Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach

Classification problems often have a large number of features in the data sets, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the performance. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing feature selection algorithms treat the task as a single objective problem. This paper presents the first study on multi-objective particle swarm optimization (PSO) for feature selection. The task is to generate a Pareto front of nondominated solutions (feature subsets). We investigate two PSO-based multi-objective feature selection algorithms. The first algorithm introduces the idea of nondominated sorting into PSO to address feature selection problems. The second algorithm applies the ideas of crowding, mutation, and dominance to PSO to search for the Pareto front solutions. The two multi-objective algorithms are compared with two conventional feature selection methods, a single objective feature selection method, a two-stage feature selection algorithm, and three well-known evolutionary multi-objective algorithms on 12 benchmark data sets. The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions. The first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm. It achieves comparable results with the existing three well-known multi-objective algorithms in most cases. The second algorithm achieves better results than the first algorithm and all other methods mentioned previously.
Session introduction

The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health-related terms to integrate consumer-friendly synonyms for medical terminologies.
Immersive Analytics

Immersive Analytics is an emerging research thrust investigating how new interaction and display technologies can be used to support analytical reasoning and decision making. The aim is to provide multi-sensory interfaces that support collaboration and allow users to immerse themselves in their data in a way that supports real-world analytics tasks. Immersive Analytics builds on technologies such as large touch surfaces, immersive virtual and augmented reality environments, sensor devices and other, rapidly evolving, natural user interface devices. While there is a great deal of past and current work on improving the display technologies themselves, our focus in this position paper is on bringing attention to the higher-level usability and design issues in creating effective user interfaces for data analytics in immersive environments.
Transportan-derived cell-penetrating peptide delivers sirna to inhibit replication of influenza virus in vivo

TP and efficient delivery of siRNA were tested in 293T, MDCK, RAW, and A549 cells. Furthermore, T9(dR)- and TP-delivered siRNAs against nucleoprotein (NP) gene segment of influenza virus (siNP) were studied in both cell lines and mice. Results: Gel retardation showed that T9(dR) effectively condensed siRNA into nanoparticles sized between 350 and 550 nm when the mole ratio of T9(dR) to siRNA was $ 4:1. In vitro studies demonstrated that T9(dR) successfully delivered siRNA with low cellular toxicity into several cell lines. It was also observed that T9(dR)-delivered siRNAs inhibited replication of influenza virus more efficiently as compared to that delivered by TP into the MDCK and A549 cells. It was also noticed that when given a combined tail vein injection of siNP and T9(dR) or TP, all mice in the 50 nmol siNP group infected with PR8 influenza virus survived and showed weight recovery at 2 weeks post-infection. Conclusion: This study indicates that T9(dR) is a promising siRNA delivery tool with potential application for nucleotide drug delivery.
Operational Ocean Modelling with the Harvard Ocean Prediction System

Abstract : In close collaboration with a team of Massachusetts Institute of Technology an ocean forecasting system has been made available within TNO. The Harvard Ocean Prediction System (HOPS) is a large numerical ocean simulation system which produces forecasts for the maritime environment. This report documents the process of large scale ocean modelling and explains the choices that have to be made during this process. The consequences of these choices are discussed, as well as the operational uses of the model outcome.
Clustering: Science or Art?

We examine whether the quality of different clustering algorithms can be compared by a general, scientifically sound procedure which is independent of particular clustering algorithms. We argue that the major obstacle is the difficulty in evaluating a clustering algorithm without taking into account the context: why does the user cluster his data in the first place, and what does he want to do with the clustering afterwards? We argue that clustering should not be treated as an application-independent mathematical problem, but should always be studied in the context of its end-use. Different techniques to evaluate clustering algorithms have to be developed for different uses of clustering. To simplify this procedure we argue that it will be useful to build a "taxonomy of clustering problems" to identify clustering applications which can be treated in a unified way and that such an effort will be more fruitful than attempting the impossible--developing "optimal" domain-independent clustering algorithms or even classifying clustering algorithms in terms of how they work.
Cross-sectional and longitudinal assessment of muscle from regular chest computed tomography scans: l1 and pectoralis muscle compared to l3 as reference in non-small cell lung cancer

Background: Computed tomography (CT) is increasingly used in clinical research for single-slice assessment of muscle mass to correlate with clinical outcome and evaluate treatment efficacy. The third lumbar level (L3) is considered as reference for muscle, but chest scans generally do not reach beyond the first lumbar level (L1). This study investigates if pectoralis muscle and L1 are appropriate alternatives for L3. Methods: CT scans of 115 stage IV non-small cell lung cancer patients were analyzed before and during tumor therapy. Skeletal muscle assessed at pectoralis and L1 muscle was compared to L3 at baseline. Furthermore, the prognostic significance of changes in muscle mass determined at different locations was investigated. Results: Pearson’s correlation coefficient between skeletal muscle at L3 and L1 was stronger ( r = 0.90, P  0.001) than between L3 and pectoralis muscle ( r = 0.71, P  0.001). Cox regression analysis revealed that L3 (HR 0.943, 95% CI: 0.92–0.97, P  0.001) and L1 muscle loss (HR 0.954, 95% CI: 0.93–0.98, P  0.001) predicted overall survival, whereas pectoralis muscle loss did not. Conclusion: L1 is a better alternative than pectoralis muscle to substitute L3 for analysis of muscle mass from
NONLINEAR MODEL IDENTIFICATION AND ADAPTIVE CONTROL OF VARIABLE SPEED WIND TURBINE USING RECURRENT NEURAL NETWORK

The best configuration for generating electricity energy form a variable-speed wind energy conversion system (WECS) is using double-output induction generator (DOIG). Controlling this system in order to optimum performance on maximum extracting power from wind in each speed were attracted the attention of many researchers. This kind of generators use a rectifier and inverter know as static Kramer drive (SKD) and changes on the firing angle of the inverter can control the operation of the generator. Achieving above purpose is difficult because the behavior of this system under classic controller is very time variant and nonlinear and need to an adaptive controller is presented. With regard to high capability of neural network in control subject, in this paper one structure of this kind of networks for controlling wind energy conversion system was proposed. This controller uses recurrent neural network based on approximation of non-linear autoregressive moving average (NARMA) model. Feasibility and effectiveness of controller are demonstrated by simulation results. Different cases, such as applying a distinct disturbance, applying noise to system and Parameters variations and uncertainties of the system in order to study the ability of proposed controllers, were considered.
A FUZZY WAVELET NEURAL NETWORK LOAD FREQUENCY CONTROLLER BASED ON GENETIC ALGORITHM

In this paper, a self tuning load frequency controller based on Fuzzy Wavelet Neural Network (FWNN) and Genetic Algorithm (GA) is developed to quench the deviations in frequency and tie line power due to load disturbances in an interconnected power system. The error between desired system output and output of control object is employed to tune the network parameters. Tuning rule is accomplished based on GA approach by minimizing a compound of control error. For the purpose of the proposed method’s evaluation, the proposed method is applied to a two area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic PI controller. Moreover, the robustness of the proposed method is tested against change of parameters. The simulation studies show that the designed controller by proposed method has a very desirable dynamic performance, better operation and improved system parameters such as settling time and step response rise time even when the system parameters change.
A perturbed gene network containing PI3K/AKT, RAS/ERK, WNT/ β -catenin pathways in leukocytes is linked to ASD genetics and symptom severity

A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity. Abstract Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1–4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes that are highly copy, download text and data-mine the content such documents, the purposes of academic research, subject to the Conditions of expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/ β catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.
Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems

Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.
LJMU Research Online

: 1 Background: Excessive energy intake that contributes to overweight and obesity is arguably 2 driven by pleasure associated with the rewarding properties of energy-dense palatable foods. 3 It is important to address influences of external food cues in food-abundant societies where 4 people make over 200 food related decisions each day. This study experimentally examines 5 protective effects of a mindful attention induction on appetitive measures, state craving and 6 food intake following exposure to energy-dense foods. 7 Method: Forty females were randomly allocated to a standard food-cue exposure condition 8 in which attention is brought to the hedonic properties of food or food-cue exposure 9 following a mindful attention induction. Appetitive reactions were measured pre, post and ten 10 minutes after post-cue exposure, after which a plate of cookies was used as a surreptitious 11 means of measuring food intake. 12 Results: Self-reported hunger remained unchanged and fullness significantly increased for 13 the mindful attention group post-cue exposure whereas hunger significantly increased for the 14 standard attention group and fullness remained unchanged. There was no significant between- 15 group difference in state craving post-cue exposure and ten minutes later. Significantly more 16 cookies were eaten by the standard attention group ten minutes post-cue exposure although 17 no significant between-group differences in appetitive and craving measures were reported at 18 that time. 19 Conclusion: 20
Text mining as integration of several related research areas: report on KDD's workshop on text mining 2000

In this paper we give an overview of the KDD'2000 Workshop on Text Mining that was held in Boston, MA on August 20, 2000. We report in detail on the research issues covered in the papers presented at the Workshop and during the group discussion held in the final session of the Workshop.
Does “hospital loyalty” matter? Factors related to the intention of using a mobile app

Purpose This study aimed to explore the association between hospital loyalty, perceived usefulness of a mobile app, perceived ease of use of that mobile app, and satisfaction with the app’s use as well as predicting patients’ intended use of the app. Patients and methods Purposive sampling was adopted in a cross-sectional survey. The participants were outpatients at the traditional Chinese medicine departments of three hospitals in northern Taiwan (n=125). The self-report questionnaire comprised information about sociodemographics and scales related to hospital loyalty, perceived usefulness of the mobile app, its perceived ease of use, satisfaction with its use, and its usage intention. The data were analyzed using structural equation modeling. Results The four factors explained 70% variance in usage intention. The perceived usefulness of the mobile app directly and indirectly affects their usage intention, but its perceived ease of use had only indirect effects on the usage intention. Perceived usefulness and perceived ease of use influence the usage intention through satisfaction. The women’s hospital loyalty does not directly affect the usage intention of the mobile app, but indirectly affects it through perceived usefulness and perceived ease of use of the mobile app. Conclusion Intended use of the app by women is mainly related to their experience of which is usefulness, ease of use and satisfaction of service are the most important factors contributing to continuous use. Hospital loyalty does not directly affect intention to use as expected. The influence of loyalty must be related to the patients’ perception of the product, in terms of usefulness, ease to use, and satisfaction. The finding is helpful to understand patients’ preference and support their behavioral adherence.
MODEL SEMIVARIOGRAM DAN ESTIMASI ORDINARY KRIGING UNTUK NILAI UJIAN NASIONAL SMP DI KOTA BANDUNG DAN CIMAHI

Proses spasial adalah proses yang dibangun oleh barisan peubah acak yang memiliki indeks parameter berupa lokasi-lokasi di suatu kawasan tertentu. Untuk mengukur kebergantungan linear antara satu observasi dengan observasi yang terpisahkan oleh suatu jarak h, maka digunakan semivariogram. Semivariogram dapat dihitung melalui semivariogram eksperimental yang kemudian akan dicocokkan dengan model-model semivariogram. Beberapa model semivariogram yang sering digunakan untuk merepresentasikan korelasi spasial adalah model eksponensial, spherikal, dan gauss. Model tersebut akan digunakan dalam kriging untuk menginterpolasi variabel terregional yang belum diketahui dan berada di sekitar observasi. Pada tesis ini, penulis menggunakan data nilai Ujian Nasional SMP di Kota Bandung dan Cimahi yang meliputi mata pelajaran Bahasa Indonesia, Bahasa Inggris, dan Matematika. Kemudian akan dibahas cara mendapatkan semivariogram eksperimental, memilih model-model semivariogram yang cocok untuk data tersebut, dan peta kontur hasil estimasi Ordinary Kriging.
Denial of Service Attacks: Tools and Categories

DoS attacks cause severe damage to services available online. This kind of attacks try to prevent legitimate users from using their rights to online services by bringing down servers. Issues in Internet security provide the hackers with ways to attack network systems. These attacks can be performed using many tools and techniques. This paper gives an overview on DoS attacks categories and tools. Also, it defines the most known Dos attack incidents and explains the IP Spoofing. Keywords— DoS Attack; RDoS Attack; IP Spoofing.
Measuring the momentum distribution of a lattice gas at the single-atom level

In this thesis, we report the demonstration of a detection technique able to probe, with a single-atom sensitivity, the momentum distribution of an ultracold gas loaded inside a 3D optical lattice. We have developed a micro-channel plate detector, able to electronically probe clouds of metastable Helium-4. The gas is detected after a time-of-flight of 325ms, long enough to reach the far-field expansion, where the spatial distribution of the cloud can be mapped to the asymptotic momentum distribution. By putting ourselves in a regime where the lattice filling is close to unity, the atomic collisions in the first instant of the expansion become negligible, so that the asymptotic momentum distribution is equal to the in situ momentum distribution. We experimentally demonstrate this equality, by comparing our far-field measurements with the momentum distribution calculated from the Bose-Hubbard Hamiltonian, thanks to ab initio quantum Monte Carlo simulations. We show a good agreement with the theory over more than 3 orders of magnitude in density. Those simulations are calculated with our experimental parameters, the temperature being the only adjustable variable. We then use this comparison to perform a precise thermometry of the lattice gas, allowing us to explore the superfluid-normal gas transition through a direct measurement of different quantities, like the condensed fraction or the two-particles correlation function.
Structural plasticity of actin-spectrin membrane skeleton and functional role of actin and spectrin in axon degeneration

Axon degeneration sculpts neuronal connectivity patterns during development and is an early hallmark of several adult-onset neurodegenerative disorders. Substantial progress has been made in identifying effector mechanisms driving axon fragmentation, but less is known about the upstream signaling pathways that initiate this process. Here, we investigate the behavior of the actin-spectrin-based Membrane-associated Periodic Skeleton (MPS), and effects of actin and spectrin manipulations in sensory axon degeneration. We show that trophic deprivation (TD) of mouse sensory neurons causes a rapid disassembly of the axonal MPS, which occurs prior to protein loss and independently of caspase activation. Actin destabilization initiates TD-related retrograde signaling needed for degeneration; actin stabilization prevents MPS disassembly and retrograde signaling during TD. Depletion of βII-spectrin, a key component of the MPS, suppresses retrograde signaling and protects axons against degeneration. These data demonstrate structural plasticity of the MPS and suggest its potential role in early steps of axon degeneration.
Learning and Reasoning for Cultural Metadata Quality

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