Reviewerly/exHarmony huggingface dataset:
Analysis method for structured big data feature based on hypernetwork model

In view of the problems existing in current big data feature analysis methods, such as long data feature retrieval delay, low comparison fitting index and low success rate of abnormal data extraction, this paper proposes a structured big data feature method based on the hypernetwork model. Based on the commonality of different components, the abnormal data features in big data are extracted. The characteristics of structured big data are analysed comprehensively by using the hypernetwork model, and the comprehensive data classification is carried out. The hypernetwork model is adopted to realise the structural and clustering analysis of big data features, so as to ensure the quality of data feature analysis. The experimental results show that the proposed method has shorter data feature retrieval delay, the comparison fitting index is basically above 0.9, the fitting effect is better than the literature method, and the success rate of abnormal data extraction is higher.
Designing a suitable learning technology for an authentic science classroom: reflection from WISE and smart classrooms

Different emerging learning technologies provide new insights into the utility of educational applications. However, their proper integration for collaborative problem solving in authentic science classrooms remains a complex challenge for researchers and stakeholders. This paper presents an in-depth analysis of two systematic works on design learning technologies for authentic science classrooms. The first study concerns the web-based inquiry science environment (WISE) research team from the University of California Berkeley, and the second involves the smart classroom research team from the University of Toronto. Such in-depth analyses will further enhance our understanding of the role of technology in the classroom context and offer a systematic reflection on designing suitable learning technologies for authentic science classrooms.
A literature Review of Cryptography on RC4 Stream Cipher and Hash-LSB Steganography

In the modern world with the emergence of technology, the entire network includes the digital data that adds new dimensions as to how data can be exchanged effectively and safely in real time from one part of the world to another. Such new opportunities are followed by obstacles such as keeping information confidential and secret or data that is a lively area to explore. It is necessary to transform the information into cryptic form to safely transfer the information from intruders, so that the information is concealed from the intruders. Researchers have always found it fascinating to establish safe techniques that would allow the data to reach from the sender without exposing it to third parties. Therefore several techniques have been developed to transfer data securely and steganography is one of them. In this paper, we review numerous research papers on different techniques of encryption and steganography. This paper contains details about the RC4 encryption algorithm, and how steganography and encryption methods are used. The cryptography and steganography techniques ensure the security of the data.
Design and Development for Forgery Currency Detection using SIFT Features based SVM Classifier

There are many methods for identifying a fake currency notes which we have discussed and each one has its own significance. But, there is no software to detect fake currencies. The first process is to get the original and fake currency image from the data set .After getting the two images are pre-analysis the both original and fake image Convert the image into gray color. To extract the black strips in both currency images. After conversion the image segmentation are applied and the post-processing are applied. After that the feature extraction are classified undergoes SVM Classifier. SIFT Algorithm are used in the training set to count the black strips from both original and forgery image. Finally, there are two types of result will be executed under the histogram feature analysis and probability map and next one is to counting the black strips from both original and fake images.
Some Key Technologies of Deep Learning in the Field of Computer Vision

In recent years, with the continuous development of science and technology, computer vision has also been a rapid development opportunity, and it is also an important research direction of deep learning.For example, from the previous large-scale visual recognition competition to the current stage of go man-machine war, we have seen the infinite potential and vigorous vitality of computer in the field of vision.The development of computer vision field cannot be separated from the important role of deep learning in it.Therefore, deep learning is of great significance to the development of computer vision and artificial intelligence, and also changes people's way of life.However, due to the complexity of deep neural network maze design and the lack of tagged training data, the development of deep learning in the field of computational vision has also encountered some difficulties.This paper analyzes some key technologies of deep learning in the field of computer vision, hoping to play a reference role for relevant researchers.
The career psychological experiences of academic department chairpersons at a South African university

The existing research on the academic department chairperson focuses on the structure of the position more than on the role in terms of career, psychological functioning, coping and well-being of the incumbent. The objective of this research was to describe the career psychological experiences of academic department chairpersons (ADC) in their organisational role. A qualitative and descriptive research design was chosen for a case study of 24 chairpersons. Data were gathered using a focus group followed by thematic analysis. The manifesting themes were crossing the boundary into the career of an ADC, relationships, experienced confl ict and loss in the role, personal well-being and future career as an ADC. A psychological profile was constructed, and the data were interpreted on the basis of various stress coping models. It was concluded that these ADCs did not cope well psychologically, which significantly influenced their work performance. Research on the depth psychology role analysis of the ADC was recommended, followed by an inter-university investigation into the career dilemmas experienced, resulting in their lack of work satisfaction and productivity.
Evaluating the Use of Action Templates to Create Suspense in Narrative Planning

The IRIS story generation system is a narrative planner that is designed to create suspenseful stories at the story-action level. It uses a set of action templates that are injected into the planning process to create suspense around a given goal. The action templates were evaluated independent of the rest of the IRIS system against a series of other techniques designed to create suspense. Results show that it is possible to use these set of action templates to create localized moments of suspense in a story at the story-action level without the need for discourse-level suspense.
Building an Honest Tree for Mass Spectra Classification Based on Prior Logarithm Normal Distribution

Structure elucidation is one of big tasks for analytical researcher and it often needs an efficient classifier.The decision tree is especially attractive for easy understanding and intuitive representation.However, small change in the data set due to the experiment error can often result in a very different series of split.In this paper, a prior logarithm normal distribution is adopted to weight the original mass spectra.It helps to building an honest tree for later structure elucidation.
A Call for Emotion Modeling in Interactive Storytelling

Artificial Intelligence (AI) techniques are widely used in video games. Recently, AI planning methods have been applied to maintain plot consistency in the face of player’s agency over the narrative. Combined with an automatically populated player model, such AI experience managers can dynamically create a consistent narrative tailored to a specific player. These tools help game narrative designers achieve narrative goals while affording players a choice. On the other hand, they increase the number of feasible plot branches making it more difficult for the author to ensure that each branch carries the player along a desired emotion arc. In this paper we discuss the problem and call for an extension of experience managers with player emotion models. When successful, interactive narrative can be then automatically produced to satisfy authorial goals not only in terms of specific events but also in terms of emotions evoked in the player.
Cloud Computing:Concept,Model,and Key Technologies

Cloud computing is a new technology for network computing under the IP architecture,and its potential lies in new ICT business applications.For the majority of operators and enterprises,the main task of cloud computing is data centre transformation.This will ensure cloud computing becomes more widespread among enterprises,institutions,organizations,and operators.Cloud computing will not only provide traditional IT resource usage and application services,but will also support full resource usage and application services—such as IT,communications,video,mobile,as well as Internet of Things under a converged network infrastructure.Some key cloud computing technologies include unified fabric,unified virtualization,and a unified computing system.The formation of an open industry alliance and promotion of open technology standards will also be strategically critical for future development of cloud computing.
A Modular Approach To Teaching “Wireless Communications And Systems” For Ecet Students

Recent development in wireless technologies has generated a high demand for wireless communications professionals.Rigorous math background is needed for students to fully understand wireless communications system fundamentals.However, Electrical and Computer Engineering Technology (ECET) students generally do not have such a math background to digest concepts and analyze issues associated with wireless communications.It is a better approach to focus on a point-to-point wireless system through experiential exploration, and extend the knowledge to wireless communications systems in general.There are different educational training stations available to help achieve educational goals in wireless communications.However, these educational trainers have their obvious shortcomings, which include: (1) most of them transmit at relatively low frequencies (in one hundred MHz range), while most of contemporary communications systems transmit in GHz range; (2) most of them are built as systems and it is hard for students to test module parameters; (3) most of them are costly and it is not affordable for many schools to purchase enough units to satisfy their lab needs.This article presents our on-going efforts of developing a modular approach to teaching a senior level "Wireless Communications and Systems" course.Mini-Circuits R RF modules are used for function block testing and system implementation.The emphasis is on the Radio Frequency (RF) modules of a communications system such as modulator, demodulator, Voltage-Controlled-Oscillator (VCO) and Frequency Mixer.The operating frequency is set at 900 MHz.Performance issues specific to wireless communications such as large and small scale fading effects are also addressed.
Sign Language Recognition

There are nearly 15 million people around the world who have difficulty in speaking or communicating. Their only way of communication is through sign language. Hand gesture is one of the methods used in sign language for non-verbal communication. It is most commonly used by deaf &amp; dumb people who have hearing or speech problems to communicate among themselves or with normal people. There are many recognized sign language standards that have been defined such as ASL(American Sign Language), IPSL(Indo Pakistan Sign Language), etc., which define what sign means what. ASL is the most widely used sign language by the deaf and dumb community. The deaf and dumb use sign language to communicate among themselves with the knowledge of the standard sign language. But they can’t communicate with the rest of the world as most of the people are unaware of the existence and the usage of the sign language. This method aims to remove this communication barrier between the disabled and the rest of the world by recognizing and translating the hand gestures and convert it into speech.
Application of Intelligent Technology in Power System Automation

This paper describes the theory of intelligent control,and introduces the implementation scheme and measures of intelligent technology in power system,including expert system control technology,fuzzy control technology,artificial intelligence technology,linear optimal control technology,monitoring technology and integrated intelligent system,etc.
Intelligent Content Generation via Abstraction, Evolution and Reinforcement

We present a system for autonomously generating puzzles in the form of a 2D, tile-based world. Puzzle design is entirely dependent on tile characteristics, which are implemented as abstract classes that can be modified by the system. Thus, the system controls not only the base-level puzzle design but also (to some extent) the meta-level component design. The result is a rich space of possible puzzles that the system explores with a combination of evolutionary computation and Q-learning. The system autonomously produces a variety of puzzles of varying difficulty to create a game called Loki's Castle. The system is almost completely autonomous, requiring only a minimal description of what a puzzle should include, and the abstraction allows extensibility so that future versions can invent entirely new classes of tiles. Several puzzle examples are presented to demonstrate the system's capability.
A Paradigm Shift In Digital System Design Education With Industry Participation

While the commercial Electronic Design Automation (EDA) and the academic/industrial research communities have been aware of the requirement for an intensive effort to study the digital system design practice in its entirety, resource needs, fuzzy objectives, and short-time horizon have handicapped progress.The Rapid Prototyping of Application Specific Signal Processors (RASSP) program is a major ARPA/Tri-Service effort that is overcoming these previous handicaps.This effort is bringing a number of new technologies that will lead to shorter prototyping times, improved product quality, and reduced life cycle costs to fruition.In an effort to ensure the successful transfer of these new technologies, the RASSP Education & Facilitation (RASSP E&F) program is working with small and medium size universities to incorporate these new technologies into their undergraduate and graduate curricula.Only by successfully inserting these rapidprototyping technologies into the curricula and research activities of the university community will the long term benefits of these technologies be realized.This paper discusses the need to change the existing embedded digital system design education paradigm, and the efforts being undertaken by the RASSP E&F program to work with the university community to effect this change.The result is a clarion call to stimulate academic participation in a progressive educational program that adopts the latest instructional methods and industrial strength tools to revolutionize the way system design is taught in United State's colleges and universities.
Talkin' Teams – Strategies for Elevating Student and Team Skill Development over Project Completion

The purpose of this paper is to share the author's experience over the past 12 years of integrating relevant theories and practices concerning authentic development of team skills and team member skills in a year-long team-based capstone design experience.An integrative learning approach to team skills is described that includes: grading and performance reviews that reward participation in building team skills, team formation and ongoing support based on cognitive diversity and collaborative skills, peer coaching based on areas of specialization, and consideration of team and individual tasks when structuring deliverables (including reports and presentations).
Understanding Processors Design Decisions for Data Analytics in Homogeneous Data Centers

Our global economy increasingly depends on our ability to gather, analyze, link, and compare very large data sets. Keeping up with such big data poses challenges in terms of both computational performance and energy efficiency, and motivates different approaches to explore data center systems and architectures. To better understand the processor design decisions in context of data analytics in data centers, we conduct comprehensive evaluations using representative data analaytics workloads on representative conventional multi-core and many-core processors. After a comprehensive analysis of performance, power, energy efficiency and performance-cost efficiency, we have the following observations: contrasted with the conventional wisdom that uses wimpy many-core processors to improve energy-efficiency, the brawny multi-core processors with SMT (simultaneous multithreading) and dynamic overclocking technologies outperform the counterparts in terms of not only execution time, but also energy-efficiency for most of data analytics workloads in our experiments.
Searching for discriminative words in multidimensional continuous feature space

Word feature vectors have been proven to improve many natural language processing tasks. With recent advances in unsupervised learning of these feature vectors, it became possible to train it with much more data, which also resulted in better quality of learned features. Since it learns joint probability of latent features of words, it has the advantage that we can train it without any prior knowledge about the goal task we want to solve. We aim to evaluate the universal applicability property of feature vectors, which has been already proven to hold for many standard NLP tasks like part-of-speech tagging or syntactic parsing. In our case, we want to understand the topical focus of text documents and design an efficient representation suitable for discriminating different topics. The discriminativeness can be evaluated adequately on text categorisation task. We propose a novel method to extract discriminative keywords from documents. We utilise word feature vectors to understand the relations between words better and also understand the latent topics which are discussed in the text and not mentioned directly but inferred logically. We also present a simple way to calculate document feature vectors out of extracted discriminative words. We evaluate our method on the four most popular datasets for text categorisation. We show how different discriminative metrics influence the overall results. We demonstrate the effectiveness of our approach by achieving state-of-the-art results on text categorisation task using just a small number of extracted keywords. We prove that word feature vectors can substantially improve the topical inference of documents’ meaning. We conclude that distributed representation of words can be used to build higher levels of abstraction as we demonstrate and build feature vectors of documents. Our method can help in any multi-domain environment to automatically extract discriminative keywords. It can be used to organise and search documents more efficiently.
Theories, Practices and Examples for Community and Social Informatics

Community Informatics is an emergent discipline with a dual focus: the conduct of research about the relationship between the design of information and communications technologies (ICTs) and local communities; and the implementation of ICT projects in local communities. While most of the Community Informatics literature focuses on empirical work and its relationship to more technically-oriented Information or Management Systems thinking, this book will use a combination of theoretical and case study approaches to explore the relationship between Community Informatics, Social Informatics, and broader social theory. Themes include: social order mediated through ICTs; community and cohesion; class and power; social psychology and technology; the relationship between personal agency and social structure mediated through technology; and the nature of institutional or community formations in the age of ICTs.
A Software Level Calibration Based on Bayesian Regression for a Successive Stochastic Approximation Analog-to-Digital Converter System

Recently, a novel low-power high-precision analog-to-digital converter (ADC) called the successive stochastic approximation ADC has been proposed which has two kinds of outputs from different modes, and which requires a software-level error correction method of combining them into a high-precision total output. From the practical viewpoint, we propose an error correction method based on the Bayesian regression with an incremental learning, in which additional data are successively selected according to the uncertainty of the corresponding predictive total output, and the uncertainty is approximately estimated by evaluating the upper bound of the standard deviations of the Bayesian predictive distributions of the outputs in each block of a partition of the all data set. Through numerical experiments, we verify the performance of the proposed method.
Hybrid capacity planning methodology for web search engines

Capacity planning studies are suitable for supporting decision making in management and operation of Web search engines deployed on large clusters of processors. Among many possibilities, they enable ensuring that a sufficient amount of computational resources are timely provisioned to efficiently deal with the ever changing streams of user queries. In this paper, we present a simulation based methodology devised to perform capacity planning in large scale Web search engines. It combines classical operational analysis formulae with discrete event simulation to significantly reduce the number of deployments that are evaluated to find an optimal assignment for a target workload. We experimentally evaluate our proposal for demanding cases such as service nodes with temporary failures. The results show that the proposed methodology is able to produce good quality results in practical running times.
Classification using margin pursuit

In this work, we study a new approach to optimizing the margin distribution realized by binary classifiers, in which the learner searches the hypothesis space in such a way that a pre-set margin level ends up being a distribution-robust estimator of the margin location. This procedure is easily implemented using gradient descent, and admits finite-sample bounds on the excess risk under unbounded inputs, yielding competitive rates under mild assumptions. Empirical tests on real-world benchmark data reinforce the basic principles highlighted by the theory.