Miracle-dz/UniFAR-FTUs huggingface dataset:
FACE RECOGNITION SYSTEM FOR MOBILE PHONE USING PCA

: Automated security systems are essential at present environment. These days most of the personal and official records and banking transactions statements, use of personal gadgets like mobile, and tablets are frequently performed using automated security system for identification. Face recognition systems accomplish the task through face acquisition, facial feature extraction and feature Matching. In the proposed face recognition system using PCA for matching of reference image with all images of database and create a suspect list. Then FFT is use to match reference image with faces in suspect list created by PCA. The result shows that proposed system provides higher accuracy in all the conditions in comparison to PCA based systems, while run time is slightly increased.
Cyber Social Threats 2021: AI, Covid-19 Vaccine, Detection and Countering Strategies

In recent years, online platforms have been utilized for promoting harmful content and behavior such as extremism, harassment, mis/disinformation, human trafﬁcking, gender-based violence among others affecting our society, often leading to real-world events. Such content and behaviors are inherently complex, making the recognition of these narratives challenging for researchers as well as social media companies. The Cyber Social Threats (CySoc) Workshop 2021 aimed to facilitate a rich forum for researchers and practitioners from both academia and industry in the areas of computing and social science, to discuss novel avenues for research on interdisciplinary aspects of harmful communications on social media, to exchange ideas and experiences, and to identify new opportunities for collaboration. The second edition of this workshop was well-received and very successful in providing a platform to explore future directions, possible research ideas, and collaborations, as it was for the ﬁrst edition last year (2020).
Shifting Silences

In Poland between 1970s and 2010s, the discursive landscape involving issues of religion and homosexuality radically changed, which influenced the possibilities for combining religiosity with non-normative sexuality at a personal level. This paper draws on biographical interviews with Polish Roman Catholics who experience homoerotic desire. It shows that the shift of homosexuality from being a phenomenon surrounded by silence to becoming a ‘gay’ identity has produced new silences and new problems in integrating the religious and the sexual spheres of life. The paper is concerned with the integration at both the cognitive level and in the context of social interactions, highlighting possibilities and limitations specific for the past and the present, respectively. However, it tells a non-linear story of gay and lesbian experience that complicates a clear division between the communist past, customarily seen as totally restricting sexually non-normative individuals, and today’s democratic conditions, seen as decidedly beneficial for their self-expression.
Intraseasonal variability of wind waves in the western South Atlantic: the role of cyclones and the Pacific South-American pattern

Abstract. Extratropical cyclones are known to generate extreme significant wave height (swh) values in the western South Atlantic (wSA), which are highly influenced by intraseasonal scales. This work aims to investigate the importance of intraseasonal time scales (30–180 days) in the regional wave climate and its atmospheric forcing. The variability is explained by analyzing the storm track modulation due to westerlies winds. These winds present time-scales and spatial patterns compatible with the intraseasonal component of the Pacific South–American (PSA) patterns. The analysis are made using ECMWF’s ERA5 from 1979 to 2019 and a database of extratropical cyclones based on the same reanalysis. Empirical orthogonal function (EOF) analysis of the 10 m zonal wind and swh are used to assess the westerlies and waves regime in the wSA. The EOF1 of u10 presented a core centred at 45° W and 40° S, while the EOF2 is represented by two cores organized into a see-saw pattern with a center between 30° S–40° S and another to the south of 40° S. Composites of cyclone genesis and track densities, and swh fields were calculated based on the phases of both EOFs. In short, EOF phases presenting cores with a positive (negative) u10 anomaly provides a favorable (unfavorable) environment for cyclone genesis and track densities and, therefore, positive (negative) swh anomalies. The modulation of the cyclones track are significant for extreme values of the swh. The spatial patterns of the EOFs of u10 are physically and statistically consistent with 200 hPa and 850 hPa geopotential height signals from the Pacific, indicating the importance of the remote influence of the PSA patterns over the wSA.
Simple Technology is an Improved Solution for a Post-Pandemic Informative System: A Reference Model

COVID-19 pandemic accelerated the digitalization and the implementation of technological tools to distribute knowledge and content to certify the instructional process's steadiness despite the restrictions posed in many nations worldwide. However, multi-models of development and integrations based on multitudes of theoretical and conceptual frameworks made it difficult for deciders during the year - especially in developing countries - to follow a clear path based on their contextual needs. Based on a literature review and historical Data, Learning Ana-lytics research, and its empirical results, this article proposes a data-analytics model for growth. Training/educational technologies help stakeholders use data as intelligence sources to implement technologies that will improve traditional learning procedures without constraining practices. As a result, the paper also suggests, according to the pragmatic results, a 4-year plan applicable in multidi-mensional contexts to enhance the organization's learning capabilities as a whole unit to face future trials. The return on experience in the last year of the pandemic contracts the methodological basis of results. Furthermore, this manuscript's aim is defined by the urgent necessity of post-pandemic solutions to positively safe-guard the future of keeping the wheel of knowledge running for the learners and warrant upcoming transition into using data as a source of developing new learn-ing technologies.
OPTIMIZING IR SYSTEM WITH RF ALGORITHMS USING QUANTUM MECHANICS

– Information retrieving is deals with indexing documents including useful data relevant to user’s data need. A class of algorithms for improving information retrieving and which consists of collecting other data representing the user’s information need and automatically creating a new query. This paper presents a class of new Relevance Feedback algorithms inspired by quantum detection to re-weight the query keywords and re-rank the relevant documents retrieved by an Information retrieving system. This paper utilizes the query vector on subspace spanned by the eigenvector which increases the distance between distribution probability of relevance and non-relevance respectively. This paper explains the how the algorithms classifying and retrieving the useful information.
OTT_A_275304 11421..11431

*These authors contributed equally to this work Background: Individuals with hepatocellular carcinoma (HCC) are at risk of tumor recurrence after surgical resection, which affects their survival. The aim of the present study was to establish a model for predicting tumor progression in patients with HCC. Methods: To develop and validate the efficacy of a novel prognostic model, a retrospective cohort with HCC (n = 1005) at Beijing Ditan Hospital was enrolled from January 2008 and June 2017. Furthermore, a prospective cohort (n = 77) was recruited to validate the association between thyroid-stimulating hormone (TSH) levels and tumor progression in patients with HCC. Results: The model used in predicting the progression of HCC included four variables (namely, Barcelona Clinic Liver Cancer [BCLC] stage, presence of portal vein tumor thrombus, alpha-fetoprotein level, and TSH level). The AUROC of the 1-year progressionfree survival (PFS) model was 0.755 and 0.753 in the deriving cohort and validation cohort, respectively, and these values were significantly higher than those of the Child–Pugh score, Model for End-stage Liver Disease (MELD), tumor–lymph node–metastasis (TNM) staging system, Okuda classification, and CLIP score. A simple assessment using a nomogram showed the 1-year PFS rate of patients with HCC. In the prospective cohort, the KM curve showed that the high TSH level group had a shorter PFS than the low TSH level (p = 0.001). Conclusion: The prognostic model of HCC progression was superior to other well-known classical tumor scoring systems. A high TSH level was correlated to poor outcome, particularly those with advanced HCC.
Concealed Pied-Piping in Russian: On Left Branch Extraction, Parasitic Gaps, and Beyond1

We use parasitic gaps to examine left branch extraction (LBE) from noun phrases in Russian. We observe that the interpretation for a parasitic gap in a context with LBE is the same as the interpretation assigned when an entire noun phrase is moved, rather than extracted from. Thus we argue that Russian LBE involves concealed pied-piping of an entire noun phrase, rather than extraction. We go on to explore the consequences of this analysis for several other topics.
Tolerating Adversarial Attacks and Byzantine Faults in Distributed Machine Learning

Adversarial attacks attempt to disrupt the training, retraining, and utilizing of artificial intelligence (AI) and machine learning models in large-scale distributed machine learning systems. This causes security risks on its prediction outcome. For example, attackers attempt to poison the model by either presenting inaccurate misrepresentative data or altering the models' parameters. In addition, Byzantine faults including software, hardware, network issues occur in distributed systems which also lead to a negative impact on the prediction outcome. In this paper, we propose a novel distributed training algorithm, partial synchronous stochastic gradient descent (ParSGD), which defends adversarial attacks and/or tolerates Byzantine faults. We demonstrate the effectiveness of our algorithm under three common adversarial attacks again the ML models and a Byzantine fault during the training phase. Our results show that using ParSGD, ML models can still produce accurate predictions as if it is not being attacked nor having failures at all when almost half of the nodes are being compromised or failed. We will report the experimental evaluations of ParSGD in comparison with other algorithms.
Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing

Distributed Stream Processing systems are becoming an increasingly essential part of Big Data processing platforms as users grow ever more reliant on their ability to provide fast access to new results. As such, making timely decisions based on these results is dependent on a system’s ability to tolerate failure. Typically, these systems achieve fault tolerance and the ability to recover automatically from partial failures by implementing checkpoint and rollback recovery. However, owing to the statistical probability of partial failures occurring in these distributed environments and the variability of workloads upon which jobs are expected to operate, static configurations will often not meet Quality of Service constraints with low overhead.In this paper we present Khaos, a new approach which utilizes the parallel processing capabilities of cloud orchestration technologies for the automatic runtime optimization of fault tolerance configurations in Distributed Stream Processing jobs. Our approach employs three subsequent phases which borrows from the principles of Chaos Engineering: establish the steady-state processing conditions, conduct experiments to better understand how the system performs under failure, and use this knowledge to continuously minimize Quality of Service violations. We implemented Khaos prototypically together with Apache Flink and demonstrate its usefulness experimentally.
Matrix hypercontractivity, streaming algorithms and LDCs: the large alphabet case

We prove a hypercontractive inequality for matrix-valued functions defined over large alphabets, generalizing the result of Ben-Aroya, Regev, de Wolf (FOCS'08) for the Boolean alphabet. For such we prove a generalization of the $2$-uniform convexity inequality of Ball, Carlen, Lieb (Inventiones Mathematicae'94). Using our inequality, we present upper and lower bounds for the communication complexity of Hidden Hypermatching when defined over large alphabets, which generalizes the well-known Boolean Hidden Matching problem. We then consider streaming algorithms for approximating the value of Unique Games on a $t$-hyperedge hypergraph: an edge-counting argument gives an $r$-approximation with $O(\log{n})$ space. On the other hand, via our communication lower bound we show that every streaming algorithm in the adversarial model achieving a $(r-\varepsilon)$-approximation requires $\Omega(n^{1-2/t})$ quantum space. This generalizes the seminal work of Kapralov, Khanna, Sudan (SODA'15), and expand to the quantum setting results from Kapralov, Krachun (STOC'19) and Chou et al. (STOC'22). We next present a lower bound for locally decodable codes ($\mathsf{LDC}$) over large alphabets. An $\mathsf{LDC}$ $C:\mathbb{Z}_r^n\to \mathbb{Z}_r^N$ is an encoding of $x$ into a codeword in such a way that one can recover an arbitrary $x_i$ (with probability at least $1/r+\varepsilon$) by making a few queries to a corrupted codeword. The main question here is the trade-off between $N$ and $n$. Via hypercontractivity, we give an exponential lower bound $N= 2^{\Omega(\varepsilon^4 n/r^4)}$ for $2$-query (possibly non-linear) $\mathsf{LDC}$s over $\mathbb{Z}_r$ and using the non-commutative Khintchine inequality we improved our bound to $N= 2^{\Omega(\varepsilon^2 n/r^2)}$. Previously exponential lower bounds were known for $r=2$ (Kerenidis, de Wolf (JCSS'04)) and linear codes (Dvir, Shpilka (SICOMP'07)).
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications

Gaussian processes have become a promising tool for various safety-critical settings, since the posterior variance can be used to directly estimate the model error and quantify risk. However, state-of-the-art techniques for safety-critical settings hinge on the assumption that the kernel hyperparameters are known, which does not apply in general. To mitigate this, we introduce robust Gaussian process uniform error bounds in settings with unknown hyperparameters. Our approach computes a conﬁdence region in the space of hyperparameters, which enables us to obtain a probabilistic upper bound for the model error of a Gaussian process with arbitrary hyperparameters. We do not require to know any bounds for the hyperparameters a priori, which is an assumption commonly found in related work. Instead, we are able to derive bounds from data in an intuitive fashion. We additionally employ the proposed technique to derive performance guarantees for a class of learning-based control problems. Experiments show that the bound performs signiﬁcantly better than vanilla and fully Bayesian Gaussian processes.
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization

Modern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize at test time to new compositions/structures that have not been observed during training. Recent research has shown that automatic generation of synthetic utterance-program pairs can alleviate the first problem, but its potential for the second has thus far been under-explored. In this work, we investigate automatic generation of synthetic utterance-program pairs for improving compositional generalization in semantic parsing. Given a small training set of annotated examples and an “infinite” pool of synthetic examples, we select a subset of synthetic examples that are structurally-diverse and use them to improve compositional generalization. We evaluate our approach on a new split of the schema2QA dataset, and show that it leads to dramatic improvements in compositional generalization as well as moderate improvements in the traditional i.i. d setup. Moreover, structurally-diverse sampling achieves these improvements with as few as 5K examples, compared to 1M examples when sampling uniformly at random – a 200x improvement in data efficiency.
Fair Federated Learning for Heterogeneous Face Data

We consider the problem of achieving fair classiﬁcation in Federated Learning (FL) under data heterogeneity. Most of the approaches proposed for fair classiﬁcation require diverse data that represent the different demographic groups involved. In contrast, it is common for each client to own data that represents only a single demographic group. Hence the existing approaches cannot be adopted for fair classiﬁcation models at the client level. To resolve this challenge, we propose several aggregation techniques. We empirically validate these techniques by comparing the resulting fairness metrics and accuracy on CelebA, UTK, and FairFace datasets.
On the square of the antipode in a connected filtered Hopf algebra

It is well-known that the antipode $S$ of a commutative or cocommutative Hopf algebra satisfies $S^{2}=\operatorname*{id}$ (where $S^{2}=S\circ S$). Recently, similar results have been obtained by Aguiar, Lauve and Mahajan for connected graded Hopf algebras: Namely, if $H$ is a connected graded Hopf algebra with grading $H=\bigoplus_{n\geq0}H_n$, then each positive integer $n$ satisfies $\left( \operatorname*{id}-S^2\right)^n \left( H_n\right) =0$ and (even stronger) \[ \left( \left( \operatorname{id}+S\right) \circ\left( \operatorname{id}-S^2\right)^{n-1}\right) \left( H_n\right) = 0. \] For some specific $H$'s such as the Malvenuto--Reutenauer Hopf algebra $\operatorname{FQSym}$, the exponents can be lowered. In this note, we generalize these results in several directions: We replace the base field by a commutative ring, replace the Hopf algebra by a coalgebra (actually, a slightly more general object, with no coassociativity required), and replace both $\operatorname{id}$ and $S^2$ by "coalgebra homomorphisms" (of sorts). Specializing back to connected graded Hopf algebras, we show that the exponent $n$ in the identity $\left( \operatorname{id}-S^2\right) ^n \left( H_n\right) =0$ can be lowered to $n-1$ (for $n>1$) if and only if $\left( \operatorname{id} - S^2\right) \left( H_2\right) =0$. (A sufficient condition for this is that every pair of elements of $H_1$ commutes; this is satisfied, e.g., for $\operatorname{FQSym}$.)
Statistical computation methods for microbiome compositional data network inference

Microbes can affect processes from food production to human health. Such microbes are not isolated, but rather interact with each other and establish connections with their living environments. Understanding these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A common and essential approach toward this objective involves the inference of microbiome interaction networks. Although network inference methods in other fields have been studied before, applying these methods to estimate microbiome associations based on compositional data will not yield valid results. On the one hand, features of microbiome data such as compositionality, sparsity and high-dimensionality challenge the data normalization and the design of computational methods. On the other hand, several issues like microbial community heterogeneity, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this paper, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various assumptions and research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks and differential networks. Their scope of applications, advantages and limitations are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has captured all the aspects of interest to date. In addition, we discuss the challenges now confronting current microbial associations study and future prospects. Finally, we highlight that the research in microbial network inference requires the joint promotion of statistical computation methods and experimental techniques.
EXAMPLES OF BACKGROUND DRIVING DISTRIBUTION FUNCTIONS ( BDDF ) FOR SOME SELFDECOMPOSABLE VARIABLES

Many classical variables (statistics) are selfdecomposable. They admit the random integral representations via Lévy processes. In this note are given formulas for their background driving distribution functions (BDDF). This may be used for a simulation of those variables. Among the examples discussed are: gamma variables, hyperbolic characteristic functions, Student t-distributions, stochastic area under planar Brownian motions, inverse Gaussian variable, logistic distributions, non-central chi-square, Bessel densities and Fisher z-distributions. Found representations might be of use in statistical applications. AMS 2020 Subject Classifications: 60E07; 60E10, 60G50, 62E10.
On Second-order Optimization Methods for Federated Learning

We consider federated learning (FL), where the training data is distributed across a large number of clients. The standard optimization method in this setting is Federated Averaging (FedAvg), which performs multiple local first-order optimization steps between communication rounds. In this work, we evaluate the performance of several second-order distributed methods with local steps in the FL setting which promise to have favorable convergence properties. We (i) show that FedAvg performs surprisingly well against its second-order competitors when evaluated under fair metrics (equal amount of local computations)—in contrast to the results of previous work. Based on our numerical study, we propose (ii) a novel variant that uses second-order local information for updates and a global line search to counteract the resulting local specificity.
Audio-Based Musical Version Identification: Elements and challenges

Creating novel interpretations of existing musical compositions is and has always been an essential part of musical practice. Before the advent of recorded music, listening to a piece of music mostly meant listening to a version of it, in many cases, performed by musicians other than the original composer or performer. While this practice, along with musical notation, allows compositions to remain known for many decades or even centuries, it also provides room for artistic expression.