Miracle-dz/UniFAR-FTUs huggingface dataset:
How to Improve Linear Fuel-Cost Function to Compete with Quadratic and Cubic Functions

To model the operating cost of thermal generating units, it is common to use polynomial relations between their power output and fuel input. These mathematical relations are known as fuel-cost functions, which are the heart of optimization algorithms. These functions could be modeled as first, second, or third order polynomial equations. The first order or linear equation is weak to explain the variability of units' operating cost. Also, the third order or cubic polynomial equation is rarely used in the literature, because its third element does not have any significant contribution to add. Thus, the second order or quadratic polynomial equation becomes the most popular fuel-cost function. Sometimes, different linear equations grouped as a piecewise function are used to accelerate the computational speed and linear programming algorithms can be directly involved. This study tries to achieve the goal of the last approach without using any piecewise function. That is, improving the preceding single linear equation to be a competitive fuel-cost function.
Authentication methods in cloud computing: a survey

The study focuses on presenting a review on the various biometric authentication mechanisms in the cloud computing environment. Cloud Computing is used to deliver computing Services over the internet and is used to store data in cloud servers. Due to the sharing of services, privacy and data security have become a major area of concern in cloud computing. The cloud service providers store and maintain client data across data centers which provides threats of data leaks. It is found that several mechanisms have emphasized on data security and in the ensuing process have ignored privacy. Authentication helps to ensure and confirms a user's identity. The existing traditional password authentication does not provide enough security for the data residing in cloud and there have been instances when the password based authentication has been manipulated to gain access into the cloud data. Multifactor authentication is a methodology that uses two or more authentication techniques along with the password but it still does not provide fool-proof data security. Since the conventional methods such as passwords do not serve the purpose of data security, research works focused on biometric traits were as a means of user authentication in cloud services. The biometric authentication is broadly classified into physical based biometric authentication and behavioral based biometric authentication. This paper presents an overview of those methods and analyzes their merits and demerits. The study has been structured to analyze the prevailing biometric authentication mechanisms to gain insights in developing a new authentication model that is more efficient than the existing methods.
Detecting Pickpocket Suspects from Large-Scale Public Transit Records

Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in urban areas. Existing studies on AFC data have primarily focused on identifying passengers’ movement patterns. However, we creatively leveraged such data for identifying pickpocket suspects. Stopping pickpockets in the public transit system has been crucial for improving passenger satisfaction and public safety. Nonetheless, in practice, it is challenging to discern thieves from regular passengers. In this paper, we developed a suspect detection and surveillance system, which can identify pickpocket suspects based on their daily transit records. Specifically, we first extracted a number of useful features from each passenger's daily activities in the transit system. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who typically exhibit abnormal traveling behaviors. Experimental results demonstrated the effectiveness of our method. We also developed a prototype system for potential uses by security personnel.
The Evidence-Based Social Work Skills Book

Preface. Organization of the Book.The Author.Acknowledgments. 1. Evidence-Based Social Work. What Is EBSW? Practice Effectiveness.What Is Evidence? Phases of Evidence-Based Social Work.EBSW in Contemporary Practice.2. Questioning. Formulating a Practice Effectiveness Question.Formulating a Practice Effectiveness Question: Exercises.Generating Synonyms and Keywords.Generating Synonyms and Keywords: Exercises.Planning the Search.Planning the Search: Exercises.3. Searching. Searching: The Basics.Searching Library Resources.Searching Library Resources: Exercises.Searching Bibliographic Databases.Searching Bibliographic Databases: Exercises.Searching Evidence-Based Collaborations and Government Resources.Searching Evidence Based Collaborations and Government Resources: Exercises.Searching the World Wide Web.Searching the World Wide Web: Exercises.4. Analyzing. Evaluating Evidence.Source. Contents.Purpose and Goals.Assessing Authority-Based Evidence.Assessing Research-Based Evidence.Validity and Reliability.Research Papers.Research Syntheses.Practice Guidelines.Grading the Evidence.Synthesizing Evidence.5. Applying and Evaluating. Applying.Evaluating. Epilogue. References.
Fundamentals of Information Theory and Coding Design

From the Publisher: "Without abandoning the theoretical foundations, Fundamentals of Information Theory and Coding Design presents working algorithms and implementation that can be used to design and create real systems. The emphasis is on the underlying concepts governing information theory and the mathematical basis for modern coding systems, but the authors also provide the practical details of important codes like Reed-Solomon, BCH, and Turbo codes. Also setting this book apart are discussions on the cascading of information channels and the additivity of information, the details of arithmetic coding, and the connection between coding of extensions and Markov modeling. " Complete, balanced coverage, an outstanding format, and a wealth of examples and exercises make this an outstanding text for upper-level students in computer science, mathematics, and engineering, and a valuable reference for telecommunications engineers and coding theory researchers.
Atrial Fibrillation Detection From Face Videos by Fusing Subtle Variations

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias, which particularly occurs in the elderly individuals with heart disease. Though AF is often asymptomatic during normal activities, it has huge potential risks for stroke and other severe diseases. Thus, early detection of AF has great importance in the field of public health. Currently, electrocardiography (ECG) is the commonly used measure for the diagnosis of AF, which presents the irregular rhythm of waveform for AF patients. However, the measurement of the ECG signal requires special medical acquisition devices, which are not comfortable for practical monitoring in daily life. In this paper, we explore a very promising algorithm to detect AF from remote face videos by analyzing the color variations of face skin. The main challenge is that the current remote photoplethysmography (rPPG) technique is rather immature, which causes difficulty in extracting accurate pulse signals for describing the cardiac rhythm. To solve this problem, we first utilize various rPPG algorithms to capture pulse rhythms from different regions on the face video. We then investigate biomedical statistical methods to extract suitable features from each pulse signal. Due to the imprecision of video-extracted pulse signals, some traditional physiological features may lose their utility since they were originally proposed for ECG signals. Furthermore, some of them are very susceptible to the influence of noise. Thus, we propose a feature fusion algorithm to select and combine reasonable information from multiple physiological features, which aims to preserve the discriminability of detecting AF in the presence of the noise and outlier disturbances. The experimental results on a real-world database demonstrate the effectiveness of the proposed method in providing useful information for AF detection.
Método generalizado de diseño de transmisores para equipos de radiolocalización y comunicaciones subterráneas

La radiolocalizacion y las comunicaciones subterraneas se desarrollan en entornos hostiles (cuevas, minas.) caracterizados por la oscuridad, presencia de barro, humedad, acceso dificultoso, etc. Debido al efecto pelicular que ofrece la roca por ser considerada como un medio "conductor", las senales electromagneticas son fuertemente atenuadas, incrementandose dicho efecto con la frecuencia y la conductividad del suelo. Por ello, los sistemas de comunicaciones subterraneas trabajan en baja frecuencia (ELF-LF). El acceso al medio con estos sistemas se realiza mediante dos tipos de soluciones: acoplamiento magnetico e inyeccion de corriente en el terreno. En el primer caso, bucles de pequeno tamano son empleados como carga y en el segundo, la corriente se introduce en el suelo por medio de electrodos. El conocimiento de estos dos tipos de carga es fundamental para el correcto diseno de la etapa de potencia de un transmisor de radiolocalizacion o comunicaciones subterraneas de voz y datos. Esta Tesis presenta, ademas de un exhaustivo estado del arte de estas aplicaciones, los factores determinantes a considerar para el desarrollo de un transmisor, para pasar a tratar en detalle los dos tipos de carga. Para cada una de ellas se presentan los modelos teoricos y los metodos de caracterizacion, desarrollando y aplicando el mas adecuado. Ademas se analizan las etapas de adaptacion a la carga. Por ultimo, todo el conocimiento teorico y experimental es aplicado en situaciones reales de comunicaciones subterraneas.
Intra-domain Cross-talk Regulates Serine-arginine Protein Kinase 1-dependent Phosphorylation and Splicing Function of Transformer 2β1*

Background: Serine-arginine-rich (SR)-like proteins regulate the alternative splicing of human genes. Results: The serine-arginine protein kinase 1 (SRPK1) phosphorylates transformer 2β1 (Tra2β1) at numerous sites regulating RNA binding, splicing of the survival motor neuron 2 gene, and catalytic function of the kinase domain. Conclusion: The two RS domains interact and regulate Tra2β1 activity in a phosphorylation-dependent manner. Significance: SRPK1 is a regulator of Tra2β1. Transformer 2β1 (Tra2β1) is a splicing effector protein composed of a core RNA recognition motif flanked by two arginine-serine-rich (RS) domains, RS1 and RS2. Although Tra2β1-dependent splicing is regulated by phosphorylation, very little is known about how protein kinases phosphorylate these two RS domains. We now show that the serine-arginine protein kinase-1 (SRPK1) is a regulator of Tra2β1 and promotes exon inclusion in the survival motor neuron gene 2 (SMN2). To understand how SRPK1 phosphorylates this splicing factor, we performed mass spectrometric and kinetic experiments. We found that SRPK1 specifically phosphorylates 21 serines in RS1, a process facilitated by a docking groove in the kinase domain. Although SRPK1 readily phosphorylates RS2 in a splice variant lacking the N-terminal RS domain (Tra2β3), RS1 blocks phosphorylation of these serines in the full-length Tra2β1. Thus, RS2 serves two new functions. First, RS2 positively regulates binding of the central RNA recognition motif to an exonic splicing enhancer sequence, a phenomenon reversed by SRPK1 phosphorylation on RS1. Second, RS2 enhances ligand exchange in the SRPK1 active site allowing highly efficient Tra2β1 phosphorylation. These studies demonstrate that SRPK1 is a regulator of Tra2β1 splicing function and that the individual RS domains engage in considerable cross-talk, assuming novel functions with regard to RNA binding, splicing, and SRPK1 catalysis.
Applying modern coexistence theory to priority effects

Significance Ecologists have embraced modern coexistence theory as a framework for understanding the interspecific differences that drive competitive exclusion versus coexistence. We extend the empirical application of this theory to examine priority effects, in which neither species in a competing pair can invade its competitor due to destabilizing differences that result in positive frequency-dependent growth rates. Using reciprocal invasion experiments between yeast strains across two environmental gradients, we reveal two fundamentally different ways in which environmental conditions alter competitive interactions: high sugar increased the likelihood of priority effects by reducing fitness differences, whereas low acidity increased the neutrality of species interactions by simultaneously reducing both fitness and destabilizing differences. This study demonstrates how modern coexistence theory can systematically explain priority effects. Modern coexistence theory is increasingly used to explain how differences between competing species lead to coexistence versus competitive exclusion. Although research testing this theory has focused on deterministic cases of competitive exclusion, in which the same species always wins, mounting evidence suggests that competitive exclusion is often historically contingent, such that whichever species happens to arrive first excludes the other. Coexistence theory predicts that historically contingent exclusion, known as priority effects, will occur when large destabilizing differences (positive frequency-dependent growth rates of competitors), combined with small fitness differences (differences in competitors’ intrinsic growth rates and sensitivity to competition), create conditions under which neither species can invade an established population of its competitor. Here we extend the empirical application of modern coexistence theory to determine the conditions that promote priority effects. We conducted pairwise invasion tests with four strains of nectar-colonizing yeasts to determine how the destabilizing and fitness differences that drive priority effects are altered by two abiotic factors characterizing the nectar environment: sugar concentration and pH. We found that higher sugar concentrations increased the likelihood of priority effects by reducing fitness differences between competing species. In contrast, higher pH did not change the likelihood of priority effects, but instead made competition more neutral by bringing both fitness differences and destabilizing differences closer to zero. This study demonstrates how the empirical partitioning of priority effects into fitness and destabilizing components can elucidate the pathways through which environmental conditions shape competitive interactions.
BioSecure Multimodal Evaluation Campaign 2007 Mobile Scenario-Experimental Results

– Ref. Syst. : The BioSecure reference system uses the standard Eigenface approach [1] to represent face images (extracted from a given video) in a lower dimensional subspace. The face space is built using the 300 images from the BANCA world model and the dimensionality of the reduced space is selected such as 99 per cent of the variance is explained by the PCA analysis. To manage video sequences, five frames are extracted at regular intervals (the number of the extracted frames are 20, 30, 40, 50 and 60). Using the eye positions detected automatically, each face image is then normalized and projected onto the face space. At the matching step, the L1-norm distance measure is used to evaluate the similarity between the five target feature vectors and the five test feature vectors. The final score corresponds to the minimum of these 25 distances.
Application and implementation of dark channel prior defogging algorithm in image restoration

In recent years, there appeared many times of serious haze weather in a lot of places in our country. According to the data published by China Meteorological Bureau, in 2013, the average number of Chinese haze days was 2.3 days more than early years at the same period, and was the most since 1961; in 2014, the haze weather did not decrease but gradually increase. Fog is the aerosol system composed of a large number of small water droplets or ice crystals suspended in the air near the ground, and a product of condensation of water vapor coagulation in the air in the ground layer. Haze is composed of dust, nitric acid, sulfuric acid, and organic hydrocarbons in the air. Both fog and haze have a series of adverse e ects, including reducing air transparency, fuzzing vision up and reducing visibility. Therefore, the haze weather causes a bad in uence on people's daily life. The formation of foggy image is a ected by many factors, and the main reasons included are: particulate composition of large radius in the atmospheric medium reduces the transparency of the air, and leads to the reduced visibility of the target; at the same time, the sun light after the scattering of these particles with a large radius is enhanced. In uenced by the above two common reasons, the foggy image quality decreases to a great extent. In order to get high quality defogging image, combined with the causes of foggy image formation and its physical model, the defogging recovery processing of foggy image is conducted. Each image objective evaluation indexes of defogging images are compared and analyzed and good results are obtained.
Influence of Thermal Imagery Resolution on Accuracy of Deep Learning based Face Recognition

Human-system interactions frequently require a retrieval of the key context information about the user and the environment. Image processing techniques have been widely applied in this area, providing details about recognized objects, people and actions. Considering remote diagnostics solutions, e.g. non-contact vital signs estimation and smart home monitoring systems that utilize person's identity, security is a very important factor. Thus, thermal imaging has become more and more popular, as it does not reveal features that are often used for person recognition, i.e. sharp edges, clear changes of pixel values between areas, etc. On the other hand, there are much more visible light data available for deep model training. Taking it into account, person recognition from thermography is much more challenging due to specific characteristics (blurring and smooth representation of features) and small amount of training data. Moreover, when low resolution data is used, features become even less visible, so this problem may become more difficult. This study focuses on verifying whether model trained to extract important facial embedding from RGB images can perform equally well if applied to thermal domain, without additional re-training. We also perform a set of experiments aim at evaluating the influence of resolution degradation by down-scaling images on the recognition accuracy. In addition, we present deep super-resolution (SR) model that by enhancing donw-scaled images can improve results for data acquired in scenarios that simulate real-life environment, i.e. mimicking facial expressions and performing head motions. Preliminary results proved that in such cases SR helps to increase accuracy by 6.5% for data 8 times smaller than original images. It has also been shown that it is possible to accurately recognize even 40 volunteers using only 4 images per person as a reference embedding. Thus, the initial profiles can be easily created in a real time, what is an additional advantage considering a solution setup in a new environment.
Rothamsted Repository Download

Background: CRISPR/Cas has recently become a widely used genome editing tool in various organisms, including plants. Applying CRISPR/Cas often requires delivering multiple expression units into plant and hence there is a need for a quick and easy cloning procedure. The modular cloning (MoClo), based on the Golden Gate (GG) method, has enabled development of cloning systems with standardised genetic parts, e.g. promoters, coding sequences or terminators, that can be easily interchanged and assembled into expression units, which in their own turn can be further assembled into higher order multigene constructs. Results: Here we present an expanded cloning toolkit that contains 103 modules encoding a variety of CRISPR/Cas-based nucleases and their corresponding guide RNA backbones. Among other components, the toolkit includes a number of promoters that allow expression of CRISPR/Cas nucleases (or any other coding sequences) and their guide RNAs in monocots and dicots. As part of the toolkit, we present a set of modules that enable quick and facile assembly of tRNA-sgRNA polycistronic units without a PCR step involved. We also demonstrate that our tRNA-sgRNA system is functional in wheat protoplasts. Conclusions: We believe the presented CRISPR/Cas toolkit is a great resource that will contribute towards wider adoption of the CRISPR/Cas genome editing technology and modular cloning by researchers across the plant science community.
AN IMPROVED GA-MILSVM CLASSIFICATION APPROACH FOR DIAGNOSIS OF BREAST LESIONS FROM STAIN IMAGES

Cancer cells spread to more distant parts of the body through the lymphatic system or bloodstream. Not all tumors are cancerous. Benign tumors do not grow uncontrollably, do not invade neighboring tissues, and do not spread throughout the body. Intraductal Carcinoma is a noninvasive condition in which abnormal cells are found in the lining of a breast duct. The abnormal cells have not spread outside the duct to other tissues in the breast. In some cases, Intraductal Carcinoma may become invasive cancer and spread to other tissues, although it is not known at this time how to predict which lesions will become invasive. Intraductal cancer is the most common type of breast cancer in women. Memory Intraductal includes 3-types of cancer: Usual Ductal Hyperplasia (UDH), Atypical Ductal Hyperplasia (ADH), and Ductal Carcinoma in Situ (DCIS). So the system of detecting the breast microscopic tissue of UDH, ADH, DCIS is proposed. The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. The system classify the tissue based on the quantitative feature derived from the images. The statistical features are obtained. The approach makes use of preprocessing, Cell region segmentation, Individual cell segmentation, Feature extraction technique and MILSVM classifier for the detection of cancer and optimized using Genetic Algorithm An overall accuracy of 87.9% precision is obtained using GA and 4.5% recall are achieved on the entire test data. The test accuracy of 82.6% precision and 3.5% recall are obtained using MILSVM. When compared with MILSVM, GA has a great potential in improving diagnostic accuracy and reproducibility.