Reviewerly/exHarmony huggingface dataset:
Eluding ML-based Adblockers With Actionable Adversarial Examples

Online advertisers have been quite successful in circumventing traditional adblockers that rely on manually curated rules to detect ads. As a result, adblockers have started to use machine learning (ML) classifiers for more robust detection and blocking of ads. Among these, AdGraph which leverages rich contextual information to classify ads, is arguably, the state of the art ML-based adblocker. In this paper, we present a4, a tool that intelligently crafts adversarial ads to evade AdGraph. Unlike traditional adversarial examples in the computer vision domain that can perturb any pixels (i.e., unconstrained), adversarial ads generated by a4 are actionable in the sense that they preserve the application semantics of the web page. Through a series of experiments we show that a4 can bypass AdGraph about 81% of the time, which surpasses the state-of-the-art attack by a significant margin of 145.5%, with an overhead of <20% and perturbations that are visually imperceptible in the rendered webpage. We envision that a4’s framework can be used to potentially launch adversarial attacks against other ML-based web applications.
Analyze Skin Histopathology Images Using Multiple Deep Learning Methods

For melanoma diagnosis, visual analysis of skin histopathology images is the gold standard. There have been researches on deep learning-based histopathology image diagnosis. However, few studies explore the use of multiple deep learning methods to analyze histopathology images. Our research used 3 popular deep learning models, and 2 recognized training methods to analyze histopathology images. 312 histopathology images were collected from the department of dermatology in Xiangya Hospital for contrast tests. In the contrast test, three models of ResNet50, InceptionV3, and MobileNet were performed in sequence. Moreover, two training methods of transfer learning method and fully trained method were performed, respectively. Firstly, the result shows that different models with the same training method got similar performance. Secondly, the models trained by the fully trained method (the accuracy of the three models ranged from 99.78% to 99.88%) performance better than the model trained by transfer learning mode (the accuracy of the three models ranged from 96.69% to 96.81%). Therefore, deep learning is suitable for the histopathology image diagnosis of melanoma. For the same deep learning model, the fully trained method is better than transfer learning in histopathology image diagnosis of melanoma.
Deep Semi-Supervised Learning for Low-Light Image Enhancement

Deep recursive band network(DRBN) is the first semi-supervised learning method applied in low-light image enhancement and achieved state-of-the-art results right now. However, lack of extra same domain unsupervised images and the separated supervised and unsupervised modules hinder the further improvement of the performance. To overcome these two problems, in this paper, we propose the first joint training semi-supervised low-light image enhancement algorithm. Our algorithm consists of two parts: the unsupervised image selection part and the semi-supervised low-light image enhancement part. The unsupervised image selection part overcomes the first problem. Specifically, a scoring mechanism based on the QTP theory is used to score unsupervised low-light images, images with lower score are selected as the extra same domain unsupervised images for low-light image enhancement tasks. In semi-supervised low-light image enhancement part, we extend the MixMatch based semi-supervised classification algorithm into its semi-supervised regression version, and utilize recursive band learning(RBL) which is the first stage of DRBN as the supervised learning part of our model to solve the second problem. As our method can solve the two problems of DRBN simultaneously, ours can achieve better performance. Comprehensive experimental results on real datasets demonstrate the effectiveness of our method.
An Overview of Security and Privacy in Smart Cities

In the twenty-first century, so many technological paradigms emerged as the result of the technological advancement of information and communication technology (ICT). Many of the technologies were used as an aid for fulfilling human needs and mitigating many of the problems associated with the environment and making everyone connected. In order to mitigate the problem of rapid urbanization smart city concept has emerged and it is being used to streamline the functions of urban cities and make the lives of citizens more easy and convenient. In a typical smart city context, citizens are already interconnected via smart mobile devices and wearable devices. The hospitals, vehicles, houses, traffic surveillance systems, public places, and other social systems in the city are all connected over the Internet, which is known as the Internet of Things (IoT). This will contribute to unprecedented changes in the condition of life in the city. In public care, public safety, emergency management, and disaster recovery, these pervasive systems which are linked through the Internet can assist citizens with greater benefits. The world is now witnessing a boom of smart cities. Although smart cities have many advantages, such as creating new economic and social possibilities, there are many challenges, such as security and privacy. These security and privacy challenges include unauthorized access to information and attacks that cause physical interruptions in the provision of city services. As all people in the city are linked to each other through the underlying ubiquitous communication technologies in the smart city, security problems can arise as a result of the vulnerabilities present in the city infrastructure, communication networks, and human errors. Different applications used in smart cities can also present new vulnerabilities, so the attackers can gain access to information which is highly confidential. The purpose of this study is to provide an extensive analysis of security and privacy problems in smart cities.
AI Bot for Academic Schedules using Rasa

Chatbots or virtual assistants are being used by industries all over the world, they can reduce human intervention and improve efficiency. These days smart-assistants such as Amazon Alexa and Google Assistant help users get quick access to most generic queries within seconds, but when it comes to students and their everyday queries, these assistants fall short in answering the queries they have related to their academic schedules i.e., timetable queries, online classes links, syllabus queries, test dates, etc. The motive behind building this chatbot is to help students get quick and accurate responses to their schedule and syllabus-related queries, this is especially beneficial for students who are taking online classes due to the COVID-19 pandemic and cannot talk to their peers face to face. This chatbot was developed with the Rasa, it is a framework for developing contextual AI assistants and chatbots. Rasa enables the use of components in the NLU pipeline to customize the intent classification, entity extraction, and response selection. This paper goes through the pipeline customizations that were necessary to process the schedule-specific queries from students. It also goes over the stories and custom actions used to generate responses once the intent and entities are extracted. Telegram was used to deploy the chatbot onto the real world to enable students to talk to this chatbot from the comfort of their smartphones.
A Hybrid CBR Classification Model by integrating Decision Tree and Random Forest into Case Retrieval

Due to the huge amount of medical data, which are stored in databases. The classification is the most demanding task for automatic decision making. This paper presents the development of ahybrid system for classifying medical data, based on the combination of learning methods with Case-Based Reasoning (CBR). The importance of this work lies in the design and implementation of an automatic classifier such as decision trees (C4.5,REPTree, LMT) and Random Forests (RF) to model the Retrieval phase of a CBR system, thus aiding inthe diagnosis or initial screening of the disease. The performance of the hybrid CBR system designed with C4.5 is compared to those designed with REPTree, Logistic Model Tree (LMT) and Random Forest (RF) for a set of dynamic and static activities. The system is trained and tested on four medicaldatasets, namely Wisconsin Breast Cancer, Thyroid, Hepatitis and Breast pathologies. Simulation results show that the proposed CBRRF and CBR-LMT methods outperform CBR-C4.5 and CBR-REPTree by achieving overall accuracy during cross-dataset evaluation of 97%, 96%, 83%, and 94%on Wisconsin Breast Cancer, Thyroid, Hepatitis, and Breast pathologies, respectively, and achievebetween0.10-0.14 root mean square error.
A Comparative Analysis of Artificial Neural Networks for Photovoltaic Power Forecast Using Remotes and Local Measurements

Abstract The inclusion of photovoltaic systems in distribution networks has raised the importance of the prediction of photovoltaic power for safe planning and operation. Artificial neural networks (ANNs) have been used in this task due to its capacity of representing nonlinearities. However, the profile of the data used may affect the forecast accuracy. This manuscript reports on a comparative analysis of the performance of four neural network models for photovoltaic power forecast regarding their input dataset. Four sets composed of photovoltaic power data (local measurements) and external weather data (remote measurements) were used, and the networks were validated through actual measurements from a photovoltaic micro plant. The ANN that dealt with only weather data showed a good level of accuracy, being a useful tool for the feasibility analysis of new photovoltaic projects. In addition, the approach that used only photovoltaic power data has excelled and can be used in electric sector companies.
“Taking out the Trash”: Why Security Behavior Change requires Intentional Forgetting

Security awareness is big business – virtually every organization in the Western world provides some form of awareness or training, mostly bought from external vendors. However, studies and industry reports show that these programs have little to no effect in terms of changing the security behavior of employees. We explain the conditions that enable behavior change, and identify one significant blocker in the implementation phase: not disabling existing (insecure) routines – failure to take out the trash – prevents embedding of new (secure) routines. Organizational Psychology offers the paradigm Intentional Forgetting (IF) and associated tools for replacing old (insecure) behaviors with new (secure) ones by identifying and eliminating different cues (sensoric, routine-based, time and space based as well as situational strength cues) that trigger old behavior. We introduce the underlying theory, examples of successful application in safety contexts, and show how its application leads to effective behavior change by reducing the information that needs to be transmitted to employees, and suppressing obsolete routines.
Geochemical Studies of some Heavy Metals’ Toxicity in Groundwater with their Plausible Sources around Gondwana Supergroup, Wardha Valley Coalfields, Maharashtra

The study is designed for groundwater assessment in terms of some sorted heavy metals' toxicity and their probable source in Wardha valley coalfields, Maharashtra. For both seasons, 45 groundwater samples were collected separately to assess Al, Cd, Cr, Cu, Fe, Ni, Pb and Zn by AAS. Among them, 22 were found contaminated as per BIS (2012) concerning Cd, Fe, Ni and Pb. The major contributors in both seasons were Fe, Zn, Cu and Al. The PCA study has shown sound relationships among Fe-Ni, Cd-Ni, and Al-Cu-Zn. The contamination intensities in various samples indicates that the mining region as a prime cause. The Kamthi and Barakar formations in the study area were considered as a contamination source. The significant role of lower pH environment in enhanced contamination was revealed in the correlation analysis. Accordingly, the probability of acid mine drainage was supposed. The EPMA and SEM-EDS/X studies reveal the presence of sulphide phases in Barakar Formation and sulphur in associated carboniferous strata confirming the source of lower pH. Accordingly, the acid mine drainage was anticipated to be responsible for heavy metals' dissolution and migration to groundwater in mining areas.
Intelligent Models for Mining Social Media Data

Social media produces enormous amounts of unutilized data. Through machine learning and text analytics techniques, organizations and individuals alike can use such data to extract a great deal of information. The type of information can be used in a plethora of ways; with techniques such as sentiment analysis allowing organizations to determine public reception to their products or finding trendy topics through the application of topic extraction. This paper outlines the process taken to develop a machine learning model that analyzes social media posts and extracts the post’s sentiment and topic. A multinomial Naïve Bayes model and a random forest model were trained for each objective to gauge which learning algorithm performs better with accuracy and F-Score being performance metrics. The results showcased that even with relatively simple implementations, and the analysis results on unseen social media posts produced promising accuracy results with short training time, with the multinomial Naïve Bayes model outperforming the random forest model.
BA-GAN: Block Attention GAN model for Underwater Image Enhancement

Since underwater images are an important route to underwater information, underwater image enhancement becomes an important fundamental study to underwater vision tasks. Regarding problems with underwater degraded images, such as color distortion causing image rendering blue or green, blur and low contrast leading to image loss of detail information, we propose a Generative Adversarial Network (GAN) based model that adds an attention mechanism to enhance underwater images. The model finally achieves the purpose of generating clear underwater images by learning the relationship between underwater degraded images and ground truth values, removing color interference in the image and enhancing the details of the image. We compared the proposed model with the the state-of-the-art methods on both EUVP and Underwater Image Enhancement Benchmark Dataset, experimental results on both datasets prove that our method performs more stable than state-of-the-art methods.
Chosen-ciphertext lattice-based public key encryption with equality test in standard model

With the rapid growth of cloud storage and cloud computing services, many organizations and users choose to store the data on a cloud server for saving costs. However, due to security concerns, data of users would be encrypted before sending to the cloud. However, this hinders a problem of computation on encrypted data in the cloud, especially in the case of performing data matching in various medical scenarios. Public key encryption with equality test (PKEET) is a powerful tool that allows the authorized cloud server to check whether two ciphertexts are generated by the same message. PKEET has then become a promising candidate for many practical applications like efficient data management on encrypted databases. Lee et al. (Information Sciences 2020) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of post-quantum PKEET schemes based on lattices. At ACISP 2019, Duong et al. proposed a direct construction of PKEET over integer lattices in the standard model. However, their scheme does not reach the CCA2-security. In this paper, we propose an efficient CCA2-secure PKEET scheme based on ideal lattices. In addition, we present a modification of the scheme by Duong et al. over integer lattices to attain the CCA2-security. Both schemes are proven secure in the standard model, and they enjoy the security in the upcoming quantum computer era.
Producing Synthetic Dataset for Human Fall Detection in AR/VR Environments

Human poses and the behaviour estimation for different activities in (virtual reality/augmented reality) VR/AR could have numerous beneficial applications. Human fall monitoring is especially important for elderly people and for non-typical activities with VR/AR applications. There are a lot of different approaches to improving the fidelity of fall monitoring systems through the use of novel sensors and deep learning architectures; however, there is still a lack of detail and diverse datasets for training deep learning fall detectors using monocular images. The issues with synthetic data generation based on digital human simulation were implemented and examined using the Unreal Engine. The proposed pipeline provides automatic “playback” of various scenarios for digital human behaviour simulation, and the result of a proposed modular pipeline for synthetic data generation of digital human interaction with the 3D environments is demonstrated in this paper. We used the generated synthetic data to train the Mask R-CNN-based segmentation of the falling person interaction area. It is shown that, by training the model with simulation data, it is possible to recognize a falling person with an accuracy of 97.6% and classify the type of person’s interaction impact. The proposed approach also allows for covering a variety of scenarios that can have a positive effect at a deep learning training stage in other human action estimation tasks in an VR/AR environment.
Optical image hiding under low light illumination based on deep learning

We propose a system that realizes optical image hiding under low light and reconstructs secret images through deep learning. Based on the improved Mach–Zehnder interferometer system, the light intensity of the two optical paths of the reference light and the object light is adjusted by the neutral density filter (NDF) until the light intensity ratio is 1:15, which constitutes the experimental system of optical image hiding under low light field in this article. The interference hologram collected by the CCD is used as the training data set of the neural network, and the neural network model is continuously optimized. Finally, the optimized neural network model reconstructs the secret image with high quality. The proposal of this scheme reduces the embedding depth of the secret image, making the secret image less noticeable. In addition, the results of optical experiments and simulated test results of different light intensity ratios show that the scheme has certain feasibility and effectiveness.
Communication-Efficient Decentralized Zeroth-order Method on Heterogeneous Data

Decentralized federated learning is an communication efficient method to learn from large-scale distributed dataset. Most existing FL algorithms are gradient-based and assume knowledge of first-order information of the objective function in client nodes. However, there are situations where the information of gradient is difficult to touch or compute accurately. In this paper, we consider a local zeroth-order algorithm based on biased stochastic zeroth-order update in decentralized federated learning setting. We prove concise convergence rates on strongly convex problems and show that it matches the rate of decentralized local stochastic gradient descent (local SGD) up to a factor proportional to the dimension of the parameter. The number of communication rounds can be reduced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\sqrt{T}/N)$</tex> times. Simulation results confirm that our algorithm is globally convergent and further reveal communication complexity improvement over traditional distributed zeroth-order methods.
An integrated remote sensing, aeromagnetic and field study of the Ablah shear zone (Asir Terrane, Saudi Arabia): Structural styles and implications for the tectonic evolution

An integration of merged remote-sensing data having different spatial/spectral resolutions with aeromagnetic data and detailed field investigation is used to identify the composite Neoproterozoic rock complexes and define the structural architecture of the Ablah Shear Zone (ASZ). The SPOT and Landsat-8 data are processed as color composite images allowed the separation of different lithologic units and associated structures. Aeromagnetic data were analyzed using the Total Magnetic Intensity (TMI) and Reduced to the Pole (RTP) methods. The resulted anomalies show seven N-S opposite-dipping thrust faults that affect the shallow crustal rocks and coalesce together at about 1.5 to 2 km depth where the rock magnetic polarities inversed. This thin-skinned thrusting was initiated by a contractional tectonic regime in the E-W direction and superposed later by a transpresssion tectonic regime in the ENE-WSW direction. This study deals with the thin-skinned thrusting deformation in the ASZ that could be occurred during the deformation of the other shear zones that affect the internal part of the Asir tectonic terrane. Two controversial tectonic meanings describe the tectonic evolution of the ASZ, the first one is the thin-skinned thrusting tectonic that indicating shallow earth s crust deformation and the second one is the occurrence of some ophiolitic slices that indicating suturing tectonic deformation. The absent of the complete ophiolitic section encourages us regarding the ASZ as a structural shearing zone rather than a suture zone.
Ghost Graph Convolutional Network for Skeleton-based Action Recognition

Skeleton-based action recognition has attracted great attention in human action recognition. Existing methods for skeleton-based action recognition improve performance by designing deeper networks without considering the efficiency of the model. In this paper, we propose a simple and effective light weight graph convolutional network for skeleton-based action recognition. Our model is composed of a lightweight temporal convolutional network and spatial graph convolutional network using depthwise convolution. In addition, we propose a novel graph convolution that can take the multi-scale relationship of joints with low computational complexities. On the NTU RGB+D dataset, our proposed model achieves comparable or higher performance with much fewer parameters compared with baseline method.
A Lightweight Interactive Graph Processing Library for Edge Computing in Smart Society

Edge computing aims to handle the data very close to the source where it is produced, and also provides an effective way to alleviate the burden of cloud computing for the processing of massive volume of data, enabling lower latency, faster response, and more comprehensive data analyses. Meanwhile, graph-based data has emerged especially in the form of social networks, World Wide Web (WWW), transportation, biological networks, and so on, which become high-impact domains of human activity to build smart society. Generally, as the resources on compact end devices are strictly constrained, a graph processing solution with low latency and low overhead is a real necessity for graph analytics in edge computing. In this paper, for the first time to our knowledge, we propose a lightweight interactive graph processing library (GPL) for edge computing in smart society, which is user-friendly and handy to be operated on a single device or among multiple devices. Evaluation is conducted to indicate that the proposed GPL is highly competitive with other established libraries such as igraph, NetworKit or NetworkX based on different graph datasets over a variety of popular algorithms.