Reviewerly/exHarmony huggingface dataset:
Revamping of Telecom ICT in India

There has been a huge change in the entire chain of ecosystem including system, business processes, and functions related to handling of Unsolicited Commercial Communications (UCC). Thus, there is an immense need for re-engineering the architecture which is robust, non-repudiative, and confidential. A framework which handles huge scalable data and keeps it distributed as well as is secure on the agenda. Telecom Regulatory Authority of India (TRAI) is cause-driven and constantly moving forward in curbing UCC. With the growth of the technology and innovations, ways of spamming the consumer has also increased. It has become very crucial to detect and prevent the various ways of UCC through the new regulations of the Telecom Commercial Communications Customer Preference Regulations (TCCCP), 2018. This move came as a result of the increasing commercial calls even after all precautionary and regulatory measure taken by the Authority. Every Service Provider has to amend Codes of Practice for Unsolicited Commercial Communications Detection before allowing any commercial communication through its networks. TRAI also faces various set of concerns like making consumers choose their preference of UCC messages they wish to receive, escalation/resolution of complaints within stipulated time frame and communication to consumers regarding their complain status, etc. This paper studies the current system and illustrates a system to cater to the rising demands of an ICT fueled environment and provides solution to handle the challenges and support the government in relieving the consumers from hassle of UCC.
Procedural Generation of Levels for the Angry Birds Videogame Using Evolutionary Computation

This paper consisted in the generation of an evolutionary computation-based system capable of generate and evolve the structures of which one level of the Angry Birds game is composed, these structures are evaluated according to the stability of the structure as well as for the complexity of said structure. For this a level generation system was designed based on the data obtained from the game, said data consist in the number and type of pieces that appear and the applied gravity of the game, the individuals of the group are evaluated by a simulation of the generated level and then checking how the structures are affected by the gravity of the game. The evolutionary computation system as the main objective of generating structures based on the existent pieces of the game and evolving said pieces by combining them in a process that simulates the rules of the Open-Ended Evolution algorithm in which the evolution of this compounds is not inclined towards a numeric objective rather than to extend the diversity from which the pieces may be selected for a level.
Acceleration of data handling in neural networks by using cascade classification model

An introduction of the 5G network type will allow growth of intelligent devices, so more data will be downloaded in small areas and further transmitted. Fast processing will need efficient classifiers, where one of the best are neural networks. However, their biggest drawback remains in a very long training time in order to obtain a good level of effectiveness. This process is influenced not only by the structure, but also by the quality and amount of the data. In this paper, we discuss using the idea of cascade neural networks to create several smaller classifiers which can focus on particular classification tasks. Since the same data will be processed by several classifiers, it is important to correctly specify the weights which burden classes. The process of composition in discussed in our approach. Proposed modifications allow to create a more precise tool based on imporved neural network classifier. The proposed architecture has been described and tested on a public image database, the effects of which have been summarized and discussed.
A Hybrid Deep Physics Neural Network Model for Unsupervised Autoencoder to Perform Data Conditioning

Denoising, smoothing, missing data, and outlier issues that occur in many fields involve finding models for noise and data. However, the models often make assumptions of the noise, such as white noise, Gaussian noise, etc. Numerical methods [wavelets, Fourier transforms, Kalman filter, principal component analysis (PCA), etc.] are available for data conditioning. However, they suffer from inaccuracies. This paper presents a new approach, in which a hybrid physics-based model and deep physics neural network (DPNN) model are used to simultaneously build a data conditioning autoencoder (DPNNAE) based on DPNN for enhancing the signal strength, compared to a conventional autoencoder (AE). It necessitates integration of domain-specific physics-based models with a data-driven approach. A one-dimensional (1D) stochastic Burgers equation is used as a prototype for demonstrating the new algorithm but can be easily applied to other systems. This paper provides a new data conditioning approach that performs denoising, smoothing, data imputation, and outlier removal using DPNN and AE. This approach offers an improved and efficient methodology to arrive at the noise-free data and fills the missing data. During this work, the DPNN was applied to predict noise-free velocity in the unsteady viscous stochastic Burgers equation. The loss function is obtained from the physics models produced from domain insight and measured data. Thus, the deep neural network (DNN) framework integrates physics-based models into its framework. The hybrid DPNN model uses measured data from sensors; both measured data and physics are simultaneously satisfied by the model resulting in noise-free measured data. The conditioned data can be further used to build the DPNNAE model for further conditioning, and the DPNN can be used for filling the missing data. The DPNN method outperformed other common denoising algorithms. The new hybrid modeling data conditioning algorithm developed during this work can be applied to any real-time modeling system with real-time measurements for denoising.
A Self-regulating Spatio-Temporal Filter for Volumetric Video Point Clouds

The following work presents a self-regulating filter that is capable of performing accurate upsampling of dynamic point cloud data sequences captured using wide-baseline multi-view camera setups. This is achieved by using two-way temporal projection of edge-aware upsampled point clouds while imposing coherence and noise filtering via a windowed, self-regulating noise filter. We use a state of the art Spatio-Temporal Edge-Aware scene flow estimation to accurately model the motion of points across a sequence and then, leveraging the spatio-temporal inconsistency of unstructured noise, we perform a weighted Hausdorff distance-based noise filter over a given window. Our results demonstrate that this approach produces temporally coherent, upsampled point clouds while mitigating both additive and unstructured noise. In addition to filtering noise, the algorithm is able to greatly reduce intermittent loss of pertinent geometry. The system performs well in dynamic real world scenarios with both stationary and non-stationary cameras as well as synthetically rendered environments for baseline study.
Integral Transforms Induced by Heaviside Perceptrons

We investigate an integral transform with kernel induced by perceptrons with the Heaviside activation function. Representation theorems are given expressing sufficiently smooth functions as “infinite Heaviside perceptron networks.” The representation is exploited to obtain estimates of rates of approximation of these functions by networks with increasing numbers of units.
A Word Clustering-Based Crime Report Categorization Technique

This work introduces a text categorization scheme based on an unsupervised learning approach for partitioning groups of words derived from a crime vocabulary into different clusters. Each pair of crime terms considered from the vocabulary is taken as a boolean search-based query into a Web search engine, and the semantic similarity between each pair of words is measured by the co-occurrence matrix. A word co-occurrence graph has been formed depending on the pairwise similarity of the words. A similarity threshold is set based on the clustering coefficient of the words in the co-occurence graph, where words are considered as nodes in the graph. The clustering algorithm applied on the graph partitions the graph into several groups of words similar in context. Further, these words from the clusters are used for categorizing crime-related news reports. The crime reports are assigned to different crime types with a certain membership values. The results show the effectiveness of the proposed methodology.
An Overview on Stochastic Global Optimization and its Multi-Domain Applications

Optimization is of great interest and it has widespread applications in engineering and science. It has become a major technology contributor to the growth of industry. It is extensively used in solving a wide variety of problems in design, operation, and analysis of engineering and technological processes. Optimization of large-scale problems pose difficulties concerning to dimensionality, differentiability, multimodality and nonlinearity in objective functions and constraints. In order to overcome such difficulties, there has been a rapidly growing interest in advanced optimization algorithms. Stochastic and evolutionary optimization algorithms are increasingly used to solve challenging optimization problems. These algorithms include genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony algorithm, and cuckoo search algorithm. These algorithms are typically inspired by some phenomena from nature and they are robust. These algorithms do not require any gradient information and are even suitable to solve discrete optimization problems. These methods are extensively used to solve the optimization problems concerning to systems that are highly nonlinear, high dimensional, and noisy or for solving problems that are not easily solved by classical deterministic methods of optimization.
Large-Scale Gender/Age Prediction of Tumblr Users

Tumblr, as a leading content provider and social media, attracts 371 million monthly visits, 280 million blogs and 53.3 million daily posts. The popularity of Tumblr provides great opportunities for advertisers to promote their products through sponsored posts. However, it is a challenging task to target specific demographic groups for ads, since Tumblr does not require user information like gender and ages during their registration. Hence, to promote ad targeting, it is essential to predict user's demography using rich content such as posts, images and social connections. In this paper, we propose graph based and deep learning models for age and gender predictions, which take into account user activities and content features. For graph based models, we come up with two approaches, network embedding and label propagation, to generate connection features as well as directly infer user's demography. For deep learning models, we leverage convolutional neural network (CNN) and multilayer perceptron (MLP) to prediction users' age and gender. Experimental results on real Tumblr daily dataset, with hundreds of millions of active users and billions of following relations, demonstrate that our approaches significantly outperform the baseline model, by improving the accuracy relatively by 81% for age, and the AUC and accuracy by 5% for gender.
Global Disaster Research and Threat Detection System

The Global Disaster and Threat Detection system is a theoretical system established to study and alert before an actual natural calamity. The system uses artificial intelligence and modern IOT concepts to study the patterns that may emerge before an event from a pre-existing but modifiable set of parameters. Through this we can learn and unlock patterns from places that we may have never looked. This project is deployed in a global scope and requires time to be useful, over time the system can collect enough data to understand signs from our planet before an actual disaster, to effectively alert its inhabitants and be a helpful tool in reducing personal and collateral property loss. This would one day lead to a civilization capable of withstanding most of the global natural events and to create a safer and secure economy and ecosystem. The system is capable of producing expected results, and developing understanding to our knowledge on natural disasters and their true causes.
Data Hiding Based on Decagon-Shaped Shell

The technique that conceals secret message within a digital media file imperceptibly is known as data hiding and is generally applied in various secret communications between acknowledging sides. A good scheme of data hiding must provide good visual quality of the stego-image, high embedding capacity. Maximum existing methods have either low embedding capacity or low visual quality which leads to the distortion of the stego-image. In the proposed technique, a novel data concealing method based on decagon-shaped shells is discovered to get better visual quality of the stego-image with better embedding rate. In this paper, a secret digit is concealed into every pair of pixels of the original cover image with the help of a reference matrix based on decagon-shaped shells. The results of the experiments show that this technique of data embedding not only gives better flexibility in balancing the trade-off with embedding rate and visual quality of the stego-image, but also gives high data concealing rate.
Development prospects of the educational Milieu of universities in the conditions of academic revolution

The article analyzes the current situation in the educational milieu on the example of a flagship university, its place and role in the regional development and the necessary transformations that meet the concept of the knowledge-based economy in the wake of the "fourth industrial revolution". The massification and global knowledge-based economy, as components of the academic revolution, predetermine the need to abandon the outdated model of "educational pipe". The use of induction method in a synthesis of descriptive and abstract-analytical forms allows the authors to identify problems of the educational system development, highlighting the role of regional flagship universities as an object and subject of management capable of transforming and causing positive changes in the educational milieu.
Formulation of Aggregate Key in cloud by Cipher Text storage Reduction for data Outsourcing in healthcare

An extravagant receptiveness to healthcare commercial enterprises is the redistributing of health files to cloud. By the applicability of attribute based encryption dispositions in specification to access control many solutions have been envisaged in order to assuage those challenges. To phase out and also to procure ascendable and decisive secure solution, a distinct key criterion has been milked as multi-signer digital signature paradigms have been made the most of as multi-signer digital signature confrontations. It is entrenched to mollify the measurable attacks. In this paper, a strategic and distinct signature scheme designated as key aggregation scheme which concedes users to decrypt conglomerate classes of file using single key that can be communed betwixt the users in demoniacally fabricated group. The consorted model encompasses aggregate key generation for accessing the files systemized in a pecking order, hashing of key to upturn the security of keys, ring signature has been inculcated to dynamically fabricated group for data admittance using cryptographic proposals. The reserved files will be in the encrypted form which the user can have ingress to capacitate the hoarded files.
We Have Discovered the Dark Matter

The main purpose of this article is to prove the existence of the dark matter based on the scientific discoveries as well as the authors’ own theory. The authors theorize that dark matter could make up as much as 90% of the mass in the Universe.
A Study on Solution Oriented Smart Factory Diagnostic System for SME

This paper is a case study result of developing a system for the diagnosis of smart factories suitable for the situation of Korean SMEs, and it is characterized by the composition of the diagnostic items centered on smart factor element technology and the generation of design reports based on the diagnosis results. The diagnostic item of this system is composed of the minimum items centered on the element technology and the representative functions of each solution. Therefore, the diagnostic time is usually less than 4 hours. The diagnostic report consists of the level of work area and construction rate by element technologies, and the report includes the road map for each step. As a result of applying this system to the actual site, the satisfaction of the person who performed the diagnosis and the company responding to the diagnosis showed relatively high satisfaction compared to the existing diagnosis method.
Modeling human attention by learning from large amount of emotional images

Recent resurgence of neural networks in computer vision have resulted in tremendous improvements in saliency prediction, eventually, saturating some saliency metrics. This leads researchers to devise higher-level concepts in images in order to match the key image regions attended to by human observers. In this paper, we propose a saliency model which utilizes the top-down attention mechanism through the involvement of emotion-inducing region information in the predictor's feature space. The proposed framework is inspired by psychological and neurological studies that emotion attracts attention. Using three publicly available datasets with emotion-rich images, we were able to show that awareness of the emotion-inducing region improves saliency prediction of images. Saliency metrics for probabilistic models, particularly information gain and KL-divergence, have improved with respect to the same architecture without emotion information. Statistical tests show that emotional regions generally have higher improvement than neutral regions corroborating psychological studies that emotion attracts attention.
English Numerals Recognition System Using Novel Curve Coding

The paper aims at redressing the effect of structural and format variations in offline recognition of handwritten English numeral samples. In printed form, some of the English numerals have only curvature parts (0, 3, 6, 8, 9) and some are with linear parts only (1, 4, 7), whereas the rest ones have hybrid structures (2, 5) containing both curvature and linear parts. It is essential to fine-tune the measurement of curvature to help better recognition of individual members. The current paper extracts the features to encode a curve in novel ways based on staircase approximation. Two digits ‘4’ and ‘7’ are written usually in two different formats. There is no reported work in the literature hitherto which treats these two different formats of the same numeral separately. These format variations have been addressed by means of the proposed classifier ‘Multiple Hidden Markov Model (MHMM).’ MHMM classifier produces the next close output class in addition to main output class. Support vector machine is used in binary mode to choose a more appropriate class out of these two classes. The overall recognition accuracy of 99.51% has been achieved on MNIST database and 98.8% on CENPARMI database which are competitive to the rates of other reported recognition systems trained and tested on these databases.
Malicious URL Linkage Analysis and Common Pattern Discovery

Malicious domain names are consistently changing. It is challenging to keep blacklists of malicious domain names up-to-date because of the time lag between its creation and detection. Even if a website is clean itself, it does not necessarily mean that it won't be used as a pivot point to redirect users to malicious destinations. To address this issue, this paper demonstrates how to use linkage analysis and open-source threat intelligence to visualize the relationship of malicious domain names whilst verifying their categories, i.e., drive-by download, unwanted software etc. Featured by a graph-based model that could present the inter-connectivity of malicious domain names in a dynamic fashion, the proposed approach proved to be helpful for revealing the group patterns of different kinds of malicious domain names. When applied to analyze a blacklisted set of URLs in a real enterprise network, it showed better effectiveness than traditional methods and yielded a clearer view of the common patterns in the data.
FEATURES OF USING CLOUD TECHNOLOGIES IN EDUCATION

The article investigates cloud technologies and features of their use in educational institutions. The essence of cloud technologies in education is determined. It is noted that the modern institution of higher education provides usage of information and communication technologies, and in particular, cloud technologies. The main characteristics (features) which characterize cloud services are described here. It is noted that usage of cloud services has benefits and problems as well. There are such mandatory characteristics of cloud computing as self-service on demand, universal access to the network, resource pooling, elasticity, consumption accounting. Four models of deployment of cloud technologies are revealed in the article. They are corporate, public, group and hybrid. It is admitted that the most appropriate model of deployment of cloud technologies in the infrastructure of higher education institutions is hybrid. Cloud technologies support such activities as: communication, collaboration and co-operation, which determines certain areas of their usage. The architecture of cloud technologies is presented in three levels: software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS). The most popular cloud services which are used in various spheres, including educational institutions, are analyzed here. Office 365, Microsoft Planner, Microsoft SkyDrive, Google Drive, Google Talk, Google Docs, these are the common cloud services which are selected in SaaS. Popular software platforms which are presented in the PaaS segment are Windows Azure, Google App Engine, Cloud9 IDE, Ideone IDE. Each of the cloud platforms that is described in the article has its own peculiarities. Possible areas of application of cloud technologies in the educational process are identified. They are free access to the programs and services used in the educational process, planning activities, implementation of the working regime of teachers and employees, the organization and conduct of scientific online conferences, seminars, forums, round tables, trainings, joint project work of students, individual independent work of students, electronic interaction with entrants.
Effort estimation of open source Android projects via transaction analysis

Abstract Transactions have been used in many software sizing methods to measure software functional size and provide a basis for effort estimation. To further investigate the effects that transactions have on software functional size and more accurately estimate project effort using the transactional information, in this paper, we propose a method to identify transactions, as well as their complexity attributes that are influential to software functional size, directly from the source code of Android projects using static and dynamic analyses. The identified transactions are classified into different complexity levels and applied with different weights based on their evaluated complexity levels to distinguish their effects on software functional size. A size metric called sum of weighted transactions (SWT) is proposed to measure software functional size. We calibrate an effort estimation model using SWT based on a data set of 34 open source Android projects, and show the effectiveness of using SWT as a software functional size measure to estimate project effort.