Reviewerly/exHarmony huggingface dataset:
The Detection of SQL Injection on Blockchain-Based Database

In today's world, the storage of data needs a huge amount of space. Meanwhile, cloud and distributed environments provide sufficient storage space for the data. One of the challenging tasks is the privacy prevention of storage data. To overcome the problem of privacy, the blockchain-based database is used to store the data. There are various attacks like denial of service attacks (DoS) and insider attacks that are performed by the adversary to compromise the security of the system. In this chapter, the authors discussed a blockchain-based database, where data are encrypted and stored. The Web API is used as an interface for the storage and sharing of data. Here, they are mainly focused on the SQL injection attack, which is performed by the adversary on Web API. To cope with this problem, they present the case study based on the Snort and Moloch for automated detection of SQL attack, network analysis, and testing of the system.
From a Scrum Reference Ontology to the Integration of Applications for Data-Driven Software Development

Organizations often use different applications to support the Scrum process, including project management tools, source repository and quality assessment tools. These applications store useful data for decision-making. However, data items often remain spread in different applications, each of which adopt different data and behavioral models, posing a barrier for integrated data usage. As a consequence, data-driven decisions in agile development are uncommon, missing valuable opportunities for informed decision making. Considering the need to address semantic issues to properly integrate applications that support the agile development process, we aim to provide a common and comprehensive conceptualization about Scrum in the software development context and apply this conceptualization to support application integration. We have developed the Scrum Reference Ontology (SRO) and used it to semantically integrate Azure DevOps and Clockify. SRO served as a reference model to build software artifacts in a semantic integration architecture that enables applications to automatically share, exchange and combine data and services. The integrated solution was used in the software development unit of a Brazilian government agency. Results demonstrate that the integrated solution contributed to improving estimates, provided data that helped allocate teams, manage team productivity and project performance, and enabled to identify and fix problems in the Scrum process execution. SRO can serve as an interlingua for application integration in the context of Scrum-process support. By capturing the conceptualization underlying Scrum, the reference ontology can address semantic conflicts and thereby support the development of integrated data-driven solutions for decision making.
Improving knowledge distillation using unified ensembles of specialized teachers

The increasing complexity of deep learning models led to the development of Knowledge Distillation (KD) approaches that enable us to transfer the knowledge between a very large network, called teacher and a smaller and faster one, called student. However, as recent evidence suggests, using powerful teachers often negatively impacts the effectiveness of the distillation process. In this paper, the reasons behind this apparent limitation are studied and an approach that transfers the knowledge to smaller models more efficiently is proposed. To this end, multiple highly specialized teachers are employed, each one for a small set of skills, overcoming the aforementioned limitation, while also achieving high distillation efficiency by diversifying the ensemble. At the same time, the employed ensemble is formulated in a unified structure, making it possible to simultaneously train multiple models. The effectiveness of the proposed method is demonstrated using three different image datasets, leading to improved distillation performance, even when compared with powerful state-of-the-art ensemble-based distillation methods.
Induced homomorphism: Kirchhoff’s law in photonics

Abstract When solving, modeling or reasoning about complex problems, it is usually convenient to use the knowledge of a parallel physical system for representing it. This is the case of lumped-circuit abstraction, which can be used for representing mechanical and acoustic systems, thermal and heat-diffusion problems and in general partial differential equations. Integrated photonic platforms hold the prospective to perform signal processing and analog computing inherently, by mapping into hardware specific operations which relies on the wave-nature of their signals, without trusting on logic gates and digital states like electronics. Here, we argue that in absence of a straightforward parallelism a homomorphism can be induced. We introduce a photonic platform capable of mimicking Kirchhoff’s law in photonics and used as node of a finite difference mesh for solving partial differential equation using monochromatic light in the telecommunication wavelength. Our approach experimentally demonstrates an arbitrary set of boundary conditions, generating a one-shot discrete solution of a Laplace partial differential equation, with an accuracy above 95% with respect to commercial solvers. Our photonic engine can provide a route to achieve chip-scale, fast (10 s of ps), and integrable reprogrammable accelerators for the next generation hybrid high-performance computing. Summary A photonic integrated platform which can mimic Kirchhoff’s law in photonics is used for approximately solve partial differential equations noniteratively using light, with high throughput and low-energy levels.
Acute-Lymphoblastic Leukemia Detection Through Deep Transfer Learning Approach of Neural Network

Manual examination of blood smears under microscope is observer subjective, time consuming, and labor intensive. A computerized system would be best choice for quantitative and qualitative inspection of blood smear images. This paper presents classification of white blood cells into healthy and unhealthy using SVM machine learning model. Features from images are extracted with transfer learning approach of deep convolutional neural network using Alex-net pretrained model. This approach validates the process of discriminating white blood cells into healthy and acute lymphoblastic leukemia affected unhealthy cells with 96.15% of accuracy and outperforms the existing methods.
Robust 3-D Gaze Estimation via Data Optimization and Saliency Aggregation for Mobile Eye-Tracking Systems

In order to precisely predict 3-D gaze points, calibration is needed for each subject prior to first use the mobile gaze tracking system. However, traditional calibration methods normally expect the user to stare at predefined targets in the scene, which is troublesome and time-consuming. In this study, we proposed a novel method to remove the explicit user calibration and achieve robust 3-D gaze estimation in the room-scale area. Our proposed framework treats salient regions in the scene as possible 3-D locations of gaze points. To improve the efficiency of predicting 3-D gaze from visual saliency, the bag-of-word algorithm is adopted for eliminating redundant scene image data based on their similarities. After the elimination, saliency maps are generated from those scene images, and the geometrical relationship among the scene and eye cameras is obtained through aggregating 3-D salient targets with eye visual directions. Finally, we calculate the 3-D point of regard (PoR) by utilizing 3-D structures of the scene. The experimental results indicate that our method enhances the reliability of saliency maps and achieves promising performances on 3-D gaze estimation with different subjects.
SOMEnv: An R package for mining environmental monitoring datasets by Self-Organizing Map and k-means algorithms with a graphical user interface

The Self-Organizing Map (SOM) algorithm belongs to the family of artificial neural networks. It is an unsupervised method that requires no a priori knowledge regarding experimental data classification. Further, it can deal with large datasets and non-linear problems, providing powerful visualization features for outcome exploration on 2D maps. For environmental pollution assessments other unsupervised techniques are widely used, such as principal component and hierarchical cluster analyses, but their application for mining large datasets and properly visualizing the results is limited, making them difficult to use for handling of large datasets obtained by high frequency environmental monitoring. This study presents an R package (SOMEnv) that allows non-expert users to elaborate by SOM algorithm environmental variables (pollutants and/or chemical physical properties) recorded with high frequency for a long monitoring period. Additionally, SOMEnv can also be used for elaborating small datasets derived from uneven sampling. All the calculations and outcome visualizations can be done using a graphical user interface (GUI), meaning that experience in R software coding is not necessary, and only a basic knowledge regarding the employed algorithm is needed to interpret the results. The benefits of the SOMEnv package are that (i) both the software environment and tool are freely available; (ii) it is able to handle large datasets; (iii) it provides heuristic rules for SOM initialization; (iv) it has a built-in GUI for performing calculations and visualizing the results. Moreover, it comes with a wide range of visualizations, several of which are dedicated to high frequency data monitoring. An example of application is presented. The package is freely available on the Comprehensive R Archive Network (CRAN) repository.
Label Leakage from Gradients in Distributed Machine Learning

Empowered by the high connectivity of manifold devices in today's world, distributed machine learning enables multiple, distributed users to build a joint model by sharing their gradients over a network. In this paper, we highlight the privacy risk of sharing gradients by proposing LLG, an algorithm to disclose the labels of the users' training data from their shared gradients. We conduct an empirical analysis on two datasets to demonstrate the validity of our algorithm. Results show that our approach effectively extracts the labels with high accuracy in different scenarios.
Cybersecurity Issues in AI

AI security is vital; however, it should be designed carefully by considering the suitable assurance processes based on system requirements. Data should be secure throughout the process of an AI system. The use case3 of self-driving cars is briefly considered.
Fast and Compact Image Segmentation Using Instance Stixels

State-of-the-art stixel methods fuse dense stereo disparity and semantic class information, e.g., from a Convolutional Neural Network (CNN), into a compact representation of driveable space, obstacles and background. However, they do not explicitly differentiate instances within the same semantic class. We investigate several ways to augment single-frame stixels with instance information, which can be extracted by a CNN from the RGB image input. As a result, our novel Instance Stixels method efficiently computes stixels that account for boundaries of individual objects, and represents instances as grouped stixels that express connectivity. Experiments on the Cityscapes dataset demonstrate that including instance information into the stixel computation itself, rather than as a post-processing step, increases the segmentation performance (i.e., Intersection over Union and Average Precision). This holds especially for overlapping objects of the same class. Furthermore, we show the superiority of our approach in terms of segmentation performance and computational efficiency compared to combining the separate outputs of Semantic Stixels and a state-of-the-art pixel-level CNN. We achieve processing throughput of 28 frames per second on average for 8 pixel wide stixels on images from the Cityscapes dataset at 1792 × 784 pixels. Our Instance Stixels software is made freely available for non-commercial research purposes.
A Modular and Unified Framework for Detecting and Localizing Video Anomalies

Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos. Extensive performance evaluations on benchmark datasets show that the proposed framework significantly outperforms the current state-of-the-art approaches.
Alluvial-Placer Gold of Northwestern Salair: Composition, Types, and Mineral Microinclusions

Abstract —On the territory of the Egor’evsk district, 3500 results of probe microanalyses of surface gold particles taken from 17 placers, weathering crusts, and orebodies were processed and summarized. High-fineness and mercury-containing gold is predominantly found within mother lodes and placers, and medium- and low-fineness gold is distributed in subordinate and dramatically subordinate quantities, especially in the placers. A unique feature of the gold composition, rarely occurring in other districts, is the constant and commonly simultaneous presence of mercury and copper impurities. Analysis of Ag, Hg, and Cu content variations has enabled us to identify five main grades of gold. The mother lodes of the predominant gold grades are metasomatites with beresite and listwaenite compositions, which are developed primarily after lower Cambrian volcanoterrigenous-carbonate rocks and ore-bearing mafic dikes. Mercury-containing gold is characteristic of beresites, but copper-bearing gold is typical of listwaenites. The relationship between corresponding grades of surface and ore gold is confirmed by the presence of microinclusions in the gold grains. Nonconformity between the content of gold from mother loads, weathering crusts, and placers is explained by the losses of Hg and Ag impurities by endogenous gold under subsurface hypergene conditions. Identification of mineral-geochemical properties of surface gold is of exceptional practical importance in ore-grade gold mineralization prediction.
Digital Marketing Analytics: Building Brand Awareness and Loyalty in UAE

The rapid changes in today's modern business due to new trends, innovative and advanced technology in addition to digitalization make companies to go digital. The marketers faces challenges to look for new ways to reach their customers. For the modern marketers digital marketing is the most important function in the business that helps them in effective customer engagement and build brand awareness and customer loyalty. The positive impact of brand awareness of brand through digital marketing can lead to word of mouth about the brand by the customers. The purpose of the study is to understand the importance of digital marketing and its relationship between brand awareness and brand loyalty. The research helps to identify different digital marketing channels and its influence on building brand awareness and loyalty among the customers. To understand the influence of digital marketing in building brand awareness and loyalty, a quantitative approach was used for the study. The data was collected through primary sources using questionnaires. Using Google forms the questionnaire made and send to different online users in the UAE and snowball sampling method was used to collect the responses. A total of 80 responses were collected for the study. The results showed that the respondents are high internet users who actively uses different digital platforms. The online users were able to gain better brand awareness through the internet and it influenced their level of perception towards the brands.
Large-scale Sparse Structural Node Representation

In the BigData era, large graph datasets are becoming increasingly popular due to their capability to integrate and interconnect large sources of data in many fields, e.g., social media, biology, communication networks, etc. Graph representation learning is a flexible tool that automatically extracts features from a graph node. These features can be directly used for machine learning tasks. Graph representation learning approaches producing features preserving the structural information of the graphs are still an open problem, especially in the context of largescale graphs. In this paper, we propose a new fast and scalable structural representation learning approach called SparseStruct. Our approach uses a sparse internal representation for each node, and we formally proved its ability to preserve structural information. Thanks to a light-weight algorithm where each iteration costs only linear time in the number of the edges, SparseStruct is able to easily process large graphs. In addition, it provides improvements in comparison with state of the art in terms of prediction and classification accuracy by also providing strong robustness to noise data.
Theoretical and methodological bases of designing the educational institution information and consulting environment

Abstract In article the definition of the educational institution information and consulting environment has been formulated. It was found the such environment is a relevant part of the information education society, within which it becomes possible exchange of information and consultative support of subjects of the educational process via the computer focused technologies. The semantic and functional content of an educational institution information and consulting environment have been specified. Its social, spatial-object, psychological and pedagogical components have been analyzed. The criteria for assessing the quality of the information and consulting environment have been described. The main factors and leading pedagogical technologies of organizing and forming of the information and consulting environment have been determined. The pattern of the educational institution information and consulting environment covering organizational, technical methodical resource areas have been established. Based on the results of empirical research with use of content analysis and factor analysis the benefits and risks of the educational institution information and consulting environment have been reviewed and summarized. It is concluded that the organization of the educational institution information and consulting environment will promote increase the efficiency of the educational process and the quality of educational services.
Accelerating the Configuration Tuning of Big Data Analytics with Similarity-aware Multitask Bayesian Optimization

One of the key challenges for data analytics deployment is configuration tuning. The existing approaches for configuration tuning are expensive and overlook the dynamic characteristics of the analytics environment (i.e. frequent changes in workload due to receiving evolving input sizes or change in the underlying cluster environment). Such workload/environment changes can cause significant performance degradation, with retuning the configuration to accommodate those changes can yield up to 85% potential execution time saving. We propose SimTune, an approach that accommodates such changes through efficient configuration tuning. SimTune combines workload characterization and Multitask Bayesian optimization to identify similarity across workloads and accelerate finding near-optimal configurations. Our experimental results show that SimTune reduces the search time for finding close-to-optimal configurations by 56-73% (at the median) when compared to existing state-of-the-art techniques. This means that the amortization of the tuning cost happens significantly faster, enabling practical tuning in the rapidly changing environment of distributed analytics.
Linear canonical Wigner distribution of noisy LFM signals via variance-SNR based inequalities system analysis

In consideration of the latest second-order moment variance type of signal-to-noise ratio (SNR), the so-called variance-SNR, a new inequality model, namely, the variance-SNR inequality between the closed-form instantaneous cross-correlation function type of Wigner distribution and the classical Wigner distribution, is introduced to characterize the detection performance improvement triggered by linear canonical transform (LCT) free parameters embedded. Combining the newly established variance-SNR inequality model with the previous output SNR inequality model gives a stronger inequalities system. Solving it with respect to noisy one-component linear frequency-modulated signals yields more accurate strategies on determination of parameters. Numerical simulations also verify the correctness and rationality of the results of the theoretical deduction, implying that there is a better noise suppression effect by using the newly derived parameters selection strategies.
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples. Inspired by the success of geodesic distance approximators such as ISOMAP, we propose to use a minimum spanning tree (MST), a graph-based algorithm, to approximate the local neighborhood structure and generate structure-preserving distances among data points. We use this MST-based distance metric to replace the euclidean distance metric in the embedding function of autoencoders and develop a new graph regularized autoencoder, which outperforms a wide range of alternative methods over 20 benchmark anomaly detection datasets. We further incorporate the MST regularizer into two generative adversarial networks and find that using the MST regularizer improves the performance of anomaly detection substantially for both generative adversarial networks. We also test our MST regularized autoencoder on two datasets in a clustering application and witness its superior performance as well.