Reviewerly/exHarmony huggingface dataset:
TaxIdMA: Towards a Taxonomy for Attacks related to Identities

Identity management refers to the technology and policies for the identification, authentication, and authorization of users in computer networks. Identity management is therefore fundamental to today's IT ecosystem. At the same time, identity management systems, where digital identities are managed, pose an attractive target for attacks. With the heterogeneity of identity management systems, every type (i. e., models, protocols, implementations) has different requirements, typical problems, and hence attack vectors. In order to provide a systematic and categorized overview, the framework Taxonomy for Identity Management Attacks (TaxIdMA) for attacks related to identities is proposed. The purpose of this framework is to classify existing attacks associated with system identities, identity management systems, and end-user identities as well as the background using an extensible structure from a scientific perspective. The taxonomy is then evaluated with eight real-world attacks resp. vulnerabilities. This analysis shows the capability of the proposed taxonomy framework TaxIdMA in describing and categorizing these attacks.
Sanadset 650K: Data on Hadith narrators

The chain of narrators (Sanad) plays a vital role in deciding the authenticity of Islamic hadiths. However, the investigation and validation of such Sanad fully depend on scientists (Hadith Scholars). They ordinarily utilize their acquired knowledge, which in this manner needs a critical sum of exertion and time. Automated Sanad evaluation using machine learning algorithms is the best way to solve this problem. Therefore, a representative Sanad dataset is required. This paper presents a full hadith dataset which is named Sanadset and is made openly accessible for researchers. Sanadset corpus contains over 650,986 records collected from 926 historical Arabic books of hadith. This dataset can be used for further investigation and classification of hadiths (Strong/Weak), and narrators (trustworthy/not) using AI techniques, and also it can be used as a linguistic resource tool for Arabic Natural Language Processing. Our dataset is collected from online Hadith sources using data scraping and web crawling. The main contribution of this dataset is the extraction of narrator chains that were originally present in textual form within Hadith books. Each observation in the dataset contains complete information about a specific hadith, such as (original book, number, Hadith text, Matn, list of narrators, and the number of narrators).
Language-independence of DisCoCirc's Text Circuits: English and Urdu

DisCoCirc [6] is a newly proposed framework for representing the grammar and semantics of texts using compositional, generative circuits.While it constitutes a development of the Categorical Distributional Compositional (DisCoCat) framework, it exposes radically new features.In particular, [14] suggested that DisCoCirc goes some way toward eliminating grammatical differences between languages.In this paper we provide a sketch that this is indeed the case for restricted fragments of English and Urdu.We first develop DisCoCirc for a fragment of Urdu, as it was done for English in [14].There is a simple translation from English grammar to Urdu grammar, and vice versa.We then show that differences in grammatical structure between English and Urdu -primarily relating to the ordering of words and phrases -vanish when passing to DisCoCirc circuits.
Phase modulation polarization encoding module applied to one-to-many QKD network based on wavelength division multiplexing

The quantum key distribution (QKD) network is a promising solution for secure communications. In this paper, we proposed a polarization-independent phase-modulated polarization encoding module, and it can be combined with a dense wavelength division multiplexer (DWDM) to achieve multi-user QKD. We experimentally test the encoding module with a repetition rate of 62.5 MHz, and its average quantum bit error rate (QBER) is as low as 0.4%. Finally, we implement a principle verification test for simultaneous QKD for 1 to 2 users in 100 min, and the average QBER of two users under the transmission distance of 1 km and 5 km is kept below 0.8%. Due to the use of polarization encoding, the module can also realize scalable network architecture in free-space QKD systems in the future.
Improving Health Mention Classification of Social Media Content Using Contrastive Adversarial Training

Health mention classification (HMC) involves the classification of an input text as health mention or not.Figurative and non-health mention of disease words makes the classification task challenging.Learning the context of the input text is the key to this problem.The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results.In this paper, we improve the word representation of the input text using adversarial training that acts as a regularizer during fine-tuning of the model.We generate adversarial examples by perturbing the word embeddings of the model and then train the model on a pair of clean and adversarial examples.Additionally, we utilize contrastive loss that tries to learn similar representations for the clean example and its perturbed version.We train and evaluate the method on three public datasets.Experiments show that contrastive adversarial training improves the performance significantly in terms of F1-score over the baseline methods of both BERT Large and RoBERTa Large on all three datasets.Furthermore, we provide a brief analysis of the results by utilizing the power of explainable AI.
Immersive Virtual Reality for Pain Control and Anxiolysis During IV Blood Draws in Adults: A Randomized Controlled Trial

To evaluate immersive virtual reality's (IVR) effectiveness as a distraction in reducing perceived pain and anxiety among adults undergoing intravenous (IV) blood draw.In this randomized controlled trial, we recruited adult patients 18-50 years of age scheduled for routine blood draws at the phlebotomy lab and randomized them into experimental and control groups. The experimental group underwent IV blood draw with IVR, and the control group underwent IV blood draw with standard practice. Before the procedure, subjects rated their anxiety level and the pain they "expected" to experience during blood draw. Immediately afterward, the subjects rated their actual "perceived" pain level. We measured the "expected" and the "perceived" pain and anxiety scores using a 100 mm visual analog scale. The primary outcome was the difference in "perceived" pain scores (0-100) between the two cohorts. Secondary outcomes were differences between the anxiety scores and the "expected" and "perceived" pain between the two cohorts, as well as degree of satisfaction with IVR during the blood draw and willingness to use IVR in future procedures.Fifty-nine subjects completed the study, 31 in the experimental group and 28 in the control group. For the primary outcome, the control group reported a perceived median pain score of 6.5 vs. the experimental group of 5; P = .55. For the secondary outcomes, the median anxiety scores were 22 (6.25-45.75) and 24 (2.00-35.00) for the control and the experimental groups, respectively, P = .44. The control group reported an expected median pain score of 20 vs. a perceived score of 6.5; P = .25, and the experimental group reported an expected median pain score of 22 vs. a perceived score of 5; P < .01. Median Likert scores were 5 (1-5) for satisfaction and preference for future use during painful procedures.The results of our study demonstrated that there was no significantly lower perceived pain or anxiety when using IVR compared to standard practice in adults undergoing IV blood draw.
Seamless Synchronization for Collaborative Web Services

Collaborative web services, which allow multiple people to work together on the same data, are becoming increasingly popular. However, current state-of-the-art frameworks for interactive client-side replication cannot handle network disruptions well, or suffer from large metadata overhead when clients are short-lived. This demonstration will show OWebSync, a generic web middleware for data synchronization in browser-based applications and interactive groupware. It offers a fine-grained data synchronization model, using state-based Conflict-free Replicated Data Types, and leverages Merkle-trees in the data model for efficient synchronization. We provide an interactive demonstration of a drawing application that workshop attendees can experiment with. We will also demonstrate the robustness in disconnected and offline settings.
Quantifying Information Leakage for Security Verification of Compiler Optimizations

Compiler optimizations can be functionally correct but not secure. In this work, we attempt to quantify the information leakage in a program for the security verification of compiler optimizations. Our work has the following contributions. We demonstrate that static taint analysis is applicable for security verification of compile optimizations. We develop a completely automated approach for quantifying the information leak in a program in the context of compiler optimizations. Our method avoids many false-positives scenarios due to implicit flow. It can handle leaks in a loop and propagates leaks over paths using the leak propagation vector. With our quantification parameters, we verify the relative security of source and transformed programs considering the optimizations phase of a compiler as a black box. Our experimental evaluations on benchmarks for various compiler optimizations in SPARK show that the SPARK compiler is actually leaky.
Factors Influencing Intention to use the COVID-19 Contact Tracing Application

Due to the emergence of the COVID-19 pandemic, governments have implemented several urgent steps to minimize the disease's effect and transmission.Supportive measures to trace contacts and warn people infected with COVID-19 were also implemented such as the COVID-19 contact tracing application.This study investigated the effects of variables influencing the intention to use the COVID-19 tracker.The extended Unified Theory of Acceptance and Use of Technology model was used to investigate user behavior using the COVID-19 tracker application.Google Form was used to construct and distribute the online survey to participants.Experiment results from 224 individuals revealed that performance expectations, trust, and privacy all have an impact on app usage intention.However, social impact, effort expectation, and facilitating conditions were not shown to be statistically significant.The conceptual model explained 60.07% of the amount of variation, suggesting that software developers, service providers, and policymakers should consider performance expectations, trust, and privacy as viable factors to encourage citizens to use the app.This study work's recommendations and limitations are thoroughly discussed.
Requirements Volatility in Multicultural Situational Contexts

Requirements’ volatility refers to additions, deletions, and modifications of requirements during the system development life cycle. Different approaches in software development, including Agile and DevOps, have addressed requirements volatility by increased user participation throughout the whole development process. In this paper we analyse requirements volatility from a situational context angle with the aim to increase understanding of the role of culture and cultural diversity in a multicultural requirements elicitation process. Research on situational context in Requirements Engineering (RE) is rather limited, despite the recognized importance of RE and requirements elicitation for improving the quality of the final system and software product. This paper builds on an extensive literature review demonstrating the importance of raising awareness and understanding of the role of culture and cultural diversity for requirements volatility, as one of the most significant situational factors in the requirements elicitation process, with the aim to improve the whole systems development process as well as the resulting products and services. The paper concludes with the presentation of the Requirements Cultural Volatility Framework which aims to reveal potential conflicts that may occur in requirements elicitation on a multiplicity of cultural dimensions, The framework proposes actions to be taken in order to address the conflicts and point out expected benefits on each dimension.
Understanding and Explaining Diagnostic Paths: Toward Augmented Decision Making

The process of finding a diagnosis in the medical domain relies on implicit knowledge and the experience of a human expert. In this article, we report on the observation of human decision making, shown by the example of pathology. By tracking the diagnostic steps, individual building blocks are identified, which not only contribute to a diagnostic finding, but can also be used in the future to train and develop artificial intelligence (AI) algorithms. This work also provides insights into the interaction of human experts regarding the observation time of so-called "hot spots," the magnification used for specific findings, and the overall observation and decision path followed. The documentation scheme yields a standardized examination procedure that shows the concept the pathologist is actually looking for as well as the possible features of findings that can be identified. This contribution indicates how important visualization is for human-centered AI, and specifically for enabling human oversight with respect to AI implementation in high-stake areas, such as medicine.
Efficient Homomorphic E-Voting Based On Batch Proof Techniques — An Improvement to Secure MPC Application

E-voting is an important application of secure MPC (multi-party computation) and its achievement of security through verifiable MPC can be based on homomorphic encryption or shuffling. Homomorphic-encryption-based e-voting is often less costly than shuffling-based e-voting, but depends on validity of the encrypted ballots. Proof and verification of validity of all the encrypted ballots is the efficiency bottleneck to overcome. So homomorphic e-voting schemes still need efficiency improvements to support users with low-capability. In this paper, a novel e-voting scheme is proposed on the basis of an efficiency-improving technology, batch ZK proof of 1-out-of-2 knowledge. It achieves much higher efficiency in ballot validity proof and can improve efficiency of homomorphic e-voting to achieve an obvious advantage over the existing solutions. It is a good example to illustrate how efficiency of secure MPC can be improved by batch cryptology.
Designing Mixed Reality Experiences that Provide Views to the Past: Reviving the Operation of an Industrial Olive Oil Factory

Mixed reality (MR) installations in museums can be a promising communication medium for intangible aspects of cultural heritage, such as practices, rituals, craftsmanship, and customs of a previous era. There are various technological approaches for such hybrid setups exploiting the reality-virtuality continuum and posing several challenges when integrated into a museum setting. In this paper, we focus on an MR setup for communicating intangible cultural heritage through a rotating screen installed on a fixed basis that works as a “window to the past”. The installation aims to augment the interior of the Museum of Industrial Olive-Oil Production of Lesvos by placing digital workers to perform actions inside the virtual reconstructed scenery and machinery. The design and development process of this installation included several stages such as: 3D scanning and modeling of the interior of the factory (including machinery), modeling and animations of the virtual characters (workers), detailed product design of the installation (including microcontroller and sensors for capturing screen rotations and design of the 3D printed parts), software development, crafting and integration. We evaluated the application's prototype in its real context in terms of overall user experience, and we report on key findings that might be helpful for other interaction designers working on similar digital heritage approaches.
An Intuitive VR-based Environment for Monitoring and Control of Electrical Power Substations

A Virtual Reality System provides an intuitive tridimensional user interface, with distinct interaction when compared to a traditional 2D layout, allowing the user to manipulate data similar to the real world. Such systems give the users physical and cognitive immersion, by granting a mental model compatible with field operation. In this work, we present the development aspects and some results of a Virtual Reality based solution to provide a more natural and intuitive environment for controlling electrical operation centers. The solution intends to minimize the issues caused by the operation of electric power substations due to the lack of spatial and functional information on the traditional operation interfaces. The research is being carried out with the collaboration between the energy electric company of Minas Gerais–Brazil (CEMIG) and the Federal University of Uberlândia. Besides operating generation and transmission actives, the environment presents great potential for the training of operators and other professionals of support and maintenance.
First Grader and Mathematics: Features of Teaching According to the Updated Standard

Teaching mathematics to younger schoolchildren in accordance with the Federal State Educational Standard for Primary General Education (FSES PGE) has a number of features. Leading among them is the development of spatial thinking, logical and algorithmic thinking, mathematical speech, functional mathematical literacy in accordance with the age capabilities of children. The article discusses the organization of teaching first-graders, focused on the implementation of the requirements of the updated version of the Federal State Educational Standard of the PGE for mathematical preparation: listening and reading a mathematical text, discussing and modeling a mathematical situation, searching for different solutions, drawing up an action plan. Appropriate skills and actions of first-graders can be successfully formed on the basis of traditional subject content.
: From real infrared eye-images to synthetic sequences of gaze behavior<i />

Current methods for segmenting eye imagery into skin, sclera, pupil, and iris cannot leverage information about eye motion. This is because the datasets on which models are trained are limited to temporally non-contiguous frames. We present Temporal RIT-Eyes, a Blender pipeline that draws data from real eye videos for the rendering of synthetic imagery depicting natural gaze dynamics. These sequences are accompanied by ground-truth segmentation maps that may be used for training image-segmentation networks. Temporal RIT-Eyes relies on a novel method for the extraction of 3D eyelid pose (top and bottom apex of eyelids/eyeball boundary) from raw eye images for the rendering of gaze-dependent eyelid pose and blink behavior. The pipeline is parameterized to vary in appearance, eye/head/camera/illuminant geometry, and environment settings (indoor/outdoor). We present two open-source datasets of synthetic eye imagery: sGiW is a set of synthetic-image sequences whose dynamics are modeled on those of the Gaze in Wild dataset, and sOpenEDS2 is a series of temporally non-contiguous eye images that approximate the OpenEDS-2019 dataset. We also analyze and demonstrate the quality of the rendered dataset qualitatively and show significant overlap between latent-space representations of the source and the rendered datasets.
Increasing Performance in Turkish by Finetuning of Multilingual Speech-to-Text Model

This study was carried out with the aim of automatically translating phone calls between customers and customer representatives of a company. The dataset used in the study was created with audio files that were taken from open source platforms and reading of short texts in various contents by the company personnel. In addition to the labbeled data, approximately 28 thousand unlabeled data were labelled, and a total of 37534 audio data were prepared to be used in the training of the model that will translate from speech to text. The Wav2Vec2-XLSR-53 model which is a pre-trained model trained in 53 languages was fine-tuned with the our Turkish dataset. It has been obtained that it gives successful results in the speech to text performed on the data that is not used in model training and validation. The model was shared as open source on HugginFace to be used and tested for similar speech to text translation problems.
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences

Sequential recommendation predicts users' next behaviors with their historical interactions. Recommending with longer sequences improves recommendation accuracy and increases the degree of personalization. As sequences get longer, existing works have not yet addressed the following two main challenges. Firstly, modeling long-range intra-sequence dependency is difficult with increasing sequence lengths. Secondly, it requires efficient memory and computational speeds. In this paper, we propose a Sparse Attentive Memory (SAM) network for long sequential user behavior modeling. SAM supports efficient training and real-time inference for user behavior sequences with lengths on the scale of thousands. In SAM, we model the target item as the query and the long sequence as the knowledge database, where the former continuously elicits relevant information from the latter. SAM simultaneously models target-sequence dependencies and long-range intra-sequence dependencies with O(L) complexity and O(1) number of sequential updates, which can only be achieved by the self-attention mechanism with O(L^2) complexity. Extensive empirical results demonstrate that our proposed solution is effective not only in long user behavior modeling but also on short sequences modeling. Implemented on sequences of length 1000, SAM is successfully deployed on one of the largest international E-commerce platforms. This inference time is within 30ms, with a substantial 7.30% click-through rate improvement for the online A/B test. To the best of our knowledge, it is the first end-to-end long user sequence modeling framework that models intra-sequence and target-sequence dependencies with the aforementioned degree of efficiency and successfully deployed on a large-scale real-time industrial recommender system.