Reviewerly/exHarmony huggingface dataset:
An overview of information extraction techniques for legal document analysis and processing

&lt;span&gt;In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.&lt;/span&gt;
Explainable zero-shot learning via attentive graph convolutional network and knowledge graphs

Zero-shot learning (ZSL) which aims to deal with new classes that have never appeared in the training data (i.e., unseen classes) has attracted massive research interests recently. Transferring of deep features learned from training classes (i.e., seen classes) are often used, but most current methods are black-box models without any explanations, especially textual explanations that are more acceptable to not only machine learning specialists but also common people without artificial intelligence expertise. In this paper, we focus on explainable ZSL, and present a knowledge graph (KG) based framework that can explain the transferability of features in ZSL in a human understandable manner. The framework has two modules: an attentive ZSL learner and an explanation generator. The former utilizes an Attentive Graph Convolutional Network (AGCN) to match class knowledge from WordNet with deep features learned from CNNs (i.e., encode inter-class relationship to predict classifiers), in which the features of unseen classes are transferred from seen classes to predict the samples of unseen classes, with impressive (important) seen classes detected, while the latter generates human understandable explanations for the transferability of features with class knowledge that are enriched by external KGs, including a domain-specific Attribute Graph and DBpedia. We evaluate our method on two benchmarks of animal recognition. Augmented by class knowledge from KGs, our framework generates promising explanations for the transferability of features, and at the same time improves the recognition accuracy.
Post-Quantum Secure Ring Signatures for Security and Privacy in the Cybertwin-Driven 6G

Cybertwin-driven-based network architecture for sixth generation (6G) is a new cloud-centric network architecture, which was put forward to address challenges of 6G, such as scalability, security, mobility, and availability. As more and more users&#x2019; data are obtained and converted into other digital asset by the cybertwin, relevant techniques of data management to enhance privacy and security becomes a key challenge for Cybertwin-driven 6G. The double authentication preventing ring signature (DAPRS) is a cryptographic primitive that meets requirements of authenticity, anonymity of users&#x2019; private data, and linkability of users&#x2019; misbehavior, so it is suitable for monitoring behaviors of risky users. In this article, we propose a post-quantum secure ring signature to enhance security and privacy in Cybertwin-driven 6G (PRSG). In the PRSG, we first put forward an accumulator based on a chameleon hash function that allows to efficiently prove knowledge of an accumulated value. Then, we construct a DAPRS based on the accumulator and the zero-knowledge argument of knowledge. Finally, we present how to use the DAPRS to build a secure and efficient privacy-preserving scheme in the Cybertwin-driven 6G. Security proof shows that this scheme achieves anonymity, unforgeability, as well as double signature extractability. Also, we make some security analysis and performance evaluation to demonstrate that the PRSG has low communication complexity, high performance, and privacy preservation in the cybertwin-driven 6G.
Compact Ring Signatures from Learning with Errors

Ring signatures allow a user to sign a message on behalf of a "ring" of signers, while hiding the true identity of the signer. As the degree of anonymity guaranteed by a ring signature is directly proportional to the size of the ring, an important goal in cryptography is to study constructions that minimize the size of the signature as a function of the number of ring members. In this work, we present the first compact ring signature scheme (i.e., where the size of the signature grows logarithmically with the size of the ring) from the (plain) learning with errors (LWE) problem. The construction is in the standard model and it does not rely on a common random string or on the random oracle heuristic. In contrast with the prior work of Backes et al. [EUROCRYPT'2019], our scheme does not rely on bilinear pairings, which allows us to show that the scheme is post-quantum secure assuming the quantum hardness of LWE. At the heart of our scheme is a new construction of compact and statistically witness indistinguishable ZAP arguments for NP $$\cap $$ coNP, that we show to be sound based on the plain LWE assumption. Prior to our work, statistical ZAPs (for all of NP) were known to exist only assuming sub-exponential LWE. We believe that this scheme might find further applications in the future.
A study of the generalizability of self-supervised representations

Recent advancements in self-supervised learning (SSL) made it possible to learn generalizable visual representations from unlabeled data. The performance of Deep Learning models fine-tuned on pretrained SSL representations is on par with models fine-tuned on the state-of-the-art supervised learning (SL) representations. Irrespective of the progress made in SSL, its generalizability has not been studied extensively. In this article, we perform a deeper analysis of the generalizability of pretrained SSL and SL representations by conducting a domain-based study for transfer learning classification tasks. The representations are learned from the ImageNet source data, which are then fine-tuned using two types of target datasets: similar to the source dataset, and significantly different from the source dataset. We study generalizability of the SSL and SL-based models via their prediction accuracy as well as prediction confidence. In addition to this, we analyze the attribution of the final convolutional layer of these models to understand how they reason about the semantic identity of the data. We show that the SSL representations are more generalizable as compared to the SL representations. We explain the generalizability of the SSL representations by investigating its invariance property, which is shown to be better than that observed in the SL representations.
PMMN: Pre-trained multi-Modal network for scene text recognition

Scene Text Recognition (STR) task needs to consume large-amount data to develop a powerful recognizer, including visual data like images and linguistic data like texts. However, existing methods mainly leverage a one-stage training manner to train the entire framework end-to-end, which deeply relies on the well-annotated images and does not effectively use the data of the two modalities mentioned above. To solve this, in this paper, we propose a pre-trained multi-modal network (PMMN) that utilizes visual and linguistic data to pre-train the vision model and language model respectively to learn modality-specific knowledge for accurate scene text recognition. In detail, we first pre-train the proposed off-the-shelf vision model and language model to convergence. And then, we combine the pre-trained models in a unified framework for end-to-end fine-tuning and utilize the learned multi-modal information to interact with each other to generate robust features for character prediction. Extensive experiments are conducted to demonstrate the effectiveness of PMMN. The evaluation results on six benchmarks show that our proposed method exceeds most existing methods, achieving state-of-the-art performance.
Digital Forensics Domain and Metamodeling Development Approaches

Metamodeling is used as a general technique for integrating and defining models from different domains. This technique can be used in diverse application domains, especially for purposes of standardization. Also, this process mainly has a focus on the identification of general concepts that exist in various problem domain and their relations and to solve complexity, interoperability, and heterogeneity aspects of different domains. Several diverse metamodeling development approaches have been proposed in the literature to develop metamodels. Each metamodeling development process has some advantages and disadvantages too. Therefore, the objective of this paper is to provide a comprehensive review of existing metamodeling development approaches and conduct a comparative study among them-eventually selecting the best approach for metamodel development in the perspective of digital forensics.
Mars Image Super-Resolution Based on Generative Adversarial Network

High-resolution (HR) Mars images have great significance for studying the land-form features of Mars and analyzing the climate on Mars.Nowadays, the mainstream image super-resolution methods are based on deep learning or CNNs, which are better than traditional methods.However, these deep learning based methods obtain low-resolution(LR) images usually by using an ideal down-sampling method (e.g.bicubic interpolation).There are two limitations in the existing SR methods: 1) The paired LR-HR data by using such methods can achieve a satisfactory results when tested on an ideal datasets.But, these methods always fail in real Mars image super-resolution, since real Mars images rarely obey an ideal down-sampling rule.2) The LR images obtained by ideal down-sampling methods have no noise while real Mars images usually have noise, which leads to the super-resolved images are not realistic in texture details.To solve the above-mentioned problems, in this article, we propose a novel two-step framework for Mars image superresolution.Specifically, to address limitation 1), we focus on designing a new degradation framework by estimating blur-kernels.To address limitation 2), a Generative Adversarial Network (GAN) is trained to generate noise distribution.Extensive experiments on the Mars32k dataset demonstrate the effectiveness of the proposed method, and we achieve better qualitative and quantitative results compared to other SOTA methods.
Design of Human Activity Recognition System Using Body Sensor Networks

The need for a developed technology in the biomedical field is in a great demand and to fulfill such demands there are many evolving technologies in this field, and one of such technology is human activity recognition system using body sensor networks which deals with recognizing human activities in order to observe and examine the user or while dealing with biomedical sector it is highly required to thoroughly examine the patients. This human activity recognition system is basically an observational system to keep track of human actions by categorizing them into some basic actions like sit, stand, walk, rest, etc. other than this field it has a wide range of applications.
Effects of Affordance State and Operation Mode on a Smart Washing Machine Touch Sensitive User Interface Design

Smart touch-sensitive screens provide a wide range of possibilities. Compared with static icons, animated icons should be effective in terms of narrative function learning and visual pleasure to provide a good interactive experience. The purpose of this study was to explore the affordance of animated visual cues for the interface design of smart washing machines. A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times3$ </tex-math></inline-formula> mixed factorial design was planned to help explore whether different affordance states and operation modes may affect users’ task performance and subjective evaluation. Adopting the convenience sampling method, 24 participants were recruited to take part in the experiment. The experimental data were collected pertinent to task performance using the system usability scale (SUS) and a 7-point Likert scale of subjective evaluation, as well as semi-structured interviews. The generated results revealed that: (1) There was a significant difference in the affordance states. Animated icons significantly affect the heuristic processing of the smart washing machine interface, and enhance the usefulness of icons, which helps improve operational efficiency. (2) There were significant differences among different operation modes. The Square type interface combines the modern smart mode and traditional learning performance to help users improve task performance. (3) The overall analysis of task performance and satisfaction consistently showed that the Square type is better than the Circle and Mesh types in all aspects, and the animated affordance had the best task performance. (4) A smart user interface design should combine user experience and touch-sensitive screen product characteristics to help improve users’ affordance perception.
UAV Waypoint Opportunistic Navigation in GNSS-Denied Environments

Navigation of an unmanned aerial vehicle (UAV) to reach a desired waypoint with provable guarantees in global navigation satellite system (GNSS)-denied environments is considered. The UAV is assumed to have an unknown initial state (position, velocity, and time) and the environment is assumed to possess multiple terrestrial signals of opportunity (SOPs) transmitters with unknown states (position and time) and one anchor SOP whose states are known. The UAV makes pseudorange measurements to all SOPs to estimate its own states simultaneously with the states of the unknown SOPs. The waypoint navigation problem is formulated as a greedy (i.e., one-step look-ahead) multiobjective motion planning (MOMP) strategy, which guarantees that the UAV gets to within a user-specified distance of the waypoint with a user-specified confidence. The MOMP strategy balances two objectives: i) navigating to the waypoint; and (ii) reducing UAV’s position estimate uncertainty. It is demonstrated that in such an environment, formulating the waypoint navigation problem in a so-called “naive” fashion by heading directly to the waypoint would result in failing to reach the waypoint. This is due to poor estimability of the environment. In contrast, the MOMP strategy guarantees (in a probabilistic sense) reaching the waypoint. Monte Carlo simulation results are presented showing that the MOMP strategy achieves the desired objective with 95% success rate compared to a 36% success rate with the naive approach. Experimental results are presented for a UAV navigating to a waypoint in a cellular SOP environment, where the MOMP strategy successfully reaches the waypoint, while the naive strategy fails to do so.
A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction

The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing structural information of a sentence is important to support this task. Currently, graph neural networks are widely implemented to support relation extraction, in which dependency trees are employed to generate adjacent matrices for encoding structural information of a sentence. Because parsing a sentence is error-prone, it influences the performance of a graph neural network. On the other hand, a sentence is structuralized by several named entities, which precisely segment a sentence into several parts. Different features can be combined by prior knowledge and experience, which are effective to initialize a symmetric adjacent matrix for a graph neural network. Based on this phenomenon, we proposed a feature combination-based graph convolutional neural network model (FC-GCN). It has the advantages of encoding structural information of a sentence, considering prior knowledge, and avoiding errors caused by parsing. In the experiments, the results show significant improvement, which outperform existing state-of-the-art performances.
Sticky notes against corporate hierarchies in South Korea: An ethnography of workplace collaboration and design co-creation

This article deploys ethnographic methods and actor-network theory (ANT) to investigate how sticky notes challenge the historical hierarchies of South Korean corporate culture. After sketching challenges of workplace collaboration in South Korea, we present an ethnography of a co-creation workshop at a food company. To detail how sticky notes support collaborative inquiry, we approach them as active nonhuman actors and analyze the workshop progression through the four moments of ANT's sociology of translation—problematization, intéressement, enrolment, and mobilization. We conclude that stickies enable an effective silent ideation by hushing domineering senior participants and amplifying opinions from junior and mid-career participants. Finally, implications are drawn for stickies' mediating ability in culturally specific collaborative practices.
Further results on optimal ternary cyclic codes

Let C(u,v) denote the ternary cyclic code with two nonzeros αu and αv, where α is a generator of F3m⁎ and 0≤u,v≤3m−2. In this paper, we present a sufficient condition such that C(u,v) is an optimal ternary cyclic code. Based on this condition, we get several classes of optimal ternary cyclic codes by choosing the proper u and v. Moreover, we show that C(3m+12,3m−12+v) and C(1,v) have the same optimality.
Learning scale awareness in keypoint extraction and description

To recover relative camera motion accurately and robustly, establishing a set of point-to-point correspondences in the pixel space is an essential yet challenging task in computer vision. Even though multi-scale design philosophy has been used with significant success in computer vision tasks, such as object detection and semantic segmentation, learning-based image matching has not been fully exploited. In this work, we explore a scale awareness learning approach in finding pixel-level correspondences based on the intuition that keypoints need to be extracted and described on an appropriate scale. With that insight, we propose a novel scale-aware network and then develop a new fusion scheme that derives high-consistency response maps and high-precision descriptions. We also revise the Second Order Similarity Regularization (SOSR) to make it more effective for the end-to-end image matching network, which leads to significant improvement in local feature descriptions. Experimental results run on multiple datasets demonstrate that our approach performs better than state-of-the-art methods under multiple criteria.
TopicBERT: A Topic-Enhanced Neural Language Model Fine-Tuned for Sentiment Classification

Sentiment classification is a form of data analytics where people's feelings and attitudes toward a topic are mined from data. This tantalizing power to "predict the zeitgeist" means that sentiment classification has long attracted interest, but with mixed results. However, the recent development of the BERT framework and its pretrained neural language models is seeing new-found success for sentiment classification. BERT models are trained to capture word-level information via mask language modeling and sentence-level contexts via next sentence prediction tasks. Out of the box, they are adequate models for some natural language processing tasks. However, most models are further fine-tuned with domain-specific information to increase accuracy and usefulness. Motivated by the idea that a further fine-tuning step would improve the performance for downstream sentiment classification tasks, we developed TopicBERT-a BERT model fine-tuned to recognize topics at the corpus level in addition to the word and sentence levels. TopicBERT comprises two variants: TopicBERT-ATP (aspect topic prediction), which captures topic information via an auxiliary training task, and TopicBERT-TA, where topic representation is directly injected into a topic augmentation layer for sentiment classification. With TopicBERT-ATP, the topics are predetermined by an LDA mechanism and collapsed Gibbs sampling. With TopicBERT-TA, the topics can change dynamically during the training. Experimental results show that both approaches deliver the state-of-the-art performance in two different domains with SemEval 2014 Task 4. However, in a test of methods, direct augmentation outperforms further training. Comprehensive analyses in the form of ablation, parameter, and complexity studies accompany the results.
Infrared Image Pedestrian Detection via YOLO-V3

Thermal imaging is often used for pedestrian detection at night to make up for the inability of traditional cameras to be used in harsh lighting conditions. However, the pedestrian detection technology based on thermal imaging is difficult to overcome the interference of high-heat objects around the human body, and it is easy to cause misidentification. This paper proposes to detect pedestrians in infrared images at night through the YOLO-V3 detection framework. Experimental results show that the proposed method can not only accurately locate pedestrians at night, but also maintain high detection accuracy when pedestrians overlap.
Intelligent Cancer Detection System

Histopathology and MRI produce different types of images and a single procedure to detect cancerous cells from both of them is considered tedious and time-consuming. Therefore, this study proposes a strategy that can perform segmentation, classification and detection over numerous categories of cancer cells. This experimentation demands the preprocessing to intensify the cells’ pictorial characteristic and to segment the intended cancerous cell k-means algorithm applied. The Convolutional Neural Network (CNN) classifier being considered as the best method classifying the test image using the feature extraction method on the filtered cell. Considering the system detects cancer, different morphological operations are applied on the extracted cancer region, and finally, we calculated the percentage of the cancerous area. The MATLAB environment has been used on Histopathology and MRI images of the infected cell from various authentic datasets of various cancers mainly Brain, Breast, Leukemia and Lung cancer. The proposed arrangements of methods give improved accuracy as compared to the other existing methods.
2021 State of the Practice in Data Privacy and Security

The data privacy and security landscape is changing drastically due to new laws and regulations, the COVID-19 pandemic, and other emerging trends. This article looks at the current state of these issues.