Reviewerly/exHarmony huggingface dataset:
A Method Combining Text Classification and Keyword Recognition to Improve Long Text Information Mining

Using a pre-trained language model for long text classification will be limited by the input length of the pre-trained language model. At the same time, we cannot effectively utilize all the text information in the long text. While using TextCNN for long text classification will be limited by the input word embedding based on a specific task, and it is unable to fully understand the semantics of the current task. In order to better understand the semantic information of the current task, we will use TextCNN with special word embedding for long text classification, we use the keyword data of the current task to finetune the pre-training task to obtain better word embedding representations. On the one hand, using the current word embedding representation as inputs to the classification task better supports the classification task, and on the other hand, it further finetunes the pre-training task to obtain a better word embedding representation. At the same time, the keyword data can be better expanded. Compared with the direct use of TextCNN for long text classification, long text classification combined with keywords is more efficiency and achieves the effect close to BERT. Experiments are carried out on the three datasets, ChnSentiCorp, NLPCC14-SC and business opportunity recommendation(BOR). The model accuracy is relatively high, with the increase of 1.8%, 1.03% and 4.03%, the relative increase of F1-score by 1.8%, 1.03% and 5.04% respectively. Experiments show that, without changing the efficiency of the TextCNN text classification model, we keep the integrity of the input text, the pre-training task finetuned by keyword discovery task enables the word embedding to have better understanding of current semantic expression. The text classification model combined with keywords discovery task by co-training can effectively improve the classification effect on different datasets.
Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps

Modern image generative models show remarkable sample quality when trained on a single domain or class of objects. In this work, we introduce a generative adversarial network that can simultaneously generate aligned image samples from multiple related domains. We leverage the fact that a variety of object classes share common attributes, with certain geometric differences. We propose Polymorphic-GAN which learns shared features across all domains and a per-domain morph layer to morph shared features according to each domain. In contrast to previous works, our framework allows simultaneous modelling of images with highly varying geometries, such as images of human faces, painted and artistic faces, as well as multiple different animal faces. We demonstrate that our model produces aligned samples for all domains and show how it can be used for applications such as segmentation transfer and cross-domain image editing, as well as training in low-data regimes. Additionally, we apply our Polymorphic-GAN on image-to-image translation tasks and show that we can greatly surpass previous approaches in cases where the geometric differences between domains are large.
An Experimental Study on Reading in High-Immersion Virtual Reality

High-immersion virtual reality (VR) is an increasingly valued environment for language learners. Although reading constitutes a core language skill, practicing reading in VR has received little attention. In this between-subject, quantitative study, 79 intermediate learners of English in Germany were randomly assigned to view an interactive, multimedia-rich story under two conditions. In the experimental condition, subjects (the VR group) experienced the story using a high-immersion VR headset. In the comparison condition, subjects (the video group) watched a video recording (i.e., screencast) of the same VR experience. In both conditions, the story was presented using captions in English, which served as the reading comprehension task. In addition to measuring the impact of VR on reading comprehension, validated questionnaires yielded data on the subjects’ intrinsic motivation, sense of presence, and cognitive load. The analysis yielded several findings. First, t-tests revealed the VR group was associated with significantly higher reading comprehension scores compared with the video group. Second, Wilcoxon signed-rank tests showed that the VR treatment elicited statistically significant higher levels of motivation and sense of presence. Cognitive load was not significantly different between the groups. Our main implication is that VR can be beneficial for reading practice.
The Transition from an Informal Financial Money Market to a Formal Financial System Through Digital Financial Inclusion

This chapter shows how digital financial inclusion is helping people make the transition from the informal financial market to the formal financial system. Specifically, the chapter focuses on how digital financial inclusion is assisting people in making the shift. The connection between digital finance and financial inclusion is based on the idea that a sizeable portion of the population that is excluded has access to a mobile phone, and that the delivery of financial services via mobile phones and other related digital devices is enhancing access to finance for people who were previously excluded, such as the underprivileged, women, and youth. This enables these individuals to move from an unregulated money market to a regulated financial market, which in turn allow them to transition from being financially excluded to being financially included.
A Conditional Privacy Preserving Heterogeneous Signcryption Scheme for Internet of Vehicles

The Internet of Vehicles (IoVs) is a wireless network of intelligent and autonomous vehicles and Roadside Units (RSUs), which work together to improve transportation services. On the one hand, this revolutionary mode of transportation optimizes traffic efficiency and reduces congestion and accidents; on the other hand, it raises security and privacy concerns. As a result, keeping the security and privacy aspects of such vital systems should be a top concern, as security and privacy flaws can have devastating implications. In addition, each vehicle's on-board unit (OBU) has a certain amount of processing and storage power. As a result, in areas with high traffic density, vehicles that need to verify messages have a very high computational overhead. In this article, we propose a conditional privacy-preserving heterogeneous signcryption scheme for IoV utilizing Hyperelliptic Curve Cryptography (HECC) operation. The security robustness of the proposed scheme has been evaluated with the Random Oracle Model (ROM), a formal security evaluation method. In addition, relevant existing schemes are compared to the proposed scheme in terms of computation and communication costs. The findings demonstrate that the proposed scheme is both efficient and secure, validating its viability.
Prototype-based One-Class-Classification Learning Using Local Representations

One-class-classification remains an important problem in machine learning, which is related to data representation and outlier detection, but different from them in several aspects. In the present contribution we propose an one-class-classifier based on a prototype vector quantization model. We modeled a corresponding cost function to account for aspects of representation learning and to appropriately evaluate the one-class classifier. The prototype-based model ensures a local representation of the target class. After this introduction, we obtain an interpretable one-class classifier model. We demonstrate the capabilities of the approach by applying the classifier to illustrative toy data examples as well as on real data in a medical context.
A Multi-Gate Encoder for Joint Entity and Relation Extraction

Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, ensuring the specificity of task-specific traits while the two tasks interact properly is a huge difficulty. We propose a multi-gate encoder that models bidirectional task interaction while keeping sufficient feature specificity based on gating mechanism in this paper. Precisely, we design two types of independent gates: task gates to generate task-specific features and interaction gates to generate instructive features to guide the opposite task. Our experiments show that our method increases the state-of-the-art (SOTA) relation F1 scores on ACE04, ACE05 and SciERC datasets to 63.8% (+1.3%), 68.2% (+1.4%), 39.4% (+1.0%), respectively, with higher inference speed over previous SOTA model.
Unsupervised Selective Labeling for More Effective Semi-supervised Learning

Given an unlabeled dataset and an annotation budget, we study how to selectively label a fixed number of instances so that semi-supervised learning (SSL) on such a partially labeled dataset is most effective. We focus on selecting the right data to label, in addition to usual SSL's propagating labels from labeled data to the rest unlabeled data. This instance selection task is challenging, as without any labeled data we do not know what the objective of learning should be. Intuitively, no matter what the downstream task is, instances to be labeled must be representative and diverse: The former would facilitate label propagation to unlabeled data, whereas the latter would ensure coverage of the entire dataset. We capture this idea by selecting cluster prototypes, either in a pretrained feature space, or along with feature optimization, both without labels. Our unsupervised selective labeling consistently improves SSL methods over state-of-the-art active learning given labeled data, by 8–25 $$\times $$ in label efficiency. For example, it boosts FixMatch by 10% (14%) in accuracy on CIFAR-10 (ImageNet-1K) with 0.08% (0.2%) labeled data, demonstrating that small computation spent on selecting what data to label brings significant gain especially under a low annotation budget. Our work sets a new standard for practical and efficient SSL.
Concept-Level Model Interpretation From the Causal Aspect

With the increasing growth of data and the ability of learning with them, machine learning models are adopted in various domains. However, few of machine learning models are able to reason their prediction, which limits their further applications in real-world tasks. With the potential to address this dilemma, model interpretation has become an important research topic because of the ability to provide the underlying reasons for model predictions at the feature level or concept level. Model interpretation at the concept level focuses on exploring the roles of concepts in model prediction, which enables more compact and understandable interpretations. Concept-level model interpretation requires the identification of the concepts that contribute to model prediction and the exploration of the rules underneath these concepts. To achieve the two objectives, we propose a Concept-level Model Interpretation framework (CMIC) from the perspective of causality. CMIC can automatically detect concepts in data and discover the causal relation between the detected concepts and the model's predicted labels. Furthermore, CMIC ranks the contributions of concepts by their causal effect on the model prediction, reflecting the detected concepts’ importance. We evaluate the proposed CMIC framework on both synthetic and real-world datasets to demonstrate the quality of the provided interpretation.
Mapping Relational Database Constraints to SHACL

Most structured data today is still stored in relational databases, which makes it important to provide a translation between relational and semantic data. A relational to RDF mapping, such as R2RML [13], provides a way to view existing relational data in the RDF data model through declarative mappings. While relational to RDF mapping translates relational instance data to RDF, it does not specify any translation of existing relational constraints such as primary and foreign key constraints. Since the introduction of R2RML, interest in RDF constraint languages has increased and SHACL [15] has been standardised. This raises the question of which SHACL constraints are guaranteed to be valid on a dataset produced by a relational to RDF mapping. For arbitrary SQL constraints and relational to RDF mappings, this is a hard problem, but we introduce a number of restrictions on the mappings that allow us to introduce a constraint rewriting for relational to RDF mappings that faithfully transfers SQL integrity constraints to SHACL constraints. We define and prove two fundamental properties, namely maximal semantics preservation and monotonicity.
Joint Covariate-Alignment and Concept-Alignment: A Framework for Domain Generalization

In this paper, we propose a novel domain generalization (DG) framework based on a new upper bound to the risk on the unseen domain. Particularly, our framework proposes to jointly minimize both the covariate-shift as well as the concept-shift between the seen domains for a better performance on the unseen domain. While the proposed approach can be implemented via an arbitrary combination of covariate-alignment and concept-alignment modules, in this work we use well-established approaches for distributional alignment namely, Maximum Mean Discrepancy (MMD) and covariance Alignment (CORAL), and use an Invariant Risk Minimization (IRM)-based approach for concept alignment. Our numerical results show that the proposed methods perform as well as or better than the state-of-the-art for domain generalization on several data sets.
A Robust Blockchain Architecture for Electronic Health Data using Efficient Lightweight Encryption Model with Re-Encryption Scheme

The handling of electronic health records (EHRs) from the Internet of Medical Things (IoMT) is one of the most challenging research areas as it consists of sensitive information which is a target for attackers. Also, it is highly complex and expensive to deal with modern healthcare systems as it requires a lot of secured storage space. However, these problems can be mitigated with the improvement in health record management using blockchain technology. To improve data security, patient privacy, and scalability, the proposed work develops a scalable lightweight framework based on blockchain technology. Initially, the COVID-19 related data records are hashed by using an enhanced Merkle tree (EMT) data structure. The hashed values are encrypted by lattice-based cryptography with a Homomorphic Proxy Re-Encryption scheme (LBC-HPRS) in which the input data are secured. After the completion of the encryption process, the blockchain uses IPFS to store secured information. Finally, the Proof of Work (PoW) concept is utilized to verify and validate the security of the input COVID-based data records. The experimental setup of the proposed work is performed by using a python tool and the performance metrics like encryption time, re-encryption time, decryption time, overall processing time and latency prove the efficacy of the proposed schemes.
A MapReduce Clustering Approach for Sentiment Analysis Using Big Data

The modern era of organizations are generating huge amount of data by digitalizing their way of promoting services and products. The companies trying to know what customers are saying in terms of products through reviews in social media analytics constitutes a prime factor to enhance the success of big data era. However, the social media data analytics is a very complex discipline due to subjectivity in the textual review and the productivity in their complexity. Different approach of framework to tackle this problem is proposed: The first stage discussed, in this paper, the sentiment analysis of social media through the different approaches of machine learning. The second stage is used to discuss the challenges faced for processing the sentiment analysis of social media. Then, an overview of the case study presented for the two stages sentiment analysis in different approaches with the help of customer reviews. The machine learning approaches will be followed to analyze the sentiment analysis of social media in processing the big data.
A Format Compliant Framework for HEVC Selective Encryption After Encoding

The security protection of Ultra-High-Definition (UHD) video is facing grand challenges due to changeable application scenarios. The video business is highly dependent on the video format structure, which makes the format compliance of the encryption algorithm essential. Existing HEVC Selective Encryption (SE) algorithms are difficult to encrypt with format compliance while independent from the encoding, which limits their practical application. In order to realize the format compliant encryption on encoded bitstream, this paper first proposes a non-diffusion rule by analyzing the coding format specification of High Efficiency Video Coding (HEVC). Following the non-diffusion rule, a Bit Flipping (BF) method and a Bit Insertion-Deletion (BID) method are proposed based on the lower bound mapping of the bypass syntax element bit coding interval. Then, considering the various binarization methods and the correlation between syntax elements when decoding, this paper proposes the Validity Principle (VP) and Independence Principle (IP) of format compliant encryption. Focusing on the binary bits of the primary bypass elements in HEVC, the encryptability of them is analyzed in detail, and the corresponding format compliant encryption schemes based on BF, BID methods are constructed. Based on the above, a Format Compliance Encryption (FCE) framework after encoding is formulated. The flexibility, security, and adaptability of the framework are analyzed. Finally, through experiments from the aspects of format compliance, encryption speed, and perception effect, the proposed methods, schemes, and framework are format compliance while ensuring the basic performance and security requirements.
Alteration Detections Using ASTER Remote Sensing data and Fractal Geometry for Mineral prospecting in Hemich Area, NE Iran

Nowadays, remote sensing studies are considered as one of the best methods in mineral prospecting. Considering that most of the important deposits of the world, such as porphyry copper and epithermal gold deposits are widely associated with alteration zones. Identification of the alterations can be a significant exploratory guide for these types of deposits. The spectral difference between altered and unaltered rocks is the most important way of detecting alteration areas and thus exploring ore deposits by means of remote sensing. In this research, ASTER image was used to identify alteration areas in the Hemich Area, NE Iran. Geometric corrections were made on images in order to reduce the spatial errors caused by displacements and distortions related to sensors. Radiometric corrections were also applied to reduce the spectral errors caused by atmospheric effects. Subsequently, mineral prospectus areas were identified using false-color composite, band ratio, and principal components analysis. Since the results of these methods are grayscale images, a concentration-area fractal method was used to distinguish the regions containing the alterations. Using the results of these methods, some areas with the highest probability of mineralization were discovered, which were compatible with the ground control points.
Comparative Study of Liver Segmentation using U-Net and ResNet50

Medical tomography has been engaged to backing medical findings and medication. Radiography revels the decisive evidence to doctors in identifying dreadful tumors and potential ill-health findings, makes ease for surgeons to treat patients with optimal surgical methods. Particularly segmentation’s of tomography images to identify the area of interest such as tumor regions and organ boundaries etc. such that assuage complexity of understanding medical images. Automatic organ segmentation can be achieved with the help of Deep Learning algorithms this excites the attention of researchers to focus on these ace areas of research. This paper mainly focuses on two imperatives of DL architectures like U-Net and ResNet50 in the experiments so that efficacy of algorithms interim’s of comparative studies. Metrics used such as accuracy and Dice coefficients and the results were analyzed. Dataset which we acquired from Monai medical imaging, our motive is to introduce usage of Monai and PyTorch with the help of python programming language along with DL model for making segmentation of liver from computer assisted tomography scan. Results discloses that both ResNet50 and U-Net shows the tremendous work in purging false positive values. Moreover, accuracy metrics alone is not sufficient in making significant measure of efficacy about segmentation. The inferences are useful in calibrating surgical operations with respect to boundaries and critical regions which helps the doctors for making right choices to treat patients.
Performance Analysis of The Effect Euler Regression on Complex YOLOv4 Model for Autonomous Driving Applications

Autonomous driving is a technology with a driving automation mode where objects will be captured using LiDAR sensors and cameras. Objects captured by LiDAR will be processed into a point cloud representing the bounding box predicted. However, the detection of 3D objects in autonomous driving in real time causes the distribution of the location of the bounding box coordinates of the predicted results to be out of accordance with the predetermined ground truth. Based on these problems, this research designed and implemented an object detection model using Complex YOLOv4 that focuses on exploiting regression parameters. Exploitation of regression parameters is carried out on upper factors, limit angles, and scaling ranges. Based on the results obtained, the highest mAP value is with an upper factor of 0.4, a limit angle of 25 degree, and a scaling range (0.8;1.05) with an mAP value of 37.7% or 4.9% superior to the Complex YOLOv4 model which has an mAP value of 32.8%. In this research obtained the value of precision, recall, and F1-Score for each class shows the car class is dominant of all classes because of the dataset is imbalanced.
Fine-Grained Fashion Representation Learning by Online Deep Clustering

Fashion designs are rich in visual details associated with various visual attributes at both global and local levels. As a result, effective modeling and analyzing fashion requires fine-grained representations for individual attributes. In this work, we present a deep learning based online clustering method to jointly learn fine-grained fashion representations for all attributes at both instance and cluster level, where the attribute-specific cluster centers are online estimated. Based on the similarity between fine-grained representations and cluster centers, attribute-specific embedding spaces are further segmented into class-specific embedding spaces for fine-grained fashion retrieval. To better regulate the learning process, we design a three-stage learning scheme, to progressively incorporate different supervisions at both instance and cluster level, from both original and augmented data, and with ground-truth and pseudo labels. Experiments on FashionAI and DARN datasets in the retrieval task demonstrated the efficacy of our method compared with competing baselines.