Miracle-dz/UniFAR-FTUs huggingface dataset:
Honeyword-based Authentication Techniques for Protecting Passwords: A Survey

Honeyword (or decoy password) based authentication, first introduced by Juels and Rivest in 2013, has emerged as a security mechanism that can provide security against server-side threats on the password-files. From the theoretical perspective, this security mechanism reduces attackers’ efficiency to a great extent as it detects the threat on a password-file so that the system administrator can be notified almost immediately as an attacker tries to take advantage of the compromised file. This paper aims to present a comprehensive survey of the relevant research and technological developments in honeyword-based authentication techniques. We cover twenty-three techniques related to honeyword, reported under different research articles since 2013. This survey paper helps the readers to (i) understand how honeyword based security mechanism works in practice, (ii) get a comparative view on the existing honeyword based techniques, and (iii) identify the existing gaps that have yet to be filled and the emergent research opportunities.
Entropy of probability kernels from the backwards tail boundary

A number of recent works have sought to generalize the Kolmogorov-Sinai entropy of probability-preserving transformations to the setting of Markov operators acting on the integrable functions on a probability space $(X,\mu)$. These have culminated in a proof by Downarovicz and Frej that these definitions all coincide, and that the resulting quantity is uniquely characterized by certain properties. On the other hand, Makarov has shown that this `operator entropy' is always dominated by the Kolmogorov-Sinai entropy of a classical system that may be constructed from a Markov operator, and that these numbers coincide under certain extra assumptions. This note proves that equality in all cases.
Caching and Distributed Storage - Models, Limits and Designs

A simple task of storing a database or transferring it to a different point via a communication channel turns far more complex as the size of the database grows large. Limited bandwidth available for transmission plays a central role in this predicament. In two broad contexts, Content Distribution Networks (CDN) and Distributed Storage Systems (DSS), the adverse effect of the growing size of the database on the transmission bandwidth can be mitigated by exploiting additional storage units. Characterizing the optimal tradeoff between the transmission bandwidth and the storage size is the central quest to numerous works in the recent literature, including this thesis. In a DSS, individual servers fail routinely and must be replicated by downloading data from the remaining servers, a task referred to as the repair process. To render this process of repairing failed servers more straightforward and efficient, various forms of redundancy can be introduced in the system. One of the benchmarks by which the reliability of a DSS is measured is availability, which refers to the number of disjoint sets of servers that can help to repair any failed server. We study the interaction of this parameter with the amount of traffic generated during the repair process (the repair bandwidth) and the storage size. In particular, we propose a novel DSS architecture which can achieve much smaller repair bandwidth for the same availability, compared to the state of the art. In the context of CDNs, the network can be highly congested during certain hours of the day and almost idle at other times. This variability of traffic can be reduced by utilizing local storage units that prefetch the data while the network is idle. This approach is referred to as caching. In this thesis we analyze a CDN that has access to independent data from various content providers. We characterize the best caching strategy in terms of the aggregate peak traffic under the constraint that coding across contents from different libraries is prohibited. Furthermore we prove that under certain set of conditions this restriction is without loss of optimality.
Considerations of a $k=+1$ Varluminopic Cosmology

Every relativistic particle has 4-speed equal to c , since g µν dx µ dτ dx ν dτ = c 2 . With the choice of k = +1 in the FRW metric, the cosmological scale factor a ( t ) has the natural interpretation of the radius of the sphere S 3 a = { x ∈ R 4 : ( x,x ) = a 2 } . Thus, a particle at rest in the cosmological frame has 4-speed equal to dadt . This leads us to infer that ˙ a = c , which respresents a simple kinematic constraint linking the speed of light to the cosmological scale factor. This drastically changes the k = +1 picture from a closed deaccelerating universe to an open accelerating universe, settles the horizon problem, and provides for a new cosmological model more appealing to our natural intuition. In this paper we shall consider ramiﬁcations of this model.
Probing the Limits of Social Data - Biases, Methods, and Domain Knowledge

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection techniques. This has led to numerous studies that leverage online social data to model or gain insights about real world phenomena, as well as to inform system or methods design for performance gains, or for providing personalized services. Alas, regardless of how large, detailed or varied the online social data is, there are limits to what can be discerned from it about real-world, or even mediaor application-specific phenomena. This thesis investigates four instances of such limits that are related to both the properties of the working data sets and of the methods used to acquire and leverage them, including: (a) online social media biases, (b) assessing and (c) reducing data collection biases, and (d) methods sensitivity to data biases and variability. For each of them, we conduct a separate case study that enables us to systematically devise and apply consistent methodologies to collect, process, compare or assess different data sets and dedicated methods. The main contributions of this thesis are: (i) To gain insights into media-specific biases, we run a comparative study juxtaposing social and mainstream media coverage of domain-specific news events for a period of 17 months. To this end, we introduce a generic methodology for comparing news agendas online based on a comparison of spikes of coverage. We expose significant differences in the type of events that are covered by the two media. (ii) To assess possible biases across data collections, we run a transversal study that systematically assembles and examines 26 distinct data sets of social media posts during a variety of crisis events spanning a 2 years period. While we find patterns and consistencies, we also uncover substantial variability across different event data sets, highlighting the pitfalls of generalizing findings from one data set to another. (iii) To improve data collections, we introduce a method that increases the recall of social media samples, while preserving the original distribution of message types and sources. To
Self-Similar Vector Fields

We propose statistically self-similar and rotation-invariant models for vector fields, study some of the more significant properties of these models, and suggest algorithms and methods for reconstructing vector fields from numerical observations, using the same notions of self-similarity and invariance that give rise to our stochastic models. We illustrate the efficacy of the proposed schemes by applying them to the problems of denoising synthetic flow phantoms and enhancing flow-sensitive magnetic resonance imaging (MRI) of blood flow in the aorta. In constructing our models and devising our applied schemes and algorithms, we rely on two fundamental notions. The first of these, referred to as ‘innovation modelling’ in the thesis, is the principle—applicable both analytically and synthetically—of reducing complex phenomena to combinations of simple independent components or ‘innovations’. The second fundamental idea is that of ‘invariance’, which indicates that in the absence of any distinguishing factor, two equally valid models or solutions should be given equal consideration.
Intra-document Block Pre-ranking for BERT-based Long Document Information Retrieval - Abstract

Information retrieval using transformer architectures, especially pretrained models like BERT, has seen great improvements. However, due to the quadratic complexity of the self-attention mechanism, for long documents, directly using such models is unsatisfactory. Truncating long documents is a widely adopted approach. Other researchers also propose to separate a long document into passages, each of which can be treated by a standard BERT model. The other solution is modifying the self-attention mechanism to make it sparser. However, these approaches either lose information or have high computational complexity and memory requirement. We propose a slightly different approach that firstly pre-ranks passages within a long document according to the query, after which the filtered top-ranking passages are combined for later ranking to obtain the document relevance score. Experiments on IR collections demonstrate the SOTA level effectiveness of the proposed approach.
Exploring Analogical Inference in Healthcare

Analogical proportions are statements of the form 𝐴 : 𝐵 :: 𝐶 : 𝐷 that are used to map similar relationships between two pairs of objects, 𝐴 , 𝐵 , and 𝐶 , 𝐷 . Analogies have long been a subject of research in the Natural Language Processing (NLP) community, where they have been applied to a variety of reasoning and classification tasks. Lately, machine and representation learning have shown to be useful for analogical reasoning. In this paper, we discuss the possibility of adapting the analogical framework to healthcare applications, in particular to medical decision support. We particularly hypothesize that as language representations help in analogical reasoning in NLP, patient representation learned from Electronic Health Records (EHRs) may help in healthcare. We define three different analogy based settings adapted to EHR data that we see as first steps to the development of analogical applications to this domain. We provide statistics on the first sets of analogies that we built from a publicly available dataset of EHRs, and report preliminary, but promising results to detect patient-stay analogies following our very first experimental setting.
Multi-element protocol on IR experiments stability: Application to the TREC-COVID test collection

The evaluation of information retrieval systems is performed using test collections. The classical Cranfield evaluation paradigm is defined on one fixed corpus of documents and topics. Following this paradigm, several systems can only be compared over the same test collections (documents, topics, assessments). In this work, we explore in a systematic way the impact of similarity of test collections on the comparability of the experiments: characterizing the minimal changes between the collections upon which the performance of IR system evaluated can be compared. To do that, we create pair instances of sub-test collections from one reference collection with controlled overlapping elements, and we compare the Ranking of Systems (RoS) of a defined list of IR systems. We can then compute the probability that the RoS are the same across the sub-test collections. We experiment with our framework proposed on the TREC-COVID collections, and two of our findings show that: a) the ranking of systems, according to the MaP, is very stable even for overlaps smaller than 10% for documents, relevance assessments and positive relevance assessments sub-collections, and b) stability is not ensured for MaP, Rprec, Bpref and ndcg evaluation measures even when considering large overlap for the topics. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Virtual reality enhances safety training in the maritime industry: An organizational training experiment with a non-WEIRD sample

Objective: Many industries struggle with training dynamic risk assessment, and how to bridge the gap between safety training and behavior in real life scenarios. In this article, we focus on dynamic risk assessment during a mooring operation and investigate the potential value of using immersive virtual reality (VR) simulations compared to standard training procedures in an international maritime training organization. Methods: In a pilot study, we compared two ways of implementing a VR simulation (stand-alone or with post-simulation reflection) to a manual and a personal trainer condition in a between-subjects design with 86 students in a maritime school. Based on the results we compared the stand-alone VR simulation to the personal trainer condition in a between-subjects design in a non-Western, Educated, Industrialized, Rich, and Democratic (WEIRD) sample of 28 seafarers from the Kiribati Islands at an international maritime training organization. Results: The VR simulation group reported significantly higher perceived enjoyment (d = 1.28), intrinsic motivation (d = 0.96), perceived learning (d = 0.90), and behavioral change (d = 0.88), and significantly lower extraneous cognitive load (d = 0.82) compared to the personal trainer group, but the differences in self-efficacy, and safety attitudes were not significant. Discussion: The results support the value of using VR to train procedures that are difficult to train in the real world and suggest that VR technologies can be useful for providing just in time training anywhere, anytime, in a global market where employees are increasingly cross-cultural and dislocated.
Determinants of Voluntary Counseling and Testing Service Uptake Among Adult Sub-Saharan Africans: A Systematic Review and Meta-Analysis

Objective: To examine the major determinants of VCT service uptake among adults in SSA. Methods: Electronic databases were searched to identify eligible English language publications. Reporting of the study selection procedure was done according to PRISMA and the selected articles were also critically appraised. Results: We found 8 significant determinants of VCT uptake among adults in SSA, such as less physical access [OR (Odds ratio): 0.77 (95% CI (Confidence interval): 0.62–0.96), p < 0.01], older age [OR: 1.36 (95% CI: 1.08–1.73), p < 0.01], higher education level [OR: 1.60 (95% CI: 1.24–2.05), p < 0.01], high knowledge of HIV and VCT awareness [OR: 1.40 (95% CI: 1.03–1.90), p < 0.01], unprotected sexual practices [OR: 1.75 (95% CI: 1.18–2.58), p < 0.01], discussion on HIV among partners and others [OR: 1.76 (95% CI: 1.10–2.81), p < 0.01], other STIs [OR: 1.40 (95% CI: 1.00–1.98), p < 0.01], and divorced/separated [OR: 1.39 (95% CI: 1.12–1.72), p < 0.01]. Conclusion: This study showed that 8 determinants were significantly associated with VCT service uptake in SSA. Thus, HIV interventions and policy initiatives should be tailored to these determinants to ensure scale-up of VCT service uptake in SSA.
Quartz crystal microbalance and atomic force microscopy to characterize mimetic systems based on supported lipids bilayer

Quartz Crystal Microbalance (QCM) with dissipation and Atomic Force Microscopy (AFM) are two characterization techniques that allow describing processes taking place at solid-liquid interfaces. Both are label-free and, when used in combination, provide kinetic, thermodynamic and structural information at the nanometer scale of events taking place at surfaces. Here we describe the basic operation principles of both techniques, addressing a non-specialized audience, and provide some examples of their use for describing biological events taking place at supported lipid bilayers (SLBs). The aim is to illustrate current strengths and limitations of the techniques and to show their potential as biophysical characterization techniques.
Metastases to the thyroid gland: ultrasonographic findings and diagnostic value of fine-needle aspiration cytology

Introduction In the present study, we aimed to analyze ultrasonographic findings of metastases to the thyroid and explore the role of fine-needle aspiration cytology (FNAC) in the diagnosis of metastases to the thyroid. Methods Twelve cases of cytologically or/and pathologically confirmed metastatic tumors of the thyroid gland were reviewed. All the primary thyroid lesions and lymphomas were excluded. The location, maximum size, echogenicity, shape, margin, presence of calcifications, vascularity, and cervical lymph nodes were assessed on ultrasonography. In addition, the results of cytology or pathology (or both) were noted retrospectively. Results Eight of 10 patients were diagnosed correctly with FNAC. Two cases presented with diffuse involvement in both thyroid lobes. Nine cases demonstrated a hypoechoic nodule with an irregular margin, four of which had microcalcifications. One case presented with a mixed solid and cystic mass with an oval shape. The lesions with cervical lymph nodes were found in seven cases. Conclusion Most metastatic thyroid cancer has similar ultrasound features to primary thyroid cancer. In some cases with atypical US features, ultrasound diagnosis should be combined with the medical history. FNAC might be helpful in the diagnosis.
Regulation of the Signal-Dependent E Protein HEBAlt Through a YYY Motif Is Required for Progression Through T Cell Development

The E protein transcription factors E2A and HEB are critical for many developmental processes, including T cell development. We have shown that the Tcf12 locus gives rise to two distinct HEB proteins, with alternative (HEBAlt) and canonical (HEBCan) N-terminal domains, which are co-expressed during early T cell development. While the functional domains of HEBCan have been well studied, the nature of the HEBAlt-specific (Alt) domain has been obscure. Here we provide compelling evidence that the Alt domain provides a site for the molecular integration of cytokine signaling and E protein activity. Our results indicate that phosphorylation of a unique YYY motif in the Alt domain increases HEBAlt activity by 10-fold, and that this increase is dependent on Janus kinase activity. To enable in vivo studies of HEBAlt in the T cell context, we generated ALT-Tg mice, which can be induced to express a HA-tagged HEBAlt coding cassette in the presence of Cre recombinases. Analysis of ALT-Tg mice on the Vav-iCre background revealed a minor change in the ratio of ISP cells to CD8+ SP cells, and a mild shift in the ratio of T cells to B cells in the spleen, but otherwise the thymus, spleen, and bone marrow lymphocyte subsets were comparable at steady state. However, kinetic analysis of T cell development in OP9-DL4 co-cultures revealed a delay in early T cell development and a partial block at the DN to DP transition when HEBAlt levels or activity were increased. We also observed that HEBCan and HEBAlt displayed significant differences in protein stability that were resolved in the thymocyte context. Finally, a proteomic screen identified STAT1 and Xpo1 as potential members of HEBAlt-containing complexes in thymocytes, consistent with JAK-induced activation of HEBAlt accompanied by translocation to the nucleus. Thus, our results show that the Alt domain confers access to multiple layers of post-translational control to HEBAlt that are not available to HEBCan, and thus may serve as a rheostat to tune E protein activity levels as cells move through different thymic signaling environments during T cell development.
A 1-dimensional family of Enriques surfaces in characteristic 2 covered by the supersingular K3 surface with Artin invariant 1

We give a 1-dimensional family of classical and supersingular Enriques surfaces in characteristic 2 covered by the supersingular K3 surface with Artin invariant 1. Moreover we show that there exist 30 nonsingular rational curves and ten non-effective (-2)-divisors on these Enriques surfaces whose reflection group is of finite index in the orthogonal group of the Neron-Severi lattice modulo torsion.
Early immune surveillance to predict cytomegalovirus outcomes after allogeneic hematopoietic stem cell transplantation

Background There is no method of predicting human cytomegalovirus (HCMV) outcomes in allogeneic hematopoietic stem cell transplant recipients clinically, leading in some cases to excessive or insufficient antiviral therapy. We evaluated the early immune response of recipients with disparate HCMV outcomes. Methods The HCMV outcomes of recipients were determined by long-term monitoring of HCMV DNA levels posttransplant. HCMV IgG and IgM concentrations at 1 week before and 1 week after transplantation, absolute lymphocyte counts, and HCMV-specific IFN-γ secreting cells at 1 month posttransplant were evaluated based on HCMV outcome. Results All recipients were negative for HCMV IgM. Significant differences between recipients with and without HCMV reactivation were observed in pre- and post-transplant HCMV IgG antibody levels, absolute lymphocyte counts, and HCMV-specific IFN-γ secreting cells (P < 0.05). HCMV IgG antibody levels significantly increased after transplantation in recipients with HCMV reactivation (P = 0.032), but not in those without reactivation. Multivariate analysis revealed that except for the absolute lymphocyte count these biomarkers were related to HCMV reactivation, independent of other clinical factors. In time-to-event analyses, lower levels of these biomarkers were associated with an increased 150-day cumulative incidence of HCMV reactivation (log-rank P < 0.05). In recipients with HCMV reactivation, the duration of HCMV DNAemia had negative correlation with HCMV-specific IFN-γ-secreting cells (P = 0.015, r = -0.372). The relationships between the peak HCMV DNA load and absolute lymphocyte count and HCMV-specific IFN-γ-secreting cells followed the same trends (P = 0.026, r = -0.181 and P = 0.010, r = -0.317). Conclusions HCMV IgG, absolute lymphocyte count, and HCMV-specific IFN-γ secreting cells represent the humoral and cellular immune response. Early monitoring of these immune markers could enable prediction of HCMV outcomes posttransplant and assessment of the severity of HCMV DNAemia.
Classification of chinese fragrant rapeseed oil based on sensory evaluation and gas chromatography-olfactometry

Fragrant rapeseed oils and traditional pressed oils are increasingly popular in China owing to their sensory advantages. Many fragrant rapeseed oils are labeled by different fragrance types; however, the scientific basis for these differences is lacking. To identify the distinctive aroma and achieve fragrance classification, the sensory characteristics and aroma components of nine different fragrant rapeseed oils were analyzed via sensory evaluation and gas-chromatography-mass spectrometry-olfactometry. A total of 35 aroma compounds were found to contribute to the overall aroma. By using chemometrics methods, rapeseed oils were categorized into three fragrance styles: “strong fragrance,” “umami fragrance,” and “delicate fragrance. ” In total, 10 aroma compounds were predicted to be the most effective compounds for distinguishing sensory characteristics of fragrant rapeseed oil. According to our results, this approach has excellent potential for the fragrance classification and quality control of rapeseed oil.