Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-layer adaptive transmission scheduling in wireless networks

A new promising approach for wireless network optimization is from a cross-layer perspective. This thesis focuses on exploiting channel state information (CSI) from the physical layer for optimal transmission scheduling at the medium access control (MAC) layer. The first part of the thesis considers exploiting CSI via a distributed channel-aware MAC protocol. The MAC protocol is analysed using a centralized design approach and a noncooperative game theoretic approach. Structural results are obtained and provably convergent stochastic approximation algorithms that can estimate the optimal transmission policies are proposed. Especially, in the game theoretic MAC formulation, it is proved that the best response transmission policies are threshold in the channel state and there exists a Nash equilibrium at which every user deploys a threshold transmission policy. This threshold result leads to a particularly efficient stochastic-approximation-based adaptive learning algorithm and a simple distributed implementation of the MAC protocol. Simulations show that the channel-aware MAC protocols result in system throughputs that increase with the number of users. The thesis also considers opportunistic transmission scheduling from the perspective of a single user using Markov Decision Process (MDP) approaches. Both channel state information and channel memory are exploited for opportunistic transmission. First, a finite horizon MDP transmission scheduling problem is considered. The finite horizon formulation is suitable for short-term delay constraints. It is proved for the finite horizon opportunistic transmission scheduling problem that the optimal transmission policy is threshold in the buffer occupancy state and the transmission time. This two-dimensional threshold structure substantially reduces the computational complexity required to compute and implement the optimal policy. Second, the opportunistic transmission scheduling problem is formulated as an infinite horizon average cost MDP with a constraint on the average waiting cost. An advantage of the infinite horizon formulation is that the optimal policy is stationary. Using the Lagrange dynamic programming theory and the supermodularity method, it is
A Novel Ranking Model for Product Name Extraction

The Internet contains large amounts of information, which are valuable to the researchers. Since manual product name collection needs large human efforts and also time consuming, extracting keywords automatically become a hot topic. This paper addresses the issue of automatic extraction for agricultural product name. Previously, this problem was formalized as classification and learning methods for classification were utilized. In this paper, we transform the product name extraction problem into a ranking problem and employ a learning to rank method to solve the task. Specifically, we utilize the ranking SVM to extract the agricultural product name. Experimental results on real life datasets demonstrate that our novel ranking method outperforms the baseline methods.
Unsupervised Cross-Database Micro-Expression Recognition Using Target-Adapted Least-Squares Regression

SUMMARY Over the past several years, the research of micro-expression recognition (MER) has become an active topic in a ﬀ ective computing and computer vision because of its potential value in many application ﬁelds, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from di ﬀ erent micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an e ﬀ ective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coe ﬃ cient matrix based on the source samples and their provided label information and also enable this learned regression co-e ﬃ cient matrix to suit the target micro-expression database. We are thus able to use the learned regression coe ﬃ cient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.
The crystal structure of (1E,2E)-2-methyl-4-((7-oxo-7H-furo[3,2-g]chromen-9-yl)oxy)but-2-enal O-isonicotinoyl oxime–trichloromethane (3/1), C67H49Cl3N6O18

Abstract C67H49Cl3N6O18, triclinic, P1̄ (no. 2), a = 12.3281(4) Å, b = 15.8887(5) Å, c = 16.9037(6) Å, α = 89.015(2)°, β = 77.885(3)°, γ = 72.021(3)°, V = 3075.20(19) Å3, Z = 2, Rgt(F) = 0.0658, wRref(F2) = 0.1558, T = 291.2(3) K.
14 Patterns of Biophilic Design: Improving Health and Well-Being in the Built Environment

Biophilic design can reduce stress, enhance creativity and clarity of thought, improve our well-being and expedite healing; as the world population continues to urbanize these qualities are ever more important. Theorists, research scientists, and design practitioners have been working for decades to define aspects of nature that most impact our satisfaction with the built environment. “14 patterns of biophilic design” articulates the relationships between nature, human biology and the design of the built environment so that we may experience the human benefits of biophilia in our design applications. biophilia in Context looks as the evolution of biophilic design in architecture and planning and presents a framework for relating the human biological science and nature. design Considerations explores a sampling of factors (e.g., scale, climate, user demographics) that may influence biophilic design decisions to bring greater clarity to why some interventions are replicable and why others may not be. the patterns lays out a series of tools for understanding design opportunities, including the roots of the science behind each pattern, then metrics, strategies and considerations for how to use each pattern. This paper moves from research on biophilic responses to design application as a way to effectively enhance health and well-being for individuals and society.
APLIKASI IDENTIFIKASI RAS MANUSIA MENGGUNAKAN METODE IMAGE REGISTRATION DAN SCALE INVARIANT FEATURE TRANSFORM (SIFT) DENGAN KLASIFIKASI K-NEAREST NEIGHBORS (KNN)

Abstrak Ras manusia dapat di identifikasi melalui bentuk dari wajahnya dan manusia cenderung dapat dengan mudah mengidentifikasi ras yang orang lain miliki jika ras orang tersebut sama dengan yang ia miliki. Hal tersebut disebabkan oleh informasi yang terdapat pada wajah manusia serta kemampuan manusia dalam memproses secara preseptual. Kemampuan tersebut merupakan salah satu kelemahan yang harus diperbaiki untuk dapat memproses informasi yang dimiliki secara lebih efektif dan efisien yang dapat digunakan untuk kebutuhan forensik. Pada penelitian ini dilakukan pengujian identifikasi informasi demografis citra wajah manusia berdasarkan metode Image Registration dan Scale Invariant Feature Transform (SIFT) dengan klasifikasi K-Nearest Neighbors (KNN). Pengujian difokuskan untuk mengidentifikasi 3 ras besar manusia yaitu Kaukasoid, Negroid, dan Mongoloid. Hasil pengujian penelitian ini mendapatkan nilai akurasi sebesar 41.67%. Kata kunci : Ras, Forensik, Image Registration, SIFT, KNN Abstract Human race can be identified through the shape of his face and humans tend to be able to easily identify races that other people have if that person's race is the same as his own. This is caused by information contained in the human face and the human ability to process in a preseptual manner. This capability is one of the weaknesses that must be corrected to be able to process information more effective and efficient that can be used for forensic needs. In this project, the identification of human facial demographic information was tested based on the Image Registration and Scale Invariant Feature Transform (SIFT) method with the K-Nearest Neighbors (KNN) classification. The tests focused on identifying 3 major human races, Caucasoid, Negroid, and Mongoloid. The results of this project are have an accuracy of 41.67%. Keywords: Races, Forensic, Image Registration, SIFT, KNN
Prototype tool for design and construction of polygonal objects with implementation in MATLAB, Simulink and FPGA

The aim of computer vision is to make useful decisions about physical objects and scenes based on sensed images. Nowadays, Computer Vision has almost an unlimited field of application in which several solutions are based on polygonal object identification; commercial, scientific, industrial and military applications are some examples. Although several techniques have been introduced and developed to solve the variety of computer vision challenges, there is no accepted methodology or paradigm. This work focuses on developing an algorithm, used to develop a software tool that allows to a computer vision designer explore in the design of applications involving the recognition of polygonal objects. Usage of this work in designing applications reduces time in the development of prototype solutions using FPGAs. The algorithm has been developed using MATLAB and I/O equivalent implementations in a Simulink block system with the aim of ease the design of hardware description entities in VHDL. A Spartan 3e FPGA Starter kit evaluation board is used as the device for the hardware implementation of the algorithm. Binary images are loaded into the FPGA through a micro SD memory card and the resulting data from the FPGA process is visualized through the Starter Kit built-in alphanumeric LCD.
A novel way to place short implants using platelet-rich fibrin (PRF): An original research

Aim: The purpose of this study was to evaluate the effects of PRF application on short implant both clinically and radiologically. Methodology: A total of fifteen short implants coated with PRF were placed in periodontally healthy patients who were above 18 years of age. All the patients were informed about the purpose of study and consent was taken. They were followed up for a period of 6 months. Results: Results showed gain in crestal bone levels along with no adverse complications. Conclusion: It was concluded that the use of PRF along with short implants is an important adjunct in implantology as it accelerates the soft and hard tissue healing around the implant without performing any extensive surgery in deficient bone height and contributes to the overall success of implants.
Effects of different pH, temperature and foils on aluminum leaching from baked fish by ICP-MS

The effect of different type of fish, marination methods, temperature, and cooking time as well as foil type on Al leaching into baked fish, was determinated. Two fish types, Al foils, and marinating ingredients were procured from a hypermarket. Fish was cooked in the baking tray of oven, using two foil types, at 150°C for 40 min and at 200°C for 20 min. Al content was determined. It was found that increase in acidity caused significant increase in Al leaching. When the relationship between temperature-time and Al leaching based on meat type was analysed, it was found that when the temperature was increased the extent of Al leaching was higher in salmon. Even in the highest estimated weekly exposure to Al (1.228 ± 0.1631 mg/kg per week), Al PTWI suggested by JECFA was not exceeded. Although using different Al foil did not leach significant amounts of Al into the fish and exposure values of Al did not exceed the PTWI, exposure of Al from fish samples may be dangerous to vulnerable groups such as children, elderly and people with kidney disease.
Probabilistic bounded reachability for stochastic hybrid systems

Stochastic parametric hybrid systems provide a means of formalising automata with continuous nonlinear dynamics, discrete interruptions, and parametric uncertainty (e.g. randomness and/or nondeterminism). They can be used for modelling a vast class of cyber-physical systems – machines comprising physical components orchestrated by a digital control (e.g. medical devices, self-driving cars, and aircraft autopilots). Assuring correct and safe behaviour of such systems is crucial as human lives are often involved. One of the main problems in system verification is reachability analysis. It amounts to determining whether the studied model reaches an unsafe state during its evolution. Introduction of parametric randomness allows the formulation of a quantitative version of the problem – computing the probability of reaching the undesired state. Reachability analysis is a highly challenging problem due to its general undecidability for hybrid systems and undecidability of nonlinear arithmetic (e.g. involving trigonometric functions) over the real numbers. A common approach in this case is to solve a simpler, yet useful, problem. In particular, there are techniques for solving reachability rigorously up to a given numerical precision. The central problem of this research is probabilistic reachability analysis of hybrid systems with random and nondeterministic parameters. In this thesis I have developed two new distinct techniques: a formal approach, based on formal reasoning which provides absolute numerical guarantees; and a statistical one, utilising Monte Carlo sampling that gives statistical guarantees. Namely, the former computes an interval which is guaranteed to contain the exact reachability probability value, while the latter returns an interval containing the probability value with some statistical confidence. By providing weaker guarantees, the statistical approach is capable of handling difficult cases more efficiently than the formal one, which in turn, can be used for parameter set synthesis in the absence of random uncertainty. The latter is one of the key problems in system modelling: identifying sets of parameter values for which a given model satisfies the desired behaviour. I have implemented the described techniques in the publicly available tool ProbReach, which I have then applied to several realistic case studies such as the synthesis of safe and robust controllers for artificial pancreas and the design of UVB treatment for psoriasis.
Mechanism and Health Effects of Heavy Metal Toxicity in Humans

Several heavy metals are found naturally in the earth crust and are exploited for various industrial and economic purposes. Among these heavy metals, a few have direct or indirect impact on the human body. Some of these heavy metals such as copper, cobalt, iron, nickel, magnesium, molybdenum, chromium, selenium, manganese and zinc have functional roles which are essential for various diverse physiological and biochemical activities in the body. However, some of these heavy metals in high doses can be harmful to the body while others such as cadmium, mercury, lead, chromium, silver, and arsenic in minute quantities have delirious effects in the body causing acute and chronic toxicities in humans. The focus of this chapter is to describe the various mechanism of intoxication of some selected heavy metals in humans along with their health effects. Therefore it aims to highlight on biochemical mechanisms of heavy metal intoxication which involves binding to proteins and enzymes, altering their activity and causing damage. More so, the mechanism by which heavy metals cause neurotoxicity, generate free radical which promotes oxidative stress damaging lipids, proteins and DNA molecules and how these free radicals propagate carcinogenesis are discussed. Alongside these mechanisms, the noxious health effects of these heavy metals are discussed.
Analysis and Design of the Dynamical Stability of Collective Behavior in Crowds.

The modeling of the dynamics of the collective behavior of multiple characters is a key problem in crowd animation. Collective behavior can be described by the solutions of large-scale nonlinear dynamical systems that describe the dynamical interaction of locomoting characters with highly nonlinear articulation dynamics. The design of the stability properties of such complex multi-component systems has been rarely studied in computer animation. We present an approach for the solution of this problem that is based on Contraction Theory, a novel framework for the analysis of the stability complex nonlinear dynamical systems. Using a learning-based realtime-capable architecture for the animation of crowds, we demonstrate the application of this novel approach for the stability design for the groups of characters that interact in various ways. The underlying dynamics specifies control rules for propagation speed and direction, and for the synchronization of the gait phases. Contraction theory is not only suitable for the derivation of conditions that guarantee global asymptotic stability, but also of minimal convergence rates. Such bounds permit to guarantee the temporal constraints for the order formation in self-organizing interactive crowds.
THE MISSING PERSONS FINDING SENSITIVE TO MOVEMENTS WITH IMAGE RECOGNITION SYSTEM AND NUMERIC DATA PEOPLE OF LOSS IN TURKEY

Lost children and adults in our country and in other countries that they are known by everyone to be a major social problem. According to the study, records from 12 April 2010 to 1 January 2006 Turkey were examined. During this review process, it was observed that a total of 29.223 recourses for missing children. Of these, 27.741 children are said to be found later. Children and young people are the future of the country and the main architect of assurance. In this study, the robot is designed to find missing persons. Thanks to the wireless camera on the robot, transmits images to the authorities in the outside world. Face images with previously saved from a computer face pictures of missing persons matched. If there is a match between the faces, the system automatically provides alerts to the authorities
Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms.
Design and analysis of three-axis cantilever type force sensor

Purpose The force sensing is used in robotic assembly tasks. The sensors developed are much advanced and costly. The force transducers are generally configured and deployed at the wrist of the robotic arm. The purpose of this paper is to describe the concept of an elastic transducer to make available cost-effective force sensor with simple construction and analysis. Design/methodology/approach The analytical formulation is developed herewith for one-, two- and three-axis elastic cantilever configuration. The force to be measured can be calculated analytically using derived strain expressions. The strains are estimated using proposed formulation, further crosschecked through FEA approach. The analytical method for strain estimation using moment equations is presented along with validation using finite element method (FEM) tool (ANSYS 15.0) with the case study. Findings The derivation of expressions for force components from strains is developed. The resulting formulation found to confirm the estimated strains from analytical methods closely to the FEM results. Theoretically, it is possible to find contact forces and angle of force on stationary force platform. It is found that the magnitude of estimated contact forces is within 1 per cent deviations. Research limitations/implications The mathematical modeling and FEA simulation of the three-axis force sensor under elastic (no deformation) conditions. Originality/value These sensors are ranging from simple crossbar structure to Stewart platform type. The subsequent development in this field pertains to performance enhancement such as accuracy and cross-sensitivity. The basic structure of the sensor has not changed drastically. The major problem, as discussed by many authors, is a complex interdependence of six components and intricate geometrical structure. Derivation of expressions for force components from strains is a breakthrough contribution by the authors. The analytical treatment for finding the strains is aimed in this paper.
Marine Internet for Internetworking in Oceans: A Tutorial

Ever-increasing human activities in oceans require handy, reliable and cost-effective high-speed network access similar to terrestrial Internet services to be available on and under water. However, terrestrial Internet cannot be extended to oceans seamlessly due to huge differences between terrestrial and oceanic environments, while satellite services are still very expensive especially for ordinary users with communication quality susceptible to weather conditions, and cannot cover underwater networks either. Therefore, marine Internet, proposed about six years ago, is the earliest scheme that tries to systematically address the internetworking issue in oceans and still in its infancy stage. This tutorial aims to introduce the principle, architecture and applications of marine Internet, along with discussion on oceanic environments for communication, currently available and under developing communication systems in oceans, as well as challenging issues necessary for further studies to foster the development of marine Internet.