Estimating Software Effort Using an ANN Model Based on Use Case Points

In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable (effort) is also introduced. Our data repository contains 240 data points in which, 214 are industrial and 26 are educational projects. Both the regression and ANN models were trained using 168 data points and tested using 72 data points. The ANN model was evaluated using the MMER and PRED criteria against the regression model, as well as the UCP model that estimates effort from use cases. Results show that the ANN model is a competitive model with respect to other regression models and can be used as an alternative to predict software effort based on the UCP method

Avatar captcha : telling computers and humans apart via face classification and mouse dynamics.

Bots are malicious, automated computer programs that execute malicious scripts and predefined functions on an affected computer. They pose cybersecurity threats and are one of the most sophisticated and common types of cybercrime tools today. They spread viruses, generate spam, steal personal sensitive information, rig online polls and commit other types of online crime and fraud. They sneak into unprotected systems through the Internet by seeking vulnerable entry points. They access the system’s resources like a human user does. Now the question arises how do we counter this? How do we prevent bots and on the other hand allow human users to access the system resources? One solution is by designing a CAPTCHA (Completely Automated Public Turing Tests to tell Computers and Humans Apart), a program that can generate and grade tests that most humans can pass but computers cannot. It is used as a tool to distinguish humans from malicious bots. They are a class of Human Interactive Proofs (HIPs) meant to be easily solvable by humans and economically infeasible for computers. Text CAPTCHAs are very popular and commonly used. For each challenge, they generate a sequence of alphabets by distorting standard fonts, requesting users to identify them and type them out. However, they are vulnerable to character segmentation attacks by bots, English language dependent and are increasingly becoming too complex for people to solve. A solution to this is to design Image CAPTCHAs that use images instead of text and require users to identify certain images to solve the challenges. They are user-friendly and convenient for human users and a much more challenging problem for bots to solve. In today’s Internet world the role of user profiling or user identification has gained a lot of significance. Identity thefts, etc. can be prevented by providing authorized access to resources. To achieve timely response to a security breach frequent user verification is needed. However, this process must be passive, transparent and non-obtrusive. In order for such a system to be practical it must be accurate, efficient and difficult to forge. Behavioral biometric systems are usually less prominent however, they provide numerous and significant advantages over traditional biometric systems. Collection of behavior data is non-obtrusive and cost-effective as it requires no special hardware. While these systems are not unique enough to provide reliable human identification, they have shown to be highly accurate in identity verification. In accomplishing everyday tasks, human beings use different styles, strategies, apply unique skills and knowledge, etc. These define the behavioral traits of the user. Behavioral biometrics attempts to quantify these traits to profile users and establish their identity. Human computer interaction (HCI)-based biometrics comprise of interaction strategies and styles between a human and a computer. These unique user traits are quantified to build profiles for identification. A specific category of HCI-based biometrics is based on recording human interactions with mouse as the input device and is known as Mouse Dynamics. By monitoring the mouse usage activities produced by a user during interaction with the GUI, a unique profile can be created for that user that can help identify him/her. Mouse-based verification approaches do not record sensitive user credentials like usernames and passwords. Thus, they avoid privacy issues. An image CAPTCHA is proposed that incorporates Mouse Dynamics to help fortify it. It displays random images obtained from Yahoo’s Flickr. To solve the challenge the user must identify and select a certain class of images. Two theme-based challenges have been designed. They are Avatar CAPTCHA and Zoo CAPTCHA. The former displays human and avatar faces whereas the latter displays different animal species. In addition to the dynamically selected images, while attempting to solve the CAPTCHA, the way each user interacts with the mouse i.e. mouse clicks, mouse movements, mouse cursor screen co-ordinates, etc. are recorded nonobtrusively at regular time intervals. These recorded mouse movements constitute the Mouse Dynamics Signature (MDS) of the user. This MDS provides an additional secure technique to segregate humans from bots. The security of the CAPTCHA is tested by an adversary executing a mouse bot attempting to solve the CAPTCHA challenges

Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

A Treeboost Model for Software Effort Estimation Based on Use Case Points

Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Treeboost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Treeboost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Treeboost model can be used with promising results to estimate software effort

Fuzzy-ExCOM Software Project Risk Assessment

A software development project can be considered to be risky project due to the uncertainty of the information (customer requirements), the complexity of the process, and the intangible nature of the product. Under these conditions, risk management in software development projects is mandatory, but often it is difficult and expensive to implement. Expert COCOMO is an efficient approach to software project risk management, which leverages existing knowledge and expertise from previous effort estimation activities to assess the risk in a new software project. However, the original method has a limitation because it cannot effectively deal with imprecise and uncertain inputs in the form of linguistic terms such as: Very Low (VL), Low (L), Nominal (N), High (H), Very High (VH) and Extra High (XH). This paper introduces the fuzzy-ExCOM methodology that combines the advantages of a fuzzy technique with Expert COCOMO methodology for risk assessment in a software project. A validation of this approach with project data shows that fuzzy-ExCOM provides better risk assessment results with a higher level of sensitivity with respect to risk identification compared to the original Expert COCOMO methodology

A Systematic Review of the State of Cyber-Security in Water Systems

Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems

An adjective selection personality assessment method using gradient boosting machine learning

Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.This work has been supported by FCT - Fundação para a Ciência e a Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. It was also partially supported by a Portuguese doctoral grant, SFRH/BD/130125/2017, issued by FCT in Portugal

One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.Comment: This work has been accepted to "Neural Computing and Applications: Special Issue on Deep Learning for Music and Audio