Reviewerly/exHarmony huggingface dataset:
Lossless Image Decomposition And Reconstruction Using Haar Wavelets In Matlab For Ecet Students

A method for introducing the topic of lossless image decomposition and reconstruction to ECET students is presented.The definition and frequency selective properties of the Haar wavelet is introduced.In addition, the application of Haar wavelets to the decomposition and reconstruction of a 1-dimensional signal is explained and serves as a stepping stone to discussing the application to digital images.
Using Uml And Security Patterns To Teach Secure Systems Design

Our introductory course on data and network security presents an overview of the main topics of security and has a conceptual and design emphasis.There is a graduate and an undergraduate version of this course.A security course should encompass all the system architectural levels and provide a unifying conceptual approach or it becomes a collection of techniques and mechanisms to solve disjoint problems.For several reasons, formal methods are not appropriate for this purpose.The Unified Modeling Language (UML) is the accepted standard for software development and it is a visual language very appropriate for the description of system architecture.Software patterns are well established for software analysis and design as a way to improve reusability and reliability.We have adopted an approach that combines UML and patterns to present models and mechanisms for security.The students' reaction to this approach has been very positive because they see the course as a way to learn not only security but also to reinforce their knowledge of object-oriented software design.We are also using this approach in a forthcoming security textbook.
Toward An Engineering Capability Maturity Model

There are many skills and capabilities considered crucial to an engineer.Colleges of engineering and engineering accreditation boards have developed curricula and criteria that assess mastery of the requisite mathematical, scientific and engineering foundation.However, other critical skills and capabilities, such as technical writing and oral communication skills, problem solving skills, interdisciplinary team collaboration skills, leadership skills, ethics and creativity are assumed to be interwoven across the curriculum.The capability and maturity of engineering students in these areas are seldom formally assessed.This paper proposes an Engineering Education Capability Maturity Model designed to improve the process of tracking, assessing and improving engineering students' capabilities in these often neglected areas across their undergraduate years.The Engineering Education Capability Maturity Model is an adaptation of an integrated process improvement model used in software systems engineering, called the Capability Maturity Model (CMM).Model-based process improvement uses a model to guide the improvement of an organization's processes and aims to increase the capability of work processes.Process capability is the inherent ability of a process to produce planned results.This paper presents an overview of the CMM and proposes three CMM-based models for improving the process capability of the engineering institution, the engineering faculty and the engineering student.
Validity of the Methodology for Establishing Baseline Water Quality for Uranium

This paper examines the research a teacher completed during the Research Experience for Teachers in Manufacturing for Competitiveness in the United States (RETainUS) summer project and the implementation of the research in a high school science classroom.The research involved the importance of establishing a baseline water quality level prior to in situ recovery mining for uranium.Water is the life line of many and the quality of that water is being questioned by many.In South Texas there is a clash between private citizen groups and uranium mining companies regarding ground water quality in uranium mining sites.Private citizens' groups are claiming that uranium mining companies are contaminating the ground water with drilling and uranium production processes and that the baseline well locations and averages are not randomly selected.However, research findings show that the drilling process is similar to that of other drilling processes.There is not a clear variation between drilling for baseline water wells and drilling for identification and production of uranium deposits.The claims that uranium companies are polluting the ground water may be valid but the phenomenon needs to be better understood before conclusions can be made on the causes of uranium contamination in ground water.There are many improvements that can be made by private citizens, uranium companies, and the regulatory agencies to maintain better water quality.Using this research experience, the teacher created a thematic unit using the question of "Is your water safe to drink?" for a high school aquatic science class following the legacy cycle format.Students will explore what makes water safe to drink, where well water comes from, drilling water well practices, and the uranium mining process.Students will test their own water samples for basic water contaminants.Student learning will be gauged by a scenario of the student working at an environmental lab and lastly sharing their new found knowledge with local publications.Students in this community are aware of uranium in the ground water, as the area has been plagued with water quality problems.This legacy cycle will give the students information need to become informed citizens.
Work in Progress: Recruiting Computing Students Through IN-COMMAND CS-0: An INtroduction to COMputing through Mobile ApplicatioN Development

As part of a larger effort to recruit and retain Hispanics in computer engineering, the first author created a weeklong summer course for college students to develop applications for Google's Android mobile phone.Ten students, having little to no prior programming experience, designed and tested applications (apps) for Google's Android platform.Students used a software development environment, complete with a software emulator for the phone to develop the application and a real platform running the Android OS to demonstrate the application.Students were exposed to the main aspects of App development, such as setting up the development environment, Graphical User Interface (GUI) design through the Extensible Markup Language (XML), Multimedia and Graphics programming, Java programming and OpenGL.At the end of the weeklong session, students initiated the development of a Sudoku application for the Android System.Students involved in the weeklong course took a survey based on social cognitive career theory (SCCT), which posits that individuals base career decisions on their confidence, interest, and perceived sense of mastery in a field (Lent, et al 2005).Following the course, students reported increased interest in computing careers and in the computing major, increased confidence that they could succeed in the major, and perceived academic support in their department.Students commented that the structure of the course and real-world problem solving associated with developing apps contributed to their learning.Future efforts will involve developing a full semester course as well as disseminating the curriculum to high school and post-secondary populations.
Towards Grounding Compositional Concept Structures in Self-organizing Neural Encodings

While the symbol grounding problem of agreeing on a mapping between symbols and sensory or even sensorimotor grounded concepts has been solved to a large extent, one possibly even deeper open problem remains: How do concepts and compositional concept structures develop in the rst place?Concepts may be described as integrative mental representations that encode certain sensory, motor, or sensorimotor states or events.Compositionality, on the other hand, determines how concepts are associated with each other in a semantically meaningful and highly exible manner.We argue that progressively complex concepts and compositional structures can be developed starting from very basic perceptual and motor control mechanisms.An experiment with a simple simulated robot gives hints about highly relevant structural ontogenetic prerequisites for their development.In the outlook, we conclude by sketching out the current most pressing challenges ahead.
Strategies For Graduate Software/Systems Engineering Education (Panel)

Software engineering is more important today than it has ever been.More consumer products include larger quantities of software than ever before.The demand for well educated software engineers continues to increase.To meet that demand colleges and universities need to offer more graduate programs in software engineering.In addition, the increasing complexity of typical applications that require the development of both software and hardware demands that systems engineers, who are responsible for overall system architecture, the allocation of functionality between hardware and software and the system's critical safety and timing requirements, be knowledgeable about both hardware and software development processes and capabilities.
The Software and Systems Engineering Masters Program at Texas Tech University: A Computer Science and Industrial Engineering Collaborative Effort

University of Miami from 1987-1989. Her research addresses integrated techniques for event, rule, and transaction processing to address data consistency and active behavior in distributed, data-centric applications.Urban has been the recipient of several grants from the National Science Foundation for her research on constraints, active rule processing in centralized and distributed environments, data consistency issues in service-oriented environments, the use of databases in engineering design, undergraduate research, and the development of innovative teaching concepts for database instruction.She has published more than 100 refereed papers and book chapters on the results of her research and is a co-author of Fundamentals of Object Databases: Object-Oriented and Object-Relational Design (Morgan Claypool, 2011).She currently serves in the Editorial Board of the Journal of Data Semantics and has previously served on the
Automatic Construction of Parallel Dialogue Corpora with Rich Information

Due to the lack of ideal resources, few researchers have investigated how to improve the machine translation (MT) of conversational materials by exploiting their internal structure. In this chapter, we will propose a novel strategy to automatically construct a parallel dialogue corpus by bridging two kinds of resources: movie subtitles and movie scripts. First, we collected parallel subtitles and their corresponding monolingual scripts from the Internet. After sentence alignment, we then projected all useful information from the script side to its corresponding subtitle side. Finally, we automatically built a Chinese-English dialogue corpus, which contains bilingual subtitle utterances, speaker names and actions, scene descriptions and boundaries, and script sentences. To demonstrate the usefulness of our data, we used speaker name tags to improve the translation performance. Our experiments showed that our approach achieved 81.79% accuracy in speaker name annotation, and the speaker-based model adaptation obtained around a 0.5 BLEU (bilingual evaluation understudy) point improvement in translation quality. We believe that our resources will benefit various tasks, such as dialogue systems, image/movie descriptions, and MT.
ANALISIS RESOLUSI KAMERA DAN SUDUT UNTUK VISUALISASI ALAT MUSIK DRUM MENGGUNAKAN MULTI TRACKING

Music favored by many people. Because the wonderful mix of instruments that can make people become complacent with its voice. One of instrument that favored people that drum. Drum can adrenaline someone for its drum. The introduction of drums still use books or media two-dimensional (2D). One of development the introduction of drums could use Augmented Reality (AR). In this study, Augmented Reality is an application that visualizes the shape of parts of the drums. In manufacturing consists of three phases, namely making marker, making 3D objects with Autodesk Maya, and manufacturing of AR application using Unity 3D. In this study using multi-tracking method that is able to scan more than one marker simultaneously. Testing of this research was conducted by two parameters, namely, the resolution of the camera, and angle. The resolution parameter consists of 2 Megapixel camera, 3.2 Megapixel, 5 Megapixel, while for angles comprised of 30 o , 45 o , and, 90 o . From the results of these tests showed that the ideal angle for scanning is at an angle of 90 o . When using angle and less than 90 o , then the time for generating 3D objects can be longer. For the resolution of the camera is a 5 Megapixel recommended, because it can detect the marker faster in comparison with the resolution 2 megapixel camera, and a 3.2 megapixel.
Collaborative Filtering Model for Recommendation Systems

With the exponential enhancement in the quantity of virtual records over the net, on-line stores, online song, video and photo libraries, search engines and recommendation systems have end up the most useful approaches to find applicable statistics in short time. In the latest times, deep learning advances have won large attention inside the subject of speech reputation, image processing and natural language processing. In the meantime, several recent studies have proven the application of deep learning to know inside the place of recommendation structures and information retrieval as well. In this paper, we cover the recent advances made inside the area of recommendation the usage of various versions of deep learning technology. We arrange the paper in 2 components: collaborative system, content based system. The paper additionally discusses the contribution of deep studying incorporated recommendation systems into numerous application domains. The paper concludes via discussion of the impact of deep learning knowledge of in recommendation system in diverse area and whether deep learning has shown any big improvement over the conventional systems for advice by using K-means Clustering algorithm. Finally, we also provide future directions of studies that are viable based on the current state of use of deep learning in recommendation systems.
QBGA–SVM for Face Recognition of Livable Cities

With the continuous expansion of urbanization, the problem of human settlements has become increasingly prominent. Green, economical, intelligent and livable cities have become the urgent needs of future urban planning. The evaluation of urban livability is not only one of the judgment criteria of urban competitiveness, but also an important factor affecting the speed of urban development. Among them, the safety factor of the city is the important guarantee of other aspects, so this paper intends to design a high-precision face recognition algorithm to make efforts for the safety construction of livable cities. Aiming at the shortcomings of the standard support vector machine (SVM), combined with the quantum-behaved mechanism, a quantum-behaved genetic algorithm–SVM (QBGA–SVM) is proposed in the paper. The experimental results for the human face databases show that QBGA–SVM is superior to the comparison algorithms in both accuracy and stability. Finally, QBGA–SVM is applied to face images of the real world, and the results are better than the other algorithms.
Fashion and Beauty Retailing with the Metaverse: A Systematic Review of 5 Years of Research

<p class="p1" style="margin-bottom: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; line-height: normal; font-family: &quot;Helvetica Neue&quot;; color: rgb(0, 0, 0); font-size: 13px;"><span style="font-size:16px;margin-bottom: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; line-height: normal; font-family: &quot;Helvetica Neue&quot;; color: rgb(0, 0, 0);">The metaverse allows retailers new opportunities to promote their products and brands by providing brand-new digital experiences to consumers. However, the academic understanding of the metaverse in fashion and beauty retailing remains yet blurred. To fill the research gap, a systematic review of 5 years of research publications was conducted following the PRISMA protocol. The distribution of publications by year showed the recent surging interest in the metaverse. Despite the scattered understanding of the term&nbsp;<i style="margin-bottom: 0px; font-variant-numeric: normal; font-variant-east-asian: normal; font-stretch: normal; font-size: 16px; line-height: normal; font-family: &quot;Helvetica Neue&quot;; color: rgb(0, 0, 0);">metaverse</i>, two major approaches to conceptualizing the metaverse were found. The dominant concepts and issues covered in the retailing and consumer behavior literature on the metaverse represent the advent of meta-commerce. In order to compensate for the lack of physical sensory experience and induce desirable consumer experience in the meta-commerce, fashion and beauty retailers are recommended to provide high responsiveness, realistic feelings, and entertainment and social values by enhancing perceived interactivity and vividness.&nbsp;</span>
Research Spotlights

What surprising patterns do fluid dynamics and neurobiological networks have in common? In this issue's first Research Spotlights article, “Bump Attractors and Waves in Networks of Leaky Integrate-and-Fire Neurons,” a connection is made between the waves at the onset of pipe turbulence and the localized traveling waves in spiking neural networks. Authors Daniele Avitabile, Joshua L. Davis, and Kyle Wedgwood consider a so-called “discrete integrate-and-fire model” to describe the dynamics of a connected neuron firing via a system of piecewise linear ordinary differential equations. Constructing a solution to this system effectively amounts to determining firing times of the neurons. One can then witness “bump” patterns, in this case characterized by a core of neurons that fire frequently, while neurons away from the core do not fire. Such a model's deterministic chaotic behavior can then be studied around a firing set and used to construct waves and analyze their stability. Simulations and analysis demonstrate oscillatory and nested branching of these waves, and such behavior can accumulate in a bump attractor and wandering wave. The authors show that such complex spatiotemporal patterns, and their conditions for stability, are not unlike those witnessed in the transition to turbulence in a pipe. The paper concludes with a challenge to future researchers: can further dynamical systems characterizations of such event-driven models uncover links between localized waves and bumps in complex spatial networks? The second Research Spotlights article addresses the problem of identifying a system from noisy observations of dynamical data. In “Learning Dynamical Systems with Side Information” authors Amir Ali Ahmadi and Bachir El Khadir present a way to incorporate system knowledge to improve the learning process beyond what is possible using data alone. Such knowledge, or “side information,” arises when one has contextual information about the unknown dynamics without knowing the dynamics precisely. Examples of side information covered by the authors' framework include known trajectories (e.g., when one has knowledge of some equilibrium points), group symmetry, and coordinatewise properties (e.g., nonnegativity or monotonicity). Restricting attention to a particular class of functions to be learned and particular side information allows the learning process to be posed as a semidefinite optimization formulation amenable to efficient solution. This approach is demonstrated numerically on systems modeling the diffusion of a contagion, behavior of a simple pendulum, and time evolution of a cancerous tumor, as well as on the classical Lorenz system. In each case, imposing side information via semidefinite programming allows the authors to learn the unknown dynamics from far fewer observations than otherwise possible and provides a potential road map for addressing even broader classes of systems and side information.
Single-shot 3D shape measurement based on RGB dot patterns and stereovision: retraction

The referenced article [ Opt. Express 30 , 28220 ( 2022 ) 10.1364/OE.466148 ] has been retracted by the authors.
Survey and Review

Marginal likelihood and Bayes factors are key components of the Bayesian analysis used in hypothesis testing and model selection. In many applications in statistics, applied mathematics, signal processing, and machine learning, the computation of normalizing constants of probability models or ratios of these constants plays a fundamental role. The first Survey and Review paper in this issue, “Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review,” by F. Llorente, L. Martino, D. Delgado, and J. López-Santiago, provides a comprehensive study of the state of the art of this topic. The authors highlight respective benefits and limitations of many computational schemes for approximating the marginal likelihood of different models or the ratio of two marginal likelihoods. Connections and differences among these techniques are worked out. Some of the most relevant methodologies are compared through theoretical comparisons and numerical experiments. The chief accomplishment is the presentation of various methods introduced independently in the literature in a unified framework. The second Survey and Review paper, “Flow-Based Algorithms for Improving Clusters: A Unifying Framework, Software, and Performance,” by Kimon Fountoulakis, Meng Liu, David F. Gleich, and Michael W. Mahoney, addresses the improvement of clusters of points in a vector space or nodes in a graph, which is a primary problem in statistical data analysis. Clustering means grouping of objects in a set such that objects in the same cluster are more similar to each other than to those in other groups. It is a main task of exploratory data analysis and a common technique for statistical data analysis, used in pattern recognition, image analysis, data compression, machine learning, and many other fields. A frequent practice is improving a given cluster such that conductance is minimized. Cluster analysis can be achieved by various algorithms. While the literature is rich on global spectral methods, flow-based methods have attracted less attention so far, since they lack the simplicity of related spectral methods and seeded graph diffusion methods like PageRank. These spectral methods are often easy to explain in terms of random walks, Markov chains, linear systems, and intuitive notions of diffusion. On the other hand, at the expense of complex graph constructions, the flow-based methods often yield impressive theoretical results. The goal of this review is to provide a unified framework for flow-based methods, based on a class of optimization methods known as fractional programming. The fractional programming optimization perspective unifies all existing flow-based cluster improvement methods, showing that these methods are attractive alternatives to global spectral methods.