Miracle-dz/UniFAR-FTUs huggingface dataset:
Bitcoin Value and Sentiment Expressed in Tweets

In recent years, traditional economic models failed to forsee several developments resulting in a considerable economic crisis. Other phenomena, such as the increase in Bitcoin value cannot be completely modeled by these traditional means either. As Bitcoin and other cryptocurrencies are a playground for technically interested people, it might be worthwhile to look into other communication channels, such as Social Media to ﬁnd clues for the development we observe. We hypothesize that sentiment expressed in, for example, might model the development of Bitcoin value better than traditional models. In this work, we present a data set of Tweets covering almost one year, which we annotated for Sentiment. Additionally, we show results from preliminary experiments which support our hypothesis that sentiment information is highly predictive of the value development.
PRAPD: A novel received signal strength–based approach for practical rogue access point detection

Rogue access point attack is one of the most important security threats for wireless local networks and has attracted great attention from both academia and industry. Utilizing received signal strength information is an effective solution to detect rogue access points. However, the received signal strength information is formed by multi-dimensional received signal strength vectors that are collected by multiple sniffers, and these received signal strength vectors are inevitably lacking in some dimensions due to the limited wireless transmission range and link instability. This will result in high false alarm rate for rogue access point detection. To solve this issue, we propose a received signal strength–based practical rogue access point detection approach, considering missing received signal strength values in received signal strength vectors collected in practical environment. First, we present a preprocessing scheme for received signal strength vectors, eliminating missing values by means of data filling, filtering, and averaging. Then, we perform clustering analysis on the received signal strength vectors, where we design a distance measurement method that dynamically uses partial components in received signal strength vectors to minimize the distance deviation due to missing values. Finally, we conduct the experiments to evaluate the performance of the practical rogue access point detection. The results demonstrate that the practical rogue access point detection can significantly reduce the false alarm rate while ensuring a high detection rate.
Revisiting consensus protocols through wait-free parallelization

The recent surge of blockchain systems has renewed the interest in traditional Byzantine fault-tolerant consensus protocols. Many such consensus protocols have a primary-backup design in which an assigned replica, the primary, is responsible for coordinating the consensus protocol. Although the primary-backup design leads to relatively simple and high performance consensus protocols, it places an unreasonable burden on a good primary and allows malicious primaries to substantially affect the system this http URL this paper, we propose a protocol-agnostic approach to improve the design of primary backup consensus protocols. At the core of our approach is a novel wait-free approach of running several instances of the underlying consensus protocol in parallel. To yield a high performance parallelized design, we present coordination-free techniques to order operations across parallel instances, deal with instance failures, and assign clients to specific instances. Consequently, the design we present is able to reduce the load on individual instances and primaries, while also reducing the adverse effects of any malicious replicas.
meso-Pyridine Substituted Boron-dipyrromethene (BDP) Dye as a pH Probe:Synthesis,Crystal Structure and Spectroscopic Properties

A new boron-dipyrromethene (BDP) dye with pyridine subunit at meso-position has been synthesized and structurally characterized by X-ray diffraction crystal structure analysis. The meso-pyridine ring is nearly perpendicular to the indacene plane with torsion angle being 88.2°. Its absorption and steady-state fluorescence properties are investigated. The compound shows strong fluorescent quenching upon increasing the solution acidity. The pKa value calculated from the pH dependent fluorescence emission spectra is 2.24. It can be used as a pH probe excited with visible light. CCDC: 297327.
Study on Extraction of Two Essential Oils,Analysis of Major Components and Antimicrobial Activities

In this study,we used steam distillation to extract clove and cinnamon essential oils.Components of the two essential oils were analyzed by GC.The antimicrobial effects of the plant oils were studied to inhibit five important foodborne pathogens:Staphylococcus aureus,Escherichia coli,Bacillus subtilis,Salmonella typhimurium and Shigella dysenteriae,to be compared with sodium benzoate and potassium sorbate,the chemical preservatives commonly used now.The experimental results showed that the content of eugenol in clove is 78.1%,and that of cinnamaldehyde in cinnamon 86.8%. The antimicrobial effects of clove and cinnamon oils and their components are stronger than those of chemical preservatives.Cinnamon oil and cinnamaldehyde showed that the strongest antimicrobial effects with their MIC range from 200 to 1600mg/L,only 1/64～1/16 of the ranging of sodium benzoate and potassium sorbate(with their MIC ranging from 6400 to 25600mg/L). Clove oil and eugenol are most active against the foodborne pathogens(with their MIC ranging from 800 to 1600mg/L).
BUKU AJAR INTERACTIVE BOOK UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KREATIF SISWA SEKOLAH DASAR

Penelitian ini bertujuan untuk menghasilkan Interactive Book yang valid, praktis, dan efektif untuk melatihkan keterampilan berpikir kreatif siswa. Penelitian ini dilaksanakan dengan menggunakan model 4-D (Four-D Model), yaitu tahap pendefinisian (define), perancangan (design), pengembangan (develop) dan diujicobakan di kelas V SD Islam As-Salam Kota Malang tahun ajaran 2016/2017 dengan Pretest-Posttest Design. Pengumpulan data menggunakan metode observasi, tes, dan angket. Teknik analisis data menggunakan analisis deskriptif kuantitatif dan kualitatif. Hasil penelitian ini menunjukkan: 1) Interactive Book yang dikembangkan berkategori sangat valid; 2) Pembelajaran menggunakan Interactive Book ditinjau dari keterlaksanaan RPP berkategori sangat valid dan aktivitas siswa sesuai dengan tahap-tahap pada model guided discovery learning; dan 3) Keefektivan Interactive Book dalam pembelajaran ditinjau dari ketuntasan adalah mengalami kenaikan dari rata-rata 65 menjadi 84 dengan N-Gain 0,53 (sedang). Berdasarkan hasil analisis data, dapat disimpulkan bahwa Interactive Book dan perangkat pembelajaran menggunakan telah valid, praktis, dan efektif untuk meningkatkan keterampilan berpikir kreatif.
Thirty days exposure to kerosene induces alteration in select serum trace element levels in rats

Trace elements are essential micronutrients required in minute amount by a number of metabolic processes. In many parts of Africa, kerosene is widely available since it is the most commonly used fuel for cooking purposes. As a result of this common availability, excessive exposure to this product has been described. The aim of this study is to identify if exposure to trace amount of kerosene is capable of causing depletion in magnesium and trace element levels. Eighteen Wistar rats divided equally into three groups were used for the study. Administration of trace amount of kerosene (0.4 ml/kg body weight) by oral or dermal route resulted in significant increase (p 0.05) in rats in dermal route. Magnesium and trace elements play significant physiologic roles and their depletion has been identified as an etiologic factor of many disease conditions. The results of this study therefore suggest that kerosene exposure even at trace quantity is capable of altering magnesium and trace element status in a mammalian species. A situation which may predispose an individual to many of the clinical conditions associated with essential element deficiencies.
Surface Tensions Measurement of Aqueous Solutions of Sodium Dodecyl Benzene Sulfonate

The surface tensions of aqueous solutions of sodium dodecyl benzene sulfonate(SDBS) are measured with surface laser light scattering method(SLLS). The experimental temperature is 294.17 K,and the concentration of SDBS spanned from 0% to 0.080%. Twenty-one experimental data points have been obtained,which are correlated as a polynomial function of SDBS concentration,the maximum and the average deviation are 0.998 mN·m-1 and 0.531 mN·m-1,respectively.By the experimental test,the critical micelle concentration(CMC) is determined to be 1.36×10-3 mol·L-1.
Influence of Chlorination Conditions on Amount of Chloroform Formation

Experiment was made, in which Shenzhen reservoir water was taken as source water to observe the influence of chlorination conditions such as chlorine dosage, reaction time, pH value, organics concentration and varieties of disinfectant on the amount of chloroform formation. Based on the experiment results, control measures were proposed for the formation of disinfection byproducts such as chloroform through optimization of disinfection.
Statistical Machine Learning Methods for the Large Scale Analysis of Neural Data

Statistical Machine Learning Methods for the Large-Scale Analysis of Neural Data Gonzalo Esteban Mena Modern neurotechnologies enable the recording of neural activity at the scale of entire brains and with single-cell resolution. However, the lack of principled approaches to extract structure from these massive data streams prevent us from fully exploiting the potential of these technologies. This thesis, divided in three parts, introduces new statistical machine learning methods to enable the large-scale analysis of some of these complex neural datasets. In the first part, I present a method that leverages Gaussian quadrature to accelerate inference of neural encoding models from a certain type of observed neural point processes — spike trains — resulting in substantial improvements over existing methods. The second part focuses on the simultaneous electrical stimulation and recording of neurons using large electrode arrays. There, identification of neural activity is hindered by stimulation artifacts that are much larger than spikes, and overlap temporally with spikes. To surmount this challenge, I develop an algorithm to infer and cancel this artifact, enabling inference of the neural signal of interest. This algorithm is based on a a bayesian generative model for recordings, where a structured gaussian process is used to represent prior knowledge of the artifact. The algorithm achieves near perfect accuracy and enables the analysis of data hundreds of time faster than previous approaches. The third part is motivated by the problem of inference of neural dynamics in the worm C.elegans: when taking a data-driven approach to this question, e.g., when using wholebrain calcium imaging data, one is faced with the need to match neural recordings to canonical neural identities, in practice resolved by tedious human labor. Alternatively, on a bayesian setup this problem may be cast as posterior inference of a latent permutation. I introduce methods that enable gradient-based approximate posterior inference of permutations, overcoming the difficulties imposed by the combinatorial and discrete nature of this object. Results suggest the feasibility of automating neural identification, and demonstrate variational inference in permutations is a sensible alternative to MCMC.
Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators

Finding appropriate low dimensional representations of high-dimensional multi-modal data can be challenging, since each modality embodies unique deformations and interferences. In this paper, we address the problem using manifold learning, where the data from each modality is assumed to lie on some manifold. In this context, the goal is to characterize the relations between the different modalities by studying their underlying manifolds. We propose two new diffusion operators that allow to isolate, enhance and attenuate the hidden components of multi-modal data in a data-driven manner. Based on these new operators, efficient low-dimensional representations can be constructed for such data, which characterize the common structures and the differences between the manifolds underlying the different modalities. The capabilities of the proposed operators are demonstrated on 3D shapes and on a fetal heart rate monitoring application.
Fully Integrable/Cascadable CM Universal Filter and CM Quadrature Oscillator Using VDCC and Only Grounded Passive Elements

This paper proposes a current mode universal filter circuit, employing two active elements along with four grounded passive components only. The derived circuit realizes all five filtering responses, i.e., low pass (LP), band pass (BP), high pass (HP), band reject (BR) and all pass (AP), simultaneously from high-impedance ports along with the input being fed to low-impedance port, thus making it a fully cascadable filter. In addition, the designed circuit exhibits independent tunability of its quality factor. With the idea of making the proposed filter fully integrable, a resistor-less approach of the configuration has also been discussed. By making slight modifications in the filter configuration, a current mode single-resistance-controlled quadrature oscillator circuit has also been derived. The ideal, nonideal, sensitivity and parasitic analysis have been conducted for the designed configurations. The functionality of the proposed structures is verified by PSPICE simulations using 0.18[Formula: see text][Formula: see text]m CMOS technology. The designs have also been verified using PSPICE macro-model of the commercially available IC, i.e., OPA860.
Gaze estimation and interaction in real-world environments

Human eye gaze has been widely used in human-computer interaction, as it is a promising modality for natural, fast, pervasive, and non-verbal interaction between humans and computers. Gaze information can either be used as explicit input to interactive systems or analysed over time to recognise user activity and cognitive states. As the foundation of gaze-related interactions, gaze estimation has been a hot research topic in recent decades. Dominant traditional gaze estimation methods require dedicated hardware including high-resolution cameras and additional infrared light sources to detect eye features, such as the pupil centre and iris boundary. Such dedicated hardware results in extra costs that can be very expensive, constrained head movement due to narrow field of view of the camera, limited distance between user and camera to capture high-resolution images, and sensitivity to illumination conditions as sunlight will interfere with infrared light sources. Consequently, most gaze-based interaction researchers conducted their studies in settings in which lighting, user position, etc. can be controlled. In contrast, appearance-based gaze estimation methods directly regress from eye images to gaze targets without eye feature detection, and therefore have great potential to work with ordinary devices, such as a cellphone, tablet, laptop, or public display. However, these methods require a large amount of domain-specific training data to cover the significant variability in eye appearance. Unfortunately, most of the previous gaze estimation datasets were collected under laboratory conditions, and thus we cannot properly study the capabilities of appearance-based methods for interactive applications in practice. In this thesis, we focus on developing appearance-based gaze estimation methods and corresponding attentive user interfaces with a single webcam for challenging real-world environments. First, we collect a large-scale gaze estimation dataset, MPIIGaze, the first of its kind, outside of controlled laboratory conditions. This dataset enables us, for the first time, to conduct cross-dataset evaluation as training and test on different datasets to study the generalisation capabilities of appearancebased methods in real-world settings. To fully utilise the large amount of data, we the propose GazeNet method, which uses convolutional neural networks (CNN) for the first time for the gaze estimation task, and achieves significant improvement over previous state-of-the-art methods. Second, we propose an appearance-based method that, in stark contrast to a long-standing tradition in gaze estimation, only takes the full face image as input. The face patch has been used in recent work as part of the input. However, it is still an open question whether there is additional information in the other regions of the face, and how to efficiently encode this. We investigate the single full-face approach for 2D and 3D appearance-based gaze estimation and improve the model
Potential Mechanisms and the Effects of Passive Warm Up on Exercise Performance

439 1. Warm-Up Mechanisms . 440 1.1 Temperature Effects Associated with Warm Up . 440 1.1.1 Decreased Viscous Resistance . 441 1.1.2 Increased Oxygen Delivery to Muscles . 441 1.1.3 Speeding of Rate-Limiting Oxidative Reactions . 442 1.1.4 Increased Anaerobic Metabolism . 442 1.1.5 Increased Nerve Conduction Rate . 443 1.1.6 Increased Thermoregulatory Strain . 443 1.2 Metabolic Effects of Active Warm Up . 443 1.3 Elevation of Baseline Oxygen Consumption . 443 1.4 Postactivation Potentiation . 444 1.5 Breaking of Actin-Myosin Bonds . 445 1.6 Psychological Effects . 445 1.7 Summary of Potential Warm-Up Mechanisms . 445 2. Passive Warm up and Performance . 446 2.1 Short-Term Performance . 446 2.1.1 Isometric Force . 446 2.1.2 Dynamic Force . 446 2.1.3 Summary of Short-Term Performance . 448 2.2 Intermediate Performance. 448 2.3 Long-Term Performance . 450 2.4 Summary of Passive Warm Up and Performance . 450 3. Conclusions . 452 Despite limited scientific evidence supporting their effectiveness, warm-up Abstract routines prior to exercise are a well-accepted practice. The majority of the effects of warm up have been attributed to temperature-related mechanisms (e.g. decreased stiffness, increased nerve-conduction rate, altered force-velocity relationship, increased anaerobic energy provision and increased thermoregulatory strain), although non-temperature-related mechanisms have also been proposed (e.g. effects of acidaemia, elevation of baseline oxygen consumption (V̇O2) and increased postactivation potentiation). It has also been hypothesised that warm up may have a number of psychological effects (e.g. increased preparedness). Warm-up techniques can be broadly classified into two major categories: passive warm up or active warm up. Passive warm up involves raising muscle or core temperature by some external means, while active warm up utilises exercise. Passive heating allows one to obtain the increase in muscle or core temperature achieved by active warm up without depleting energy substrates. Passive warm up, although not practical for most athletes, also allows one to test the hypothesis that
Role of Nitric Oxide Carried by Hemoglobin in Cardiovascular Physiology: Developments on a Three-Gas Respiratory Cycle.

A continuous supply of oxygen is essential for the survival of multi-cellular organisms. The understanding of how this supply is regulated in the microvasculature has evolved from viewing erythrocytes (red blood cells, RBCs) as passive carriers of oxygen to recognizing the complex interplay between hemoglobin and oxygen, carbon dioxide, and nitric oxide - the three-gas respiratory cycle - that insures adequate oxygen and nutrient delivery to meet local metabolic demand. In this context, it is blood flow not blood oxygen content that is the main driver of tissue oxygenation by RBCs. Herein we review the lines of experimentation that led to this understanding of RBC function; from the foundational understanding of allosteric regulation of oxygen binding in hemoglobin in the stereochemical model of Perutz, to blood flow autoregulation (hypoxic vasodilation governing oxygen delivery) observed by Guyton, to current understanding that centers on S-nitrosylation of hemoglobin (aka S nitrosohemoglobin; SNO-Hb) as a purveyor of oxygen-dependent vasodilatory activity. Notably, hypoxic vasodilation is recapitulated by native SNO-replete RBCs and by SNO-Hb itself, whereby SNO is released from hemoglobin and RBCs during deoxygenation, in proportion to the degree of hemoglobin deoxygenation, to regulate vessels directly. In addition, we discuss how dysregulation of this system through genetic mutation in hemoglobin or through disease is a common factor in oxygenation pathologies resulting from microcirculatory impairment, including sickle cell disease, ischemic heart disease and heart failure. We then conclude by identifying potential therapeutic interventions to correct deficits in RBC-mediated vasodilation to improve oxygen delivery-steps towards effective microvasculature-targeted therapies. To the extent that diseases of the heart, lungs, and blood are associated with impaired tissue oxygenation, the development of new therapies based on the three-gas respiratory system have the potential to improve the well-being of millions of patients.
Migration Studies of Two Common Components of UV-curing Inks into Food Simulants

The Rapid Alert System for Food and Feed (RASFF) has reported many cases of different UV curing inks components in foodstuffs during the last few years. These contaminants reach foodstuffs mainly by set-off, their principal migration mechanism from the package. Under this premise, this work has tried to characterize the process of migration of two common UV ink components: a photoinitiator (4-Methylbenzophenone) and a coinitiator (Ethyl-4-(dimethylamino) benzoate), from the most common plastic material used in food packaging low-density polyethylene (LDPE) into six different food simulants. The migration kinetics tests were performed at four different common storage temperatures, obtaining the key migration parameters for both molecules: the coefficients of diffusion and partition. The migration process was highly dependent on the storage conditions, the photoinitiator properties and the pH of the foodstuff.
Autochthonous Saccharomyces cerevisiae Starter Cultures Enhance Polyphenols Content, Antioxidant Activity, and Anti-Inflammatory Response of Apulian Red Wines

Several biotic and abiotic factors can influence the amount of polyphenols in grape tissues. During vinification, the temperature, presence of grape seeds and peel, and use of enzymes, can influence the extraction of polyphenols. However, little information is available on the effects of yeast strains used in the polyphenolic composition of wine. With this aim, two selected Saccharomyces cerevisiae strains, ITEM 14093 and ITEM 14077 were used to produce wine from two Italian grape cultivars, Primitivo and Negroamaro. At the end of the alcoholic fermentation, the content of total polyphenols and of particular classes of polyphenols (stilbenes, phenolic acids, flavonols, and flavanols) and the antioxidant activity were evaluated and compared in the obtained wines. We also examined the effects of red wine extracts in a culture model of vascular inflammation. The results obtained comparing wine extracts obtained by utilizing commercial or autochthonous yeast showed that wines obtained with selected yeast significantly inhibited vascular inflammation. The results are positively directed towards the healthy properties of wine drinking.