Reviewerly/exHarmony huggingface dataset:
$p$-Norm Broad Learning for Negative Emotion Classification in Social Networks

Negative emotion classification refers to the automatic classification of negative emotion of texts in social networks. Most existing methods are based on deep learning models, facing challenges such as complex structures and too many hyperparameters. To meet these challenges, in this paper, we propose a method for negative emotion classification utilizing a Robustly Optimized BERT Pretraining Approach (RoBERTa) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -norm Broad Learning ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -BL). Specifically, there are mainly three contributions in this paper. Firstly, we fine-tune the RoBERTa to adapt it to the task of negative emotion classification. Then, we employ the fine-tuned RoBERTa to extract features of original texts and generate sentence vectors. Secondly, we adopt <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -BL to construct a classifier and then predict negative emotions of texts using the classifier. Compared with deep learning models, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -BL has advantages such as a simple structure that is only 3-layer and fewer parameters to be trained. Moreover, it can suppress the adverse effects of more outliers and noise in data by flexibly changing the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> . Thirdly, we conduct extensive experiments on the public datasets, and the experimental results show that our proposed method outperforms the baseline methods on the tested datasets.
More Inputs Makes Difference: Implementations of Linear Layers Using Gates with More Than Two Inputs

Lightweight cryptography ensures cryptography applications to devices with limited resources. Low-area implementations of linear layers usually play an essential role in lightweight cryptography. The previous works have provided plenty of methods to generate low-area implementations using 2-input xor gates for various linear layers. However, it is still challenging to search for smaller implementations using two or more inputs xor gates. This paper, inspired by Banik et al., proposes a novel approach to construct a quantity of lower area implementations with (n + 1)- input gates based on the given implementations with n-input gates. Based on the novel algorithm, we present the corresponding search algorithms for n = 2 and n = 3, which means that we can efficiently convert an implementation with 2-input xor gates and 3-input xor gates to lower-area implementations with 3-input xor gates and 4-input xor gates, respectively.We improve the previous implementations of linear layers for many block ciphers according to the area with these search algorithms. For example, we achieve a better implementation with 4-input xor gates for AES MixColumns, which only requires 243 GE in the STM 130 nm library, while the previous public result is 258.9 GE. Besides, we obtain better implementations for all 5500 lightweight matrices proposed by Li et al. at FSE 2019, and the area for them is decreased by about 21% on average.
Enhancing Intrinsic Motivation of Librarian Students using Virtual Reality for Education in the Context of Culture Heritage Museums

In this study the effects of intrinsic motivation and learning competency of virtual realitybased learning is compared to traditional text-based learning. In the study 28 students were involved from the second and third years of the library and information science faculty. The application of the fully immersive virtual 3D environment of Wieng Yong House was developed to support the virtual realitybased learning in the context of museums for culture heritage. To verify the hypothesis, the control group (traditional text-based learning), consisted of 14 students, used the course module of metadata for culture heritage in the museum whereas the experimental group (control group), consisted of 14 students, was provided virtual reality-based learning in the virtual museum. The tools of measurement were the pre-test/post-test quiz for learning competency and Intrinsic Motivation Inventory (IMI) with interview. The statistical model of paired sampled t-test assumptions was chosen for the statistical investigation for the hypothesis. The results suggest that constructing a virtual reality for learning in the context of a museum for cultural heritage might be a good way to enhance intrinsic motivation but cannot sufficiently and significantly improve the student's learning competency.
Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper is to analyze and propose—through DEtection TRansformer (DETR) models, architectures, hyperparameters—recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis. The development of real-time video conference translation based on artificial intelligence supported solutions has a relevant impact in the health sector, especially on clinical practice via better video consultation (VC) or remote diagnosis. The importance of this development was augmented by the COVID-19 pandemic. The challenge of this topic is connected to the variety of languages and dialects that the involved specialists speak and that usually needs human translator proxies which can be substituted by AI-enabled technological pipelines. The sensitivity of visual textual element localization is directly connected to complexity, quality, and the variety of collected training data sets. In this research, we investigated the DETR model with several variations. The research highlights the differences of the most prominent real-time object detectors: YOLO4, DETR, and Detectron2, and brings AI-based novelty to collaborative solutions combined with OCR. The performance of the procedures was evaluated through two research phases: a 248/512 (Phase1/Phase2) record train data set, with a 55/110 set of validated data instances for 7/10 application categories and 3/3 object categories, using the same object categories for annotation. The achieved score breaks the expected values in terms of visual text detection scope, giving high detection accuracy of textual data, the mean average precision ranging from 0.4 to 0.65.
An Effective Bootstrapping Framework for Web Services Discovery Using Trigram Approach

Web services are progressively being used to comprehend service-oriented architectures. Web services facilitate the integration of applications and simplify interoperability. Additionally, it assists in wrapping accessible applications in order for developers to access them using standard languages and protocols. The user faces a difficult challenge in selecting the appropriate service in accordance with the user request as the behavior of the participating service affects the overall performance in discovery, selection, and composition. As a result, it is critical to select a high-quality service provider for these activities. Existing approaches rely on nonfunctional qualities for discovery and selection, but the user cannot always rely on these features, and these QoS values cannot be used to determine the user’s or quality perspective. Additionally, the user indicates an interest in a high-quality service based on quality attributes or service with a good reputation throughout the selection process rather than a newly registered service. As a result, a proper bootstrapping mechanism is required to evaluate newly registered services prior to their use by service requestors. This paper proposes a novel bootstrapping mechanism. The contribution of this paper involves (a) a method for evaluating the quality of service (QoS) by focusing on performance-related indicators such as response time, execution time, throughput, latency, and dependability; (b) a methodology for evaluating the QoE attributes based on user reviews that take into account both attributes and opinions; (c) bootstrap the newly registered service based on quality of service and quality of experience; and (d) building a recommender system that suggests the top-rated service for composition. The evaluation results are used to augment currently available online services by providing up-to-date quality of service and quality of experience attributes for discovery, selection, and composition.
Application of Fuzzy Immune Algorithm and Soft Computing in the Design of 2-DOF PID Controller

To solve the difficulty in selecting the crossover probability and mutation probability in genetic algorithms, a fuzzy immune algorithm based on adaptive estimation of crossover probability and mutation probability in a fuzzy reasoning system is proposed, and it is used in the parameter optimization design of a two-degree-of-freedom PID controller. According to the experiment and simulation results, classic genetic algorithm evolution tends to halt after 37 generations, with a fitness value of 7.135, whereas fuzzy genetic algorithm evolution tends to stop after 20 generations, with a fitness value of 7.486. The 2-DOF PID controller that was created can give the system strong target value following and interference suppression features at the same time.
Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution

Birds are a kind of environmental indicator organism, which can reflect the changes in the ecological environment and biodiversity, and recognition of birdsongs can further help understand and protect birds and natural environment. Extreme learning machine (ELM) has the advantages of fast learning speed and good generalization ability, which is widely used in classification and recognition problems. Input layer weights and hidden layer thresholds are two key factors affecting ELM performance. As one of swarm intelligence optimization methods, differential evolution (DE) can be used to optimize the parameters of ELM. In order to enhance the diversity, convergence speed and global search ability of the DE population, and improve the accuracy and stability of the classification model, this paper proposes a multi-strategy differential evolution method (M-SDE) to optimize the parameters of the ELM. And the differential MFCC feature parameters, extracted from birdsongs, are applied to build classification models of M-SDE_ELM and an ensemble M-SDE_EnELM with optimized ELM for bird species recognition. In the experiments, the ELM models optimized by the swarm intelligence algorithms PSO and GOA are compared and analyzed by hypothesis tests with the M-SDE_ELM and M-SDE_EnELM. Results show that the M-SDE_ELM and M-SDE_EnELM can achieve a classification accuracy of 86.70% and 89.05% in the classification of nine species of birds respectively, and the recognition effect and stability of the M-SDE_EnELM model outperform other models.
Towards Increase Reading Habit For Preschool Children Through Interactive Augmented Reality Storybook

The reading habit is crucial in children for developing their cognitive, communication and understanding. Generally, children learn to read and write as early as three years old and can read by their own at the age of eight years old. However, the reading habit in children becoming less attractive to the kids as apart from their lack of interest, the technology has replaced the reading activity in children. Instead of reading, the kids spend more time playing digital game, watch video online or view the updates in social media. In Malaysia particularly, the reading habit in children to read English storybook became an issue. Due to lack of understanding the written story, lack of engagement in reading the story and less motivation to sustain reading the story are among the main factors of it. This research aims to increase the reading habit for preschool children through interactive augmented reality storybook. With the AR technology, the story can be presented in attractive environment and children able to interact with the story. The interaction used in the AR English storybook is slightly different from a normal AR interaction that based on click, touch, drag or slide. It’s required to user to play against the main character in the storybook. The finding shows that children show positive feedback with such approach in reading the English storybook. Substantially, it instills the good reading habit in children.
A New Method for Remote Sensing Satellite Observation Effectiveness Evaluation

The number of remote sensing satellites has increased rapidly in parallel with the advancement of space technology and the rising demand in the space industry. Consequently, the observation effectiveness evaluation of remote sensing satellites has received extensive attention. As the core content of the effectiveness evaluation, index systems are usually established and screened using qualitative or quantitative methods. They can hardly satisfy the construction principles such as completeness and independence simultaneously. To address this issue, we propose a new method for remote sensing satellite observation effectiveness evaluation that considers various principles. Firstly, a three-layer evaluation index system structure is constructed. The principle of completeness, hierarchy, and measurability of the index system is ensured by decomposition, clustering, and preliminary screening. Secondly, the principal component contribution rate is obtained through principal component analysis. Finally, we introduce a comprehensive scoring method (ICCLR) based on the combination of independence coefficient and principal component comprehensive loss rate. It realizes the screening of an index system from the index set containing correlation relationships. The validity and optimality of the proposed method are verified through experiments and analysis of three typical tasks.
Impacts of augmented reality on foreign language teaching: a case study of Persian language

The use of information technology in the field of foreign language teaching as an auxiliary tool is very important. In a foreign language classroom, place is just an abstract concept; where the language is separated from the community, culture and places in which it is used. Augmented reality is a technology in which virtual components are simultaneously combined with the real environment. Our aim in this study is to investigate the effects of location-based augmented reality in teaching Persian as a foreign language. In this study, after consulting with professors in the field of Persian language teaching and reviewing similar researches, we came to the conclusion that nothing has been done to teach Persian language using augmented reality. Therefore, a Persian game based on augmented reality was designed and implemented and then evaluated. For evaluation, two methods have been used; the user and the heuristic evaluation. Experts in the field of Persian language teaching, human-computer interaction and a number of language learners participated in the evaluation. Their feedback shows that the use of augmented reality increases satisfaction, enthusiasm and interaction with the environment and people, and also makes the process of learning and memorizing concepts more efficient.
Class-Wise Denoising for Robust Learning under Label Noise

Label noise is ubiquitous in many real-world scenarios which often misleads training algorithm and brings about the degraded classification performance. Therefore, many approaches have been proposed to correct the loss function given corrupted labels to combat such label noise. Among them, a trend of works unbiasedly estimate the data centroid, which plays an important role in constructing an unbiased risk estimator. However, they usually handle the noisy labels in different classes all at once, so the local information inherited by each class is ignored. To address this defect, this paper presents a novel robust learning algorithm dubbed &#x201C;Class-Wise Denoising&#x201D; (CWD), which tackles the noisy labels in a class-wise way to ease the entire noise correction task. Specifically, two virtual auxiliary sets are respectively constructed by presuming that the positive and negative labels in the training set are clean, so the original false-negative labels and false-positive ones are tackled separately. As a result, an improved centroid estimator can be designed which helps to yield more accurate risk estimator. Our CWD can produce the improved classification performance under label noise, which is also demonstrated by the comparisons with ten representative state-of-the-art methods on a variety of benchmark datasets.
Multitask Feature Learning as Multiobjective Optimization: A New Genetic Programming Approach to Image Classification

Feature learning is a promising approach to image classification. However, it is difficult due to high image variations. When the training data are small, it becomes even more challenging, due to the risk of overfitting. Multitask feature learning has shown the potential for improving generalization. However, existing methods are not effective for handling the case that multiple tasks are partially conflicting. Therefore, for the first time, this article proposes to solve a multitask feature learning problem as a multiobjective optimization problem by developing a genetic programming approach with a new representation to image classification. In the new approach, all the tasks share the same solution space and each solution is evaluated on multiple tasks so that the objectives of all the tasks can be optimized simultaneously using a single population. To learn effective features, a new and compact program representation is developed to allow the new approach to evolving solutions shared across tasks. The new approach can automatically find a diverse set of nondominated solutions that achieve good tradeoffs between different tasks. To further reduce the risk of overfitting, an ensemble is created by selecting nondominated solutions to solve each image classification task. The results show that the new approach significantly outperforms a large number of benchmark methods on six problems consisting of 15 image classification datasets of varying difficulty. Further analysis shows that these new designs are effective for improving the performance. The detailed analysis clearly reveals the benefits of solving multitask feature learning as multiobjective optimization in improving the generalization.
Designing qualitative interfaces: Experiences from studio education

Interaction designers tend to use quantification as a default to present information and a way to enable interactions with technologies.There is a notion that quantification is valued to be the most actionable and legitimate form of presentation, while our actual experiences of the world are largely qualitative.But can we design 'qualitative interfaces'?What would they be like?In this paper, we explore insights and experiences from four years of applying the notion of qualitative interfaces in interaction design student projects in two countries.We introduce, review, and compare projects across different application areas ranging from running training schemes to electricity use, and discuss questions around the relationships between the underlying phenomena and links to the ways in which they are displayed or represented, around the variety of ways in which students arrived at their designs, and suggest considerations for others interested in this kind of approach.
Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras

This paper describes a method that precisely estimates the position of images of traffic surveillance camera objects. We suggest a projection method with multiple traffic surveillance cameras through a local coordinate system into a global coordinate system. The transformation of coordinates uses detected objects, parameters of the camera and the geometric information of high- definition (HD) maps. Traffic surveillance cameras that pursue traffic safety and convenience use various sensors to generate traffic information. We suggest a transformation method with images of the camera and HD maps and an evaluation method. Therefore, it is necessary to improve the sensor-related technology to increase the efficiency and reliability of the traffic information. Recently, the role of the camera in collecting video information has become more important due to advances in artificial intelligence (AI) technology. The objects projected from the traffic surveillance camera domain to the HD domain are helpful to identify imperceptible zones, such as blind spots, on roads for autonomous driving assistance. In this study, we proposed to identify and track dynamic objects (vehicles, pedestrian, etc.) with traffic surveillance cameras, and to analyze and provide information about them in various environments. To this end, we conducted the identification of dynamic objects using the Yolov4 and DeepSort algorithms, established real-time multi-user support servers based on Kafka, defined transformation matrices between images and spatial coordinate systems, and implemented map-based dynamic object visualization. In addition, a positional consistency evaluation was performed to confirm its usefulness. Through the proposed scheme, we confirmed that multiple traffic surveillance cameras can serve as important sensors to provide relevant information by analyzing road conditions in real-time in terms of road infrastructure beyond a simple monitoring role.
Multi-UAV cooperative reconnaissance mission planning novel method under multi-radar detection

Past swarm intelligence algorithms for solving UAV path planning problems have suffered from slow convergence, lack of complex constraints and guidance for local optimisation. It no longer meets the requirements of the Multi-UAV Cooperative Reconnaissance Mission Planning (MUCRMP) problem in the context of multi-radar detection. In this paper, a global optimisation model with the objective of a shorter distance within radar detection range of the UAV is proposed at first, including the planning of reconnaissance sequence between and within target groups, relative position to targets. More importantly, the imaging characteristics of the UAV and its minimum turning radius have been considered in depth in this study. Then an improved synthetic heuristic algorithm is proposed to solve the model, which obtains valuable reconnaissance mission plan. Finally, an example solution for a problem with 68 target point sizes is carried out, and the validity and feasibility of the model and algorithm are illustrated through the analysis given. Compared with the existing algorithms, the improved synthetic heuristic algorithm can give better anti-radar attributes to the UAV and efficiently improved the convergence speed in the specific reconnaissance mission.