Reviewerly/exHarmony huggingface dataset:
A Facial Expression Recognition System for Smart Learning Based on YOLO and Vision Transformer

This paper proposes a facial expression recognition system for smart learning on classroom. Firstly, YOLO is used to extract face images of multiple students from high-resolution video; secondly, face images are preprocessed, then a self-attention based model named Vision Transformer (ViT) is used to recognize facial expressions; finally, the classified facial expression is used to assist teacher to analyze students’ learning status, so as to provide suggestions for improving teaching effect.
Hardware Implementation of a Chaos Based Image Encryption Using High-Level Synthesis

In recent years, the use of digital images in data transmission networks and digital devices has increased greatly. Therefore, the security of images in transmission or storage has become very important due to security attacks. For this reason, cryptography is used to protect images from unauthorized access and attacks. In this paper, an image encryption algorithm using chaos theory is proposed in which, besides providing the appropriate level of security, the speed of encryption operations is also considered. The 1D tent map is used for the encryption process. Its security analyses have been performed to verify its efficiency in terms of security. The hardware implementation of the algorithm was performed on FPGA with the help of high-level synthesis (HLS), architectural techniques, time and resource optimization. Based on our experimental results, the hardware designed for image encryption is robust in terms of security and it can achieve an encryption and decryption time of 21.14 milliseconds for an image with a size of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${256\times 256}$</tex> .
Recognition system for masked face based on deep learning

With the spread of the epidemic in the world, wearing masks has become the most simple and effective way to block the COVID-19. For the lack of data and model design to fit the epidemic scene, we propose an integrated masked face recognition system with three cascaded convolutional neural networks. Firstly, a SSD model is used to detect masked face to eliminate the interference of irrelevant background. Then, we use an Hourglass network to regress the key points of the occluded face and crop the aligned eye-brow area without mask. Finally, we finetune a pretrained FaceNet to fully adapt to the data of eye-brow regions. Experiments on numbers of laboratory and wild images proved that our method can recognize the subjects with mask effectively.
Cells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification

Digitization of histology images and the advent of new computational methods, like deep learning, have helped the automatic grading of colorectal adenocarcinoma cancer (CRA). Present automated CRA grading methods, however, usually use tiny image patches and thus fail to integrate the entire tissue micro-architecture for grading purposes. To tackle these challenges, we propose to use a statistical network analysis method to describe the complex structure of the tissue micro-environment by modelling nuclei and their connections as a network. We show that by analyzing only the interactions between the cells in a network, we can extract highly discriminative statistical features for CRA grading. Unlike other deep learning or convolutional graph-based approaches, our method is highly scalable (can be used for cell networks consist of millions of nodes), completely explainable, and computationally inexpensive. We create cell networks on a broad CRC histology image dataset, experiment with our method, and report state-of-the-art performance for the prediction of three-class CRA grading.
Face Detection And Recognition In Complex Environments

With the development of deep learning, the perception of facial image has wide prospects of application.In a crowded environment, there is a problem that faces cannot be detected due to mutual occlusion of faces, and the low resolution causes the face to be blurred. In order to detect the missing faces which were occluded in the dense environments, a kind of model, which based on multi-task cascaded convolutional network, was put forth. We improve the detection scheme by using Soft non-maximum suppression algorithm.Aiming at the problem of faces with proportion of inconsistent caused by the inconsistency of the distance between the face and the camera device, this paper extracts facial features based on the deep res-net. For high and low resolution facial images, a dual-branch extraction feature network is used to make images of different resolutions are clustered in a public feature space. At the same time, the central loss function, parameterized rectification linear unit and batch normalization are used to improve the network training process, which improves the network's feature classification performance and the convergence speed in the training process. WIDER FACE database is used to verify the face detection method. LFW database and YALE face database are used to verify face recognition method. Experiments show that the network proposed in this paper has good robustness against occlusion and low resolution in face detection, and effectively expands the distance between classes in face recognition and improves the recognition accuracy.
Hyperspectral Data Preprocessing of the Northwestern Algeria Region

Hyperspectral images provide a rich source of information and allow us to broaden the field of applications like material identification, agriculture, and environmental studies. However, these images commonly suffer from atmospheric effects, sensor noise, etc. Preprocessing is a crucial task to reduce/remove these effects for better extraction of the desired information. The present paper aims to apply some preprocessing techniques on Djebel Meni zone, situated in the Northwestern of Algeria, to prepare reliable images for the next processing stages. The database used in this work is the Hyperion level L1R dataset. The preprocessing approach adopted in this work involves: bad band removal, i.e., removing the bands with no information, radiometric calibration, and atmospheric correction in order to handle the atmospheric effects, correcting the continuous vertical stripes and the abnormal pixels in bands. Also, we have applied dimensionality and noise reduction to deal with the huge volume of spectral data and the noise present in the Hyperion hyperspectral dataset. These preprocessing steps are an indispensable prerequisite for any further processing and accurate interpretation of spectra of different surface objects. Also this study recommends that QUAC atmospheric correction has a capability to compensate the atmospheric effects than IARR method for mineralogy studies
Extreme Learning Machines (ELM) as Smart and Successful Tools in Prediction Cost and Delay in Construction Projects Management

The construction industry is subject to a high level of risks and uncertainties than any other industry. In reality, most participants experience cost and time overruns risks and often fail to meet quality standards and operational requirements. This paper aimed to investigate the accuracy of artificial intelligent models such as Extreme Learning Machines (ELM) to demonstrate the impact of risk factors on predicting the cost and delay of construction projects. Risk factors (RF) and delay factors (DF) were identified and analyzed using Probability and Impact analysis adopted as the model's inputs. At the same time, the outputs for the models were represented by the ratio of contractor's profit to project costs and the period of delay occurring in the construction projects. Root mean squared error (RMSE), correlation coefficient (R), and coefficient of determination (R2) were employed as performance indices of the models to evaluate the accuracy of the results. The study results showed that the ELM model has demonstrated acceptable performance in predicting the cost and delay, resulting in superior performance indices.
Path Planning Method for Mobile Robot Based on Multiple Improved PSO

The problem of easy to fall into the local optimum for the traditional particle swarm optimization (PSO) in robot path planning has gained attention constantly. For solving the problems such as discrete and optimization problems, the path is not smooth, etc., multiple improved PSO algorithm is introduced in this paper. The improved algorithm adapts to the adjustment, optimizes the inertia weight coefficients, and learns factors, thus increase the optimal value of the path. It constructs a penalty function and proposes constraint indexes that can be optimized for evaluation, the robot can avoid obstacles autonomously, and get a smooth path by the cubic spline. The improved algorithm was verified by experiments. The experimental results show that: the optimal value of the path length obtained by the improved algorithm is 18.2% less than that of the classic PSO. Comparing with other improved algorithms mentioned in this paper, the path length is reduced by 6.2%, but it saves at least 5.04 seconds in convergence speed.
Investigating Malware Propagation and Behaviour Using System and Network Pixel-Based Visualisation

Abstract Malicious software, known as malware, is a perpetual game of cat and mouse between malicious software developers and security professionals. Recent years have seen many high profile cyber attacks, including the WannaCry and NotPetya ransomware attacks that resulted in major financial damages to many businesses and institutions. Understanding the characteristics of such malware, including how malware can propagate and interact between systems and networks is key for mitigating these threats and containing the infection to avoid further damage. In this study, we present visualisation techniques for understanding the propagation characteristics in dynamic malware analysis. We propose the use of pixel-based visualisations to convey large-scale complex information about network hosts in a scalable and informative manner. We demonstrate our approach using a virtualised network environment, whereby we can deploy malware variants and observe their propagation behaviours. As a novel form of visualising system and network activity data across a complex environment, we can begin to understand visual signatures that can help analysts identify key characteristics of the malicious behaviours, and, therefore, provoke response and mitigation against such attacks.
A Domain-Specific Language to Specify Planned Architectures of Adaptive Systems

Adaptive Systems (ASs) are able to monitor their own behavior and adapt it when quality goals are not accomplished. MAPE-K is a reference model that provides abstractions to design ASs. Structuring such systems with these abstractions brings many benefits, some related to maintenance and some related to comprehension of the architecture. However, many existing ASs are not designed according to MAPE-K, making those abstractions hidden in their architecture. Architectural Conformance Checking (ACC) is a technique for checking if the current architecture (CA) of a system is obeying the rules prescribed by a planned architecture (PA) or even by a reference model, like MAPE-K. In this paper we present DSL-Remedy, a language for specifying PAs in the context of Adaptive Systems. Our language provides adaptive-systems specific abstractions so that engineers can be more precise when describing the planned architecture. Besides, as our DSL is ASs-specific, it incorporates some rules already known in MAPE-K. We have evaluated our DSL comparing it with a general-purpose DSL and the results show improvements in the productivity.
A Similarity Constraint Divergent Activation Method for Weakly Supervised Object Detection in Remote Sensing Images

With the development of remote sensing technology and object detection technology, many fully-supervised convolutional neural networks (CNN) methods based on object labeling information such as bounding box have achieved good results in remote sensing image object detection. However, due to the wide detection range of remote sensing images, diversity of objects, and the complexity of background, it is very difficult to manually label large-scale remote sensing images. Therefore, in recent years, more and more attention has been paid to the weakly supervision method using only image-level labels in object detection. Class activation mapping (CAM) method based on weakly supervision works well for object detection in natural scene images, but it has the problem when it is used in remote sensing images: a large number of small objects are lost. In this paper, we propose an object detection method for remote sensing image based on similarity constraint divergent activation (SCDA). The divergent activation (DA) module in SCDA improves the response intensity of the low response regions in the shallow layer feature map. According to the similarity between the objects, the similarity constraint module (SCM) is used to further improve the feature distribution and suppress background noise. By fusing DA and SCM, the missed rate of small objects can be reduced. Comprehensive experiments and comparisons with state-of-the-art methods on WSADD and DIOR data sets demonstrate the superiority of our proposed method.
Classism Hate Speech in Katherine Mansfield's Short Story ‘The Doll’s House’: A Pragmatic Study

Hate speech (henceforth HS) has recently spread and become an important issue. This type of speech in children's writings has a particular formulation and specific objectives that the authors intend to convey. Thus, the study aims at examining qualitatively and quantitatively the classism HS and its pragmatic functions via identifying the speech acts used to express classism HS, the implicature instigated as well as impoliteness. Since pragmatics is the study of language in context, which is greatly related to the situations and speaker’s intention, this study depends on pragmatic theoriespeech acts, impoliteness and conversational implicature) to analyze the data which are taken from Katherine Mansfield's short story (The Doll’s House). The data has been analyzed qualitatively and quantitatively. It is qualitative, as it is dedicated to describe HS phenomenon that is found in the selected short story, depending on an eclectic model. Regarding the quantitative analysis, the researchers have used SPSS 23 program to determine the frequencies and percentages of the strategies that are intended to be measured. The study has concluded that HS has multiple dimensions that are difficult to interpret outside the context of speech. It can be conveyed by many strategies, both explicit and covert. Further, the simplest form of HS involves an insult in addition to other functions, such as disapproval and humiliation.
Cloud Security: The Future of Data Storage

In this modern era, all the organizations seem to be shifting their data and services to cloud because of the increase in information leaks and data thefts. It has become easier for intruders to break into the organization’s data stored locally. As cloud provides a significant hike in security levels, it is slowly turning into the future of data storage. It has become crucial to acknowledge the issues regarding cloud security. Cloud security is the security of all the services provided by the cloud—storage, servers, networking, and databases. In this paper, the issues related to the same are covered along with the mitigation proposed to deal with those problems. The key to the overall security of the cloud is identity access management (IAM). IAM is inherently more secure than a simple username and password combinations because of the profile of information IAM collects. It can make access to data and networks a much more convenient process.
Novel Radio-Frequency Fingerprint Recognition Scheme Using Multiwavelets-Based Cyclic-Spectrum Graph Analysis

A novel radio-frequency (RF) fingerprint recognition scheme using finite-element-multiwavelets-based cyclic-spectrum graph analysis is introduced in this work. Recognizing or classifying RF signals emitted from different devices without any help of spread/embedded identification codes or sequences is intriguing but challenging nowadays. In our proposed new approach, an RF signal emitted by an electronic device is first decomposed into multiple subbands using finite-element multiwavelets with a certain appropriate multiplicity. Thus, the cyclic spectra of extracted subband signals are formed and then the corresponding graphs can be established thereby. Such graph features may represent the RF fingerprints and be utilized to distinguish (recognize) devices by use of the discrepancies among them. Our simulations are based on realworld RF signals and the results demonstrate that the proposed finite-element-multiwavelets-based scheme can lead to higher correct recognition rates but lower computational complexities than the prevalent GHM-multiwavelets-based scheme and the discrete-Fourier-transform (DFT) method.
9th International Workshop on Human Activity Sensing Corpus and Applications (HASCA)

The recognition of complex and subtle human behaviors from wearable sensors will enable next-generation human-oriented computing in scenarios of high societal value (e.g., dementia care). This will require large-scale human activity corpus and much improved methods to recognize activities and the context in which they occur. This workshop deals with the challenges of designing reproducible experimental setups, running large-scale dataset collection campaigns, designing activity and context recognition methods that are robust and adaptive, and evaluating systems in the real world. We wish to reflect on future methods, such as lifelong learning approaches that allow open-ended activity recognition. This year HASCA will welcome papers from participants to the Fourth Sussex-Huawei Locomotion and Transportation Recognition Challenge and the Third Nursing Activity Recognition Challenge in special sessions.
What Will Affect the Diffusion of AI Agents?

For billions of people, the threat of the Novel Coronavirus SARS-CoV-2 and its variants has precipitated the adoption of new behaviors. Pandemics are radical events that disrupt the gradual course of societal change, offering the possibility that some rapidly adopted innovations will persist in use past the time period of the event and, thus, diffuse more rapidly than in the absence of such an event. Human-machine communication includes a range of technologies with which many of us have quickly become more familiar due to stay-athome orders, distancing, workplace closures, remote instruction, home-bound entertainment, fear of contracting COVID-19, and boredom. In this commentary I focus on Artificial Intelligence (AI) agents, and specifically chatbots, in considering the factors that may affect chatbot diffusion. I consider anthropomorphism and expectancy violations, the characteristics of chatbots, business imperatives, millennials and younger users, and from the user perspective, uses and gratifications.
Fast Convolutional Factorization Machine with Enhanced Robustness

Recently, factorization machine and its variants have shown promising results for context-aware recommender systems (CARS), especially when combined with deep neural networks. Among them, convolutional factorization machine (CFM) is a prominent example. The key to the success of CFM is its 3D convolutional architecture for capturing complex interactions on top of embedded features. However, the resultant computational cost can also be demanding. Moreover, the feature embedding scheme of CFM and other factorization models can be potentially vulnerable to noise. To tackle these issues, in this study we propose two models, namely, the fast convolutional factorization machine (FCFM) that slims down the complete pairwise feature interaction for higher computational efficiency, and adversarial fast convolutional factorization machine (AFCFM) that further enhances the robustness of the model by introducing adversarial noise to the feature interaction image generated by the model. Experimental results on four benchmark datasets prove that the proposed FCFM is nearly five times faster than CFM with competitive performance, while AFCFM improves the performance of the state-of-the-art models by about 8\% with higher efficiency than CFM.
Establishing the Informational Requirements for Modelling Open Domain Dialogue and Prototyping a Retrieval Open Domain Dialogue System

Open domain dialogue systems aim to coherently respond to users over long conversations through multiple conversational turns. Modelling open domain dialogue is challenging as both the syntactic and semantic features of language play a role in response formation. As similarity to human dialogue has been considered the goal of open domain dialogue systems, this paper takes the view that human linguistic reasoning research can be informative to the requirement engineering process of modelling open domain dialogue. Through a review of linguistic reasoning research and modern approaches in open domain dialogue systems, the authors present informational hypotheses impacting the modelling of open domain dialogue systems. Furthermore, this paper discusses the design and testing of an open domain dialogue system presenting response BLEU-1 scores of 35.41% based on the DailyDialogue Dataset.
From Pixel to Whole Slide: Automatic Detection of Microvascular Invasion in Hepatocellular Carcinoma on Histopathological Image via Cascaded Networks

Microvascular invasion (MVI) is a histological feature of hepatocellular carcinoma (HCC). It is the strongest feature related to HCC recurrence and survival after liver resection. However, its diagnosis is time consuming which requires pathologist to examine histopathological images at high resolution. A computer aided MVI detection system that improves the diagnosis efficiency and consistency is in demand. There are two challenges in MVI detection (1) MVI is formed by the same type of tumor cells as common cancer tissue (CCT) and (2) both MVI and CCT's size varies significantly – from a few cells to thousands of cells. Inspired by pathologists' routine reading procedure, we propose a 3-stage solution composed by cascaded networks to tackle this problem. In this framework, images are first analyzed by pixel-level cancer tissue segmentation, followed by region-level instance feature extraction, and then by slide-level comparison to detect MVI. To reduce inter-stage error accumulation, the system is designed in the way that later stage can learn and correct errors in the previous stages. To effectively conduct slide-level analysis, a novel convolutional graph neural network with short cut (sc-GCN) is proposed to solve the over-smoothing problem in classic GCN methods. Testing results on 90 WSI samples show that the proposed system achieves state-of-the-art performance on MVI detection.
An Assessment on Classification in Python Using Data Science

This study examines approaches across developing platforms in the current period, which is nothing more than Data Science. Informatics, software engineering, forecasting, decision making, arithmetic, task research, measurements, and the applied sciences have an effect on data science in a logical order. At the moment, the data science industry is introducing a brand-new data model. The activation values of hidden units inside the network are dissected, and classification rules are developed based on the findings of this research analysis. Python programming is made up of many imperative data modelling and algorithms that allow users to create duplicate analyses and produce meaningful analyses. Pycharm, spyder, Pydev, and other Python programming interfaces are widely used for creating reports that support various current trends models such as support vector machine, C4.5, random forest k-means, Apriori, EM, Page, Rank, logistic regression, KNN, Nave Bayes, and CART. This review focuses on existing classification methods that employ data science approaches, as well as applications that are often used in Python programming.