Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Kalman/H∞ Filtering for Sensor Networks with Partial Unknown System Parameters

This paper studies the distributed filtering problem for sensor networks in which each sensor’s measurement matrix is not observable and target’s system matrix is not completely and precisely known. A distributed Kalman/$\mathrm{H}_{\infty}$ filtering algorithm is proposed. The algorithm is composed of two parts. The first part is a consensus Kalman filter to estimate the parameters of the target’s system matrix. It is proved that the mean square estimation errors of the system parameters converge to zero, if and only if for any one system parameter, its accessible node subset where each node knows this parameter with noise is globally reachable. The second part is a consensus Kalman/$\mathrm{H}_{\infty}$ filtering algorithm based on the system parameter estimations to estimate the target’s state. In the state estimation, the consensus weights are fully distributed and adaptively designed based on the estimation errors of the sensors. Under the algorithm, as long as there further exists a globally reachable node which is collaboratively observable using its neighbors’ available measurements, the mean square estimation errors of all sensors must be bounded.
Hidden Dot Patterns Recognition using CNNs on Raspberry Pi Zero W

A talking pen is the evolutional way of learning especially for children because it is fun and interactive. Commercial talking pen use a small infrared camera to capture the hidden dot patterns that hidden around a book's page. Specific sounds are generated according to the location of the pen on the pages. This image processing for recognition technique is complex and need calculation power because it had to calculate relative location and orientation of dots in a group. Although, the talking pen is successful product but it is not flexible to add new pattern or uses different set of patterns. In this research, the new approach for hidden dot pattern recognition is proposed. Convolutional neural networks or CNNs is selected as the recognition software. The system is built on Raspberry Pi Zero W hardware with Raspbian operating system and TensorFlow platform. MobileNet is used in the research since it small and effective for limited resources platform. MobileNet is trained by using the infrared images that captured by infrared camera module that connected to Raspberry Pi. Result shows that the accuracy of the proposed method is good enough and the response is fast enough to be implemented into the real-time commercial product and has ability to expand to any other kind of patterns without limit because the learned network can be updated easily without alter recognition software.
An Analysis of Music Lyrics by Measuring the Distance of Emotion and Sentiment

The present world has an oversupply problem in regard to current products. In order to the solve oversupply problem, we are breaking down the trade barriers between countries. However, in the future, it can be an era of breaking down individual trade barriers of a person and resolving oversupply of goods. This means that it will be an important issue in analyzing the characteristics of each person and providing the goods that match them. Music is an invisible and intangible product even though we can see this as a commodity as well, meaning that the music market is also a time period of oversupply because the paradigm has changed from analog to electronic music. In other words, the time it takes to make music has been shortened radically. After the paradigm shifted to the era of electronic music, the value of music streaming market exploded. In other words, it was a concept of music ownership in the past, but now it can be said that it has turned into a concept of approaching music. The market value of approaching concepts such as streaming is exploding. In order to overcome the oversupply problem and to recommend it, it is obviously a limit to distinguish music as the current profile elements such as genres, titles, and artists. So we work on classifying on of the greatest features of music: the lyrics. Since the lyrics of music consist of texts, music can be considered to contain human emotions because humans can convey their feelings in texts. We can use sensibility of the lyrics of music to classify music. We used about 11,000 lyrics based on the Billboard Chart from Hot 100. The Natural Language Processing (NLP) work was done to refine each of the lyrics, and the music was digitized as sentiment and emotion. After selecting a new song using the K-Nearest Neighbor (K-NN) algorithm, we propose a method for recommending music with the most similar lyrics. In conclusion, we can inform users of songs with lyrics that are most similar to the overproduction of music.
Efficient Signature Scheme for Delivering Authentic Control Commands in the Smart Grid

Enabling secure delivery of control commands and alert messages is one of the critical requirements of smart grid communications. If adversaries were to gain access, they could alter or send malicious commands from the Control Center (CC), intended to the Remote Terminal Units (RTUs) and Intelligent Electronic Devices (IEDs). These malicious commands could disrupt power system operations and cause damage to power devices. In this paper, we propose an efficient signature scheme for ensuring the delivery of only legitimate commands over the communication network. We propose efficient signatures using hash that are sent by the CC along with commands for protecting command alteration over the network, which also provide sender authenticity by verifying the signatures at the RTU/IED. By using the proposed scheme, both random and malicious commands sent by an adversary are discarded. The security analysis shows that the scheme is secure against replay, repudiation and impersonation attacks. The performance evaluation and prototype simulation show that our signature scheme using SHA1, SHA256 and SHA384 is feasible, and can be used for communications from the CC to the field.
Improving Domain Independent Question Parsing with Synthetic Treebanks

Automatic syntactic parsing for question constructions is a challenging task due to the paucity of training examples in most treebanks. The near absence of question constructions is due to the dominance of the news domain in treebanking efforts. In this paper, we compare two synthetic low-cost question treebank creation methods with a conventional manual high-cost annotation method in the context of three domains (news questions, political talk shows, and chatbots) for Modern Standard Arabic, a language with relatively low resources and rich morphology. Our results show that synthetic methods can be effective at significantly reducing parsing errors for a target domain without having to invest large resources on manual annotation; and the combination of manual and synthetic methods is our best domain-independent performer.
Open Source Business Intelligence Platforms' Assessment using OSSpal Methodology

The knowledge that can be acquired from existing data in organizations is critical to increasing organizations competitive advantage in today's changing markets. The use of Business Intelligence (BI) platforms is an effective choice in support of decision-making. BI platforms are a major asset for any enterprise, as they have multiple benefits, such as efficient use of resources, identification of business opportunities and negative trends that become a competitive advantage. Open source BI platforms provide most of the functionalities available in commercial solutions without increasing costs for enterprises. However, it is important to know which open source BI platform to choose. In this paper, it is used OSSpal, an open source assessment methodology to evaluate two of the most popular open source BI platforms: Knowage and Pentaho.
The utility of cross‐sectional imaging in the management of suspected scaphoid fractures

Scaphoid fractures are the commonest carpal bone fracture. If untreated they pose significant risk to patients, thus if a scaphoid fracture is suspected, patients are managed with immobilisation. Although scaphoid fractures may be difficult to diagnose on plain radiography, sometimes for months after injury, ongoing radiographic surveillance is preferred due to its low upfront cost. Patients in immobilising casts for long periods experience significant personal and social ramifications such as difficulty working and self‐caring. This study examines whether cross‐sectional imaging by computed tomography (CT) or magnetic resonance imaging (MRI) is quicker than serial X‐ray surveillance at allowing a scaphoid fracture to be either excluded or confirmed.
Mobile Robot Trajectory Generation from Ordered Point-Set Using Time-Optimal Bezier Segments

This paper addresses the problem of constructing a trajectory from a given set of points, considering a mobile robot. Following the initial pose of the robot, the points are connected in sequence by cubic Bezier segments. After selecting the tangential velocity at the connection points, these segments are time-optimized under the velocity and acceleration constraints of the robot, both tangential and angular, using nonlinear constrained optimization. The optimization problem is simplified by exploitation of polynomials. Experimental studies demonstrate the effectiveness of the algorithm.
A Summary and Comparative Study of Different Metrics for Machine Translation Evaluation

Assessment of the Translation done by machines has always been a major topic of interest for the Natural Language Researchers. Every day, a new translator is out there in the market claiming to be the best one. But to challenge such claims, one needs to have a powerful assessment tool that can judge the output of any translator on different parameters like fluency of the language, adequacy as well as the accuracy of the output generated. In this paper, we have discussed the major issues with the algorithms used for the evaluation of Machine translation. We have also presented a brief description and comparative study of different metrics used to evaluate the outcome of a machine translator over English to Hindi Language parallel sentences.
Privacy-preserving Distributed Access Control for Medical Data

The availability of wearable devices such as smartwatches and fitness trackers are a recent development. Among other things, these devices can measure the activity and vital signs of their wearers. As the types of data these devices are able to gather increases the potential for them to be used as a source of data grows. This calls for a secure method of controlling the digital exchange of medical data between wearables and healthcare providers, and healthcare providers in general. By enforcing the exchange of data to go through a central authority, a patient can be given more control over who is able to access his medical data. This central authority is then given the task of monitoring access and ensuring that all access requirements are met. Though effective, this solution relies on a highly trusted central authority. In this work, we propose a scheme using Polymorphic Encryption and Pseudonomysation and Secret Sharing to provide anonymous data storage and data exchange. Our proposal removes the need for a central authority, and instead uses a group of authorities, of which a quorum is needed to facilitate the exchange of data.
The role and impact of community health workers in childhood obesity interventions: a systematic review and meta‐analysis

Childhood obesity increases the risk for poor health during childhood, as well as for adult obesity and its associated comorbidities. Children from racial/ethnic minority groups or who live in poverty experience elevated rates of obesity. One potential method for reducing childhood obesity disparities is to involve community health workers (frontline public health workers who are trusted members of and/or have an unusually close understanding of the community served). The purpose of this systematic review and meta‐analysis was to explore the role and effectiveness of community health workers in childhood obesity interventions. Eleven studies met inclusion criteria, of which nine were eligible for inclusion in the meta‐analysis. Results demonstrated that community health workers played various roles in childhood obesity interventions in the home, clinic, school, and community setting. Interventions focused primarily on children from underserved populations. Meta‐analytic findings demonstrated a small but significant impact on BMIz and BMI percentile (BMIz [7 studies]: −0.08, 95% CI: −0.15, −0.01, p = 0.03, I2 = 39.4%; BMI percentile [2 studies]: −0.25, 95% CI: −0.38, −0.11, p < 0.01, I2 = 0%). Findings from this review demonstrate that partnering with community health workers may be an important strategy for reducing childhood obesity disparities and advancing health equity.
Circular Hough Transform and Integral Intensity Projection for Computing Automatic Footprint Arch Index

This paper demonstrates approaches for automatic computing footprint arch index by Circular Hough Transform and Intensity Projection methods. We applied k-mean clustering approach in order to classify the cluster of the color in the footprint image. Next, the image is converted to a binary image and divided into left and right side. The Circular Hough Transform method and flood-fill operation are then utilized to detect and to remove the toe prints, respectively. After that the positions of the anterior and the posterior aspect of the toeless foot prints are defined by using the horizontal integral intensity projection method. Subsequently, an angle of the toeless foot print is then estimated from these two positions. The toeless foot print is then adjusted to the perpendicular line. Finally, the length of toeless foot print and the footprint arch index values were calculated by applying Euclidean method to this adjusted toeless foot print. Footprint arch types are then classified by using the cutoff value of arch index. In this experiment, the average accuracy for automatic computing arch index values in the footprint image is 93.99% and the accuracy for classification of the footprint arch type is 84.40% in comparison with the manual computation from the expert.
Copper regulates the interactions of antimicrobial piscidin peptides from fish mast cells with formyl peptide receptors and heparin

Phagocytic cells in fish secrete antimicrobial peptides (AMPs) such as piscidins, glycosaminoglycans such as heparin, and copper ions as first-line immune defenses. Recently, we established that Cu2+ coordination by piscidins 1 (P1) and 3 (P3) enhances their antibacterial activity against membranes and DNA. Interestingly, we noted that physicochemical similarities exist between both piscidins and other AMPs that interact with heparin and induce immune-cell chemotaxis through formyl peptide receptors (FPRs) involved in innate immunity. Thus, we postulated that P1 and P3 interact with heparin and FPRs but that these interactions distinctively depend on Cu2+. Here, we investigate the interactome potentiated by piscidins, heparin, FPR, and Cu2+. Utilizing FPR-transfected cells and neutrophils, we demonstrate that both piscidins exclusively use FPR1 and FPR2 to induce chemotaxis and that Cu2+ reduces their chemotaxis induction. P1 is more effective at activating FPR1 than P3 and other known AMP ligands. Furthermore, the expression of Fpr2 on the surface of neutrophils is down-regulated by both peptides. Copper conjugation of the peptides does not further increase down-regulation, suggesting that the conformational changes induced by the metal translate into reduced FPR efficacy without altering the binding affinity. Using surface plasmon resonance, we show that piscidin–heparin interactions are Cu2+-dependent and reduced at the acidic pH of phagosomes. Although heparin decreases the antimicrobial activity of P3–Cu2+, it does not affect bacterial killing by P1–Cu2+. Copper's effects on modulating the micromolar-range interactions of both piscidins with FPR and heparin suggest that the interactome of these distinct immune agents plays an important role in innate immunity. The interactions between diverse host-defense molecules uncovered here may help inform the design of novel therapeutics to treat immune-related diseases.
Robust Visual Loop Closure Detection with Repetitive Features

Loop closure detection problem is an essential issue in simultaneous localization and mapping (SLAM) problem. In particular, visual loop closure detection, which using a visual sensor, should be robust to environmental conditions like confusion caused by repeated structures. In this paper, we propose a robust visual loop closure detection algorithm through restrained repetitive features observed in repeating structures. The proposed algorithm aims to extract bag of visual words (BoVW) for each image frame with RootSIFT extraction, improve it by restrain dominantly repetitive features, calculates histogram similarity score with histogram comparing method and finally decides loop closure pair(s). Experimental results show that the proposed algorithm robustly performs loop closure detection.
Internal Simulation for Autonomous Robot Exploration of Lava Tubes

Space Exploration stands as one of the most challenging endeavors of our time. Extraterrestrial caves in particular have been identified by the scientific community as of great interest. They could be suitable for allocating astronaut planetary stations, but little is known about them. In this paper, we describe and analyze robotic cave exploration using internal simulation and SLAM technologies and provide experimental results. The experiments were performed in a lava tube selected due to its representativeness as space analog. Expensive mission costs, communication constraints and navigational challenges in space missions demand highly developed degrees of autonomy and safety on the robots. For this reason, our solution incorporates methods for the validation of paths based on on-board, internal simulation. The methods provide the means to increase the confidence of successful executions by simulating using physics models the planned path. A highly realistic model of the robot and an on-line generated model of the environment are required. Assuming the demands of the space robotics scenario, all software runs on-board.
Upgrading Voltage Control Method Based on Photovoltaic Penetration Rate

In this paper, we propose a comprehensive scheme to determine a suitable method and timing for upgrading the voltage control method. Voltage control methods are expected to be upgraded in accordance with the photovoltaic (PV) penetration in distribution systems. The suitable method and timing detailed in this paper are based on the limit of the PV penetration rate, which is constrained by the regulated voltage deviation. The upgrade process involves moving the on-load tap changer (OLTC) control method from the conventional scalar line drop compensator (LDC) method to the vector LDC method or centralized control method. Then, a static var compensator (SVC) or step voltage regulator (SVR) is installed. The locations of the SVR and SVC are determined to maximize the PV penetration rate. The suitable method and timing are demonstrated using a general distribution system. In addition to the numerical simulations, experiments are performed using an active network system with energy resources. The experimental results are consistent with the numerical simulation results, thus validating the proposed scheme. The maximum PV penetration rate obtained using the OLTC control method is 55%. Whereas, the installation of the SVR and SVC increased the rate to 95% and 100%, respectively.
An Investigation into the Energy Consumption of HTTP POST Request Methods for Android App Development

Producing energy efficient applications without compromising performance is a difficult job for developers as it affects the utility of smart devices. In this paper, we conducted a small-scale evaluation of selected implementations using different methods for making HTTP POST requests. In the evaluation, we measured how much energy is consumed by each implementation and how varying message payload size effects the energy consumption of each implementation. Our results provide useful guidance for mobile app developers. In particular, we found that implementation using OkHttp consumes less energy than the implementation using HttpURLConnection or Volley libraries. These results serve to inform the developers about the energy consumption of different HTTP POST request methods.
Event-triggered Distributed Pose Estimation for Networked Vehicles

This paper is concerned with the pose estimation problem for networked vehicles by designing distributed filtering with an event-triggered communication mechanism. By utilizing the event-based distributed filter structure and event-induced behaviors, we obtain the augmented system equations for networked vehicles. Based on the augmented system, an upper bound of the distributed filtering error variance is obtained by a recursive algorithm and can be minimized at each iteration with proper filter parameters computed via a simplified matrix approach. Simulative studies are given to show the effectiveness of the proposed filtering design method.
Intelligent Decision Support System for Industrial Site Classification: A GIS-Based Hierarchical Neuro-Fuzzy Approach

Industrial site selection involves a large number of criteria and location alternatives; consequently, the selection process leads to extended decision-making periods and requires complex knowledge management, classification, and analysis skills. The selection criteria are generally described by a number of different features expressed as both quantitative and qualitative measures that can involve some uncertainty. Moreover, the goals considered in the selection process are frequently nonlinearly related to the criteria; thus, they give rise to an optimization problem that is nonlinear with respect to each goal. Consequently, decision making requires appropriate support to enable efficient data optimization and classification under uncertainty before the final selection of an industrial site is made. This paper presents a novel intelligent decision support system for classifying industrial sites according to quality criteria estimated by exploiting a geographic information system, expert knowledge, and machine learning techniques. The proposed system is based on a geographic information system for generating location alternatives and a hierarchical neuro-fuzzy approach for site classification. The neuro-fuzzy method is based on a knowledge base designed by experts in the field and uses a neural approach to tune the parameters of the membership functions. Experimental results on real-world problems show that the proposed system provides accurate results for industrial site classification at the local level (micro locations).