ABSTRACT Although JavaScript dominates modern software development, research on its quality attributes remains scarce, despite the fundamental differences that distinguish it from other languages. This motivates dedicated research related to JavaScript quality attributes and metrics. This paper aims to identify (a) the quality attributes of the JavaScript language that are mainly studied and (b) the quality metrics that are used to quantify them. Additionally, the paper provides information on the tools that can be used to measure quality metrics. To achieve these goals, we have conducted a mapping study on seven journals and eight conferences of high quality. A total of 142 primary studies, published between 2002 and February 2025, have been selected and analyzed, to identify and classify software metrics to high‐level quality attributes, as described in ISO/IEC 25010:2011. Maintainability, Security, Reliability, and Usability quality attributes are the most studied ones. Furthermore, 78 generic and 48 JavaScript‐specific metrics were identified. A wide dispersion of metrics has been identified for assessing each quality attribute, based on different development tasks. Moreover, a variety of tools and benchmarks were identified. A clear research trend in JavaScript quality assessment related to issues that involve software reuse, code testing, and dynamic code analysis has been identified. Yet differences among primary studies in quality assessment and quantification, along with tool adoption indicate the need for further exploration of these recurring topics.

JavaScript (JS) is one of the most popular programming languages on GitHub. Most JavaScript applications are reusing third-party components to acquire various functionalities. Despite the benefits offered by software reuse there are still challenges, during the evolution of JavaScript applications, related to the management and maintenance of the third-party dependencies. Our key objective is to explore the evolution of library dependencies constraints in the context of JavaScript applications in terms of (a) the changeability (i.e., number of removed, added, or maintained libraries) (b) the update frequency of the library dependencies. For this purpose, we conducted a case study on the 86 most forked JavaScript applications hosted on GitHub and analyzed reuse data from a total of 2.363 successive releases. In general, 39% of the packages introduced in the first version of the project are being reused in the entire project’s lifetime. The number of package dependencies slightly grows over time, while several other are being permanently removed. Regarding the evolution of third-party applications, it is observed that developers do not update the dependencies constraints to a most recent version, waiting to reach probably “breaking points” when the updates will be inevitable.

Motivated by the emergence of next-generation internet-of-things applications, this paper introduces a novel non-orthogonal semi-grant-free multiple access protocol that enables reliable massive connectivity of scheduled and random access devices with varying data rate requirements. Specifically, the protocol divides devices into two classes, namely primary and secondary devices. Primary devices (PDs) have high data rate requirements, while secondary devices (SDs) transmit data at a lower frequency compared to primary devices, and have a lower data rate requirement. The base-station (BS) enables a single PD and two SDs to access the same radio resource block (RRB). The PD uses grant-based (GB) access to the RRB, while the secondary devices use grant-free (GF) access. The BS first decodes the PD signal by treating the SDs signals as interference. Then, it decodes the signal of the SD with the higher channel gain. To assess the performance of the proposed protocol, we carried out simulations and measured the achieved throughput of each device under various parameters, such as power-to-noise gain, signal-to-noise threshold, and transmission probability.

Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Undoubtedly, energy consumption in communication networks poses a significant threat to the environmental stability. Access networks contribute to this consumption by being composed of numerous energy inefficient devices and network equipment. Passive Optical Networks (PONs), one of the most promising candidates in the field of access networking, should avoid this bottleneck in the backhaul power consumption by lowering the energy use of the optical devices. In this paper, we move towards that direction by introducing an energy efficient power management scheme that encompasses two major goals: a) to reduce the energy consumption by allowing the optical devices to enter the sleep mode longer, and b) to concurrently maintain the network performance. To this end, we focus on the energy consumed by the optical network units (ONUs). The intelligence of the ONUs is stimulated by enhancing the decision making in determining the duration of the sleep period with learning from experience mechanism. Learning automata (LAs) are charged to address this challenge. The evaluation of the proposed enhanced power management scheme reveals considerable improvements in terms of energy savings, while at the same time the network performance remains in high levels.

Electrooculographic (EOG) artefact is one of the most common contaminations of Electroencephalographic (EEG) recordings. The corruption of EEG characteristics from Blinking Artefacts (BAs) affects the results of EEG signal processing methods and also impairs the visual analysis of EEGs. In this paper, our scope was a comparative analysis of the performance of three standard denoising methods like continuous Empirical Mode Decomposition (EMD), Discrete Wavelet Transform (DWT) and Kalman Filter (KF). In order to evaluate the performance of EMD, DWT and KF of noise reduction and to express the quality of the denoised EEG, we calculate several indexes such as the Signal-to-Noise Ratio (SNR). All the results obtained from noise simulated EEG data show that WT achieved the greatest SNR difference and also the mode mixing issue of EMD affected this method's performance.

Recent vehicular networking activities include public vehicle to vehicle/infrastructure (V2X) large scale deployment, machine-to-machine (M2M) integration scenarios and more automotive applications. eHealth is about the use of the Internet to disseminate health related information, and is one of the promising Internet of Things (IoT) applications. Combining vehicular networking and eHealth to record and transmit a patient's vital signs is a special telemedicine application that helps hospital resident health professionals to optimally prepare the patient's admittance. From the automotive perspective, this is a typical Vehicle-to-Infrastructure (V2I) communication scenario. This proposal provides an IPv6 vehicular platform which integrates eHealth devices and allows sending captured health-related data to a Personal Health Record (PHR) application server in the IPv6 Internet. The collected data is viewed remotely by a doctor and supports a diagnostic decision. This paper introduces the integration of vehicular and eHealth testbeds, describes related work and presents a lightweight auto-configuration method based on a DHCPv6 extension to provide IPv6 connectivity for resource constrained devices.

We present in this paper the evaluation of the energy consumption of PRCSMA, an 802.11-based medium access control protocol designed to coordinate the retransmissions from the relays in a wireless network implementing a Cooperative Automatic Retransmission Request (C-ARQ) scheme. A comparison in terms of energy efficiency with non-cooperative ARQ schemes (retransmissions performed only from the source) and with ideal C-ARQ (with perfect scheduling among the relays) is included in this paper. The main results show the conditions under which a C-ARQ scheme with PRCSMA outperforms, in terms of energy efficiency, non-cooperative ARQ schemes and also show that the overhead of the MAC layer cannot be neglected in order to accurately evaluate the performance of a C-ARQ scheme.

Communication networks' energy consumption poses a considerable threat to the environment stability. The expansion of access networks, which constitute the main playground of the Internet backhaul, is accompanied by numerous energy inefficient devices and equipments. Passive optical networks (PONs) is a potential dominant technology on the field of access networking, hence the reduction of the consumed energy of the optical devices forms a critical issue. In this paper, an efficient green PON is introduced, having two main targets: a) to reduce the energy consumption, by allowing optical devices to operate longer in sleep mode, and b) to maintain PON's good performance. Beyond the green provisioning, the proposed scheme is able to increase occasionally the network performance in terms of mean packet delay and packet drop ratio. This is accomplished by reducing the amount of control messages between subscribers, operating in sleep mode, and central office, allowing more bandwidth to be allocated for data delivering.

Whenever DFT (discrete Fourier transform) processing of a multidimensional discrete signal is required, one can apply either a multidimensional FFT (fast Fourier transform) algorithm, or a single-dimension FFT algorithm, both using the same number of points. That is, the dimensions of a "multidimensional" signal, and of its spectrum, are a matter of choice. Every multidimensional sequence is completely equivalent to a one-dimensional function in both "time" and "frequency" domains. This statement applied to MRI (magnetic resonance imaging) explains why one can reconstruct the slice by using either one-dimensional or two-dimensional methods, as it is already done in echo planar methods. In the commonly used spin warp methods, the image can be also reconstructed by either one- or two-dimensional processing. However, some artifacts in the images reconstructed from the original "zig-zag" echo planar trajectory, are shown to be due to the wrong dimensionality of the FFT applied.