN models on real world sequence labeling problems.
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance. Several recent effective methods for handling noisy labels have two key steps: 1) dividing samples into cleanly labeled and wrongly labeled sets by training loss, 2) using semi-supervised methods to generate pseudo-labels for samples in the wrongly labeled set. However, current methods always hurt the informative hard samples due to the similar loss distribution between the hard samples and the noisy ones. In this paper, we proposed PGDF (Prior Guided Denoising Framework), a novel framework to learn a deep model to suppress noisy label by using the training history to generate the sample prior knowledge, which is integrated into both sample dividing step and semi-supervised step. Our framework can save more informative hard clean samples into the cleanly labeled set. Besides, our framework also promotes the quality of pseudo-labels during the semi-supervised step by suppressing the noise in the current pseudo-labels generating scheme. To further enhance the hard samples, we reweight the samples in the cleanly labeled set during training. We evaluated our method using synthetic datasets based on CIFAR-10 and CIFAR-100, as well as on the real-world datasets WebVision and Clothing1M. The results demonstrate substantial improvements over state-of-the-art methods. The code is available at https://github.com/bupt-ai-cz/PGDF .
Rényi Divergence Deep Mutual Learning

This paper revisits Deep Mutual Learning (DML), a simple yet effective computing paradigm. We propose using Rényi divergence instead of the Kullback-Leibler divergence, which is more flexible and tunable, to improve vanilla DML. This modification is able to consistently improve performance over vanilla DML with limited additional complexity. The convergence properties of the proposed paradigm are analyzed theoretically, and Stochastic Gradient Descent with a constant learning rate is shown to converge with $$\mathcal {O}(1)$$ -bias in the worst case scenario for nonconvex optimization tasks. That is, learning will reach nearby local optima but continue searching within a bounded scope, which may help mitigate overfitting. Finally, our extensive empirical results demonstrate the advantage of combining DML and the Rényi divergence, leading to further improvement in model generalization.
Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks

As large-scale pre-trained models have become the major choices of various applications, new challenges arise for model pruning, e.g., can we avoid pruning the same model from scratch for downstream tasks? How to reuse the pruning results of previous tasks to accelerate the pruning for new tasks? To address these challenges, we create a small model for a new task from the pruned models of similar tasks. We show that a few fine-tuning steps on this model suffice to produce a promising pruned model for the new task. We study this “meta-pruning” from nearest tasks on two major classes of pre-trained models, convolutional neural network and vision transformer, under a limited budget of pruning iterations. Our study begins by investigating the overlap of pruned models for similar tasks and how the overlap changes over different layers and blocks. Inspired by these discoveries, we develop a simple but effective “Meta-Vote Pruning” method that significantly reduces the pruning iterations for a new task by initializing a sub-network from the pruned models of its nearest tasks. In experiments, we demonstrate MVP’s accuracy, efficiency, and generalization advantages through extensive empirical studies and comparisons with popular pruning methods over several datasets.
The Effectiveness of the Cooperative Integrated Reading and Composition (CIRC) Method on Reading Comprehension Ability based on Reading Interest in Grade IV Elementary School Students

Reading is a very important means or medium for obtaining information or knowledge, reading is also a type of language, speaking has a very important role in human life. Reading can be done anytime and anywhere according to the wishes of the reader. The aim of this research is to determine the effectiveness of the method in teaching reading comprehension skills based on the reading interest of fourth grade elementary school students. The type of research used is experimental research. The population in this study were all class IV students at SDN cluster 3, Kediri sub-district, West Lombok Regency, while the sample used in this research was class IV at SDN 2 Rumak. The results of this research in the form of quantitative data were analyzed into data using pretest and posttest then analyzed using the normality test with Kolmogrov Smirnov, then homogeneity test with f variance. To determine the hypothesis test, it can be done by comparing the calculated t value with the t table. This is proven by the students' high interest in reading, the average value of the t-test, namely the calculated t value -8.200&gt; t table 1.725 with a significance value = 0.000 &lt; 0.05. For students interested in reading, the average value of the examiner's t calculated value is -8.444&gt; t table 1.812 with a significance value = 0.000&lt; 0.05. So, it can be concluded that the CIRC learning method is effectively used in teaching Reading Comprehension Skills for fourth grade elementary school students. Keywords: CIRC Method, Reading Comprehension, Reading Interest
A New and Optimized Sorting Algorithm (Bi-Way Sort)

A sorting algorithm organizes a large number of data items in a specific order, either ascending or descending. Organized data is helpful in analysis. Therefore, researchers are working to develop a sorting algorithm despite many already developed algorithms. In this paper, a new sorting algorithm is proposed. The main target of this algorithm is to reduce the multiplication and division operations. This is also a 2-loop algorithm without multiplication and division operations. Comparative analysis is also performed with various other sorting algorithms. Results show that the proposed algorithm is better in terms of computation.
Plausible Reasoning by Solving Open Mathematical Problems

The article deals with the problem of forming plausible reasoning of students through open mathematical problems, justifies its necessity in preparing young people for real life.As a rule, in real life, the initial data are not always clearly formulated, the goal is not clearly set, and there are situations when there is not the only way out of this situation. Therefore, a person often faces open-type tasks. Open tasks are understood as tasks in which the initial data or the final goal are not clearly formulated. At the same time, students are to some extent given the freedom to set a task, choose a way of thinking and introduce additional parameters necessary for its solution. Within the framework of this study, a review of existing scientific literature, publications published in scientific journals was conducted, which necessitated the analysis of plausible reasoning among students, as well as the compilation and solution of open mathematical problems. The algorithm of actions for solving open problems and an example of identifying the possibili
Blockchain-Enabled Ridesharing Platform

The ridesharing industry has experienced rapid growth in recent years, but it still faces challenges such as security concerns, trust issues, and intermediaries’ involvement. Blockchain technology has the potential to overcome these issues by providing a secure, decentralized, and transparent platform. This project paper aims to design and develop a blockchain-enabled ridesharing platform that utilizes smart contracts to automate the ride-booking process, ensure secure transactions, and reduce intermediaries’ involvement. The proposed platform’s architecture involves a decentralized network of nodes that maintain the ledger, and users can join the network as either riders or drivers. The results of this project paper will contribute to the research and development of blockchain-based applications in the transportation industry, particularly ridesharing.
Identification of Image Spoofing Using Deep Convolution Neural Network

Image plays a prominent role in forensic and biometric system authentication. Biometrics has emerged as a reliable security system solution. However, biometric applications become more prevalent, criminals are developing techniques to circumvent them by simulating physical or behavioural characteristics of legal users (image spoofing attacks). Face recognition systems are extremely vulnerable to such frauds because they can easily be fooled with common printed facial photographs, despite being a promising characteristic due to its universality, acceptability, and presence of cameras almost everywhere. Modern approaches based on convolutional neural networks (CNNs) produce good results in detecting face spoofing. However, these methods ignore the importance of learning deep local features from each facial region, despite the fact that face recognition has shown that each facial region has distinct visual characteristics that can be used to detect face spoofing. In this paper, we propose a novel deep CNN architecture for such tasks that is trained in two steps. At first, each component of the neural network learns features from a specific facial region. Following that, the entire model is fine-tuned on the entire set of images. The results show that such pre-training allows the CNN to learn different local spoofing cues, improving the final model’s performance and convergence speed and outperforming state-of-the-art approaches.
Multiresolution Analysis Based on Variants of Wavelet Transforms for Illumination Normalized Face Recognition

Face recognition has been recognized as an extensive biometric system in recent years that effectively identifies digital face images under any uncontrolled illumination situations. This often results in intrapersonal differences, thus effective feature extraction is prudent in designing face recognition systems, to extract prominent discriminative facial features. The wavelet transforms domain-based techniques have been widely applied for face recognition due to their ability to apprehend localized time–frequency-based facial information in the feature extraction process. Hence, this work presents a comparative study for multiresolution analysis of illumination normalized face recognition based on variants of wavelet transforms. Initially, the effects of varied illuminations are normalized for face images based on homomorphic filtering process. Thereafter, an empirical study is performed to extract substantial normalized facial features in four well-known variants of wavelet transforms namely, discrete wavelet transform (DWT), integer wavelet transforms (IWT), dual tree complex wavelet transform (DD-DTCWT), and double density dual tree complex wavelet transform, respectively. Furthermore, to reduce the dimensionality of face feature vector space using principal component analysis, which is then classified using simple K-nearest neighbor linear classifier. The performance of illumination normalized face recognition in stated wavelet transforms is assessed on Extended Yale B database in terms of recognition rates (%). The conducted comparative assessment validates that superior results are attained in DD-DTCWT domain with significant illumination normalized facial features that also outperform other state of art approaches also.
A Novel Multi Features Deep Learning Architecture for Breast Cancer Detection Using Loss Function

Identifying and diagnosing breast cancer is a well-known problem in the biomedical domain. Breast images obtained from medical scanning were taken for classifying different classes like types of breast tumour and which magnification category it belongs. Computer-aided diagnosis has gained immense progress over the past few years because of new insights and developments in deep learning. Often the performance of an efficient deep learning model is prone to drawbacks like imbalanced datasets, inconsistent annotations, less number of images, selection of inappropriate evaluation metrics, and selection of inappropriate loss functions. In this paper, we have done a comparative analysis by comparing how the deep learning model handles class imbalance problems by modifications done at the algorithmic level (i.e.) modifying the loss functions. This research lays a foundation for future research on handling imbalanced datasets and how modifications at the algorithmic level can benefit deep learning architectures.
Hybrid: (NH–DH) a New Hope and Diffie–Hellman for Transmission Data in Cloud Environment

Since the creation of cloud computing and its widespread usage, data security was a significant problem. Different frameworks were suggested; however, data breaches still happen often. The advent regarding quantum computing suggests a pressing need to present a paradigm that would present acceptable data security for both conventional and quantum computings, as encryption is now the predominant approach related to cloud data security. As a result, although certain cryptosystems will withstand the test of quantum computing, the majority of cryptosystems will become vulnerable and out-of-date. In order to lessen the likelihood of Diffie–Hellman algorithm attacks and advance the New Hope algorithm to the next level, the study suggests a model that combines Diffie–Hellman (DH) and New Hope to key exchange between server and client. This suggested public-key exchange protocol depends on New Hope lattice-based cryptography. In this research, we expand the New Hope and DH algorithms with the use of algorithm of the DH for getting a stronger technique to transmit data further exchanged between receiver and sender such that for every message, a new approach to would be formed.
Using Optical Character Recognition Techniques, Classification of Documents Extracted from Images

We now have autonomous cars, speech recognition, efficient online search, and a far greater grasp of the human genome thanks to machine learning techniques developed during the previous ten years. Today, machine learning is employed so often that many times are mistakenly made. It is possible to educate a computer to anticipate outcomes that are challenging for the human brain by trying to teach it certain processes or scenarios. Additionally, these techniques enable us to quickly do various tasks that are frequently impractical or challenging for humans to complete. These factors make machine learning so crucial in today's world. There are two distinct machine learning techniques that were employed in this study. The manuscript materials were moved to the computer and then categorized to address a real-world issue. To complete the procedure, we relied on three fundamental techniques. A scanner or digital camera has converted handwriting or printed materials to digital format. Two alternative optical character recognition (OCR) operations have been used to process these papers. Next, the Naive Bayes method is used to categorize the sentences that were created. The entire project was created using the Windows operating system and Microsoft Visual Studio 12. All components of the study were written in the C# programming language. DLLs and prepared codes were also employed. Exploited.
Advanced Blockchain-Based Scheme for Efficient and Secured Sharing of Customers Information in Logistics Management Model Using RSA Encryption Method

Due to the logistic 4.0 era, global logistics providers now rely more on the use of intelligent technologies in logistics operations. These technologies not only aids in the data gathering and transmission of logistical data, but they also create some security and efficiency issue in the logistics system. These issues can be solved with the use of blockchain solutions. Blockchain is a new technology that is reshaping different sectors, and it has the potential to offer a solution to the issues mentioned above with its unique features such as immutability, transparency, and anonymity. This research proposed a blockchain-based logistics management model that deployed the Rivest-Shamir-Adleman (RSA) Asymmetric encryption method. The RSA encryption is utilized to protect the logistics system from cyber-attacks and provide privacy to customers’ private information from authorized access while being shared. Furthermore, the security evaluation of the proposed model was done with the prevalent logistics attacks and the performance evaluation was done based on throughput and latency. The comparison analysis demonstrates that the suggested model is more efficient and secure than the current one. The suggested model offers a better solution for the security and privacy of the logistics management system and makes recommendations to important players in the logistics sector. In addition to the study’s methodological flaw, it is additionally constrained by a lack of reference resources.