Miracle-dz/UniFAR-FTUs huggingface dataset:
Ethically Aligned Multi-agent Coordination to Enhance Social Welfare Extended Abstract

In multi-agent systems (MASs), the complex interactions among self-interested agents can be modelled as stochastic games. Existing decision support approaches dealing with such situations focus on minimizing individual agent’s regret through outperforming other agents in the competitive aspect of the game. Such an approach often results in social welfare not being maximized in the process. In this paper, we propose the regret-minimization-social-welfare-maximization (RMSM) approach. It contains a novel method to quantify how an agent’s sacrifice increases and decreases over time based on queueing system dynamics. In this way, ensuring fairness of distribution of sacrifice among agents and compensating for their previous sacrifices can be translated into maintaining the stability of a queueing system. ACM Reference Format: Han Yu, Zhiqi Shen, Lizhen Cui, Yongqing Zheng, and Victor R. Lesser. 2019. Ethically Aligned Multi-agent Coordination to Enhance Social Welfare. In Proc. of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019, IFAAMAS, 3 pages.
Molecular Dynamics Simulations for Biological Systems

Molecular dynamics simulation is an important tool to capture the dynamicity of biological molecule and the atomistic insights. These insights are helpful to explore biological functions. Molecular dynamics simulation from femto seconds to milli seconds scale give a large ensemble of conformations that can reveal many biological mysteries. The main focus of the chapter is to throw light on theories, requirement of molecular dynamics for biological studies and application of molecular dynamics simulations. Molecular dynamics simulations are widely used to study protein-protein interaction, protein-ligand docking, effects of mutation on interactions, protein folding and flexibility of the biological molecules. This chapter also deals with various methods/algorithms of protein tertiary structure prediction, their strengths and weaknesses
Multi-stages Visualized Parallel Beam CT Reconstruction with Performance Evaluation

Computed tomography is a technique for estimating the interior of an object from measurements of radiation collected around the object. This radiation is projected either through or emitted from the object and the projections are reconstructed to form the object image. The reconstruction quality & time depend on many factors such as: filteration domain, filter type and interpolation level. The objective of this paper is to develop an algorithm to simulate CT image reconstruction from projections (image simulated projections or raw data projections file). The developed program enables the user to investigate the effects of many factors mentioned above on the quality of the reconstructed images. The system has agraphical user interface (GUI) to facilitate changing the effictive factors and measuring the performance of the reconstruction. This system helps for studying the CT reconstruction process by introducing many factors selections for development as well as for training
Why Do We Pin? New Gratifications Explain Unique Activities in Pinterest

Pinterest is now the fourth most popular social network site after Facebook, Twitter, and LinkedIn in the United States, offering its own suite of functions. This study investigated why individuals use specific features of Pinterest such as pinning, creating, liking, following, commenting, inviting, sharing, checking, searching, and browsing different categories. An online survey (N = 113) revealed that a brand new set of gratifications (specific to digital media) predicted a large number of user behaviors in Pinterest. The results showcased the predictive value of affordance-based gratifications in shaping specific user behaviors on social-media.
Impact of Penetration of Photovoltaic on Rotor Angle Stability of Power System

The small signal and transient behaviour of large multi-machine system is minimal when the smallscale PV farm power is connected to it. But the oscillation on dynamics or states of the multi-machine system begins to increase as the large-scale PV farm power is connected. In this work investigations are carried out on small signal stability and transient of large multi-machine system with PV connected to it. Eigenvalue analysis is carried out for both cases with and without the PVs and are compared. Transient stability is analysed by time domain simulation by executing three phase short circuit fault at critical location. When PV are integrated without the replacement of conventional generators, small signal stability and transient stability both improves. Eigenvalue analysis shows that the damping of critical modes increases. And the magnitude of the peak of rotor angle of generator 2 decreases from 17.984o to 13.069o due to addition of PV which shows improvement in transient stability. In contrast, the stability of system in terms of small signal and large signal both degrades as the addition of PV is done by the replacement of the conventional generators. The trend of critical mode 3 towards the RHP (right half plane) of the complex plane indicates that the small signal stability is degrading. For the same case, the peak of the speed of generator 1 increases form 1.015 p.u to 1.0311 p.u from no PV to various level of PV which indicates the degradation of transient stable. The reduction in inertia due to replacement of synchronous generators reduces the damping of the critical modes which is also verified by the transient analysis.
Fusion of Contourlet Transform and Zernike Moments using Content based Image Retrieval for MRI Brain Tumor Images

Background: Content based Image Retrieval (CBIR) is employed to search and retrieve the expected image from the database. Magnetic Resonance Imaging (MRI) technique plays a crucial role in diagnosing many diseases in human brain. Methods: In this paper, we proposed a texture fusion technique for T1 and T2 weighted MRI scans. Our proposed technique has three parts. First, texture and shape features are extracted from a brain tumor images. Next, the feature selection techniques like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to combine the texture and shape features. Finally, the popular supervised learning machine techniques like Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are used to classify the brain tumor based on the selected features. Findings: The results of proposed MRI brain tumor diagnosis method are robust, efficient, effective, reduces the retrieval time and improves the retrieval accuracy significantly. Best overall classification accuracy results were obtained using the given DiCom Images. Application: The proposed MRI image based brain tumor retrieval would efficiently deal with a medical decision system based on the CT+ZM fusion method provides more accurate results, so this method can yield better result of brain tumor diagnosis in advance where this method using in medical fields.
Damage to DNA induced by low energy electrons and photons: mechanisms and analysis at the molecular level

In this thesis studies of interactions of both VUV light and low energy (below 20 eV) electrons with dry DNA films and DNA samples in aqueous solutions are reported. The damage induced by such radiation is quantified by monitoring both loss of supercoiled DNA and formation of single and double strand breaks using agarose gel electrophoresis. In addition VUV spectra of DNA were recorded and, for the first time, spectral signatures of both the structural water in DNA and individual contributions of separate DNA nucleotides were resolved. Such investigations required the assembly and calibration of new apparatus. New protocols for the synthesis and purification of high quality samples of plasmid DNA were also developed. Alternative methods for analysis of DNA damage were explored, such as the atomic force microscopy technique, which allows for lesion imaging, and a new method, based on the ELISA assay was developed for labelling damage sites. VUV irradiation experiments revealed enhanced damage upon irradiation with 170 nm photons that was shown, via VUV absorption experiments, to be due to the excitation of structural water leading to increased hydroxyl (OH) radical formation causing chemical damage to the plasmid DNA. In agreement with earlier work both single (SSBs) and double (DSBs) strand breaks were induced during by electron irradiation at 4.5 eV, 5 eV and 10 eV but only SSB were observed for electron irradiation at 1 eV.
Overview of the H.264/AVC error concealment

The problem of error concealment in video communication is becoming increasingly important(because) of the growing interest in video delivery over unreliable channels such as wireless networks and the(Internet.)This paper reviews the techniques that have been developed for error concealment in the literature. (Error) concealment by postprocessing refers to operations at the decoder to recover the damaged areas based on characteristics of image and video signals.This paper summarized the error concealment methods in space and temporal,including some hybrid temporal-spatial error concealment method.
A Statistical Analysis of the Minimal SUSY B-L Theory

The structure of the B-L MSSM theory--specifically, the relevant mass scales and soft supersymmetric breaking parameters--is discussed. The space of initial soft parameters is explored at the high scale using random statistical sampling subject to a constraint on the range of dimensionful parameters. For every chosen initial point, the complete set of renormalization group equations is solved. The low energy results are then constrained to be consistent with present experimental data. It is shown that a large set of initial conditions satisfy these constraints and lead to acceptable low energy particle physics. Each such initial point has explicit predictions, such as the exact physical sparticle spectrum--which is presented for two such points. There are also statistical predictions for the masses of the sparticles and the LSP species which are displayed as histograms. Finally, the fine-tuning of the $\mu$ parameter--which is always equivalent to or smaller than in the MSSM--is discussed.
A 3.3 kV / 800 A Ultra-High Power Density SiC Power Module

A 3.3 kV/800 A diode-less (D-less) SiC power module has been developed adopting the next High Power Density Dual (nHPD(exp 2)) package. The ultra-high power density value of 37.7 kVA/cm(exp 2) is realized by fulfilment with only SiC-MOSFETs. Furthermore, as a countermeasure for "bipolar degradation" issues related to body diodes in the SiC-MOSFET structure, a high throughput screening process is deployed. The low loss and high reliability characteristics of the D-less SiC module are set out herein.
Text Segmentation wit h Topic Models

This article presents a general method to use information retrieved from the Latent Dirichlet Allocation (LDA) topic model for Text Segmentation: Using topic assignments instead of words in two well-known Text Segmentation algorithms, namely TextTiling and C99, leads to significant improvements. Further, we introduce our own algorithm called TopicTiling, which is a simplified version of TextTiling (Hearst, 1997). In our study, we evaluate and optimize parameters of LDA and TopicTiling. A further contribution to improve the segmentation accuracy is obtained through stabilizing topic assignments by using information from all LDA inference iterations. Finally, we show that TopicTiling outperforms previous Text Segmentation algorithms on two widely used datasets, while being computationally less expensive than other algorithms.
Influence of Digital Signal Processing on Precision of Power Quality Parameters Measurement

The paper deals with errors, which rise up during Fast Fourier Transform (FFT) realising harmonic analysis for power quality measurements, if it is performed by digital signal processors (DSP). For FFT realisation there exist different algorithms based on the same principal but differing in the calculation operations sequence. The paper compares results obtained by radix-2 decimation in frequency and decimation in time algorithms. Then it gives possible improvements for overall error suppression.
Assessment of Fuzzy Gaussian Naive Bayes for Classification Tasks

Statistical methods have been used in order to classify data from random samples. In general, if we know the statistical distribution of data, we can use specific classifiers designed for that distribution and expect good results. This work assesses the accuracy of a Fuzzy Gaussian Naive Bayes (FGNB) classifier for tasks using data from five different statistical distributions: Negative Binomial, Logistic, Log-Normal, Weibull and Gamma. The FGNB classifier was recently proposed as a fuzzy extension of Gaussian Naive Bayes for training assessment in virtual environments. Results of assessment are provided and show different accuracy according to the statistical distribution of data. Keywords–Fuzzy Gaussian Naive Bayes Classifier, Classification, Accuracy Assessment.
Size effect for samples with blunt and sharp notches using linear cohesive crack law

The object of this work is to provide a more general formulation of the cohesive size effect curve to include structural configurations with blunt and sharp notches. An extensive campaign of accurate numerical simulations, based on the cohesive crack model, is performed to compute the Generalized Cohesive Size Effect Curves (GCSEC) for typical test configurations featuring both sharp and blunt notches. The results are analyzed with reference to the classical Bažant’s Size Effect Law (SEL) to investigate the relationship between GCSEC and SEL. This analysis shows that as specimen size tends to infinity, the SEL represents the asymptote of the GCSEC in the case of sharp notches, and that the SEL parameter known as the effective fracture process zone length is a material property, which can be expressed as a function of the Cohesive Crack Law (CCL) parameters. For blunt notches, however, the nominal strength of infinite size samples tends to a horizontal asymptote corresponding to the elastic limit. It is shown that the two results are not in contradiction because the effective fracture process zone length tends to zero as the radius of curvature tends to zero.
Combination Model-based Self-similarity Traffic Prediction

In view of mode mixing caused by EMD(Empirical Mode Decomposition),this paper proposed a self-similarity traffic prediction method based on the combination models.Through the EEMD(Ensemble Empirical Mode Decomposition) process,the long-term dependence existing in network traffic was removed effectively.Additionally,according to the different characteristics of each IMF(Intrinsic Mode Function) produced by EEMD,ANN(Artificial Neural Network) and ARMA(Auto Regressive Moving Average) were adopted for different IMFs.The simulation results demonstrate that the proposed method can effectively predict the traffic and has high precision.
TR-Classifier and kNN Evaluation for Topic Identification tasks

This paper focuses on studying topic identification for Arabic language by using two methods. The first method is the well-known kNN (k Nearest Neighbors) which is used as baseline. The second one is the TR-Classifier, mainly based on computing triggers. The experiments show that TR-Classifier has the advantage to give best performances compared to kNN, by using much reduced sizes of Topic Vocabularies. TR-Classifier performance is enhanced by increasing jointly the number of triggers and the size of topic vocabularies. It should be noted that topic vocabularies are used by the TR-Classifier. Whereas, a general vocabulary is needed for kNN, and it is obtained by the concatenation of those used by the TR-Classifier. In addition to the standard measures Recall and Precision used for the evaluation step, we have drawn ROC curves for some topics to illustrate more clearly the difference in performance between the two classifiers. The corpus used in our experiments is downloaded from an online Arabic newspaper. Its size is about 10 millions words, distributed over six selected topics, in this case: culture, religion, economy, local news, international news and sports.
An attribute weighting K-means algorithm based on mean-square-deviation

K-means with its rapidity,simplicity and high scalability,has become one of the most widely used text clustering techniques.However,it treats all features fairly and sets weights of all features equally.Thus,the resulting clusters may be not strongly reveal intra-similarities of data and reflect non-natural groupings.To address this issue,this paper proposes an attribute weighting K-means algorithm based on mean-square-deviation in order to improve the clustering quality.The experimental results on real life datasets show that it is superior to algorithms without weight with respect to adjusted rand index measures.
Pycnocline Flow from the Subtropical to the Equatorial Pacific Ocean *

Interior circulation pathways from the subtropics to the equator are markedly different in the Northern and Southern Hemispheres of the Pacific Ocean. In the North Pacific the pycnocline shoals and strengthens dramatically under the intertropical convergence zone, separating the North Equatorial Current from the North Equatorial Countercurrent. While the high potential vorticity between these currents would intuitively seem to inhibit meridional water-property exchange between the subtopics and the equator, transient tracer analyses and some modeling studies have suggested an interior pathway from the subtropics to the equator in the pycnocline of the central North Pacific. This study delineates this pathway and estimates an upper bound for its magnitude at 5 (61) 3 109 kg s21. In contrast, the southern branch of the South Equatorial Current clearly brings pycnocline water estimated at 15 (61) 3 109 kg s21 from the southern subtropics directly to the equator in the South Pacific through an interior region of low and relatively uniform potential vorticity. In both hemispheres, these interior pathways extend downward as far as the lightest waters of the equatorial pycnostad. The subsurface countercurrents flanking the pycnostad form the equatorward limbs of tropical subsurface cyclonic gyres. These deep gyres are consistent with the absence of interior ventilation of the equator from the subtropics below the pycnocline. Measured and derived fields from a hydrographic climatology are presented on neutral surfaces and in meridional–vertical sections to show that salinity, potential vorticity, and acceleration potential are all consonant with these arguments. The vertical extent of interior communication is also in agreement with the transient
FACIAL COMPONENT SEGMENTATION USING CONVOLUTIONAL NEURAL NETWORK

Facial components are important for many face image analysis applications. Facial component segmentation is a challenging task due to variations in illumination conditions, pose, scale, skin color etc. Deep learning is a novel branch of machine learning, very efficient in solving complex problems. In this study, we developed a deep Convolutional Neural Network (CNN) to automatically segment facial components in face images. The network has been trained with face images in Radboud face database. Training labels have been created using Face++ SDK. The developed CNN produces a segmentation mask where mouth, eyes, and eyebrows components of the face are marked as foreground. We have focused on these components because they can include very important information for facial image analysis studies such as facial expression recognition. The segmentation success rate of the study is 98.01 according to average accuracy.
Feedback LSTM network based on attention for image description generator

: Images are complex multimedia data which contain rich semantic information. Most of current image description generator algorithms only generate plain description, with the lack of distinction between primary and secondary object, leading to insufficient high-level semantic and accuracy under public evaluation criteria. The major issue is the lack of effective network on high-level semantic sentences generation, which contains detailed description for motion and state of the principal object. To address the issue, this paper proposes the Attention-based Feedback Long Short-Term Memory Network (AFLN). Based on existing codec framework, there are two independent sub tasks in our method: attention-based feedback LSTM network during decoding and the Convolutional Block Attention Module (CBAM) in the coding phase. First, we propose an attention-based network to feedback the features corresponding to the generated word from the previous LSTM decoding unit. We implement feedback guidance through the related field mapping algorithm, which quantifies the correlation between previous word and latter word, so that the main object can be tracked with highlighted detailed description. Second, we exploit the attention idea and apply a lightweight and general module called CBAM after the last layer of VGG 16 pretraining network, which can enhance the expression of image coding features by combining channel and spatial dimension attention maps with negligible overheads. Extensive experiments on COCO dataset validate the superiority of our network over the state-of-the-art algorithms. Both scores and actual effects are proved. The BLEU 4 score increases from 0.291 to 0.301 while the CIDEr score rising from 0.912 to 0.952.
Opportunities for Near Data Computing in MapReduce Workloads

In-memory big data applications are growing in popularity, including in-memory versions of the MapReduce framework. The move away from disk-based datasets shifts the performance bottleneck from slow disk accesses to memory bandwidth. MapReduce is a data-parallel application, and is therefore amenable to being executed on as many parallel processors as possible, with each processor requiring high amounts of memory bandwidth. We propose using Near Data Computing (NDC) as a means to develop systems that are optimized for in-memory MapReduce workloads, offering high compute parallelism and even higher memory bandwidth. This dissertation explores three different implementations and styles of NDC to improve MapReduce execution. First, we use 3D-stacked memory+logic devices to process the Map phase on compute elements in close proximity to database splits. Second, we attempt to replicate the performance characteristics of the 3D-stacked NDC using only commodity memory and inexpensive processors to improve performance of both Map and Reduce phases. Finally, we incorporate fixed-function hardware accelerators to improve sorting performance within the Map phase. This dissertation shows that it is possible to improve in-memory MapReduce performance by potentially two orders of magnitude by designing system and memory architectures that are specifically tailored to that end. Dedicated to my wife, Melina.
Impedance and Dielectric Spectroscopy of Nanoparticulate Films and Composites

Nanoparticulate materials are becoming ubiquitous in a wide range of industrial applications. Such materials can be used as individual devices with appropiate doping and contacting methods. They are often made into films or used as one of the functional components in a nanocomposite. They are made into nanoparticulate films via vacuum filtration, layer by layer assembly, spin coating, dip pen nanolithography and many other techniques. When they are used in composites, they are often dispersed inside a polymer matrix via melt-extrusion, solution dispersion or microcontact printing to name a few. In all cases, these materials behave as combinations of two or more materials. Therefore, their electrical characterization is challenging due to the extreme smallness of the nanoparticulate materials and/or the properties of the materials that they are surrounded by. In this paper, we will focus on the usage of impedance and dielectric spectroscopy as an ideal method for characterization of such materials.