Reviewerly/exHarmony huggingface dataset:
An Approach Based on Semantic Relationship Embeddings for Text Classification

Semantic relationships between words provide relevant information about the whole idea in the texts. Existing embedding representation models characterize each word as a vector of numbers with a fixed length. These models have been used in tasks involving text classification, such as recommendation and question–answer systems. However, the embedded information provided by semantic relationships has been neglected. Therefore, this paper proposes an approach that involves semantic relationships in embedding models for text classification, which is evaluated. Three embedding models based on semantic relations extracted from Wikipedia are presented and compared with existing word-based models. Our approach considers the following relationships: synonymy, hyponymy, and hyperonymy. They were considered since previous experiments have shown that they provide semantic knowledge. The relationships are extracted from Wikipedia using lexical-syntactic patterns identified in the literature. The extracted relationships are embedded as a vector: synonymy, hyponymy–hyperonymy, and a combination of all relationships. A Convolutional Neural Network using semantic relationship embeddings was trained for text classification. An evaluation was carried out for the proposed relationship embedding configurations and existing word-based models to compare them based on two corpora. The results were obtained with the metrics of precision, accuracy, recall, and F1-measure. The best results for the 20-Newsgroup corpus were obtained with the hyponymy–hyperonymy embeddings, achieving an accuracy of 0.79. For the Reuters corpus, F1-measure and recall of 0.87 were obtained using synonymy–hyponymy–hyperonymy.
Transformations as Denoising: A Robust Approach to Weaken Adversarial Facial Images

While facial recognition (FR) has been widely used by businesses and governments for various purposes, it gives rise to privacy concerns once the consent of users is not handled properly. Hence, researchers have proposed methods to evade FR technology by attaching adversarial perturbations to user profile images. Nonetheless, image denoising-based methods have been proposed to increase the model robustness over adversarial examples. This paper investigates the impact of transformations on adversarial facial images. In particular, a simple but effective framework, TaD (Transformations as Denoising), is proposed to remove possible adversarial perturbations from user images generated by popular FR privacy protection frameworks. Extensive evaluations show the reliability of Fawkes and LowKey with various simple transformations. Experimental results indicate that simple transformations can impact the protection performance, and the choice of DNN-based facial feature extractors can enhance the robustness of facial images with adversarial perturbations. The experimental results also demonstrate strengths and weaknesses of FR methods and give suggestions for further improvements of privacy safeguard tools.
Medical Image Analysis Using Federated Learning Frameworks: Technical Review

Learning from various types of Medical Imaging data have been a area of research since it involve variation in data distribution bias factor, privacy and legal issues. Hence ML algorithms can not be generalized to take care diversity in data-sets taken from various organizations hospitals locations or devices. The federated learning (FL) paradigm helps to overcome such challenges and allowing the algorithms to learn on various medical imaging data sets from various locations through collaborations at central server. In this paper we are presenting a technical review on federated learning architectures for medical image analysis. The manuscript illustrate how federated learning architectures applied for medical image analysis in effective way.
Distributed Swift and Stealthy Backdoor Attack on Federated Learning

Federated Learning (FL) provides enhanced privacy over traditional centralized learning; unfortunately, it is also as susceptible to backdoor attacks, just like its centralized counterpart. Conventionally, in data poisoning-based backdoor attacks, all the malicious participants overlay the same single trigger pattern on a subset of their private data during local training. The same trigger is used to induce the backdoor in the otherwise benign global model at inference time. Such single trigger attacks can be detected and removed with relative ease as they undermine the distributed nature of FL. In this work, we focus on building an attack scheme where each batch of malicious clients uses sizably discrete local triggers during local training, with the ability to invoke the attack with a single small inference trigger during the global model testing. The larger size of the trigger pattern ensures prolonged attack longevity even after the termination of the attack. We conduct extensive experiments to show that our approach is far faster, stealthier, and more effective than the centralized trigger approach. The stealthiness of our work is explained using the DeepLIFT visual feature interpretation method.
Hardware-Efficient Template-Based Deep CNNs Accelerator Design

Acceleration of Convolutional Neural Network (CNN) on edge devices has recently achieved a remarkable performance in image classification and object detection applications. This paper proposes an efficient and scalable CNN-based SoC-FPGA accelerator design that takes pre-trained weights with a 16-bit fixed-point quantization and target hardware specification to generate an optimized template capable of achieving higher performance versus resource utilization trade-off. The template analyzed the computational workload, data dependency, and external memory bandwidth and utilized loop tiling transformation along with dataflow modeling to convert convolutional and fully connected layers into vector multiplication between input and output feature maps, which resulted in a single compute unit on-chip. Furthermore, the accelerator was examined among AlexNet, VGG16, and LeNet networks and ran at 200-MHz with a peak performance of 230 GOP/s depending on ZYNQ boards and state-space exploration of different compute unit configurations during simulation and synthesis. Lastly, our proposed methodology was benchmarked against the previous development on Ultra96 for higher performance measurement.
Learning Moiré Pattern Elimination in Both Frequency and Spatial Domains for Image Demoiréing

Recently, with the rapid development of mobile sensing technology, capturing scene information by mobile sensing devices in the form of images or videos has become a prevalent recording method. However, the moiré pattern phenomenon may occur when the scene contains digital screens or regular strips, which greatly degrade the visual performance and image quality. In this paper, considering the complexity and diversity of moiré patterns, we propose a novel end-to-end image demoiré method, which can learn moiré pattern elimination in both the frequency and spatial domains. To be specific, in the frequency domain, considering the signal energy of moiré pattern is widely distributed in the frequency, we introduce a wavelet transform to decompose the multi-scale image features, which can help the model identify the moiré features more precisely to suppress them effectively. On the other hand, we also design a spatial domain demoiré block (SDDB). The SDDB module can extract moiré features from the mixed features, then subtract them to obtain clean image features. The combination of the frequency domain and the spatial domain enhances the model's ability in terms of moiré feature recognition and elimination. Finally, extensive experiments demonstrate the superior performance of our proposed method to other state-of-the-art methods. The Grad-CAM results in our ablation study fully indicate the effectiveness of the two proposed blocks in our method.
Human action recognition method based on Motion Excitation and Temporal Aggregation module

Aiming at the problem of low modeling efficiency and feature loss of temporal modeling in human action recognition, we propose a human action recognition method based on Motion Excitation and Temporal Aggregation module (META). The method can capture multi-state and multi-scale temporal information to achieve effective motion excitation. Firstly, temporal relational sampling is performed on video frames; Secondly, META is proposed to capture multi-state and multi-scale temporal information. META is composed of Multi-scale Motion Excitation module (MME) and Squeeze and Excitation Temporal Aggregation module (SETA). MME captures the feature level temporal difference by transforming the features into the temporal channel, which directly establishes the relationship between features and temporal channel, and solves the problem of low modeling efficiency. SETA transforms the local convolution into a set of sub-convolutions. Multiple sub-convolutions form hierarchies to extract features together and share the results of the upper convolutional layer, which increases the final temporal receptive field and solves the problem of feature loss. Moreover, the optical flow features are extracted through Cross modality pre-training to improve the utilization of temporal information. Finally, the result of human action recognition is carried out by combining spatiotemporal two stream features. Experimental results show that the accuracy of this method in UCF101 and HMDB-51 is 96.0% and 71.2% respectively, which is higher than other studies in the same period.
Dual-Proxy Modeling for Masked Face Recognition

With the recent worldwide COVID-19 pandemic, almost everyone wears a mask daily, leading to severe degradation in the accuracy of conventional face recognition systems. Several works improve the performance of masked faces by adopting synthetic masked face images for training. However, such methods often cause performance degradation on unmasked faces, raising the contradiction between the face recognition system's accuracy on unmasked and masked faces. In this paper, we propose a dual-proxy face recognition training method to improve masked faces' performance while maintaining unmasked faces' performance. Specifically, we design two fully-connected layers as the unmasked and masked feature space proxies to alleviate the significant difference between the two data distributions. The cross-space constraints are adopted to ensure the intra-class compactness and inter-class discrepancy. Extensive experiments on popular unmasked face benchmarks and masked face benchmarks, including real-world mask faces and the generated mask faces, demonstrate our method's superiority over the state-of-the-art methods on masked faces without incurring a notable accuracy degradation on unmasked faces.
Effect of Nursing Students’ Growth Mindset on Perceived Academic Achievement: Mediating Effect of Self-Directed Learning Ability

Objectives This study was conducted to confirm the mediating effect of self-directed learning ability on the relationship between growth mindset and perceived academic achievement for nursing students in a non-face-to-face learning environment. Methods Data collection was conducted from December 20 to 31, 2021 for nursing college students in one area, and a total of 118 data were used for the final analysis. For data analysis, independent t-test, ANOVA, Pearson's correlation coefficients, and linear regression analysis were used. Results Growth mindset and self-directed learning ability (r=.57 p&lt;.001), Growth mindset and perceived academic achievement (r=.32 p=.001), self-directed learning ability and perceived academic achievement (r=.39, p&lt;.001) showed a significant static correlation. In growth mindset and perceived academic achievement, self-directed learning ability had complete mediating effect (Z=2.36, p=.020), and explanatory power was 19.0%. Conclusions It is expected that the results of this study can be used as basic data to develop a self-directed learning ability program to improve the perceived academic achievement of nursing students in the future.
Financial sector development, trade and income inequality: regional perspectives using evidence from macro and firm-specific data

Purpose The purpose of this study is to firstly, find out does the relationship between finance, trade and income inequality provides matching evidence using the macro- and firm-level data? Secondly, whether a causal relationship from the firm-specific data can be established. Design/methodology/approach The authors employed a panel data fixed effect regression and two-stage least square (2sls) using key instruments. Their analysis is conducted based on a sub-sample analysis that emphasizes Latin America Region (LAR), Africa (AFR) and Asia by merging South Asia, East Asia and Pacific Regions (SEAR). These areas are characterized by an unequal society represented by a mounting gini index (Robilliard, 2020; De Rosa et al . 2020). Accordingly, the authors estimated a comparable model to unfold the impact of finance and trade. Besides, the authors exploited the interaction between their predictors with labor productivity and ownership structure to claim the difference between the results using the two data sets. The effort to establish a causal relationship from the combined firm-level data challenges the current literature that leans towards either macro or micro perspectives. Findings The result obtained from macro-level data shows that while financial development widens income inequality, the impact of trade on income inequality is negative. However, the estimation result from the combined firm-level data portrays that the proportion of investment financed by banks and trade widens income inequality. Given the contrasting result concerning trade, the authors test whether the firm-level evidence is causal following different identification strategies. The exercise shows that the correlation presented in the paper is causal. That challenges the current literature that falls short of providing firm-specific evidence. Social implications The authors adds to a growing body of literature on finance, trade and income inequality by paying due emphasis on firms from 2006 to 2020. The authors show the private sector development effect on income inequality by linking topics from the firm and country-level data. Originality/value The authors extend the macro-level discussion and contribute to the existing literature by offering firm-specific evidence on the relationship between finance, trade and income inequality.
Hybrid extreme learning machine based bidirectional long <scp>short‐term</scp> memory for crop prediction

Summary This article introduces a new crop prediction method using hybrid machine learning (ML) and deep learning (DL) models. The proposed model comprises four phases: data preprocessing, feature fusion, feature selection, and prediction. Initially, the dataset is built with the information collected by 250 sensors located at different places in Maharashtra. The constructed dataset has provided sample data for 31 crops, each with four attributes: temperature, humidity, rainfall, and soil potential of hydrogen. After constructing the dataset, preprocessing is the initial step of the proposed framework. Then, feature fusion and selection were performed using the remora‐based partial least squares regression method to achieve the best accuracy. Eventually, the most discriminatory features are incorporated into the hybrid ML and DL model known as the extreme learning machine based on the bi‐directional long short‐term memory for final prediction. The proposed method is implemented in the python platform, and the performance is evaluated in terms of accuracy, precision, recall, F‐measure, Kappa, MAE, and log loss. Then, the performance of the proposed method is compared with recent existing methods. As a result, the simulated outcomes proved that the proposed method had achieved better performance than the existing methods.
Development of Data Center based on Cloud Computing Technology

The author firstly builds the energy green scheduling model of cloud computing data center, then trains the model in deep neural network based on artificial intelligence technology, and finally designs a distributed energy management method from the perspective of cost minimization and carbon emission minimization. According to the design of the scheme, some key technologies are used to compare and implement the relevant modules in the laboratory environment, and the security of the data center can be controlled through an intuitive interface, thus ensuring the security of the data center. Computational resources, storage Design resources, network resources and their virtualization, put forward a cross-campus master and backup data center system implementation plan, and briefly analyze potential problems and drawbacks
Smart Supply Chain Management using Big Data Analysis and Machine Learning

Supply chain management (SCM) is concerned with the movement of products, services, and information from points of origin to consumers via a network of interconnected organizations and activities. It is believed that capacity, demand, and cost are well-known variables in typical SCM challenges. In practice, however, there are uncertainties brought on by changes in client demand, supply chain management, organizational risks, and lead times. Demand uncertainty in particular has a significant impact on SC performance with wide-ranging implications for scheduling production, planning inventories, and organizing transportation. In this regard, demand forecasting is a crucial strategy for tackling supply chain uncertainty. Forecasting supply chain demand using big data analytics has been published in the supervised and unsupervised learning categories. In supervised learning, the inputs and outputs are known because the data are tagged with labels. Given a fresh unlabeled dataset, the supervised learning algorithms attempt to map the inputs to matching outputs by identifying the underlying connections between the inputs and outputs. The main motive for the authors is to prove that supply chain can be managed through machine learning and to get a novel accuracy along with comparison of the machine learning models.
Object Detection-Based Reinforcement Learning for Autonomous Point-to-Point Navigation

Autonomous navigation has been a fundamental area of research for real-world mobile robotic applications, having widespread utility across many industries from warehouse package delivery to residential cleaning services. Because of the complex nature of the robot’s environment, several challenges have prevented effectively implementing reinforcement learning-based algorithms trained in simulation. While difficulties can arise from the virtual environment lacking the sophistication to represent such a large and complex state space based on data-heavy sensor observations, the variance in MDP representations across related studies biases their fair comparison, performance, and repeatability. In this study, it is found that the design of the reward function used for training a vision-based mobile agent to perform collision-free point-goal navigation in simulation plays a significant role in overall performance. A novel approach is introduced where reward is also granted for successfully detecting a target object scaled according to prediction confidence. This strategy was found to significantly improve the point-goal navigation behavior compared to simpler reward function designs seen in similar related studies.
Training Deep Architectures Without End-to-End Backpropagation: A Survey on the Provably Optimal Methods

This tutorial paper surveys provably optimal alternatives to end-to-end backpropagation (E2EBP) — the de facto standard for training deep architectures. Modular training refers to strictly local training without both the forward and the backward pass, i.e., dividing a deep architecture into several nonoverlapping modules and training them separately without any end-to-end operation. Between the fully global E2EBP and the strictly local modular training, there are weakly modular hybrids performing training without the backward pass only. These alternatives can match or surpass the performance of E2EBP on challenging datasets such as ImageNet, and are gaining increasing attention primarily because they offer practical advantages over E2EBP, which will be enumerated herein. In particular, they allow for greater modularity and transparency in deep learning workflows, aligning deep learning with the mainstream computer science engineering that heavily exploits modularization for scalability. Modular training has also revealed novel insights about learning and has further implications on other important research domains. Specifically, it induces natural and effective solutions to some important practical problems such as data efficiency and transferability estimation.
The National Library of Medicine indexer assignment dataset: A new large‐scale dataset for reviewer assignment research

MEDLINE is the National Library of Medicine's (NLM) journal citation database. It contains over 28 million references to biomedical and life science journal articles, and a key feature of the database is that all articles are indexed with NLM Medical Subject Headings (MeSH). The library employs a team of MeSH indexers, and in recent years they have been asked to index close to 1 million articles per year in order to keep MEDLINE up to date. An important part of the MEDLINE indexing process is the assignment of articles to indexers. High quality and timely indexing is only possible when articles are assigned to indexers with suitable expertise. This paper introduces the NLM indexer assignment dataset: a large dataset of 4.2 million indexer article assignments for articles indexed between 2011 and 2019. The dataset is shown to be a valuable testbed for expert matching and assignment algorithms, and indexer article assignment is also found to be useful domain-adaptive pre-training for the closely related task of reviewer assignment.
CREMMA Medii Aevi: Literary Manuscript Text Recognition in Latin

This paper presents a novel segmentation and handwritten text recognition dataset for Medieval Latin from the 11th to the 16th century. It connects with Medieval French datasets, as well as earlier Latin datasets, by enforcing common guidelines, bringing 263,000 new characters and now totaling over a million characters for medieval manuscripts in both languages. We provide our own addition to Ariane Pinche’s Old French guidelines to deal with specific Latin cases. We also offer an overview of how we addressed this dataset compilation through the use of pre-existing resources. With a higher abbreviation ratio and a better representation of abbreviating marks, we offer new models that outperform the Old French base model on Latin datasets, improving accuracy by 5% on unknown Latin manuscripts.
Estimation and Comparison of Monthly Global Solar Radiation Between Empirical Models and ANN Method at Visakhapatnam, India

The sun’s global radiation is a crucial aspect for evaluating the radiation of sun, as it gives the entire solar availability at a given place and can be calculated by the equipment. However, it is unfeasible to measure solar radiation in many areas due to maintenance and high price of the equipment used for measurement. Unfortunately, every place can’t spare the price for the equipment attributable to the above factors. Hence, empirical models were established as a substitute to roughly calculate the data. This work is to compare the empirical models and find the suitable one to approximate the global monthly sun’s radiation on parallel plane surfaces in the city, Visakhapatnam. The value of measured global solar radiance data facilitates the approximation of global radiation. And the execution of the empirical models is assessed using statistical error tests and in the end following the observations, it’s declared that ANN model was reliable and accurate.