Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotional profiling through supervised machine learning of interrupted EEG interpolation

It has been reported that the construction of emotion profiling models using supervised machine learning involves data acquisition, signal pre-processing, feature extraction and classification. However, almost all papers do not address the issue of profiling emotion using supervised machine learning on the interrupted encephalogram (EEG) signals. Based on a preliminary study, emotion profiling on interrupted EEG signals produces low classification accuracy, using multilayer perceptron (MLP). Furthermore, lower emotion classification accuracy is produced from interrupted EEG signals with higher number of segments. Thus, the objective of this paper is to propose a technique and present the outcomes of handling interrupted EEG signals for emotion profiling. This is done by the suppression and interpolation of originally interrupted EEG signals at pre-process stage. As a result, emotion classification using MLP on interpolated data improves from 80.1% to 95%.
Complementing Bibliometrics with Network Visualization to Support Scientific Spheres

Science evaluation in both quantitative and qualitative terms is imperative to ascertain the growth and trends in a knowledge domain, strengths and gaps in research, symbiotic collaboration opportunities at national and international level, and impact of research reflected in terms of quality and performance indicators, viz. h-index, impact factor, number of citations, etc. Bibliometrics and its alternatives including scientometrics, altmetrics, webometrics, and librametrics are being widely used as tools for exploring the growth and structure of scholarly communications in different forms and formats encompassing various disciplines. The Library and Information Science Professionals (LISPs) have experience and expertise in bibliographic data handling. Therefore, LISPs can actively execute bibliometric studies. However, the volume and complexity of datasets in science disciplines have grown so enormous that use of special tools has become essential for inferring from the data available in form of various units of analysis. Network visualization software is used as supplemental aid to support bibliometrics in an interactive mode. These software usually have text mining functionality to facilitate the construction and visualization of network maps based on the co-occurrence of key terms extracted from a body of scholarly communication, viz. co-authorship, co-occurrence of institutions, bibliographic coupling and co-citation links. This paper discusses as detailed below the need for scientific evaluation, application of bibliometrics in science evaluation, role of data/network visualization in complimenting bibliometrics and various network visualization software.
Vision based obstacle detection and mapping for unmanned surface vehicles

An unmanned surface vehicle (USV) is a speed boat that comes with a suit of sensors to understand maritime environment and navigational intelligence to know how to navigate itself autonomously. In this thesis, we focus on exploring and enhancing the perception ability of USV based on vision. Detecting and mapping obstacles in maritime environments with high accuracy and robustness are two important issues for USV in real-life applications, such as surveillance and navigation. Solutions based on vision (cameras) are more costeffective than its counter-part, radar. What’s more, vision can compensate the blind area of radar at short distances. This thesis addresses the tasks of improving the accuracy and robustness of vision based obstacle detection and mapping in open sea. For obstacle detection, first, we present a novel image-based algorithm, in that the obstacle patches are separated from sea patches using the proposed patch distinctiveness measure, named as global sparsity potentials. Secondly, we develop a real-time long range obstacle detection and tracking system for USV based on binocular stereo vision, which improves the image-based methods in reducing false positives caused by noise (white wake, waves, sun reflection, etc.) and obtaining additional information of the detected obstacles’ distances. In this system, we propose to implement the obstacle detection algorithm in an image-pyramid manner to speed up the processing, and we also propose a new solution for multiple-obstacle tracking with scale adapting and occlusion handling. Then, an approach by fusing 2D and 3D clues for further enhancing the performance of obstacle detection is proposed in the same binocular vision system. In this approach, the 2D and 3D information are combined in a weighting model, which gives more weights to the 2D detecting results when obstacles are distant, while gives more weights to the 3D detecting results when obstacles are nearby. After detecting the obstacles, it is necessary to build the obstacle map for the navigation of USV. To this end, we develop a system for obstacle mapping based on motion stereo vision, which raises the ranging ability from 500 meters in the previous binocular stereo system to 1,000 meters with a even larger baseline obtained from
Optimizing the investments in mobile networks and subscriber migrations for a telecommunication operator

We consider the context of a telecommunication company that is at the same time an infrastructure operator and a service provider. When planning its network expansion, the company can leverage over its knowledge of the subscriber dynamic to better optimize the network dimensioning, therefore avoiding unnecessary costs. In this work, the network expansion represents the deployment and/or reinforcement of several technologies (e.g., 2G, 3G, 4G), assuming that subscribers to a given technology can be served by this technology or older ones. The operator can influence subscriber dynamic by subsidies. The planning is made over a discretized time horizon while some strategic guideline requirements are required at the end of the time horizon. Following classical models, we consider that the willingness of customers for shifting to a new technology follows an S‐shape piecewise constant function. We propose a mixed‐integer linear programming formulation, improved through several valid inequalities and a heuristic algorithm. We assess the formulation numerically on real instances.
Applicability Statement for Provider Provisioned CE-based Virtual Private Networks using IPsec

This document is an applicability statement for Provider Provisioned CE-based IPsec VPNs, as discribed in [CEVPN]. This document describes how provider provisioned CE-based approaches meet the key requirements that are outlined in the PPVPN Applicability Statements Guideline document [ASGUIDE] and the key security requirements according to the template in section 8 of the VPN security framework document [SEC-FW].
Integrated optical phased arrays: LiDAR, augmented reality, and beyond

Recent integrated optical phased array architectures, results, and applications will be reviewed. Beam-steering optical phased arrays monolithically integrated with on-chip rare-earth-doped lasers and heterogeneously integrated with CMOS driving electronics will be shown. Passive integrated optical phased arrays that focus radiated light to tightly-confined spots in the near field and that generate quasi-Bessel beams will be discussed. Finally, integrated-phased-array-based visible-light holographic displays will be proposed as a scalable solution towards the next generation of augmented-reality head-mounted displays; passive near-eye holographic displays, visible-light liquid-crystal modulators, and liquid-crystal-based visible-light phased arrays will be presented.
SCOP-Aided Fragment Assembly Protein Structure Prediction

Despite some limited success, computational biology has not been able to produce reliable results in the field of protein structure prediction. Although the fragment assembly approach has shown a lot of potential, it still requires substantial improvements. Not only are its predictions largely inaccurate whenever a protein exceeds 150 amino acids in length, but also, even for short targets, inconsistencies of the energy function associated with the enormous search space too often lead to the generation of erroneous conformations. Moreover, as it relies on the creation of a large number of decoys, it is highly computational expensive. Based on its secondary structure content, a protein can generally be classified into one of the standard structural classes, i.e. all-alpha, all-beta or alpha-beta. Since structural class prediction has reached a prominent accuracy, it is proposed to amend the standard pipeline of fragment-based methods by including some constraints on the template proteins from which fragments are extracted. Using Rosetta, a state-of-the-art fragment-based protein structure prediction package, the suggested customized method was evaluated on 67 former CASP targets ranging from 47 to 149 amino acids in length. Using SCOP-based structural class annotations, improvement of structure prediction performance is highly significant in terms of GDT (53 out of 67 targets show higher scores of 6.1% on average, $\mathbf{p}-\mathbf{value} < 0.0005$).
A 3.89-GOPS/mW Scalable Recurrent Neural Network Processor With Improved Efficiency on Memory and Computation

Recurrent neural networks (RNNs) perform excellently on sequencing tasks but are severely restricted by the complex computations and intensive memory consumption due to their internal fully connected topologies, thereby making it a great challenge to implement RNNs on embedded devices. In this brief, we propose an energy-efficient RNN processor by exploiting the data locality in network compression using an innovative quantified sparse matrix encoding format. Compared with the conventional processors for compressed RNNs, more than 80% of the weight fetching and matrix–vector multiplications can be further reduced in applications, such as natural language and keyword spotting. To handle different scales of RNN models without introducing significant interactive overhead, scalable hardware architecture is presented to organize multiple processor engines in a spatial fashion with the assistance of the network cross-division strategy. Synthesized in the SMIC 40LL CMOS process, the prototype processor has a total area of 0.65 mm2 with 95.5 kB of static random-access memory capacity. Based on the simulation, this processor achieves a peak performance of 24 GOPS and dissipates 6.16-mW power with 1.1 V supply and 200 MHz. The peak energy efficiency reaches 3.89 GOPS/mW, which is state of the art among existing RNN accelerators.
Ground object recognition and segmentation from aerial image‐based 3D point cloud

Several attempts have been made to grasp three‐dimensional (3D) ground shape from a 3D point cloud generated by aerial vehicles, which help fast situation recognition. However, identifying such objects on the ground from a 3D point cloud, which consists of 3D coordinates and color information, is not straightforward due to the gap between the low‐level point information (coordinates and colors) and high‐level context information (objects). In this paper, we propose a ground object recognition and segmentation method from a geo‐referenced point cloud. Basically, we rely on some existing tools to generate such a point cloud from aerial images, and our method tries to give semantics to each set of clustered points. In our method, firstly, such points that correspond to the ground surface are removed using the elevation data from the Geographical Survey Institute. Next, we apply an interpoint distance‐based clustering and color‐based clustering. Then, such clusters that share some regions are merged to correctly identify a cluster that corresponds to a single object. We have evaluated our method in several experiments in real fields. We have confirmed that our method can remove the ground surface within 20 cm error and can recognize most of the objects.
Statistical Physics of High Dimensional Inference

To model modern large-scale datasets, we need efficient algorithms to infer a set of P unknown model parameters from N noisy measurements. What are fundamental limits on the accuracy of parameter inference, given finite signal-to-noise ratios, limited measurements, prior information, and computational tractability requirements? How can we combine prior information with measurements to achieve these limits? Classical statistics gives incisive answers to these questions as the measurement density α = N P →∞. However, these classical results are not relevant to modern high-dimensional inference problems, which instead occur at finite α. We formulate and analyze high-dimensional inference as a problem in the statistical physics of quenched disorder. Our analysis uncovers fundamental limits on the accuracy of inference in high dimensions, and reveals that widely cherished inference algorithms like maximum likelihood (ML) and maximum-a posteriori (MAP) inference cannot achieve these limits. We further find optimal, computationally tractable algorithms that can achieve these limits. Intriguingly, in high dimensions, these optimal algorithms become computationally simpler than MAP and ML, while still outperforming them. For example, such optimal algorithms can lead to as much as a 20% reduction in the amount of data to achieve the same performance relative to MAP. Moreover, our analysis reveals simple relations between optimal high dimensional inference and low dimensional scalar Bayesian inference, insights into the nature of generalization and predictive power in high dimensions, information theoretic limits on compressed sensing, phase transitions in quadratic inference, and connections to central mathematical objects in convex optimization theory and random matrix theory.
How to assess A meaningful technology-integrated tool for the reflective educators

In this paper we intend to present a tool for self-assess, support and improve reflective thinking in the educators towards the use of digital storytelling. First, we present an overview on the theoretical framework for conceptualizing a reflective educator, who needs to have a conscious approach on the practice and inquiries on the ethical consideration and important outcomes of eductional practice, with self-reflection, deep analysis of personal beliefs, and expectations on men and women and potential in general, and on learning. Educators’ expectations are self-generating, and often hard to be changed. Moreover, educators stay trapped in unexamined rulings, judgements, interpretations, conventions, potentials and hopes. Second, we describe the process of constructing a DST for approaching to the reflective practice infusing personal beliefs and values into a professional identity. Third, we describe the process of constructing the rubric, its elements, indicators and levels to value the product every storyteller made. We conclude with the self-assessment tool we want to propose in our on-going research project.
Computational Method for a Gradient-Enhanced Damage Theory

In the last years different continuum damage theories have been proposed to describe the behavior of elastic materials. Among these theories, some introduce higher order gradients of the damage variable in the constitutive model, in order to avoid the loss of well-posedness in the postlocalization range. Although such theories allow a mathematically correct modelling of the strain localization phenomena, they are usually considered complex to handle from the numerical point of view. The present work is concerned with the numerical implementation of a gradient-enhanced damage theory for elastic materials. A simple numerical technique, based on the finite element method, is proposed to approximate the solution of the resulting nonlinear mathematical problem. The coupling between damage and strain variables is circumvented by means of a splitting technique, which permit to transform the nonlinear coupled problem in a sequence of simpler linear problems. In order to evaluate the physical coherence of the model and to access the main features of the numerical method some problems and different numerical techniques are analyzed.
The Journal of Systems and Software

Code comments are crucial to program comprehension. In this paper, we propose a novel approach Con-textCC to automatically generate concise comments for Java methods based on neural networks, leveraging techniques of program analysis and natural language processing. Firstly, ContextCC employs program analysis techniques, especially abstract syntax tree parsing, to extract context information including methods and their dependency. Secondly, it ﬁlters code and comments out of the context information to build up a high-quality data set based on a set of pre-deﬁned templates and rules. Finally, ContextCC trains a code comment generation model based on recurrent neural networks. Experiments are conducted on Java projects crawled from GitHub. We show empirically that the performance of ContextCC is superior to state-of-the-art baseline methods.
A Distributed Algorithm for Large-Scale Graph Clustering

Graph clustering is one of the key techniques to understand the structures present in the graph data. In addition to cluster detection, the identification of hubs and outliers is also a critical task as it plays an important role in the understanding of graph data. Recently, several graph clustering algorithms have been proposed and used in many application domains such as biological network analysis, recommendation systems and community detection. Most of these algorithms are based on structural clustering. Yet, these algorithms have been evaluated on small graph database. In this paper, we propose DSCAN, a novel distributed structural graph clustering algorithm. We present an implementation of DSCAN on top of BLADYG, a distributed graph processing framework. We experimentally show that DSCAN significantly outperforms existing clustering algorithm in terms of scalability and performance in the case of large graphs.
Sensing Matrix Design for MMV Compressive Sensing: An MVDR Approach

Compressive sensing (CS) has been widely used in vehicular technology including compressive spectrum sensing, sparse channel estimation, and vehicular communications. The complete procedure of CS consists of sparse representation, sparse measurement, and sparse recovery. In the CS, measurement matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {\Phi }$</tex-math></inline-formula> is utilized to sample a sparse signal, while sensing matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {\Psi }$</tex-math></inline-formula> is exploited for sparse recovery. It is important to properly design the sensing matrix that significantly affects the signal recovery performance. This paper addresses the issue of the sensing matrix design for the CS with multiple measurement vectors (MMV). We first study sufficient conditions for the MMV-CS and then develop three sensing matrix design approaches in different situations. The proposed methods have their roots in the principle of minimum variance distortionless response (MVDR) beamformer. This allows us to optimally determine the sensing matrix in the framework of the MVDR, which amounts to minimizing local cumulative cross-coherence between <inline-formula><tex-math notation="LaTeX">$\mathbf {\Phi }$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\mathbf {\Psi }$</tex-math></inline-formula> while keeping the columns of <inline-formula><tex-math notation="LaTeX">$\mathbf {\Psi }$</tex-math></inline-formula> highly correlated to their counterparts of <inline-formula><tex-math notation="LaTeX">$\mathbf {\Phi }$</tex-math></inline-formula>. Simulation results are presented to verify the effectiveness of the proposed methods.
The construction of well-being

My thesis develops an alternative to orthodox theories of well-being. I argue that well-being is not a property of people or the world that exists separately from attempts to define and measure it. Instead, assessments of well-being are largely shaped by the purposes and interests of the people making the measurement. In part I, I argue that philosophy of well-being should take at face value the variety of ways that well-being is understood and measured. This points towards a pluralist account of well-being. I go on to argue that a theory of well-being is incomplete unless it says something about how to determine the extent to which life is going well for someone. I argue that the identification of well-being in individuals amounts to a form of measurement. Theories of well-being must therefore be theories of the measurement of well-being. In part II, I look closely at three approaches to defining and measuring well-being—in terms of objective goods, preferences, and subjective experience. I argue that, in each case, appraisals of whether someone is doing well or not, and how well they are doing, depend on the context of measurement, the tools used to measure well-being, and the goals and purposes of the people making the assessment. In part III, I propose that well-being should not be treated as a property of people or of the world, but rather something which is largely constructed in the process of measurement. I draw on contemporary model theories of measurement to argue that well-being is best understood as representing a relation between the person whose well-being is being measured, a measuring instrument, and the environment. This relation is modelled by the people who are making the measurement in order to produce information about well-being and ascriptions of well-being are therefore unavoidably attitude dependent.
Hazard and Operability Analysis(HAZOP) of a Plastic Manufacturing Plant at Karachi, Pakistan.

The basic objective of this study is to analyze the hazards and operational problems of plastic manufacturing plant and provide some recommendation to operate plant in a hazard free environment. HAZOP technique is used to study the whole process of the unit from the storage Silos (Propylene) to Winding machine (collection point of plastic film). Initially eight (08) nodes were selected and analyzed qualitatively as per process flow diagram (PFD) and piping and instrumentation diagram (P&IDs) of the plant. The analysis includes the deviation of the process parameters from the standard conditions, the causes of the deviation, the resulting consequences, and the existing safety measures and finally recommendations were suggested to avoid the damage and loss to equipment, product and personnel. Result shows 25 causes of deviations of process parameters, 19 consequences out of which 53% of the consequences were related to the product, 42 % were related to the equipment and 5% were related to the surrounding and personnel. Moreover, about 11 different existing safety measures were installed and practiced by the company and 16 recommendations were suggested.
Energy efficient downlink massive MIMO: Is 1-bit quantization a solution ?

Massive MIMO aims to build wireless base stations with hundreds of coherently operating antennas serving tens of single antenna users in order to improve both the transmission capacity by a factor 10-50 and the energy-efficiency trade-off by up to a thousand times. Pre-coding at the base station has been proposed to efficiently implement digital beamforming. It implies a high signal dynamic range and therefore a power backoff resulting in less energy-efficiency. One-bit quantized Zero-Forcing precoding has been proposed to efficiently handle the RF front-end when the array is implemented with so many antennas. In this paper, we analyze the energy-efficiency of the quantized Zero-Forcing precoded systems for a large number of users and a massive MIMO base station.