Reviewerly/exHarmony huggingface dataset:
A Steganographic Apps-based Patient‟s Information Encryption-Decryption

A steganographic apps-based patient’s information communication system has been designed, developed and implemented in Java programming language that can hide patient confidential data in an image. The Playfair cipher encryption-decryption technique with a set of keywords has been used in this transaction system. For this, the patient’s information is first encrypted with the Playfair encryption technique and produces the cipher text that are embedded with an image in a hidden format and then the image is sent to the destination. In the receiving end, the encrypted hidden information is extracted and retrieve the information by using the reverse process. The process has been applied on several patient’s information and steganographic images and found the results successfully. This proposed steganographic process is a higher layer of security methods in the communications and can be applied where high security is needed.
Threshold-based empirical validation of object-oriented metrics on different severity levels

Software metrics has become desideratum for the fault-proneness, reusability and effort prediction. To enhance and intensify the sufficiency of object-oriented (OO) metrics, it is crucial to perceive the relationship between OO metrics and fault-proneness at distinct severity levels. This paper characterise on the investigation of the software parts with higher probability of occurrence of faults. We examined the effect of thresholds on the OO metrics and build the predictive model based on those threshold values. This paper also instanced on the empirical validation of threshold values calculated for the OO metrics for predicting faults at different severity levels and builds the statistical model using logistic regression. This paper depicts the detection of fault-proneness by extracting the relevant OO metrics and focus on those projects that falls outside the specified risk level for allocating the more resources to them. We presented the effects of threshold values at different risk levels and also validated results on the KC1 dataset using machine learning and different classifiers.
Authorship Attribution using Content based Features and N-gram features

The internet is increasing exponentially with textual content primarily through social websites. The problems were also increasing with anonymous textual data in the internet. The researchers are searching for alternative techniques to know the author of an unknown document. Authorship Attribution is one such technique to predict the details of an unknown document. The researchers extracted various classes of stylistic features like character, lexical, syntactic, structural, content and semantic features to distinguish the authors writing style. In this work, the experiment performed with most frequent content specific features, n-grams of character, word and POS tags. A standard dataset is used for experimentation and identified that the combination of content based and n-gram features achieved best accuracy for prediction of author. Two standard classification algorithms were used for author prediction. The Random forest classifier attained best accuracy for prediction of author when compared with Naïve Bayes Multinomial classifier. The achieved results were good compared to many existing solutions to the Authorship Attribution.
Algorithmic Antitrust

This paper provides an overview of recent developments in algorithmic antitrust, and the economics and legal issues raised in the areas of abuse of dominance, algorithmic pricing and collusion, and mergers and acquisition. The general theme is that while much has been made of the possible anticompetitive effects of the large digital platforms there is little hard evidence to support the core premises of the current ‘tech lash’.
Quantum Computing

In recent years Quantum Computing has attracted a great deal of attention in the scientific and technical communities. Interest in the field has expanded to include the popular press and various funding agencies. We discuss the origins of the idea of using quantum systems for computing. We then give an overview in recent developments in quantum hardware and software, as well as some potential applications for high energy physics.
Advanced Digital Forensic IoT Based Secure Communication

With the explosive growth of the Internet-of-Things (IoT) due to its widespread applicability, it is imperative that IoT designers and architects must incorporate ensuring security of communication in IoT as a key requirement. This emerging field of study, communication in IoT, is concerned with ensuring secure collaboration between smart sensors, actuators and devices and the external environment that constitute the overarching arena of IoT. The field poses new challenges to security and privacy in communication. Most of the cloud service providers such as Amazon, IBM, Microsoft, and Google facilitate various cloud services at reduced cost. The architecture of IoT must be designed to have many features such as scalability, efficiency, accessibility, availability, and flexibility so that applications can be built that can benefit both public and commercial entities. Key factors in driving the success of the IoT will be an accurate specification of security parameters with various security measures that must be enforced. This paper gives an overview of security and privacy aspects in IoT communications using an advanced digital forensic approach for security enhancement. The researchers face various challenges in the investigation of security breaches in IoT. The challenges get magnified multiple folds as cloud service providers utilize many advanced techniques such as virtualization and a multi-tenant usage model to allocate its resources to users securely. These models and architectures make it difficult for investigators who try identify sources that launched various malicious activities and attacks on cloud services. The investigators resort to advanced digital forensic investigation to analyze such attacks. There exist various digital forensic techniques and tools which are not necessarily applied in the context of security breach in IoT. When any security breach occurs, investigators face challenges in collecting evidence as they cannot physically access the evidence buried in devices connected to a local host. The advanced digital forensic approach consists of identifying the evidence related to the source of the crime, and examining and reporting the results and conclusions. The proposed methodology, which employs an advanced digital forensic approach, describes an improved, efficient, and reliable method to identify the source of the crime and collect evidence responsible for security breach during communication.
An enhanced, efficient and affordable wearable elderly monitoring system with fall detection and indoor localisation

According to the statistics of National Council on Aging (NCOA) nowadays, the rate of death among old-aged people has reached a critical state that, for every 11 seconds an older adult is being treated for a fall and for every 19 minutes a death is reported. Hence, it is obvious that an innovation is required to detect these falls in the elderly to provide them timely treatment. We have built a wearable device for the fall detection. The wearable device can detect falls indoor and outdoor. Also, we provide exact location of fall, health attributes of the user to the caretaker and with hospital suggestions. Indoor localisation is also implemented. Importantly, without any wearables or camera, the rest room falls have been identified in an innovative yet economical way.
Secure PDF Text Steganography by Transforming Secret Into Imperceptible Coding

In this paper, we have proposed an approach of PDF based text Steganography by considering the hiding capacity and security of secret information, and improved imperceptibility of stego-cover file. In proposed approach secret information are transformed into compact and encrypted form of imperceptible coding, and then translated into bits form, thereafter embedded into targeted locations of PDF file by applying new cross-reference coding technique in incremental updates of PDF file with less computation complexity. The proposed extraction process provides authentication of received stego-cover file such that only desired file is accepted for the extraction process otherwise fake file is discarded by recipient. Time complexity has been improved significantly by implementing a novel method of PDF steganography for embedding the secret data. Experimental result demonstrates that proposed method provides efficient algorithms in terms of improved security of hidden information.
A Decision-making Module for Fertilization and Irrigation Control System in Rice Farming Using Markov Chain Process and SARSA Algorithms

The merging of modern sensor technologies, the Internet, and advanced control of irrigation and fertilization with an Internet of Things (IoT) approach allow a relatively precise control of agriculture.This IoT approach can thereby increase the resilience of agricultural systems in the face of complex demands for water and fertilizer use, even in countries such as Rwanda with low levels of economic development as long as appropriate and low cost technologies are used.In this work, we add to our previous IoT design for an irrigation system by adding a fertilization system.The proposed low cost system will provide individual farmers fertilization and irrigation options informed by reservoir capacity, water level, predicted rainfall, and temperature along with soil condition and pH.IoT data are assessed in the context of rice growth stages as a Markov Chain process, or in the case of IoT system fault, assessed using the SARSA temporal difference technique.Simulations for the Muvumba Valley rice project in Northeast Rwanda demonstrate the potential of this IoT system to increase rice yield while decreasing fertilizer and water use.
Image Colorization

Image colorization is the process of adding color to an originally black and white image. This means the artist needs to plan the color scheme and then spend time painstakingly filling in the colors manually. The current tool of choice is Photoshop or an equivalent. A single picture can take up to one month to colorize. In this chapter, we will implement a simple convolutional neural network (CNN) model to understand how image colorization works.
Multimedia-aided English online translation platform based on Bayesian theorem

In order to overcome the problems of the traditional online English translation platform, such as low translation efficiency, poor translation accuracy and small translation database capacity, a multimedia-aided online English translation platform based on Bayesian theorem is designed. The translation platform consists of display layer, permission control layer, logic control layer and data processing layer. This paper introduces Bayes' theorem and calculates the probability of translation from English to Chinese. In the design of query module, the translation of search words and thesaurus is selected based on Bayes' theorem, and the retrieval method is optimised. SQLite management system is used to manage the vocabulary data in the vocabulary, so as to complete the design of multimedia-assisted English online translation platform. Experimental results show that the translation accuracy of the platform designed in this paper fluctuates in the range of 86-95, and the translation time is always lower than 0.4 s, indicating that the platform not only has high translation efficiency and accuracy, but also can complete the translation of large volume data.
Leukocyte Detection in Blood Smear Image Based on Improved YOLOv3

The classification of leukocytes is an important indicator for the detection of a variety of blood diseases in routine blood tests.Although there have been many papers studying the detection of WBCs (white blood cell) or classification independently, few papers consider them together.In this paper, we propose an end-to-end white blood cell localization and classification method based on improved YOLOv3.Firstly, we utilize the k-means clustering algorithm to generate the anchor boxes suitable for WBC.Secondly, to identify white blood cells of different sizes, we use multi-scale predictions with YOLOv3 network structure.Experimental results on both the LISC dataset [1] and a dataset of 7500[2] leukocyte smear images of five categories demonstrate that the proposed improved-YOLOv3 can achieve efficient detection performance in terms of accuracy.The recognition accuracy of the two data sets is reached respectively 96.4% and 95.5%.
Access and Equity: Computers for Schools Burundi

Although information and communication technology (ICT) has been used in various parts of the world to improve access to and the quality of education, educational systems in many African nations still face challenges around access to, equity in, and accessibility of ICT. Such issues are widespread in public education throughout Burundi. To resolve these issues, all stakeholders, including nongovernmental organizations, not-for-profit organizations, schools, communities, and employers in the education sector, must recognize and facilitate educational liberation leading to the social transformation of Burundi’s educational system. It is especially important to include previously disadvantaged communities. This paper outlines and contextualizes the quest of Computers for Schools Burundi to improve access to and equity in ICT literacy skills for Burundian youth from kindergarten–Grade 12.
Completely Automated CNN Architecture Design Based on Blocks

© 2019 IEEE. The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessarily available to every interested user. To address this problem, we propose to automatically evolve CNN architectures by using a genetic algorithm (GA) based on ResNet and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures. In particular, neither preprocessing before it starts nor postprocessing in terms of CNNs is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem, or even GAs. The proposed algorithm is evaluated on the CIFAR10 and CIFAR100 benchmark data sets against 18 state-of-the-art peer competitors. Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors. In addition, the proposed algorithm consumes much less computational resource than most peer competitors in finding the best CNN architectures.
A Novel Statistical Based Methodology for The Feature Extraction of both MRI and CT images

Design of a common methodology for the diagnosis of different image types is the objective of the work presented in this paper. The software is developed and can be used to diagnoseMRI and CT images by the laboratory technician The paper presents a statistical method for the feature extraction of MRI and CT images. About thirteen features are extracted using the methodology adopted for the proposed work. The thirteen features are based on texture, shape and intensity. The data dimensionality is reduced using the Principle Component Analysis (PCA). The common features are extracted using the Gray level co-occurrence matrix method. The software is developed using MATLAB and PYTHON for IoT support.
DESIGN OF CENTRALIZED WASTEWATER SEWERAGE SYSTEM IN SMALL LENGKANG ISLAND, BATAM CITY, INDONESIA

One of the small islands within the Indonesia-Singapore border is Lengkang Island. Wastewater collection infrastructure services on Lengkang Island need to be assessed to improve Sustainable Development Goals (SDGs), where access to wastewater services must reach 100%. The purpose of this study is to determine the existing conditions of wastewater management and to plan a centralized wastewater sewerage system on Lengkang Island involving the community. The existing condition shows that all residents channel wastewater into the sea. Only a few houses have toilets, semi-permanent houses on land. Non-permanent houses built on the sea do not have latrines, where stools are dumped directly into the sea. Tripikon-S is one way to overcome the problem of impermanent homes that do not have latrines. Latrine does not provide a solution to the effects of eutropication. Centralized wastewater collection system and wastewater treatment is one way to overcome this. The results of the planning of wastewater collection systems using PVC pipes mentioned the pipe diameter between 32 mm - 60 mm, with a speed of 0.3 m/sec -1.31 m/sec. With this centralized wastewater sewerage system infrastructure, community participation is expected to increase environmentally friendly development.
Corporate Social Responsibility Practices and Reputation of Listed Firms in Nigeria: A Structural Equation Modeling Approach

With the advent of globalization, societal and economic concern increases; this did not give clear standards or regulations for measuring CSR that has been created by the organization.Thus many problems occur due to the inconsistency in which companies are capable of claiming activities that might not meet the general understanding of CSR initiatives.It may improve their reputation, or in some cases, companies do not have a clear understanding of what CSR means.Furthermore, the impacts of CSR practices on firms' reputation are not well delineated in the literature.Hence, this study examined the impact of CSR practices and reputation of listed firms in Nigeria.Primary data collected through the use of a structured and validated questionnaire.Cronbach's alpha reliability coefficients for the constructs ranged from 0.85 to 0.90.The response rate was 98%.Data were analyzed using descriptive and inferential statistics.Findings revealed that corporate social responsibility had a positive and significant effect on business reputation (R2 = 0.43, β = 0.654, t(389) = 15.697,p < 0.05), Business ethics and innovation significantly moderated the relationship between CSR and business reputation (ΔAdj.R2= 0.562, ΔF(3,386) = 167.55,p < 0.05) respectively.The study concluded that corporate social responsibility affects the corporate performance of firms in Nigeria.Recommended that the practice of corporate social responsibility should be intensified by corporate firms to improve on their performance.
Efficient deep convolutional model compression with an active stepwise pruning approach

Deep models are structurally tremendous and complex, thus making it hard to deploy on the embedded hardware with restricted memory and computing power. Although, the existing compression methods have pruned the deep models effectively, some issues exist in those methods, such as multiple iterations needed in fine-tuning phase, difficulty in pruning granularity control and numerous hyperparameters needed to set. In this paper, we propose an active stepwise pruning method of a logarithmic function which only needs to set three hyperparameters and a few epochs. We also propose a recovery strategy to repair the incorrect pruning thus ensuring the prediction accuracy of model. Pruning and repairing alternately constitute cyclic process along with updating the weights in layers. Our method can prune the parameters of MobileNet, AlexNet, VGG-16 and ZFNet by a factor of 5.6×, 11.7×, 16.6× and 15× respectively without any accuracy loss, which precedes the existing methods.
Preemptive Fault Tolerance in DDS based Distributed System using Application Migration

Software availability is the main concern of distributed systems regardless of design and framework. Faults are inevitable to any system and system should continue to function even in presence of these faults. Fault tolerance (FT) techniques should make the system fault tolerant by detecting and handling these faults. This paper introduces the technique of proactively anticipating the possible fault and then avoiding that fault by application migration. The possible fault is predicted through sudden or unusual utilization of CPU, RAM, fan speed or CPU temperature. Whenever CPU, RAM, fan speed or temperature breaches the permissible limits of any machine or an alert is generated based on historical data analysis; system takes the necessary steps to redistribute the application load based on software or hardware analytics. The proposed solution has proved to be more efficient than recovery from failure via traditional reactive FT mechanisms such as checkpoint/restart. The use of data distribution service (DDS) as communication middleware has helped in convenient state transfer and state maintenance of migrating application.
Review Paper On Sentiment Analysis Technique By Different Machine Learning Approach

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
HAMM A Hybrid Algorithm of Min-Min and Max-Min Task Scheduling Algorithms in Cloud Computing

Nowadays, with the huge development of information and computing technologies, the cloud computing is becoming the highly scalable and widely computing technology used in the world that bases on pay-per-use, remotely access, Internet-based and on-demand concepts in which providing customers with a shared of configurable resources. But, with the highly incoming user’s requests, the task scheduling and resource allocation are becoming major requirements for efficient and effective load balancing of a workload among cloud resources to enhance the overall cloud system performance. For these reasons, various types of task scheduling algorithms are introduced such as traditional, heuristic, and meta-heuristic. A heuristic task scheduling algorithms like MET, MCT, Min-Min, and Max-Min are playing an important role for solving the task scheduling problem. This paper proposes a new hybrid algorithm in cloud computing environment that based on two heuristic algorithms; Min-Min and Max-Min algorithms. To evaluate this algorithm, the Cloudsim simulator has been used with different optimization parameters; makespan, average of resource utilization, load balancing, average of waiting time and concurrent execution between small length tasks and long size tasks. The results show that the proposed algorithm is better than the two algorithms Min-Min and Max-Min for those parameters.