Reviewerly/exHarmony huggingface dataset:
Adoption of Agile Software Methodology Among the SMEs Developing an IOT Applications

Agile software methodology framework has been used frequently in the software development industry especially among the small medium (SME) companies. In this paper, we conducted a case study among the four selected SME companies that have been developing the Internet of Things application (IoT) as their business core. The results found that all the companies under study are using extreme programming (XP), Dynamic System Development Methodology (DSDM), Feature Driven Development (FDD) and Lean as chosen development methodology. However, the most used agile methodology framework is the Scrum techniques. The study shows that companies applied Agile SDM. This is because, the Agile SDM help the software developer teams to organize their task efficaciously, and able to make frequent delivery to their stakeholders and users.
Patterns and Antipatterns, Principles, and Pitfalls: Accountability and Transparency in Artificial Intelligence

This article discusses a set of principles for accountability and transparency in AI as well as a set of antipatterns or harmful trends too often seen in deployed systems. It provides concrete suggestions for what can be done to shift the balance away from these antipatterns and toward more positive ones.
Machine Learning-based Software Effort Estimation: An Analysis

Estimating the effort behind a software project is the approximation time and resources an engineer need to create a software application. The estimation is one of the most important phase in the developing process to set the cost of project and ultimately to attract the client. In the preliminary stage of a project, the accuracy of estimation is to be extremely precise and dependable, which may not be easy to achieve. Therefore, use of machine learning algorithms is a possible solution for the estimation process on which the decision can be made. In this study, we have analyzed various studies and machine learning trends conducted in this field. Doing this effective reductions in the cost and parameter for the project to be accomplished. Accuracy, root mean and relative absolute errors are used to compute the effort estimation accuracy.
Implementation of Autonomous Visual Detection, Tracking and Landing for AR.Drone 2.0 Quadcopter

This paper presents the design and implementation of a system where an autonomous aerial vehicle (UAV) has to detect, track and land in the vicinity of a moving Unmanned Ground Vehicle (UGV). The quadcopter needs, using camerabased data, to identify and track the UGV platform. A colorbased detection algorithm is employed to detect the UGV position and Kernelized Correlation Filters (KCF) are taken for its tracking. For suppressing the false positive cases of the detection algorithm, and to double check the presence of the UGV in an image frame, the ORB (Oriented FAST and rotated BRIEF) feature detector is used. Then the UGV position detected in the image frame is used in a PD feedback control loop in the quadcopter's controller that enables the actual tracking of the moving UGV in the horizontal plane, while another PID controller stabilizes the UAV altitude. The proposed approach was initially tested in Gazebo simulator, and then in real environment with Parrot AR.Drone 2.0 and Plato robots (as UAV and UGV correspondingly). The results show that the proposed system can detect and track the UGV in online mode without artificial markers and perform robust UAV landing.
Blockchain-based Land Record Management in Pakistan

A number of issues regarding Land Record Management has been pointed out in the literature. In particular, developing countries like Pakistan face severe Land Record Management issues, such as data tempering with land record, provision of no means to fetch out a complete history of property ownership, working of various related Land Record Management System in isolation, etc. Prevalent traditional Land Record Management Solutions do not address such issues. To address these issues, we propose a Blockchain-based Land Record Management solution for Pakistan. The proposed system has been designed and implemented, details of which are presented in the paper.
A Survey on Sarcasm detection and challenges

Sarcasm is a way of expressing feelings in which people says or write something, which is completely different of what they actually intended. Due to the obscurity nature of sarcasm, it is really hard to detect it. Sarcasm is a type of irony. Criticism is one of the main purpose to which sarcasm is being used. People generally use sarcasm to express their opinions or feelings especially in the social networking sites like Twitter and Facebook. Perfect analysis and understanding of the sarcasm sentences can improve the accuracy of sentiment analysis. Sentiment analysis means understanding the attitude or opinions of individuals or society about a particular event or topic. In this paper, we tried to detailing the general architecture of sarcasm detection, existing methods, and different types of sarcasm, issues, challenges and future scope.
Adaptive Cat Swarm Optimization Algorithm and Its Applications in Vehicle Routing Problems

This paper proposes a novel hybrid algorithm named Adaptive Cat Swarm Optimization (ACSO). It combines the benefits of two swarm intelligence algorithms, CSO and APSO, and presents better search results. Firstly, some strategies are implemented to improve the performance of the proposed hybrid algorithm. The tracing radius of the cat group is limited, and the random number parameter r is adaptive adjusted. In addition, a scaling factor update method, called a memory factor y , is introduced into the proposed algorithm. They can be learnt very well so as to jump out of local optimums and speed up the global convergence. Secondly, by comparing the proposed algorithm with PSO, APSO, and CSO, 23 benchmark functions are verified by simulation experiments, which consists of unimodal, multimodal, and fixed-dimension multimodal. The results show the effectiveness and efficiency of the innovative hybrid algorithm. Lastly, the proposed ACSO is utilized to solve the Vehicle Routing Problem (VRP). Experimental findings also reveal the practicability of the ACSO through a comparison with certain existing methods.
Classification of periodic arrivals in event time data for filtering computer network traffic

Abstract Periodic patterns can often be observed in real-world event time data, possibly mixed with non-periodic arrival times. For modelling purposes, it is necessary to correctly distinguish the two types of events. This task has particularly important implications in computer network security; there, separating automated polling traffic and human-generated activity in a computer network is important for building realistic statistical models for normal activity, which in turn can be used for anomaly detection. Since automated events commonly occur at a fixed periodicity, statistical tests using Fourier analysis can efficiently detect whether the arrival times present an automated component. In this article, sequences of arrival times which contain automated events are further examined, to separate polling and non-periodic activity. This is first achieved using a simple mixture model on the unit circle based on the angular positions of each event time on the p -clock, where p represents the main periodicity associated with the automated activity; this model is then extended by combining a second source of information, the time of day of each event. Efficient implementations exploiting conjugate Bayesian models are discussed, and performance is assessed on real network flow data collected at Imperial College London.
Smart Digital Marketing of Financial Services to Millennial Generation using emerging technological tools and buyer persona

Digital Marketing is term used to describe marketing products or services using digital technologies. It is mainly based on the internet, and it can refer to advertising through digital channels such as search engines, websites, social media, email, and mobile apps. Millennial generation consists of individuals born between 1982 and 2002, they are the demographic social influencers. Financial Institutions such as banks are searching for new ways to market products and services to attract millennials. It is important for banks to use digital marketing for millennial generations since they are avid fans of the visual platform. Buyer Persona is a semi-fictional representation to represent what prospective customers are thinking and doing, as it gives real data about existing customers. In order to focus on each category of customers, its necessarily to create buyer persona. It is one of the unique methods banks could use for marketing. This research has examined digital marketing tools and consumer behaviour of millennial generation and have proposed the techniques of creating specific buyer persona through a framework. This framework could be used by banks in order to create specific marketing strategies for marketing financial services to individuals of the millennial generations.
When hip-hop meets CMC: digital discourse in Nigerian hip-hop

Mobile telephony and the Internet have ensured that digitized communication is a routine in which everyone engages. Since music artistes use their songs as archival tools in documenting social events and changes, we consider, in this study, the (re)presentation of Computer Mediated Communication in Nigerian Hip-hop. To this end, six purposively selected songs by six Nigerian hip-hoppers are subjected to textual analysis. It is noted that the cross re-territorialization of CMC in Hip-hop was explicated thematically through depictions of internet crime, romance and the necessity to be successful regardless of prevailing challenges. Slangification using technology-based lexis was further identified as a means to code online criminal activities as well as sexual relations. The findings reinvigorate a post-digital popular culture phenomenon and reflect Nigerian Hip-Hop as a cultural signifier of the contemporary crisis of identity and prevailing post-colonial realities
DCT-DWT Based Digital Watermarking and Extraction using Neural Networks

Recent trends in information technology have miraculously changed the way people interact with multimedia devices. The steady and consistent developments in communication technologies have created a situation where a massive amount of data and thereby information is created, stored and shared every second. With an increased number of devices, backed by enhanced communication technologies the insurgence towards originality and ownership of the data is increasing. Among various other methods of protecting the originality and ownership of the digital data, watermarking has emerged to be a prominent one. Watermarking allows the copyrighting of the data through algorithm-driven encoding techniques. Along with imperceptibility and robustness, digital watermarking also requires default requirements such as security, fidelity, inseparability, payload capacity and effectiveness. For a digital watermark to be inseparable, the carrier signal(secret key) should remain undistorted outside certain specific conditions. The aim here is to create an algorithm which can efficiently create strong a digital watermark and which can also extract the watermark without the need of the original image using neural networks. Here we propose a neural network-driven digital watermarking with functions in frequency domain transformation. A strong DCT-DWT hybrid algorithm is used to embed a visible watermark into the original image and at later stages neural networks are deployed to extract the watermark without the use of the original pure image.
A Service-oriented Network Infrastructure for Crossover Service Ecosystems

Crossover service networks have become a trend promoting cross-enterprise, field, and -industry cooperation. As an innovation process for enterprises, crossover services can provide users creative, novel, and amalgamated services by breaking traditional organization, business, and domain boundaries. However, business and interface inconsistencies in today's crossover service ecosystems make crossover cooperation difficult and time-consuming. We designed a crossover service ecosystem-oriented network that provides a sound framework for efficiently deploying, publishing, discovering, composing, monitoring, and optimizing access on crossover services to fully deliver the potential of them. Furthermore, a prototype of the service network, namely JTangYdrail, is introduced, where service switches and service routers are designed to provide the transparency between the complex crossover service environment and the convenient user service access.
Retracted: Smart Wheelchair for Physically Challenged People

According to the World Health Organization (WHO), in the world population, there around 70 million people who requires the need for wheelchairs, yet only 5-15% have access to it. The wheelchair we developed here is very cost effective and aids the people who are disabled and physically challenged. The wheelchair allows them to maneuver around their surrounding with ease without the intervention of another person, allowing them to be independent.
New unified insights on deep learning in radiological and pathological images: Beyond quantitative performances to qualitative interpretation

Deep learning (DL) has become the main focus of research in the field of artificial intelligence, despite its lack of explainability and interpretability. DL mainly involves automated feature extraction using deep neural networks (DNNs) that can classify radiological and pathological images. Convolutional neural network (CNNs) can be also applied to pathological image analysis, such as the detection of tumors and the quantification of cellular features. However, to our knowledge, no attempts have been made to identify interpretable signatures from CNN features, and few studies have examined the use of CNNs for cytopathology images. Therefore, the aim of the present paper is to provide new unified insights to aid the development of more interpretable CNN-based methods to classify radiological and pathological images and explain the reason for this classification in the form of if-then rules. We first describe the “black box” problem of shallow NNs, the concept of rule extraction, the renewed attack of the “black box” problem in DNN architectures, and the paradigm shift regarding the transparency of DL using rule extraction. Next, we review limitations of DL in pathology in regard to histopathology and cytopathology. We then investigate the discrimination of cytological features and explanations and review recent techniques for interpretable CNN-based methods in histopathology, as well as current approaches being taken to enhance the interpretability of CNN-based methods for radiological images. Finally, we provide new unified insights to extract qualitative interpretable rules for radiological and pathological images.
Image Classification of Abnormal Red Blood Cells Using Decision Tree Algorithm

The study aims to increase the number of abnormal red blood cells that can be detected using image processing. This study used Decision-Tree Algorithm as a machine learning algorithm in classifying. As a result, the system detected and classified a total of ten abnormal red blood cells. Images used in the system came from hospitals' past patients. In addition, a camera is used to capture the slides. The image was then inserted into the program. The system processed and classified the image. In effect, the results show the name of the abnormal red blood cells detected in the image within the system including the soft copy of the list.
Object detection in autonomous driving - from large to small datasets

The purpose of the paper is to analyze the current capacity of pedestrian and vehicle detection through four state of the art detectors -Yolo, SSD, Faster R-CNN and RetinaNet on a big dataset (BDD100K). Also, we analyzed if the results are transferable from one dataset to another - we used a small dataset from our campus, we offered some quantitative results and we made an error analysis based on the dataset characteristics (e.g. weather, light, size of the object).
Readiness model for requirements change management in global software development

Abstract Requirements Change Management (RCM) is one of the challenges faced by Global Software Development (GSD) organisations as requirements evolution is inevitable due to dynamic business and operating environments. GSD organisations face issues when dealing with RCM because many organisations embark on GSD without understanding their readiness to undertake such an initiative. Currently, there is no readiness model to assess the RCM process in the context of GSD. The objective of this study is to develop a requirements change management readiness model (RCMRM) for GSD organisations. A Systematic Mapping Study (SMS) was conducted to identify the primary studies related to RCM in the GSD projects. By using SMS, 109 primary studies were selected and 73 RCM practices were identified which were used to design the readiness levels of the proposed RCMRM. To validate the RCMRM, initially, two case studies were conducted in two GSD organisations. Based on the suggestions and recommendations of the case study participants,the RCMRM was further modified. The updated version of RCMRM was further validated by two different GSD organisations. The results of the second case study indicate that RCMRM is effective in assessing the readiness of the RCM process in the context of GSD.
DoubleType: Authentication Using Relationship Between Typing Behavior on Multiple Devices

Authentication using Keystroke Dynamics (KD), has customarily focused on one device at a time, either desktop or phone. It is imperative that authentication systems adapt to an environment where the users consistently switch between multiple devices. We use the typing behavior of users on different devices, extract the relationship between them and show that these relationships can be used to authenticate users in a multi-device environment when a user switches between devices. We design an authentication system for three scenarios, using the relationship between typing behaviors on a) desktop and phone, b) desktop and tablet, and c) tablet and phone. We find that these are highly separable for individuals with data form 70 users. With Gaussian Naive Bayes (GNB) and Random Forests (RF) classifiers, we found the accuracies for verification to be very high. Using GNB we achieved mean accuracies of 99.15%, 99.23% and 98.72% for relationships between desktop-phone, desktop-tablet and tablet-phone respectively. RF classifiers performed similarly with mean accuracies of 99.31%, 99.33% and 99.12% for relationships between desktop-phone, desktop-tablet and tablet-phone respectively.
Millimeter-Wave AWR1642 RADAR for Obstacle Detection: Autonomous Vehicles

To know the nature of the surrounding is very crucial, and the profile of the terrain geometry must be known to decide the motion path in autonomous cars. The obstacles must be detected along with their coordinates with the vehicle as origin (reference) and relative velocity to determine a collision free path. Most autonomous vehicle manufacturers depend extensively on LIDAR such as Uber and Google, whereas Tesla uses RADAR as a primary sensor. RADAR is relatively less expensive and works equally well in all weather conditions such as fog, rain, snow, and dust. It can also determine relative traffic speed using Doppler frequency shift which is not possible in case of LIDAR. Hence, RADAR is a viable solution for autonomous cars considering above parameters, and it provides good results when used with secondary sensors such as ultrasonic and camera. This paper proposes a methodology for testing RADAR sensor AWR1642 in various configurations with respect to distance and angle of coverage. The testing of the above RADAR is carried out on real vehicle. The results obtained verify that RADAR provides accurate results within 50 m range for medium-sized objects with appropriate tuning of parameters such as best range and best range resolution.
Cloud Computing Trends and Cloud Migration Tuple

Cloud computing has come a long way to become an established platform to host the ICT infrastructure. Almost all of the Fortune 500 companies have cloud presence. And the latest announcement of Pentagon to offer a contract worth USD 10 billion to migrate to cloud shows the confidence level in the cloud infrastructure. Migration to cloud needs careful planning, strong business case, credible migration strategy, and robust migration frameworks. This paper reviews the area of cloud computing migration frameworks. It utilizes the co-word analysis technique of bibliometric or science mapping. The keywords of 299 research articles downloaded from the Web of science database have been analyzed. A 10 elements cloud migration tuple has been developed. Additionally, this paper gives cloud computing technology trends that have evolved to address the challenges of latency and jitter, context awareness, Internet of Things, voluminous data, and mobility support. The results of this paper will help in understanding the cloud computing and migration thereof.
Plunge into the Underworld: A Survey on Emergence of Darknet

The availability of sophisticated technologies and methods of perpetrating criminogenic activities in the cyberspace is a pertinent societal problem. Darknet is an encrypted network technology that uses the internet infrastructure and can only be accessed using special network configuration and software tools to access its contents which are not indexed by search engines. Over the years darknets traditionally are used for criminogenic activities and famously acclaimed to promote cybercrime, procurements of illegal drugs, arms deals, and cryptocurrency markets. In countries with oppressive regimes, censorship of digital communications, and strict policies prompted journalists and freedom fighters to seek freedom using darknet technologies anonymously while others simply exploit it for illegal activities. Recently, MIT's Lincoln Laboratory of Artificial Intelligence augmented a tool that can be used to expose illegal activities behind the darknet. We studied relevant literature reviews to help researchers to better understand the darknet technologies, identify future areas of research on the darknet and ultimately to optimize how data-driven insights can be utilized to support governmental agencies in unraveling the depths of darkent technologies. This paper focuses on the use of internet for crimes, deanonymization of TOR-services, darknet a new digital street for illicit drugs, research questions and hypothesis to guide researchers in further studies. Finally, in this study, we propose a model to examine and investigate anonymous online illicit markets.
Exploring Internet of Things Adoption in Malaysian Oil and Gas Industry

Internet of Things (IoT) is one of the top digital topics recently emerging within the oil and gas industry. For IoT to arise from the conceptual level, organizations must be ready to invest and adopt this new technology. Several studies have looked into the prospective of IoT in the industrial sector. However, IoT is still not properly adopted across different industries including the oil and gas industry. The existing literatures identify lack of knowledges in IoT adoption and inadequate IoT adoption researches especially in oil and gas industry. IoT adoption is relatively sparse and lack of focus in a specific industry. This study aims to identify factors influencing IoT adoption in oil and gas industry, statistically evaluate the influencing factors, propose the IoT adoption model, recommend suitable strategies for IoT adoption in Malaysian oil and gas industry and spur the research community to explore into more research in IoT adoption.