Reviewerly/exHarmony huggingface dataset:
Authenticated Encryption Engine for IoT Application

With the increase of connected devices in IoT paradigm for various domains that include wireless sensor networks, edge computing, embedded systems. Hence the cryptographic primitives deployed on these devices have to be lightweight as the devices used are low cost and low energy. The cryptographic techniques and algorithms for data confidentiality only aim at providing data privacy, but the authenticity of the data is not being addressed. Hence Authenticated Encryption (AE) is used to provide a higher level of security. Authenticated encryption is a scheme used to provide authenticity along with confidentiality of the data. In this pper, AE is implemented using a lightweight PRESENT encryption algorithm and hashing SPONGENT algorithm. These algorithms have the smallest foot-print compared to other lightweight algorithms. The proposed design uses a PRESENT block cipher for the key size variants of 80 bits and 128 bits for a block size of 64-bit and SPONGENT variants of 88, 128, and 256 bits for authentication. Simulation, analysis, inference, and synthesis of the proposed architectures i.e. Encryption then MAC (EtM) is implemented on the target platform ARTY A7 100T. A comparative analysis states that the combination of the PRESENT 80 block cipher and SPONGENT 88 variant is best suited for the resource-constrained Internet of Things applications as the world is slowly approaching the brink of mankind’s next technological revolution.
Detection of Presentation Attacks on Facial Authentication Systems Using Intel RealSense Depth Cameras

In this paper, we explore the prospects for using Intel RealSense depth cameras to solve the problem of presentation attack detection in facial authentication systems. Studies known to date use quantized depth data. Such an approach makes it impossible to connect these data with anthropometric facial features and scene geometry. In addition, in recent papers, some researchers declared the limitation of using depth cameras in this problem only at small (less than 1 m) distances to the face. In this regard, we have made our collection of 480 samples containing bonafide images and two types of presentation attacks. The samples were taken at distances up to 2 m, as well as in different face positions relative to the camera axis. We also designed a set of features based on texture analysis, anthropometric properties, and face oval localization. The conducted experiments show the advantage of using the original, not quantized depth data. Our study also refutes the thesis that depth cameras can be used for presentation attack detection only at short distances. Additionally, we demonstrate the applicability of the proposed features on one of the publicly available datasets.
Literature Review on Recommender Systems: Techniques, Trends and Challenges

Nowadays, Recommender Systems (RSs) have become a necessity especially with the rapid increasing of the numerical data volume. In fact, internet’s users need an automatic system that help them to filter the huge flow of information provided bay websites or even by research engines. Therefore, a Recommender System can be seen as an Information Retrieval system that can respond to an implicit user’s query. The RS draw this implicit user’s query based on a user’s profile that can be created using some semantic or statistic knowledge. In this paper, we provide an in-depth literature review on main RS approaches. Basically, RS’s techniques can be divide into three classes: collaborative Filtering-based, Content-based and hybrid approaches. Also, we show the challenges and the potential trends in this domain.
A Study on Written Communication About Client-Side Web Security

Today, web services services are widely used by ordinary people with little technical know-how. End user cybersecurity in web applications has become an essential aspect to consider in web development. One important part of online cybersecurity is the HTTPS protocol that encrypts the web traffic between endpoints. This paper explores how the relevant end user cybersecurity instructions are communicated to users. Using text-focused analysis, we study and assess the cybersecurity instructions online banks and browser vendors provide with regards to HTTPS. We find that security benefits of HTTPS are often exaggerated and can give users a false sense of security.
Human rights and good governance to identify hate crimes on social networks

Nowadays, we notice an increase in the use of technology, and on the other hand, we can say that it has an impact on the increase in cases of hate speech. As Wilson and Land (2021) have pointed out, crimes and hate speech in recent years have taken a trend of rapid development, where as a consequence the language of hatred, which however is not direct can contribute to violence. The purpose of this paper is to research the behavior and approach of individuals to the forms and causes of hate speech as well as to find the factors that manifest these acts towards the persons who are attacked. In this paper, the analytical method, the true study, and legal positivism are used. First, the role of technology and highlights are analyzed in the increase of hate crimes as well as the forms of performances of these acts. The causes that increase these influences of hatred nowadays are the social categories. With the method of legal positivism, the local and regional jurisdiction and the role of the provisions determined by the Constitution in the country and the laws in force have been reflected. The results of the survey show a high assessment of people who have been attacked by the use of technology and hate crimes.
From Instrumentalization to Integration of Technologies for Creative Teaching: Transformations in Times of Crisis

There is an urgency to build a critical view of information and communication technologies in society and education in order to harness their pedagogical potential. Generally, the Covid-19 pandemic demanded transformation, which is one of the possibilities in times of crisis. Despite the pandemic exposing digital exclusion in many parts of the planet, digital technologies concurrently have overcome, at least in part, social isolation through the provision of remote activities. The functionality of technologies motivated the question that guides the present investigation: how does the use of technologies before, during, and after the pandemic period appear in the products of the ISATT academic community? The objective is to come to know the contribution of ISATT and the degree to which its input is current and relevant. To meet our goal, we conducted an exploratory study gathering data by means of Google Form questionnaire. ISATT members invited to answer the questionnaire were also participants in the survey. Our intent is to map and describe the relationship between technology and creative teaching and categorize the different approaches used. Our analysis will allow ISATT community members to evaluate their interests, foci, trends, and contributions.
Applying Machine Learning to Gaze Data in Software Development: a Mapping Study

Eye tracking has been used as part of software engineering and computer science research for a long time, and during this time new techniques for machine learning (ML) have emerged. Some of those techniques are applicable to the analysis of eye-tracking data, and to some extent have been applied. However, there is no structured summary available on which ML techniques are used for analysis in different types of eye-tracking research studies.
Gaze-based Interaction on Handheld Mobile Devices

With the advancement of smartphone technology, it is now possible for smartphones to run eye-tracking using the front-facing camera, enabling hands-free interaction by empowering mobile users with novel gaze-based input techniques. While several gaze-based interaction techniques have been proposed in the literature, these techniques were deployed in settings different from daily gaze interaction with mobile devices, posing several unique challenges. The user’s holding posture may hinder the camera’s view of their face during the interaction, the front-facing camera may be obstructed by the user’s clothing or hands, or the environment is shaky due to the user’s movements and the dynamic environment. This PhD research investigates the usability of state-of-the-art gaze-based input techniques in mobile settings, develops a novel concept of combining multiple gaze-based techniques, and addresses the challenges imposed by the unique aspects of these devices.
Representing (Dis)Similarities Between Prediction and Fixation Maps Using Intersection-over-Union Features

A classic evaluation of the quality of gaze prediction models consists in comparing a set of ground truth fixation maps against a set of predictions. The quality of the prediction depends on the spatial similarity between the predicted and the observed fixated and non-fixated areas. Typically, (dis)similarity is evaluated by computing distribution-based metrics. However, the shortcoming of the metric scores is that they provide no information about the different types of (dis)similarities present in the prediction, for example, to determine whether the prediction fails wholly or partially to account for all the fixations. In this paper, we propose a set of features for representing the spatial (dis)similarities using intersection-over-union features, which provide helpful information that cannot be retrieved with traditional metrics for analyzing and evaluating prediction maps. We exemplify the usage of the features by analyzing the performance of different prediction models on a saliency benchmark dataset.
Evaluating Human Eye Features for Objective Measure of Working Memory Capacity

Eye tracking measures can provide means to understand the underlying development of human working memory. In this study, we propose to develop machine learning algorithms to find an objective relationship between human eye movements via oculomotor plant and their working memory capacity, which determines subjective cognitive load. Here we evaluate oculomotor plant features extracted from saccadic eye movements, traditional positional gaze metrics, and advanced eye metrics such as ambient/focal coefficient , gaze transition entropy, low/high index of pupillary activity (LHIPA), and real-time index of pupillary activity (RIPA). This paper outlines the proposed approach of evaluating eye movements for obtaining an objective measure of the working memory capacity and a study to investigate how working memory capacity is affected when reading AI-generated fake news.
Multi-Rate Sensor Fusion for Unconstrained Near-Eye Gaze Estimation

The power requirements of video-oculography systems can be prohibitive for high-speed operation on portable devices. Recently, low-power alternatives such as photosensors have been evaluated, providing gaze estimates at high frequency with a trade-off in accuracy and robustness. Potentially, an approach combining slow/high-fidelity and fast/low-fidelity sensors should be able to exploit their complementarity to track fast eye motion accurately and robustly. To foster research on this topic, we introduce OpenSFEDS, a near-eye gaze estimation dataset containing approximately 2M synthetic camera-photosensor image pairs sampled at 500 Hz under varied appearance and camera position. We also formulate the task of sensor fusion for gaze estimation, proposing a deep learning framework consisting in appearance-based encoding and temporal eye-state dynamics. We evaluate several single- and multi-rate fusion baselines on OpenSFEDS, achieving 8.7% error decrease when tracking fast eye movements with a multi-rate approach vs. a gaze forecasting approach operating with a low-speed sensor alone.
Program Code Navigation Model for Individuals based on LSTM with Co-clustering

To improve program learning tools with eye tracking technology, it is crucial to understand fixation points and individual information, providing personalized navigation cues for different levels of expertise programmers. Meanwhile, long short-term memory (LSTM) and clustering techniques revealed important characteristics for eye movement data regarding comprehension performance. This paper is about a spatial analysis by co-clustering with the gaze location among different levels of expertise programmers. Then it predicts the next fixation point based on the human eye movement data by LSTM. Finally, combining the individual background information and the eye movement information, it generates a new indicator, named ‘code comprehension index’, to indicate the current code understanding level on real-time gaze information for individuals, which can be applied to improve the program navigation tools’ effectiveness and efficiency for different levels of expertise programmers.
Universal Design of Gaze Interactive Applications for People with Special Needs

Within the last 20 years, gaze interaction has become a successful communication solution for numerous people with motor challenges. In this paper, we present two new cases of gaze interactive assistive technology: i) gaze control of an exoskeleton for stroke rehabilitation, and ii) gaze interactive reading support for people with low vision. By applying a Universal Design approach [Mace 1998] both cases are assessed through an ability analysis to identify issues with gaze interaction specific to our applications that need to be further addressed. Finally, we suggest how solutions in our applications may be mainstreamed for a broader user group.
A Deep Learning Architecture for Egocentric Time-to-Saccade Prediction using Weibull Mixture-Models and Historic Priors

Real-time detection of saccades is of major interest for many applications in human-computer interaction and mixed reality. However, due to relatively low update rates and high latencies of current commercially available eye trackers, gaze events are typically detected after they occur with some delay. This limits interaction scenarios such as intent-based gaze interaction, redirected walking, or gaze forecasting.
Calibration free eye tracking solution for mobile and embedded devices

In this study we propose a competent low-cost eye tracking solution that is able to run on any mobile device, independently of the hardware that is equipped with. The rapid evolution of technologies has enabled to work with many neural network structures that some years ago were out of reach. The project will start from a solution which Irisbond (https://www.irisbond.com/) company has been working on, which gives precision values of 3 and 6 degrees for calibration and calibration-free use cases respectively. The goal of the solution is to try to develop a usable solution in the Augmented and Alternative Communication (AAC) field across different types of devices, from mobile to embedded devices. To achieve such an objective, two main goals have been set out during this study. One the one hand I (we) aim at removing the initial calibration step to reach a calibration-free solution. On the other hand, I (we) seek to separate the functionality of a software into independent, interchangeable modules to fit the different target device limitations.
Investigating Phishing Awareness Using Virtual Agents and Eye Movements

Phishing emails typically attempt to persuade recipients to reveal private or confidential information (e.g., passwords or bank details). Interaction with such emails places individuals at risk of financial loss and identity theft. We present an ongoing study using eye tracking metrics and varying interface components to assess users’ ability to spot simulated phishing attempts. Findings seek to establish how users interact with email inbox interfaces and will inform future design of usable security tools.
TF-IDF based Scene-Object Relations Correlate With Visual Attention

The relative contribution of bottom-up and top-down attentional guidance is a central topic in vision research. Whereas attention is guided bottom-up by low-level saliency, top-down guidance involves the viewer’s knowledge and expectations accumulated throughout a lifetime. Here we explore the influence of high-level scene-object relations on viewing behavior. To assess top-down guidance, we score the relevance of linguistic object labels using methods from document analysis. Specifically, we computed the term frequency-inverse document frequency (TF-IDF), a statistic that reflects how important a term is to a document. We use object TF-IDF to measure how important a specific object is to a scene category and use these scores to predict eye movement distributions over scenes. Our results show that scene-specific objects are more likely to be fixated. Object TF-IDF had an effect partially independent of image saliency, suggesting that an object’s relevance for a scene category affects attention during scene perception.
Collaboration Assistance Through Object Based User Intent Detection Using Gaze Data

As eye-tracking technology becomes increasingly prevalent in augmented reality (AR), new opportunities arise for collaborative applications. In this paper, we propose a novel approach to improve collaborative interaction through object-based user intent detection using gaze data. Our system uses reinforcement learning (RL) to dynamically adapt the user interface based on the context of the collaborative task. The system visualizes the user’s intent on a shared environment, allowing for improved collaborative awareness between users. We evaluate our approach in a user study scenario focused on visual search tasks. The results demonstrate that our system significantly improves task completion times and reduces cognitive load for users. Additionally, subjective feedback suggests that users are more aware of each other’s activity, further highlighting the benefits of our approach. We encourage conducting future user studies to assess the suitability of our approach for additional collaborative tasks.
Visual Expertise in Code Reviews

This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).
Detecting Blinks from Wearable Cameras using Spatial-Temporal-Aware Deep Network Learning

Blinks have been widely studied in various fields including medical and human computer interactions, and in driver fatigue. Automatic detection of blinks has valuable practical importance. While existing deep neural networks excel in extracting spatial features from images and demonstrate impressive performance in visual object recognition, their application for blink detection in videos on a frame-by-frame basis is suboptimal, as they only consider spatial features from single images. In this paper, we developed a spatial-temporal-aware deep learning framework that capitalizes on the rapid advancements of the existing state-of-the-art visual object recognition networks, aiming to enhance their performance specifically in blink detection. Our framework takes consecutive frames as input to extract spatial and temporal features simultaneously for better detection of eye movements. We also propose a sliding window re-sampling strategy to mitigate overfitting on training data. Extensive experimental evaluations and comparisons demonstrate the feasibility of the proposed algorithm, which delivers excellent performance for detecting blinks.
Model-based deep gaze estimation using incrementally updated face-shape parameters

In this paper, we propose a method to improve the performance of deep gaze estimation using face-shape parameters adapted to a specific target person based on multiple observations. Our gaze estimation network contains a predefined computation module that calculates gaze directions using known geometric relationships among head poses, eye-ball positions, and gaze directions. Updated face-shape parameters contribute to improving the performance of the process. In addition, the computation module enables a network to acquire the ability to induce hidden parameters such as eyeball position and eyeball radius from observed information through a training process. Experimental results reveal improvement in gaze estimation accuracy by introducing a sequential update process for face-shape parameters and a predefined computation module.
Predicting the Allocation of Attention: Using contextual guidance of eye movements to examine the distribution of attention

Eye movements are often taken as a marker of where attention is allocated, but it is possible that the attentional window can be either tightly or broadly focused around the fixation point. Using target objects whose location could either be strongly predicted by scene context (High Certainty) or not (Low Certainty), we examined how attention was initially distributed across a scene image during search. To do so, an unexpected distractor object suddenly appeared either in the relevant or irrelevant scene region for each target type. Distractors will be more disruptive where attention is allocated. We found that for High Certainty targets, the distractors were fixated significantly more often when they appeared in relevant than irrelevant regions, but there was no such difference for Low Certainty targets. This finding demonstrated differential patterns of attentional distribution around the fixation point based on the predicted location of target objects within a scene.
A Preliminary Investigation on Eye Gaze-based Estimation of Self-efficacy during a Dexterity Task

We investigated the relationship between eye gaze and self-efficacy based on anticipatory gaze. The experimental results showed that the correlation coefficient between saccade distance and self-efficacy from the questionnaire was more than 0.5, suggesting that self-efficacy could be measured from eye gaze.
Automated Detection of Geometric Structures in Gaze Data

Automated detection of individual problem-solving strategies is mandatory for the realization of adaptive learning systems (ALS). In this context, we present a new algorithm which is able to detect dynamic geometric structures (slope triangles) in gaze data of 62 learners using automated fixation-clustering and temporal networks. Preliminary results show a promising performance for fixed bandwidth parameters of the clustering algorithm (average recall: 0.60, maximum precision: 0.66).