Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Individual Susceptibility of Social Media Users to Fake News: Polish Perspective

Scientifi c objective: The subject of research presented in the paper is the issue of fake news and the impact of the user’s analytical thinking on his / her ability to detect fake news. The hypothesis underlying the research is that a high level of analytical thinking has a positive effect on the ability to distinguish fake from real news. Research methods: The method of diagnostic survey, containing the scale of fake news and psychological measurement of the level of analytical thinking. Results and conclusions: The analysis proves that there is a fundamental relationship between the level of analytical thinking and the accuracy in fake news detection. In addition, some groups that were particularly susceptible to fake news have been identifi ed. Cognitive value: The paper presents a media-psychological analysis of the phenomenon of fake news in Poland, focusing on the susceptibility of social media users to fake news. This is the fi rst interdisciplinary study of this kind conducted in Polish media studies academia.
Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages

Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient data for pre-training such models, or are predominantly oral vernaculars without a standardised writing system, precluding fine-tuning. Query-by-example spoken term detection (QbE-STD) offers an alternative for iteratively indexing untranscribed speech corpora by locating spoken query terms. Using data from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we show that QbE-STD can be improved by leveraging representations developed for ASR (wav2vec 2.0: the English monolingual model and XLSR53 multilingual model). Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD. Nevertheless, we find that wav2vec 2.0 representations (either English or XLSR53) offer large improvements (56-86% relative) over state-of-the-art approaches on our endangered language datasets.
Quest for vortices in photon condensates

We predict that a photon condensate inside a dye-filled microcavity forms long-lived spatial structures that resemble vortices when incoherently excited by a focused pump orbiting around the cavity axis. The finely structured density of the condensates have a discrete rotational symmetry that is controlled by the orbital frequency of the pump spot and is phase-coherent over its full spatial extent despite the absence of any effective photon-photon interactions.
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning

While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications. We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations. We design self-supervised learning objectives to recover masked-out event and temporal indicators and to discriminate sentences from their corrupted counterparts (where event or temporal indicators got replaced). By further pre-training a PTLM with these objectives jointly, we reinforce its attention to event and temporal information, yielding enhanced capability on event temporal reasoning. This **E**ffective **CON**tinual pre-training framework for **E**vent **T**emporal reasoning (ECONET) improves the PTLMs’ fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art performances in most of our downstream tasks.
Rethinking Deinterlacing for Early Interlaced Videos

In recent years, high-definition restoration of early videos have received much attention. Real-world interlaced videos usually contain various degradations mixed with interlacing artifacts, such as noises and compression artifacts. Unfortunately, traditional deinterlacing methods only focus on the inverse process of interlacing scanning, and cannot remove these complex and complicated artifacts. Hence, this paper proposes an image deinterlacing network (DIN), which is specifically designed for joint removal of interlacing mixed with other artifacts. The DIN is composed of two stages, i.e., a cooperative vertical interpolation stage for splitting and fully using the information of adjacent fields, and a field-merging stage to perceive movements and suppress ghost artifacts. Experimental results demonstrate the effectiveness of the proposed DIN on both synthetic and real-world test sets.
Statistica Sinica Preprint No: SS-2022-0057

: A central topic in functional data analysis is how to design an optimal decision rule, based on training samples, to classify a data function. We exploit the optimal classiﬁcation problem in which the data functions are Gaussian processes. We derive sharp convergence rates for the minimax excess misclassiﬁcation risk both when the data functions are fully observed and when they are discretely observed. We explore two easily implementable classiﬁers, based on a discriminant analysis and on a deep neural network, respectively, which both achieve optimality in Gaussian settings. Our deep neural network classiﬁer is new in the literature, and demonstrates outstanding performance, even when the data functions are nonGaussian. For discretely observed data, we discover a novel critical sampling frequency that governs the sharp convergence rates. The proposed classiﬁers perform favorably in ﬁnite-sample applications, shown in comparisons with other functional clas-siﬁers in simulations and one real-data application.
Cox Point Process Regression

Point processes in time have a wide range of applications that include the claims arrival process in insurance or the analysis of queues in operations research. Due to advances in technology, such samples of point processes are increasingly encountered. A key object of interest is the local intensity function. It has a straightforward interpretation that allows to understand and explore point process data. We consider functional approaches for point processes, where one has a sample of repeated realizations of the point process. This situation is inherently connected with Cox processes, where the intensity functions of the replications are modeled as random functions. Here we study a situation where one records covariates for each replication of the process, such as the daily temperature for bike rentals. For modeling point processes as responses with vector covariates as predictors we propose a novel regression approach for the intensity function that is intrinsically nonparametric. While the intensity function of a point process that is only observed once on a fixed domain cannot be identified, we show how covariates and repeated observations of the process can be utilized to make consistent estimation possible, and we also derive asymptotic rates of convergence without invoking parametric assumptions.
Zero-Shot Open Set Detection Based on a Pretrained Language-Vision Model

In a regular open set detection problem, samples of known classes (also called closed set classes ) are used to train a special classiﬁer. In testing, the classiﬁer should (1) classify the test samples of known classes to their respective classes and also (2) detect samples that do not belong to any of the known classes (i.e., they belong to some unknown or open set classes ). This paper studies the problem of zero-shot open set detection , which still performs the same two tasks in testing but has no training except using the given known class names. This paper proposes a novel and yet simple method (called ZO-CLIP) to solve the problem. ZO-CLIP builds on top of the recent advances in zero-shot classiﬁcation through multi-modal representation learning. It ﬁrst extends the pretrained language-vision model CLIP by training a text-based image description generator on top of CLIP. In testing, it uses the extended model to generate some candidate unknown class names for each test sample and computes a conﬁdence score based on both the known class names and candidate unknown class names for zero-shot open set detection. Experimental re-sults on 5 benchmark datasets for open set detection conﬁrm that ZO-CLIP outperforms the baselines by a large margin.
The Effect of Polymers Combination of Hydroxypropylmethylcellulose and Carboxymethylcellulose Sodium Polymers to The Physical Properties of Patch of Celery (Apium graveolens L.) Ethanolic Extract

Celery herbs (Apium graveolens L.) contain apigenin compounds (flavonoids) that are effective as antihypertensiv agent. The use of Celery herbs in the form of decoction and oral preparations has disadvantage that is voluminous and can cause gastric irritation. Patches were chosen because it can prevent gastric irritation and reduce the frequency of drug administration because the drug is delivered over the skin with a continuously. This study aimed to evaluate of combination of hydroxypropyl methylcellulose (HPMC) and carboxymethylcellulose sodium (CMC sodium) to the physical properties of patch of celery herbs ethanolic extract. This study was conducted with maceration of celery herbs using ethanol in order to obtaine of polar active constituents and then dispersed in patch made with variations in the concentrations ratio of HPMC and CMC sodium polymers of 3:1, 1:1, and 1:3. Patches were tested for physical properties including organoleptic, pH, thickness, weight, folding endurance, and moisture content. Statistical analysis of test data used One Way ANOVA and Paired Samples T Test. The results of statistical analysis showed that the increase concentrations ratio of CMC sodium polymer in the combination of HPMC and CMC sodium polymers could increase the thickness, weight, and moisture content of celery herbs ethanol extract patch. The best patch was formula 2 with the same concentration ratio of HPMC and CMC sodium polymers because it showed the best results with a pH 6,12-6,29, thickness 1,37-1,53 mm, weighting 2,24-2,52 gram, folding endurance more than 300 times, and the moisture content was quite stable.
Intelligibility and naturalness of articulatory synthesis with VocalTractLab compared to established speech synthesis technologies

In this work, the current state-of-the-art of articulatory speech synthesis (V OCAL T RACT L AB ) is compared to a wide range of different text-to-speech systems that once represented or still represent the continuously evolving state-of-the-art of speech synthesis technology. The comparison systems include neural and concatenative synthesis by Google and Microsoft, as well as Hidden Markov Model-based, unit-selection and diphone synthesis developed at universities (using M ARY TTS, MBROLA and DRESS). A small corpus of 15 German sentences was synthesized using the text-to-speech (and, if available, re-synthesis) functionalities of each system. The intelligibility of the synthesized utterances was evaluated in an ASR experiment. The naturalness of the utterances was evaluated in a multi-stimulus Likert test by 50 German native speakers. As an additional reference, recordings of natural speech were used in the experiments. It was found that the articulatory synthesis can achieve a performance on par with the non-commercial synthesis systems in terms of intelligibility and naturalness, while being signiﬁ-cantly outperformed by the commercial synthesis systems.
Metodi Monte Carlo gran-canonici per simulare modelli a grana grossa di proteine

In questa tesi si cerca di riprodurre il processo di ripiegamento di una proteina, descritta con un modello a grana grossa, tramite una simulazione Monte Carlo. L'obiettivo e testare una nuova mossa Monte Carlo che modifichi la forma della catena proteica smontandola e poi rimontandola in maniera differente. In particolare la proteina studiata e la "serine proteinase inhibitor" o "2CI2".
(Single Lead ECG-Apnea Recordings Assessment for Improved Accuracy in Classification of Sleep Disorder based on AdaBoost)

Sleep disorder is a disturbed breathing flow (collapse) during sleep. The symptoms are generally undiagnosed and untreated properly so that repeated respiratory interruptions have the potential for severe sleep disorders. Electrocardiogram (ECG) recordings are practical tools used to examine the existence of sleep disorders in the heart rhythm. The ECG represents heart electrical activity in the form of P, QRS, and T waves. The number of ECG sensors is uncomfortable for the patient to record the data, increasing the recording complexity, slowing the computation, causing misinterpretation and loss of clinical information. Therefore, an early warning system is needed as a medical aid that can be diagnosed using single-lead ECG. In conducting this study, the system consists of five stages, which include the acquisition of ECG records, pre-processing, extraction of features, selection of features, and the classification process. ECG-record feature sets consist of time-domain, frequency-domain, and non-linear analysis. The AdaBoost method confirms that the model had the highest performance than the SVM, k-NN and NN. The results of the experiments thus measure the outperformed of method performance and achieved 90.1% classification accuracy for the AdaBoost classification method. Moreover, the F1 score, precision, recall, sensitivity, and specificity was reported as 90.1%, 90.3%, 90.1%, 86.9%, and 93.3%, respectively. Intisari—Gangguan tidur adalah terganggunya aliran pernapasan (kolaps) yang berulang saat tidur. Gangguan tidur umumnya tidak terdiagnosis dan tidak tertangani dengan baik sehingga terjadi interupsi berulang pada saluran pernapasan. Kondisi tersebut berpotensi menyebabkan penderita mengalami gangguan tidur yang serius. Salah satu alat bantu yang digunakan untuk menganalisis adanya gangguan tidur pada ritme jantung adalah melalui rekaman electrocardiogram (ECG). ECG merupakan suatu grafik yang merepresentasikan aktivitas listrik pada jantung berupa gelombang P, gelombang QRS, dan gelombang T. Banyaknya sensor untuk merekam ECG menyebabkan ketidaknyamanan pasien saat perekaman data, menyebabkan kompleksitas rekaman, lambatnya komputasi, kesalahan dalam interpretasi, dan kehilangan informasi klinis. Oleh karena itu, dibutuhkan suatu sistem peringatan dini sebagai alat bantu medis yang mampu mendiagnosis rekaman ECG menggunakan satu sadapan. Sistem didesain menjadi lima tahapan, yaitu akuisisi rekaman ECG, praproses, ekstraksi fitur, seleksi fitur, dan proses klasifikasi. Fitur set yang diperoleh dari rekaman ECG berupa variabel-variabel yang diperoleh dari domain waktu, domain frekuensi, dan analisis nonlinear. Pengujian menunjukkan metode AdaBoost memiliki nilai kinerja paling tinggi dibandingkan dengan SVM, k-NN, dan NN. Metode AdaBoost secara berurutan memiliki kinerja classification accuracy, F1 score, precision, recall, sensitivity, dan specificity sebesar 90,1%, 90,1%, 90,3%, 90,1%, 86,9%, dan 93,3%. Kata Kunci— Rekaman ECG, Gangguan Tidur, SVM, AdaBoost, Mesin Pembelajaran.
A Qualitative Case Study on the Hiring Practices of the President at a Four-Year Public University in Alabama

The topic of this dissertation was the hiring practices of college and university presidents. This particular study focused on a rural college in the Southeast United States and perceived governance as it pertains to selecting a president to lead the institution. This qualitative study utilized a case study format to answer the question of the relationship between hiring a university president and the influence of governing board members, key faculty and staff, student/alumni leaders, and individuals that have the knowledge or were associated with the hiring practices of the university president at a rural university in the southeast United States. Data were collected through a series of interviews and the use of historical documentation such as board minutes and newspaper articles.
On Ultra-Short Block-Codes on the Binary Asymmetric Channel

—Block-codes with very short blocklength over the most general binary channel, the binary asymmetric channel (BAC), are investigated. It is shown that for only two possible messages, ﬂip codes are optimal, however, depending on the blocklength and the channel parameters, not necessarily the linear ﬂip code. Further it is shown that the optimal decoding rule is a threshold rule. Finally some fundamental dependencies of the best code on the channel are given.
Real-time Analysis of Video Surveillance using Machine Learning and Object Recognition

- Widely accepted standards for video surveillance are very poor and inadequate in critical situations and often they fail to recognize or even identify suspicious activities. Our goal is to explore the feasibility of our proposed methodology of implementing a novel video surveillance system. This paper aims at studying the various existing algorithms in computer vision and implement algorithms and techniques best suited for our system. The objective is to develop a better system which utilizing machine learning, computer vision and image processing algorithms to detect and analyze objects of interest in a scenario. We employ algorithms for detection and recognition of faces as well as suspicious objects and persons on the input feed provided by CCTV cameras, various criminal activities can be detected, and authorities will be assisted to take the desired action early as possible. Implementing effective security measures in critical environments is of the utmost importance and is very difficult as it involves a lot of IT infrastructure as well as human inputs.
Current perspective on the treatment of advanced/metastatic colorectal cancer

Metastatický rozsev je zodpovědný za většinu nádorových úmrtí (1). U kolorektálního karcinomu (CRC) tomu, bohužel, není jinak. Multimodální kombinovaná léčba však výrazně zlepšuje prognózu pacientů, u některých pak můžeme dosáhnout dlouhodobé remise onemocnění nebo i úplného vyléčení. Lépe dostupné a přesnější molekulární analýzy by nám měly pomoci ve snaze určení diagnózy, prognózy a vhodných terapeutických cílů k přísně individualizované léčbě. Z klinických studií pronikají do léčby nové postupy, ale rychlost jejich implementace se v každé zemi liší. V dalších řádcích jsem se pokusil shrnout některá aktuální doporučení s mírným přesahem do blízké, a doufejme nadějnější, budoucnosti léčby metastatického CRC (mCRC). Klíčová slova: kolorektální karcinom, cílená léčba.
Review on Implementation of Efficient Method of Aggregate Key for Encrypted Cloud Data.

— Potential of selectively sharing encrypted data with various users with the help of public cloud storage may very much easiness security concerns over unintentional data leaks in the cloud. A key brave to designing such encryption schemes lies in the well-organized management of encryption keys. The preferred flexibility of sharing any group of selected documents with any group of users demands dissimilar encryption keys to be used for dissimilar documents. On the other hand, this also implies the necessity of robustly spreading to users a huge amount of keys for both encryption and search, and those users’ needs to confidentially store the keys received by them, and then submit a uniformly huge amount of keyword trapdoors to the cloud in order to complete search on the shared data. The implied need for secure communication, storage, and complexity precisely converts the approach impractical. In this paper, we deal with this practical problem, which is largely neglected in the previous studies, by proposing the key aggregate searchable encryption (KASE) concept and to initialize the concept through a concrete KASE scheme, in which a data vendor only needs to distribute a single key to a user for sharing a large amount of documents, and the user needs to submit only one trapdoor on the cloud for asking the shared documents. The security analysis and performance evaluation both verify that our proposed schemes are provably and practically well-organized.
Antibody responses to BNT162b2 mRNA vaccine: Infection‐naïve individuals with abdominal obesity warrant attention

The excess of visceral adipose tissue might hinder and delay immune response. How people with abdominal obesity (AO) will respond to mRNA vaccines against SARS‐CoV‐2 is yet to be established. SARS‐CoV‐2‐specific antibody responses were evaluated after the first and second dose of the BNT162b2 mRNA vaccine, comparing the response of individuals with AO with the response of those without, and discerning between individuals with or without prior infection.
Academic and Professional Qualification of Higher Education Teachers in Angola: Instrument of Analysis and its Validation

The intervention framework of Higher Education Institutions (HEIs) in Angola has registered a markedly quantitative evolution, reflected in the resizing of Higher Education (HE), in its expansion, in the multiplication of HEIs, in the increase of the training offer and, as a consequence, in increasing student numbers. However, the qualitative dimension is often questioned, in terms of the performance of the institutions, the entry profile of students and the academic and professional qualification of teachers (APQT). As the Institutional Development Plan (IDP) is a guiding document for HEIs in the country, it must include elements that enhance its quality, particularly with regard to APQT. The APQT and the IDP are the structuring axes of this study. However, the existence of an analysis instrument (AI) that can contribute to the construction and monitoring of the IDP is not known. In this sense, this article aims to describe, reasonably, the development of an AI in this context, as well as the process of its validation. The construction of the AI was made based on the referencing methodology. The validation of the AI involved (i) an interview with two actors with responsibilities in the management of HE in Angola and (ii) its application to the IDP of an HEI. Although the focus of the AI is the dimension of the APQT, it is believed that it can contribute to the management of institutional practices, as well as to research.
MAKING HEALYHCARE CHATBOT USING NLP

: To start a good life healthcare is very important. But it is very difficult to the consult the doctor if any health issues. The proposed idea is to create a healthcare chatbot using Natural Language Processing technique. It is the part of Artificial Intelligence that can diagnose the disease and provide basic details about the disease before consulting a doctor. To reduce the healthcare costs and improve accessibility to medical knowledge the Healthcare chatbot is built. Some chatbots acts as a medical reference books, which helps the patient know more about their disease and helps to improve their health. The user can achieve the benefit of a healthcare chatbot only when it can diagnose all kind of disease and provide necessary information. The system provides text or voice assistance. Bot will provides which type of disease based on the user symptoms provides doctor details and analagesics for further treatment respective to user disease. and also provides food suggestion that means which type of food you have to take. Thus, people will have an idea about their health and have the right protection.