Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Detection via Eclarans Algorithm

Data mining is extrication of concealed prescient information from huge dataset & furthermore an amazing latest innovation with incredible ability to break down significant data in the information warehouses. In this paper, Data mining is used to extract data from complete set of sample. Data objects which don't agree to normal conduct or prototype of data set known as anomaly detection. We want to detect this anomaly by applying ECLARANS-DB-scan clustering. Outlier Detection in dataset has various implementations, for example, fraud recognition, modified marketing, quest for terrorism. In any case, utilization of Outlier Detection for different reasons for existing isn't a simple undertaking. We introduce a framework for anomaly detection through ECLARANS-DB-scan clustering because this method is much efficient and easy as compared to the existing methods. We break down method to plainly recognize digital information from outliers.
Intersection Coordination of Mixed Autonomous and Human Vehicles with Heterogeneous Social Preferences

—Current methods for autonomous management assume full collaboration between autonomous vehicles, using a ﬁrst-come, ﬁrst-serve (FCFS) ordering to manage incoming autonomous vehicles at an intersection. In this work, we present a coordination policy that swaps agent ordering to increase the system-wide performance while taking into account communicating and non-communicating vehicles, as well as, heterogeneous social preferences. By considering an agent’s Social Value Orientation (SVO), a social psychology metric for their willingness to help another vehicle, the central coordinator can enable system-wide optimization across agents will ensuring that each individual utility increases. The FCFS-SVO algorithm is both computationally tractable and accounts for a variety of real-world agent types. In addition, we show that the proportion of human drivers, as well as, the distribution of pro-social and egoistic vehicles in the system can have a prominent effect on the performance of the system.
Exploring Smart Speakers as Cognitive Tracking Tools

Dementia has no cure but some symptoms can be treated. Therefore, early detection could greatly improve a person’s quality of life. Smart speakers offer a potential platform for voice user interface (VUI) tools that can aid the diagnosis of cognitive decline and dementia in older adults. A cognitive screener was implemented as a VUI with an accompanying mockup of a clinical dashboard. Experts were inter-viewed about their perceptions of both artifacts. We evaluated our prototype with seniors via a lab-based usability study and a 5-week field deployment. Results show the approach is promising and reveal various design implications for future fully implemented smart speaker based cognitive tracking systems.
CLASSIFYING FISH BY SPECIES USING CONVOLUTIONAL NEURAL NETWORKS

There is an increasing need for automated ﬁsh classiﬁ-cation to help properly identify ﬁsh species and characteristics in a standardized, non-invasive, and cost-effective manner. Machine learning is a promising method to do this. In this paper, we present the results of a convolutional neural network (CNN) used to identify ﬁsh species across datasets. Our proposed model improves on a previously-built model by Rathi et al. (2018). The performance of our improved model is demonstrated with real-world data from a research organization called The Nature Conservancy.
Advanced Velocity Planning for Robotic Systems

—Many industrial tasks ( e.g. , welding) require advanced velocity planning to achieve the target ﬁnal product quality. In this paper, a novel approach is proposed to perform robotic trajectory planning. The developed algorithm exploits Fuzzy Logic (FL) to relate path features (such as curves or sharp edges) to the proper execution velocity. Such a computed velocity reference is then used as an input for Dynamical Movement Primitives (DMP), providing the reference signals to the robot controller. The main improved methodology features are: path-based velocity planning, extension of DMP to variable velocity reference, the smoothing of the velocity reference including robot velocity/acceleration limits.
The Effects of Organizational Justice, Organizational Reputation and Self-Esteem on Job Seeker Attraction: A Moderated Mediation Model

Article History Article Received: 24 July 2019 Revised: 12 September 2019 Accepted: 15 February 2020 Publication: 11 April 2020 Abstract: The present study examined the roles of organizational justice, organizational reputation perceptions and self-esteem in determining the expectations of success in attracting job seekers to an organization. We hypothesized that organizational reputation mediated the relationship between organizational justice and job seeker attraction and that self-esteem then moderated the relationship. Our sample consisted of 327 respondents undergoing internships who assumed the role of job seekers. The study required the respondents to assess the organizations in which they were undergoing internshipssince they would havegained experience and knowledge about the organizations during their internships and thus would have formed informed opinions about organizational justice, organizational reputation, and self-esteem and their attractiveness as job seekers. A moderated mediation model indicated that organizational reputation mediates the organizational justice and job seeker attraction relationship and that it was stronger for undergraduates with high self-esteem. The findings showed that organizations could improve organizational reputation through organizational justice,which would attract job seekers, especially those with high self-esteem. It was suggested that rather than focusing solely on undergraduate competency, the government and stakeholders should collaborate with each other to assist organizations to promote organizational justice, organizational reputation,as well asimprove self-esteem of undergraduates, to attract undergraduates to apply for jobswhich is mitigates the choosiness attitude.
The Human Wearable Perception System: a Human-Robot Collaboration Application

The human-robot collaboration (HRC) is more and more requested in several occupational fields, e.g., in assemblyline factories, in medical rehabilitation centers for patients or for domestic assistance. Although today’s robots are equipped with sophisticated sensors, their collaborative success is not always guaranteed by the onboard technologies. The perception that the robots have of the human collaborators during a shared task is very limited due to unpredictability of the human movements [1]. For this reason we introduced a novel framework endowing robots with the ability to perceive humans while fulfilling a cooperative task. The framework is composed of i) a wearable sensor system (i.e., the Xsens motion tracking system, a pair o portable sensorized shoes equipped with 6-axis force/torque sensors and tactile insoles [2], developed at Istituto Italiano di Tecnologia) and ii) a software architecture implementing a real-time whole-body kinematics and dynamics estimation. Within this framework, the robot perceives i) the position and motion of the human, ii) the exchanged contact forces and iii) the human joint torques for the task ergonomy control [3]. During the HRC, robots can make the human ergonomics better by embodying the human estimated quantities and adjusting their collaborative interaction strategy.
Graph Sparsification via Meta-Learning

We present a novel graph sparsification approach for semisupervised learning on undirected attributed graphs. The main challenge is to retain few edges while minimize the loss of node classification accuracy. The task can be mathematically formulated as a bi-level optimization problem. We propose to use meta-gradients, which have traditionally been used in meta-learning, to solve the optimization problem, essentially treating the graph adjacency matrix as hyperparameter to optimize. Experimental results show the effectiveness of the proposed approach. Remarkably, with the resulting sparse and light graph, in many cases the classification accuracy is significantly improved.
Semantic Analysis of Sentiments through Web-Mined Twitter Corpus

A huge amount of textual data is generated due to the boom of microblogging. Microblogging sites such as Facebook, Twitter and Google+ are used by millions of people to express their views and emotions on different subjects. In this paper, we discuss sentiment analysis on a Twitter dataset having various tweets from different users. Sentiment analysis is useful for gaining the opinion of people using large volumes of text data where texts are highly unstructured and heterogeneous. In this paper, different classification techniques like Support Vector Machine, Logistic Regression, Logistic Regression with Stochastic Gradient Descent optimizer, Decision Tree Classification, Naive Bayes, Bidirectional LSTM and Random Forest Classification have been applied to analyze the sentiment of people, i.e., whether their tweets are positive or negative. The corpus has been analyzed by plotting descriptive insights such as the word cloud and frequency of positive and negative tweets. The best classifier was selected by comparing the results of accuracy, recall, precision, F1 score, AUC score and ROC curve.
Pharmacological Effect of Muntingia calabura Leaves on the Expression of sod1 and sod2 in Drosophila

The lifespan of Drosophila melanogaster can last longer when consuming ethanol extract of Muntingia calabura leaves (EMCL) before starvation. Lifespan extension can be affected by several factors, including the increased expression of endogenous antioxidants. This study aimed to assess the level of endogenous antioxidants sod1 and sod2 in D. melanogaster upon ingestion of ethanol extract of M. calabura leaves (EMCL) before starvation. Fifty D. melanogaster were divided into five groups: healthy control group, starvation control group, and three groups of EMCL-treated: 0.2% group, 1% group, and 5% group. Each group was fed with fly food in the presence or absence of EMCL for seven days, and all groups, except the healthy control group, were subsequently subjected to a starvation procedure for seven hours. RNA extraction was performed on each group before sod1 and sod2 measurements using the reverse-transcriptase quantitative PCR method. Our results revealed that the expression of sod2 was increased in response to starvation. On the contrary, reduced expression of sod1 was observed in the starvation-treated group. Treatment of flies with EMCL at 1% and 5% increased the mRNA level of sod1, but did not affect the mRNA level of sod2. Taken together, our results demonstrated the potential effect of Muntingia calabura leaves in the modulation of Drosophila sod1 and sod2 gene expression in Drosophila. Similar endogenous antioxidants are present in humans and have been associated with aging, implying that prospective pharmacological investigations are feasible to pursue in future studies.
Challenges and Solutions in translating sepsis guidelines into practice in resource-limited settings

Sepsis and septic shock are major contributors to the global burden of disease, with a large proportion of patients and deaths with sepsis estimated to occur in low- and middle-income countries (LMICs). There are numerous barriers to reducing the large global burden of sepsis including challenges in quantifying attributable morbidity and mortality, poverty, inadequate awareness, health inequity, under-resourced public health, and low-resilient acute health care delivery systems. Context-specific approaches to this significant problem are necessary on account of important differences in populations at-risk, the nature of infecting pathogens, and the healthcare capacity to manage sepsis in LMIC. We review these challenges and propose an outline of some solutions to tackle them which include strengthening the healthcare systems, accurate and early identification of sepsis the need for inclusive research and context-specific treatment guidelines, and advocacy. Specifically, strengthening pediatric intensive care units (PICU) services can effectively treat the life-threatening complications of common diseases, such as diarrhoea, respiratory infections, severe malaria, and dengue, thereby improving the quality of pediatric care overall without the need for expensive interventions. A thoughtful approach to developing paediatric intensive care services in LMICs begins with basic fundamentals: training healthcare providers in knowledge and skills, selecting effective equipment that is resource-appropriate, and having an enabling leadership to provide location-appropriate care. These basics, if built in sustainable manner, have the potential to permit an efficient pediatric critical care service to be established that can significantly improve sepsis and other critical care outcomes.
Odyssey: A Systems Approach to Machine Learning Security

Using software exposes vulnerabilities that are susceptible to attacks with serious consequences. While there are many different vulnerabilities, the consequences fall into a small number of categories. This paper explains how consequences of attacks on Machine Learning (ML) vulnerabilities fall into these same categories. These consequences are then aligned to ML-specific attacks, the contexts in which they occur, and the established methods for mitigating them. These defensive countermeasures can support the security of the ML elements of a system and lead to greater assurance that systems using ML will operate as planned. This paper provides a systems approach to addressing attacks, consequences, and mitigations for systems using ML. It explains each of these over the lifecycle of an ML technology, providing clear explanations of what to worry about, when to worry about it, and how to mitigate it while presuming little incoming knowledge of ML specifics.
Effect of deep neck flexor strengthening on forward head posture: A systemic review and meta-analyses

Aim: This study aimed to systematically review and summarize the best evidence of the efficiency of deep neck flexor (DNF) strengthening for forward head posture (FHP). Material and Methods: A literature search from January 2000 to 2020 was performed using PubMed, Cochrane Library,Science Direct, and PEDro by two authors who independently selected studies. The methodological quality of studies was assessed using the PEDro scale. The primary outcomes were pain, disability, and craniovertebral angle (CVA),and the secondary outcomes were forced vital capacity and forced expiratory volume in 1 second. Results: A total of 7 randomized controlled trials of low-to-moderate quality evidence that compared the short-term effect of DNF strengthening on pain, disability, CVA, and respiratory function with that of another treatment; 4 studies were enrolled in the meta-analyses. In 3 studies that investigated the effect of DNF strengthening on the cervicovertebral angle, the overall effect was statistically insignificant in patients with FHP (mean difference [MD], 0.23; 95% confidence interval [CI], −2.58 to 3.04; P=0.23). In 2 studies that examined the effect of DNF strengthening on pain, the overall effect was statistically insignificant (MD, −0.07; 95% CI, −1.77 to 1.62; P=0.87). Discussion: The findings of this review suggest that the evidence of DNF strengthening in FHP is not sufficient.
Solving Weighted Orthogonal Procrustes Problems via a Projected Gradient Method

This paper proposes a new line search method to deal with weighted orthogonal procrustes problems. The nonmonotone line search of Zhang and Hager and an adaptive Barzilai–Borwein step size are the chosen tools in order to speed up the convergence of the iterative method. The new approach uses a search direction based on a weighted sum between the Euclidean gradient and the Riemannian gradient of the cost function. Some numerical experiments are provided in order to demonstrate the eﬀectiveness of the new proposal.
Leveraging Lexical Features for Chinese Named Entity Recognition via Static and Dynamic Weighting

—Named entity recognition (NER) is an essential research assignment in natural language processing. Entity boundaries and word segmentation information are crucial determinants that affect the accuracy of Chinese named entity recognition. Researchers generally utilize two methods: word-based method and character-based method. Word-based methods rely heavily on segmentation tools. These methods can cause error propagation problems from word segmentation in-correctly. Character-based methods lack word-level information and word boundaries information, which will cause the problem of missing information. A novel neural entity recognition model fused with self-matched lexical word features is proposed for Chinese NER tasks in response to these problems. In the embedding layer, they were using the attention mechanism to extract the semantic features of word sequences, using the statistical character frequency to extract the syntactic features. Our proposed model is validated in many datasets. The F1 score of 73.9% and 57.65% was obtained on the OntoNotes 4 and Weibo datasets. The F1 score of 90.76% and 95.58% was obtained on the MSRA and Resume datasets, and the state-of-the-art experimental results are obtained on the Resume benchmark dataset.
Can real-time Artificial Intelligence techniques be applied to Synthetic Environments?

The increasing proliferation of source data poses a significant challenge to the capabilities of Synthetic Environment (SE) generation pipelines to transform this data with the necessary agility, velocity, productivity and affordability required to meet future demands. Previous research has demonstrated the potential to apply Artificial Intelligence (AI) throughout the entire generation pipeline. But the ability to capture large volumes of imagery, video, point cloud and other real-world data within days, hours or in real-time places specific demands on the capacity of SE pipelines to extract and process features, attributes and properties from this data, and update or create SE’s at a comparable tempo. Faced with a similar and potentially overwhelming data glut, fields such as security, autonomous vehicle and Intelligence, Surveillance and Reconnaissance (ISR) systems are performing automated, real-time analysis and computer vision tasks, from facial recognition and human pose estimation to object detection, classification and segmentation. These are made possible by the application of AI, particularly Deep Learning (DL), to perform critical data processing and analysis in real-time, often on edge devices. If similar techniques can be applied to SE generation it offers a path to genuinely rapid SE construction. Today’s largely offline data preparation and processing tasks could be encapsulated into a pipeline that can intelligently analyze, process and exploit input data, extract features, derive and generate content on-the-fly, in an end to end, data to run-time SE construction process. This paper will examine the state and application of AI to rapid or real-time data processing and analysis. It will assess if, how and where such implementations of AI and DL could be applied to introduce similarly fast, dynamic data exploitation into the SE pipeline, and consider potential issues such solutions may pose in areas such as SE interoperability.
Volterra integral equations with highly oscillatory kernels: a new numerical method with applications

Abstract. The aim of this paper is to present a Nyström-type method for the numerical approximation of the solution of Volterra integral equations of the second kind having highly oscillatory kernels. The method is based on a mixed quadrature scheme which combines the classical product rule with a dilation quadrature formula. The convergence and the stability of the method are investigated and the accuracy of the presented approach is assessed by some numerical tests. The proposed procedure is also applied to the computation of initial scattering data related to the initial value problem associated to the Korteweg-de Vries equation.
Development of Simulation Model for a Combined Activated‐Sludge and Biofilm Process to Remove Nitrogen and Phosphorus

A simulation model was developed for a combined activated‐sludge and biofilm process that was developed in Japan to achieve nitrogen and phosphorus removal without extending existing treatment facilities. The process consists of an activated‐sludge process of three zones (anaerobic, anoxic, and aerobic) and a biofilm process in the form of fluidized media in the aerobic zone. In this process, nitrification is carried out by nitrifiers on the fluidized media and denitrification and phosphorus removal are performed by activated sludge. First, a biofilm model was developed by modifying components, biological processes, and detachment mechanism of an existing model using experimental results from the fluidized‐bed process. Second, the biofilm model was incorporated in the International Association on Water Quality Activated Sludge Model No. 2 with the addition of a mechanism of transferring detached biofilm to activated sludge. Parameter values of this model were fitted using data from a pilot plant during a period of no rainfall. The model was then evaluated with pilot‐plant data for a period of rainfall. The model developed for the combined activated‐sludge and biofilm process described water quality of the pilot plant well. Necessary improvements are also discussed.
Energy Efficient NK-RLE Data Compression Scheme

Wireless sensor network vigorous research area now a day . In WSN sensor nodes are outfitted with tiny batteries having low power where power conservation is main concern to increase the network life time. In order to conserve energy by reduce the data to be send from sense nodes to sink node. Data compression is effective and well known technique, to get better compression results with varied data from data sources it is essential to introduce competent processing technique. In this paper we proposed improved data compression schemes motivated from RLE and K RLE. The proposed algorithm enhances the compression ratio in comparison of K-RLE and RLE. The result shows that our compression algorithms efficiently enhance the data compression ratio and hence improving energy efficiency in WSNs
Dissipative Light Bullets in Passively Mode-Locked Semiconductor Lasers

We demonstrate the existence of stable three dimensional dissipative localized structures in the output of a laser coupled to a distant saturable absorber. These phase invariant light bullets are individually addressable and can be envisioned for three dimensional optical information storage. An effective theory provides for an intuitive picture and allows to relate their formation to the morphogenesis of static auto-solitons and cellular patterns. The complexity incurred by the widely different time scales present in the problem as well as by non-local couplings that stem from the material degrees of freedom is circumvented by the use of a multiple time-scale analysis. This provides a powerful model enabling to tackle effectively the three dimensional case.