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This paper presents "Biotic Browser," an innovative AI assistant leveraging StreamingLLM to transform web navigation and task execution. Characterized by its ability to simulate the experience of a passenger in an autonomous vehicle, the Biotic Browser excels in managing extended interactions and complex, multi-step web-based tasks. It marks a significant advancement in AI technology, particularly in the realm of long-term context management, and offers promising applications for enhancing productivity and efficiency in both personal and professional settings., Comment: Written December 2023

Search engine results often misalign with users' goals due to opaque algorithms, leading to unhelpful or detrimental information consumption. To address this, we developed a Google Chrome plugin that provides "content labels" for webpages in Google search results, assessing Actionability (guiding actions), Knowledge (enhancing understanding), and Emotion. Using natural language processing and machine learning, the plugin predicts these properties from webpage text based on models trained on participants' ratings, effectively reflecting user perceptions. The implications include enhanced user control over information consumption and promotion of healthier engagement with online content, potentially improving decision-making and well-being.

The use of the un-indexed web, commonly known as the deep web and dark web, to commit or facilitate criminal activity has drastically increased over the past decade. The dark web is an in-famously dangerous place where all kinds of criminal activities take place [1-2], despite advances in web forensics techniques, tools, and methodologies, few studies have formally tackled the dark and deep web forensics and the technical differences in terms of investigative techniques and artefacts identification and extraction. This research proposes a novel and comprehensive protocol to guide and assist digital forensics professionals in investigating crimes committed on or via the deep and dark web, The protocol named D2WFP establishes a new sequential approach for performing investigative activities by observing the order of volatility and implementing a systemic approach covering all browsing related hives and artefacts which ultimately resulted into improv-ing the accuracy and effectiveness. Rigorous quantitative and qualitative research has been conducted by assessing D2WFP following a scientifically-sound and comprehensive process in different scenarios and the obtained results show an apparent increase in the number of artefacts re-covered when adopting D2WFP which outperform any current industry or opensource browsing forensics tools. The second contribution of D2WFP is the robust formulation of artefact correlation and cross-validation within D2WFP which enables digital forensics professionals to better document and structure their analysis of host-based deep and dark web browsing artefacts.

User quality of experience in the context of Web browsing is being researched widely, with plenty of developments occurring alongside technological advances, not seldom driven by big industry players. With the huge reach and infrastructure of Google, the Chrome User Experience Report (CrUX) provides quantitative real-life measurement data of a vast magnitude. Analysis of this steadily expanding dataset aggregating different user experience metrics, yields tangible insights into actual trends and developments. Hence, this paper is the first to study the CrUX dataset from the viewpoint of relevant metrics by quantitative evaluation of users Web browsing experience across three device types and nine European countries. Analysis of data segmented by connection type in the device dimension shows desktops outperforming other device types for all metrics. Similar analysis in the country dimension, shows North European countries (Sweden, Finland) having maximum 4G connections (85.99%, 81.41% respectively) and steadily performing 25%-36% better at the 75th percentile across all metrics compared to the worst performing country. Such a high-level longitudinal analysis of real-life Web browsing experience provides an extensive base for future research., Comment: 9 pages, 6 figures and 1 table. Accepted for publication at 2024 IFIP Networking Conference

Even though the web environment facilitates daily life, emotional problems caused by its incompatibility with human cognition are becoming increasingly serious. To alleviate negative emotions during web use, we developed a browser extension that presents memorized product images to users, in the form of web advertisements. This system utilizes the cognitive architecture Adaptive Control of Thought-Rational (ACT-R) as a model of memory and emotion. A heart rate sensor modulates the ACT-R model parameters: The emotional states of the model are synchronized or counterbalanced with the physiological state of the user. An experiment demonstrates that the counterbalance model suppresses negative ruminative web browsing. The authors claim that this approach is advantageous in terms of explainability.

Motives or goals are recognized in psychology literature as the most fundamental drive that explains and predicts why people do what they do, including when they browse the web. Although providing enormous value, these higher-ordered goals are often unobserved, and little is known about how to leverage such goals to assist people's browsing activities. This paper proposes to take a new approach to address this problem, which is fulfilled through a novel neural framework, Goal-directed Web Browsing (GoWeB). We adopt a psychologically-sound taxonomy of higher-ordered goals and learn to build their representations in a structure-preserving manner. Then we incorporate the resulting representations for enhancing the experiences of common activities people perform on the web. Experiments on large-scale data from Microsoft Edge web browser show that GoWeB significantly outperforms competitive baselines for in-session web page recommendation, re-visitation classification, and goal-based web page grouping. A follow-up analysis further characterizes how the variety of human motives can affect the difference observed in human behavioral patterns., Comment: Accepted by RecSys 2021

In this paper we explore the opportunities brought by cognitive augmentation to provide a more natural and accessible web browsing experience. We explore these opportunities through \textit{conversational web browsing}, an emerging interaction paradigm for the Web that enables blind and visually impaired users (BVIP), as well as regular users, to access the contents and features of websites through conversational agents. Informed by the literature, our previous work and prototyping exercises, we derive a conceptual framework for supporting BVIP conversational web browsing needs, to then focus on the challenges of automatically providing this support, describing our early work and prototype that leverage heuristics that consider structural and content features only.

Clickstreams on individual websites have been studied for decades to gain insights into user interests and to improve website experiences. This paper proposes and examines a novel sequence modeling approach for web clickstreams, that also considers multi-tab branching and backtracking actions across websites to capture the full action sequence of a user while browsing. All of this is done using machine learning on the client side to obtain a more comprehensive view and at the same time preserve privacy. We evaluate our formalism with a model trained on data collected in a user study with three different browsing tasks based on different human information seeking strategies from psychological literature. Our results show that the model can successfully distinguish between browsing behaviors and correctly predict future actions. A subsequent qualitative analysis identified five common web browsing patterns from our collected behavior data, which help to interpret the model. More generally, this illustrates the power of overparameterization in ML and offers a new way of modeling, reasoning with, and prediction of observable sequential human interaction behaviors.

Organizations such as government departments and financial institutions provide online service facilities accessible via an increasing number of internet connected devices which make their operational environment vulnerable to cyber attacks. Consequently, there is a need to have mechanisms in place to detect cyber security attacks in a timely manner. A variety of Network Intrusion Detection Systems (NIDS) have been proposed and can be categorized into signature-based NIDS and anomaly-based NIDS. The signature-based NIDS, which identify the misuse through scanning the activity signature against the list of known attack activities, are criticized for their inability to identify new attacks (never-before-seen attacks). Among anomaly-based NIDS, which declare a connection anomalous if it expresses deviation from a trained model, the unsupervised learning algorithms circumvent this issue since they have the ability to identify new attacks. In this study, we use an unsupervised learning algorithm based on principal component analysis to detect cyber attacks. In the training phase, our approach has the advantage of also identifying outliers in the training dataset. In the monitoring phase, our approach first identifies the affected dimensions and then calculates an anomaly score by aggregating across only those components that are affected by the anomalies. We explore the performance of the algorithm via simulations and through two applications, namely to the UNSW-NB15 dataset recently released by the Australian Centre for Cyber Security and to the well-known KDD'99 dataset. The algorithm is scalable to large datasets in both training and monitoring phases, and the results from both the simulated and real datasets show that the method has promise in detecting suspicious network activities.

In this paper, we describe the foundations for generating a chatbot out of a website equipped with simple, bot-specific HTML annotations. The approach is part of what we call conversational web browsing, i.e., a dialog-based, natural language interaction with websites. The goal is to enable users to use content and functionality accessible through rendered UIs by "talking to websites" instead of by operating the graphical UI using keyboard and mouse. The chatbot mediates between the user and the website, operates its graphical UI on behalf of the user, and informs the user about the state of interaction. We describe the conceptual vocabulary and annotation format, the supporting conversational middleware and techniques, and the implementation of a demo able to deliver conversational web browsing experiences through Amazon Alexa., Comment: typos corrected, metadata fixed

Web browsing is the main Internet Service and every customer wants the minimum web page loading times. To satisfy this perpetual need, web browsers offer new processing engines and increased functionalities. Web developers make use of new programming languages and paradigms, new transport protocols have been introduced, network operators offer increased bandwidth and Content Delivery Networks (CDN) providers deploy web servers closest to end-users. To assess the efficiency of these evolutions, delivered performance is often evaluated into a standalone manner, without considering the whole chain of web browsing. We propose Web View, a measurement platform, which performs automated web browsing sessions on popular websites in a user-representative environment. Through different configurable parameters, Web View measures a wide set of information in order to evaluate the impact of the different parameters (transport protocols, web browsers, CDN, access networks, etc.). Based on those measurements, to ease the understanding of web browsing and the corresponding evolutions, Web View offers a public visualization website (https://webview.orange.com). For instance, with Web View, we were able to detect that the use of different CDNs at different times of the day can decrease loading times up to 400% and that the delivery of content through different transport protocols can decrease loading times up to 79%. This paper presents the Web View measurement platform as well as remarkable events we noticed during more than one year of measurements.

In this paper, we study the causal impact of the web-search activity on the research publication. We considered observational prospective study design, where research activity of 267 scientists is being studied. We considered the Poisson and negative binomial regression model for our analysis. Based on the Akaike's Model selection criterion, we found the negative binomial regression performs better than the Poisson regression. Detailed analysis indicates that the higher web-search activity of 2016 related to the sci-indexed website has a positive significant impact on the research publication of 2017. We observed that unique collaborations of 2016 and web-search activity of 2016 have a non-linear but significant positive impact on the research publication of 2017. What-if analysis indicates the high web browsing activity leads to more number of the publication. However, interestingly we see a scientist with low web activity can be as productive as others if her/his maximum hits are the sci-indexed journal. That is if the scientist uses web browsing only for research-related activity, then she/he can be equally productive even if her/his web activity is lower than fellow scientists.

This paper proposes a set of techniques to investigate eye gaze and fixation patterns while users interact with electronic user interfaces. In particular, two case studies are presented - one on analysing eye gaze while interacting with deceptive materials in web pages and another on analysing graphs in standard computer monitor and virtual reality displays. We analysed spatial and temporal distributions of eye gaze fixations and sequence of eye gaze movements. We used this information to propose new design guidelines to avoid deceptive materials in web and user-friendly representation of data in 2D graphs. In 2D graph study we identified that area graph has lowest number of clusters for user's gaze fixations and lowest average response time. The results of 2D graph study were implemented in virtual and mixed reality environment. Along with this, it was ob-served that the duration while interacting with deceptive materials in web pages is independent of the number of fixations. Furthermore, web-based data visualization tool for analysing eye tracking data from single and multiple users was developed., Comment: arXiv admin note: text overlap with arXiv:2005.05025

Quality of Experience (QoE) for various types of websites has gained significant attention in recent years. In order to design and evaluate websites, a metric that can estimate a user's experienced quality robustly for diverse content is necessary. SpeedIndex (SI) has been widely adopted to estimate perceived web page loading progress. It measures the speed of rendering pixels for the webpage that is visible in the browser window. This is termed Above-The-Fold (ATF). The influence of animated content on the perception of ATF has been less comprehensively explored. In this paper, we present an experimental design and methodology to measure ATF perception for websites with and without animated elements for various page content categories. We found that pages with animated elements caused people to have more varied perceptions of ATF under different network conditions. Animated content also impacts the page load estimation accuracy of SI for websites. We discuss how the difference in the perception of ATF will impact the QoE management of web applications. We explain the necessity of revisiting the visual assessment of ATF to include the animated contents and improve the robustness of metrics like SI.