Jordan, like other countries in the world, is facing the Coronavirus pandemic and is making a great effort to continue education in Jordanian schools and universities through distance education. To evaluate this novel experience, this study aimed at investigating the effectiveness of distance education in Jordanian universities in light of the Coronavirus pandemic and identifying the obstacles faced by university students. To reach the research objectives, a questionnaire was developed by the researchers followed by semi-structured interviews. A sample of 1000 students was selected randomly from public and private universities. The results revealed that the majority of the Jordanian university students are not pleased with this distance learning experience as most of the students encountered some obstacles such as internet speed, technological difficulties, and online content design.

With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.

Epilepsy is a chronic neurological disorder that significantly affects the quality of life (QoL), often causing irreversible brain damage and physical impairment. Electroencephalography (EEG) signal analysis is crucial for monitoring epilepsy, enabling early seizure detection and timely intervention. Effective seizure detection requires the identification of interpretable features from the EEG signal to improve clinical outcomes. This study proposes a novel interpretable multi-view feature learning approach (IMV-FL), in which the time-domain signals and Discrete Fourier Transform (DFT) are applied to convert the time-domain EEG signal into frequency-domain representations. To develop initial multiview feature extraction and compression, spatial and temporal morphological features are extracted from optimal layers of ResNet and Long Short-Term Memory (LSTM) models, with feature compression performed using a Deep Neural Network (DNN). To construct an interpretable multi-view feature fusion, linear and nonlinear properties are calculated for the feature and with fusion strategies. The selected features are processed using the Mutual Information-Based Feature (MIBF) selection algorithm, and a Stacking Ensemble Classifier (SAEC) is adopted for unified view classification. To enhance clinical interpretability, SHapley Additive exPlanations (SHAP) is applied. The proposed framework outperforms single-view feature learning methods by 3% on average and state-of-the-art techniques by 2% in classification accuracy, sensitivity, specificity, and F1-score using the CHB-MIT Scalp and Bonn EEG datasets. This approach offers an effective tool for EEG-based seizure detection (ESD) in clinical and healthcare settings.

This paper presents the issues and challenges faced for steganographic techniques that uses MP3 (Moving Picture Experts Group-1 "MPEG-1" Audio Layer 3) files as the cover or carrier file. The problem of steganographic techniques for MP3 is variety of MP3 states, where it exists at higher or lower bit rates, with higher or lower resulting quality. These have profound effects on strength of techniques that will be used for a different state of MP3 files. Several techniques have already been proposed for embedding data in MP3 files using steganography systems. Unfortunately, there are lacks of general emergence research on the issues and the challenges faced with the MP3 file in terms of steganography. Some techniques for embedding information in MP3 files were reviewed, and recommendations are being proposed for the best strategy of hide information in MP3 files and the possibility of finding other new techniques hiding information in MP3 files.

Software visualization is one of the main techniques used to simplify the presentation of software systems and enhance their understandability. It is used to present the software system in a visual manner using simple, clear and meaningful symbols. This study proposes a new 2D software visualization approach. In this approach, each class is represented by rectangle, the name of the class placed above the rectangle, the size of class (Line of Code) represented by the height of the rectangle. The methods and the attributes are represented by circles and triangles respectively. The relationships among classes correspond to arrows. The proposed visualization approach was evaluated in terms of applicability and efficiency. Results have confirmed successful implementation of the proposed approach, and its ability to provide a simple and effective graphical presentation of extracted software components and properties.