Is cloned code really stable?

Clone has emerged as a controversial term in software engineering research and practice. The impact of clones is of great importance from software maintenance perspectives. Stability is a well investigated term in assessing the impacts of clones on software maintenance. If code clones appear to exhibit a higher instability (i.e., higher change-proneness) than non-cloned code, then we can expect that code clones require higher maintenance effort and cost than non-cloned code. A number of studies have been done on the comparative stability of cloned and non-cloned code. However, these studies could not come to a consensus. While some studies show that code clones are more stable than non-cloned code, the other studies provide empirical evidence of higher instability of code clones. The possible reasons behind these contradictory findings are that different studies investigated different aspects of stability using different clone detection tools on different subject systems using different experimental setups. Also, the subject systems were not of wide varieties. Emphasizing these issues (with several others mentioned in the motivation) we have conducted a comprehensive empirical study where we have - (i) implemented and investigated seven existing methodologies that explored different aspects of stability, (ii) used two clone detection tools (NiCad and CCFinderX) to implement each of these seven methodologies, and (iii) investigated the stability of three types (Type-1, Type-2, Type-3) of clones. Our investigation on 12 diverse subject systems covering three programming languages (Java, C, C#) with a list of 8 stability assessment metrics suggest that (i) cloned code is often more unstable (change-prone) than non-cloned code in the maintenance phase, (ii) both Type 1 and Type 3 clones appear to exhibit higher instability than Type 2 clones, (iii) clones in Java and C programming languages are more change-prone than the clones in C#, and (iv) changes to the clones in procedural programming languages seem to be more dispersed than the changes to the clones in object oriented languages. We also systematically replicated the original studies with their original settings and found mostly equivalent results as of the original studies. We believe that our findings are important for prioritizing code clones from management perspectives.     

Graph bandit for diverse user coverage in online recommendation

We study a recommendation system problem, in which the system must be able to cover as many users’ preferences as possible while these preferences change over time. This problem can be formulated as a variation of the maximum coverage problem; specifically we introduced a novel problem of Online k-Hitting Set, where the number of sets and elements within the sets can change dynamically. When the number of distinctive elements is large, an exhaustive search for even a fixed number of elements is known to be computationally expensive. Even the static problem is known to be NP-hard (Hochba, ACM SIGACT News 28(2):40–52, 1997) and many known algorithms tend to have exponential growth in complexity. We propose a novel graph based UCB1 algorithm that effectively minimizes the number of elements to consider, thereby reducing the search space greatly. The algorithm utilizes a new rewarding scheme to choose items that satisfy more users by balancing coverage and diversity as it construct a relational graph between items to recommend. Experiments show that the new graph based algorithm performs better than existing techniques such as Ranked Bandit (Radlinski et al. 2008) and Independent Bandits (Kohli et al. 2013) in terms of satisfying diverse types of users while minimizing computational complexity.     

An improved Urdu stemming algorithm for text mining based on multi-step hybrid approach

Stemming is the basic operation in Natural language processing (NLP) to remove derivational and inflectional affixes without performing a morphological analysis. This practice is essential to extract the root or stem. In NLP domains, the stemmer is used to improve the process of information retrieval (IR), text classifications (TC), text mining (TM) and related applications. In particular, Urdu stemmers utilize only uni-gram words from the input text by ignoring bigrams, trigrams, and n-gram words. To improve the process and efficiency of stemming, bigrams and trigram words must be included. Despite this fact, there are a few developed methods for Urdu stemmers in the past studies. Therefore, in this paper, we proposed an improved Urdu stemmer, using hybrid approach divided into multi-step operation, to deal with unigram, bigram, and trigram features as well. To evaluate the proposed Urdu stemming method, we have used two corpora; word corpus and text corpus. Moreover, two different evaluation metrics have been applied to measure the performance of the proposed algorithm. The proposed algorithm achieved an accuracy of 92.97% and compression rate of 55%. These experimental results indicate that the proposed system can be used to increase the effectiveness and efficiency of the Urdu stemmer for better information retrieval and text mining applications.     

Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

With the development and wide adoption of industrial wireless sensor networks to support various domain applications, the boundary detection of continuous objects has become an important research challenge, where improving the accuracy of boundary area while reducing the energy consumption are the first-class citizens to be considered. To address this research challenge, this article proposes a two-stage boundary face detection mechanism, where sensor nodes are duty-cycled and to be deployed in a dense fashion. When the occurrence of potential events are recognized using the initially activated sensor nodes, the boundary faces of continuous objects are constructed through adopting planarization algorithms. Thereafter, sensor nodes contained in certain boundary faces are examined, where their sensory data are estimated using spatial interpolation methods. Certain sensor nodes are selected to be woken up, only when their sensory data suggest that they should be more appropriate candidates of boundary sensor nodes. Consequently, the size of boundary faces is reduced, and this coarse-to-fine refinement procedure iterates, until all sensor nodes contained in the boundary faces have been examined. Experimental evaluation result shows that the boundary area can be refined significantly and be more precise, where the half of the initial boundary face area should be reduced in most situations.     

New look on relay selection strategies for full-duplex multiple-relay NOMA over Nakagami-m fading channels

Wireless Networks - By removing the orthogonal use of radio-resources, non-orthogonal multiple access (NOMA) has been introduced to improve the spectral efficiency of fifth generation (5G) and...     

A comprehensive survey of AI-enabled phishing attacks detection techniques

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

     

Biomimetic Nanomaterial Strategies for Virus Targeting: Antiviral Therapies and Vaccines

The ongoing coronavirus disease 2019 (COVID-19) pandemic highlights the importance of developing effective virus targeting strategies to treat and prevent viral infections. Since virus particles are nanoscale entities, nanomaterial design strategies are ideally suited to create advanced materials that can interact with and mimic virus particles. In this progress report, the latest advances in biomimetic nanomaterials are critically discussed for combating viral infections, including in the areas of nanomaterial-enhanced viral replication inhibitors, biomimetic virus particle capture schemes, and nanoparticle vaccines. Particular focus is placed on nanomaterial design concepts and material innovations that can be readily developed to thwart future viral threats. Pertinent nanomaterial examples from the COVID-19 situation are also covered along with discussion of human clinical trial efforts underway that might lead to next-generation antiviral therapies and vaccines.     

A new denoising method for removing salt & pepper noise from image

Multimedia Tools and Applications - Digital image has a significant importance in many fields in human life such as, in medicine, photography, biology, astronomy, industry and defense. Thus, it...     

Usability evaluation of a virtual reality smartphone app for a living museum

This paper elaborates the empirical evidence of a usability evaluation of a VR and non-VR virtual tour application for a living museum. The System Usability Scale (SUS) was used in between participants experiments (Group 1: non-VR version and Group 2: VR version) with 40 participants. The results show that the mean scores of all components for the VR version are higher compared to the non-VR version, overall SUS score (72.10 vs 68.10), usability score (75.50 vs 71.70), and learnability (58.40 vs 57.00). Further analysis using a two-tailed independent t test showed no difference between the non-VR and VR versions. Additionally, no significant difference was observed between the groups in the context of gender, nationality, and prior experience (other VR tour applications) for overall SUS score, usability score, and learnability score. Α two-tailed independent t test indicated no significant difference in the usability score between participants with VR experience and no VR experience. However, a significant difference was found between participants with VR experience and no VR experience for both SUS score (t(38) = 2.17, p = 0.037) and learnability score (t(38) = 2.40, p = 0.021). The independent t test results indicated a significant difference between participant with and without previous visits to SCV for the usability score (t(38) = −2.31, p = 0.027), while there was no significant differences observed in other components. It can be concluded that both versions passed based on the SUS score. However, the sub-scale usability and learnability scores indicated some usability issue.

     

The development of spectral indices for early detection of Ganoderma disease in oil palm seedlings

Field spectroscopy is a rapid and non-destructive analytical technique that may be used for assessing plant stress and disease. The objective of this study was to develop spectral indices for detection of Ganoderma disease in oil palm seedlings. The reflectance spectra of oil palm seedlings from three levels of Ganoderma disease severity were acquired using a spectroradiometer. Denoizing and data transformation using first derivative analysis was conducted on the original reflectance spectra. Then, comparative statistical analysis was used to select significant wavelength from transformed data. Wavelength pairs of spectral indices were selected using optimum index factor. The spectral indices were produced using the wavelength ratios and a modified simple ratio method. The relationship analysis between spectral indices and total leaf chlorophyll (TLC) was conducted using regression technique. The results suggested that six spectral indices are suitable for the early detection of Ganoderma disease in oil palm seedlings. Final results after regression with TLC showed that Ratio 3 is the best spectral index for the early detection of Ganoderma infection in oil palm seedlings. For future works, this can be used for the development of robust spectral indices for Ganoderma disease detection in young and mature oil palm using airborne hyperspectral imaging.