Reviewerly/exHarmony huggingface dataset:
Common Clustering Algorithms

The major steps of an overall clustering task are preclustering, clustering, and postclustering. Preclustering involves data preparation, including feature extraction, selection, transformation normalization, cleaning, and data reduction, whereas postclustering involves cluster usability encompassing cluster validity, reasoning, interpretation, and visualization. This article focuses on the second step, “clustering,” which is further divided into two key modules: clustering criterion and clustering method. This clustering step takes a set X = {x1, x2, …, xn} of preprocessed points (synonymously elements, objects, instances, cases or patterns) as an input and produces a clustered result as an output (either partitioning or hierarchical) for postclustering. It first requires a clustering criterion to be built and needs a clustering algorithm to optimize the clustering criterion.
The Carnegie Mellon/Sirsi Corporation Alliance

This article dcscribcs the special relationship between Carnegie Mellon University Libraries and Sirsi Corporation, which was forged in fall 1994. It discusses the motivational context in which this relationship emerged, negotiation strategies, and the elements and ongoing activities of the alliance. The University Libraries’ expertise in library automation research and development was instrumental in forging the relationship that was a key factor in selecting Sirsi as Carnegie Mcllon’s integrated library management system vendor. This article discusses the three primary elements of the alliance-research, collaboration, and marketing-and the benefits of these activities for Sirsi, Carnegie Mellon and other academic customers of Sirs’s integrated online library system, Unicorn. [Article copies available for a fee from The Haworth Document Delivery Service: 1–800-342-9678, E-mail address: getinfo@haworthpressinc.com]
How Games Improve Language in People With Language Dysfunctions

Many people as they age face a greater challenge of muscular dexterity around their facial muscles. This results in difficulty producing certain sounds, and sometimes the problem is so severe that they are unintelligible. People who could benefit from the methods in this chapter are those who are hard of hearing and do not have feedback readily accessible and people with ALS. This chapter describes a method that uses a computer learning algorithm that predicts what people are about to say based on earlier content and learns what the natural sound of their voice sounds like. This chapter illustrates speech trajectory and voice shaping. Clear Audio is a biologically inspired framework for studying natural language. Like the story behind Jurassic Park, Clear Audio attempts to make predictions about data from existing data, inspired by biological processes. Its main goal is to give feedback for speech pathology purposes.
Assessing the Performance of Image Quality Assessment Metrics

Quality assessment of tone-mapped images has been an active research area over the past years. Humans have a personal liking of image attributes such as vividness of colors, mean brightness level, and contrast. Therefore, image quality is often determined through subjective studies. The participants are asked to assign scores to the images, and the average scores are used to determine the relative quality of the images or the algorithms that produced them. Several metrics have been proposed that try to replicate this process and assign quantitative scores to the images. While these metrics perform reasonably in general and their scores correlate well with subjective scores, they can fail badly in some situations. This paper proposes a novel method to put these metrics to test under extreme conditions and observe their performance. For this, we use the differential evolution approach that keeps modifying the tone-mapping function iteratively, such that the assigned score by the metric keeps increasing over iterations. We show that visible distortions start showing up in the image in many situations, yet the score given by the metric remains very high.
Lithology indicators

Rock and mineral typing today can rely on technologies that did not exist in the pre-digital era. In particular, the spectrographic methods in use today were barely in the experimental stages then and the spectral gamma ray log was only introduced at the end of the pre-digital era. In place of these modern conveniences, which we all now take for granted, the analysts using pre-digital logs will find that basic lithological indicators are just old friends that are still in use today. Here we give them an opportunity to remind us of our ABCs when it comes to figuring out what those F R D Ks1 are.
VR in Higher Education

The purpose of this chapter is to examine VR within education to assess the impact of its ability to apply instructional design principles in the context of virtual reality technology-based instruction. VR can improve learning outcomes from enhancing learning activities to supporting simulation studies and encourage feedback educational tasks. Learner performance will be improved when the gameplay is mapped to gamification and group-based challenges. Due to the diversity of VR, individuals are supported through the educational life cycle via sensory stimuli to improve and enhance cognitive development. Research has shown that VR has changed individual attitudes, cultural beliefs, and context when exploring the new innovative characteristics of these new devices. Virtual reality can enhance and replace reality with digital objects so that learners can instantly enjoy various activities, which make a positive outcome.
Reducing Model Complexity and Cost in the Generation of Efficient Error Detection Mechanisms

The design and location of error detection mechanisms (EDMs) is fundamental to the design of a dependable software system. The application of machine learning algorithms to fault injection data has been shown to be an effective approach for the generation of efficient EDMs. However, the complexity of the generated models and initial cost of generation represent barriers to the adoption of the approach. Addressing these challenges directly, this paper demonstrates that genetic programming can be used as an approach to reduce the complexity of the models generated and obviate the computational cost associated with the sampling and refinement stages of EDM generation. More specifically, it is shown that (i) genetic programming can be used to project the instance space of fault injection data sets into a space more amenable to learning, (ii) machine learning algorithms can be applied to the resultant projection to permit the generation of efficient EDMs with reduced model complexity, and (iii) the cost of generating efficient EDMs can be reduced by the approach because it obviates the need for data set sampling methods and model refinement.
Dual Controlled Smart Wheelchair for Physically Disabled

There are many different types of wheel chairs on the market, some of which are manual, some of which are semi-automatic, and some of which are totally automatic. The development of the wheel chair system is carried out on a daily basis in order to provide support to differently abled people. In this regard, our smart wheel chair system is likewise a work in progress. Provide assistance to this group of people. The goal of this project is to create a Smart Wheel Chair System for the physically challenged. Handicapped persons at a reasonable price. An Android messenger application controls the system, which consists of for navigating aid, use the HC-06 Bluetooth module and proximity sensor. Our wheel chair system is linked to a Bluetooth module, which allows it to automatically move to the desired horizontal position. The system is controlled by an Android application. We used G-chat to link the Wheel Chair to the Bluetooth module at work, but any Bluetooth messenger application can be utilized. Two proximity sensors are installed at the back and front of the system to safeguard it from collisions. Our wheel chair assembly has a dual password security system, with the first password needed to connect the Bluetooth device and the second password used to control the wheel chair's movement.
Route Planning Method of Rescue Aircraft Safe Flight based on the improved Astar Algorithm and Time Window

research-article Route Planning Method of Rescue Aircraft Safe Flight based on the improved Astar Algorithm and Time Window Share on Authors: Wei Li Civil Aviation Management Institute of China, China and General Aviation Research Institute of Zhejiang Jiande, China Civil Aviation Management Institute of China, China and General Aviation Research Institute of Zhejiang Jiande, ChinaSearch about this author , Ming Zhang Nanjing University of Aeronautics and Astronautics, China Nanjing University of Aeronautics and Astronautics, ChinaSearch about this author Authors Info & Claims AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced ManufactureOctober 2021 Pages 2021–2027https://doi.org/10.1145/3495018.3501041Online:14 March 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Data Analysis

We analyze four diverse datasets about time expressions for their intrinsic characteristics and find five such common characteristics; similarly we analyze two well-known benchmark datasets about named entities for their intrinsic characteristics and find three such common characteristics. For the common characteristics of time expressions, firstly, most time expressions are very short, consisting of about 2 words on average; secondly, most time expressions contain at least one time-related word that can distinguish time expressions from common text; thirdly, only a small group of words are used to express time information; fourthly, words in time expressions demonstrate similar syntactic behaviour; and finally, time expressions are formed by loose structure, with more than 53.5% of time tokens appearing in different positions within time expressions. For the common characteristics of named entities, firstly, most named entities contain uncommon words, which mainly appear in named entities and hardly appear in common text; secondly, named entities are mainly made up of proper nouns, with more than 84.8% of proper nouns appear in named entities under the whole text and more than 80.1% of the words are proper nouns within named entities; thirdly, named entities are formed by loose structure, with more than 53.77% of distinct words that appear in different positions within named entities.
Chapter 7: Hierarchical Clustering Techniques

Hard clustering algorithms are subdivided into hierarchical algorithms and partitional algorithms. A partitional algorithm divides a data set into a single partition, whereas a hierarchical algorithm divides a data set into a sequence of nested partitions. As we mentioned in Chapter 1, hierarchical algorithms are subdivided into agglomerative hierarchical algorithms and divisive hierarchical algorithms (see Figure 1.5).
Introduction

Swarm intelligence is one of the most beautiful and strange phenomena in nature. It emerges from the interaction of a group of agents (individuals) with their environment.
CCCE

Cloud computing technology is a modern emerging trend in the distributed computing technology that is rapidly gaining popularity in network communication field. Despite the advantages that the cloud platforms bolstered, it suffers from many security issues such as secure communication, consumer authentication, and intrusion caused by attacks. These security issues relevant to customer data filtering and lost the connection at any time. In order to address these issues, this chapter, introduces an innovative cloud computing cryptographic environment, that entails both Quantum Cryptography-as-service and Quantum Advanced Encryption Standard. CCCE poses more secure data transmission channels by provisioning secret key among cloud's instances and consumers. In addition, the QCaaS solves the key generation and key distribution problems that emerged through the online negotiation between the communication parties. It is important to note that the CCCE solves the distance limitation coverage problem that is stemmed from the quantum state property.
Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector

Recent years, textual semantic similarity measurements play an important role in Natural Language Processing. The semantic similarity between concepts or terms can be measured by various resources like corpora, ontologies, taxonomies, etc. With the development of deep learning, distributed vector models are constructed for extracting the latent semantic information from corpora. Most of existing models create a single prototype vector to represent the meaning of a word such as CBOW. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. In this work, the authors propose a knowledge-augmented multiple-prototype model by using corpora and ontologies. Based on the distributed word vector learned by the CBOW model, the authors append the concept definition and the relational knowledge vector into the target word vector to enrich the semantic information of the word. Finally, the authors perform the experiments on well-known datasets to verify the efficiency of the authors' approach.
Data Extrapolation via Curve Modeling in Analyzing Risk

Risk analysis needs suitable methods of data extrapolation and decision making. Proposed method of Hurwitz-Radon Matrices (MHR) can be used in extrapolation and interpolation of curves in the plane. For example quotations from the Stock Exchange, the market prices or rate of a currency form a curve. This chapter contains the way of data anticipation and extrapolation via MHR method and decision making: to buy or not, to sell or not. Proposed method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz-Radon (OHR), built from these matrices, is described. Two-dimensional data are represented by the set of curve points. It is shown how to create the orthogonal and discrete OHR and how to use it in a process of data foreseeing and extrapolation. MHR method is interpolating and extrapolating the curve point by point without using any formula or function.
Semantic Health Mediation and Access Control Manager for Interoperability Among Healthcare Systems

Healthcare systems have evolved to become more patient-centric. Many efforts have been made to transform paper-based patient data to automated medical information by developing electronic healthcare records (EHRs). Several international EHRs standards have been enabling healthcare interoperability and communication among a wide variety of medical centres. It is a dual-model methodology which comprises a reference information model and an archetype model. The archetype is responsible for the definition of clinical concepts which has limitations in terms of supporting complex reasoning and knowledge discovery requirements. The objective of this article is to propose a semantic-mediation architecture to support semantic interoperability among healthcare organizations. It provides an intermediate semantic layer to exploit clinical information based on richer ontological representations to create a “model of meaning” for enabling semantic mediation. The proposed model also provides secure mechanisms to allow interoperable sharing of patient data between healthcare organizations.
Propositional and Predicate Logic

Propositional logic is the study of propositions, where a proposition is a statement that is either true or false. Propositional logic may be used to encode simple arguments that are expressed in natural language, and to determine their validity. The validity of an argument may be determined from truth tables, or using the inference rules such as modus ponens to establish the conclusion. Predicate logic allows complex facts about the world to be represented, and new facts may be determined via deductive reasoning. Predicate calculus includes predicates, variables and quantifiers, and a predicate is a characteristic or property that the subject of a statement can have. The universal quantifier is used to express a statement such as that all members of the domain of discourse have property P, and the existential quantifier states that there is at least one value of x that has property P.
Metadata Standards in Digital Audio

Audio metadata is an essential tool that supports control and management of systems that create, transmit, describe, manage, and store digital audio. Technical and structural metadata enable audio in devices and software applications; descriptive metadata provide context. Metadata also relate and incorporate audio in a multimedia environment resulting in profound effects on conception, reception, and consumption. This chapter explores metadata standards in digital audio.
Sorting of Civic Issues using Machine Learning with Image Processing

In our day to day life there are several civic issues which are being faced by each&amp; every person in the world.&amp; if we take countries just like India or any other country where population is too much, in such countries there are several civic issues which are faced by each citizen in such countries. Larger the country larger the issues, because there are several complaints regarding a single issue. If we take just an example out of those like street damages, garbage management problems (garbage bin over owing), Electricity problem, Water problem etc. For that there is a system also available in such countries but problem is just like that it’s time consuming. If any one of us go to register complaint into the municipality then they will register our complaint, after registration there are several days required to solve their civic issues of each individual person who are residing at that place. So, from this project we are just reducing the time of the process from both sides, i.e. if any one of the persons sees that garbage or can say that potholes on the road but he / she neglect that&amp; move on, just because of they think that when will we go to the municipality &amp; when they will register complaint against it. Nobody has a time in today’s 21stcentury. There is another kind of people also present in respective countries, they are very sciatic who really wants to act regarding such as garbage wasting or any other issues. They register their complaint but they don’t know the status of their complaint, just because of that they leave from that topic &amp; easily move on. So, this is just an effort for finding remedy for all above mentioned issues.
Query Rewriting

It is well known that there is a lexical chasm between web documents and user queries. As a result, even when the queries can fully capture users’ information needs, the search engines could not retrieve relevant web documents to match these queries. Query rewriting aims to bridge this gap by rewriting a given query to alternative queries such that the mismatches can be reduced and the relevance performance can be improved. Query rewriting has been extensively studied and recent advances from deep learning have further fostered this research field. In this chapter, we give an overview about the achievements that have been made on query rewriting. In particular, we review representative algorithms with both shallow and deep architectures.
Understanding User Experience

User experience determines the quality of an interaction being used by an actor in order to achieve a specific outcome. The actor can have varying roles and evolving needs, thus reviewing and predicting experiences are important. As an actor uses and gains feedback, the feedback guides individual and group behavior, thus becoming pertinent to how interactions occur. This chapter questions how artefacts are designed to promote such interactions and what processes should be incorporated to ensure successful interpretation, use, (physical) reaction, and conation. This chapter discusses the effects of user experiences today based on societal needs and expectations. It shows how the field is delineated into numerous sub-topics, all of which can stand on their own yet still draw from each other. The discussions will include fields such as cognitive science, human-computer interaction, learning sciences, and even ergonomics to show how design and subsequently interactions can assist in having successful user experiences.
Construction and application of constitution online learning Platform based on mobile Internet technology

Based on Mobile Internet technology (Mobile Internet) constitution online learning platform is a suitable for the current college students group application, relying on Mobile terminals, in the Mobile communication network environment can be used in the network online learning platform. Under the mobile Internet technology, relying on the widespread use of mobile terminal equipment and comprehensive coverage of mobile communication network, mobile Internet applications are gradually diversified, and the functions covered are further enriched and powerful. Successively set up by the constitution on the basis of online learning platform is designed to enrich college students knowledge of the constitution of the way to learn, such as online learning, games, problem solving, interactive communication function module, convenient for students to use of fragmented time to improve the in-depth study and comprehensive understanding of the constitution of knowledge, make up for the shortcomings of traditional passive rigid teaching way, it can effectively improve the enthusiasm of students to study the constitution.
A Robust Random Matrix Prediction Model for Extended Object Rotations

This paper proposes a robust random matrix prediction model for the tracking of extended objects performing turn-rate manoeuvres. A key innovation is to derive exact solutions for two matrix-valued expectations that are currently approximated by third-order Taylor series expansions. Moreover, we prove that the use of such Taylor series expansions will lead to tracking failure when the turn-rate variance exceeds a known threshold. The exact solution can be computed in a robust and computationally efficient manner, and we demonstrate its utility in simulation compared with state-of-the-art alternative approaches.
Bioimage Databases

In bioinformatics, image data is as important as textual data in churning out new discoveries in science. Image data in biology includes high content screening, molecular, cellular, medical, real-time earth and other related images in biology. Harnessing the power of biological images using proper methodologies and techniques are necessary as images provide rich visual information that could assist in generating valuable research outputs. This article presents the current status of bioimage databases, focusing on two different types which are text based query databases and content based image retrieval databases. The issues in bioimage databases are discussed and a proposal for the development of viable bioimage databases is presented.
Belief Revision

This chapter introduces the classical AGM approach to belief revision as well as the basic ideas of belief base revision theory. Finally, we describe a number of open problems that will be tackled in Part II of the investigation.
Light Deflection

This chapter focuses on the light deflection. We begin with a point mass and derive the deflection angle in both the Newtonian and Relativistic limits. Then, we generalize the result to the cases of ensembles of point masses and extended mass distributions.