Reviewerly/exHarmony huggingface dataset:
End-to-End Compound Table Understanding with Multi-Modal Modeling

Table is a widely used data form in webpages, spreadsheets, or PDFs to organize and present structural data. Although studies on table structure recognition have been successfully used to convert image-based tables into digital structural formats, solving many real problems still relies on further understanding of the table, such as cell relationship extraction. The current datasets related to table understanding are all based on the digit format. To boost research development, we release a new benchmark named ComFinTab with rich annotations that support both table recognition and understanding tasks. Unlike previous datasets containing the basic tables, ComFinTab contains a large ratio of compound tables, which is much more challenging and requires methods using multiple information sources. Based on the dataset, we also propose a uniform, concise task form with the evaluation metric to better evaluate the model's performance on the table understanding task in compound tables. Finally, a framework named CTUNet is proposed to integrate the compromised visual, semantic, and position features with a graph attention network, which can solve the table recognition task and the challenging table understanding task as a whole. Experimental results compared with some previous advanced table understanding methods demonstrate the effectiveness of our proposed model. Code and dataset are available at \urlhttps://github.com/hikopensource/DAVAR-Lab-OCR.
CALM: Commen-Sense Knowledge Augmentation for Document Image Understanding

Performance of document image understanding has been significantly fueled by encoding multi-modal information in recent years. However, existing works heavily rely on the superficial appearance of the observed data, resulting in counter-intuitive model behavior in many critical cases. To overcome this issue, this paper proposes a common-sense knowledge augmented model CALM for document image understanding tasks. It firstly produces purified representations of document contents to extract key information and learn common-sense augmented representation for inputs. Then, relevant common-sense knowledge is extracted from the external ConceptNet knowledge base, and a derived knowledge graph is built to enhance the common-sense reasoning capability of CALM jointly. In order to further highlight the importance of common-sense knowledge in document image understanding, we propose the first question-answering dataset, CS-DVQA, focused on common-sense reasoning for document images, in which questions are answered by taking both document contents and common-sense knowledge into consideration. Through extensive evaluation, the proposed CALM approach outperforms the state-of-the-art models in three document image understanding tasks, including key information extraction(from 85.37 to 86.52), document image classification(from 96.08 to 96.17), document visual question answering(from 86.72 to 88.03).
A blockchain-based Aadhar system: distributed authentication system

An Aadhaar is a unique number issued to every citizen in India. Aadhar's current identity authentication relies on the central identities data repository (CDIR) of the unique identification authority of India (UIDAI), which is at risk of a single-point fault attack. Perhaps worse, internal attacks can tamper with the sensitive data of authenticated devices without being detected. In this paper, the proposed system utilizes emerging technology: blockchain for solving the issue of centralized authentication. The proposed system provides a distributed, secure, and tamper-proof ledger platform for Aadhar in that Aadhar is implemented using blockchain ethereum technology. The proposed system considers the unique Aadhar identification (ID) for each citizen of India and registered it on the smart contract of ethereum so that this unique ID may be authenticated by each other in a peer-to-peer network without a central authority. For securing the data, the proposed framework uses hashing technique for significant data (i.e. firmware). Blockchain stores hashed data and instantly any change in the state of the data may be possible to detect.
Parallel field programmable gate array implementation of the sum of absolute differences algorithm used in the stereoscopic system

Stereo vision is a popular method for an artificial vision-based environment perception system used in various applications such as intelligent transportation. With two cameras, the disparity map is calculated to find the distance and depth of objects in front of a moving vehicle. The key element of the stereoscopic system is based on the sum of absolute differences (SAD) algorithm, which is the most repeated operation in the stereo matching subsystem; however, this algorithm requires a very intensive processing time, statistical analysis show that the SAD block can consume more than 80% of the overall processing time of the algorithm. In this paper we propose a highly efficient hardware architecture of the SAD algorithm for real time stereo matching, the proposed architecture is established by a hierarchical parallel architecture of the SAD block, and verified by simulation and successfully implemented in Cyclone IV field programmable gate array (FPGA), it provides a significant reduction of processing time and the performance of the stereo imaging system is able to achieve 30 frames per second of 640×480 resolution color images.
Comparison of the efficiency of machine learning algorithms for phishing detection from uniform resource locator

We are using cyberspace for completing our daily life activities because of the growth of Internet. Attackers use some approachs, such as phishing, with the use of false websites to collect personal information of users. Although, software companies launch products to prevent phishing attacks, identifying a webpage as legitimate or phishing, is a very defficult and these products cannot protect from attacks. In this paper, an anti-phishing system has been introduced that can extract feature from website’s URL as instant basis and use four classification algorithms named as K-Nearest neighbor, decision tree, support vector machine, random forest on these features. According to the comparison of the experimental results from these algorithms, random forest algorithm with the selected features gives the highest performance with the 95.67% accuracy rate. Then we have used one deep learning algorithm as enhanced of our experiment named as deep neural decision forests which have given performance with the 92.67% accuracy rate. Then we have created a system which can extract the features from raw URL and pass the features to our deep neural decision forest trained model and can classify the URL as Phishing or legitimate.
Sound -&gt; Object -&gt; Gesture: Physical Affordances of Virtual Materials

This paper explores the influence of materials communicated through the sound of digital musical instruments. We are particularly interested in how musicians approach the design of new instruments when the sounds they make are constrained to recognisable material timbres and behaviours. We present a digital musical instrument (DMI) design study in which 20 participants design 80 new DMIs using an instrument kit. We use enactive approaches to design to enable the participants to borrow from everyday understandings of interaction in the real world, revealing aspects of their environment mediated purely through sound during the study tasks. We demonstrate that the influence of virtual material is strong, with participants not only taking material cues from the sound, but in places interaction strategies and performance approaches. We suggest that in addition to commonly referenced modalities of sight and touch, sound can be both precursor and mediator to our selection of, and gestural interaction with, materials at the design stage. We present two specific approaches to instrument design (Sound, Object Gesture – SOG, and Sound Gesture Object - SGO) which we found to underpin participant’s approaches to the design task and have wider effects on the instruments they designed, and the model of creativity employed in their use.
Precipitation forecast in China based on reservoir computing

Precipitation as the meteorological data is closely related to human life. For this reason, we hope to propose new method to forecast it more accurately. In this article, we aim to forecast precipitation by reservoir computing with some additional processes. The concept of reservoir computing emerged from a specific machine learning paradigm, which is characterized by a three-layered architecture (input, reservoir and output layers). What is different from other machine learning algorithms is that only the output layer is trained and optimized for particular tasks. Since the precipitation data is non-smooth, its prediction is very difficult via the classical methods of prediction of the nonlinear time series. For the predicated precipitation data, we take its first-order moving average to make it smoother, then take the logarithm of smoothed nonzero data and the same negative constant for smoothed zero data to obtain a new series. We train the obtained series by reservoir computing and get the predicated result of its future. After taking its exponent function, the predicated data for original precipitation data are obtained. It indicates that reservoir computing combined with other processes can potentially bring about the accurate precipitation forecast.
Cross-modal Semantic Alignment Pre-training for Vision-and-Language Navigation

Vision-and-Language Navigation needs an agent to navigate to a target location by progressively grounding and following the relevant instruction conditioning on its memory and current observation. Existing works utilize the cross-modal transformer to pass the message between visual modality and textual modality. However, they are still limited to mining the fine-grained matching between the underlying components of trajectories and instructions. Inspired by the significant progress achieved by large-scale pre-training methods, in this paper, we propose CSAP, a new method of Cross-modal Semantic Alignment Pre-training for Vision-and-Language Navigation. It is designed to learn the alignment from trajectory-instruction pairs through two novel tasks, including trajectory-conditioned masked fragment modeling and contrastive semantic-alignment modeling. Specifically, the trajectory-conditioned masked fragment modeling encourages the agent to extract useful visual information to reconstruct the masked fragment. The contrastive semantic-alignment modeling is designed to align the visual representation with corresponding phrase embeddings. By showing experimental results on the benchmark dataset, we demonstrate that transformer architecture-based navigation agent pre-trained with our proposed CSAP outperforms existing methods on both SR and SPL scores.
Internet of Things-Based Home Education Interactive System and Parent-Teacher Relationship Cultivation

In today’s society, young parents have no time to take care of their children under the pressure of life and leave their children to their own parents, resulting in less and less interaction between parents and children. At present, the main problem with family education is that the education method is unscientific. The family education interactive system refers to building a platform for parents and children through the Internet to solve communication problems, improve parents’ educational concepts, and improve the overall level of family education. The level of tacit understanding between parents and teachers has a profound impact on children’s learning. Parents and teachers keep the same educational ideas and form a joint force, which makes students’ subjective learning willingness stronger and effectively avoids negative psychology such as confusion and weariness. Compared with the traditional education model, the new education model is more information-based and digital. The Internet of Things is the product of the expansion and extension of the Internet, which refers to the connection of various information-sensing devices with the Internet to form a broader network system. This study takes parents, students, and teachers as the research objects, mainly applies the embedded system technology in the Internet of Things, and aims to solve some problems in family education and poor communication between parents and teachers. This not only improves the original interactive system of family education but also effectively improves the communication efficiency between parents and teachers by using the Internet of Things technology. Overall satisfaction with the improved home education system increased by 19 percent, and communication efficiency between parents and teachers increased by 8 percent. It has practical reference significance for the research on the Internet of Things in family education. At the same time, the structural family therapy model is used to expand the family’s construction of the problem, obtain a family interaction model to maintain symptoms, and explore the influence of family members’ past on the present and the related changes in family members. In the process of implementation, explore and evaluate the structure of the family, create a situation that changes the family structure, and transform the family system.
MRBENet: A Multiresolution Boundary Enhancement Network for Salient Object Detection

Salient Object Detection (SOD) simulates the human visual perception in locating the most attractive objects in the images. Existing methods based on convolutional neural networks have proven to be highly effective for SOD. However, in some cases, these methods cannot satisfy the need of both accurately detecting intact objects and maintaining their boundary details. In this paper, we present a Multiresolution Boundary Enhancement Network (MRBENet) that exploits edge features to optimize the location and boundary fineness of salient objects. We incorporate a deeper convolutional layer into the backbone network to extract high-level semantic features and indicate the location of salient objects. Edge features of different resolutions are extracted by a U-shaped network. We designed a Feature Fusion Module (FFM) to fuse edge features and salient features. Feature Aggregation Module (FAM) based on spatial attention performs multiscale convolutions to enhance salient features. The FFM and FAM allow the model to accurately locate salient objects and enhance boundary fineness. Extensive experiments on six benchmark datasets demonstrate that the proposed method is highly effective and improves the accuracy of salient object detection compared with state-of-the-art methods.
AI-Assisted Decision-Making and Risk Evaluation in Uncertain Environment Using Stochastic Inverse Reinforcement Learning: American Football as a Case Study

In this work, we focus on the development of an AI technology to support decision making for people in leadership positions while facing uncertain environments. We demonstrate an efficient approach based on a stochastic inverse reinforcement leaning (IRL) algorithm constructed by hybridizing the conventional Max-entropy IRL and mixture density network (MDN) for the prediction of transition probability. We took the case study of American football, a sports game with stochastic environment, since the number of yards gainable on the next offence in real American football is usually uncertain during strategy planning and decision making. The expert data for IRL are built using the American football 2017 season data in National Football League (NFL). The American football simulation environment was built by training MDN using the annual NFL data to generate the state transition probability for IRL. Under the framework of Max-Entropy IRL, optimal strategy was successfully obtained through a learnt reward function by trial-and-error communication with the MDN environment. To precisely evaluate the validity of the learnt policy, we have conducted a risk-return analysis and revealed that the trained IRL agent showed higher return and lower risk than the expert data, indicating that it is possible for the proposed IRL algorithm to learn superior policy than the one derived directly from the expert teaching data. Decision-making in an uncertain environment is a general issue, ranging from business operation to management. Our work presented here will likely serve as a general framework for optimal business operation and risk management and contribute especially to the portfolio’s optimization in the financial and energy trading market.
Differential Analysis of a Cryptographic Hashing Algorithm HBC-256

The article observes the new hashing algorithm HBC-256. The HBC-256 algorithm is based on the block cipher of the compression function CF (Compression Function) and produces a 256-bits hash value. Like any new cryptographic structure, the HBC-256 algorithm requires careful research process in order to confirm its cryptographic properties, namely: pre-image resistance and resistance to collisions of the first and second order. As a result of the research, for the HBC-256 hashing algorithm differential properties of nonlinear elements (S-boxes) and various options for constructing round characteristics are considered. A hypothesis has been advanced about the existence of paired differences, which will make it possible to construct round characteristics for hashing and for the function of round keys generating. It is shown that even for the most optimal way of constructing chains of differences, the probability of finding correct pairs of texts is less than the probability of a complete enumeration of one 128-bit block of input data, which makes the method of differential cryptanalysis unsuitable for finding collisions.
Application Of The Group Investigation Model, Using Whatsapp To Increase Learning Outcomes For Class VI Students Of Sd Negeri Trayu Samigaluh Academic Year 2020/2021

The purpose of this study was to improve learning outcomes for grade VI students at SD Negeri Trayu Samigaluh Kulon Progo for the 2020/2021 school year and the ability of teachers to manage learning through the application of the Group Investigation model with WhatsApp Group Classes. This research is a classroom action research. This research was carried out in two cycles, in which each cycle had two meetings covering planning, action, observation, and reflection. The research subjects were students of Class VI SD Negeri Trayu Samigaluh, Kulon Progo Regency. Data and data sources come from students, and the learning process. Data collection techniques through observation and tests. Data analysis with qualitative descriptive technique. The results showed that the application of the Group Investigation model using the WhatsApp Group class could improve the learning outcomes of grade VI students at SD Negeri Trayu Samigaluh for the 2020/2021 school year. It can be seen the increase in cognitive test scores from 35.29% (cycle I) to 58.82% (cycle II). Furthermore, in the management of learning by teachers, the average result was 3.49 (cycle I), with the category "Very Satisfactory", while in Cycle II the average value was obtained at 3.74 with the category "Very Satisfactory".
JPEG Compression-aware Image Forgery Localization

Image forgery localization, which aims to find suspicious regions tampered with splicing, copy-move or removal manipulations, has attracted increasing attention. Existing image forgery localization methods have made great progress on public datasets. However, these methods suffer a severe performance drop when the forged images are JPEG compressed, which is widely applied in social media transmission. To tackle this issue, we propose a wavelet-based compression representation learning scheme for the specific JPEG-resistant image forgery localization. Specifically, to improve the performance against JPEG compression, we first learn the abstract representations to distinguish various compression levels through wavelet integrated contrastive learning strategy. Then, based on the learned representations, we introduce a JPEG compression-aware image forgery localization network to flexibly handle forged images compressed with various JPEG quality factors. Moreover, a boundary correction branch is designed to alleviate the edge artifacts caused by JPEG compression. Extensive experiments demonstrate the superiority of our method to existing state-of-the-art approaches, not only on standard datasets, but also on the JPEG forged images with multiple compression quality factors.
Spatial scene memories contain a fixed amount of semantic information

Scene memory has known spatial biases. Boundary extension is a well-known bias whereby observers remember visual information beyond an image’s boundaries. While recent studies demonstrate that boundary contraction also reliably occurs based on intrinsic image properties, the specific properties that drive the effect are unknown. This study assesses the extent to which scene memory might have a fixed capacity for information. We assessed both visual and semantic information in a scene database using techniques from image processing and natural language processing, respectively. We then assessed how both types of information predicted memory errors for scene boundaries using a standard rapid serial visual presentation (RSVP) forced error paradigm. A linear regression model indicated that scene boundaries were significantly predicted by semantic, but not visual, information and that this effect persisted when scene depth was considered. Boundary extension was observed for images with low semantic information, and contraction was observed for images with high semantic information. This suggests a cognitive process that normalizes the amount of semantic information held in memory.
Learning behavior in augmented reality–mediated mobile game–based learning

Purpose This paper aims to investigate whether the effects of children's current learning are related to their learning efficiency and behavior when they are exposed to two different gaming media. Design/methodology/approach In this paper the authors used a quasi-experimental design to determine whether game-based learning can be improved by using mobile devices equipped with augmented reality (AR). Findings The control group using the card game was careful to find the correct answer, with the intention of “obtaining the maximum score with the highest rate of correctness,” whereas the experimental group using the AR board game played aggressively by “obtaining the maximum score with the highest number.” Research limitations/implications Although integrating an AR board game into the curriculum is an effective approach, the need to implement such a game in response to different learning attitudes and behaviors of students should be addressed. Practical implications Depending on the learning situation, different teaching methods and aids can be used to help students effectively learn. The recommendations based on this experiment can broaden the teaching field and allow for a wider range of experimental studies. Originality/value Learning behavior was observed, and user attention was interpreted using MindWave Mobile.