Reviewerly/exHarmony huggingface dataset:
Learning to Remove Bad Weather: Towards Robust Visual Perception for Self-Driving

Visual perception plays a vital role in generating the intelligent actions of autonomous vehicles. However, various bad weather degradations can create visibility problems that impair the performance of high-level tasks. Though image enhancement techniques have been extensively studied for safe self-driving in bad weather, few studies have dealt with both enhancement and perception simultaneously. To cope with bad weather conditions for high-level perception, we propose an end-to-end deep learning-based framework, which connects the enhancement network with the perception network. To this end, we design a universal enhancement network that can address multiple bad weather conditions and assist in producing promising perception results. To this end, we design a universal enhancement network that can address multiple bad weather conditions and assist in producing promising perception results. To this end, we design a universal enhancement network that can address multiple bad weather conditions and assist in producing promising perception results.
Security, Privacy, and Trust of Emerging Intelligent Transportation: Cognitive Internet of Vehicles

The recent advancement in cloud technology offers unparallel strength to support intelligent computations and advanced services to assist with automated decisions to improve road transportation safety and comfort. Besides, the rise of machine intelligence propels the technological evolution of transportation systems one step further and leads to a new framework known as Cognitive Internet of Vehicles (C-IoV). The redefined cognitive technology in this framework promises significant enhancements and optimized network capacities compared with its predecessor framework, the Internet of Vehicles (IoV). CIoV offers additional security measures and introduces security and privacy concerns, such as evasion attacks, additional threats of data poisoning, and learning errors, which may likely lead to system failure and road user fatalities. Similar to many other public enterprise systems, transportation has a significant impact on the population. Therefore, it is crucial to understand the evolution and equally essential to identify potential security vulnerabilities and issues to offer mitigation towards success. This chapter offers discussions framing answers to the following two questions, (1) how and in what ways the penetration of the latest technologies are reshaping the transportation system? (2) whether the evolved system is capable of addressing the concerns of cybersecurity? This chapter, therefore, starts presenting the evolution of the transportation system followed by a quick overview of the evolved CIoV, highlighting the evolved cognitive design. Later it presents how a cognitive engine can overcome legacy security concerns and also be subjected to further potential security, privacy, and trust issues that this cloud-based evolved transportation system may encounter.
A Robust Image Retrieval Method Using Multi-Hierarchical Agglomerative Clustering and Davis-Bouldin Index

An image retrieval system is required to provide high accuracy in a short time.Combining various features will usually increase accuracy but also increase retrieval time.This study developed a CBIR (Content-Based Image Retrieval) method based on hierarchical clustering on low-level features.Low-level features consisting of color, texture, and shape are extracted and then clustered hierarchically.The resulting clusters are then validated to obtain their optimal number.In the retrieval process, the query image features are extracted and compared with the cluster centroid on each feature.The scores of query results on each feature are normalized, and then the normalized scores are weighted to get the total score.The experiment was carried out using three datasets, namely DIKE20, Corel-1k, and Corel-10k.Based on the experimental result, the proposed method shows better performance compared to the existing state-of-the-art method.On the Corel-1k and Corel-10k datasets, the proposed method obtained precision scores of 0.81 and 0.62, respectively.
Special Issue on Cryptography and Its Applications in Information Security

Nowadays, mankind is living in a cyber world [...]
IT Blueprint for an Effective Online Learning System with a Blended Approach for Upper Secondary Education System During COVID-19 Pandemic

Online learning is becoming the main teaching method since the COVID-19 pandemic. What happens after the pandemic is over? Will the online education be still going? The schools have implemented online learning systems to replace the current teaching methods to ensure the teaching and learning process keeps going. However, research have found that not all students respond well to fully online learning methods, therefore a blended learning system is needed to facilitate the difference in student performance. This IT blueprint will ensure that an upper secondary school is equipped with the proper IT system for its purposes as well as propose blended learning system where the school will apply both online learning and face-to-face teaching method at the same time to maximize student potential.
Evaluating knowledge-based security questions for fallback authentication

Failed user authentication is a common event. Forgotten passwords and fingerprint non-recognition are the most common causes. Therefore, there is a need for efficient backup authentication methods, known as fallback authentication. However, fallback authentication methods suffer from different security and usability issues. This study aims to improve the security and usability of knowledge-based fallback authentication in the form of static security questions. The approach proposed in this study was designed considering different factors, such as question features, authentication mechanisms, and the use of tools to aid in composing memorable and secure answers. This study used a two-part experiment with 23 participants to evaluate the proposed approach based on security model testing. The results show that the proposed approach offered improved resistance to blind guess, focused guess, and observation guess attacks. While usability was clearly improved with questions that were based on recognition mechanisms, our results indicate that fallback authentication systems need a flexible level of security and avoidance of complexity in composing answers. In addition, our results indicate the effectiveness of using user behavioral details in the choice of topics for questions, where behavioral questions must have both high recall levels and resistance against guessing attacks. This work theoretically extends the knowledge of fallback authentication research by evaluating new security questions for fallback authentication considering replace of classical topics of security questions by introducing new topics of security questions based on user behavior and personal preferences. Also, this study applies methods of managing answers to security questions by encouraging users to compose answers based on free strict rules that inspire them to create strong and memorable answers based on their own rules. In addition, the findings of this study could support the deployment of knowledge-based authentication in fallback systems as a practical contribution to the user authentication field.
Automatic Generation of Fuzzy Classification Rules from Data

In this paper, we propose a method for automatic generation of fuzzy rules for data classification. The proposed method is based on subtractive clustering optimized using genetic algorithm. It searches for the FIS structure and number of rules that have the highest fitness value. Multiple performance measures are incorporated into the fitness function to address the problem of imbalanced data. Fitness function includes both training and validation to avoid data over-fitting. Classification performance of the proposed method is evaluated using different data sets and results are compared to those of a number of models generated by fuzzy cmeans clustering with various cluster numbers. Results show that the proposed method has better accuracy and a well compromised sensitivity and specificity.
On the Vulnerability of Large Corpora Source Code

This paper is a part of a continual effort to score functions in source code for vulnerability. For practical reasons we've restricted our attention to the C and C++ programming languages. We demonstrate an auto-encoder network and techniques to embed source code into a low-dimensional Euclidean space and some of the issues encountered where dealing with a very large code base. We also describe a process of developing ‘code smell’ features and a classifier when data is extremely unbalanced. Finally we explore how the workflow may generalize to other projects and programming languages.
Data Analysis and Processing Application of Deep Learning in Engineering Cost Teaching Evaluation

Based on the poor quality of engineering cost teaching, this paper puts forward the application method of in-depth learning in engineering cost teaching evaluation. This study aims to construct the engineering cost teaching evaluation system, optimize the engineering cost teaching evaluation algorithm combined with the in-depth learning method, standardize the evaluation index of the engineering cost teaching system, and realize the research goal of engineering cost teaching evaluation. Finally, the experiment proves that the application effect of in-depth learning in engineering cost teaching evaluation is obviously better, which can better guide the quality of engineering cost teaching.
Improved face detection algorithm based on multitask convolutional neural network for unmanned aerial vehicles view

With the rapid development of automation technology, unmanned aerial vehicles (UAV) have been widely used to assist troops in long-distance combat missions. However, considering that the UAV needs to keep flying and maintain a certain distance from the target, face detection using UAV usually achieves poor results, which hinders the application of the face recognition technology in UAV systems. We proposed an improved Mish-L2-multitask convolutional neural network (MTCNN) model based on MTCNN model to further improve the accuracy of small-size face detection. First, the maximum pooling layer of the P-Net CNN was removed. Second, a regularization term was added to the crossentropy loss function. Finally, the activation functions exerted in three subneural networks of MTCNN model were replaced with Mish activation functions. The result shows that the proposed Mish-L2-MTCNN model could improve the accuracy of small-size face detection efficiently under the UAV view. Comparing with the result obtained from the original MTCNN model, the accuracy was improved by 3.62%, and the missing rate was evidently reduced. This work can provide methodological guidance for the development of a UAV-based face recognition system in the real airborne scenario and guarantee the validity and efficiency of the system. The study can also guide further research concerning the detection effect in the cases of side face and half mask.
Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model

A BERT-MDLP-Bayesian Network model (BMB) is proposed to analyze the improvement strategy of e-commerce products based on user generated content (UGC). The proposed model can be represented into four parts: clearing redundant data on the obtained UGC, extracting product attributes and word vector to generate product attributes, establishing product attribute Bayesian network corresponding to UGC, and inferring the causal relationship between product attributes. In order to verify the effectiveness of the proposed model, an amazon tablet product is used for empirical analysis. Compared with the traditional model, BMB model has better performance in product feature mining in three aspects of feature diversity, feature long tail and attribute difference. In application, the model can effectively describe the core problems of products, and provide suggestions for e-commerce to modify marketing strategies and determine the new direction of product development.
Dual resolution deep learning network with self-attention mechanism for classification and localisation of colorectal cancer in histopathological images

Aims Microscopic examination is a basic diagnostic technology for colorectal cancer (CRC), but it is very laborious. We developed a dual resolution deep learning network with self-attention mechanism (DRSANet) which combines context and details for CRC binary classification and localisation in whole slide images (WSIs), and as a computer-aided diagnosis (CAD) to improve the sensitivity and specificity of doctors’ diagnosis. Methods Representative regions of interest (ROI) of each tissue type were manually delineated in WSIs by pathologists. Based on the same coordinates of centre position, patches were extracted at different magnification levels from the ROI. Specifically, patches from low magnification level contain contextual information, while from high magnification level provide important details. A dual-inputs network was designed to learn context and details simultaneously, and self-attention mechanism was used to selectively learn different positions in the images to enhance the performance. Results In classification task, DRSANet outperformed the benchmark networks which only depended on the high magnification patches on two test set. Furthermore, in localisation task, DRSANet demonstrated a better localisation capability of tumour area in WSI with less areas of misidentification. Conclusions We compared DRSANet with benchmark networks which only use the patches from high magnification level. Experimental results reveal that the performance of DRSANet is better than the benchmark networks. Both context and details should be considered in deep learning method.
A deep learning approach in detection of malaria and acute lymphoblastic leukemia diseases utilising blood smear microscopic images

The numerous rising infections and deaths of malaria and acute lymphoblastic leukaemia (ALL) highlights the urgent need for early, useful, and efficient diagnosis methods. Recently, the framework of artificial intelligence has been applied to minimize time-consuming tasks, to increase the accuracy and flexibility of clinical diagnoses, and to reduce the pressure on physicians, diagnosticians, and clinical experts. In this study, a detection system for malaria and ALL is proposed that utilizes blood smear microscopic images with the aid of deep learning algorithms to identify and classify these two diseases automatically. The blood smear microscopic images consist of 1503 ALL images, 891 malaria images, and 1503 normal images that were divided into a training, validation, and testing sets in ratios of 50, 25, and 25%, respectively. The proposed model was built into three stages including the first stage for segmentation-applied modified UNet pre-trained model, the second stage for classification based on the convolution neural network model, and the final stage for classification utilizing perceptron as the combining model. As a result, the proposed system provides an alternative and interpretable method to detect abnormal leukocytes for ALL and malaria-infected blood cells with a 93% overall accuracy including the detection rate for ALL of 95% and the detection rate for malaria of 92%.
Universal Criticality in Reservoir Computing Using Asynchronous

Elementary cellular automata (ECAs) generate critical spacetime patterns in a few local rules, which are expected to have advantages in reservoir computing (RC). However, previous studies have not revealed the advantages of critical spacetime patterns in RC. In this paper, we focus on the distractor’s length in the time series data for learning and clarify the advantages of the critical spacetime patterns. Furthermore, we propose asynchronously tuned ECAs (AT_ECAs) to generate universally critical spacetime patterns in many local rules. Based on the results achieved in this study, we propose RC based on AT_ECAs. Moreover, we show that the universal criticality of AT_ECAs is effective for learning time series data.
Students’ Perception of Online Learning in English Subject at SMK Negeri 3 Karang Baru

This study aimed to determine students' perceptions of online learning English at SMK Negeri 3 Karang Baru. This study used a qualitative method. The researcher used questionnaire as the instrument of research. In this study, the researcher found that many students did not agree with online learning. The material was difficult to understand, it was constrained by internet signals, so they preferred to study as usual. Based on the explanation above, it can be concluded that students' perceptions of online learning English were negative, and they were not satisfied with online learning English.
Comparison of Convolution Neural Network Architecture for Colon Cancer Classification

In 2021, colon cancer is the second most common cause of death for this type of cancer. Therefore, in this study, a colon cancer classification system was developed to help medical staff classify 2 types of cancer colon adenocarcinomas and benign colonic tissues. The classification method uses the Convolution Neural Network (CNN) with the architecture VGG16, VGG19, ResNet101, ResNet152, MobileNetV2, DenseNet201 and InceptionV3. We used 10.000 image datasets that divided into 7200 training data, 1800 validation data and 1000 test data. Pre-trained models are used to extract new features and training data. The best performance parameter based on accuracy, precision, recall and f1-score and confusion matrix are obtained in 3 architectures, namely VGG19, ResNet101 and ResNet152. These architectures can identify and classify both types of colon cancer with 100% accuracy.
Strategies of Reading Digital Texts for Performing Educational Reading Tasks: Study Based on the Think-Aloud Protocols

For success in education and life in our informationally saturated digital society, one must be able to select and interpret digital texts of different genres, choose optimal ways of interacting with these texts, and extract and assess information from them. Contemporary education specialists believe that skills of working with digital texts are an integral part of reading literacy; their publications model successful results of interacting with digital texts. Nevertheless, the means of attaining these results remains a very important and topical question for the education system. What strategies allow one to interact with digital texts effectively? How should one teach these strategies to contemporary schoolchildren? The present article aims to identify and classify metacognitive strategies used by competent Russian-speaking lower secondary students for performing learning assignments based on digital texts. It is based on the analysis of think-aloud protocols and data from the online monitoring of readers’ activities on the screen. The study describes and analyzes seven groups of digital reading strategies. The results contribute to basic knowledge about the processes at the root of effective digital reading and hence of the development of approaches to teaching and assessing reading literacy in the digital age.
Smart Bot for Handwritten Digit String Recognition

Handwritten digit string recognition is more sophisticated than determining a single digit individually. The repeated recognition of single digits is applicable for recognizing a handwritten digit string. A similar approach is exercised in this paper. The proposed approach could be advantageous in banks to recognize the digits written on the cheque and processes the cheque. Further, the banks could send the audio message of the recognized handwritten digits to the cheque issuer for confirmation before cashing the cheque. The pro-posed model is developed in the python platform and is lightweight, robust, and cross-platform. In this approach, we have trained a neural network model with MNIST handwritten digits dataset and some samples of our own for recognizing the handwritten digits. The Convolution Neural network models are widely used in the present-day technologies for object recognition, image processing, segmentation, face recognizing and also many identifications related tasks. The CNN model used in the project determines the digit in the image provided. Finally, the system plays the [pre-]recorded audio and displays the output for the recognized digits in the given digit string.
UIT at VBS 2022: An Unified and Interactive Video Retrieval System with Temporal Search

This paper introduces our multimedia retrieval system for the Video Browser Showdown 2022 competition. The system was built for interactive retrieval task in a large video collection by focusing on four fundamental methods. First, we allow users to search by object features such as position and color. Secondly, our system also supports searching by text instances appearing in video segments. Next, we support searching by visual-textual association. And finally, the system can also search for videos containing a specific audio category. Moreover, we extend our framework to support temporal queries for all of the mentioned features.
Signed random walk diffusion for effective representation learning in signed graphs

How can we model node representations to accurately infer the signs of missing edges in a signed social graph? Signed social graphs have attracted considerable attention to model trust relationships between people. Various representation learning methods such as network embedding and graph convolutional network (GCN) have been proposed to analyze signed graphs. However, existing network embedding models are not end-to-end for a specific task, and GCN-based models exhibit a performance degradation issue when their depth increases. In this paper, we propose Signed Diffusion Network (SidNet), a novel graph neural network that achieves end-to-end node representation learning for link sign prediction in signed social graphs. We propose a new random walk based feature aggregation, which is specially designed for signed graphs, so that SidNet effectively diffuses hidden node features and uses more information from neighboring nodes. Through extensive experiments, we show that SidNet significantly outperforms state-of-the-art models in terms of link sign prediction accuracy.
Privacy-Preserving of Human Identification in CCTV Data using a Novel Deep Learning-Based Method

With the rapid development of Information Tech-nologies, the number of surveillance cameras has increased, leading to a decrease in the rate of violence and crimes. Despite the advantages, it has some drawbacks, such as the lack of privacy-preserving in human identification when video data is shared. This paper focuses on the privacy-preserving issue and proposes a novel deep learning-based solution. The proposed approach uses the recent state-of-the-art models such as RetinaFace and TinaFace for human face detection from the input videos and other Computer Vision tools such as OpenCV for framing the input video and connecting them again for restoring the initial form of the video in the output. Moreover, computer vision tools such as Blur and Gaussian Blurring are used for anonymizing faces. Our proposed approach allows the closed-circuit television (CCTV) data to be shared for public use, where human identification is perfectly preserved. Experimental results show the effectiveness of our proposed method by outperforming the state-of-the-art methods in constrained conditions. Furthermore, we have created a face dataset from the input CCTV videos, where the face detection tools have failed. The created dataset is annotated with the five face landmarks and can be used for the face detection task.