Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding and changing human behaviour—antibiotic mainstreaming as an approach to facilitate modification of provider and consumer behaviour

Abstract This paper addresses: 1) Situations where human behaviour is involved in relation to antibiotics, focusing on providers and consumers; 2) Theories about human behaviour and factors influencing behaviour in relation to antibiotics; 3) How behaviour in relation to antibiotics can change; and, 4) Antibiotic mainstreaming as an approach to facilitate changes in human behaviour as regards antibiotics. Influencing human behaviour in relation to antibiotics is a complex process which includes factors like knowledge, attitudes, social norms, socio-economic conditions, peer pressure, experiences, and bio-physical and socio-behavioural environment. Further, key concepts are often perceived in different ways by different individuals. While designing and implementing projects or programmes for behavioural change with respect to antibiotics for professionals or consumers it is helpful to consider theories or models of behaviour change, e.g. the ‘stages of change model', including pre-contemplation, contemplation, preparation, action, and maintenance. People in different stages of change are susceptible to different behaviour modification strategies. Application of marketing principles to ‘global good', so-called ‘social marketing', to improve ‘welfare of the individual and society' is gaining increased attention in public health. In conclusion, just providing correct knowledge is not sufficient although it is a pre-requisite for behaviour modification in the desired direction. We can never change the behaviour of any other human, but we can facilitate for others to change their own behaviour. One possibility is to implement ‘antibiotic mainstreaming' as a potentially effective way for behaviour modification, i.e. to address consequences for maintaining effective antibiotics in all activities and decisions in society.
Prototypes based discriminative appearance model for object tracking

Occlusion is one of the major challenges for object tracking in real life scenario. Various techniques in particle filter framework have been developed to solve this problem. This framework depends on two issues: motion model and observation (likelihood) model. Due to the lack of effective observation model and efficient motion model, problem of occlusion still remains unsolvable in the tracking task. In this article, an effective observation model is proposed based on confidence (classification) score provided by the developing online prototypes based discriminative appearance model. This appearance model is constructed with the prior knowledge of two classes (object and background) and tries to discriminate between three classes such as object, background and occluded part of the object. The considered composite motion model can handle both the object motion as well as scale change of the object. The proposed update mechanism is able to adapt the appearance changes during tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of the results concludes that the proposed technique can track (fully or partially) occluded objects as well as objects in various complex environments in a better way as compared to the existing ones.
Capturing the real influencing factors of traffic for accurate traffic identification

In this paper we introduce a novel framework for traffic identification that employs machine learning techniques focusing on the estimation of multiple traffic influencing factors. The effect of these factors is handled with the training of several machine learning models. We utilize the outcome of the multiple models via a recombination algorithm to achieve high overall true positive and true negative and low overall false positive and false negative classification ratio. The proposed method can improve the performance of every kind of machine learning based traffic identification engine making them capable of efficient operation in changing network environment i.e., when the probing node is trained and tested in different sites.
Implementation of an ALU using modified carry select adder for low power and area-efficient applications

In digital computer, an Arithmetic logic unit (ALU) is a powerful combinational circuit that executes arithmetic and logical functions. Parallel adder in ALU plays an essential role, however the carry propagation (CP) takes most of the time for addition. For Low power and area-efficient applications, ALU using modified Square Root Carry Select Adder (SQRT CSLA) is proposed and for better speed applications, ALU using modified SQRT CSLA by Carry Look Ahead (CLA) Adder is implemented. The paper delivers the design and implementation of 8-Bit, 16-Bit, 32-Bit and 64-Bit ALU using modified SQRT CSLA and also compares it with the ALU using regular SQRT CSLA in terms of total number of basic gates. The design entry is done in Verilog Hardware Description Language (HDL) and simulated using ISIM Simulator. It is synthesized and implemented using Xilinx ISE 12.2. By using ALU with modified SQRT CSLA, 20.44% reduction in basic gates is observed.
Book Review: AbdouMaliq Simone, Jakarta: Drawing the City Near

Julia M Bernard, PhD, is an Assistant Professor in the Department of Social Sciences at McNeese State University. She teaches sociology and family and child studies courses. She is a marriage and family therapist, a certified family life educator, and a family researcher. She has traveled and presented abroad, including a graduate study experience in India. Address: Department of Social Sciences, McNeese State University, 4205 Ryan Street, Lake Charles, LA 70609, USA. Email: jbernard@mcneese.edu
Performance Capability Modeling And Optimization Of RF/Millimeter Wave Integrated Functions And Modules Using A Hybrid Statistical/Electromagnetic Technique That Includes Process Variations

Various uses of statistical tools combined with deterministic electromagnetic simulators in the analysis, design and optimization of RF and microwave systems are presented. First, the statistical methods are introduced, showing the advantages over the conventional techniques. The statistical tools used in the methodology include sources of variation tools such as ANOVA (analysis of variance), SPC (statistical process control), and MC (Monte Carlo) simulation, to account for the process variability. Using this methodology, the developed transfer functions predict both the nominal values and the variation expected for system performance. This is of great value for complex 3D RF integrated modules, RF MEMS and reconfigurable systems, especially at high frequencies where the fabrication tolerances affect the system more due to the smaller circuit features. The methodology can be extended to predict performance of multi-level systems, for which the outputs of the lower-level system become the inputs of the higher-level system. The presented methodology is applied for the analysis, design and optimization of a benchmarking geometry of 60 GHz cavity filters in LTCC (low temperature cofired ceramic) technology
Propositional approximations for bounded model checking of partial circuit designs

Bounded model checking of partial circuit designs enables the detection of errors even when the implementation of the design is not finished. The behavior of the missing parts can be modeled by a conservative extension of propositional logic, called 01X-logic. Then the transitions of the underlying (incomplete) sequential circuit under verification have to be represented adequately. In this work, we investigate the difference between a relation-oriented and a function-oriented approach for this issue. Experimental results on a large set of examples show that the function-oriented representation is most often superior w. r. t. (1) CPU runtime and (2) accuracy regarding the ability to find a counterexample, such that by using the function-oriented approach an increase of accuracy up to 210% and a speed-up of the CPU runtime up to 390% compared to the relation-oriented approach are achieved. But there are also relevant examples, e. g. a VLIW-ALU, for which the relation-oriented approach outperforms the function-oriented one by 300% in terms of CPU-time, showing that both approaches are efficient for different scenarios.
Real-time speech-driven animation of expressive talking faces

In this paper, we present a real-time facial animation system in which speech drives mouth movements and facial expressions synchronously. Considering five basic emotions, a hierarchical structure with an upper layer of emotion classification is established. Based on the recognized emotion label, the under-layer classification at sub-phonemic level has been modelled on the relationship between acoustic features of frames and audio labels in phonemes. Using certain constraint, the predicted emotion labels of speech are adjusted to gain the facial expression labels which are combined with sub-phonemic labels. The combinations are mapped into facial action units (FAUs), and audio-visual synchronized animation with mouth movements and facial expressions is generated by morphing between FAUs. The experimental results demonstrate that the two-layer structure succeeds in both emotion and sub-phonemic classifications, and the synthesized facial sequences reach a comparative convincing quality.
High-Rate Space-Time Coded Large MIMO: Low-Complexity Detection and Channel Estimation

In this paper, we present a low-complexity algorithm for detection in high-rate, non-orthogonal spacetime block coded (STBC) large MIMO systems that achieve high spectral efficiencies of the order of tens of bps/Hz. We also present a training-based iterative detection/channel est imation scheme for such large STBC MIMO systems. Our simulation results show that excellent bit error rate and nearness-tocapacity performance are achieved by the proposed multistage likeli hood ascent search (M-LAS) detector in conjunction with the proposed iterative detection/channel estimation scheme at low comp lexities. The fact that we could show such good results for large STBCs like 16 × 16 and 32 × 32 STBCs from Cyclic Division Algebras (CDA) operating at spectral efficiencies in excess of 20 bps/ Hz (even after accounting for the overheads meant for training-based channel estimation and turbo coding) establishes the effec tiveness of the proposed detector and channel estimator. We decode perfect codes of large dimensions using the proposed detect or. With the feasibility of such a low-complexity detection/channel estimation scheme, large MIMO systems with tens of antennas operating at high spectral efficiencies can become practica l, enabling interesting high data rate wireless applications .
Polish phoneme Statistics obtained on Large Set of Written Texts

The phonetical statistics were collected from several Polish corpora. The paper is a summaryof the data which are phoneme n-grams and some phenomena in the statistics. Triphonestatistics apply context-dependent speech units which have an important role in speech recognitionsystems and were never calculated for a large set of Polish written texts. The standardphonetic alphabet for Polish, SAMPA, and methods of providing phonetic transcriptions are described.
FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.
Mars Entry Navigation With Uncertain Parameters Based on Desensitized Extended Kalman Filter

Mars entry phase is the most challenging part among Mars entry, descent, and landing (EDL). One of the main reasons is that the lander suffers tough tests from the uncertainties, which include atmospheric density, lift over drag ratio, ballistic coefficient, and initial conditions. In this paper, the desensitized extended Kalman filter (DEKF) is introduced. State estimations from DEKF are obtained by minimizing a cost function that is composed of the trace of posterior covariance matrix and the weighted norm of the posterior state estimation error sensitivities (PSEES). By applying inertial measurement unit (IMU), Mars orbiters and Mars surface beacons (MSBs) integrated navigation simulations demonstrate that the introduced DEKF is far less sensitive to uncertain parameters than standard EKF during Mars entry. With the increase of uncertainties one by one, the root-mean-square errors (RMSE) of the states are still convergent in case of DEKF as compared to standard EKF where they become larger. At last, the consistency test is carried on to further validate the proposed DEKF.
Pictures of Facial Affect-Ulm (PFA-U): A New Facs-Based set of Pictures for Basic Emotions

Abstract Emotion recognition and emotion expression/regulation are important aspects of emotional intelligence (EI). Although the construct of EI is widely used and its components are part of many investigations, there is still no sufficient picture set that can be used for systematic research of facial emotion recognition and practical applications of individual assessments. In this research we present a new Facial Action Coding System validated picture set consisting of six emotions (anger, disgust, fear, happiness, sadness, and surprise). Basic principles of stimulus development and evaluation process are described. The PFA-U can be used for future studies in organization for the assessment of emotion recognition, emotion stimulation, and emotion management.
Analysis for dynamic of analog circuits by using HSPN

Since the ability to describe dynamic in high level, Petri Nets has already been used extensively in logic circuits and systems community for state analysis. As others formal methods, extending Petri Nets to analog circuits analysis requires continuous feature enhanced. In this paper, the object of study is dynamic of circuits with random nonlinear feature (such as random noise and random interface) by using a Hybrid Statistical Petri Net (HSPN). A formal definition of hybrid Petri Net is derived from hybrid automata to model the dynamic of nonlinear analog circuits. Furthermore, we improve the hybrid Petri Net to abstract the statistical feature by enhancing the ability of describe random continuous transition. And a formal definition of the novel Hybrid Statistical Petri Net is presented. Within this HSPN, the fundamental method of analyzing the dynamic of random analog circuits is studied. A sample circuit with random noise illustrates this technique.
Light deflection and time delay in the gravitational field of a spinning mass

In this paper we consider the possibility of measuring the corrections induced by the square of the parameter a_g of the Kerr metric to the general relativistic deflection of electromagnetic waves and time delay in an Earth based experiment. It turns out that, while at astronomical scale the well known gravitoelectric effects are far larger than the gravitomagnetic ones, at laboratory scale the situation is reversed: the gravitomagnetic effects exceed definitely the gravitoelectric ones which are totally negligible. By using a small rapidly rotating sphere as gravitating source on the Earth the deflection of a grazing light ray amounts to 10^{-13} rad and the time delay is proportional to 10^{-23} s. These figures are determined by the upper limit in the attainable values of a_g due to the need of preventing the body from exploding under the action of the centrifugal forces. Possible criticisms to the use of the Kerr metric at a_g^2 level are discussed.
Suzaku Observation of the TeV Blazar 1ES 1218+304: Clues on Particle Acceleration in an Extreme TeV Blazar

We observed the TeV blazar 1ES 1218+304 with the X-ray astronomy satellite Suzaku in 2006 May. At the beginning of the 2 day continuous observation, we detected a large flare in which the 5-10 keV flux changed by a factor of ~2 on a timescale of 5 × 104 s. During the flare, the increase in the hard X-ray flux clearly lagged behind that observed in the soft X-rays, with the maximum lag of 2.3 × 104 s observed between the 0.3-1 keV and 5-10 keV bands. Furthermore, we discovered that the temporal profile of the flare clearly changes with energy, being more symmetric at higher energies. From the spectral fitting of multiwavelength data assuming a one-zone, homogeneous synchrotron self-Compton model, we obtain a magnetic field strength B ∼ 0.047 G and an emission region size R = 3.0 × 1016 cm for an appropriate beaming with a Doppler factor of δ = 20. This value of B is in good agreement with an independent estimate through the model fit to the observed time lag ascribing the energy-dependent variability to the differential acceleration timescale of relativistic electrons provided that the gyrofactor ξ is 105.
Autonomic Operations in Cooperative Stream Processing Systems

System S is a large-scale distributed streaming data analysis environment, designed to handle extreme data rates. Multiple System S sites can cooperate to further improve the scale, breadth and depth of data analysis. We describe three autonomic features in the operation of such a cooperative stream processing environment: interoperation models, planning, and failover, They enable the distributed environment to deal with dynamic and rapid changes in imposed workload, available resources, and the priorities of administrators and users. Thus, the system can minimize the human effort needed to operate such large, complex sys-
Features selection for human activity recognition with iPhone inertial sensors

The recognition of human activities through sensors embedded in smart-phone devices, such as iPhone, is attracting researchers due to its relevance. The advances of this kind of technology are making possible the widespread and pervasiveness of sensing technology to take advantage of multiple sources of sensing to enrich users experience or to achieve proactive, context-aware applications and services. Human activity recognition and monitoring involves a continuing analysis of large amounts of data so, any increase or decrease in accuracy results in a wide variation in the number of activities correctly classified and incorrectly classified, so it is very important to increase the rate of correct classification. We have researched on a vector with 159 different features and on the vector subsets in order to improve the human activities recognition. We extracted features from the Magnitude of the Signal, the raw signal data, the vertical acceleration, the Horizontal acceleration, and the filtered Raw data. In the evaluation process we used the classifiers: Naive Bayes, K-Nearest Neighbor and Random Forest. The features were extracted using the java programming language and the evaluation was done with WEKA. The maximum accuracy was obtained, as expected, with Random Forest using all the 159 features. The best subset found has twelve features: the Pearson correlation between vertical acceleration and horizontal acceleration, the Pearson correlation between x and y, the Pearson correlation between x and z, the STD of acceleration z, the STD of digital compass y, the STD of digital compass z, the STD of digital compass x, the mean between axis, the energy of digital compass x, the mean of acceleration x, the mean of acceleration z, the median of acceleration z.
Reconstructing the drawing process of reproductions from medieval images

Based on 14th century paintings and their 18th century manual reproductions we reconstruct the temporal order of the sketching process and analyze how images were altered by artists. Therefore, we present a novel algorithm for decomposing images into groups of parts that were similarly transformed between images. Moreover, a method is proposed that orders all parts based on the relatedness of their transformation. Based on this ordering, a third dimension, which corresponds to the timeline of the drawing process, is added to 2D paintings.
Grid security research based on middleware

Considering OGSA architecture, this paper introduces a new Grid Security System Based on Middleware (GSSBM). The grid includes many different domains; it can make use of middleware technology to implement grid security. Integrating the GSI and PKI technologies, the architecture can solve the single sign-on, authentication, proxy and user-based trust model.
Model transformation of dependability-focused requirements models

Recent research has focused on extending standard requirements elicitation processes to address potential abnormal situations that can interrupt normal system interaction at run-time. We proposed a process, DREP, that extends use case-driven modelling with elements that allow the modelling of system behaviour in exceptional situations. This paper discusses the challenge of using the notions of exceptional behaviour and outcomes defined in use cases within a MDE process. In order to create a more formal specification model with activity diagrams, the use cases have to be well-formed to begin with. We describe precise transformation rules to systematically create an activity diagram corresponding to each use case. Special stereotypes are introduced to document partial or degraded outcomes and handling activities. The model resulting from the transformation unambiguously specifies the system interactions required to satisfy the user, as well as exceptional interactions that can lead to degraded service provision.