Reviewerly/exHarmony huggingface dataset:
Cost optimization in cloud environment based on task deadline

Abstract The popularity of cloud and fog services has raised the number of users exponentially. Main advantage of Cloud/fog infrastructure and services are crucial specially for commercial users from diverse areas. The variety of service requests with different deadlines makes the task of a service broker challenging. The fog and cloud users always lookfor a suitable compromise between cost and quality of service in terms of response time therefore, the cost optimization is vital for the cloud/fog service providers to capture the market. In this paper an algorithm, Cost Optimization in the cloud/fog environment based on Task Deadline (COTD) is proposed that optimizes cost without compromising the response time. In this algorithm the task deadline is considered as a constraint and an appropriate data center for task processing is selected. The proposed algorithm is suitable for runtime decision making due to its low complexity. The proposed algorithm is evluated using a well-known simulation tool Cloud Analyst. Our comprehensive testbed simulations show that COTD outperforms the existing schemes, Service Proximity Based Routing and Performance-Optimized Routing. The proposed algorithm successfully minimizes the cost by 35% on average while maintaining the response time.
Non-Intrusive Real Time Eye Tracking Using Facial Alignment for Assistive Technologies

Most affordable eye tracking systems use either intrusive setup such as head-mounted cameras or use fixed cameras with infrared corneal reflections via illuminators. In the case of assistive technologies, using intrusive eye tracking systems can be a burden to wear for extended periods of time and infrared based solutions generally do not work in all environments, especially outside or inside if the sunlight reaches the space. Therefore, we propose an eye-tracking solution using state-of-the-art convolutional neural network face alignment algorithms that is both accurate and lightweight for assistive tasks such as selecting an object for use with assistive robotics arms. This solution uses a simple webcam for gaze and face position and pose estimation. We achieve a much faster computation time than the current state-of-the-art while maintaining comparable accuracy. This paves the way for accurate appearance-based gaze estimation even on mobile devices, giving an average error of around 4.5°on the MPIIGaze dataset [1] and state-of-the-art average errors of 3.9°and 3.3°on the UTMultiview [2] and GazeCapture [3], [4] datasets respectively, while achieving a decrease in computation time of up to 91%.
Toward Correlated Data Trading for Private Web Browsing History

The trading of social media data has attracted wide research interests over years. In particular the trading for web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous datasets, poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer’s utility. In this paper, we propose CEATSE, a CorrElated dAta Trading framework for various kinds of private web browSing historiEs. CEATSE first models the correlation among multiple dimensional features, and then generates the optimal feature clustering scheme. Combined with this scheme, CEATSE next incorporates a correlated data perturbation strategy on each feature cluster, in order to balance the privacy-utility tradeoff. It then quantifies each chosen data contributor’s privacy loss on optimal feature clusters. Through real-data based experiments, our analysis and evaluation results demonstrate CEATSE indeed achieves user privacy protection, the data consumer’s accuracy requirement, and truthfulness, individual rationality as well as budget balance.
Routing Performance Based On Software Defined Quantum Key Distribution Network

Quantum Key Distribution (QKD) takes a short time to process encryption and authentication messages as an advanced security key distribution. Open Shortest Path First (OSPF) routing used on existing QKD networks uses more security keys. Software-Defined Networking (SDN) exists to control the core network, make routes easier to find and reduce the use of security keys. One of the benefits of SDN is that the controller can customize the routing scheme and save the use of secure keys. Based on the topology and scenarios, the link state protocol test results show that using keys increased the number of hops traversed by five to fourteen keys. We can see the five keys constant when testing the distance vector protocol. The average test time difference between the two protocols is about 3.438 seconds for the link state protocol and 2.687 seconds for the distance vector algorithm.
Oculomotoric Biometric Identification under the Influence of Alcohol and Fatigue

Patterns of micro- and macro-movements of the eyes are highly individual and can serve as a biometric characteristic. It is also known that both alcohol inebriation and fatigue can reduce saccadic velocity and accuracy. This prompts the question of whether changes of gaze patterns caused by alcohol consumption and fatigue impact the accuracy of oculomotoric biometric identification. We collect an eye tracking data set from 66 participants in sober, fatigued and alcohol-intoxicated states. We find that after enrollment in a rested and sober state, identity verification based on a deep neural embedding of gaze sequences is significantly less accurate when probe sequences are taken in either an inebriated or a fatigued state. Moreover, we find that fatigue and intoxication appear to randomize gaze patterns: when the model is fine-tuned for invariance with respect to inebriation and fatigue, and even when it is trained exclusively on inebriated training person, the model still performs significantly better for sober than for sleep-deprived or intoxicated subjects.
Does lossy image compression affect racial bias within face recognition?

Yes - This study investigates the impact of commonplace lossy image compression on face recognition algorithms with regard to the racial characteristics of the subject. We adopt a recently proposed racial phenotype-based bias analysis methodology to measure the effect of varying levels of lossy compression across racial phenotype categories. Additionally, we determine the relationship between chroma-subsampling and race-related phenotypes for recognition performance. Prior work investigates the impact of lossy JPEG compression algorithm on contemporary face recognition performance. However, there is a gap in how this impact varies with different race-related inter-sectional groups and the cause of this impact. Via an extensive experimental setup, we demonstrate that common lossy image compression approaches have a more pronounced negative impact on facial recognition performance for specific racial phenotype categories such as darker skin tones (by up to 34.55%). Furthermore, removing chroma-subsampling during compression improves the false matching rate (up to 15.95%) across all phenotype categories affected by the compression, including darker skin tones, wide noses, big lips, and monolid eye categories. In addition, we outline the characteristics that may be attributable as the underlying cause of such phenomenon for lossy compression algorithms such as JPEG.
TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks

With today's technologies, the possibility to generate highly realistic visual fakes is within everyone's reach, leading to major threats in terms of misinformation and data trustworthiness. This holds in particular for synthetically generated faces, which are able to deceive even the most experienced observers, and can be exploited to create fake digital identities with synthetic facial attributes, to be used on social networks and online services. In response to this threat, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem “in the wild”, more realistic image databases are needed to train specialised detectors. In this work, we present TrueFace, a first dataset of social-media-processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram. The dataset is used to validate a ResNet-based image classification model addressing the discrimination of synthetic-vs-real faces in both presocial and post-social scenarios. The results demonstrate that even detectors with extremely high performance on non-shared images struggle to retain their accuracy on images from social media, while fine-tuning with shared images strongly mitigates such performance issues.
Design and Development of a Daily Recording System for Vocational High School Field Work Practice Participants

Online Prakerin is a program implemented by the Information and Communication Technology Research Center of the Bandung Institute of Technology since 2021 which allows Vocational High Schools throughout Indonesia to register their students to carry out field work practices online. Monitoring and Evaluation is one form of activity that must be carried out in running an online internship program with the aim that field work practice participants can benefit from the online internship program. In 2021 there will be 90 SMKs and 4,198 students enrolled in the online prakerin program. Monitoring and evaluating 4198 students require enormous energy and effort. Seeing these conditions, the researcher proposes the design and development of an Android-based application with the aim of making it easier for online internship participants to report daily activities so that the daily activities of online internship program participants can be monitored and can later be evaluated as consideration for providing value results from online internships. The stages carried out in this study are as follows: The first stage is the analysis of system requirements at this stage the authors identify problems by conducting interviews with the Information and Communication Technology Research Center, Bandung Institute of Technology. The two stages of system design at this stage the authors design the requirements that have been obtained in the first stage and describe them in the form of use case diagrams. The three stages of making the system at this stage the researchers carried out the implementation of making the system from the use case diagram that had been made in the second stage. The result of the third stage is an android-based application that can be used by students to report daily activities and a website-based application that can be used by the Information and Communication Technology Research Center of the Bandung Institute of Technology to monitor the activities of online internship participants. The fourth system testing at this stage the authors conducted a system test by giving questionnaires to students and to the Information and Communication Technology Research Center, Bandung Institute of Technology.
OCFR 2022: Competition on Occluded Face Recognition from Synthetically Generated Structure-Aware Occlusions

This work summarizes the IJCB Occluded Face Recognition Competition 2022 (IJCB-OCFR-2022) embraced by the 2022 International Joint Conference on Biometrics (IJCB 2022). OCFR-2022 attracted a total of 3 participating teams, from academia. Eventually, six valid submissions were submitted and then evaluated by the organizers. The competition was held to address the challenge of face recognition in the presence of severe face occlusions. The participants were free to use any training data and the testing data was built by the organisers by synthetically occluding parts of the face images using a well-known dataset. The submitted solutions presented innovations and performed very competitively with the considered baseline. A major output of this competition is a challenging, realistic, and diverse, and publicly available occluded face recognition benchmark with well defined evaluation protocols.
Towards Explaining Demographic Bias through the Eyes of Face Recognition Models

Biases inherent in both data and algorithms make the fairness of widespread machine learning (ML)-based decision-making systems less than optimal. To improve the trustfulness of such ML decision systems, it is crucial to be aware of the inherent biases in these solutions and to make them more transparent to the public and developers. In this work, we aim at providing a set of explainability tool that analyse the difference in the face recognition models' behaviors when processing different demographic groups. We do that by leveraging higher-order statistical information based on activation maps to build explainability tools that link the FR models' behavior differences to certain facial regions. The experimental results on two datasets and two face recognition models pointed out certain areas of the face where the FR models react differently for certain demographic groups compared to reference groups. The outcome of these analyses interestingly aligns well with the results of studies that analyzed the anthropometric differences and the human judgment differences on the faces of different demographic groups. This is thus the first study that specifically tries to explain the biased behavior of FR models on different demographic groups and link it directly to the spatial facial features. The code is publicly available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/fbiying87/Demographic-Bias-Visualization.git.
Exploring Face De-Identification using Latent Spaces

We explore a new method to hide identity information in a facial image from face recognition (FR) systems, while only minimally changing the appearance of the image as perceived by humans. We train a decoder network that reverses the mapping of an FR system and use the dissimilarity score function of this FR system to teach the decoder to return images with as little identity information as possible, while using a visual loss to change the image as little as possible visually. We show that these obfuscation attacks are also successful when the FR system is unknown. We analyse the obfuscated images in latent space and show that our approach as well as an existing method can be easily circumvented by applying the same obfuscation method to the enrolled faces as to the probe images. We suggest an adaptation that can help prevent this circumvention.
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises

Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of ob-jects, such as faces, animals, and automobiles. It is not surprising these models can also generate ISO-compliant, yet synthetic iris images, which can be used to augment training data for iris matchers and liveness detectors. In this work, we trained one of the most recent GAN mod-els (StyleGAN3 [15]) to generate fake iris images with two primary goals: (i) to understand the GAN's ability to produce “never-before-seen” irises, and (ii) to investigate the phenomenon of identity leakage as a function of the GAN's training time. Previous work has shown that personal biometric data can inadvertently flow from training data into synthetic samples, raising a privacy concern for subjects who accidentally appear in the training dataset. This paper presents analysis for three different iris matchers at varying points in the GAN training process to diagnose where and when authentic training samples are in jeopardy of leaking through the generative process. Our results show that while most synthetic samples do not show signs of identity leak-age, a handful of generated samples match authentic (training) samples nearly perfectly, with consensus across all matchers. In order to prioritize privacy, security, and trust in the machine learning model development process, the re-search community must strike a delicate balance between the benefits of using synthetic data and the corresponding threats against privacy from potential identity leakage.
Action Invariant IMU-Gait for Continuous Authentication

Continuous Authentication (CA) is proposed as an alter-native authentication scheme for modern personal devices. Gait is a suitable biometric for CA due to its availability and low resource requirements. However, the drastic change in the gait pattern with changes in actions (such as walking, running or climbing stairs) and changes in terrain (such as walking on a flat surface or down an incline) makes it chal-lenging to deploy in a real-world CA system. We show that standard gait features are influenced by different actions. The gait pattern of an action is also in-fluenced by the actions performed before and immediately after. Therefore, gait features are usually not robust to these action variations. We propose action invariant gait features to address this robustness issue. Our proposed method learns action invariant gait features utilizing a Siamese Net-work architecture with triplet loss and a unique triplet mining protocol. Our evaluations highlight that our action in-variant features are robust to pre and post action impacts and real world action variations. These features allows for a CA system to be enrolled using a single action (walk) and be used across multiple different actions encountered throughout the day.
On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolled subjects, MasterFace attacks represent a potential security risk for widely-used face recognition systems. Previous works proposed methods for generating such images and demonstrated that these attacks can strongly compromise face recognition. However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data. This makes it hard to state the generalizability of these attacks. In this work, we comprehensively analyse the generalizability of MasterFace attacks in empirical and theoretical investigations. The empirical investigations include the use of six state-of-the-art face recognition models, cross-dataset and cross-model evaluation protocols, and utilizing testing datasets of significantly higher size and variance. The results indicate a low generalizability when MasterFaces are training on a different face recognition model than the one used for testing. In these cases, the attack performance is similar to zero-effort imposter attacks. In the theoretical investigations, we define and estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. The current trend of increasing the fairness and generalizability in face recognition indicates that the vulnerability of future systems might further decrease. Future works might analyse the utility of MasterFaces for understanding and enhancing the robustness of face recognition models.
DFGC 2022: The Second DeepFake Game Competition

This paper presents the summary report on our DFGC 2022 competition. The DeepFake is rapidly evolving, and realistic face-swaps are becoming more deceptive and difficult to detect. On the other hand, methods for detecting DeepFakes are also improving. There is a two-party game between DeepFake creators and defenders. This competition provides a common platform for benchmarking the game between the current state-of-the-arts in Deep-Fake creation and detection methods. The main research question to be answered by this competition is the current state of the two adversaries when competed with each other. This is the second edition after the last year's DFGC 2021, with a new, more diverse video dataset, a more realistic game setting, and more reasonable evaluation metrics. With this competition, we aim to stimulate research ideas for building better defenses against the DeepFake threats. We also release our DFGC 2022 dataset contributed by both our participants and ourselves to enrich the DeepFake data resources for the research community (https://github.com/NiCE-X/DFGC-2022).
Machine Learning and Health Care

We discuss the potential for machine learning (ML) and artificial intelligence (AI) to improve health care, while detailing caveats and important considerations to ensure unbiased and equitable implementation. If disparities exist in the data used to train ML algorithms, they must be recognized and accounted for, so they do not bias performance accuracy or are not interpreted by the algorithm as simply a lack of need. We pay particular attention to an area in which bias in data composition is particularly striking, that is in large-scale genetics databases, as people of European descent are vastly overrepresented in the existing resources.
Guiding Feature Models Synthesis from User-Stories: An Exploratory Approach

User-stories are commonly used to define requirements in agile project management. In Software Product Lines (SPL), a user-story corresponds to a feature description (or part of it), that can be shared by several products. In practice, large SPL include a huge number of user-stories, making variability hard to grasp and handle. In this paper we present an exploratory approach that aims to guide the synthesis of Feature Models that capture and structure the commonalities and the variability expressed in these user-stories. The built Feature Models aim to help the project understanding, maintenance and evolution. Our approach first decomposes the user-stories to extract the roles and the features, using natural language processing techniques. In a second step, we group user-stories having the same topics thanks to a clustering method. This contributes to extract more general features. In a third step, we leverage the use of Formal Concept Analysis to extract logical constraints between the features that guide Feature Model synthesis. We illustrate our approach using a dataset from our industrial partner.
Evaluating the predictive performance of subtyping: A criterion for cluster mean‐based prediction

Heterogeneity is a frequent issue in population data analyses in medicine, biology, and the social sciences. A common approach for handling heterogeneity is to use a clustering algorithm to group similar samples, considering samples within the same group to be homogeneous. This approach is known as "subtyping" or "subgrouping." Methods for evaluating the validity of subtyping have yet to be fully established. In this study, we propose the cost of cluster mean-based prediction (CCMP) as a metric for evaluating the accuracy of predictions based on subtyping. By selecting the minimum CCMP among several candidate clustering results, the optimal subtype classification in terms of prediction accuracy can be determined. The computational implementation of the CCMP is validated with numerical experiments. We also examine some properties of subtype classification selected by CCMP.