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The biosensors on a human body form a wireless body area network (WBAN) that can examine various physiological parameters, such as body temperature, electrooculography, electromyography, electroencephalography, and electrocardiography. Deep learning can use health information from the embedded sensors on the human body that can help monitoring diseases and medical disorders, including breathing issues and fever. In the context of communication, the links between the sensors are influenced by fading due to diffraction, reflection, shadowing by the body, clothes, body movement, and the surrounding environment. Hence, the channel between sensors and the central unit (CU), which collects data from sensors, is practically imperfect. Therefore, in this article, we propose a deep learning-based COVID-19 detection scheme using a WBAN setup in the presence of an imperfect channel between the sensors and the CU. Moreover, we also analyze the impact of correlation on WBAN by considering the imperfect channel. Our proposed algorithm shows promising results for real-time monitoring of COVID-19 patients.

The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%.

Mobile platforms are rapidly and continuously changing, with support for new sensors, APIs, and programming abstractions. Static analysis is gaining a growing interest, allowing developers to predict properties about the run-time behavior of mobile apps without executing them. Over the years, literally hundreds of static analysis techniques have been proposed, ranging from structural and control-flow analysis to state-based analysis.In this paper, we present a systematic mapping study aimed at identifying, evaluating and classifying characteristics, trends and potential for industrial adoption of existing research in static analysis of mobile apps. Starting from over 12,000 potentially relevant studies, we applied a rigorous selection procedure resulting in 261 primary studies along a time span of 9 years. We analyzed each primary study according to a rigorously-defined classification framework. The results of this study give a solid foundation for assessing existing and future approaches for static analysis of mobile apps, especially in terms of their industrial adoptability.Researchers and practitioners can use the results of this study to (i) identify existing research/technical gaps to target, (ii) understand how approaches developed in academia can be successfully transferred to industry, and (iii) better position their (past and future) approaches for static analysis of mobile apps.

With the development of online retailing on third-party e-commerce platforms such as Taobao, the churn of individual shops is becoming increasingly severe. Platform operators are eager to learn the causes of this eroding trust. Based on Hofstede’s cultural dimensions theory, this paper aims to investigate how masculinity, uncertainty avoidance and long-term orientation impact platform trust and the interaction between these three cultural variables. Based on data collected from sellers on the largest Chinese C2C platform, we found that uncertainty avoidance had a negative effect on platform trust, while masculinity and long-term orientation exerted a positive impact on platform trust. A significant association among the cultural factors was also found.

Owing to the recent fuel in investigation of lip as a biometrics trait both in physiological and behavioral aspect, and in healthcare application, it is imperative to comprehensively study and document the literature. Hence, in this work we enlist the works related to the lip as a biometirc trait and other applications-based on its behavior and physiology. Consequently, in this survey we first discuss the lip anatomy that permits to identify the physiological and behavioral properties. Followed by the list of challenges to characterize lip behavior and physiology. Next, we proceed to provide an insight of several inspiring ideas proposed in the literature to establish lip behavior and physiology for identity and other applications such as health monitoring and forensic. Whilst, it can be concluded at the end of the survey that their are several open research area and unsolved issues, which we discuss in details at the end of this survey to attract the attention of researchers.

This paper introduces a novel and simple approach of high-level scene classification. Knowing that objects are the essence of any given scene, the proposed method uses them to construct a well-structured background knowledge, which is composed of ranking functions and a statistical collection, in order to support the scene classification process. Since not all objects are relevant, only the most salient ones are identified and used in computing the appropriate scene category. To prove the efficiency of the proposed method, experiments are conducted on state of the art datasets: MIT Indoor, SUN900, SUN2012, SUN397 and LabelMe+.Comparisons with other methods were also introduced. The obtained results are reported and discussed.

The privacy and utility are treated as a major factor in influencing the role of data privacy preservation in cloud environments. There exists a trade-off between these two factors, where one factor should compromise its functionality over the other. It is hence necessary to maintain both utility and privacy for a data offloaded or accessed across cloud computing environment. In this paper, we develop a utility privacy model that established utility using Deep Adaptive Clustering (DAC) and privacy using Elliptic Curve Digital Signature Algorithm (ECDSA). The utility is performed using clustering of the input datasets using DAC and the privacy is maintained using ECDSA. The simulation is conducted on specific datasets to test the efficacy of the model and the results shows improved accuracy on clustering, and efficient privacy metrics than existing methods.

High utility itemset (HUI) mining is a powerful data mining technique to discover profitable patterns. The utility of an item is computed by using two measures named quantity and per-unit profit. All existing HUI mining algorithms consider a single value of external utility (per unit profit) for the entire database. However, the per-unit profit of items might fluctuate over time in many applications. This research introduces three novel strategies to comprise the external utilities of items as input for the HUI mining algorithm. Traditional HUI mining algorithms have been developed for the standalone system and do not fit for big data processing due to the limited computing resources (CPU, memory). Big data are efficiently processed on distributed frameworks like Apache Hadoop, Spark, etc. This paper introduces a distributed HUI mining algorithm named Spark-based Top-k high utility itemset (k-SHUI) miner. We also propose a fair load distribution strategy to divide the search space equally among the cluster nodes. The k-SHUI produces top-k HUIs without the requirement of the minimum utility threshold. We conducted extensive experiments on six real-life datasets to compare the proposed algorithm's performance with the existing algorithm. The experimental results demonstrate that the proposed algorithm outperforms the existing algorithms.

Transformation in higher education is one of the main objectives to meet the global challenges and deliver higher sustainable and knowledge-based development levels. It is pivotal to inspire the quality of teaching and innovative pedagogy. Advancement in technology has led to growth of many effectual methods for state-of-the-art pedagogies in the field of education. Engineering education is built on a innovative standard in which engineering is to be practiced with technical expertise as well as social responsibility. The teaching community has assimilated a lot of teaching strategies for changing teacher-centric education to student-centric education. This paper targets to present the facets of the teaching–learning process, teaching pedagogies, and curricular approaches of engineering pedagogy and study the teaching strategies followed by engineering teaching fraternity. In order to study the different teaching strategies followed by engineering teachingfraternity, an online survey on teaching strategies was conducted for the engineering teaching fraternity all over India. This online survey shows that there is a shift in the teaching process from teacher-centric education to student-centric education. Nearly 85–90% of the teaching fraternity follow student-centric teaching approach by using many strategies and tools, which consecutively results in Outcome-Based Education.

Supporting transfers of science big data over Wide Area Networks (WANs) with Data Transfer Nodes (DTNs) requires optimizing multiple parameters within the underlying infrastructure. New solutions for such data movement require new paradigms and technologies, such as NVMe over Fabrics, which provides high-performance data movement with direct remote NVMe device access over traditional fabrics. However, recent NVMe over Fabrics studies have been limited to local storage fabrics. To support increasing demands for the large volume of science data movement during Supercomputing (SC) conferences, we proposed a SCinet DTN-as-a-Service framework orchestrating the desired optimization to meet users, applications, and providers’ requirements. Furthermore, we extend the SCinet DTN-as-a-Service framework to incorporate new techniques, solve optimization issues in data-intensive science and evaluate NVMe over Fabrics with multiple WAN testbeds to examine its performance and discover new opportunities for optimization.

In cloud, sensitive data is stored in an encrypted form, and searchable encryption is a technique which allows user to search over encrypted data by keyword and it leaks no useful information. However, the secure searchable encryption scheme which can prevent inside keyword guessing attacks (IKGA) and satisfy multi-trapdoor privacy has not been proposed. We first review the security of Wu et al.’s scheme and demonstrate that their scheme is insecure under IKGA and multi-keyword guessing attacks in this paper. To solve these issues, we propose an improved certificateless public key searchable encryption scheme. Furthermore, we give the proof of the security of our scheme under the computational bilinear Diffie-Hellman assumption. It shows that our scheme can resist IKGA successfully, and provides multi-ciphertext indistinguishability and multi-trapdoor privacy. The better performance is shown in our experiment.

In this article, we compare the impact of state-of-the-art light field compression methods. It addresses quality of (a) refocused images and (b) point clouds reconstructed from 4D light field data. The methods include recent video compression formats, specifically H.265, AV1, XVC, and H.266/VVC (finalized in 2020). In addition, we have extended a standard image compression method into four dimensions and compared it with the video compression formats. It turned out that the new VVC format demonstrated superior performance, closely followed by the underrated XVC. Apart from the comparison, we show that the four-dimensional light field data can be compressed with a higher ratio than independent still images while maintaining the same visual quality of a perceived picture.

In today’s world most of the people are becoming more and more dependent on visual media information particularly digital images and videos. However, the advancement in technology has lead to increase in number of editing tools that make the image and video tampering easier and faster. The content of the digital video can be manipulated or altered effectively with the help of such editing tools without leaving any noticeable signs. Numerous attempts have been made over the previous decade to recognize the altered videos and localization of the altered frames with high exactness dependent on some extraordinarily structured system. This paper gives a detailed review of existing methodologies for recognizing tampered videos, localization of the altered frames and reconstruction of the tampered video.

Privacy image classification can help people detect privacy images when people share images. In this paper, we propose a novel method using multi-level and multi-scale features for privacy image classification. We first use CNN (Convolutional Neural Network) to extract multi-levels features. Then, max-pooling layers are employed to obtain multi-scale features at each level. Finally, we propose two feature aggregation models, called Privacy-MSML and Privacy-MLMS to fuse those features for image privacy classification. In Privacy-MSML, multi-scale features of the same level are first integrated and then the integrated features are fused. In Privacy-MLMS, multi-level features of the same scale are first integrated and then the integrated features are fused. Our experiments on a real-world dataset demonstrate the proposed method can achieve better performance compared with the state-of-the-art solutions.

Fog computing is considered a formidable next-generation complement to cloud computing. Nowadays, in light of the dramatic rise in the number of IoT devices, several problems have been raised in cloud architectures. By introducing fog computing as a mediate layer between the user devices and the cloud, one can extend cloud computing's processing and storage capability. Offloading can be utilized as a mechanism that transfers computations, data, and energy consumption from the resource-limited user devices to resource-rich fog/cloud layers to achieve an optimal experience in the quality of applications and improve the system performance. This paper provides a systematic and comprehensive study to evaluate fog offloading mechanisms' current and recent works. Each selected paper's pros and cons are explored and analyzed to state and address the present potentialities and issues of offloading mechanisms in a fog environment efficiently. We classify offloading mechanisms in a fog system into four groups, including computation-based, energy-based, storage-based, and hybrid approaches. Furthermore, this paper explores offloading metrics, applied algorithms, and evaluation methods related to the chosen offloading mechanisms in fog systems. Additionally, the open challenges and future trends derived from the reviewed studies are discussed.

The development of web cameras and smart phones is mature, and more and more facial recognition-related applications are implemented on embedded systems. The demand for real-time face recognition on embedded systems is also increasing. In order to improve the accuracy of face recognition, most of the modern face recognition systems consist of multiple deep neural network models for recognition. However, in an embedded system, integrating these complex neural network models and execute simultaneously is not easy to achieve the goal of real-time recognition of human faces and their identities. In view of this, this study proposes a new frame analysis mechanism, continuous frames skipping mechanism (CFSM), which can analyze the frame in real time to determine whether it is necessary to perform face recognition on the current frame. Through the analysis of CFSM, the frames that do not need to be re-recognized for face are omitted. In this way, the workload of the face recognition system will be greatly reduced to achieve the goal of real-time face recognition in the embedded system. The experimental results show that the proposed CFSM mechanism can greatly increase the speed of face recognition in the video on the embedded system, achieving the goal of real-time face recognition.

Accessible websites are a necessity for everyone, especially for people with disabilities. However, this is an objective that has not been achieved yet and requires initiatives that continue guiding the way towards an increasingly inclusive web and lowering accessibility barriers. One of the first steps in this regard is to identify the accessibility level of a particular website, and for this purpose, a set of automatic tools and manual instruments have been developed. These tools allow calculating an accessibility level from the expert’s perspective and from the website’s source code using as a reference the accessibility guidelines defined by the W3C. The authors state that evaluating accessibility with just one of these methods is not enough, so therefore its integration is required. This article presents a tool called Accessibility Evaluation Tool, which allows and facilitates the integration of the accessibility evaluation delivered by an automatic validator with the manual evaluation of elements by experts.

ASCON is one of the elegant designs of authenticated encryption with associated data (AEAD) that was selected as the first choice for lightweight applications in the CAESAR competition, which also has been submitted to NIST lightweight cryptography standardization. ASCON has been in the literature for a while; however, there has been no successful AEAD which is secure and at the same time lighter than ASCON. In this article, we have overcome the challenge of constructing a permutation that is lighter than the ASCON permutation while ensuring a similar performance, and based on which we achieve a more lightweight AEAD which we call Sycon. Extensive security analysis of Sycon confirms that it provides the same level of security as that of ASCON. Our hardware implementation result shows that the Sycon permutation has 5.35% reduced area, compared to the ASCON permutation. This leads to a remarkable area reduction for Sycon AEAD which is about 14.95% as compared to ASCON AEAD. We regard Sycon as a new milestone as it is the lightest among all the AEADs belonging to the ASCON family.

There have been considerable advancements in multimedia technologies over the past 5 years. It has been observed that state-of-the-art multimedia systems face three broad categories of challenges: (1) Dependency on continuous network connections, (2) Data-sharing applications & collaboration, and (3) Security issues. Among these, security vulnerability poses a major threat to modern multimedia systems. Therefore, it is imperative to carefully investigate the security issues that can endanger wireless and mobile communications. At present, multimedia security research mainly focuses on wireless traffic monitoring, wireless system attacks, and wireless and mobile security. In this paper, we have used the network attack-type, “Reconnaissance”, which contains two types of malicious activities: (1) OS scanning, and (2) Fuzzing. The goal of this paper is to quantify multimedia security risks due to Fuzzing by using various types of machine learning models. The highest accuracy i.e., 96.8%, is obtained using the XGBoost classifier, which is good compared to the existing models present in the literature. This is the first paper, to the best of our knowledge, that uses machine learning methods to differentiate between benign and malignant Fuzzing attacks.

In ciphertext-policy attribute-based encryption (CP-ABE), once malicious users successfully decrypt the encrypted data, they can obtain the real original personal privacy data, leading to serious privacy leakages problems. Thus, if the user does not access the original private data but the perturbed data while guaranteeing statistical characteristics, the privacy protection capabilities of CP-ABE will be greatly improved. Motivated by this, an integration of basic CP-ABE and local differential privacy (LDP) or achieving multiple privacy levels data sharing (MPLDS) is constructed to provide double privacy protection for data owners, which is with a relatively lower complexity and higher data utility. To prevent different trusted users from colluding and gaining more privacy beyond their trust levels, a randomized perturbation strategy is elaborately designed for resisting collusion attacks (RCA) while ensuring the fact that the output of RCA perturbation strategy is the same as that of the original perturbation, which has been proved from the theoretical level. Finally, the proposed MPLDS scheme is simulated and verified on both synthetic and real data sets, which indicates that the proposed MPLDS scheme outperforms the existing MPPDS scheme while greatly reducing the complexity.

Forward privacy of RFID systems and its relaxed version, narrow forward privacy, are generally considered satisfactory for practical needs. Unfortunately, the attempt to get forward privacy by symmetric-key cryptography failed. Also, all symmetric-key cryptography-based RFID systems proposed so far that are strictly narrow forward private (that is, narrow forward private but not forward private) suffer from desynchronization. Under these circumstances, the question whether the attempt to exceed these limits is doomed to failure or not was frequently asked. The paper we are proposing wants to clarify this matter. Thus, we show that forward privacy in Vaudenay’s RFID model cannot be achieved with symmetric-key cryptography. Then, we show that strictly narrow forward privacy can be achieved with symmetric-key cryptography only by RFID systems with unbounded desynchronization. This last result holds for strictly narrow destructive privacy and strictly narrow strong privacy too, if one wants to achieve them with symmetric-key cryptography.

Background Communication failures secondary to damaged infrastructure have caused difficulties in coordinating disaster responses. Two-way radios commonly serve as backup communication for hospitals. However, text messaging has become widely adopted in daily life and new technologies such as wireless mesh network (WMN) devices allow for text messaging independent of cellular towers, Wi-Fi networks, and electrical grids. Objective To examine the accuracy of communication using text-based messaging transmitted over WMN devices (TEXT-WMN) compared to voice transmitted over two-way radios (VOICE-TWR) in disaster simulations. Secondary outcomes were patient triage accuracy, perceived workload, and device preference. Methods 2 × 2 Latin square crossover design: 2 simulations (each involving 15 min of simulated hospital-wide disaster communication) by 2 modalities (TEXT-WMN and VOICE-TWR). Physicians were randomized to one of two sequences: VOICE-TWR first and TEXT-WMN second; or TEXT-WMN first and VOICE-TWR second. Analyses were conducted using linear mixed effects modeling. Results On average, communication accuracy significantly improved with TEXT-WMN compared to VOICE-TWR. Communication accuracy also significantly improved, on average, during the second simulation compared to the first. There was no significant change in triage accuracy with either TEXT-WMN or VOICE-TWR; however, triage accuracy significantly improved, on average, during the second simulation compared to the first. On average, perceived workload was significantly lower with TEXT-WMN compared to VOICE-TWR, and was also significantly lower during the second simulation compared to the first. Most participants preferred TEXT-WMN to VOICE-TWR. Conclusion TEXT-WMN technology may be more effective and less burdensome than VOICE-TWR in facilitating accurate communication during disasters.

Image captioning is a challenging task of computer vision and natural language processing. The big challenge lies in obtaining semantic information from images and translating that into the human language using machines. The interaction of computer vision and natural language processing further increases the complexity of image captioning. Notably, research has been carried out in image captioning to narrow down the semantic gap using deep learning techniques effectively. Deep learning techniques are proficient in dealing with the complexities of image captioning. A detailed study is carried out to identify the various state-of-the-art techniques for image captioning. The working algorithm of technique, positive highlights, and weakness of every technique is discussed in this paper. We also discussed the quantitative evaluation measures used for deep learning techniques and available datasets.

The current onboard wireless and sensors technologies have a significant role in improving day-to-day users’ activities. Examples of these technologies are WiFi, Bluetooth, Cellular, Global Navigation Satellite System (GNSS), proximity, camera, and inertial sensors. Using these technologies provides many applications from the range of communication to the location-based systems (LBS). The LBSs locate people and objects from outdoors into indoors for many purposes, including navigation, entertainment services, and even for security issues. However, the most notable limitations of these technologies include a lack of accuracy and cost-efficiency. Several research types have been attempted to tackle these issues by combining the various technologies, using smart models, and applying optimization algorithms. Therefore, this study reviews many models on the shelf and analyzes the models according to their improvement of accuracy, reduction of time to fix, and cost-efficiency. Specifically, in this study, the optimization algorithms used for positioning purposes are investigated and classified into two groups of algorithms: statistical and non-statistical algorithms. Further, the study also illustrates the weaknesses and limitations of the surveyed algorithms. Finally, the famous challenges and future trends are listed to provide a useful guide for the current readers.

In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. OCT images can be used to differentiate between a healthy eye and an eye with CNV disease. In CNV the Retinal Pigment Epithelial layer experiences changes in various properties which can be related to the assistance of OCT Images. This paper proposes a technique for finding CNV in OCTA pictures. Among the few attributes of CNV, the bigger turning point of veins is a moderately clear element, so we will utilize this property to see if there is CNV in an OCTA picture. DenseNet and Vgg16 Architectures of Deep Learning were used in the study and the hyper parameters of both of the architectures were changed to diagnose the disease properly. After the detection of the disease, the diseased OCT images are segmented from the background for the Region of Interest detection using Python OpenCV library that is used for the processing of images. The results of implementation of the Architectures showed that Vgg16 showed better results in detecting the images rather than Dense Net Architecture with an accuracy percentage of 97.53% approximately a percent greater than Dense Net.

Vehicular Adhoc Network (VANET) is based on the principles of Mobile Adhoc NETwork (MANET) where vehicles are considered as nodes and secure communication is established to provide a safe driving experience. Due to its unique characteristics, it has various issues and challenges. These issues can be resolved by ensuring security requirements like authentication, privacy preservation, message integrity, non-repudiation, linkability, availability etc. Authentication plays a vital role since it is the first step to establish secure communication in the vehicular network. It also distinguishes malicious vehicles from legitimate vehicles. Different authentication schemes have been proposed to establish secure vehicular communications. A survey of the existing authentication schemes is given in this paper. At first, the existing authentication schemes are broadly classified based on message signing and verification methods. Then, each category is clearly explained with its sub-categories. At last, the existing schemes in each category are compared based on security requirements, security attacks and performance parameters.

Computer-assisted Collaborative Learning (CCL) is a good teaching technique to enhance students' learning and intellectual performance consistency. Several studies have been undertaken to recognize variables that affect student intellectual ability. Observation obtained from the studies helped students in the right way and improved their habits and personal condition in learning and intellectual performance. In this paper, the proposed method has a multi-level multi-class classification algorithm. The dataset is transformed into various problem syntheses, and adaptive methods such as Binary Significance (BS), Label Powerset (LP), Classifier Series (CS), K-Nearest Neighbor (KNN), and Machine Learning integrated K-Nearest Neighbor (MLiKNN) adaptive classifier algorithm. Finally, the variables that significantly impact the student's success are identified, and recommendations for enhancing those factors are suggested to enhance the student's intellectual ability. The outcomes observed from the proposed classifier framework provide more recommended strategies to enhance students' learning environment and intellectual ability.

In this research work, a circularly polarized (CP) monopole antenna is designed for Ultra-Wideband (UWB) applications. The CP UWB antenna is be made up of a reformed ring patch and ground plane. The slots and stubs are inserted in the ground to achieve CP in the UWB antenna. This antenna attained an Axial Ratio Bandwidth (ARBW) of 5 GHz (4.0–9.0 GHz) that lies in the UWB frequency range that is from 3.1 to 10.6 GHz. The designed antenna has a radiation efficiency of around 80% for the complete UWB frequency range. The CP UWB antenna is designed and fabricated using the FR4 with a compact size of 32 × 30 × 1.6 mm3 and with a peak gain of 6.8 dBi. Tested results are in good resembles with simulated ones.

Both Visual Question Answering (VQA) and image captioning are the problems which involve Computer Vision (CV) and Natural Language Processing (NLP) domains. In general, computer vision models are effectively utilized to represent visual contents. While NLP algorithms are used to represent the sentences. In recent years, VQA and image captioning tasks are tackled independently although they require similar type of algorithms. In this paper, a joint relationship between these two tasks is established and exploited. We present an image captioning based VQA model that uses the knowledge learnt from the image captioning task and transfers that knowledge to VQA task. We integrate the image captioning module into the VQA model by fusing the features obtained from captioning model and the attention-based visual feature. The experimental results demonstrate the improvement in the answer generation accuracy by a margin 3.45 % on VQA 1.0, 3.33% on VQA 2.0 and 1.73% on VQA-CP v2 datasets over the state-of-the-art VQA models.

Advances in artificial intelligence and machine learning (AI/ML) algorithms are not only the fastest growing areas but also provide endless possibilities in many different science and engineering disciplines including computer communication networks. These technologies are used by billions of people. Any person who has a smartphone can tangibly experience advances in communication networks, social media, natural language processing, and computer vision that were not possible just 10 years ago. Research is therefore needed to understand and improve the potential and suitability of AI/ML in general for communications and networking technologies, but also in particular for systems and networks operations and management.

This research reviews challenges in building sustainable relationships between the parties involved in the crowdfunding and crowdsourcing projects, which are running in extreme situations, such as the COVID-19 pandemic. This study aims to solve problems that generate the crowdsourcing concerns and to find better alternatives to increase trust for crowdfunding among donors, as this impacts their strategic sustainability in the conditions of turbulence and COVID-induced financial crisis. It was found that factors influence donor decisions in different ways, yet the common tendency for donor activity is non-monotonicity. Future development in the field of sustainable relationships should focus on creating a donor classification system.

An intelligent tutoring system (ITS) provides universal access to education, which is an emerging paradigm of modern education. ITS is an educational tool that provides learners with adaptive and real-time instructional content, wherever they happen to be. Learner acceptance is a critical factor for the success of an ITS. Thus, this study investigated learner acceptance of ITS based on the technology acceptance model. Additionally, this study further investigated the effects of pedagogical agents on learners’ acceptance of ITSs. A total of 102 high school students were recruited to use an ITS that was incorporated as a pedagogical agent in their educational process to learn mathematics over a nine-day period. The results reveal that students’ perceived usefulness had the largest effect on their intentions to use the ITS. Furthermore, the social presence of the pedagogical agent has a significant effect on students’ perceived usefulness. Additionally, interpersonal attraction of the agent has a significant effect on its perceived ease of use. This study’s empirical findings provide implications for both theoretical research and the practical development of ITSs that will help education professionals make full use of ITSs and pedagogical agents.

The last decade has seen data becoming an important resource which drives innovation in areas such as big data and machine learning. Privacy concerns, however, impede the use of personal data in many contexts. Non-Personal Data (NPD)—which comprises either data that does not contain any personal data in the first place, or data from which all personal data has been removed through digital techniques—can be used for driving innovation. This paper critiques the report of the Committee of Experts on non-personal data governance framework released by the Government of India in December 2020, from different standpoints.

Clickjacking is a newly discovered breach in network security. It is based on the functionality of web-designing in which two or more web frames are overlapped over each other. The analysis shows that there is a need for in-depth study on click-jacking attacks (client-side vulnerability) and preventive measures so that early prevention and detection of such kinds of attacks can be implemented in a timely manner. In this research work, most of the client-side attacks are studied and an overview of the clickjacking techniques is presented to provide insights into the area of network security to the researchers. The overview of tools and techniques used by attackers are also investigated along with the prevention measures.

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

We proposed a method employing deep learning (DL) on eye-tracking data and applied this method to detect intentions to use apparel websites that differed in factors of depth, breadth, and location of navigation. Results showed that users’ intentions could be predicted by combining a deep neural network algorithm and metrics recorded from an eye-tracker. Using all of the eye-tracking metric features attained the best accuracy when predicting usage/not-usage intention to websites. In addition, the results suggest that for apparel websites with the same depth, designers can increase usage intention by using a larger number of navigation items and placing the navigation at the top and left of the homepage. The results show that building intelligent usage intention-detection systems is possible for the range of websites we examined and is also computationally practical. Hence, the study motivates future investigations that focus on design of such systems.

Many difficulties are encountered during evacuation from construction sites in hazardous situations, which may lead to severe fatalities. These fatalities, especially caused by fire, may be significantly reduced by ensuring personal protective equipment (PPE) compliance of construction site workers and fire detection through proper surveillance. Thus, the detection of PPEs, fire and injured or trapped persons, can greatly assist in the reduction of fatalities and economic loss. This article presents a novel approach towards the detection of fire and PPEs to assist in the monitoring and evacuation tasks. This work utilizes the YOLOv4 and YOLOv4-tiny algorithms based on deep learning for carrying out the detection task. A self-made dataset has been utilized to train the model in the Darknet neural network framework. Moreover, a comparative analysis with previous works has been carried out in order to endorse the real-time efficacy of the proposed work. The results verify the strength of YOLOv4 algorithm in real-time detection and surveillance at construction sites with maximum mean average precision (mAP) of 76.86 %.

The massive use of multimedia technologies has enabled the exploration of information in many data such as texts, audio, videos, and images. Computational methods are being developed for several purposes such as monitoring, security, business, and even health through the automatic diagnosis of diseases by medical images. Among these diseases, we have melanoma skin cancer. Melanoma is a skin cancer that causes a large number of fatalities worldwide. Several methods for the automatic diagnosis of melanoma in dermoscopic images have been developed. For these methods to be more efficient, it is essential to isolate the lesion region. This study used a melanoma segmentation method based on U-net and LinkNet deep learning networks combined with transfer learning and fine-tuning techniques. Additionally, we evaluate the model’s ability to learn to segment the disease or just the dataset by combining datasets. The experiments were carried out in three datasets (PH2, ISIC 2018, and DermIS) and obtained promising results, emphasizing the U-net that obtained an average Dice of 0.923 in the PH2 dataset, Dice = 0.893 in ISIC 2018, and Dice = 0.879 in the DermIS dataset.

Side-channels are unintended pathways within target systems that leak internal information, exploitable via side-channel attack techniques that extract the target information, compromising the system’s security and privacy. Side-channel attacks are well established within the cybersecurity domain, and thus their cyber-physical systems are actively defended with countermeasures. Non-cyber systems are equally as vulnerable to side-channel attacks; however, this is largely unrecognised and therefore countermeasures to defend them are limited. This paper surveys side-channel attacks against non-cyber systems and investigates the consequent security and privacy ramifications. Side-channel attack techniques rely on respective side-channel properties in order to succeed; therefore, countermeasures that disrupt each side-channel property are identified, effectively thwarting the side-channel attack. This principle is captured within a countermeasure algorithm: a systematic and extensible approach to identifying candidate countermeasures for non-cyber systems. We validate the output of this process by showing how the candidate countermeasures could be applied in the context of each non-cyber system and in the real world. This work provides an extensible platform for translating cybersecurity-derived side-channel attack research into defending systems from non-cyber domains.

Small-scale Wireless Internet Service Providers in rural areas (WISP) are always looking for secure and cost-effective solutions for their infrastructure. Therefore, this paper proposes an architecture for low-cost authentication Rural small-scale WISP Using Mikrotik hotspot and Freeradius server installed on Raspberry Pi 3. As the performance of this architecture testing was conducted, the result of using such a system was highly positive. The significance of this study is derived from the tested solution for low-cost infrastructure of WISP that it introduces.

The internet of things (IoT) is the networking of interrelated devices and sensors connected through the internet to transfer and share data. The data gathered from these devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is found in several domains, such as fault detection and health monitoring systems. In this paper, we review and analyze the relevant literature on existing anomaly detection techniques that apply different machine learning approaches in the IoT. In addition, we examine different anomaly detection datasets used in IoT and highlight the most concerning issues with these datasets for different approaches, and list several future research directions. We believe this survey will serve as a starting point for researchers to gain knowledge from the IoT that employs machine learning approaches to detect anomalies. Moreover, the datasets that associate with anomaly detection in IoT, issues and future directions from a dataset perspective.

The continuous increase of data traffic for present-day applications necessitates the development of Elastic Optical Networks (EONs). Significant advancements in efficient Routing and Spectrum Assignment (RSA) algorithms for EONs have been noticed in the recent past. These existing algorithms did not mention constraints on the number of transceivers per node in a network. However, for the planning of a realistic network, it is necessary to estimate the number of transceivers required at each node for the efficient operation of a network. Therefore, transceiver constraints should be taken into account while designing the RSA algorithms. In this paper, we present the impact of putting a limit to the number of transmitters and receivers available at each node of an EON. Moreover, the cost of a network heavily depends on the number of transceivers that each node in the network may offer. Hence, estimating the required number of transceivers per node in a network is vital to approximate the design cost of a network. Here, we present an Integer Linear Programming (ILP) formulation that includes the transceiver constraints and also develop a transceiver-aware heuristic algorithm for routing and spectrum assignment in EONs. Simulation results help us provide a proper design tool to estimate the number of transceivers per node in elastic optical networks.

The light problem and the complicated environment of underwater images make target detection difficult. These images are usually blurry because tiny inorganic and organic particles in the water have a great impact on light. To solve this problem, we add squeeze and excitation modules after the deep convolution layers of the YOLOv3 model to learn the relationship between channels and enhance the semantic information of deep features. In addition, many small targets will lose too much information after five downsamples. This is not conducive to detection. By expanding the detection scale, we combine the deep semantic information with the location information of the shallower layer to improve the detection performance of small targets. The experimental results show that the YOLOv3-brackish model greatly improved the detection of small fish, crabs, shrimp and starfish. In addition, there were minor improvements in the detection of big fish and jellyfish. The mean average precision increased by 4.43%.

The implementation of IoT related technologies in tourism is promoting the achievement of smart tourism. In this context, Fifth generation (5G) technology, which aims to address the limitations of previous cellular systems, massively expands the implementation of IoT and gradually drives the Internet future to the edge. In order to facilitate the implementation, the work tries to formulate a definition of IoT enabled tourism and make a visualization review on the relevant literature to IoT enabled tourism in recent 10 years. The definition of IoT enabled tourism is helpful for extending the current research topics related to smart tourism. The visualization review identifies the intellectual bases and influential studies of IoT enabled tourism. Meanwhile, the outstanding cooperation relationships among authors, institutions and countries are detected out. These findings are helpful for the academic circle making further efforts on IoT enabled tourism and finally facilitating the achievement of smart tourism.

Password guessers are instrumental for assessing the strength of passwords. Despite their diversity and abundance, little is known about how different guessers compare to each other. We perform in-depth analyses and comparisons of the guessing abilities and behavior of password guessers. To extend analyses beyond number of passwords cracked, we devise an analytical framework to compare the types of passwords that guessers generate under various conditions (e.g., limited training data, limited number of guesses, and dissimilar training and target data). Our results show that guessers often produce dissimilar guesses, even when trained on the same data. We leverage this result to show that combinations of computationally-cheap guessers are as effective as computationally intensive guessers, but more efficient. Our insights allow us to provide a concrete set of recommendations for system administrators when performing password checking., Comment: 14 pages, 20 individual figures including 9 scale bars; clarified that default settings were used for the guessing tools; modified table formats

Modern block ciphers have been used to design security schemes to fulfil the security requirements in different areas of applications. The substitution boxes, significant components, used in block ciphers must have strong cryptographic strength so as to mitigate different attacks. There is no obvious distribution in the cryptographic properties of S-boxes, which is the main difficulty in creating an S-box. This paper proposes a novel group theoretic construction which can inherit the algebraic properties of S-box and able to construct as many as 2,359,296 different S-boxes. As an illustration, the proposed approach is applied to AES S-box and few S-boxes out of 2,359,296 are reported and assessed against standard performance parameters to validate the proposed findings.