Reviewerly/exHarmony huggingface dataset:
A Real-Time Flight Safety Alarm System for Multiple UAVs

Airborne heterogeneous UAVs can encounter flight control or sensor failures while performing flight tasks. To protect against this, safety alarm systems (SAS) are typically implemented in ground control systems (GCS) to prevent crashes or other failures. Tracing the failure for a single UAV is relatively easy, but it is difficult to manually monitor multiple UAVs flying in a distributed open area. The current paper aims to fulfil this SAS deficiency, a deficiency that is still frequently ignored during GCS development. Our SAS alerts users of no-fly zones as well as navigation, battery, GPS and communication failures. The system can monitor simultaneous multiple UAV-flight failures in real time so that GCS can send appropriate commands to each UAV encountering a problem.
Real Virtual IDentification (RVID): Providing a Virtual, Secure and Anonymous ID Service to Indian Users

Today using (sharing) Aadhaar with several welfare schemes/organisations, provide several essential benefits (to get government schemes benefits) to Indian citizens. But on the other hand, revealing/sharing their Aadhaar number with government and organisation raised several issues like security and revealing of personal information of the user. For that the government makes several enhancements from time to time like recently, government forces and give an option to use virtual IDs irrespective of using Aadhaar number. But virtual ID is not fixed and a user can generate multiple VIDs, it means it is a confusing process to user/people belonging to rural area. So to solve this issue of VID, this work describes a novel approach to provide a secure and anonymous (yet transparent and immutable) identification management system, which provide a Real Virtual ID (RVID) with respect to every user’s Aadhaar number. This ID (16-digit ID, i.e. containing combination characters and letters) can be used to take several benefits or can be used irrespective of sharing Aadhaar number with organisations. A prototype shows that such ID management system is feasible, and solves the problem of duplication of data. It is also immune to many ID attacks like guessing attack, etc.
Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line Enhancing

In this paper, our goal is salient building outline enhancement and extraction from images taken from consumer cameras using L 0 smoothing. We address weak outlines and over-smoothing problem. Weak outlines are often undetected by edge extractors or easily smoothed out. We propose an iterative method, including the smoothing cell and sharpening cell. In the smoothing cell, we iteratively enlarge the smoothing level of the L 0 smoothing. In the sharpening cell, we use Hough Transform to extract lines, based on the assumption that salient outlines for buildings are usually straight, and enhance those extracted lines. Our goal is to enhance line structures and do the L 0 smoothing simultaneously. Also, we propose to create building masks from semantic segmentation using an encoder-decoder network. The masks filter out irrelevant edges. We also provide an evaluation dataset on this task.
Fabricating high-quality composite laminates by magnetic compaction

This paper demonstrates the feasibility of using high-power, permanent magnets in developing high consolidation pressure during a variety of fabrication methods for composite laminates. First, the salient features of this new manufacturing method, expected enhancement of the mechanical properties, and possible improvements in part quality are discussed. Then, the method of applying magnetic pressure over composite laminates with relative ease is presented. To validate this new method, a number of E-glass/epoxy and carbon/epoxy composite laminates were manufactured by utilizing Neodymium Iron Boron (NdFeB) magnets. Specific results will be given for: (i) wet lay-up/vacuum bag, (ii) VARTM, and (iii) oven curing (i.e. out-of-autoclave) processes, where the laminates fabricated with and without the magnetic compaction. The mechanical properties were shown to improve substantially, primarily due to increase in fiber volume fraction and decrease in process-induced voids. In most cases, both the flexural strength and modulus of the composites increased even as much as 60%, while the void content was reduced from 4-5% to less than 1%.
Clustering Algorithms for Query Expansion Based Information Retrieval

Clustering is by far the most commonly used unsupervised data mining techniques for discovering interesting knowledge and patterns. It aims to group a set of data objects into clusters that are coherent internally but basically different from each other. In this work, we involve clustering algorithms in Information Retrieval (IR) to strengthen the user’s original query with appropriate additional terms and return more relevant information. The overall procedure consists of the following steps: (i) use the k-medoids clustering algorithm to group terms into clusters with similar characteristics, (ii) involve k-means algorithm to calculate the centroid of query terms, (iii) select the relevant clusters to the original query and return the expansion term candidates, (iv) evaluate the expansion term candidates to the centroid and add the best ones to the original query, (v) run a search with the expanded query. We present numerical experiments based on real data from a large online health database. The results of our numerical testing demonstrate the effectiveness of the proposed method compared to prior state-of-the-art.
Utilization of Smartphones for Retrieving Research Information Resources in Libraries by Undergraduates

This chapter examined the utilization of smart phones for retrieving research information resources in libraries by undergraduates. The study covers awareness, utilization, benefits, effects, and constraints that affect smart phones for retrieving research information resources by undergraduates. These concepts generally implied that awareness facilitates interest for use. It was concluded that smartphones were used by undergraduate students to retrieve research information resources, and this had positive impact on research out of students will produce positive impact on academic performance of students. It was recommended among others that efforts should be made by the university to inculcate mobile teaching and learning curriculum in to the school syllabuses at the various levels, and library staff, lecturers, and university staff generally should be sensitized and trained in the use of mobile devices for teaching and learning.
Linear Algebra, Secret Agencies, and Zombies: Applications to Enhance Learning and Creativity

This article will discuss activities and assignments created for a linear algebra course that aim to excite students about learning the course content. The linear algebra course taught at the University of Wisconsin-Eau Claire covers linear algebra, its applications, and serves as the introduction to proofs course. Students often become overwhelmed with all the content and proof techniques in this course. This leads to them not enjoying, nor engaging in, what they are learning and they become discouraged with the material. Throughout the semester, interactive projects are introduced which cover applications of the course content. These activities center around the idea that the students have been recruited to work with the “Zombie Containment Task Force” under the supervision of Agent Frank Larson. Throughout the semester, students must complete several missions to uncover secrets about the workings of the task force, discover knowledge about zombies, and reveal double agents. At the end of the semester, students use clues given throughout the semester to make a final decision which informs them of their future in this fictional world. This article will give the outline of five interconnected projects that are used in the course as well as discuss the implementation of these missions.
To Inspire and Enhance Research Interests with Reading Assignments before Dissertation Topic Selection

Dissertation plays an important role in postgraduate students' schoolwork, which makes it a mandatory topic in software engineering education. However, the diversity of research concerns and the wide range of dissertation topics bring great challenges for students to determine their dissertation topic, especially when students have diverse culture and education background. The purpose of this paper is to enhance the research process and explore a more generic education pattern for dissertation topic selection. An empirical study involved 88 students was conducted to stimulate students' research interest and explore students' interest distribution, which could guide teachers to better prepare dissertation topics. In this study, we found that (1) before selecting dissertation topics, lots of research paper reading is recommended; (2) to better help students select their interested dissertation topics, professor should expand the scope of topics and increase the number of topics students interested in; (3) paying attention to differences between students and balancing the difficulty of topics are fundamental and necessary. The results indicate the key factors towards a generic education pattern of dissertation topic selection and help to better course planning.
optimization of Time Domain Moving Target Detection Algorithm Based on Improved FT

In order to solve the problems of GMM algorithm in time-domain moving target detection, such as computational complexity, shadows and missed detection, an improved FT algorithm based time-domain moving target detection optimization algorithm is proposed in this paper. The algorithm aims to optimize the speed and accuracy of time-domain moving target detection algorithm by using the idea of combining frequency-domain with time-domain. Firstly, the original FT algorithm is used to perform the preliminary moving target detection, and then the whole moving target detection system is completed by the GMM algorithm. In order to improve the detection rate and ensure better accuracy, this paper improves the original FT algorithm from two aspects of distance measurement and fusion. Experiments show that the proposed algorithm can effectively optimize the detection rate and accuracy of GMM algorithm.
Machine Learning and Artificial Intelligence

This chapter proposes a cost-effective and scalable approach to obtain information on the current living standards and development in rural areas across India. The model utilizes a CNN to analyze satellite images of an area and predict its land type and level of development. A decision tree classifies a region as rural or urban based on the analysis. A summary describing the area is generated from inferences made on the recorded statistics. The CNN is able to predict the land and development distribution with an accuracy of 95.1%. The decision tree predicts rural areas with a precision of 99.6% and recall of 88.9%. The statistics obtained for a dataset of more than 1000 villages in India are cross-validated against the Census of India 2011 data. The proposed technique is in contrast to traditional door-to-door surveying methods as the information retrieved is relevant and obtained without human intervention. Hence, it can aid efforts in tracking poverty at a finer level and provide insight on improving the economic livelihood in rural areas.
Towards Object Shape Translation Through Unsupervised Generative Deep Models

This paper focuses on the problem of unsupervised image-to-image translation. More specifically, we aim at finding a translation network such that objects and shapes that only appear in the source domain are translated to objects and shapes only appearing in the target domain, while style color features present in the source domain remain the same. To achieve this, we use a domain-specific variational autoencoder and represent each image in its latent space representation. In a second step, we learn a translation between latent spaces of different domains using generative adversarial networks. We evaluate this framework on multiple datasets and verify the effect of multiple perceptual losses. Experiments on the MNIST and SVHN datasets show the effectiveness of the proposed translation method.
A Study of English Neologisms Through Large-Scale Probabilistic Indexing of Bentham’s Manuscripts

Probabilistic indexes (PI) are obtained from untranscribed handwritten text images by means of recently introduced lexicon-free, query-by-string, probabilistic keyword spotting techniques. PIs have proven to be a powerful tool that allow efficient, free textual searching in very large collections of handwritten historical documents. PIs convey uncertain information about the textual contents of the document images. However, text uncertainty is accurately modeled by the associated lexical probability distributions, which can be conveniently exploited in many applications. As an example of these applications, here we study the dating of a number of English neologisms in the large collection of Bentham’s manuscripts, which encompass $$90\,000$$ images. The statistical techniques used for neologism dating are theoretically motivated and experiments on this collection are reported. Among other interesting contributions of this study, it provides sound evidence that some commonly assumed neologism introduction dates need to be revised.
A Reliability Task Scheduling for High-Throughput Computing Platform

In order to meet the different quality of service (QoS), it is necessary to consider the performance of the system not only to consider the performance of the system, but also to consider the user’s service quality requirements. Aiming at the existing problem of task scheduling algorithm with the goal of user service quality, in this paper we proposed a task scheduling algorithm which satisfies the service quality requirement of users, and carried on the three goals from the reliability, time and cost of service quality task scheduling research. We described the multi-objective constraint processing method used in this paper. For the target requirements of reliability, time and cost, the multi-objective optimization problem is transformed into a single objective optimization problem with constraints the time and cost are constrained by the deadline and the budget, and the reliability is used as the final goal of the task scheduling when the constraint is adopted. Finally, according to the task scheduling goal of this paper, the only solution with the highest reliability is obtained. The experimental results show that the RSL algorithm has better reliability than other algorithms, and it has better advantages in other performance indicators while satisfying the scheduling deadline.
A Functional Analysis of the Prime Suffixes in Igbo Morpho-Syntax

The Igbo language is one of the languages of the Benue Congo family spoken predominantly in the south-east part of Nigeria.Although, some works have been done on the inflectional and extensional suffixes in the language but no detailed study so far has been done on the prime suffixes in the language.It is on this premise that this paper attempts to examine the functional analysis of the prime suffixes in Igbo morpho-syntax with the aim of identifying primary suffixes that exist in the language, classifying them based on the functions they perform in the verb root where they are attached, explore their morphological structures, syntactic patterns, and semantic behaviours in the constructions.The X-bar theory is the theoretical framework for the study.The data for the study were obtained through the recording of the naturally occurring speeches of the native speakers during discourses, and conversations.The findings reveal that the prime suffixes are overtly morphologically marked on the verbs of the language.They have [V], [CV], [CVCV] or [CVCVCV] syllable structures and can be monosyllabic, disyllabic or trisyllabic in nature.They can perform the following semantic functions on the verbs where they are attached in the syntactic structures respectively: imperative, negative, past tense, progressive aspect, preposition as well as adverbial function.Some vowels of the verb roots do harmonize with the vowels of the prime suffixes while some do not.Again, the tones of these suffixes do change when they are mapped unto the verb roots.We, therefore, recommend that more research works be done in the verbs of the language to enhance its growth and development by applying some of the linguistic theories in order to find out how they operate in the language.
The Support System Decision the Determination of Poor Community Welfare with the Methods Web-Based SMARTER: Case Studies Regency Lebak the Province of Banten

Abstract In this country, population problem is not a problem that new again. One of the problems of population that often discussed is a matter of public welfare. Welfare society can be measured from / condition the state or the ability of each family. There are some data sources can be used as reference by the government of them data from bkkbn (coordination agency national family planning). Data collection family activities implemented since 1994 based on statute no. 10 year 1992 concerning the development of population and prosperous family development. Data and information results data this family besides used for operational needs population program, family planning and development own family has also frequently used by other development sector, particularly to identify a really poor family. In this research in a method of smarter apply them in the support system decision the determination of the level of community welfare aims to construct a model of the determination of the level of community welfare computer-based thereby increasing the accuracy and effectiveness in doing the process of determining the level of community welfare bkkbn kabupaten lebak the province of banten based on with smarter ( simple multi-attribute rating technique exploiting ranks ) refined.
Improving Data Reduction by Merging Prototypes

A well-known and adaptable classifier is the k-Nearest Neighbor (kNN) that requires a training set of relatively small size in order to perform adequately. Training sets can be reduced in size by using conventional data reduction techniques. Unfortunately, these techniques are inappropriate in streaming environments or when executed in devices with limited resources. dRHC is a prototype generation algorithm that works in streaming environments by maintaining a condensed training set that can be updated by continuously arriving training data segments. Prototypes in dRHC carry an appropriate weight to indicate the number of instances of the same class that they represent. dRHC2 is an improvement over dRHC since it can handle fixed size condensing sets by removing the least important prototypes whenever the condensing set exceeds a predefined size. In this paper, we exploit the idea that dRHC or dRHC2 prototypes could be merged whenever they are close enough and represent the same class. Hence, we propose two new prototype merging algorithms. The first algorithm performs a single pass over a newly updated condensing set and merges all prototype pairs of the same class under the condition that each prototype is the nearest neighbor of the other. The second algorithm performs repetitive merging passes until there are no prototypes to be merged. The proposed algorithms are tested against several datasets and the experimental results reveal that the single pass variation performs better for both dRHC and dRHC2 taking into account the trade-off between preprocessing cost, reduction rate and accuracy. In addition, the merging appears to be more appropriate for the static version of the algorithm (dRHC) since it offers higher data reduction without sacrificing accuracy.
Data Visualization Scenarios for the Analysis of Computational Evolutionary Techniques

There has been an increasing demand to understand and describe Evolutionary Computing techniques. Information Visualization may contribute with interactive data visualizations that help explore the population of individual solutions over the data search space and generations, convergent behavior, individual fitness, the dynamic of the evolutionary process among other possible scenarios. Although there are previous works on the use of visualization to analyze evolutionary techniques, there has been little diversity among the approached visualization techniques. Also, most related works consider only genetic algorithms and ignore other evolutionary approaches. Therefore the goal of this paper is to suggest the appropriate InfoVis techniques for the analyzed scenarios to better understand the behavior of evolutionary computing algorithms. Furthermore, we present a case study that applies the proposed scenarios to AutoClustering, a tool based on Estimation of Distribution Algorithms. We hope the proposed scenarios and techniques provide a set of good practices for the analysis of Evolutionary Computing techniques.
Training a CNN for Guidewire Detection

Guidewire detection is an important and challenging problem in image-guided interventions. The guidewire is barely visible in fluoroscopic sequences, since it is thin and the image has poor quality. Most recent methods for guidewire localization have a first level of pixel-wise detection based on a trained classifier on hand-crafted features. A Convolutional Neural Network (CNN) could in principle learn its own features, however training a CNN for guidewire detection has proved to be difficult because the wire is very thin and can have any orientation. In this paper we present a method to train a Fully Convolutional Neural Network for guidewire detection, and highlight what challenges are encountered during training for this particular problem. We also introduce the Spherical Quadrature Filters (SQF) for guidewire detection and show how they can be used to improve the training data. Experiments show that the trained CNN outperforms many popular approaches such as the Frangi filter, the SQF and a trained classifier based on hand-crafted feature. Furthermore, we observe that a CNN approach that uses the SQF to obtain better aligned training examples further improves the detection accuracy.
The Methodology for Calculating the Importance Factors of Disciplines and Their Modules Involved in the Formation of Competencies by the Example of the Direction of Training «Information Systems and Technologies»

The article proposes and describes the method of calculating the importance of disciplines, their modules and other types of educational activities involved in the formation of competencies. The calculation is made for the “Information Systems and Technologies” training direction, in order to show how important is the calculation of these characteristics when forming the fi nal assessment of the graduate’s professional orientation. Such an assessment is advisable to carry out for any profi le of training. The described method of calculation can be used for any training profi les and adjusted to suit their characteristics.