Reviewerly/exHarmony huggingface dataset:
Masked Facial Recognition in Security Systems Using Transfer Learning

The COVID-19 is a crisis of unprecedented magnitude, which has resulted in countless casualties and security troubles. In view of recent events of corona virus people are required to wear face masks to protect themselves from getting infected. As a result, a good portion of face (nose and mouth) is hidden by the mask and hence the facial recognition becomes difficult. Many organizations use facial recognition as a means of authentication. Researchers focus on developing rapid and efficient solutions to deal with the ongoing coronavirus pandemic by coming up with suggestions for handling the facial recognition problem. This research paper aims to identify the person, while the face is covered with a facial mask with only eyes and forehead being exposed. The first step involves marking the facial region. Next, using the data set, we will implement an object detection model YOLOv3 to identify unmasked and masked faces. The YOLO v3 object detection model is the best performing model with a detection time of 0.012 s, F1 score of 0.90 and mAP score of 0.92. Experimental results on Real-World Masked-Face-Data set show high recognition performance.
Modelling of Solar Radiation for Photovoltaic Applications

This chapter explores the different ways in which solar radiation (SR) can be quantified for use in photovoltaic applications. Some solar radiation models that incorporate different combinations of parameters are presented. The parameters mostly used include the clearness index (Kt), the sunshine fraction (SF), cloud cover (CC) and air mass (m). Some of the models are linear while others are nonlinear. These models will be developed for the estimation of the direct (Hb) and diffuse (Hd) components of global solar radiation (H) on both the horizontal and tilted surfaces. Models to determine the optimal tilt and azimuthal angles for solar photovoltaic (PV) collectors in terms of geographical parameters are equally presented. The applicable, statistical evaluation models that ascertain the validity of the SR mathematical models are also highlighted.
Introduction

Libraries, archives, and museums (LAMs) have a long, interrelated history and, since the turn of the century, growing relations between LAMs have become more apparent.Internationally, the number of collaborative projects and partnerships has increased and the incidence of libraries, archives, or museums sharing premises or even merging has grown.Many of the collaborations follow trends in digitalization, which can be seen in the development of shared digital cultural heritage platforms and content sharing.Accordingly, digital convergence among LAMs is a growing field of research.The number of cross-sectoral textbooks and other publications is slowly increasing and collaborations between sector-specific educational environments are discussed in tertiary education.Collectively, this indicates that a common LAM perspective is in the process of becoming an established phenomenon (Hvenegaard Rasmussen and Hjørland 2021).Scandinavia (Denmark, Norway, and Sweden) offers a fruitful region for LAM research for several reasons.First, Scandinavian LAM institutions are relatively well funded and developed compared to the situation in other parts of the world.Second, LAMs in Scandinavia are governed by a cultural policy rooted in the welfare states' values of enlightenment, community building, and participatory democracy.Third, some of the tertiary education of the respective LAM professionals in Scandinavia is in cross-sectoral departments, or so-called "LAM departments."In Sweden and Denmark, some departments offer programs and specializations in library and information science, archival science, and museum studies, and in Norway there are programs in library and information science and archival science in joint departments.In this book, we will address all three sectors.The book encompasses a common LAM perspective and addresses issues related to LAM institutions' environment, collections, and challenges.
INVESTIGATING THE ROLE OF TOTAL QUALITY MANAGEMENT, ENTREPRENEURIAL ORIENTATION, AND ORGANIZATIONAL EXCELLENCE IN DETERMINING THE ORGANIZATIONAL PERFORMANCE IN DUBAI POLICE

The purpose of this study is to examine the role of total quality management, entrepreneurial orientation, organizational excellence towards organizational performance from the context of Dubai Police. A sample of 453 valid responses through online survey questionnaire was collected and analzyed. More specifically, descriptive statistics, correlational and demographic analyses and both measurement model and structural model was assessed. The study findings through measurement model confirm the reliability, validity, internal consistency, and discriminant validity of the latent constructs. Finally, the findings through PLS-SEM approach confirms that direct and significant relationship between total quality management, entrepreneurial orientation, organizational excellence, and organizational performance, respectively. Besides, some useful policy implications and study limitations are also discussed to determine the future directions of the upcoming studies.
MEDIATING EFFECT OF TALENT MANAGEMENT ON THE RELATIONSHIP BETWEEN TOTAL QUALITY MANAGEMENT, ENTREPRENEURIAL ORIENTATION, ORGANIZATIONAL EXCELLENCE, AND ORGANIZATIONAL PERFORMANCE IN DUBAI POLICE

Both public and private organizations are facing various challenges in the current environment due to range of complexities as exists in the market. However, to achieve higher organizational performance, more attention is required towards different tangible and intangible organizational resources along with strategic capabilities. The purpose of this study is to examine the role of total quality management, entrepreneurial orientation, organizational excellence towards organizational performance from the context of Dubai Police. Additionally, it also explores the mediating effect of talent management on the relationship between total quality management, entrepreneurial orientation, organizational excellence, and organizational performance. A sample of 453 valid responses through online survey questionnaire was collected and analzyed. More specifically, descriptive statistics, correlational and demographic analyses and both measurement model and structural model was assessed. The study findings through measurement model confirm the reliability, validity, internal consistency, and discriminant validity of the latent constructs. Finally, the findings through PLS-SEM approach confirms that direct and significant relationship between total quality management, entrepreneurial orientation, organizational excellence, and organizational performance, respectively. The testing of mediating effect confirms that talent management significantly mediates the relationship between TQM-OP, between EO-OP, and between OE-OP, accordingly. The study findings would be of great support to various policy makers specifically in the Dubai Police.
Emergency Remote Teaching In The Dictation Of Statistics In Economic Sciences. Opportunities For Educational Quality Improvement Management

The pandemic during 2020 and its consequent preventive and compulsory social isolation forced the migration to an “emergency remote teaching” model abruptly. Although it had its own theoretical material, this reality required teachers to be more versatile and adaptable in record time. This paper describes how this new normality allowed rethinking the teaching practice by using technologies for teaching classes by virtual means, either through video recording, weekly synchronous meetings and the use of free mobile applications, being an opportunity to achieve an improvement in educational quality. La pandemia durante 2020 y su consecuente Aislamiento Social Preventivo y Obligatorio forzó a migrar a un modelo de “enseñanza remota de emergencia” en forma abrupta. Si bien se contaba con material teórico propio, esta realidad exigió a los docentes una mayor versatilidad y capacidad de adaptación en tiempo récord. En el presente trabajo se describe cómo esta nueva normalidad permitió repensar la práctica docente mediante la utilización de tecnologías para el dictado de clases por medios virtuales, ya sea a través de la grabación de videos, encuentros sincrónicos semanales y el uso de aplicaciones móviles gratuitas, siendo una oportunidad para lograr una mejora en la calidad educativa.
An Efficient Quantum Decoder for Prime-Power Fields

We consider a version of the nearest-codeword problem on finite fields $\mathbb{F}_q$ using the Manhattan distance, an analog of the Hamming metric for non-binary alphabets. Similarly to other lattice related problems, this problem is NP-hard even up to constant factor approximation. We show, however, that for $q = p^m$ where $p$ is small relative to the code block-size $n$, there is a quantum algorithm that solves the problem in time ${\rm poly}(n)$, for approximation factor $1/n^2$, for any $p$. On the other hand, to the best of our knowledge, classical algorithms can efficiently solve the problem only for much smaller inverse polynomial factors. Hence, the decoder provides an exponential improvement over classical algorithms, and places limitations on the cryptographic security of large-alphabet extensions of code-based cryptosystems like Classic McEliece.
H4VDM: H.264 Video Device Matching

Methods that can determine if two given video sequences are captured by the same device (e.g., mobile telephone or digital camera) can be used in many forensics tasks. In this paper we refer to this as "video device matching". In open-set video forensics scenarios it is easier to determine if two video sequences were captured with the same device than identifying the specific device. In this paper, we propose a technique for open-set video device matching. Given two H.264 compressed video sequences, our method can determine if they are captured by the same device, even if our method has never encountered the device in training. We denote our proposed technique as H.264 Video Device Matching (H4VDM). H4VDM uses H.264 compression information extracted from video sequences to make decisions. It is more robust against artifacts that alter camera sensor fingerprints, and it can be used to analyze relatively small fragments of the H.264 sequence. We trained and tested our method on a publicly available video forensics dataset consisting of 35 devices, where our proposed method demonstrated good performance.
Preliminary investigation method for segregating malware-encrypted files from the regular traffic

regarding AI-based real-time noise suppression.She has led several projects in the fields of Artificial Intelligence and Cybersecurity during her course of undergraduate study.
Unsupervised Text Deidentification

Deidentification seeks to anonymize textual data prior to distribution. Automatic deidentification primarily uses supervised named entity recognition from human-labeled data points. We propose an unsupervised deidentification method that masks words that leak personally-identifying information. The approach utilizes a specially trained reidentification model to identify individuals from redacted personal documents. Motivated by K-anonymity based privacy, we generate redactions that ensure a minimum reidentification rank for the correct profile of the document. To evaluate this approach, we consider the task of deidentifying Wikipedia Biographies, and evaluate using an adversarial reidentification metric. Compared to a set of unsupervised baselines, our approach deidentifies documents more completely while removing fewer words. Qualitatively, we see that the approach eliminates many identifying aspects that would fall outside of the common named entity based approach.
Blockchain-based Guarantees of Origin issuing platform

Guarantees of Origin (GoO) issued have the sole function of proving to a final customer that a given share or quantity of energy was produced from renewable sources. A GoO can be transferred, independently of the energy to which it relates, from one holder to another. However, with a view to ensuring that a unit of electricity from renewable energy sources is disclosed to a customer only once, double counting and double disclosure of Guarantees of Origin should be avoided [1].For this reason, TRINITY project [2] will ensure the traceability of GoOs using a blockchain-based issuing platform (compatible with the EECS model [3]). The blockchain-based platform proposed in TRINITY, and presented in this paper, does not rely on the entire miner network to approve transactions. Instead, it uses the Federated Byzantine Agreement (FBA) algorithm, which allows a fast processing of transactions and negligible use of energy.
Efficient solar PV integration to data centers in a Nordic climate

The demand for data services, predominantly provided by data centers, has grown rapidly in the last decade as global internet traffic has increased. To provide data services, data centers consume vast amounts of electricity, making them a significant prospect to reduce carbon emissions in the power sector. Thus, efforts to de-carbonize and integrate renewables into the energy supply of data centers will be essential to curb carbon emissions of these facilities in the future. For this purpose, both utilization of waste heat and wide-scale implementation of local renewable energy sources in data centers are needed. In this paper, we show how a solar PV system could be integrated into a forthcoming data center in Finland, with options for battery storage and prosumer grid utilization. We also evaluate the cost-effectiveness of this approach in a Nordic climate with various levels of self-production.
Blind Polynomial Regression

Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at all, rendering conventional regression approaches not applicable. In this paper, we formally state the (potentially partial) blind regression problem, illustrate some of its theoretical properties, and propose algorithmic approaches to solve it. As a case-study, we apply our methods to a jitter-correction problem and corroborate its performance.
TCAB: A Large-Scale Text Classification Attack Benchmark

We introduce the Text Classification Attack Benchmark (TCAB), a dataset for analyzing, understanding, detecting, and labeling adversarial attacks against text classifiers. TCAB includes 1.5 million attack instances, generated by twelve adversarial attacks targeting three classifiers trained on six source datasets for sentiment analysis and abuse detection in English. Unlike standard text classification, text attacks must be understood in the context of the target classifier that is being attacked, and thus features of the target classifier are important as well. TCAB includes all attack instances that are successful in flipping the predicted label; a subset of the attacks are also labeled by human annotators to determine how frequently the primary semantics are preserved. The process of generating attacks is automated, so that TCAB can easily be extended to incorporate new text attacks and better classifiers as they are developed. In addition to the primary tasks of detecting and labeling attacks, TCAB can also be used for attack localization, attack target labeling, and attack characterization. TCAB code and dataset are available at https://react-nlp.github.io/tcab/.
Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes

Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference -- abilities lacking in even the best AI systems today. Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little attention in the NLP community, with most research focusing on simple word analogies. Work that tackled more complex analogies relied heavily on manually constructed, hard-to-scale input representations. In this work, we explore a more realistic, challenging setup: our input is a pair of natural language procedural texts, describing a situation or a process (e.g., how the heart works/how a pump works). Our goal is to automatically extract entities and their relations from the text and find a mapping between the different domains based on relational similarity (e.g., blood is mapped to water). We develop an interpretable, scalable algorithm and demonstrate that it identifies the correct mappings 87% of the time for procedural texts and 94% for stories from cognitive-psychology literature. We show it can extract analogies from a large dataset of procedural texts, achieving 79% precision (analogy prevalence in data: 3%). Lastly, we demonstrate that our algorithm is robust to paraphrasing the input texts.
Reducing Training Sample Memorization in GANs by Training with Memorization Rejection

Generative adversarial network (GAN) continues to be a popular research direction due to its high generation quality. It is observed that many state-of-the-art GANs generate samples that are more similar to the training set than a holdout testing set from the same distribution, hinting some training samples are implicitly memorized in these models. This memorization behavior is unfavorable in many applications that demand the generated samples to be sufficiently distinct from known samples. Nevertheless, it is unclear whether it is possible to reduce memorization without compromising the generation quality. In this paper, we propose memorization rejection, a training scheme that rejects generated samples that are near-duplicates of training samples during training. Our scheme is simple, generic and can be directly applied to any GAN architecture. Experiments on multiple datasets and GAN models validate that memorization rejection effectively reduces training sample memorization, and in many cases does not sacrifice the generation quality. Code to reproduce the experiment results can be found at $\texttt{https://github.com/jybai/MRGAN}$.
Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this setting in an actionable, trustworthy way. To this end, we conduct a qualitative study with cardiac critical care clinicians. Our results reveal that clinician trust may be impacted most not by the degree of uncertainty, but rather by how transparent the visualization of what the sources of uncertainty are. Our results show a clear connection between feature interpretability and clinical actionability.
Motion Policy Networks

Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliable enough for practical deployment. A wide variety of methods have been proposed ranging from local controllers to global planners, often being combined to offset their shortcomings. We present an end-to-end neural model called Motion Policy Networks (M$\pi$Nets) to generate collision-free, smooth motion from just a single depth camera observation. M$\pi$Nets are trained on over 3 million motion planning problems in over 500,000 environments. Our experiments show that M$\pi$Nets are significantly faster than global planners while exhibiting the reactivity needed to deal with dynamic scenes. They are 46% better than prior neural planners and more robust than local control policies. Despite being only trained in simulation, M$\pi$Nets transfer well to the real robot with noisy partial point clouds. Code and data are publicly available at https://mpinets.github.io.
An Exploration of Neural Radiance Field Scene Reconstruction: Synthetic, Real-world and Dynamic Scenes

This project presents an exploration into 3D scene reconstruction of synthetic and real-world scenes using Neural Radiance Field (NeRF) approaches. We primarily take advantage of the reduction in training and rendering time of neural graphic primitives multi-resolution hash encoding, to reconstruct static video game scenes and real-world scenes, comparing and observing reconstruction detail and limitations. Additionally, we explore dynamic scene reconstruction using Neural Radiance Fields for Dynamic Scenes(D-NeRF). Finally, we extend the implementation of D-NeRF, originally constrained to handle synthetic scenes to also handle real-world dynamic scenes.
Uncertain Evidence in Probabilistic Models and Stochastic Simulators

We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as "uncertain evidence." We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables. We consider a recently-proposed method "distributional evidence" as well as revisit two older methods: Jeffrey's rule and virtual evidence. We devise guidelines on how to account for uncertain evidence and we provide new insights, particularly regarding consistency. To showcase the impact of different interpretations of the same uncertain evidence, we carry out experiments in which one interpretation is defined as "correct." We then compare inference results from each different interpretation illustrating the importance of careful consideration of uncertain evidence.
A Comprehensive Comparison of Neural Networks as Cognitive Models of Inflection

Neural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology. This debate has gravitated into NLP by way of the question: Are neural networks a feasible account for human behavior in morphological inflection? We address that question by measuring the correlation between human judgments and neural network probabilities for unknown word inflections. We test a larger range of architectures than previously studied on two important tasks for the cognitive processing debate: English past tense, and German number inflection. We find evidence that the Transformer may be a better account of human behavior than LSTMs on these datasets, and that LSTM features known to increase inflection accuracy do not always result in more human-like behavior.