Computers, Volume 11, Issue 9 (September 2022) – 16 articles

Dependency injection (DI) is generally known to improve maintainability by keeping application classes separate from the library. Particularly within the Java environment, there are many applications using the principles of DI with the aim to improve maintainability. There exists some work that provides an inference on the impact of DI on maintainability, but no conclusive evidence is provided. The fact that there are no publicly available tools for quantifying DI makes such evidence more difficult to be produced. In this paper, we propose two novel metrics, dependency injection-weighted afferent couplings (DCE) and dependency injection-weighted coupling between objects (DCBO), to measure the proportion of DI in a project based on weighted couplings. We describe how DCBO can serve as a more meaningful metric in assessing maintainability when DI is also considered. The metric is implemented in the CKJM-Analyzer, an extension of the CKJM tool to perform static analysis on DI detection. We discuss the algorithmic approach behind the static analysis and prove the soundness of the tool using a set of open-source Java projects. Full article

Iris recognition as a biometric identification method is one of the most reliable biometric human identification methods. It exploits the distinctive pattern of the iris area. Typically, several steps are performed for iris recognition, namely, pre-processing, segmentation, normalization, extraction, coding and classification. In this article, we present a novel algorithm for iris recognition that includes in addition to iris features extraction and coding the step of feature selection. Furthermore, it enables selecting a variable length of features for iris recognition by adapting our recent algorithm variable length black hole optimization (VLBHO). It is the first variable length feature selection for iris recognition. Our proposed algorithm enables segments-based decomposition of features according to their relevance which makes the optimization more efficient in terms of both memory and computation and more promising in terms of convergence. For classification, the article uses the famous support vector machine (SVM) and the Logistic model. The proposed algorithm has been evaluated based on two iris datasets, namely, IITD and CASIA. The finding is that optimizing feature encoding and selection based on VLBHO is superior to the benchmarks with an improvement percentage of 0.21%. Full article

Communication establishment is crucial for rescue operations in disaster affected areas. A standard tool for communication is the use of cell phones. However, they can be useless in situations where the cellular network’s base stations are damaged in a disaster. A contemporary approach to re-establishing a communication network is by hosting base stations in drones. However, low battery life and difficulty in calculating the number of drones needed in different terrains are limitations of the above approach. This paper introduces a novel terrain-aware algorithm that calculates the minimum number of drones needed to cover an area with no voids in the network coverage. Our method ensures that the drones are deployed at optimal heights to maximize the average leftover energy in the network. We apply the algorithm for an actual location in Pettimudi, India and find the optimal number and positions of the drones to cover the area effectively without voids. In addition, we provide a simulation of the the communication establishment using above drones, and our experiments yield an average network efficiency of 98%, showing the effectiveness of our method. Full article

The paper presents the results of an experimental investigation and statistical analysis of the data streams generated by popular Internet services. It is found that every investigated Internet service generates data streams of a specific type, possessing specific statistical characteristics. On this basis, it is possible to develop an analog of the classical metrology approach for the data streams generated by Internet services. Furthermore, i the problem of the data streams superposition “at the source” (i.e., on a user’s computer or on a local network) is investigated. It is found that the data streams are additive, with sufficient accuracy for engineering applications (but not exactly additive). Full article

Vulnerability lifecycles and the vulnerability markets are related in a manner that can lead to serious security and economic risks, especially regarding black markets. In the current era, this is a relationship that requires careful scrutiny from society as a whole. Therefore, in this study, we analyzed the actual data relating to vulnerability-regulated markets in the case of two well-known browsers, Firefox and Chrome. Our analysis shows that financial reward is the main motivation for most discoverers, whose numbers are increasing every year. In addition, we studied the correlation between vulnerability markets and the vulnerability lifecycle from many perspectives, including theoretical concepts, and statistical approaches. Furthermore, we discussed the potential risks for people and organizations in terms of security and economics. We believe that money is the main motivation in vulnerability markets and that the latter are, in turn, the main driver of the vulnerability lifecycle, which presents several risks to the software industry and to society itself. Thus, in our opinion, if vulnerability markets can be controlled, the vulnerability lifecycle will be reduced or eliminated, along with its associated risks. Full article

Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately. Full article

Vertical farming is a new agricultural system which aims to utilize the limited access to land, especially in big cities. Vertical agriculture is the answer to meet the challenges posed by land and water shortages, including urban agriculture with limited access to land and water. This research study uses the Preferred Reporting for Systematic Review and Meta-analysis (PRISMA) item as one of the literary approaches. PRISMA is one way to check the validity of articles for a literature review or a systematic review resulting from this paper. One of the aims of this study is to review a survey of scientific literature related to vertical farming published in the last six years. Artificial intelligence with machine learning, deep learning, and the Internet of Things (IoT) in supporting precision agriculture has been optimally utilized, especially in its application to vertical farming. The results of this study provide information regarding all of the challenges and technological trends in the area of vertical agriculture, as well as exploring future opportunities. Full article

Face swapping technology is approaching maturity, and it is difficult to distinguish between real images and fake images. In order to prevent malicious face swapping and ensure the privacy and security of personal photos, we propose a new way to disable the face detector in the face detection stage, which is to add a black line structure to the face part. Using neural network visualization, we found that the black line structure can interrupt the continuity of facial features extracted by the face detector, thus making the three face detectors MTCNN, S3FD, and SSD fail simultaneously. By widening the width of the black line, MTCNN, S3FD, and SSD are able to reach probability of failure levels up to 95.7%. To reduce the amount of perturbation added and determine the effective range of perturbation addition, we firstly experimentally prove that adding perturbation to the background cannot interfere with the detector’s detection of faces. Full article

The global expansion of the sports betting industry has brought the prediction of outcomes of sport events into the foreground of scientific research. In this work, soccer outcome prediction methods are evaluated, focusing on the Greek Super League. Data analysis, including data cleaning, Sequential Forward Selection (SFS), feature engineering methods and data augmentation is conducted. The most important features are used to train five machine learning models: k-Nearest Neighbor (k-NN), LogitBoost (LB), Support Vector Machine (SVM), Random Forest (RF) and CatBoost (CB). For comparative reasons, the best model is also tested on the English Premier League and the Dutch Eredivisie, exploiting data statistics from six seasons from 2014 to 2020. Convolutional neural networks (CNN) and transfer learning are also tested by encoding tabular data to images, using 10-fold cross-validation, after applying grid and randomized hyperparameter tuning: DenseNet201, InceptionV3, MobileNetV2 and ResNet101V2. This is the first time the Greek Super League is investigated in depth, providing important features and comparative performance between several machine and deep learning models, as well as between other leagues. Experimental results in all cases demonstrate that the most accurate prediction model is the CB, reporting 67.73% accuracy, while the Greek Super League is the most predictable league. Full article

Recently, many new network paths have been introduced while old paths are still in use. The trade-offs remain vague and should be further addressed. Since last decade, the Internet is playing a major role in people’s lives, and the demand on the Internet in all fields has increased rapidly. In order to get a fast and secure connection to the Internet, the networks providing the service should get faster and more reliable. Many network data paths have been proposed in order to achieve the previous objectives since the 1970s. It started with the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) and later followed by several more modern paths including Quick UDP Internet Connections (QUIC), remote direct memory access (RDMA), and the Data Plane Development Kit (DPDK). This raised the question on which data path should be adopted and based on which features. In this work, we try to answer this question using different perspectives such as the protocol techniques, latency and congestion control, head of line blocking, the achieved throughput, middleboxes consideration, loss recovery mechanisms, developer productivity, host resources utilization and targeted application. Full article

CONTROLAR provides Bosch with an intelligent functional testing machine used to test the correct functioning of the car radios produced. During this process, the radios are submitted to several tests, raising the problem of how the machine detects errors in several radios consecutively, making it impossible to know if the device has a problem since it has no module to see if it works correctly. This article arises from the need to find a solution to solve this problem, which was to develop a self-diagnostic system that will ensure the reliability and integrity of the cyber-physical system, passing a detailed state of the art. The development of this system was based on the design of an architecture that combines the KDT methodology with a DSL to manage and configure the tests to integrate the self-diagnostic test system into a CPS. A total of 28 test cases were performed to cover all its functionalities. The results show that all test cases passed. Therefore, the system meets all the proposed objectives. Full article

The aim of the research work is to investigate the operability of the entire 23 pulmonary function parameters, which are stipulated by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), to design a medical decision support system capable of classifying the pulmonary function tests into normal, obstructive, restrictive, or mixed cases. The 23 respiratory parameters specified by the ATS and the ERS guidelines, obtained from the Pulmonary Function Test (PFT) device, were employed as input features to a Multi-Layer Perceptron (MLP) neural network. Thirteen possible MLP Back Propagation (BP) algorithms were assessed. Three different categories of respiratory diseases were evaluated, namely obstructive, restrictive, and mixed conditions. The framework was applied on 201 PFT examinations: 103 normal and 98 abnormal cases. The PFT decision support system’s outcomes were compared with both the clinical truth (physician decision) and the PFT built-in diagnostic software. It yielded 92–99% and 87–92% accuracies on the training and the test sets, respectively. An 88–94% area under the receiver operating characteristic curve (ROC) was recorded on the test set. The system exceeded the performance of the PFT machine by 9%. All 23 ATS\ERS standard PFT parameters can be used as inputs to design a PFT decision support system, yielding a favorable performance compared with the literature and the PFT machine’s diagnosis program. Full article

We present how artificial intelligence (AI)-based technologies create new opportunities to capture and assess sensory marketing elements. Based on the Online Sensory Marketing Index (OSMI), a sensory assessment framework designed to evaluate e-commerce websites manually, the goal is to offer an alternative procedure to assess sensory elements such as text and images automatically. This approach aims to provide marketing managers with valuable insights and potential for sensory marketing improvements. To accomplish the task, we initially reviewed 469 related peer-reviewed scientific publications. In this process, manual reading is complemented by a validated AI methodology. We identify relevant topics and check if they exhibit a comprehensible distribution over the last years. We recognize and discuss similar approaches from machine learning and the big data environment. We apply state-of-the-art methods from the natural language processing domain for the principal analysis, such as word embedding techniques GloVe and Word2Vec, and leverage transformers such as BERT. To validate the performance of our newly developed AI approach, we compare results with manually collected parameters from previous studies and observe similar findings in both procedures. Our results reveal a functional and scalable AI approach for determining the OSMI for industries, companies, or even individual (sub-) websites. In addition, the new AI selection and assessment procedures are extremely fast, with only a small loss in performance compared to a manual evaluation. It resembles an efficient way to evaluate sensory marketing efforts. Full article

Knowledge is a key competitive advantage for small and medium enterprises (SMEs) as a way of competing with other organisations. There is a need to investigate SME adoption of knowledge management systems (KMSs). Knowledge management systems can only assist in this task if they are sufficiently adopted. The purpose of this research was to develop a conceptual framework for KMS adoption within an SME context. The research aimed to explore the interdependencies between various contextual KMS adoption factors, namely the technology, organization, environmental and human behavioural contexts. Four mini-focus groups were conducted and included employees in SMEs. Thematic analysis identified nine themes that describe the dynamics that either promote or prevent KMS adoption. The findings provide deeper insights into the influencing factors in KMS adoption to enhance SME performance and competitiveness. The KMS adoption framework can be applied to improve the adoption of technology in SMEs. Future research could include SMEs in specific industries to compare adoption factors and could also include larger organisations. Full article

Although web survey has been a popular method of data collection in the academic community, it presents meagre response rates, which primarily affect the validity of the results as well as the reliability of the outcomes. Surveys worldwide that study the response rate only of teachers have not been found in the relevant literature. In this survey, with a sample of 263 Greek teachers, we investigate possible factors that explain teachers’ intention to participate in web surveys that are conducted by online questionnaires indicating, therefore, the factors that probably influence the response rate of web surveys. Our findings support those factors such as (a) authority, (b) incentives, (c) survey structure/form, (d) ethical issues, (e) reminders and pre-notifications, and (f) survey time received, which seem to explain the teachers’ intention to participate in web surveys with questionnaires. Based on the findings, methodology implications and limitations for researchers are discussed. Full article

Traffic accidents are a major concern worldwide, since they have a significant impact on people’s safety, health, and well-being, and thus, they constitute an important field of research on the use of state-of-the-art techniques and algorithms to analyze and predict them. The study of traffic accidents has been conducted using the information published by traffic entities and road police forces, but thanks to the ubiquity and availability of social media platforms, it is possible to have detailed and real-time information about road accidents in a given region, which allows for detailed studies that include unrecorded road accident events. The focus of this paper is to propose a model to predict traffic accidents using information gathered from social media and open data, applying an ensemble Deep Learning Model, composed of Gated Recurrent Units and Convolutional Neural Networks. The results obtained are compared with baseline algorithms and results published by other researchers. The results show promising outcomes, indicating that in the context of the problem, the proposed ensemble Deep Learning model outperforms the baseline algorithms and other Deep Learning models reported by literature. The information provided by the model can be valuable for traffic control agencies to plan road accident prevention activities. Full article