Reviewerly/exHarmony huggingface dataset:
Spatial Image Steganography Using a Correlational Model

Nowadays, the content-adaptive strategy is widely utilized in the image steganographic algorithms, among which the model-based methods, e.g., MiPOD, can be recognized as ones of the state-of-the-arts. However, the existing model-based designs merely adopt independent probability distribution, which could not make full use of the spatial information. In this paper, we propose a novel spatial image steganography based on a correlational model, i.e., Gaussian Markov random field, and utilize its conditional independence to decompose the graph into simple cliques and nodes, and then derive a practical form of KL divergence to minimize the statistical undetectability. Experiment results demonstrate that the proposed method can outperform or approximate the state-of-the-art algorithms when resisting detection of various steganalysis features.
Theory of Deep Integration of Information Technology and Integrated Subjects Teaching

Early 1960s to the mid-1980s is the period when computer was used mainly to help teachers solve some difficulties in instruction by using the demonstrative functions of fast computing, such as graphic animation and simulation. This was the first development stage of application of information technology in education.
Hate and Offensive Language Identification from Social Media: A Machine Learning Approach

Social media has evolved into a valuable tool for disseminating information while also ensuring that everyone on the site has complete freedom of expression. This ability of social media to openly express oneself to the world has resulted in major issues such as hate speech, cyberbullying, aggression, and general profanity, all of which harm society's well-being. It's critical to pay attention to these issues as it has a significant impact on an individual's life and, in some situations, be suicidal, negatively impacting the victim's mental health. Our key aim is that our approach will automate and quicken the detection of offensive content that has been posted, making it easier to take appropriate steps and moderate these offenses. In this work, we have implemented several popular machine learning classifiers with character N-gram TF-IDF features. Along with this, we have also proposed dense neural network (DNN) and convolutional neural network (CNN) models for the identification of hate speech. In the case of DNN, character N-gram TF-IDF features wherein CNN, word embedding features are used. In the extensive experiments, we found AdaBoost classifiers performed best with the weighted F1-score of 0.86 in the identification of hate speech.
Code Tamper-Proofing Using Return Oriented Programming in IoT Devices

An application running on the Internet of Things (IoT) device is vulnerable to code reuse attacks, which makes control flow highjacking relatively easy. Control Flow Integrity (CFI) is widely known to make code tampering harder. Available CFI solutions mostly work for x86 (Intel architecture), but most of the IoT devices use the ARM architecture. This paper presents a novel control-flow obfuscation technique for IoT devices, especially for the ARM architecture. This paper uses Return Oriented Programming (ROP), used for code reuse attacks, to prevent control flow in IoT devices. The proposed method is a selfcontained code integrity verification approach that protects control flow by ROP gadgets and implicitly verifies integrity by “U-probe”.
A Projection-Based Characteristic Mapping Method for Tracer Transport on the Sphere

A semi-Lagrangian Characteristic Mapping method for the solution of the tracer transport equations on the sphere is presented. The method solves for the solution operator of the equations by approximating the inverse of the diffeomorphism generated by a given velocity field. The evolution of any tracer and mass density can then be computed via pullback with this map. We present a novel spatial discretization of the manifold-valued map using a projection-based approach with spherical spline interpolation. The numerical scheme yields C1 continuity for the map and global second-order accuracy for the solution of the tracer transport equations. Error estimates are provided and supported by convergence tests involving solid body rotation, moving vortices, deformational, and compressible flows. Additionally, we illustrate some unique features of computing the solution operator using a numerical mixing test and the transport of a fractal set in a complex flow environment.
Swarm Intelligence Using Collision Avoidance System

In this paper, we seek to study different swarm intelligence models and create a system of multiple quad copters with a decentralised control. There are different swarm intelligence models present which are based on swarms existing in nature. These models use algorithms that are suitable for different applications. We studied and compared the results of different models and have explained the ant colony optimization model and particle swarm optimization algorithm. Swarm models focus on decentralising the control and ramping up the capabilities and intelligence of individual entities. In the considered scenario of our system, a number of autonomous quads are assigned a Unique System ID on a control station. From the control station, they can operate individually by sending the MAVLink messages on their unique channel to perform multiple tasks such as rendezvousing to a point, scanning a desired land area, and returning to a beacon location. This provides individual simplicity to each quad and enhances their abilities as a group. The system is highly scalable and hence suitable for reconnaissance operations. The model is highly based on natural swarms in the environment.
Performance Analysis of Different Deep Neural Architectures for Automated Metastases Detection of Lymph Node Sections in Hematoxylin and Eosin-Stained Whole-Slide images

Dangi, Naman Deulkar, KhushaliIn medical imaging, digital pathology is a rapidly growing field, where glass slides containing tissue specimens are digitized using whole-slide scanners at very high resolutions. Virtual microscopy, also known as whole-slide imaging, aids digital pathology in the analysis, assessment, and diagnosis of tissue slides. Lymph node metastases occur in most cancer types like breast, colon, prostate, etc. Metastatic involvement of lymph nodes is a very important variable in the prognosis of breast cancer, where the diagnostic procedure for the pathologists is tedious, prone to misinterpretation, and requires large amounts of reading time from pathologists. Automated disease detection has been a long-standing challenge for computer-aided diagnostic systems; however, within the past few years, the field has been moving toward grand goals with strong potential diagnostic impact: fully automated analysis of whole-slide images to detect or grade cancer, to predict prognosis or identify metastases. In this paper, we focus on the detection of micro- and macro-metastases in hematoxylin and eosin-stained whole-slide images of lymph node sections with an aim to improve the detection of cancer metastasis, potentially reducing the workload of pathologists by a great amount while at the same time, reducing the subjectivity in diagnosis. This paper demonstrates performance analysis of different deep neural architectures deployed for automated metastases detection in whole slide images of lymph node sections and draws analogies based on the recorded results.
Comparative Study on Video Based Face Recognition Methods

Face recognition has received tremendous support in the discipline of computer, which is used for automatic attendance from CCTV (closed circuit television) cameras, monitoring criminal activity, and person tracking systems, etc. Tracking through video surveillances includes pre-processing, feature extraction, face detection and face recognition. Many problems are there when detecting faces during process of face recognition through surveillance camera like low quality camera, illumination problem, variation in face angle, occlusion problem, variation in facial expression, etc. some of these are still required more attention which is illumination and occlusion. When human faces are covered with some objects like face mask, sunglasses, beard, scarf and many other than face recognition accuracy have been drastically decreased. As many researchers have worked on tracking individuals from Surveillance camera, which found that still occlusion along with illumination is major problem when CCTV camera recognizes the face from the live feed as specially faces with the face mask. There are numerous algorithms and classifiers that can identify and comprehend faces of person from the front as well as with certain changes in an orientation of the face, but not many researches are utilized for unconstrained and occluded with illumination video-based face Recognition. So, this research review concentrates on various diverse algorithm for face recognition with occlusion and illumination from the surveillance camera and its parameter like execution time as well as recognition accuracy.
Summary and Conclusions

Gold deposits form when gold is precipitated from hot waters in the Earth’s crust in sufficient quantity and at adequate gold grade to be economic. Hydrothermal fluids, using this term to describe these hot waters, form around 10 km deep in the crust as the surrounding rocks are metamorphosed around 500oC and lose the H2O that is bonded in mineral structures. These hydrothermal fluids start their life with some dissolved gold, and then rise through the crust to be focused by faults and rocks that can fracture under the high fluid pressures. Ideal situations for fracturing of one rock type will be where rocks of different competence are juxtaposed; an example would be small competent igneous stocks within less-competent metasedimentary sequences. Larger gold deposits coincide with structural complexity and multiple orientations of fault sets each made up of multiple parallel faults. As the auriferous fluid rises to 3-5 km depth and cools to 300 – 400oC it reacts with its surrounding rocks and precipitates gold especially if there is chemical interaction of the fluid with Fe-rich or carbonaceous rocks. It is common for the gold to be deposited with quartz veins and pyrite (FeS2, also known as fool’s gold).
Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network

Medical image registration allows comparing images from different patients, modalities or time-points, but often suffers from missing correspondences due to pathologies and inter-patient variations.
Assisting Mentors in Selecting Newcomers’ Next Task in Software Product Lines: A Recommender System Approach

Onboarding (i.e., the process of incorporating new people) is relevant because it introduces employees to their role, the company’s culture, and what the company has to offer. Onboarding is then dependent on the company’s culture and practices. When it comes to software development, these practices include the methods, the tools or the developers’ organigram. Accordingly, there is not a one-size-fits-all onboarding, rather this procedure needs to be tuned for the practice at hand. This work tackles the specifics brought about by Software Product Line Engineering w.r.t. traditional software development, namely: larger code base, larger code variability, and larger and more heterogeneous teams. Specifically, this works advocates for feature-centric onboarding. Features (i.e., functional characteristics that are visible for a user) already play a key role throughout the SPL lifecycle. In this context, we advocate for defining the onboarding process as a journey where milestones are equated with features. Unfortunately, finding the most appropriate feature for a newcomer, if conducted manually by mentors, would be time-consuming, given the sheer number of features. To face this problem, we advocate for Recommender Systems based on the similarity between the feature’s codebase and the code previously explored by the newcomer. To this end, we resort to Topic Modeling, and specifically, Latent Dirichlet Allocation. We provide proof-of-concept through RecomMentor, a recommender system for pure-variants as the variability management system. RecomMentor is put to test against ranking metrics of the Information Retrieval literature. The first evaluation suggests that LDA could be an appropriate technique, paving the way towards using Recommender Systems in feature-based onboarding scenarios.
Association Rule Chains (ARC): A Novel Data Mining Technique for Profiling and Analysis of Terrorist Attacks

In data mining, association rule mining algorithms executing over massive datasets, generate vast amounts of rules. These rules pose a gruesome strain of knowledge post-processing on the user. This compels the user to dig through the rules to find relevant knowledge for decision making. For simplification of this practice, we propose the concept of association rule chains. Contrary to conventional association rules, the association rule chains map the values from dataset according to a user defined hierarchy of features, irrespective of the value constraints of interestingness measures. This mapping of values into associations rule chains relies on feature-wise hierarchical clustering of data items with the feature at the root of hierarchy termed as profile. The discovered association rule chains allow the user to assemble the domain relevant information from limited profile based subsets of the search space into a comprehensive feature specific knowledge base. In this paper, we demonstrate our approach by implementing it on a section of global terrorism database (GTD) of terrorist attacks for 4 regions. Our approach facilitates modeling of event profiles for specific classes of terrorist attack events by generating the relevant association rule chains for preemptive event analysis. Finally, we evaluate our approach against occurrences of terrorist attacks from the test dataset.
Data Analysis University Teaching Management Based on Association Rule Algorithm

With the development of social informatization, it has become an inevitable trend for university project management to use informatization tools to organize teaching decisions. Administrators can more efficiently arrange educational affairs for schools by analyzing teaching data. However, university data includes not only teacher management data but also student learning data. Faced with a huge amount of data and information, manual analysis methods will consume a lot of time and cost. And with the help of association rule algorithm, data centralized processing can be realized. This article mainly uses student learning performance information as a data set, and puts forward an analysis and application of data mining for teaching management (TM) in colleges and universities (CAU) around the basis of Apriori algorithm and algorithm ideas. In this application process, the aim is to use the Apriori algorithm to obtain some association rules (AR) in the student achievement database that can help us in teaching in CAU, so as to better arrange courses and class hours for students.
Application of 3D Human Pose Estimation for Behavioral Reproduction

Human pose estimation is a challenging problem in computer vision and shares all the difficulties of object detection. This task is particularly problematic when it comes to the estimation of the human pose in three dimensions. In fact, while the perception of an object in three dimensions is easy for humans due to the mastering of 3D mental model through years, this task is harder to replicate in computer vision. In this paper, we describe an approach that aims at estimating poses from video with the objective of reproducing the observed behaviors by a virtual avatar. We are motivated by two main objectives. First, we aim at the estimation of 3D poses in video. We use a fully convolutional model based on temporal (dilated) convolutions over 2D keypoints to achieve the estimation of these 3D poses. Secondly, we set the objective to transfer 3D coordinates for previously estimated poses to a virtual avatar. The idea behind this transfer is to reproduce observed behavior in a video by a virtual avatar. Our strategy of employing temporal convolutions in lifting 2D keypoints to 3D keypoints yields better results than previous methods that attempt similar reconstruction. Finally, we use an image-based position transfer to recreate the behaviors derived from the video on the skeleton of a virtual avatar. With our approach we create learning avatar that could be used in different applications.
On Twin Bounded Support Vector Machine with Pinball Loss

With the introduction of pinball loss for misclassification, we present a novel pinball loss-based twin bounded support vector machine (Pin-TBSVM) having regularization term, scatter and misclassification loss term to enhance noise robustness. Pin-TBSVM is a non-parallel classifier where two kernel generated surfaces are constructed by solving quadratic programming problems. Experimental results on fourteen benchmark datasets show that the proposed method achieves improved accuracy performance than the popular traditional methods considered for comparison clearly indicating the advantage of the proposed method.
The impact of digital tools on the education process in times of pandemic in Ecuador

In the context presented by COVID 19, given the closure of educational centers worldwide, the frequent and varied use of technological tools in the classroom has been evidenced, which requires an analysis of the impact of its application in the educational context. For this reason, it has been raised as an objective of this study to know the influence that technology has had on the educational process. To this end, an issue that generates great interest in the current educational scenario was addressed: digital tools applied within the current situation experienced in the pandemic; which is eminent because through its use you can continue with the teaching/learning process between teacher and student in difficult times such as those that society in general is currently experiencing. This in turn is quickly shaping what education will look like and how it will be delivered in the coming years. With the advanced pace of educational technology in the world of information and communication technologies, essential enablers have been detected. These currently help institutions to find opportunities and broader solutions in education and have demanded of teachers a rapid adaptation to digital work tools that were not usual in their daily work; that had to learn to work cooperatively by sharing knowledge about the use of tools, impressions, doubts, resources, among other elements within their daily work. The proposed methodology is positivist, with a quantitative approach and confirmatory type because it required a previous explanation or a series of assumptions or hypotheses. The sample was made up of 250 teachers from the Sierra educational system in Ecuador. The survey technique was applied and a questionnaire was applied as an instrument, with an overall score in the dimensions of perception on the impact of ICT in education. In this dimension they were asked about their perception of various aspects related to the teaching-learning process, by means of a five-point Likert scale and the use of inferential probabilistic statistics by the causal study of variables to determine the digital tools used in the educational field during the time of pandemic in Ecuador. As a result, it was obtained that the communication tools used were Microsoft Teams and gamification kahoot and quizziz. The hypothesis that allowed knowing about the use and use of information and communication technologies in virtual contexts was validated, which although they were forced due to the new reality that the pandemic has generated, caused a positive impact within the Ecuadorian educational process.
Features of the Development of the Export Potential of the Russian Higher Education System in the Digital Economy

The article, based on the materials of copyright research with the help of systemic, neo-institutional, historical, comparative analysis, identifies and critically explores the problems and specifics of the competitive advantages of the Russian higher school of Russia in the digital world, and also outlines the path of effective development of its export potential. It is concluded that it is necessary to look for opportunities and introduce ways of constructively combining the classical university strategy oriented to the interests of society and science and the principles of “academic capitalism”; it is necessary to develop within the framework of the Bologna model of the national form and content of the higher school system, which would reflect the unique achievements of the culture of our society. In this direction, it seems promising to expand e-learning in the form of short modular programs with an emphasis on practical skills, required in daily work on the basis of traditional universities. The driving forces, competitive advantages, and peculiarities of the development of this model are taking into account the Soviet experience of teaching foreign students, the fundamentality of training, the relatively low cost of services, the orientation to study in the areas of natural sciences and mass specialties of students from China, India, Central Asia and the Middle East, South America.
Feature-Aware Network Based on Upper Triangular Interaction for Single Shot Detector

Dong, Xiang Li, Feng Bai, Huihui Zhao, YaoConsidering the impact of the balance between real-time performance and detection accuracy, single-stage detectors have received more attention than two-stage detectors. However, due to a large number of detection objects with different sizes, the existing detectors still have limitations in terms of scale variations. To improve the situation, we propose a feature-aware network based on upper triangular interaction (UTINet), which introduces an upper triangular interaction module (UTIM) to focus on features of different scales and progressively interact with each other. Moreover, we build a feature-aware enhancement module (FAEM) to learn more comprehensive feature representations by amplifying the feature differences before and after fusion. Experiments are performed on MS COCO, and our UTINet with a $$300 \times 300$$ input achieves superior performance of 33.3% mAP compared with existing methods.
The Comparative Performance of Machine Learning Models for COVID-19 Sentiment Analysis

The COVID-19 outbreak is moving individuals globally. Monitoring social media and internet news is now vital to grasping this phenomenon and its impact. This study’s goal is to offer a method for capturing important concepts and themes addressed in the mainstream media and social networks and then apply it to the COVID-19 outbreak. This study compares articles and news, then visualizes the evolution and influence of the COVID-19 epidemic articles. The COVID-19 articles dataset from Kaggle is utilized for experiments. Various articles use the dataset to examine COVID-19 reviews. The studies use datasets and models such as Decision Tree (DT), Logistic Regression (LR), and Extra Tree Classifier (ETC), with F1 score, precision, and recall being evaluated. To improve accuracy, Term Frequency-Inverse Document Frequency (TF-IDF) is applied to the extracted keywords. The assembled model enhances this research, and the Logistic Regression (LR) model provides the highest accuracy at 90%.
Disciplines of AI: An Overview of Approaches and Techniques

This chapter provides an introduction to AI for people without a background in technology. After examining different definitions of AIDefinitions of AI and a discussion of the scope of the concept AI, five different disciplines of AI are discussed: Machine LearningMachine learning, Automated ReasoningAutomated reasoning, Computer VisionComputer vision, Affective ComputingAffective computing and Natural Language ProcessingNatural language processing. For each discipline of AI, approaches and techniques are discussed.