Reviewerly/exHarmony huggingface dataset:
Identification of crowd behaviour patterns using stability analysis

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.
Combining Adversarial Training and Relational Graph Attention Network for Aspect-Based Sentiment Analysis with BERT

Aspect-Based Sentiment Analysis (ABSA), also called Aspect Level Sentiment Classification (ALSC), is a common task in Natural Language Processing (NLP). Aspect-Based Sentiment Analysis mainly aims to extract and classify the sentiments objects in texts. In this paper, we propose a novel BERT-based ABSA model, which combines an adversarial training procedure with relational graph attention neural network (R-GAT). To our best knowledge, it is the first model that simultaneously using adversarial training, relational graph attention neural network and BERT for aspect-based sentiment analysis. In our proposed model, the BERT Encoder is used to extract the context feature vector, R-GAT is applied to integrate the typed syntactic dependency information. The proposed model also includes an adversarial training method to ensure the robustness of neural network, which is realized by artificially increasing data samples similar to the real-world examples using adversarial processes in the embedding space. Our experimental results on three benchmark datasets demonstrate that the proposed model is competitive compared to the other state-of-the-art methods.
Architecting a scalable e-election system using Blockchain technologies

The ability to vote in state elections represents a basic right and expressing the vote is a civic obligation. However, the pandemics of COVID-19 showed us how such a simple process for a citizen may generate indirect barriers in expressing the electoral options. The risks of getting in contact with the virus made the classical voting method infeasible in times of crisis. Thus, a new opportunity arises, anticipated since 2016 by the Eurobarometer - the need to provide methods to vote over the Internet. Currently, in the European Union, only Estonia can provide citizens with such services. To overcome the current situation, we propose an architecture of a highly scalable e-election system implemented over the blockchain technology. The aim is to provide a general viable solution and to reduce the time to market, as a quick response to the current situation. The latter aspect is ensured by the Hyperledger Fabric, a permissioned, highly customizable, and integration-focused blockchain implementation. The paper provides a qualitative solution evaluation covering essential security aspects (e.g., resilience to Sybil Attack) and performance measurements (block sizing, state database performance). The contrast established with other platforms, is the simple but tamper-proof architectural view, focused on isolation and distribution of functions.
Safe and Fast Path Planner for Minimally Invasive Surgery

Path planning of a tool in Minimally Invasive Surgery (MIS) can provide assistance to the surgeons by giving solutions for faster and safe tool movements during the surgery. However, the main challenge in this problem is to address non-uniform tool shape for planning that can change due to the tool’s dexterity. A typical robotic path planning approach by describing the robot’s feasible movements using C-space is applied in this work. Unlike the robotic path planning problem, the C-space description capturing the movement does not give any closed-form solution due to high degree of freedom associated with the tool moved by human hands. Hence, an interval-based approach is used for describing the C-space. The proposed interval-based approach is capable of dividing the space into feasible and non-feasible intervals of different sizes which helps to reduce the search area and cover the obstacles in a refined manner. This paper presents collision-free and fast path computation using interval arithmetic between any two points in a 2D- surgical environment cluttered with obstacles for a surgical tool robot.
A Prototypical Network-Based Approach for Low-Resource Font Typeface Feature Extraction and Utilization

This paper introduces a framework for retrieving low-resource font typeface databases by handwritten input. A new deep learning model structure based on metric learning is proposed to extract the features of a character typeface and predict the category of handwrittten input queries. Rather than using sufficient training data, we aim to utilize ancient character font typefaces with only one sample per category. Our research aims to achieve decent retrieval performances over more than 600 categories of handwritten characters automatically. We consider utilizing generic handcrafted features to train a model to help the voting classifier make the final prediction. The proposed method is implemented on the ‘Shirakawa font oracle bone script’ dataset as an isolated ancient-character-recognition system based on free ordering and connective strokes. We evaluate the proposed model on several standard character and symbol datasets. The experimental results showed that the proposed method provides good performance in extracting the features of symbols or characters’ font images necessary to perform further retrieval tasks. The demo system has been released, and it requires only one sample for each character to predict the user input. The extracted features have a better effect in finding the highest-ranked relevant item in retrieval tasks and can also be utilized in various technical frameworks for ancient character recognition and can be applied to educational application development.
A Compact Neural Architecture Search for Accelerating Image Classification Models

Nowadays, Automated Machine Learning (AutoML) has gradually become an inevitable trend providing automatic and suitable solutions to address AI tasks without needing more efforts from experts. Neural Architecture Search (NAS), a subfield of AutoML, has generated automated models solving fundamental problems in computer vision such as image recognition, objects detection. NAS with differentiable search strategies has reduced significantly the GPU time that occupancy on calculation. In this paper, we present an effective algorithm that allows expanding search spaces by selecting operation candidates from the initial set with different ways in concurrent execution. The extended search space makes NAS having more opportunities to find good architectures simultaneously by running the group of search spaces in overlapping time periods instead of sequentially. Our approach, is called Accelerated NAS, shortens 1.8x searching time when comparing to previous works. In addition, the Accelerated NAS generates potential neural architectures having comparable performances with the low inference time.
Is AI Augmenting or Substituting Humans?

In this paper, the authors focus on Artificial Intelligence as a tangible technology that is designed to sense, comprehend, act, and learn. There are two manifestations of AI in the medical service: an algorithm that analyzes and interprets the test result and a virtual assistant that communicates the result to the patient. The aim of this paper is to consider how AI can substitute a doctor in measuring human health and how the interaction with virtual assistant impacts one’s visual attention processes. Theoretically, the article refers to the following research strands: Human-Computer Interaction, technology in services, implementation of AI in the medical sector, and behavioral economy. By conducting an eye-tracking experimental study, it is demonstrated that the perception of medical diagnosis does not differ across experimental groups (human vs. AI). However, it is observed that participants exposed to AI-based assistant focused more on button allowing to contact a real doctor.
Application of Information and Communication Technologies in Hybrid Learning of Engineering Students

The article considers the application of a hybrid learning strategy in higher education as a didactic tool for transition from the traditional educational model to an integrated one. The authors analyse possibilities to implement the model in teaching a foreign language to engineering students following the results of the survey conducted among them. The didactic principles of organising the the educational process based on hybrid learning are formulated in the article. The results of the experimental research on implementing the hybrid learning strategy are provided.
Optimization of Discrete Anamorphic Stretch Transform and Phase Recovery for ECG Signal Compression

An ECG signal compression scheme using both Discrete Anamorphic Stretch Transform (DAST) and Discrete wavelet transform (DWT) is reported in the literature. In this scheme, a linear filter is used for the recovery of the phase of the DAST for the computation of inverse DAST. However, at a higher compression ratio, the phase recovered and the reconstructed signal became increasingly inaccurate. To overcome this problem, a phase recovery technique that uses only an adder with fixed offset is proposed in this paper. A scheme for optimizing the DAST kernels to increase the compression ratio is also proposed. To evaluate their effectiveness, the ECG signals of the MIT-BIH Arrhythmia database are compressed using 6 level 1D DWT and run-length encoding with and without DAST pre-compressor and their performances are compared. The phase of DAST of the ECG signals recovered using both linear filter and adder are computed and compared for different kernels. The proposed phase recovery scheme with the optimized sublinear and linear kernels provides a 12.2% and 10.91% increase in data compression ratio and 24.63% and 28.73% decrease in percentage root mean square difference (PRD) with 99.9% of energy packing efficiency (EPE) compared to the scheme using the filter. The compression scheme using DAST provides a 2.81 times higher compression ratio compared to that without DAST. The proposed compression scheme yields a higher Compression Ratio and lower PRD than those reported in the literature. It can be used as pre-compressor for the real time transmission of ECG signal with lower bandwidth.
Combination of Modified LSB Steganography and Huffman Compression for Data Security

This paper was carried out on the basis of the need for data security on digital media in the form of methods that can help secure confidential data so that confidential data can only be read by the desired person and to anticipate that the data is not read by unauthorized persons. This paper suggests the use of steganography techniques in securing messages, where confidential messages will be inserted into the cover image. The method in this paper uses a combination of two methods, namely the Huffman method for data compression and the Least Significant Bit (LSB) method as a method in Steganography. The Huffman method is used to compress the data before it is inserted into the cover image so that it can reduce the size of the data to be inserted, and the resulting stego image does not change significantly. The results of the experiments carried out, message insertion without using Huffman compression, resulted in an average MSE value of 1.25 and an average PSNR value of 42.35. Meanwhile, the results of the combination of LSB and Huffman compression methods can produce an average MSE value of 0.37 and an average PSNR value of 53.96.
An Attention-based Text Detection and Recognition Method for Terminals of Current Transformer’s Secondary Circuit

Abstract Recognizing irregular text in real industrial scenes is a challenging task due to the background clutter, low resolutions or distortions. In this work, an attention-based text detection and recognition method for terminals of current transformer’s secondary circuit is proposed. It consists of three major components: pre-processing, text detection and text recognition. In text recognition module, a novel spatial temporal embedding is designed to better utilize the positional information. During training, the proposed framework only requires sequence-level annotations, instead of extra fine-grained character-level boxes or segmentation masks as in previous work. Despite its simplicity, the proposed method achieves good performance on the dataset collected in actual working scene.
Is an Emoji Worth a Thousand Words? The Effect of Emoji Usage on Nonprofessional Investors' Perceptions

ABSTRACT Emojis act as non-verbal cues to disambiguate and communicate affect and are increasingly used in online corporate disclosures. Emotion work, a concept founded in social psychology, suggests that individuals adjust their behavior as emotions are evoked or suppressed. Despite the growing evidence that emojis may influence judgments and decisions due to their deliberate expression of context and affect, the accounting research community has yet to investigate emojis' impact. We experimentally explore whether emojis can soften nonprofessional investors' perceptions of bad news or enhance perceptions of good news. We find that emojis modestly suppress participants' positive emotions on positive news, influencing their investment-related judgments and decision-making. Subsequent data collection fails to replicate the initial findings in a less experienced participant pool, suggesting that investing experience may play a role. Our study enhances our understanding of the unintended consequences of emojis and introduces a sociology-based principle into the accounting literature. Data Availability: Please contact authors.
Intelligent Error Correction of College English Spoken Grammar Based on the GA-MLP-NN Algorithm

With the development of neural networks in deep learning, artificial intelligence machine learning has become the main focus of researchers. In College English grammar detection, oral grammar is the most error rate content. So, this paper optimizes MLP based on GA in the deep learning neural network and then studies the intelligent image correction of College English spoken grammar. The main direction is to discuss and analyze GA-MLP-NN algorithm technology first and then predict the error correction model of spoken language grammar by combining the optimized algorithm. The results show that GA-MLP-NN provides excellent accuracy for the prediction of the whole syntax error correction model. Then, the paper studies the deep learning technology to build an intelligent image error correction model of College English spoken grammar. The results show that the effect of intelligent correction of spoken grammar is very fast and accurate.
A Mobile Platform for Movement Tracking Based on a Fast-Execution-Time Optical-Flow Algorithm

A multi-purpose mechanical platform to track moving objects in three-dimensional space has been developed. It is composed of one main microcontroller board that processes all system data, two cameras, three motors, and one secondary microcontroller board to position a platform with three degrees of freedom. The system computes the optical flow and moves the cameras accordingly, tracking motion within the visual scene. The platform operates autonomously. To the best of our knowledge, there are no similar systems reported with low-resolution image sensors and low-cost microcontrollers. Existing solutions rely on personal computers and advanced FPGAs to process image data. This article concludes that the optical flow operation is efficient even using an image sensor with very low resolution. Thus, the system complexity and image data processing are alleviated significantly. The platform can be easily adapted to different application scenarios by adding new peripherals, sensors, or image processing algorithms. A detailed description of the system design and experimental results are provided.
Research and Application of a Multidimensional Association Rules Mining Algorithm Based on Hadoop

The traditional Apriori algorithm has to scan the data set multiple times. With the rapid growth of data volume, it cannot be applied to big data analysis. For this problem, an improved parallel Apriori algorithm is designed. Firstly, an IApriori algorithm for multidimensional data is designed by pruning strategy. Secondly, the IApriori algorithm is combined with the Hadoop distributed framework to realize the parallelization of multidimensional association rules mining algorithm. This paper applies the IPApriori algorithm to the correlation analysis of mobile phone user behavior prediction, analyzes some main factors affecting the behavior of mobile phone user, and discovers the possible correlation between mobile phone user behavior and some attributes such as age dimension, gender dimension, time dimension, location dimension and mobile phone brand dimension. Finally, experiments prove that this parallelization algorithm process and the structure building method can reduce the I/O load of the system and improve the execution efficiency of the algorithm.
Handling ambiguous annotations for facial expression recognition in the wild

Annotation ambiguity due to subjectivity of annotators, crowd-sourcing, inter-class similarity and poor quality of facial expression images has been a key challenge towards robust Facial Expression Recognition (FER). Recent deep learning (DL) solutions for this problem select clean samples for training by using two or more networks simultaneously. Based on the observation that wrongly annotated samples have inconsistent predictions compared to clean samples when transformed using different augmentations, we propose a simple and effective single network FER framework robust to noisy annotations. Specifically, we qualify an image to be clean (correctly labeled) if the Jenson-Shannon (JS) divergence between its ground truth distribution and the predicted distribution for its weak augmented version is smaller than a threshold. The threshold is dynamically tuned. The qualified clean samples facilitate supervision during training. Further, to learn hard samples (correctly labeled but difficult to classify), we enforce consistency between the predicted distributions of weak and strong augmented versions of every training image through a consistency loss. Comprehensive experiments on FER datasets like RAFDB, FERPlus, curated FEC and AffectNet in the presence of both synthetic and real noisy annotation settings demonstrate the robustness of the proposed method. The source codes are publicly available at https://github.com/1980x/HandlingAmbigiousFERAnnotations.
Research on Mobile Data Model System for Optimizing the Performance of Computer Web Front End

With the continuous development of Web front-ends, Web pages have become more and more complex, and strong interactivity has brought about the skyrocketing of page status, and more and more DOM operations are used to update pages. However, frequent DOM operations make page rendering slow and cause front-end performance bottlenecks. In view of the low performance of the Web front-end, we analyze and summarize the B/S architecture principles from the back-end to the front-end of the Web, browser caching, browser loading methods, server configuration related to HTTP, and other processes that affect the front-end performance optimization. Factors, systematically propose a complete Web front-end performance optimization solution designed to improve web page loading speed, rendering speed and user experience, with strong integrity and versatility. In order to effectively reduce the waiting time of users, a calculation model of connection request scheduling weight is proposed, and a request scheduling algorithm SACC is proposed on the basis of this model. Experiments have proved that the algorithm can effectively reduce the TDD and TSR indicators of web pages. In the experimental platform IE7, the average reduction rate is 13% and 25% respectively.
A Strongly Unlinkable Group Signature Scheme with Matching-Based Verifier-Local Revocation for Privacy-Enhancing Crowdsensing

A group signature scheme allows us to anonymously sign a message on behalf of a group. One of important issues in the group signatures is user revocation, and thus lots of revocable group signature (RGS) schemes have been proposed so far. One of the applications suitable to the group signature is privacy-enhancing crowdsensing, where the group signature allows mobile sensing users to be anonymously authenticated to hide the location. In the mobile environment, verifier-local revocation (VLR) type of RGS schemes are suitable, since revocation list (RL) is not needed in the user side. However, in the conventional VLR-RGS schemes, the revocation check in the verifier needs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(R)$</tex> cryptographic operations for the number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex> of revoked users. On this background, VLR-RGS schemes with efficient revocation check have been recently proposed, where the revocation check is just (bit-string) matching. However, in the existing schemes, signatures are linkable in the same interval or in the same task with a public index. In this paper, by introducing a limitation that at most <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> signatures can be issued by a signer during each interval for a fixed integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> , we propose a VLR-RGS scheme with the revocation token matching. In our scheme, even the signatures during the same interval are unlinkable. Furthermore, since used indexes are hidden, the strong anonymity remains. The overheads are the computational costs of the revocation algorithm and the RL size. We show that the overheads are practical in use cases of crowdsensing.