Reviewerly/exHarmony huggingface dataset:
Hierarchical Graph Convolutional Networks for Structured Long Document Classification

Long document classification (LDC) has been a focused interest in natural language processing (NLP) recently with the exponential increase of publications. Based on the pretrained language models, many LDC methods have been proposed and achieved considerable progression. However, most of the existing methods model long documents as sequences of text while omitting the document structure, thus limiting the capability of effectively representing long texts carrying structure information. To mitigate such limitation, we propose a novel hierarchical graph convolutional network (HGCN) for structured LDC in this article, in which a section graph network is proposed to model the macrostructure of a document and a word graph network with a decoupled graph convolutional block is designed to extract the fine-grained features of a document. In addition, an interaction strategy is proposed to integrate these two networks as a whole by propagating features between them. To verify the effectiveness of the proposed model, four structured long document datasets are constructed, and the extensive experiments conducted on these datasets and another unstructured dataset show that the proposed method outperforms the state-of-the-art related classification methods.
Deep Learning: Traffic Accident Captioning Model in Madagascar Mother Language

This paper presents a deep learning model for road accident captioning in the Malagasy language. As we have observed, the road accident is a deserved study case due to the relentless increase in Madagascar. In addition, image description in the Malagasy language is a subject of research that is not yet realized. Thus, we have created our dataset in the Malagasy language to do this. We have also adopted deep learning and word embedding as a method. As a result, we obtained a deep learning model which can describe a road accident image in the Malagasy language at 95.9898 percent of accuracy. The results obtained in this research are beneficial to protecting the life of Malagasy people in a road accidents. Moreover, our research is distinguished from all the related works in this domain by collaborating with an expert in the Malagasy language during the creation of the dataset, by the innovation of this domain in Madagascar, and by the relevance of our result.
Research on deep neural network model construction and overfitting

In recent years, deep neural network has been widely used in image recognition, natural language processing, computer vision and other fields, but it is prone to overfitting during network training. To solve this problem, this paper uses TensorFlow2.0 framework to construct multilayer perceptron deep network for Fashion-MNIST dataset, and uses dropout algorithm to solve the overfitting problem in the process of network training. The research results show that the dropout algorithm is applied to the deep neural network, which can make the deep neural network model have strong generalization ability and can effectively solve the overfitting problem of the training network. The research on overfitting problem has important practical significance for reducing the identification error of deep network.
Bilateral Pose Transformer for Human Pose Estimation

Human Pose is a well-defined fundamental task researched by the computer vision community for years. Previous Convolutional Neural Network (CNN) based works have achieved significant success in the human pose. Recently, Vision Transformer (VT) has shown superior performance on computer vision tasks. However, current VT methods emphasize local information less and often focus on only a single scale feature that may not be suitable for dense image prediction tasks, which essentially requires multi-scale representations. In this paper, we propose a novel Bilateral Pose Transformer (BPT) framework to handle the human pose. Specifically, BPT consists of an innovated bilateral branch encoder and a multi-scale integrating decoder. The bilateral branch encoder contains a Context Branch (CB) and Spatial Branch (SB). The CB involves a VT-based backbone to capture the context clues and produce multi-scale context features. The CNN-based SB maintains high-resolution representations containing rich spatial information to introduce the local spatial information that supplements the CB explicitly. About the decoder, a Mixed Feature Module consisting of local attention CNN is proposed to integrate the various-scale context and spatial features effectively. Experiments demonstrate that our approach achieves competitive performances in human pose estimation. Specifically, compared to the HRNet [1], the BPT saves 43% GFLOPs and drops only 0.1 points AP, achieving 75.7% AP with 9.0 GFLOPs, on the COCO keypoints dataset.
Face information forensics analysis based on facial aging: A Survey

Face recognition systems are now confronted with the problem of “aging”. The difficulty arises from age-related biological changes, which might result in considerable differences in face traits between two photographs obtained at various ages of the same individual. Because the face is the area of the body that is most impacted by aging, the extraction of strong facial traits for age-invariant face recognition is becoming increasingly important, especially when there are huge age disparities between the same person’s face photos. Face Age Progression (FAP) is the process of synthesizing face photos while simulating aging processes to anticipate an individual’s future look. The production of age-progressed face photographs has advantages for a variety of applications; Face recognition methods, private investigators, and entertainment content are just a few examples, particularly for recognizing and protecting missing abducted children using childhood photographs or Alzheimer’s people. Deep generative networks’ success recently, in particular, has considerably improved the maturity level of face’s qualities pictures about visual clarity, aging correctness, as well as identity preservation. This paper present a comparison of contemporary approaches to face age growth deep learning-based for both adulthood and youth face aged, face age progression FAP is classified into three rising concepts: translation-based, condition-based, & sequence-based. This paper provides a complete overview of one of the most frequently used methods of achievement assessment and a comprehensive list of available datasets.
Application of IOT in Exploring the Development Path of the Whole Agricultural Industry Chain under the Perspective of Ecological Environment

The green development of the entire agricultural industry chain is not only an important way to enhance the competitiveness of agricultural products and promote the value-added of the agricultural industry but also an inevitable choice to improve the efficiency of resource utilization and ensure sustainable economic development from the perspective of the ecological environment. This paper defines the connotation and characteristics of the green development of the whole agricultural industry chain and deeply analyzes the development status, major challenges, and feasible paths of the whole agricultural industry chain in China. It has built a whole industry chain development model of “Internet + modern agriculture” and proposed a decentralized agricultural data management system based on the IoT to solve the problem of untraceable of traditional agricultural products and other data. The agricultural IoT system is designed with a unique blockchain double-chain structure, using the global hash chain and the mutual verification between the transaction chains in each partition. It proposes the PMCP protocol, which improves the performance of the system by producing blocks in parallel for several consensus groups in each consensus cycle. The experimental results show that the proposed model is queryable, traceable, and trustworthy for whole-life agricultural data, and the proposed model outperforms other schemes in terms of throughput and delay.
Literary value in the era of big data. Operationalizing critical distance in professional and non-professional reviews

New phenomena such as digital social reading, instapoets, and the “rating culture” expressed in online reviews challenge traditional literary criticism in newspapers and journals. Millions of reviews on platforms such as Amazon or Goodreads are part of this culture of participation and a counterweight to professional criticism. At the same time, successful instapoets such as Rupi Kaur reject the expertise of the gatekeepers of “prestigious literary circles” and try to establish a direct connection with readers. The aim of this paper is to build the proper methodological framework to capture these changes in the current literary system. To do this, the phenomenon of online reviewing has to be contextualized within the history and the praxis of assigning literary value to literary texts, the so-called canonization. In addition, literary theory needs to be able to analyze quantitative data and to integrate numbers into its models (engaging in a procedure that is called operationalization).
Deterministic secure quantum communication against collective noise

Collective noise is one of the main obstacles that reduce the efficiency of quantum communication. In this paper, we present two robust deterministic secure quantum communication (DSQC) schemes based on logical qubits. The first scheme employs a store-and-measure strategy along with the exclusive OR operation. Then we propose a second scheme without storage by exploiting hidden permutations and one-way hash functions. To achieve the performance of robustness against collective noise, both schemes take advantage of the compensability of decoherent-free subspace (DFS) for noise. Moreover, except for decoy qubits, all quantum states are used to convey the secret message. In addition, we provide a rigorous security analysis of potential attacks and prove the security of our protocols.
Research and Application of Smart Government Approval Based on Blockchain

Traditional government approvals have low transparency and low openness. And government affairs data is stored in a centralized organization, its security depends on the security and fault tolerance of the centralized organization server. Since the server is a single point of failure, security cannot be guaranteed. Therefore, the application of blockchain technology, combined with the characteristics of blockchain’s decentralization, non-tampering and traceability, solves the security and openness issues of government approval, and improve the traceability of government data. In the process of on-chain, multi-node verification and participation, are building the entire government approval data chain. At the same time, in multiple scenarios, the government approval application combined with blockchain through experimental verification has the characteristics of security, usability, transparency and openness.
RetrofittAR: Supporting Hardware-Centered Expertise Sharing in Manufacturing Settings through Augmented Reality

Abstract Since almost the onset of computer-supported cooperative work (CSCW), the community has been concerned with how expertise sharing can be supported in different settings. Here, the complex handling of machines based on experience and knowledge is increasingly becoming a challenge. In our study, we investigated expertise sharing in a medium-sized manufacturing company in an effort to support the fostering of hardware-based expertise sharing by using augmented reality (AR) to ‘retrofit’ machines. We, therefore, conducted a preliminary empirical study to understand how expertise is shared in practice and what current support is available. Based on the findings, we derived design challenges and implications for the design of AR systems in manufacturing settings. The main challenges, we found, had to do with existing socio-technical infrastructure and the contextual nature of expertise. We implemented a HoloLens application called RetrofittAR that supports learning on the production machine during actual use. We evaluated the system during the company’s actual production process. The results show which data types are necessary to support expertise sharing and how our design supports the retrofitting of old machines. We contribute to the current state of research in two ways. First, we present the knowledge-intensive practice of operating older production machines through novel AR interfaces. Second, we outline how retrofitting measures with new visualisation technologies can support knowledge-intensive production processes.
Exploring text representation impact on K-means based arabic text documents clustering

Clustering is the task of grouping data into homogeneous groups called clusters. Arabic Internet users have led to a rapid evolution of arabic content on the Web. As a result, many researchers have been interested in solving various problems related to Arabic information retrieval applications (search engines, recommender systems, etc.) using arabic textual data clustering. In this paper, we present a study that explores the impact of text representation on K-Means-based clustering of Arabic documents. For this purpose, we combine K-means clustering algorithm with TF-IDF and FastText embedding approaches. The data set was collected from the Omani newspaper Alwatan2 and consists of 20,291 arabic articles.
Towards mesh saliency in 6 degrees of freedom

In this work, a novel 6DoF mesh saliency database is developed which provides both the subject’s 6DoF data and eye-movement data. Different from traditional databases, subjects in the experiment are allowed to move freely to view 3D meshes in the virtual reality environment. Based on the database, we first analyze the inter-observer variation and the influence of viewing direction toward subject’s visual attention, then we provide investigations about the subject’s visual attention bias and head movement during observation. As traditional 3D mesh saliency detection algorithms do not taking the subject’s head movement into consideration, we further propose a 6DoF mesh saliency detection algorithm based on the uniqueness measure and the bias preference. To evaluate the proposed approach, we design an evaluation metric accordingly which takes the 6DoF information into consideration, and extend some state-of-the-art 3D saliency detection methods to make comparisons. The experimental results demonstrate the superior performance of our approach for 6DoF mesh saliency detection, in addition to providing benchmarks for the presented 6DoF mesh saliency database. The database and proposed method will be made publicly available for research purposes.
Interactive Teaching System for Remote Vocal Singing Based on Decision Tree Algorithm

As the global pandemic rapidly spreads, distance learning is emerging as a new method of instruction. Particularly, traditional courses that must be taught in-person, such as vocal singing instruction, require immediate adaptation to the COVID-19 and the new distance learning model. However, in the process of distance learning, it is frequently impossible to tailor and personalize instruction, particularly for vocal singing courses. Educational researchers are confronted with the pressing issue of how to extract useful and personalized patterns from a large volume of learner data in order to customize and individualize instruction. In this paper, we propose applying the decision tree method from data mining technology to a vocal singing education system by categorizing students according to the model. Once the characteristics of various learners have been stored in the corresponding user database, teachers can access timely information regarding the learners’ most recent learning situation. This can be used as the basis for differentiating instructional strategies for various learners. This allows the instructor to designate individualized teaching and learning strategies for each student.
Reducing confirmation reversal probability of PoW blockchains using checkpoints

Existing PoW blockchains adopt a simple k-block confirmation rule. A block is confirmed after it is buried sufficiently deep in the blockchain. Such confirmation does not guarantee transaction finality. Specifically, after a block is confirmed, a miner may later confirm a different block in the same position of the blockchain. The previously confirmed block effectively gets reversed. A common reason for block reversal is that an adversary could privately maintain a malicious chain. The adversary releases the malicious chain once it is longer than the blockchain of at least one honest miner.In this paper, we propose a new confirmation method that relies on checkpoints to prevent the adversary from withholding its blocks. A checkpoint contains one or more blocks at the same position of the blockchain. Unlike existing protocols, we propose a simple mechanism to establish checkpoint blocks without introducing any trusted third parties. A committee is selected periodically to vote for checkpoint blocks. Miners only confirm a block when it is referenced by all checkpoint blocks at some block height, among other criteria. We prove the liveness and safety of this new confirmation rule. We show that conditioning on honest majority within a committee, miners achieve 0 confirmation reversal probability. We demonstrate that with majority mining power being honest, we could always select a proper checkpoint period to ensure the assumption of the majority of a committee being honest holds for hundreds of years on average. In the rare case where this assumption is broken, miners in the checkpoint-enabled blockchain still incur the same reversal probability as in the current k-block confirmation.
A Case Study of a Blockchain-GDPR Adaptation

Blockchain (BC) applications face many challenges to comply with the European General Data Protection Regulation (GDPR) i.e., enabling users to hold on to their rights for deleting or modifying their data stored on publicly accessible and immutable BCs. In this regard, this paper proposes an on-chain solution that allows for fine-grained modification (update and erasure) operations on transactions’ (TX) data fields within a BC. The proposed solution is based on a cryptographic primitive called Chameleon Hashing. Via this approach, BC users have the authority to update their data, which are addressed at the TX level with no side effects on the block or chain. By performing and storing data updates, all on-chain traceability and verifiability characteristics of the BC are preserved.
Cybersecurity threats in the banking sector

In 2021 cybersecurity was again in spotlight. The number of data breaches until September 30, 2021, has already exceeded by 17 percent the total number of events in 2020 [1]. The banking sector marks another “worst year ever” for cyber-attacks. The sector was disproportionately affected by the attacks. In the first half of 2021 blackmail virus attacks increased by 1318 percent on a y-o-y basis [2]. Therefore, in addition to taking advantage of the development of information technology, new potential attack surfaces appeared. The threat of cyberattacks significantly increased. The sector will be exposed to more and more sophisticated attacks. Thus, cyber-security is becoming a priority in the future. With the development of digital technology hackers are also improving their skills. Building maximum protection against their malicious attacks is an increasing challenge for professionals. In this continuous war between the cybersecurity experts and cybercriminals the first step is to learn about the latest threat trends, the tools, and the techniques.
Predicting Episodic Video Memorability using Deep Features Fusion Strategy

Video memorability prediction has become an important research topic in computer vision in recent years. The movie&#x0027;s input is highly remembered that gains much attention with unbounded time constraints. Episodic memory is a fascinating research area that needs much attention using video processing tools and techniques. Episodic memories are long-lasting with complete detail. Movies are one of the best instances of episodic memory. This paper proposes a novel framework to fuse deep features to predict the probability of recalling episodic events. Memories are reproducible and sensitive to sophisticated set of properties rather than low-level propertiesthe proposed framework pin up the fusion of text, visual and motion features. A fuzzy-based FastText model, a supervised text extraction module, is designed to extract the annotations with their relevant classes. The colour histogram analysis is done to determine the dominant colour region that performs as a connected fragment to form episodic video sequences. A novel Faster R-CNN is designed to discover the scene objects using an informative regional proposal network formation. Here, the modified loss function sorts out the lowest overlapping regions yielding the best proposals. The &#x2018;high-level&#x2019; properties are collected using Principal Component Analysis (PCA) to form episodic shots. These are fused to estimate the memorability score. The proposed framework is implemented in Mediaeval 2018 datasets. A superior spearman&#x0027;s rank correlation result is achieved as 0.6428 short-term and 0.4285 long-term memorability than the latest comparable methods.
Virtual Finger-Point Reading Behaviors: A Case Study of Mouse Cursor Movements on a Website

Web analytics has changed significantly in recent years. As part of the big data revolution, frequent low-level user actions, such as mouse movements and clicks, are often used in modern web analytics. Various studies show that when a user moves or clicks the mouse, the position of the mouse cursor is relatively close to the position of the eye gaze on the screen. Accordingly, mouse cursor positions can indicate user attention and interest in specific areas of web pages. This study focuses on mouse movement directions and speeds rather than on mouse cursor positions. A statistical analysis of mouse movements on an online learning website, which was selected for this study, sheds light on several interesting patterns. For example, most mouse movements in the examined usage data are either approximately horizontal or approximately vertical, and horizontal mouse movements are more frequent than vertical mouse movements. Besides, horizontal movements to the left are not equivalent to horizontal movements to the right, in terms of moving time and speed. As this study shows, these statistical findings are related to Pointer Assisted Reading (PAR), a reading behavior consisting of moving the mouse cursor (also known as the mouse pointer) along sentences, marking the reading position, similarly to finger-pointing when reading a book. Associating mouse movements with text reading may potentially highlight content that most users tend to skip, and therefore, might not interest the website's audience, as well as content that many readers read more than once or slowly, suggesting a lack of clarity or ambiguity. As discussed in this paper, this could be useful in locating issues in the textual content of websites and especially in online learning and educational technology applications.