Reviewerly/exHarmony huggingface dataset:
Cost Optimization For Intelligent Enterprises Using Modified GWO

Today digital revolution is playing a vital role in many intelligent enterprises and hence there is a need to manage the applications efficiently with optimal cost and a reduced end to end processing time. In this paper, a novel algorithm “grey wolf optimization for the shortest path (GWOSP)” is devised using the concept of grey wolf optimization. Here the algorithm dynamically determines the short path of a given graph to reduce the overall cost and time. Later a performance evaluation is carried out between GWOSP and Dijkstra's algorithm based on execution times. It is realized that the performance of GWOSP algorithm is better when compared to Dijkstra's algorithm. The proposed methodology can be vastly adapted in cost and risk optimization and it aids towards addressing few challenges of intelligent enterprises.
Types of Keyloggers Technologies – Survey

Keyloggers are rootkit malware that record the keystrokes of the victim's system and log it into the attacker's system. It can be used to capture sensitive data like passwords, PINs, usernames, some kind of confidential messages shared between two entities etc. We would be explaining different types of keyloggers and their working. We have also explained the different applications and measures needed to avoid keylogging activities on your system.
POSTER: Protecting Location Privacy on Road Networks

Our recent proposed notion Geo-Graph-Indistinguishability (GeoGI) is a theoretically rigorous location privacy notion which extends differential privacy to the setting of location privacy on a road network modeled by a graph. GeoGI is practically more useful for protecting location privacy in location-based services (LBSs) such as UBER than a previously proposed Geo-Indistinguishability (Geo-I), which is a popular notion of location privacy defined on the Euclidean plane. We also proposed a Graph-Exponential Mechanism (GEM) based on GeoGI. In this paper, we analyze the utility and privacy of GEM. This analysis makes following two contributions to build a user-friendly location perturbation system. First, our proposed system makes a privacy parameter easy to interpret. The privacy parameter of GEM is not interpretable for ordinary people. By converting to the interpretable value, a user can choose his/her preferred privacy level and utility. Second, we formalize an optimization problem to find the optimal range of GEM according to an arbitrary LBS w.r.t the utility-privacy trade-off for a certain user. Experimental results with real-world data show that our optimization improves the utility-privacy trade-off.
An Improved ViBe Algorithm for Quickly Suppressing Ghosts

ViBe algorithm is a general background difference algorithm for video sequences. Compared with other algorithms, the ViBe algorithm has low calculation amount and excellent real-time performance. However, there are some problems with the ViBe algorithm, such as the ghost area, shadows of objects, and sudden changes in the scene. The ViBe algorithm solely relies on the first frame to initialize the background model, which leads to the appearance of ghosts. If the first frame of the video contains moving objects, ghosts will be introduced, which will affect the detection accuracy. So as to solve the ghost problem, this paper proposes a improved ViBe algorithm to speed up ghost removal by locating the ghost area and re-initializing the area. The experimental results prove that the improved algorithm can quickly eliminate ghosts and greatly increase detection accuracy.
Breast Malignant Detection using Deep Learning Model

Malignant growth is the maximum hazardous maladies in the world, it principally viable for women. The main objective is essential to fix the malignancy through rational examination and the subsequent fundamental objective ought to be the early location of the disease because the early discovery of disease can be useful for eject the disease ended. This paper proposes a deep learning-based scheme for analyzed bosom disease on breast cancer datasets. The paper adopts deep learning techniques for decision bosom disease by utilizing breast cancer data. During the experiment, it tends to resolve the dataset imbalance, skewness, and insufficient data of the dataset. And also, the hyper-parameters to train the model is discussed to achieve the optimal accuracy. In addition to that, the proposed model is compared with existing machine learning models including Logistic regression, K-Nearest Neighbors, and decision tree algorithms for the performance of system measurement including accuracy score, precision score, recall score, and f1-score.
Sentiment Analysis and Classification on Twitter Spam Account Dataset

The amount of people using social media is very large and is increasing day by day. The impact of public figures in social media is quite big. Fake accounts are created in social media platforms and are used for various purposes like inflating the follower list of a particular account. These accounts also called spam accounts usually post spam messages which are used for marketing certain products or spreading particular agendas. Such accounts can be dangerous as they may alter a normal user's perspective on certain topics. These accounts are used to modify and help in creating a fake sense of popularity which can influence political and social situations. In this project, we try to examine some of the existing methods and approaches for fake Twitter accounts detection. We will make use of a public dataset which contains tweets and account information of both Legitimate accounts as well as spam accounts. We make use of account information to create a classifier which can easily classify whether the given account is a fake account or a legitimate account. We also apply sentiment analysis algorithms on the tweet to find patterns among them. We try to analyse the sentiments behind the tweets of different accounts. Comparing our model with the existing model we will improve the features present in our model. In the process of building a better model, we try to reduce overfitting. The final result is an optimum classifier, which can be used to separate a fake account from a list of legitimate accounts.
GAN Based Photo-Realistic Image Generation from Sketch using Nested U-Net

In computer vision generative Image modelling is a vast area of research that many studies have been carried out to address such problems as an image to image translation. In this study, we mainly discuss how we can bridge the gap between the industrial designer and their production workflow to reduce the cost of the time they spend on prototyping. We demonstrate an image synthesizing technique to generate a photo-realistic image of a real-world object from a sketch. We integrate a new network architecture to the existing network to improve the system in generating photo realistic images. Compared to the existing systems our system can generate images with more accuracy and more photo-realism.
An Abstractive Summarization Method Based on Global Gated Dual Encoder

The sequence-to-sequence model based on the RNN attention mechanism has been well applied in abstractive summarization, but the existing models generally cannot capture long-term information because of the defects of RNN. So an abstractive text summarization method is proposed in this paper, which is based on global gated double encoding (GDE). Combined with Transformer to extract global semantics, a global gating unit based on dual encoder is designed that can filter the key information to prevent the redundant information, and the problem of insufficient semantics is compensated dynamically. Many experiments on the LCSTS Chinese and CNN/Daily Mail English datasets show that our model is superior to the current advanced generative methods.
Recognition of Human Activities in a Controlled Environment using CNN

Human-centered computing is an emerging field of research aiming at understanding human activity and integrating users with computer systems and their social context. Human activity recognition (HAR) can be done using sensors, smartphones or datasets for images/videos. Designing a method in order to understand physical activities such as standing, downstairs, jogging, walking, running, and sitting is very crucial. Convolution neural network (CNN) is used to classify the aforementioned activities with the help of dataset and machine learning. The dataset is used as an input to directly train CNN without any specific pre-treatment. Human activity recognition requires the experimental data so the initial step is to analyze the data after that suitable method is applied to acquire the position of independence. The similarity in activity recognition process was calculated and lastly the study was implemented by compiling experimental data and the effect of different methods. The proposed solution has achieved a high accuracy on identification with low computational costs.
Transparent Blockchain-Based Voting System: Guide to Massive Deployments

With the development of society and its people democratic consciousness, voting as a crucial canal of democracy has to satisfy high expectations of modern society, new technologies and techniques must be deployed for better voting experience, and to replace traditional cost inefficient and laborious traditional paper voting. Blockchain technology is currently disrupting every industry where security and data integrity are prioritized. In this paper, we leveraged this technology to propose a design and implementation of a blockchain-based electronic voting system for large scale election. The novelty in this paper when compared to other state of the art blockchain-based voting systems is that it respects voter's privacy with a full transparency for auditing and user-friendly terminals, which will boost the confidence of people in the voting system and therefor increase the number of participants in the election.
Performance Prediction by an SVM with a Firefly Optimization Method

Since software system is becoming more and more complex than before, performance degradation and even abrupt download, which are called software aging phenomena, bring about a great deal of economic loss. To counter these problems, some methods are used. Support vector machine is an effective method to tackle software aging problems, but its performance is influenced by the selection of hyper-parameters. A method is proposed to optimize the hyper-parameter selection of support vector machine in this work. The proposed method which is used as a training algorithm to optimize the parameter selection of support vector machine, utilizes the global exploration power of firefly method to achieve faster convergence and also a better accuracy. In the experiment, we use two metrics to test the effect of the proposed method. The results indicate that the presented method owns the highest accuracy in both the available memory prediction and heap memory prediction of Web server for software aging predictions.
Blockchain Technologies for IoT Applications: Use-cases and Limitations

The Internet of Things is set to bring value to a broad range of networked applications. However, its high heterogeneity, scale, and connectivity will also carry challenging security risks. The advent of blockchains encompassing promising function and security features has paved the way for the development of several tailored solutions. This paper presents a survey on IoT-focused blockchain technologies targeting multiple industrial applications. A critical discussion regarding their limitations is also explored. Complementarily, key blockchain concepts, and their technical mapping are examined for completeness.
A Survey On Evaluation of Similarity Measures for Content-based Image Retrieval Using Hybrid Features

With the invention in digital technology, has the lead rise in tremendous growth in a large volume of digital image databases. So searching and retrieving the user's interest image from large data repositories has become a challenging task. From the last couple of years, Content Based Image Retrieval (CBIR) had attained ample attention from researchers as it gives a solution to the above problem. CBIR system is such that it retrieves the user required results of images from the vast collection of the database by extracting image features like color, texture, shape. These features give the detailed information about the image.
Tightly‐secure two‐pass authenticated key exchange protocol using twin Diffie–Hellman problem

Tight security is an important requirement of practical cryptographic schemes. Compared with loosely-secure schemes, tightly-secure schemes allow shorter security parameters hence are more efficient. In CRYPTO 2018, Gjøsteen and Jager proposed a tightly-secure authenticated key exchange (AKE) protocol. They used ‘commitment trick’ to construct a tight security reduction for their protocol. However, this technique leads to a three-pass execution in their protocol, and their protocol cannot achieve key confirmation unless it is modified to have a four-pass execution. In this study, the authors propose a tightly-secure two-pass AKE protocol. They use the twin Diffie–Hellman problem and the ‘re-patch’ trick of random oracles to construct a tight security reduction for their protocol. This technique allows their protocol to have a two-pass execution. Their protocol provides several security properties such as key-compromise-impersonation security, unknown-key-share security, and weak perfect forward secrecy. Moreover, a three-pass variant of their protocol provides key confirmation.
A study of various classification techniques used for very high-resolution remote sensing [VHRRS] images

Image classification the most predominant applications in wide range of domains like image processing, computer vision etc. This paper analyses various image classification algorithm based on various machine learning technology and also most commonly used accuracy assessment metrics has been addressed. A wide range of methods increases its accuracy in image classification have attained higher range. Even though still enhancement is still undergoing to attain newer accuracy scorer. This paper provides a study of various image classification algorithms supported in machine learning technology. This study also gives brief information of essential accuracy testing metrics required for image classification.
Twitter Sentiment Analysis: Case Study on the Revision of the Indonesia’s Corruption Eradication Commission (KPK) Law 2019

Pros and cons of the Revision of the Indonesia's Corruption Eradication Commission (Komisi Pemberantasan Korupsi or KPK) Law 2019 encourage a wave of demonstrations throughout September 2019. In addition, some Indonesian people also expressed their opinions regarding the Revision of KPK Law 2019 through social media Twitter. This research was conducted to analyze public opinion sentiments towards the Revision of KPK Law 2019 and is expected to be an input for the government to obtain public views, as well as a reference in the decision-making process as needed. Sentiment analysis is performed using Support Vector Machine (SVM), Decision Tree, and Naive Bayes algorithm. The result shows the SVM algorithm has the highest performance and accuracy at 81.70%, followed by Naive Bayes at 80.90%, and the Decision Tree at 74.55%. Based on the results of testing in this study, it was found that more than 75% of Indonesian people who voiced their opinions through Twitter had negative sentiments towards the Revision of KPK Law 2019. The negative sentiments are caused by people who consider this regulation to be ratified in a hurry, while people who have positive sentiments assume that the Revision of KPK Law 2019 can encourage the performance of the KPK to be more objective.
Membership Encoding for Deep Learning

Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires massive labeled data and advanced learning algorithms. The resulting models are considered as intellectual property of the model owners and their copyright should be protected. Also, MLaaS needs to be trusted not to embed secret information about the training data into the model, such that it could be later retrieved when the model is deployed.
LAIDR: A Robotics Research Platform for Entertainment Applications

In this paper, a Lighter Than Air Indoor Robot (LAIDR) is developed for entertainment applications. An Adaptive Propulsion Mechanism (APM) is proposed to actuate the LAIDR using the minimal number of Propulsion Units (PUs) and facilitate the variable in-mission PU configurations within the same airframe. Usually, the helium-inflated vehicle poses instability due to body inertia and indoor disturbance, i.e., air stream. Therefore, a customized control system is designed using MathWorks PX4 autopilots support from embedded coder. The designed controllers stabilize the motion by controlling PU's in-frame angle and rotational speed. Moreover, the Ultra-wide Band (UWB) sensors are used to localize the LAIDR in the indoor environment. Finally, a real-time implementation is elaborated to justify the LAIDR applicability in the entertainment applications.
Convolutional Neural Networks with Hebbian-Based Rules in Online Transfer Learning

In 1949, Donald Hebb proposed its Neurophysiologic Principle, which models the weight change of connected neurons. Although similar mechanisms have been experimentally proven to exists in several brain areas, traditional Deep Learning methods do not implement it, generally using Gradient-based algorithms instead. On the other hand, Convolutional Neural Networks (CNNs) has slight inspiration on the structure of Visual Cortex, particularly on the Hierarchical model of Hubel-Wiesel. Using convolutional layers it is possible to perform feature extraction and a final classification with dense layers. In this paper, we propose a combined technique of using pre-trained convolutional layers and a final classification using Hebbian-based rules (Basic Hebb, Covariance, Oja, and BCM). Once the feature extraction is done, Hebbian rules can discriminate the classes with high accuracy. These theoretical ideas were tested using this MNIST database, reaching 99.43% of test accuracy training CNNs layers with a final Hebb layer. Similar results were found using Hebbian-based rules in other datasets with RGB images and Transfer Learning. Even when these results are slightly lower than Gradient-based methods, Hebbian learning can perform online learning, which suggests that this combined strategy might be useful to design Online Machine Learning Algorithms for Image Classification.
A Blockchain-based Trading Matching Scheme in Energy Internet

With the advent of the Industry 4.0 era, the energy industry system revolution will also face new challenges. Against the backdrop of the global energy crisis, renewable energy has received great attention from countries around the world. Due to the characteristics of geographical dispersion, randomness, volatility and uncontrollability of renewable energy, the centralized traditional electrical energy networks cannot meet the management requirements of large-scale access to renewable energy. The emergence of the energy internet has enabled the effective use of renewable energy. As a distributed trading and data management technology, blockchain solves the problem of distributed electrical energy trading in the energy Internet. However, the distributed energy trading based on blockchain cannot satisfy the rational matching of trading objects. Therefore, we propose a blockchain-based electrical energy trading system BC-TMS Blockchain-based trading matching system, which can reasonably match trading objects according to the user's personalized needs and maximize the benefits of both parties to the trading. At the same time, the system also provides a certain degree of privacy protection. Finally, through security and application prospect analysis, we prove the usability and practicability of our proposed scheme.
A panel data examination of income inequality in 24 African countries

Income inequality measures the highlighted gaps between the household disposable income between individuals, groups, etc. Rising concern for financial access to all has greatly influenced this macroeconomic indicator. Africa has majority of countries with worse equality conditions. Though African countries are recipients of external aids from different entities, a high level of corruption directs the benefits to private wealth. The gain of economic growth is disproportionately shared and is skewed. The purpose of the study is to determine the factors responsible for income inequality. We have used unbalanced panel data of 24 African countries for the period 1990–2015 (5‐year averages) of several macroeconomic indicators such as GINI index, literacy, control of corruption, gross domestic product (GDP) growth, household consumption, inflation, etc. We estimated four regression models (OLS fixed effects and random effects) with an increasing number of independent variables. Further, we chose the best model according to Hausman test and conducted a restricted F ‐test to check for non‐spurious variables. We concluded one of the models as statistically significant and robust to explain Income Inequality in Africa. Also, we tested for multicollinearity in variables via the correlation matrix and variance inflation index (VIF) and eliminated the population variable, which was significant. We concluded that six factors are non‐spurious and explain the income inequality variable in a statistically significant manner. Implications of this report can be used for making policies and schemes with respect to the underline variables so that the nations will be able to reduce the inequality gap.
Energy-Optimized Elastic Application Distribution for Automotive Systems in Hybrid Cloud Architectures

The increase of resource-intensive applications in modern vehicles used for streaming, gaming or autonomous driving results in rising energy-consumption of its advanced computing and connectivity hardware. Especially in electric vehicles, this leads to much higher hardware costs and a decreased vehicle range. Modern premium cars use distributed heterogeneous hardware and mostly communicate via APIs to large cloud backends. Current approaches to reduce on-board energy consumption offload applications partly and make use of limited network connectivity assumptions to their backends. In this paper, we propose a hybrid electric and electronic architecture that manages vehicle hardware by using cloud computing frameworks. Our hybrid cloud architecture is a connection of the local vehicle cloud and a large data centre community cloud. We propose an online optimization algorithm that shifts applications from on-board ECUs to data centre servers and vice-versa. The optimization algorithm minimizes the local energy-consumption while satisfying predicatively dynamic constraints like data rate limitations, application policies and resource limitations. Our approach outperforms the non-predictive approach in average by 16%, in the best case by 21% and in the worst case both behave equally well.