Reviewerly/exHarmony huggingface dataset:
Algorithms Towards the Automated Customer Inquiry Classification

Classification is an important field of research due to the increase of unstructured text, especially in form of customer inquiries. The problem has two phases such as generally identifying a customer inquiry and automatically assigning an order of a product requested by customer with predefined categories based on its characteristics. These two phases can be accomplished by using various techniques at each interior steps. Choosing the proper technique can affect the efficiency of the text classification performance by saving a time and physical effort. The aim of this paper is to present a classification model that supports efficiency while working with Russian texts, since it is known that machine learning algorithms proved to be working well with English texts. After performing most challenging task which was the preprocessing of unstructured text by stemming, parsing and indexing, the following some logical sequence of steps and analysis with compatible combination of the embedded techniques gives us a chance for comparing algorithms and their behavior on different type of normalized and Unnormalized data. The experimental results over 33000 dataset have been performed using bi-grams, TF-IDF scores along with their parsed frequencies and shows that SVM and Naive Bayesian algorithms outperform others for normalized data. Moreover, optimization using stochastic gradient descent was applied along with neural network, and the results were compared with the traditional machine learning algorithm. The results have proven the capability of the proposed model’s performance can be improved by identifying outliers and their patterns.
Adaptive Robust Watermarking Algorithm Based on Image Texture

Digital watermarking is a key technology to solve copyright protection and content authentication. Most existing watermarking algorithms are based on global embedding, which cannot well balance the imperceptibility and robustness of watermarking. This paper proposes an adaptive robust watermarking algorithm based on image texture, which mainly includes: (1) color image is converted from RGB space to Lab space and the stable scale invariant feature transform(SIFT) points are extracted based on L component as the embedding position of watermark; (2)the structured forest edge is extracted using machine learning as the watermark image which is decomposed by using lifting wavelet transform (LWT) and then encrypted using logical chaos transform; (3)in consideration of human visual system, the strength factor is adaptively selected by using the brightness information and texture complexity of the L component. Experimental results show that the proposed algorithm in Lab space has the better visual invisibility and robustness to resist various attacks, especially for cropping, noise and JPEG compression attacks in comparison with other related algorithms.
An automated classifier and counter for microbiological objects

Many scientific works rely on analysis of images of microbiological objects. it can be object count, object classification, shape and color analysis etc. Such analysis al- ways is time consuming and there is also a window for errors. Threrefore this process was from its beggining a target for numerous attempts of automation. Most successfull tries are based on different segmentation methods. All mentioned approaches are using deterministic calculations. Aim of this work is to bring another kind of image segmentation to this task - neural networks. To successfully implement to image segmentation a set of examples is required. Size of such set should be at least no less than several hundreds of images. So for this purpose photographs of irradiated yeast were chosen mainly because it is easy to segment them manually with enough accuracy. A secondary task was to make an attempt to distinguish life cell from the dead ones together with segmentation. All calculation are done using Google Colaboratory as a computational background.
Influencing factors of employees’ information systems security police compliance: An empirical research in China

It is widely agreed that information systems security police compliance plays a pivotal role in safeguarding organizational information security. This study empirically investigated organizational and individual factors in predicting employees’ ISSP compliance. With a survey data of 525 civil servants in China, results showed that organizational information security training and information security climate were significantly related to employees’ ISSP compliance. Specifically, information security climate had stronger effect on ISSP compliance than information security training. Furthermore, it was found that employees’ perceived severity, perceived vulnerability and response efficacy were positively related to employees’ ISSP compliance. We discussed the key implications of our findings for managers and researchers.
An Effective Semantic Security Metric for Industrial Cyber-Physical Systems

The emergence of Industrial Cyber-Physical Systems (ICPS) in today's business world is still steadily progressing to new dimensions. Although they bring many new advantages to business processes and enable automation and a wider range of service capability, they also propose a variety of new challenges. One major challenge, which is introduced by such System-of-Systems (SoS), lies in the security aspect. As security may not have had that significant role in traditional embedded system engineering, a generic way to measure the level of security within an ICPS would provide a significant benefit for system engineers and involved stakeholders. Even though many security metrics and frameworks exist, most of them insufficiently consider an SoS context and the challenges of such environments. Therefore, we aim to define a security metric for ICPS, which measures the level of security during the system design, tests, and integration as well as at runtime. For this, we try to focus on a semantic point of view, which on one hand has not been considered in security metric definitions yet, and on the other hand allows us to handle the complexity of SoS architectures. Furthermore, our approach allows combining the critical characteristics of an ICPS, like uncertainty, required reliability, multi-criticality and safety aspects.
Wholeness Egg II

This paper introduces different applications of the 'Wholeness Egg' method in everyday life. It was originally invented by Iba as a way of designing workshops, based on Christopher Alexander's idea of wholeness and centers. Since we presented a paper 'Wholeness Egg- Methodology of Designing a 'Living' Workshop by Differentiating a Whole' in the EuroPLoP Conference 2018, its usage has been extended and getting utilized by people in everyday life situations. In this paper, we present the overview of the Wholeness Egg approach and then introduce two ways of application; life designing and reflection on experience. Showing some example cases, this paper concludes that this method supports people to conduct a design of everyday life, based on Alexander's idea of beauty in design, without asking them to know such a background theory. We believe that this technique can provide a good practical assistance to enhance the quality of our lives.
Feature Selection Techniques in High Dimensional Data With Machine Learning and Deep Learning

High-dimensional data inspection is one of the major disputes for researchers plus engineers in domains of deep learning (DL), machine learning (ML), as well as data mining. Feature selection (FS) endows with proficient manner to determine these difficulties through eradicating unrelated and outdated data, which be capable of reducing calculation time, progress learns precision, and smooth the progress of an enhanced understanding of the learning representation or information. To eradicate an inappropriate feature, an FS standard was essential, which can determine the significance of every feature in the company of the output class/labels. Filter schemes employ variable status procedure as the standard criterion for variable collection by means of ordering. Ranking schemes utilized since their straightforwardness and high-quality accomplishment are detailed for handy appliances. The goal of this chapter is to produce complete information on FS approaches, its applications, and future research directions.
Separating real-world photos from computer graphics: comparative study of classification algorithms

The growing quality of computer graphics makes it difficult to classify real photos from rendered images. At the same time, a high-quality classifier that separates real-world photos from the images created by computer graphics allow solving many applied problems: from automatic evaluation of the quality of the image to detecting manually modified photos. The main directions of research in this area are associated with the identification of distinctive features in the images for use in classification models. In this paper, we provide experimental studies of various classification models and groups of features on the image dataset from Vkontakte social network. We show that the best choice from a variety of features for solving the classification problem is the combination of Haralick features, Bag of Visual Words (BoVW) with Speeded-Up Robust Features (SURF) descriptors and Local Binary Pattern (LBP) histograms. In addition, we show that Gradient Boosting model being trained on the specified combination of feature groups outperform other machine learning models with 90% accuracy.
Are There More Finite Rings than Finite Groups?

We compare the number of finite groups of order n with the number of finite rings of order n, with some surprising results.
Stochastic Compositional Gradient Descent Under Compositional Constraints

This work studies constrained stochastic optimization problems where the objective and constraint functions are convex and expressed as compositions of stochastic functions. The problem arises in the context of fair classification, fair regression, and the design of queuing systems. Of particular interest is the large-scale setting where an oracle provides the stochastic gradients of the constituent functions, and the goal is to solve the problem with a minimal number of calls to the oracle. Owing to the compositional form, the stochastic gradients provided by the oracle do not yield unbiased estimates of the objective or constraint gradients. Instead, we construct approximate gradients by tracking the inner function evaluations, resulting in a quasi-gradient saddle point algorithm. We prove that the proposed algorithm is guaranteed to find the optimal and feasible solution almost surely. We further establish that the proposed algorithm requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(1/\epsilon ^{4})$</tex-math></inline-formula> data samples in order to obtain an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -approximate optimal point while also ensuring zero constraint violation. The result matches the sample complexity of the stochastic compositional gradient descent method for unconstrained problems and improves upon the best-known sample complexity results for the constrained settings. The efficacy of the proposed algorithm is tested on both fair classification and fair regression problems. The numerical results show that the proposed algorithm outperforms the state-of-the-art algorithms in terms of the convergence rate.
Digital Transformations in Forensic Science and Their Impact on Policing

Forensic technologies are now more easily transportable, fast and useable in the field by non-specialists. They help detect, collect and analyse a large volume of new and diverse traces generated by criminal activities. These changes, in order of magnitude, induce rapid digital transformations of forensic processes, requiring a radical shift in roles and tasks for traditional forensic and police structures. Examples are presented to help characterise these developments that are subject to many tensions. The new complex situation prompts interrogations about the suitability of a law enforcement paradigm of policing that mainly define the scope of forensic science as ancillary to the criminal justice system. The current context offers instead many opportunities to express the value of forensic thinking in proactive policing. Traceology, as the science of traces, is proposed as the overarching approach to pave the way for balanced, regulated and efficient approaches to intelligence and policing.
A Multi-metric Selection Strategy for Evolutionary Symbolic Regression

Evaluation metrics play an important role in accessing the performance of a regression method. In practice, these multiple evaluation metrics can be used in two ways. The first way defines a loss function by aggregating multiple metrics, while the second way defines a multiobjective loss function by considering each metric as an objective function. In this paper, we propose a new way to use multiple evaluation metrics, which is different from the aggregating method and the mutliobjective method. Our method is based on genetic programming. The idea is to randomly use one metric in each iteration of the selection operator. Therefore, multiple metrics can be used alternatively in the running process. To validate the effectiveness of our new approach, we conduct experiments on ten benchmark datasets. The experimental results show that the new approach can improve the population diversity, and can achieve the performance better than or similar to that of the traditional symbolic regression algorithms.
SQLID Framework In Order ToPerceive SQL Injection Attack on Web Application

The opportunity to admittance the web anytime is great help to the technical field. Web is more common, attacks on web also growing. SQL injection is a risk to servers like military and banking systems. SQL injection deals with to retrieve or insert information from database.
A Comprehensive Hand Gesture Dataset

Currently, a bunch of research is going in the field of human computer interaction, such as hand gesture recognition, estimation, and hand tracking, one of the important and popular research directions. However existing hand gesture datasets have their limitation such as shortage of number of data, lack of various background and enough volunteers. In order to deal with these problems, this paper proposes a novel and comprehensive dataset of hand gesture. This dataset consists of two parts, Dataset1 and Dataset2. Both datasets include various backgrounds and abundant involved persons. In this paper, a detailed comparison of existing popular datasets is listed. Dataset1, has ten thousand images in total for images classification and Dataset2 contains seven thousand in total for hand tracking. Details about how to build these datasets are represented. Both of datasets are shared online for usage in imaged-based hand gesture recognition research.
Task Scheduling-Energy Efficient in Cloud Computing

Cloud Computing is a computing model that provides computing resources as distributed on-demand services over a network. The data centers are on the rise and are projected to increase and lead to more energy consumption with the grown use of this technology. In fact, in recent years, the scientific community has tackled the issue of reducing data center power consumption. Our solution, which is essentially focused on task scheduling strategy, is critical to the overall success of cloud workflow energy systems by proposing a new model based on a deep neural network for regression. It aims to reduce energy consumption, to select the best placement with the minimum energy consumption allocated to the VM, and that by supporting the TOPSIS method.
The Effectiveness of Augmented Reality-Based Board Game as Learning Media in Comprehending Family Functions Concept

This study aims at observing the effectiveness of augmented reality-based learning media in facilitating students in understanding family functions concept. This study used a quantitative approach with one group of pretest-posttest. The respondents were 115 students majoring at Non-Formal Education (PLS) who were chosen using a quota sampling technique. The data collected in the form of scores, which were then analyzed using descriptive and inferential statistical analysis. The study results showed that augmented realitybased board game as a learning media is very effective in increasing students' mastery level of the family functions concept. The finding is confirmed by students' scores showing the pre-test score of 61.82 compared to the post-test results that are 77.63, with significance (sig.) as high as 0.001 <; 0.05. The difference is described by the calculation of the T-test where t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">count</sub> 3.486 > t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">table</sub> 1.658. Thus, Augmented Reality-Based Game Board can be an innovative learning media that helps students understand the material.
Towards Realistic Membership Inferences: The Case of Survey Data

We consider the problem of membership inference attacks on aggregate survey data through the use of several real-world datasets and a published study as a model for the survey. We apply membership inference attacks from the literature, and discover that methodological assumptions of existing attacks produce a misleading picture of the risk. When using a more realistic methodology, experiments reveal a more nuanced picture of the risk: membership inferences do succeed, but only a small subset of individuals are highly vulnerable to them. In fact, if the adversary wishes to avoid a high false positive rate, she should perform membership inferences only when she has a reason to believe that the target participated in the survey. However, our results do not imply that publishing survey data is inherently safe. Indeed, when applying membership inference not to individuals but to hospitals, we find that highly accurate inferences are possible.
Motion planning for mobile robots using uncertain estimations about the environment

The motion planning for mobile robots is a challenging task even if the agent has to reach the target position in a dense, dynamic environment. In this paper, our goal is to develop a motion planning algorithm using the changing uncertainties of the sensor-based data of the obstacles. The collision-free motion must be ensured by the algorithm using a cost function optimization method. As an assumption, some of the data of the obstacles (e.g. positions of the static obstacles) are already known at the beginning of the planning, and the other information (e.g. velocity vectors of moving obstacles) must be measured using the sensors. The algorithm is tested in simulations.
Instance-Based Learning for Human Action Recognition

Along with the exponential growth of online video creation platforms such as Tik Tok and Instagram, state of the art research involving quick and effective action/gesture recognition applications remains crucial. This work addresses the challenge of classifying such short video clips, using a domain-specific feature design approach, capable of performing significantly well using little training data. The method is based on Gunner Farneback dense optical flow (GF-OF) estimation strategy, Gaussian mixture models, and information divergence. We first aim to obtain accurate 3D representations of the human movements/actions through clustering the results given by GF-OF using K-means method of vector quantization. We then proceed by representing the result of one instance of each action by a Gaussian mixture model. Furthermore, using Kullback-Leibler divergence (KL-divergence), we attempt to find similarities between the trained actions and the ones in the test videos. Classification is done by matching each testing video to the trained action with the highest similarity (lowest KL-divergence). We have performed experiments on the KTH and Weizmann Human Action datasets, and the results reveal the discriminative nature of our proposed methodology in comparison with other state of the art techniques.
Effect of Security Controls on Patching Window: A Causal Inference based Approach

In many organisations there are up to 15 security controls that help defenders accurately identify and prioritise information security risks. Due to the lack of clarity into the effectiveness and capabilities of these defences, and poor visibility to overall risk posture has led to a crisis of prioritisation. Lately, organisations rely on scenario based red teaming exercises which test the contribution of a security control to the security preparedness of the organisation, and testing the resilience of a control. However, these assessments don’t quantify the effect of controls on the security policies already in place. Measuring this effect can help stakeholders to re-calibrate and effectively prioritise their risks.
Exploring Group Sparsity using Dynamic Sparse Training

Group-level pruning is a model compression method that can accelerate models on general hardware while maintaining the accuracy of models even at high sparsity. Existing group-level pruning studies have a limitation in that pruning is performed by evaluating the importance of the weight group based on a single global threshold, and thus the accuracy loss is significant. In this paper, we propose Grouped DST, which applies Dynamic Sparse Training to group-level pruning to learn the criteria for each filter to determine the importance of weight groups. We validate our technique at ResNet-20 on CIFAR-10. The model compressed with Grouped DST achieve 3.77% a lower accuracy loss compared to that pruned with a global pruning threshold at 95% of sparsity.
Memory‐augmented cognitive radar for obstacle avoidance using nearest steering vector search

This study describes a cognitive radar architecture with application to real-time obstacle avoidance in mobile robotic platforms. The concept of a world memory map is introduced as a means of providing an enhanced perception of the environment around the robotic platform. This is combined with a specially designed obstacle avoidance algorithm, Nearest Steering Vector Searching, all capable of operating in real-time. The study analytically derives the radar signal processing algorithm, starting from range-angle maps, so that a collision free course to a set destination point can be robustly navigated. Finally, the performance of this cognitive approach is examined through a number of proof-of-concept experiments using a commercial off-the-shelf radar mounted on a mobile ground robotic platform.
Federated Learning Algorithms: Towards Next Generation Communication Systems

We provide a survey of four different categories of Federated Learning algorithms and their limitations as these were unveiled through experiments using commonly accepted data sets. The level of data heterogeneity forms a potential benchmark to compare Federated Averaging, Gradient Descent, Evolutionary, and Differential Privacy methods and, among other criteria, identifies the gaps that need to be addressed from future approaches.
Wi-Fi based smart wireless flipchart

Abstract Notice Board plays a vital role in many places like educational institutes, railway stations, school’s colleges and everywhere. Presently a person will stick a notice on notice board in offices, schools and colleges. This causes a lot of time to stick the information on notice board. So reduce the time to build a new device as wireless flip chart by using this flip chart we reduced the time, manual power, paper and printer ink. The main aim of this paper is to display the information on LCD screen from the user’s mobile device through internet.