Miracle-dz/UniFAR-FTUs huggingface dataset:
Nudging in the Digital Big Data Era

In the digital age, social media revolutionized human communication around the globe, yet also opened opportunities to unprecedentedly reap benefits from information sharing and big data generation. To this day completely undescribed remains that the implicit hidden persuasion opens a gate to deception and is an unprecedented social class division means. Social media forces are captures as unfolding a class dividing nudgital society, in which the provider of social communication tools can reap surplus value from the information shared of social media users. The law of motion of the nudging societies holds an unequal concentration of power of those who have access to compiled data and who abuse their position under the cloak of hidden persuasion and in the shadow of paternalism. In the nudgital society, information, education and differing social classes determine who the nudgers and who the nudged are. Humans end in different silos or bubbles that differ in who has power and control and who is deceived and being ruled. The owners of the means of governance are able to reap a surplus value in a hidden persuasion, protected by the legal vacuum to curb libertarian paternalism, in the moral shadow of the unnoticeable guidance and under the cloak of the presumption that some know what is more rational than others.
An adaptive population importance sampler

Monte Carlo (MC) methods are widely used in signal processing, machine learning and communications for statistical inference and stochastic optimization. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this work, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples generated. The cloud of proposals is adapted by learning from a subset of previously generated samples, in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. Numerical results show the advantages of the proposed sampling scheme in terms of mean absolute error and robustness to initialization.
Fault Tolerance Through Redundant Execution on COTS Multicores: Exploring Trade-Offs

High availability and integrity are paramount in systems deployed in life-and mission-critical scenarios. Such fault-tolerance can be achieved through redundant co-execution (RCoE) on replicated hardware, now cheaply available with multicore processors. RCoE replicates almost all software, including OS kernel, drivers, and applications, achieving a sphere of replication that covers everything except the minimal interfaces to non-replicated peripherals. We complement our original, loosely-coupled RCoE with a closely-coupled version that improves transparency of replication to application code, and investigate the functionality, performance and vulnerability trade-offs.
GPS correlated jammer mitigation utilizing MUSIC algorithm

This paper study the behavior of multiple signal classification (MUSIC) algorithm for suppressing correlated and uncorrelated jammers to GPS receiver. Simulation indicates that MUSIC can cancel dramatically high power jammers for both types of signals and perfectly detects their direction of arrivals. It also can suppress both jammers of decreased powers up to -120 dBW. At that power level the suppression behavior regarding to uncorrelated jammer is better than that for correlated one but the algorithm still detects the correlated and uncorrelated jammers direction of arrivals.
From Millennials to Gen Z: Changes in student attitudes about group projects

Abstract The authors compare a sample of students enrolled in business classes from 2005 to 2007 with a 2017–2018 sample of business students in terms of their attitudes toward group projects. The two cohorts of students are classified as Millennials, those born before 1995, and Generation Z, those born in or after 1995. Our analyses demonstrate significant changes in some attitudes about group projects and greater anxiety by Gen Z students than by Millennials about the contributions of other team members.
Personalized optimization and its implementation in computer experiments

Abstract Optimization problems with both control variables and environmental variables often arise in quality engineering. This article introduces a personalized optimization strategy to handle such problems when the environmental variables can be observed or measured. Unlike traditional robust optimization, personalized optimization aims to find the values of control variables that yield the optimal value of the objective function for given values of environmental variables. Therefore, the solution from personalized optimization, which consists of optimal surfaces defined on the domain of environmental variables, is more reasonable and better than that from robust optimization. The implementation of personalized optimization for expensive black-box computer models is discussed. Based on statistical modeling of computer experiments, we provide two algorithms to sequentially design input values for approximating the optimal surfaces. Numerical examples including a real application show the effectiveness of our algorithms.
Integrating In-Silico Models with In-Vitro Data to Generate Novel Insights into Biological Systems

Models and computational predictions are useful in identifying certain key parameters that play a central role in defining the overall behavior of the system, and thus lead to new and more informative experiments. In this thesis, in-silico models are developed over a range of individual biological scales (macroscopic, mesoscopic and microscopic) for a range of cellular phenomena (cellular interactions, migration and signalling pathways) in order to highlight the importance of combined in-vitro – in-silico investigations. It is widely accepted that Systems Biology aims to provide a simpler and more abstract framework to explain complex biological phenomena. However, integration of these models with experimental data is often underutilised. Incorporation of experimentally derived data sets into the mathematical framework of in-silico modelling results in reliable, well parameterised systems capable of replicating dynamical properties of the biological systems. Work in this thesis includes the development of a continuous macroscopic in-silico model to identify the key mechanisms of interaction between cells present within the gastric tumour microenvironment. This model of discovery is used in a predictive capacity to accept or reject hypotheses. Next, the construction of a discrete cell based model of fibroblast migration is used to determine the degree of bias fibroblast cells experience when migrating over different surface topologies. The key results from this model show that particular surface topographies can have an effect on migratory cell behaviour. Then, the parameterisation of a differential equation model is used to quantify the key mechanisms of Nrf2 regulation in the cytoplasm and nucleus. Validation with experimentally derived datasets results in the quantification of rate ratios important to the dynamics of this signalling pathway. Finally, a stochastic Petri-net model capable of simulating the dynamical behaviour of functional cross-talk between the Nrf2 and NF-κB pathways is developed. This approach allows for the evaluation of a wide array of network responses, without the need for computationally expensive parameterisation. Together, these models exhibit how integration of in-silico models with in-vitro datasets can be used to generate new knowledge, or testable hypotheses.
WSN-AI based Cloud Computing Architectures for Energy Efficient Climate Smart Agriculture with Big Data analysis

These days, the conventional database worldview does not have enough stockpiling for the information created by Wireless Sensor Network (WSN) framework for climate smart agriculture continuously, prompts the need of cloud storage. These information’s are examined by an Artificial Intelligent (AI) module with the assistance of Big Data mining methods. Cloud based big data investigation and the WSN-AI innovation plays out an essential job in the practicality investigation of savvy farming. Sharp or exactness agricultural frameworks are assessed to assume a vital job in improving cultivation exercises. In this paper, WSN framework is utilized to detect the horticulture variable parameters and it is put away into the Cloud database. Big data examination utilizing Cloud is used to look at the information viz. compost prerequisites, break down the yields, soil PH, stickiness, temperature and other important parameters. At that point the expectation is performed dependent on data mining strategy by the AI module which will additionally pick the proper advance to drive the actuator for re-establishing the inadequacy happened for the most extreme harvest yield. Our definitive point is to build up a energy efficient smart agriculture system which will expand the yield generation and control the rural expense of the items utilizing this anticipated data.
Saliency detection in deep learning era: trends of development

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.
Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

Mechanical fault datasets are always highly imbalanced with abundant common mechanical fault samples but a paucity of samples from rare fault conditions. To overcome this weakness, the simulation of rare fault signals is proposed in this paper. Specifically, frequency spectra are employed as model signals, then Wasserstein generative adversarial network (WGAN) is implemented to generate simulated signals based on a labeled dataset. Finally, the real and artificial signals are combined to train stacked autoencoders (SAE) to detect mechanical health conditions. To validate the effectiveness of the proposed WGAN-SAE method, two specially designed experiments are carried out and some traditional methods are adopted for comparison. The diagnosis results show that the proposed method can deal with imbalanced fault classification problem much more effectively. The improved performance is mainly due to the artificial fault signals generated from the WGAN to balance the dataset, where the signals that are lacking in training dataset are effectively augmented. Furthermore, the learned features in each layer of the generator network are also analyzed via visualization, which may help us understand the working process of the WGAN.
How do Computational Models Help us Develop Better Theories

Computational models are a good thing, or so most of us believe. But, what are they actually good for, and how do they help us develop better theories? Here I assume that even if there is somebody out there who doesn't think that models are a good thing, we all think that theories are fundamental to any scientific enterprise. Although consideration of the nature of scientific theories is a big enough topic to occupy the entire academic career of a sizeable proportion of the planet's philosophers, I will focus on what I consider to be the central component of a scientific theory: To provide an explanation of some phenomena, observation, or set of data. That is, to explain how and why something happens.
A RARE CASE OF AIRWAY FOREIGN BODY (CEMENT & SAND)

INTRODUCTION An object is considered a "foreign body" (FB) if the object is in a location in the body where it is not normally found. Common foreign bodies found in children include coins, small toys, foods (like peas, beans, nuts, or even candy), and, other small objects like beads or pills, dislodged teeth , even bugs, may be found. Probably the most concerning object is a button type battery (like camera and watch batteries), as these can leak harmful substances [1-3]. Foreign body occurrence is a commonest cause of accidental home death in the children under 6 years of age in USA [4]. Children are in a habit of putting every thing in there mouth to detect the taste and texture especially around teething age. Depending on the size, shape and type of FBs, they can impact in the larynx, trachea or more distally to bronchi. Children may experience symptoms differently; some children can even have vague symptoms that do not immediately suggest ingestion. The clinical presentation varies from acute severe respiratory syndrome to clinically no obvious signs. Usually the laryngeal and tracheal FBs are fatal and produce sign and symptoms of sever choking. While the more distal FBs usually present with more variable forms, starting from acute severe respiratory distress to minor wheeze. They may present in the form of persistent or recurrent pneumonia. Other may lie silent and are diagnosed accidentally on diagnostic bronchoscopy. Long standing FBs especially of organic in origin may present with haemoptysis. Parents should suspect their child might have swallowed a foreign object if breathing or swallowing difficulties persist longer than two weeks despite medical treatment. For example, continuing asthma or upper respiratory treatment without seeing improvement [1-4]. Radiological findings vary from absolutely normal X-ray chest to emphysema, collapse of lung to opaque FBs [5]. The management of the FB varies depending upon the severity of the symptoms , site of impaction & type of the FBs. Tracheal & laryngeal FBs usually present in emergency conservative measures like Hemlich’s may be tried but one should not persistent with this method after the 4th try. The gold standard principal of FBs management is bronchscopic removal . Even the most experienced endoscopists would agree that the prospect of having to deal with a very young child with a history of possible inhalation of FB fills them with some trepidation- not only because of the demands that the removal of a FB makes on their skill as an endoscopist, but also because of the unpredictability in the degree of difficulty in the nature of the procedure [6-7].
Rice Classification Using Hyperspectral Imaging and Deep Convolutional Neural Network

Rice has been one of the staple foods that contribute significantly to human food supplies. Numerous rice varieties have been cultivated, imported, and exported worldwide. Different rice varieties could be mixed during rice production and trading. Rice impurities could damage the trust between rice importers and exporters, calling for the need to develop a rice variety inspection system. In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN). The proposed method uses a hyperspectral imaging system to simultaneously acquire complementary spatial and spectral information of rice seeds. The rice varieties are then determined from the acquired spatio-spectral data using a deep CNN. As opposed to several existing rice variety classification methods that require hand-engineered features, the proposed method automatically extracts features from the raw sensor data. As demonstrated using two types of rice datasets, the proposed method achieved up to 8% relative improvement in the classification accuracy compared to the commonly used classification methods based on support vector machines.
Collocation Classification with Unsupervised Relation Vectors

Lexical relation classification is the task of predicting whether a certain relation holds between a given pair of words. In this paper, we explore to which extent the current distributional landscape based on word embeddings provides a suitable basis for classification of collocations, i.e., pairs of words between which idiosyncratic lexical relations hold. First, we introduce a novel dataset with collocations categorized according to lexical functions. Second, we conduct experiments on a subset of this benchmark, comparing it in particular to the well known DiffVec dataset. In these experiments, in addition to simple word vector arithmetic operations, we also investigate the role of unsupervised relation vectors as a complementary input. While these relation vectors indeed help, we also show that lexical function classification poses a greater challenge than the syntactic and semantic relations that are typically used for benchmarks in the literature.
A Survey on Adaptive Authentication

Adaptive Authentication allows a system to dynamically select the best mechanism(s) for authenticating a user depending on contextual factors, such as location, proximity to devices, and other attributes. Though this technology has the potential to change the current password-dominated authentication landscape, research to date has not led to practical solutions that transcend to our daily lives. Motivated to find out how to improve adaptive authentication design, we provide a structured survey of the existing literature to date and analyze it to identify and discuss current research challenges and future directions.
Population Coding of Object Contour Shape in V4 and Posterior Inferotemporal Cortex

In primates, visual object perception and recognition is based on information processing within the ventral visual pathway (Ungerleider and Mishkin, 1982; Felleman and Van Essen, 1991). This multistage, hierarchical network runs from area V1 to V2, V4, and finally to the posterior and anterior subregions of inferotemporal (IT) cortex (Felleman and Van Essen, 1991). In each of these processing stages, visual information is encoded in the patterns of activity across populations of neurons. The neural code reflected in these activity patterns is gradually transformed across the ventral pathway into a representation thought to be optimized for robust object recognition, association, and memory. The nature of the neural code in the lowestlevel stage, V1, is now relatively clear. Research over the last half-century has identified local orientation and spatial frequency as the basis dimensions of V1 form representation (Hubel and Wiesel, 1959, 1965, 1968; Baizer, Robinson, and Dow, 1977; Burkhalter and Van Essen, 1986; Hubel and Livingstone, 1987). Specifically, each small patch of the retinal image is represented by a local population of V1 neurons tuned to different orientations and narrow spatial frequency (scale) bands. The distribution of activity within these local V1 populations encodes the local orientation and scale of image features, and the full retinal image is represented by a tiling of the visual field with thousands of such local populations. Representation in terms of local orientation and spatial frequency first arises in V1, and these representational bases are fundamental to our understanding of what V1 does. The bases of representation in subsequent processing stages are less clear, though past results suggest that activity in higher-level neurons reflects a progressively more nonlinear and complex function of the retinal image. V2 neurons, like their V1 inputs, are tuned for local orientation and spatial frequency, but also show some selectivity for line conjunctions (Ito and Komatsu, 2004, Hegde and Van Essen, 2000), orientation combinations (Anzai, Peng, and Van Essen, 2007), and illusory contours (von der Heydt and Peterhans, 1989). V4 neurons show selectivity for radial or concentric gratings and moderately complex shapes (Gallant, Braun, and Van Essen, 1993, Kobatake and Tanaka 1994). And in IT, many neurons exhibit 7
Documentation Mocap Database HDM05

Preface In the past two decades, motion capture (mocap) systems have been developed that allow to track and record human motions at high spatial and temporal resolutions. The resulting motion capture data is used to analyze human motions in fields such as sports sciences and biometrics (person identification), and to synthesize realistic motion sequences in data-driven computer animation. Such applications require efficient methods and tools for the automatic analysis, synthesis and classification of motion capture data, which constitutes an active research area with many yet unsolved problems. Even though there is a rapidly growing corpus of motion capture data, the academic research community still lacks publicly available motion data, as supplied by [4], that can be freely used for systematic research on motion analysis, synthesis, and classification. Furthermore, a common dataset of annotated and well-documented motion capture data would be extremely valuable to the research community in view of an objective comparison and evaluation of the achieved research results. It is the objective of our motion capture database HDM05 1 to supply free motion capture data for research purposes. HDM05 contains more than tree hours of systematically recorded and well-documented motion capture data in the C3D as well as in the ASF/AMC data format. Furthermore, HDM05 contains for each of roughly 70 motion classes 10 to 50 realizations executed by various actors amounting to roughly 1, 500 motion clips. In this documentation, we give a detailed description of our mocap database HDM05. In Sect. 1, we provide some general information on motion capture data including references to various application fields. A detailed description of the database structure of HDM05 as well as of the content of each mocap file can be found in Sect. 2. We also provide several MATLAB tools comprising a parser for ASF/AMC and C3D as well as visualization, renaming and cutting tools, which are described in Sect. 3. Finally, Sect. 4 summarizes some facts on the mocap file formats ASF/AMC and C3D as used in our database. We appreciate any comments and suggestions for improvement. 1 The motion capture data has been recorded at the Hochschule der Medien (HDM) in the year 2005 under the supervision of Bernhard Eberhardt.