Miracle-dz/UniFAR-FTUs huggingface dataset:
Altmetrics Spectrum of Bioinformatics Literature

Social media is increasingly used for the dissemination of scientific publications by most science, technology and management (STM) journals. Bioinformatics is one of the new areas of research emerging in the subject tree. The role of social media in increasing the awareness of published works in bioinformatics has not been previously explored. Here, a qualitative analysis of the highest trending works in bioinformatics along with a correlation analysis with their social media metrics (altmetrics) is made. A comprehensive search has been performed for articles related to the bioinformatics using the SCOPUS Database. Further, a highly-cited article with more than 1000 citations has been filtered out. Another comprehensive search for bioinformatics articles using the Altmetric database was conducted. The Altmetric database provides a total weighted score of all online mentions for an article received on Facebook, Twitter, blogs, and mainstream media sources. There is a significant correlation has been found between the patent mention and citations. Altmetrics Spectrum of Bioinformatics Literature: An Analysis of Most Cited Literature
Participatory Design of a Social Robot Toolkit for and with Adults with Autism

Autism impacts around 5 million people in the EU (Autism-Europe). Research has shown that social robots, due to their deterministic nature, simplified appearance and technological capabilities, can enable therapy or become assistive technology for empowering autistic individuals with household activities. Consequently, toolkits have emerged for prototyping social robots. Regarding such toolkits, there is a methodological, inclusion gap: there is no comprehensive co-design process to include cognitively disadvantaged users in decision-making regarding robots’ fundamental design choices. To overcome this gap and empower autistic adults to truly design their own (non-preprogrammed) robots, this research explores a social robot toolkit’s well-scaffolded participatory design.
Optimal location management in LTE networks using evolutionary techniques

Wireless network is considered a vital enabler in the world of information technology, specifically, LTE and LTE advanced networks, which are the latest technologies owing to their fast speed, robustness, and large bandwidth. However, in spite of the aforementioned advancements, signaling overhead poses critical challenges in terms of network availability, especially those caused by location management messages which are related to users’ mobility behavior. This paper seeks to address the problem of signaling overhead caused by the location management messages specifically, tracking area update (TAU) and paging by deploying three evolutionary algorithms, namely particle swarm optimization (PSO), artificial bee colony (ABC), and gravitational search algorithm (GSA). The deployed algorithms guarantee yielding the minimum values of the signaling overhead for TAU, paging, and the battery power consumption of the user. It is shown that ABC‐based algorithm has faster convergence and better signaling overhead when compared with other implemented algorithms. Moreover, the measured relative standard deviation (RSD) value of all algorithms shows low uncertainty of around 1% for the objective function and 3% for the paging, TAU, and power. Hence, the three applied optimization algorithms have proven to be efficient and reliable for solving the problem in a large‐scale environment.
Impact of Display Shapes on Symmetric 360° Video Communication For Remote Collaboration

– Field-of-view limitation has been a long-standing issue in video communication systems. With the development of omnidirectional panoramic technology, the omnidirectional camera, which can provide a 360 ° ﬁeld-of-view, has been getting more and more popular within the last few years. Previous research indicated that one-way video communication systems with a wider ﬁeld-of-view improve task efﬁciency with fewer collisions. Therefore, we propose to utilize omnidirectional cameras in a symmetrical video communication system and study how the shape of the display affects the performance of the panoramic video communication in remote collaboration. By conducting experiments with two conditions (omnidirectional camera + spherical display vs. omnidirectional camera + horizontally placed 2D ﬂat display), we analyzed what is the impact of display types on 360 ° video communication. Our results show that the spherical display is slightly better and both types of displays have their advantages and disadvantages. The ﬁndings contribute to our understanding of how to design an environment for remote collaboration with a 360 ° shared view of spaces.
Deep Learning on Graphs using Graph Convolutional Networks

Graphs are a powerful way to model network data with the objects as nodes and the relationship between the various objects as links. Such graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge to this task is that graphs are non-Euclidean structures which means that they cannot be directly used with ML techniques because ML techniques only work with Euclidean structures like grids or sequences. In order to overcome this challenge, the graph structure first needs to be encoded into an equivalent Euclidean representation in the form of a low-dimensional vector. This low-dimensional vector is called an embedding vector, and the encoding process is called node embedding. Traditionally, user-defined heuristics and matrix- factorization based methods were used for node embedding. However, these methods are slow and perform poorly on large and complex graphs. During the recent years, various ML techniques have been developed that learn the encoding of the graph automatically, and in a faster and more efficient way. A few of these techniques called Graph Convolutional Networks (GCNs) use variants of the convolutional neural networks adapted for graphs, and are implemented using deep neural networks. The aim of this project is two-fold. Firstly, to develop a unified framework focusing on three major GCN techniques in order to analyze, evaluate, and compare their performance on select benchmark datasets for the task of node classification. And secondly, to implement a new aggregator for one of the techniques — GraphSAGE, and compare the performance of the aggregator with the existing GCN methods as well as the other aggregators provided by GraphSAGE.
Effectiveness of a proposed system design on academic management of Kampala International University: design, development and implementation

This study aimed at answering questions concerning the significant effect of the proposed system design on the academic management of KIU in terms of supportability, reliability, maintainability and availability. Using a quasi-experimental design on a total sample of 750 respondents (i.e. the Users, Academic Staff and Students) and t-sample test, the study revealed that there was a significant effect by the proposed design on the academic management system. The study thus recommends that all stakeholders should be trained to interact and use it which will save a lot of time for the students, academic staff and university branches in processing academic data. Key Words : Proposed Academic management System, Supportability, Reliability, availability, Maintainability
Attitude controller design for the aerial trees-pruning robot based on nonsingular fast terminal sliding mode

In this article, the attitude control problem of a new-designed aerial trees-pruning robot is addressed. During the tree cutting process, the aerial trees-pruning robot will be disturbed by unknown external disturbances. At the same time, the model uncertainties will also affect the attitude controller. To overcome the above problems, an attitude controller is designed with a nonsingular fast terminal sliding mode method. First, the extended state observer is designed to estimate the modeling uncertainties and unknown disturbances. Then, the extended state observer-based nonsingular fast terminal sliding mode controller can make the tracking error of the attitude converge to zero in a finite time. Finally, a control allocation matrix switching strategy is proposed to solve the problem of the change of the aerial robot model in the cutting process. The final simulation and experimental results show that the extended state observer-based nonsingular fast terminal sliding mode controller designed in this article has good attitude control performance and can effectively overcome the modeling uncertainties and unknown disturbances. The attitude controller and control allocation matrix switching strategy ensure that the attitude angles of the aerial robot can quickly track the reference signals.
Performance Analysis of Full-Duplex Amplify-and-Forward Relay System with Hardware Impairments and Imperfect Self-Interference Cancellation

In this paper, we analyze the performance of a full-duplex (FD) amplify-and-forward (AF) relay system with imperfect hardware. Besides the aggregate hardware impairments of the imperfect transceiver, we also consider the impact of residual self-interference (RSI) due to imperfect cancellation at the FD relay node. An analytical framework for analyzing the system performance including exact outage probability (OP), asymptotic OP, and approximate symbol error probability (SEP) is developed. In order to tackle these impacts, we propose an optimal power allocation scheme which can improve the outage performance of the FD relay node, especially at the high signal-to-noise ratio (SNR) regime. Numerical results are presented for various evaluation scenarios and verified using the Monte Carlo simulations.
Portfolio Construction Using Predictive Linear Model – An Adaptive Multi-Objective Approach

The purpose of this study is to incorporate some of the influential findings in the forecasting literature in an integrated framework to examine whether a real-time optimizing investor can benefit from the stock market by allocating assets based on a predictive model that only uses industry portfolio and interest rate data as predictors. The proposed method aims to allow economic performance measures to have an impact on all steps of model building from variable selection to model combination without undermining the statistical performance measure. The predictors/models are selected from almost 300 variables by a multi-objective genetic algorithm considering both statistical and economic measures. I chose a subset of models from the Pareto-optimal frontier using a number of heuristic methods from the multi-criteria decision making (MCDM) literature and the concept of knee-point of a curve. The forecast of the next period is obtained by combining the forecasts of the selected subset of predictive linear regressions using a Bayesian variable selection and model averaging method called Stochastic Search Variable Selection (SSVS). The investor’s utility function is used to obtain the weight of a risky asset based on the output of the forecasting model. All aforementioned steps only use data up to the current time and before the forecasting time. The results are compared to common benchmarks such as the buy-and-hold strategy and additional benchmarks that are based on the findings of previous literature. The findings indicate that using the proposed approach can improve the portfolio performance measures relative to all benchmarks.
A Relational Theory of Locality

In many areas of program and system analysis and optimization, locality is a common concept and has been defined and measured in many ways. This article aims to formally establish relations between these previously disparate types of locality. It categorizes locality definitions in three groups and shows whether and how they can be interconverted. For the footprint, a recent metric, it gives a new measurement algorithm that is asymptotically more time/space efficient than previous approaches. Using the conversion relations, the new algorithm derives with the same efficiency different locality metrics developed and used in program analysis, memory management, and cache design.
Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.
Scheduling óptimo de procesos Batch de duración interdependiente

espanolCuando distintos equipos similares trabajan de manera concurrente compartiendo un recurso, los perfiles de trabajo de cada uno se pueden ver afectados por un consumo simultaneo. De esta manera, surge un problema de programacion de las tareas o scheduling, para encontrar la planificacion temporal y de asignacion optima que tenga en cuenta estas interdependencias entre los equipos. Este problema se puede complicar aun mas si la duracion de los procesos depende de los tipos de producto a tratar. En este trabajo se presenta una herramienta que optimiza el scheduling de una seccion de esterilizado de una planta de conservas, donde el vapor se comparte entre distintos autoclaves paralelos. Los autoclaves se emplean en el proceso de esterilizado de las latas de conserva, que tienen distintos perfiles de calentamiento dependiendo del producto que contienen y de la forma de las mismas. Con un enfoque de horizonte deslizante, la herramienta puede ser ejecutada en tiempo real y asi poder adaptarse a los cambios que muy probablemente se produciran a lo largo de los turnos de trabajo. El modelo se genera como un problema de programacion lineal mixta-entera, y con un planteamiento de slots continuos. Se presenta una mejora de los algoritmos de precedencia que se han venido utilizando en la literatura. Finalmente, se muestran con un ejemplo los resultados obtenidos que demuestran la posibilidad de su implantacion real en la planta. EnglishWhen similar pieces of equipment work concurrently sharing one resource, the working profiles can be affected by the consumption of the other equipment. Therefore, a scheduling problem arises in order to find the best temporal planification taking into account these alterations. This problem could get more complex if the profiles are not fixed, but depends on the products introduced in the equipment. In this work the authors present one tool to optimize the scheduling in a sterilization section inside a tuna canning plant, keeping in mind the alterations that the heating procedure of the autoclaves suffer if two or more autoclaves start at the same time. This tool has a moving horizon approach in order to be run iteratively, and to adapt the solution to the actual state of the plant.
Food and Nutrient Consumption Patterns of the Korean Adult Population by Income Level - 2001 National Health and Nutrition Survey

To explore the relationship between economic status and food and nutrient intake patterns, the 2001 National Health and Nutrition Survey result was analyzed. Dietary intake data of 6,978 Korean adults of 20 years and older who participated in the 2001 National Health and Nutrition Survey were used along with their demographic data. Economic status of the subjects was classified into following 4 groups based on the self-reported average monthly income of household with reference to the minimum monthly living expenses (MLE) in 2001 : low < 100% MLE ≤ middle < 200% MLE ≤ high < 300% MLE ≤ higher. Individuals in the higher income class had significantly higher mean intake for most of the nutrients including energy, protein, carbohydrate, fat, calcium, thiamin, riboflavin, niacin and vitamin C, and a higher percentage of energy intake from fat. In addition, they consumed more animal foods including meats, eggs, fish/shellfish, milk/dairy products and fats. On the other hand, the mean intakes of individuals in the lower economic class for calcium, vitamin A, and riboflavin were lower than 75% of RDAs. And, there was a predominant difference in contribution of fat to total energy intake among the groups of different economic status. These results showed that household income is an important factor influencing the food and nutrient intake patterns of the Korean adult population. Although individuals at different age classes may respond differently to a change in economic status, developing and implementing nutrition policy and intervention programs for those nutritionally vulnerable groups should consider the economic status as an important factor to customize and differentiate the content of the program.
Acquired and inherited lipodystrophies.

to the editor: The pathogenesis and management of lipodystrophy in patients with human immunodeficiency virus (HIV) infection are discussed in Garg’s review article (March 18 issue). 1 However, the author should have noted that several lines of evidence point toward nucleoside reverse-transcriptase inhibitors in general, and the mitochondrial toxicity of stavudine in particular, as having a contributory role in this form of fat wasting. Clinical trials have demonstrated that the presence or absence of stavudine in the nucleoside reversetranscriptase–inhibitor “backbone” of antiretroviral regimens has an influence on the risk of lipoatrophy.2 Nucleoside reverse-transcriptase inhibitors may inhibit gamma-polymerase, the enzyme necessary for the replication of mitochondrial DNA. Consistent with such mitochondrial toxicity, reduced levels of mitochondrial DNA have been linked with the use of stavudine in HIV-infected patients.3 Ultrastructural abnormalities of mitochondria within the adipocytes of subjects with lipoatrophy also point toward mitochondrial toxicity related to nucleoside reverse-transcriptase inhibitors in the pathogenesis of lipoatrophy.4 Finally, it has been demonstrated that lipoatrophy can be improved by switching patients from stavudine to other agents.5 Therefore, this option should be considered for HIV-infected patients with lipodystrophy.
Verification problems for timed and probabilistic extensions of Petri Nets

In the first part of the thesis, we prove the decidability (and PSPACE-completeness) of the universal safety property on a timed extension of Petri Nets, called Timed Petri Nets. Every token has a real-valued clock (a.k.a. age), and transition firing is constrained by the clock values that have integer bounds (using strict and non-strict inequalities). The newly created tokens can either inherit the age from an input token of the transition or it can be reset to zero. In the second part of the thesis, we refer to systems with controlled behaviour that are probabilistic extensions of VASS and One-Counter Automata. Firstly, we consider infinite state Markov Decision Processes (MDPs) that are induced by probabilistic extensions of VASS, called VASS-MDPs. We show that most of the qualitative problems for general VASS-MDPs are undecidable, and consider a monotone subclass in which only the controller can change the counter values, called 1-VASS-MDPs. In particular, we show that limit-sure control state reachability for 1-VASS-MDPs is decidable, i.e., checking whether one can reach a set of control states with probability arbitrarily close to 1. Unlike for finite state MDPs, the control state reachability property may hold limit surely (i.e. using an infinite family of strategies, each of which achieving the objective with probability ≥ 1−ε, for every ε > 0), but not almost surely (i.e. with probability 1). Secondly, we consider infinite state MDPs that are induced by probabilistic extensions of One-Counter Automata, called One-Counter Markov Decision Processes (OC-MDPs). We show that the almost-sure {1,2,3}-Parity problem for OC-MDPs is at least as hard as the limit-sure selective termination problem for OC-MDPs, in which one would like to reach a particular set of control states and counter value zero with probability arbitrarily close to 1.
DIVORCE PREDICTION USING CORRELATION BASED FEATURE SELECTION AND ARTIFICIAL NEURAL NETWORKS

Within the scope of this research, the divorce prediction was carried out by using the Divorce Predictors Scale (DPS) on the basis of Gottman couples therapy. Of the participants, 84 (49%) were divorced and 86 (51%) were married couples. Participants completed the “Personal Information Form” and “Divorce Predictors Scale”. In this study, the success of DPS, was investigated using Multilayer Perceptron Neural Network and C4.5 Decision tree algorithms. In addition, the study also aims to find the most significant features/items in the Divorce Predictors Scale that affect the divorce. The most effective 6 features and their values of significance obtained by applying the correlation-based feature selection method on the divorce data set. When we look at these features, they are related to creating a common meaning and failed attempts to repair, love map and negative conflict behaviors. When the direct classification methods were applied to the divorce data set, the highest success rate was 98.23% obtained with the RBF neural network. After selecting the most effective 6 features using the correlation-based feature selection method on the same data set, the highest accuracy rate obtained was 98.82% with ANN. According to the results, DPS can predict divorce. Family counselors and family therapists can use this scale for contribute to the preparation of case formulation and intervention plan. Also it can be said that the divorce predictors in the Gottman couples therapy were confirmed in the Turkish sampling.