A user-oriented network forensic analyser: the design of a high-level protocol analyser

Network forensics is becoming an increasingly important tool in the investigation of cyber and computer-assisted crimes. Unfortunately, whilst much effort has been undertaken in developing computer forensic file system analysers (e.g. Encase and FTK), such focus has not been given to Network Forensic Analysis Tools (NFATs). The single biggest barrier to effective NFATs is the handling of large volumes of low-level traffic and being able to exact and interpret forensic artefacts and their context – for example, being able extract and render application-level objects (such as emails, web pages and documents) from the low-level TCP/IP traffic but also understand how these applications/artefacts are being used. Whilst some studies and tools are beginning to achieve object extraction, results to date are limited to basic objects. No research has focused upon analysing network traffic to understand the nature of its use – not simply looking at the fact a person requested a webpage, but how long they spend on the application and what interactions did they have with whilst using the service (e.g. posting an image, or engaging in an instant message chat). This additional layer of information can provide an investigator with a far more rich and complete understanding of a suspect’s activities. To this end, this paper presents an investigation into the ability to derive high-level application usage characteristics from low-level network traffic meta-data. The paper presents a three application scenarios – web surfing, communications and social networking and demonstrates it is possible to derive the user interactions (e.g. page loading, chatting and file sharing ) within these systems. The paper continues to present a framework that builds upon this capability to provide a robust, flexible and user-friendly NFAT that provides access to a greater range of forensic information in a far easier format

Optical BVRI Photometry of Common Proper Motion F/G/K+M Wide Separation Binaries

We present optical (BVRI) photometric measurements of a sample of 76 common proper motion wide separation main sequence binary pairs. The pairs are composed of a F-, G-, or K-type primary star and an M-type secondary. The sample is selected from the revised NLTT catalog and the LSPM catalog. The photometry is generally precise to 0.03 mag in all bands. We separate our sample into two groups, dwarf candidates and subdwarf candidates, using the reduced proper motion (RPM) diagram constructed with our improved photometry. The M subdwarf candidates in general have larger $V-R$ colors than the M dwarf candidates at a given $V-I$ color. This is consistent with an average metallicity difference between the two groups, as predicted by the PHOENIX/BT-Settl models. The improved photometry will be used as input into a technique to determine the metallicities of the M-type stars.Comment: 26 pages, 8 figures, accepted for publication in A

Learning-by-Teaching in CS Education: A Systematic Review

To investigate the strategies and approaches in teaching Computer Science (CS), we searched the literature review in CS education in the past ten years. The reviews show that learning-by-teaching with the use of technologies is helpful for improving student learning. To further investigate the strategies that are applied to learning-by-teaching, three categories are identified: peer tutoring, game-based flipped classroom, and teachable agents. In each category, we further searched and investigated prior studies. The results reveal the effectiveness and challenges of each strategy and provide insights for future studies

Revolutionizing Attention Training in Autism Spectrum Disorder: Pioneering Virtual Reality and Artificial Intelligence

In the realm of educational technology, attention training plays a critical role in tailoring learning experiences to individual needs, especially for learners with autism spectrum disorder (ASD). This study presents an advanced framework that provides insights into the efficacy of reinforcement strategies by predicting their impact on attention enhancement in educational virtual reality (VR) settings, utilizing physiological biomarkers such as eye-tracking (ET), heart rate (HR), and electrodermal activity (EDA). A comprehensive comparative analysis was undertaken to evaluate the performance metrics of various machine learning (ML) and deep learning (DL) algorithms. The results showcased the robustness of gradient boosting (GB) and random forest (RF) in predicting the impact of reinforcement training in attention increase with high F1-score and ROC\_AUC values. GB achieved remarkable performance on all features dataset with 77.7\% F1-score and 77.08\% ROC\_AUC, while RF excelled on selected features dataset with 80\% F1-score and 81.94\% ROC\_AUC. The study also explores pattern recognition between autistic and non-autistic individuals, providing insights into the distinctive attentional profiles. An LSTM time-series model was also developed for real-time prediction, offering a pathway for personalized and adaptive learning experiences. The integration of artificial intelligence (AI) models and physiological data holds significant promise for enhancing attention training, with implications extending to personalized education for ASD. The study sets the stage for future enhancements in LSTM prediction accuracy and the development of real-time, tailored educational interventions

Cell Fragmentation and Permeabilization by a 1 ns Pulse Driven Triple-Point Electrode

Ultrashort electric pulses (ns-ps) are useful in gaining understanding as to how pulsed electric fields act upon biological cells, but the electric field intensity to induce biological responses is typically higher than longer pulses and therefore a high voltage ultrashort pulse generator is required. To deliver 1 ns pulses with sufficient electric field but at a relatively low voltage, we used a glass-encapsulated tungsten wire triple-point electrode (TPE) at the interface among glass, tungsten wire, and water when it is immersed in water. A high electric field (2MV/cm) can be created when pulses are applied. However, such a high electric field was found to cause bubble emission and temperature rise in the water near the electrode. They can be attributed to Joule heating near the electrode. Adherent cells on a cover slip treated by the combination of these stimuli showed two major effects: (1) cells in a crater (\u3c100 m from electrode) were fragmented and the debris was blown away. The principal mechanism for the damage is presumed to be shear forces due to bubble collapse; and (2) cells in the periphery of the crater were permeabilized, which was due to the combination of bubble movement and microstreaming as well as pulsed electric fields. These results show that ultrashort electric fields assisted by microbubbles can cause significant cell response and therefore a triple-point electrode is a useful ablation tool for applications that require submillimeter precision

On The Anisotropy Of Perceived Ground Extents And The Interpretation Of Walked Distance As A Measure Of Perception

Two experiments are reported concerning the perception of ground extent to discover whether prior reports of anisotropy between frontal extents and extents in depth were consistent across different measures (visual matching and pantomime walking) and test environments (outdoor environments and virtual environments). In Experiment 1 it was found that depth extents of up to 7 m are indeed perceptually compressed relative to frontal extents in an outdoor environment, and that perceptual matching provided more precise estimates than did pantomime walking. In Experiment 2, similar anisotropies were found using similar tasks in a similar (but virtual) environment. In both experiments pantomime walking measures seemed to additionally compress the range of responses. Experiment 3 supported the hypothesis that range compression in walking measures of perceived distance might be due to proactive interference (memory contamination). It is concluded that walking measures are calibrated for perceived egocentric distance, but that pantomime walking measures may suffer range compression. Depth extents along the ground are perceptually compressed relative to frontal ground extents in a manner consistent with the angular scale expansion hypothesis. (PsycINFO Database Record (c) 2013 APA, all rights reserved)(journal abstract

Microbial communities and arsenic biogeochemistry at the outflow of an alkaline sulfide-rich hot spring.

Alkaline sulfide-rich hot springs provide a unique environment for microbial community and arsenic (As) biogeochemistry. In this study, a representative alkaline sulfide-rich hot spring, Zimeiquan in the Tengchong geothermal area, was chosen to study arsenic geochemistry and microbial community using Illumina MiSeq sequencing. Over 0.26 million 16S rRNA sequence reads were obtained from 5-paired parallel water and sediment samples along the hot spring's outflow channel. High ratios of As(V)/AsSum (total combined arsenate and arsenite concentrations) (0.59-0.78), coupled with high sulfide (up to 5.87 mg/L), were present in the hot spring's pools, which suggested As(III) oxidation occurred. Along the outflow channel, AsSum increased from 5.45 to 13.86 μmol/L, and the combined sulfide and sulfate concentrations increased from 292.02 to 364.28 μmol/L. These increases were primarily attributed to thioarsenic transformation. Temperature, sulfide, As and dissolved oxygen significantly shaped the microbial communities between not only the pools and downstream samples, but also water and sediment samples. Results implied that the upstream Thermocrinis was responsible for the transformation of thioarsenic to As(III) and the downstream Thermus contributed to derived As(III) oxidation. This study improves our understanding of microbially-mediated As transformation in alkaline sulfide-rich hot springs