Reviewerly/exHarmony huggingface dataset:
Edge detection algorithm based on valley lines and watershed

Abstract The purpose of edge detection algorithm is to highlight the edge in the image, which is also the premise and key of image segmentation. However, the traditional edge detection algorithm will amplify the influence of noise in the process of derivative operation, and the noisy edges will often appear, which is not conducive to the accurate extraction of edge. To solve this problem, we propose an edge detection algorithm combining valley lines and watershed algorithm. In this algorithm, the bilateral filter is used to replace the Gaussian filter to achieve edge preserving and denoising. The rewritten Canny operator is used to calculate the low value points and get the valley lines of the image. This process can achieve the first denoising. Then, the valley lines are used to replace the discrete points of water injection and the watershed algorithm is used to calculate the segmentation results of the image. At this time, there are many pseudo edges around the main boundary, and the pseudo edges are deleted by setting the threshold, so as to achieve the effect of secondary denoising. The experimental results show that our method has good performance in noise resistance and edge continuity compared with other and traditional edge detection algorithms.
Self-supervised multi-task learning for semantic segmentation of urban scenes

The task of semantic segmentation plays a vital role in the analysis of remotely sensed imagery. Currently, this task is mainly solved using supervised pre-training, where very Deep Convolutional Neural Networks (DCNNs) are trained on large annotated datasets for mostly solving a classification problem. They are useful for many visual recognition tasks but heavily depend on the amount and quality of the annotations to learn a mapping function for predicting on new data. Motivated by the plethora of data generated everyday, researchers have come up with alternatives such as Self-Supervised Learning (SSL). These methods play a deciding role in boosting the progress of deep learning without the need of expensive labeling. They entirely explore the data, find supervision signals and solve a challenge known as Pretext Task (PTT) to learn robust representations. Thereafter, the learned features are transferred to resolve the so-called Downstream Task (DST), which can represent a group of computer vision applications such as classification or object detection. The current work explores the conception of a DCNN and training strategy to jointly predict on multiple PTTs in order to learn general visual representations that could lead more accurate semantic segmentations. The first Pretext Task is Image Colorization (IC) that identifies different objects and related parts present in a grayscale image to paint those areas with the right color. The second task is Spatial Context Prediction (SCP), which captures visual similarity across images to discover the spatial configuration of patches generated out of an image. The DCNN architecture is constructed considering each particular objective of the Pretext Tasks. It is subsequently trained and its acquired knowledge is transferred into a SSL trunk network to build a Fully Convolutional Network (FCN) on top of it. The FCN with SSL trunk learns a compound of features through fine-tuning to ultimately predict the semantic segmentation. With the aim of evaluating the quality of the learned representations, the performance of the trained model will be compared with inference results of a FCN-ResNet101 architecture pre-trained on ImageNet. This comparison employs the F1-Score as quality metric. Experiments show that the method is capable of achieving general feature representations that can definitely be employed for semantic segmentation purposes.
Update on the World Wide Web Consortium (W3C)

The World Wide Web Consortium <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (W3C) continues its efforts to bring support for rich audio-visual experiences to the web platform. Below is a selection of current and exploratory activities.
Visual-Textual Semantic Alignment Network for Visual Question Answering

VQA task requires deep understanding of visual and textual content and access to key information to better answer the question. Most of current works only use image and question as the input of the network, where the image features are over-sampling and the text features are under-sampling, resulting in insufficient alignment between image regions and question words. In this paper, we propose a Visual-Textual Semantic Alignment Network (VTSAN). Our network acquires tags for visual semantics from a target detector and takes the Image-Tag-Question\(\mathbf {\left\langle {I, T, Q} \right\rangle }\) triad as the input. The tags can serve as an intermediate medium between the key regions of image and the key words of question, and can greatly enrich the text features. Thereby, the visual-textual semantic alignment is significantly improved. We demonstrate the effectiveness of our proposed network on the standard VQAv2 and VQA-CPv2 benchmarks. The experimental results show that the proposed network outperforms the baseline significantly, especially on the counting questions.
Hierarchical and Multi-Resolution Preference Modeling for Next POI Recommendation

Next Point-Of-Interest (POI) recommendation has attracted extensive attention recently, benefiting from the large volumes of check-in records on location-based social networks (LBSNs). Most of the existing works either consider user's long- and short-term preferences or only one of them, ignoring users' preferences have multiple different resolutions. Moreover, these methods suffer from the high sparsity issue. Although hierarchical category information contributes to alleviating this problem, it has not been fully exploited and integrated with spatiotemporal contexts in next POI recommendation. To this end, we propose a novel method named Hierarchical and Multi-Resolution Preference Modeling (HMRPM), which simultaneously models hierarchical personalized preferences at three different resolutions, i.e., we learn long-term, short-term and transient preferences. HMRPM consists of three corresponding preference modeling modules: (1) The long-term module captures general preferences of users by modeling user-POI interactions and hierarchical category-level interactions. (2) The short-term module utilizes a hierarchical category-aware self-attention mechanism to capture users' recent preferences. Spatiotemporal position embeddings and user-aware attention are also proposed. (3) The transient module captures hierarchical current preferences by modeling hierarchical transient transition patterns. Extensive experimental results on two public datasets demonstrate the proposed method significantly outperforms other state-of-the-art methods.
Attention-based Multi-proximity Preserved Attributed Network Embedding

Network embedding is an essential task in network analysis, which aims to learn low-dimensional representations for nodes in the networks. To obtain better embeddings, the structural information and inherent properties of networks should be preserved during the network embedding procedure. Recent studies only take into consideration the lower-order or microscopic structure of networks, such as the first- and second-order proximities between nodes. Most of them, however, fail to capture the mesoscopic structure and higher-order structure of networks which are also prominent features of networks, such as community structure and motifs (i.e. the frequently occurred small dense subgraph patterns of networks). In this paper, we propose a novel attention-based graph neural network called Multi-proximity Attention Network (MAN) for network embedding that simultaneously utilizes the underlying proximity dependencies between nodes from multiple aspects. Extensive experiments are conducted in two downstream tasks, namely node classification and node clustering. Experimental results demonstrate the effectiveness of the proposed method.
An Empirical Analysis on the Prediction of Web Service Anti-patterns Using Source Code Metrics and Ensemble Techniques

Today’s software program enterprise uses web services to construct distributed software systems based on the Service Oriented Architecture (SOA) paradigm. The web service description is posted by a web service provider, which may be observed and invoked by a distributed application. Service-Based Systems (SBS) need to conform themselves through years to fit within the new user necessities. These may result in the deterioration of the quality and design of the software systems and might reason the materialization of insufficient solutions called Anti-patterns. Anti-pattern detection using object-oriented source code metrics may be used as part of the software program improvement life cycle to lessen the maintenance of the software system and enhance the quality of the software. The work is motivated by developing an automatic predictive model for predicting web services anti-patterns using static evaluations of the source code metrics. The center ideology of this work is to empirically investigate the effectiveness of different variants of data sampling technique, Synthetic Minority Over Sampling TEchnique (SMOTE), and the ensemble learning techniques in the prediction of web service anti-patterns.
Anonymous Group Signature Scheme with Publicly Verifiable General Access Structure

The group signature scheme allows a group member or defined subsets of all group members to sign a message on behalf of the group. Any party can verify the validity of the group signature, as well as whether it was created by member of the group or members of some subgroup. However, without the participation of the group manager it is not possible to determine exactly who is part of the group. This paper proposes a new generalised group signature scheme in which members of an authorised set can jointly create a group signature without revealing the identities of its members. Furthermore, the authorised sets can be dynamically modified. The scheme is existentially unforgeable against adaptively chosen message attacks assuming the hardness of Computational Diffie-Hellman problem and meets other requirements imposed on group signature schemes. The scheme has been implemented and performance tests have shown that the scheme is suitable for practical application.
Automatic Generation of Interrelated Organisms on Virtual Environments

This article presents a novel method for generating multiple species of organisms on virtual environments, with interrelated behaviors. The proposed system (AGIO) strives to keep a balance between the exploration of diverse species, the evolution of interesting and connected behaviors, and the control of the user over the generated organisms. Experimental results of a C++ implementation proves that AGIO is capable of generating different species and basic animal conducts, while keeping behaviors of the organisms interrelated. Multiple relations between different species were observed on the generated organisms. Furthermore, the performance of the organisms was comparable to that of a human player.
Development of Digital Competences of Elementary School Teachers with a Life-Long Learning Approach

The dynamic development of digital technology has an increasing influence on all areas of our lives. New technologies are also changing the face of education. Young people are particularly attracted to technological innovations. It is important that students consciously and constructively use the newest information technologies, which they should learn at school. The responsibility to take on new educational challenges lies with the teacher, who must have high digital competences and learn to act effectively in a rapidly changing reality. Between June 2017 and December 2019, more than 140 early-education school teachers were trained in the programming teaching course. Before 36-h training and after 30 h of lessons assessments among teachers based on pre- and post-questionnaires about digital competences were performed. The questionnaires were prepared accordingly to the Digital Competence Framework 2.0 (DigComp 2.0). Obtained results gave a picture of the effectiveness of developing the digital competences of the primary school teachers.
Extraction of metamorphic minerals by multiscale segmentation combined with random forest

The identification of metamorphic minerals is the basis of metamorphic rock research. Extraction of mineral information by remote sensing technology has been widely used. Digital image processing technology is also effectively applied to remote sensing image processing. Results show that the band ratio of remote sensing images can enhance mineral information, while the variogram function can describe the spatial correlation and variability of image pixels and extract more detailed texture information. The metamorphic minerals are found to present a block or strip distribution. The object-oriented remote sensing image information extraction method can avoid the salt and pepper phenomenon based on pixel extraction. Meanwhile, the random forest classification method has a fast calculation speed and high parameter accuracy. It is not sensitive to the noise caused by more lithologic components and its classification effect is found to be stable. To improve the extraction accuracy of metamorphic minerals from remote sensing images and further improve the recognition effect of metamorphic zones, this paper combined the ratio operation, multiscale segmentation, and random forest classification to extract metamorphic mineral information from ASTER images in Beishan area in Gansu Province. Initially, the image was enhanced by the ratio formula of the characteristic spectral structure of the target mineral. Multiscale image segmentation was then performed based on the spectrum and variogram. Finally, the accuracy was evaluated by the thin film identification results of the field exploration samples after the extraction of the target mineral by random forest. Results show that biotite, muscovite, and amphibole have identification characteristics on the ASTER image with an extraction accuracy of 85.4088%, 84.7640%, and 85.7308%, respectively. The extraction accuracy of other metamorphic minerals with less content are found to reach more than 60%. Multiscale segmentation can make full use of the clustering features of minerals and the variogram texture can enhance the ability of morphological features to distinguish the minerals. Random forest is not sensitive to noise and the extraction results are observed to be stable.
Robust System for Counting Movement-Specific Vehicle at Crowded Intersections in HCM City

Traffic jam is a difficult problem in many countries around the world, especially Asian countries. In addition to messing up human timetables, it also affects people’s physical and mental health because of waiting for a long time under an atmosphere full of dust and pollution. Traffic congestion will also increase local emissions that have a negative impact on the environment. Therefore, more and more researches are being done on counting, tracking, and predicting vehicle traffic on the road. However, traditional traffic management systems focus on counting total vehicles in one frame or in consecutive frames regardless of the direction of movement. This makes it difficult to predict traffic situations in neighboring areas in the near future. Besides, Asian environments, where motorbikes are used with high density making it difficult to count, have yet to be paid much attention. In this paper, we proposed a robust vehicle flow counting system with movements of interest (MOI) which can work in complex environments like Asian countries. In other words, we will detect and track objects in the region of interest (ROI) then count traffic in different directions. The experimental results conducted on HCM AI-Challenge 2020 dataset show that our system can work effectively in almost any environment.
Online Higher Education in Bangladesh: Challenges and Prospects

Online education has become a necessity during the recent Corona pandemic.It has been a popular medium of education in universities of many developed and developing countries around the world for a considerable period of time.This mode of education is now a necessity for higher education as well.But in Bangladesh it has not gained popularity for a number of reasons and a switch to it has always been seen as a challenge.This research investigates the challenges in implementing online higher education and prospects for this type of education for Bangladeshi universities.This research follows the qualitative method and depends on secondary data.The study finds out that in spite of a number of rational challenges in implementing online education, it offers a lot of prospects for Bangladeshi higher education for all time.It suggests that online education can be a good alternative of program delivery for universities of Bangladesh in view of a number of practical factors.It recommends that at least a specific percentage of courses in any program of universities may be offered online in the backdrop of challenges problems prevailing in universities and the prospects it will offer to our higher education providers.
Fire Risk Assessment and Application in Business Circle Based on ISM-BN Model

Based on the ISM-Bn model, a business circle fire assessment system is constructed, and the Interpretive Structure Model (ISM) is used to find the hierarchical relationship between the various influencing factors of the business circle fire, and the Bayesian Network (BN) is used to calculate the probability and Sort by importance. The calculation results are verified by combining examples, and preventive measures are formulated to provide technical support for reducing the probability of fire occurrence in the business circle.
A Proposal to Integrate Deep Q-Learning with Automated Planning to Improve the Performance of a Planning-Based Agent

In this work we propose an architecture which learns to select subgoals with Deep Q-Learning in order to decrease the load of a planner when faced with scenarios with tight time restrictions, such as online execution systems. We have trained this architecture on a video game environment used as a standard testbed for intelligent systems applications. We experiment with different values of the discount rate \(\gamma \) and show the importance of long-term thinking when selecting subgoals. We also compare our approach against a classical planner and show how it is able to greatly reduce time requirements, although obtaining plans with 25% more actions on average. We conclude our approach is competitive with a classical planner and presents better generalization properties than most Reinforcement Learning algorithms when applied to new levels of the same game.
Face Mask Detection Using MobileNetV2 and Implementation Using Different Face Detectors

Face recognition and object detection have been around the artificial intelligence field for a couple of years now and are constantly evolving and being pushed in many devices which we might not be even aware of. Many of such face and object detection techniques use Convolution Neural Network (CNN) architecture at the core for understanding and classifying any image passed on to the system. The neural networks identify many characteristics and distinguishing features present in the image and then provide us with a prediction. In this paper we discuss the implementation of a face mask detection technique using Mobile NetV2, observe the accuracy of our model and compare the performance of the trained model by incorporating three different face detector models. The result achieved from the trained model brings forth the opportunity for implementing such techniques on low computational powerful devices thereby making mask detection algorithm integration much easier than other techniques.
Graph-Encoder and Multi-decoders Solution Framework with Multi-attention

The goal of math word problem (MWP) is to design an automatic solution model to answer the mathematical questions given in the text. In recent years, many neural network models have achieved good results. However, most models do not consider the unique language features of mathematical problems in feature extraction, and ignore the structural information of mathematical problems and equations in the design of encoder and decoder. In order to solve these problems, we propose a graph-encoder and multi-decoders solution framework with multi-attention. On the one hand, we introduce the multi-attention mechanism in the encoding part to capture multiple features, and construct graph to express quantitative information. On the other hand, in the decoding part, we combine tree-structured and sequence-structured decoders. The experimental results on the Math23K dataset show that our model outperforms the existing state-of-the-art methods.
Research on “Attack Graph” Development Trend

With the development of cloud computing, big data and other technologies, cyber space security has become increasingly prominent. Attack graphs are a powerful tool for security risk assessment by analyzing network vulnerabilities and the paths attackers can use to compromise network resources. Attack graphs and model analysis have done a lot of research, and made a large number of achievements. In this paper we chose the advanced search in the “WoS core dataset (E-SCI)”, with a retrieval type of TS = attack graph, finally 2936 papers in the fields are retrieved. Based on the 2936 literature, we carry out time series, co-citation analysis and keywords co-occurrence analysis. The result shows the current hot spot and development trend, and predicts the future research direction in the attack graph field.
The D-Wave quantum computer – advantages and disadvantages of moving away from the circuit model

The paper is based on a thesis with the same title. The purpose of this thesis is to analyse D-Wave devices using quantum effects. The research focuses on demonstrating the advantages and disadvantages of a company moving away from the circuit model in its computers. The subject of the research is the used adiabatic model of quantum computing based on the mechanism of quantum annealing. The research is based on publicly available, comprehensive documentation of D-Wave Systems. On the basis of scientific papers, conferences and information contained in websites, controversies, disadvantages and advantages of the solutions adopted have been described.
Single Image Super-Resolution Algorithm Based on Enhanced Generative Adversarial Network

With the advancement of technology, the processing of increasing image data becomes particularly important. Computer vision can process the hidden information behind these data. Image super-resolution aims to use a low-resolution image to generate a corresponding high-resolution image through the convolutional neural network, improving the quality of the image and the display effect. This paper proposes an Enhanced Generative Adversarial Network (EGAN), designing a new loss function, so that the generated image has more texture details than the previous method. On three benchmark datasets, Set5, Set14 and BSD100, the proposed method has a great improvement on the peak signal-to-noise ratio and structural similarity of the generated images.