Reviewerly/exHarmony huggingface dataset:
Delayed Adversarial Training with Non-Sequential Adversarial Epochs

Adversarial Training (AT) has so far been considered as the most effective approach for building robust computer vision architectures that can adequately handle adversarial attacks. However, its high complexity, rising from the need to repeatedly generate attacks against image batches over training epochs, has been a controlling factor to its generalized use. Therefore, to date, AT has only been used for training on vanilla datasets, casting doubt on how soon it can be applied to real world computer vision systems in safety critical domains. Few researchers have to a certain extent addressed the issue by proposing AT variations which have moderately improved the complexity. Extending these advances, this work explores simple yet effective ways to further improve the complexity of AT in terms of training time. Specifically, it is explored through this work whether a) training sequential adversarial epochs and b) attacking the entire dataset during adversarial epochs are necessary for a robust learned model.
Education in Theoretical Interpretations of Scientific Disciplines: Subject Construction Practices

The relevance of the research is determined, on the one hand, by the fact that education, being the object of multidisciplinary research, is comprehended through the prism of subject practices of its theoretical construction, on the other hand, such subject determinism determines the specifics and nature of subject design practices within the «educationally oriented» scientific disciplines, sets the logic of relevant research programs and suggests that the construction of subject disciplinary theorizations of the phenomenon of education has a common conceptual logic. Objective: to identify the specifics and key theoretical constructs of the subject design practices formed in the «educationally oriented» scientific disciplines by understanding the multidisciplinary theoretical interpretations of education.
How to protect your practice from cybercriminals

There has been a recent surge in cyberattacks against veterinary practices, which has left many reeling in losses and sending wake‐up calls to others in the industry that their practices might be next. This article explains the different forms of cyberattacks that are aimed at veterinary practices, and gives some useful cybersecurity tips to follow to prevent them.
Overview of Basic Azure Security Components

Azure is a leading cloud service platform that provides many services to implement modern cloud solutions. With the identification of growth of security vulnerabilities due to the development of technology and the wide adoption of cloud services, introduction of several security mechanisms in Azure is essential. You will be able to explore a few of the security components available with Azure in this chapter.
DeepGRASS: Graph, Sequence and Scaled Embeddings on large scale transactions data

Representation learning has redefined large scale data mining applications. The high dimensional embeddings learn complex associations that transcend the human cognitive understanding and have achieved great success in different business applications that encounter the curse of dimensionality, including fin-tech. Different algorithms learn embeddings that capture different types of associations, and it would be useful to learn embeddings that holistically learn multi-dimensional associations. In this paper, we propose DeepGRASS – an algorithm that embeds financial transactions using graph and sequence-based topologies. Our results show that these embeddings learn associations that are very comprehensive, holistic, and multi-dimensional.We deploy DeepGRASS in PayPal, and train it on multitude of transaction data with multi-dimensional features. The algorithm is two-fold: it embeds a bipartite graph with customer and merchant nodes and parallelly learns sequential associations using historical transactions along with other transactional features. These embeddings are then scaled and combined to learn multidimensional associations. We tested this on different predictive applications and find that the learning is generic and shows benchmarking performance in different predictive contexts. Based on offline metrics, back-tests, and sensitivity analysis on offline transaction data, we find very strong evidence to suggest that these embeddings provide the highest AUC score in predictive applications, highest co-efficient of determination in explaining variance and the features explain different types of associations. To our knowledge, this is the first application of embeddings that learn both graph and sequence-based associations on large scale financial transaction data and paves the way for a new generation of feature engineering in fin-tech.
Development of A Real-Time Emotion Recognition System using Facial Expressions

The face depicts the identity of the individual and draws attention towards their psychological state. The face shows a wide range of expressions in reaction to various instincts and their surrounding environment. In this paper, we determine the algorithm used to recognize the facial expression of a person. Four different types of expressions are fed to the system. Machine Learning and Deep Learning-based models are trained with datasets to unveil human expressions. The experimental result from the network is successful as it matches with the virtual learning of the student. The sample is trained and tested, through which the machine builds a predictive model. This model achieved an accuracy of 99%.
Android Remote Unlocking Service using Synthetic Password: A Hardware Security-preserving Approach

Remote unlocking for Android devices may benefit both users and manufacturers. Users can continue using the device without factory-resetting when they unexpectedly forget their passphrases. Manufacturers can improve non-face-to-face customer services in the COVID-19 era. Nevertheless, not many manufacturers support remote unlocking services for Android devices. If the remote unlocking service is triggered by requests over-the-air, it may increase the attack surface of Android security. Android security is hardware-based (e.g., hardware-backed Keystore), so we seek to preserve this security level by designing a new remote unlocking service without modifying trusted execution environments. Our design supports two-factor authentication, distributed authority, trust-boundary minimization, and key management. Since a synthetic password used for remote unlocking is not exposed to the outside of an Android device, the manufacturer still cannot unlock the device without user consent. We identify 208 security threats in the proposed remote unlocking service using the STRIDE model and ensure that our design has countermeasures for all high-level security threats. After passing quality verification and penetration tests, the proposed remote unlocking service has been officially installed on commercial devices.
Baseball Rotation Identification Algorithm Based on Seam Curvature Matching and Pixel Similarity

In this paper, an automatic algorithm for baseball rotation analysis. To analyze the rotation angle of a baseball, the seam feature and its curvature play very important roles. Therefore, after performing background removal and denoising, we first adopt ridge detection to extract the seam on the baseball. Then, the curvature is calculated. According to the pixel-wise curvature information, one can determine the ration angle based on template sifting and matching. With the proposed algorithm, the rotation angle of the baseball can be determined accurately. It is helpful for determining the rotation rate of the baseball and beneficial for automatic sport video analysis.
Research on Construction and Application of Tourism Sequential Eventic Graph

In order to obtain travel advice and route information more intuitively, efficiently and intelligently, it's necessary to establish a more intelligent information retrieval way. By defining events in the tourism field, this paper constructs the vocabulary of sequential cue phrases, completes the explicit sequential relationship recognition based on pattern matching, and stores the explicit sequential relationship in Neo4j graph database in the form of triples to establish an Eventic Graph. Based on constructed Eventic Graph, the tourism sequential Eventic Graph application system has been designed and established. With the layered B/S structure design, the system is based on Flask framework, and the query function of the tourism route based on city and planning days is realized. Using D3.js to visually display the Eventic Graph by the graphical method. The experimental results show that the accuracy rate of explicit sequential relationship extraction based on pattern matching reaches 82.84%. The visual query function based on Eventic Graph can help people to retrieve tourist information more efficiently and intuitively, and provide references for users to make decision.
Insights into Image Compression Techniques: Tradeoff between Image Quality and Compression Ratio

Images play a vital role as far as multimedia communication is considered. With the increased use of the image for communication, it becomes crucial and convenient to reduce its size so that less bandwidth can be used in times of the communication channel. The basic idea of image compression is to get rid of all the redundant and repetitive amount of data from the image such that the crucial, non-redundant information can be kept handy which will on the other hand reduce the storage space, bandwidth of transmission and also time to transmit. With the virtue of various transform techniques, the high quality of the image is also not lost when reconstructed and critical information is being extracted. These compression techniques play a vital role. Now there are considerable amounts of compression techniques in the market one needs to efficiently examine, to what extent it is affecting the quality of the image. There are many objective quality measures today, but they alone cannot suffice the need for adequate measure for quality. In this paper comparative analysis of image compression is finished with the aid of four transform methods. The paper proceeds by the introduction to various quality measures viz. objective and subjective measures and deciding upon the quality of the compressed as well as the original image.
Safety Supervision and Management of Construction Site Based on Modern Information Technology

The traditional construction safety supervision mode has the problems of low-management efficiency, inaccurate information, and poor real-time performance. To do a good job of prevention in advance, this paper analyzes the current situation of construction safety management, and takes the construction safety monitoring system as the goal, and uses BIM. The technology improves the implementation path of construction safety information supervision process, and puts forward the real-time collection method and analysis method of unsafe behavior and psychological state data of workers who are changed into the construction site under the working state, so as to monitor the safety behavior of workers on site in real time, fully start the safety production enthusiasm of the construction personnel, effectively reduce the incidence of construction accidents, and form the safety management suitable for the construction industry theory, to achieve a more comprehensive, real-time, effective monitoring process of the construction site.
Data Mining Techniques to Build A Recommender System

Recommender systems are not a new topic but they are still current due to their broad applications and the impact that they can have in sales or revenues. There are different approaches to build a system that makes recommendations, the selection of a method depends on several factors. For example, the available data to extract knowledge and the technical resources of the company. In this article, we focus on the use of data mining techniques to build a recommender system. Then, we describe in more detail two methods: communities finding and market basket analysis. Both methods are easy to implement and they are efficient. We present our methodology to implement them as well as the advantages and disadvantages that we found in them. We used a publicly available dataset to test both methods offline. We were interested in evaluating both methods without a ground truth reference because the model selection is one of the challenges when building a new system. For the communities finding method, we used fitness functions that allowed us to compare and select a set of communities. For the market basket analysis, we tested different values for the parameters that can be controlled: support, confidence, and the number of items in transactions. With both methods, we obtained easy to interpret results that can be used to implement a recommender system. The tested methods are suggested when there is only historical data about transactions. When more information is available, there are more robust methods that can be implemented. For example, content-based methods.
Implementing Cognitive Procurement and its Influence on Supply Chain During the Era of Digital Transformation in Oil and Gas Industry 4.0

Abstract The digital transformation is key for every oil and gas organization to implement its fourth industrial revolution (Industry 4.0). Digital transformation in procurement uses artificial intelligence, big data, market intelligence, etc. to create self-learning systems; for the procurement of goods and services. This is also known as cognitive procurement or procurement 4.0. The objective of this paper is to examine the methods of implementing cognitive procurement, the impact it creates in the procurement life cycle, and in the supply chain. The paper also provides insight into three governance models of human-machine interaction that is implemented in stages based on procurement requirement and technology adoption maturity of the organization.Human-in-the-loop modelHuman on-the-loop modelHuman out-of-loop model The data for this study is obtained by preparing a list of relevant questions from multiple sources. An online survey questionnaire is sent to the procurement professionals in different organizations in the oil and gas industry. The questions reveal procurement expert's responses for:Potential challenges for the digital transformation in procurement and supply chain.Methods of enabling technologies to leverage procurement procedures and processes. The results and the observations of the study are used to measure the benefits of implementing cognitive procurement in the oil and gas industry 4.0. The new transformed roles of procurement for industry 4.0 are also discussed in the conclusion, which includes:Daily activitiesProcurement decision-making activitiesStrategic SourcingProcurement to drive organizational profitability, effectiveness and efficiency The paper will benefit oil and gas organizations that are planning to implement cognitive procurement as part of the digital transformation. The paper will also benefit any parties interested in understanding more about cognitive procurement.
A kernel-free support vector machine with Q-margin

Support vector machine (SVM) is one of the most well-known machine learning algorithms highly adept at handling both linear and non-linear binary classification problems. Linear separation margin deals easily with linearly separable data but is unfortunate to handle non-linear data. SVM with various kernel functions has great success in non-linear binary classification tasks, which map data into a higher plane to perform the classification. Unfortunately, it has been proved difficult and cumbersome to explicitly define and test these kernels, making them computationally expensive. Recently, some quadratic separation SVM models were introduced which uses quadratic surfaces for non-linear binary separations. In this study, a kernel-free quadratic SVM is proposed by the integration of a novel Q-margin for binary classification. The properties and the existence of a unique solution of the Q-margin have been discussed. Furthermore, the performance analyses on nine public data sets are done. The numerical results demonstrate the effectiveness and significance of the proposed model. It is seen that the proposed model performs 2-3% better on average when compared with well-known SVM models with or without kernels and other state-of-the-art classifiers.
Ecological and philosophical aspects of the pre-professional and professional training of agro-industrial complex specialists

Abstract Annotation. The article dedicated to substantiation necessity for serious ecologically oriented pre-professional professional training for future specialists agro-industrial complex of the country based on a holistic and systematic approaches. The vital importance of preservation natural wealth is attributable to the global environmental crisis closely related to the crisis of the spirituality of humanity. The purpose of our study was in the identification the degree of dominance of such a universal human value as “nature” using the express methods “Dominant”. The processing of the results showed that the overwhelming majority of respondents do not see and do not understand the importance of nature in ensuring the life and health of each person and humanity as a whole. This confirms the authors’ idea of the need for reviewing the curriculum and programs with the aim of strengthening their environmental component for the formation a high level of environmental culture of future specialists in the agro-industrial complex, because in their professional activities they are directly related to the use of the natural resources of our country.
Depolarization’s Dynamic: Exponential and q-Exponential Decay

Light polarization is an important property that has being extensively used for quantum communication purposes. However, light polarization is also fragile and during propagation in a noisy channel the light becomes partially or even completely unpolarized, making impossible the realization of quantum protocols without polarization control. In this direction, the present work studies the dynamic of the depolarization of some noisy channels found in the literature identifying when the polarization vanishes exponentially and q- exponentially.
Vehicle Tracking and Trajectory Estimation for Detection of Traffic Road Violation

Intelligent transport systems (ITS) are part of our daily lives and are the future of modes of transport. They allow solutions to certain problems such as improving road safety and solving the problem of traffic congestion. This paper presents an effective vehicle tracking and trajectory estimation system to prevent road and highway accident. The system is composed of (i) moving-object detection, (ii) filtering of vehicle candidates, (iii) vehicle tracking, and (iv) trajectory estimation and infraction detection. First, all objects in movement are detected in the first frames. Then, the detected moving objects will be filtered by a set of appropriate processing in order to leave only the objects likely to be vehicles. Kalman filter considered as a good motion predictor will be used in vehicle tracking and trajectory estimation in order to detect offending vehicles. The tests carried out on several roads and highway scene have given very satisfactory results on detecting and tracking vehicle and detecting of continuous line crossing that can be exploited for accident prevention. In addition, Kalman filter gave good results regarding the cars tracking and provides a reliable region for eliminating the interference of shadows and decreasing the false detection rate.
CATAQ: Concise Answer to any Question

In this modern age of information, the answer to any question is readily available on the internet. The chronology of the solution to open-domain question-answering capabilities is primitive manual question mining, followed by search engine revolution. Later, intelligent assistants became a robust method, which is being used for a long time. However, it has been noticed that the intelligent assistant responds to simple questions but not to the complicated ones that demand comprehension of the question's abstract. This paper introduces an innovative, dynamic technique called CATAQ, i.e., Concise Answer to Any Question that responds to complicated questions by providing accurate answers. Furthermore, CATAQ introduces a solution for dynamic information retrieval, which involves the selection of the most appropriate webpage links, based on the link parsing the content of the webpage irrespective of its structure, pre-processing the content, extracting the pertinent information with semantic search and summarizing by prioritizing question's keywords. It is observed that CAT AQ outperformed the present intelligent assistants while answering complex questions which include anunusual and large variety of topics and keeping the performance consistent while answering simple questions. Concluding, CATAQ can answer any question concisely, assuming the related information is available on the internet.
Expression Recognition Based on Attention Mechanism and Length Feature of Facial Landmark

In view of the fact that the proportion of face in the whole image is small and the face posture is skew, which leads to the low accuracy of facial expression recognition, this paper proposes a two-branch neural network based on attention mechanism and length feature of facial landmark. The first branch is based on the modified VGG19 and the integration of the convolutional block attention module (CBAM), which focuses on the extraction of important features of expression. The second branch extracts 68 feature points from the face image, and calculates the Euler distance between these feature points to acquire the length information of 2278 dimensions, and is finally normalized and fed into the full connection layer. The network composed of the first branch and the second branch is trained by joint fine-tuning, where L2 regularization is added to prevent overfitting. The proposed method is tested and verified on Jaffe, CK + and FER +, and the highest accuracy is 93.48%,96.12% and 84.73% respectively. The performance of the proposed method is more advance than many existing algorithms, which shows that the proposed model has good accuracy and robustness.
Dynamic Image Super-resolution Network Based on Semantic Prior

In recent years, due to the vigorous development of deep learning, the super-resolution of a single image has made great progress, but there are still challenges in texture restoration. To recover natural textures, we build a dynamic convolutional network to model the diverse mapping relations. Faced with different low-resolution images, it can reconstruct high-resolution images through different mapping relationships. The parameters of dynamic layers are adaptively generated according to the semantic prior described by deep features of the input. In the convolution neural network, the deep features can often extract the regions related to the semantics of the image. according to different regions, our model can change the transmission parameters in the super-resolution network, to recover different texture details. Finally, the experimental results show that, compared with the most advanced SFT -GAN method, this method has better performance in texture restoration, especially for images of complex scenes.