Reviewerly/exHarmony huggingface dataset:
Model construction of artificial intelligence technology in interior residential space design

With the progress of science and technology and the improvement of residents’ aesthetic level, indoor living space has brought a broader application prospect. In this direction, it is undoubtedly very important to explore the application mode of artificial intelligence technology in interior living space design. In this paper, through the three-dimensional model construction of space design, auxiliary design software, intelligent scene analysis in-depth exploration, using three-dimensional three-dimensional model to establish a comprehensive space design system model, can efficiently and scientifically carry out space design work. And expounds the modern intelligent design system based on the combination of artificial intelligence technology and space design industry, strengthening the digital process of space design industry, and then expounds the application of artificial intelligence technology in space design mode, improving the innovative development of the current artificial intelligence technology.
Proactive, Preventive or Indifference?

Students enter classes with mobile devices and use them for learning; however, these are also distracting devices. Some teacher educators display positive attitudes; others display negative attitudes, depending on their perception of the advantages and disadvantages of mobile technology for learning. This paper represents findings of a study that examined teacher educators' attitudes towards the use of mobile technology in classes, and their reactions to its use. The study identified three types of reactions: proactive, preventive and indifference. Findings show that teacher educators perceive the benefits of using mobile technology in their classes as outweighing the disadvantages. However, the majority is indifferent, e.g. do not initiate new uses of mobile technologies in class, but do not prevent its usage. A correlation was identified between the lecturers' reactions to uses of mobile technology and the familiarity with its possibilities and potential in class.
Development and Management of Digital Libraries in the Regime of IPR Paradigm

This article reports the concept of digital library (DL) with its definitions, concept, generic architecture, ethics and librarianship related to DL. DLs are created by Library professionals, publishers, Government initiatives, societies and other higher educational institutions. There are different types of files and file formats are created and stored on DL. For uploading the files, the copyright / IPR issues are involved. As one cannot upload directly someone's file without the permission of that person. In information communication technology (ICT) environment, the Internet and web enables everyone to have access to the e-contents available from anywhere at any time. Due to this, DL creators cannot take it granted to upload them and make them available online. The IPR issues in digital environment plays a key role for identifying and taking permissions from the respective authors / publishers / content creators to upload the digital content. DL and IPR issues are discussed with the digital rights issues.
Emoji in Advertising

Emoji have become an ipso facto universal language that fit in perfectly with informal routine digital communications, especially on mobile devices and on social media. Marketers and advertisers have taken notice of this communicative phenomenon and have started tapping into the emotive power of the emoji code since at least 2010. But is emoji advertising truly effective? Almost no study exists to examine this question. This article thus has a two-fold purpose. First, it looks at the use of emoji in advertising generally and then it presents a pilot study that aims to assay if such advertising is indeed effective. The overall conclusion is that effectiveness relates to the increase in interpretations, or connotations, that emoji ads seem to generate. The use of emoji in advertising is, thus, a field laboratory for gauging where emoji writing is heading and what it entails more broadly for communication.
Improving Student Learning Through Required Exposure to Other Students' Code via Discussion Boards

Abstract In a typical lower-division programming course, students rarely see other students’ code outside of a paired programming exercise. This limits their exposure to the potentially powerful learning experience of seeing examples from other programmers. In this work, we explore the result of having required code sharing via discussion posts to increase a student’s exposure to coding solutions, styles and practices (both good and bad). For each module in a Data Structures and Algorithms course, students post a small section of code, typically a single method or function, and also get to see the code posted from every other student in the class. They can choose to share a section of code that worked particularly well for them or submit code they are struggling with and want some help. The students are then required to respond to entries posted by other students. This creates a dialog between students and provides a mechanism for students to see how other students are coding a solution. The code students submit is from a low-stakes assignment. Students are allowed to see other student’s submissions from the very beginning. They are not required to have completed the assignment or posted their own code to enter the discussion board. To identify the benefits of this assignment, the comments during one semester are analyzed and the results tracked over the course of the semester. The code posted is analyzed for a variety of quality markers such as variable names, commenting, syntax errors, logic errors, correctness, and handling of edge cases. The responses are analyzed for effective error corrections, alternative solutions provided, formatting changes recommended, etc. There is also an analysis of student expressions. Finally, and perhaps most interesting of all is how these details change over the course of the semester.
Index Storage Fundamentals

Where the previous chapter discussed the logical design and syntax of indexes, this chapter will focus on the physical implementation of indexes. An understanding of the way in which indexes are laid out and interact with each other at the implementation and storage level will help you become better acquainted with the benefits that indexes provide and why they behave in certain ways.
Reviews

Machine Learning Refined: Foundations, Algorithms, and Applications, second edition, by Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos, ISBN 978-1-108-48072-7, 2020, Cambridge University Press, 594 p. Earth Science for Civil and Environmental Engineers, by Richard E. Jackson, ISBN 978-0-521-84725-4, 2019, Cambridge University Press, 492 p.
3 Sparse Inertial Poser

This thesis explores approaches to capture human motions with a small number of sensors. In the first part of this thesis an approach is presented that reconstructs the body pose from only six inertial sensors. Instead of relying on pre-recorded motion databases, a global optimization problem is solved to maximize the consistency of measurements and model over an entire recording sequence. The second part of this thesis deals with a hybrid approach to fuse visual information from a single hand-held camera with inertial sensor data. First, a discrete optimization problem is solved to automatically associate people detections in the video with inertial sensor
Jazz Drums

It is no secret that music educators are faced with unique challenges when engaging young musicians who play drum sets. Many educators aren’t drummers themselves, which can create fear and uncertainty and ultimately lead them to avoid, rather than embrace, the wonderful and exciting world of jazz percussion. This chapter provides conceptual and technical approaches to understanding the role each component of the drum set contributes to the ensemble, as well as the role of the drum set in its entirety. It also touches on how dynamics can change the function and style of a groove as well as creative ways to encourage students to move beyond pattern playing. In particular, discussion and examples are provided regarding setup, sound sources, keeping time, functioning within the rhythm section, transitioning from timekeeping to improvising, and various rhythms and grooves.
Effective Bandwidth Prediction through Statistical Technique over Heterogeneous Networks

Real time Streaming Media (Video Streaming) applications are mostly popular on the mobiles and computers using Internet. Due to higher demand of video streaming through wireless network and mobile devices, video are being transmitted through various heterogeneous networks so as to efficiently deliver to the clients devices. This has resulted in the lower quality of real time video, since real time streaming media has quality of parameter requirements like high bandwidth, low packet loss ratio, higher delay and jitter. Streaming media such as video through heterogeneous networks has more challenges due to unreliable wireless networks and device mobility; moreover bandwidth, delay and loss are unknown in advance and are unbounded. In this paper, effective bandwidth prediction through statistical technique over heterogeneous wireless communication networks is proposed. Statistical technique offers computationally efficient bandwidth prediction with reasonably better accuracy. Especially with mobile devices with limited computational power and battery life, necessitates better bandwidth prediction with efficient but computationally simpler algorithms. Bandwidth predictions assist in selecting effective network for video streaming when various heterogeneous networks are available. Detailed bandwidth prediction algorithm is presented with use of quality of service (QoS) parameters data sets available online.
Using PHP to Manage Files

PHP has a huge range of functions designed to work with the server’s file system, but finding the right one for the job isn’t always easy. This chapter cuts through the tangle to show you some practical uses of these functions, such as reading and writing text files to store small amounts of information without a database. Loops play an important role in inspecting the contents of the file system, so you’ll also explore some of the Standard PHP Library (SPL) iterators that are designed to make loops more efficient.
Conditional random field based image and video content analysis

Image and video content analysis is an interesting, meaningful and challenging topic. In recent years much of the research effort in the multimedia field focuses on indexing and retrieval. Semantic gap between low-level features and high-level content is a bottleneck in most systems. To bridge the semantic gap, new content analysis models need to be developed. In this thesis, algorithms based on a relatively new graphical model, called the conditional random field (CRF) model, are developed for two closely-related problems in content analysis: image labeling and video content analysis. The CRF model can represent spatial interactions in image labeling and temporal interactions in video content analysis. New feature functions are designed to better represent the feature distributions. The mixture feature functions are used in image labeling for databases with nature images, and the independent component analysis (ICA) mixture function is applied in sports video content analysis. The spatial dependence of image parts and the temporal dependence of video frames can be explored by the CRF model more effectively using new feature functions. For image labeling with large databases, the content-based image retrieval method is combined with the CRF image labeling model successfully.
A quantum future: preparing Canada for the potential impacts of quantum computing

Quantum computation has the potential to transform the way various Canadian industries do business. Unlike classical computers which use bits, computers built using the principles of quantum mechanics use qubits, which allows them to perform several complex tasks simultaneously and at an exponentially faster speed. This research will analyze the potential impact of quantum computing on Canada's cybersecurity and workforce. It will also highlight barriers to entry for this burgeoning technology and provide recommendations that address current and future challenges. It is proposed that if Canada embraces this technology's opportunities and addresses its challenges, it can continue to be a global leader in the field of quantum computing.
Specification and Enforcement of Access Policies in Emerging Scenarios

The enforcement of access restrictions modeled by access patterns requires a revision of the traditional query evaluation strategies. Users as well as companies access, disseminate, and share information with other parties to offer services, to perform distributed computations, or to simply make information of their own available. View-based access control solutions have been developed for centralized scenarios, but they can be adapted to operate also in distributed database systems. The presence of access patterns may complicate the process of query evaluation. Decryption permits accessing plaintext values of encrypted attributes when needed in the computation. Exchanges of data and collaborative computations should be controlled to ensure that authorizations are properly enforced and that information is not improperly accessed, released, or leaked. In the relational database context, access restrictions can be defined as views that provide access to only certain portions of the underlying relations.
Learning from Unbalanced Stream Data in Non-Stationary Environments Using Logistic Regression Model

There are several deep learning approaches that can be applied for analyzing situations in real world problems and inventing their solution in a scientific technique. Supervised data mining methods that predicts instance values, using previously obtained results from already collected data are pretty popular due to their intelligence in machine learning area. Stream data is continuous form of data which can be handled by using incremental learning approach. Stream data learning may face several challenges in real world like concept drift or class imbalance. Concept drift occurs in non-stationary environment where data distribution generation function is dynamic in nature and has no fixed formula to predict the future data distribution nature. Neural network techniques are intelligent enough to improve performance of algorithmic systems that work in such problem domains. This chapter briefly describes how MLP technique is integrated in system so that the system becomes a complete framework for handling unbalanced data with concept drift in the incremental learning strategies.
Simultaneous multiple low-dimensional subspace dimensionality reduction and classification

Fisher linear discriminant (FLD) for supervised learning has recently emerged as a computationally powerful tool for extracting features for a variety of pattern classification problems. However, it works poorly with multimodal data. Local Fisher linear discriminant (LFLD) is proposed to reduce the dimensionality of multimodal data. Through experiments tried out on the multimodal but binary datasets created from several multi-class datasets, it has been shown to be better than FLD in favour of performance. However, LFLD has a serious limitation, which is that it is limited to the use on small-scale datasets. In order to address the above disadvantages, in this paper, we develop a multiple low-dimensionality dimensionality reduction technique (MSDR) of performing the dimensionality reduction (DR) of input data. In contrast to FLD and LFLD finding an optimal low-dimensional subspace, the new algorithm attempts to seek multiple optimal low-dimensional subspaces that best make the data sharing the same labels more compact. Inheriting the advantages of NC, MSDR reduces the dimensionality of data and directly performs classification tasks without needing to train the model. Experiments of comparing MSDR with the existing traditional approaches tried out on UCI, show the effectiveness and efficiency of MSDR.
Chebyshev Centre of a Set. The Problem of Simultaneous Approximation of a Class by a Singleton Set

In earlier chapters we have been mostly concentred with the problem of best approximation of an element of a normed space by a given set from that space. In this chapter, we consider the problem of approximating a set by a class of sets. In this problem, it is not only the evaluation of the approximation that is important, but also the set that best approximates this class (an optimal set).
Attribute Ibase Cloudidata Iintegrity I Check Out Ifor Isecure I Outsourced Istorage

Property based Encryption (ABE) is viewed as a promising cryptographic leading instrument to ensure information proprietors’ immediate authority over their information in open distributed storage. The prior ABE plans include just a single position to keep up the entire trait set, which can bring a solitary point bottleneck on both security and execution. Accordingly, some multi-authority plans are proposed, in which different specialists independently keep up disjoint trait subsets. Be that as it may, the single-point bottleneck issue stays unsolved. In this paper, from another viewpoint, we direct an edge multi-authority CP-ABE access control conspire for open distributed storage, named SMAAC, in which various specialists together deal with a uniform quality set. In SMAAC, exploiting (t; in) edge mystery sharing, the ace key can be shared among various specialists, and a legitimate client can create his/her mystery key by associating with any it specialists. Security and execution investigation results show that SMAAC isn’t just obvious secure when not as much as it specialists are undermined, yet in addition strong when no not as much as it specialists are alive in the framework. Besides, by productively consolidating the customary multi-authority conspire with SMAAC, we build a half and half one, which fulfills the situation of properties originating from various specialists just as accomplishing security and framework level strength
Performance Model for Media Streaming Bandwidth Allocation using p2p

Media streaming applications are becoming quite popular in the Internet due to the enhancements that are taking place in Internet Protocol (IP) networks to support the required Quality of Service (QoS) for multimedia applications. However, the centralized data center approach or a single Internet Service Provider (ISP) handling the load on its own is not a feasible solution to support the ever increasing number of users. In other words the conventional way of supplying the media content to the users suffers from scalability problem. This system follows the arrangement of a set of Internet Service Provider (ISP) that collaborate among them to utilize their resources optimally. This helps the content providers and the viewers to provide and view the videos without the bottleneck that may be caused by the centralized approach. Moreover the ISPs follow a non-linear time tariff pattern where it is necessary for the customers to pay a predetermined amount to the ISPs irrespective of the usage pattern. In this approach it is difficult to decide the time and the amount of bandwidth required optimally. However, the non-linear time tariff model permits the discount scheme. Hence this system follows the Prediction Based Resource Allocation (PBRA) algorithm that helps the ISPs to allot the right amount of bandwidth at right time to the content providers. The prediction based resource allocation algorithm and the collaborative approach among the ISPs help the ISPs and media providers to solve the scalability problem. However, a typical pay per use model cannot support any discount scheme. Hence, in this project work a hybrid approach has been followed
Artificial intelligence in cardiology

In the first wave of artificial intelligence (AI), rule-based expert systems were developed, with modest success, to help generalists who lacked expertise in a specific domain. The second wave of AI, originally called artificial neural networks but now described as machine learning, began to have an impact with multilayer networks in the 1980s. Deep learning, which enables automated feature discovery, has enjoyed spectacular success in several medical disciplines, including cardiology, from automated image analysis to the identification of the electrocardiographic signature of atrial fibrillation during sinus rhythm. Machine learning is now embedded within the NHS Long-Term Plan in England, but its widespread adoption may be limited by the “black-box” nature of deep neural networks.
High-Speed Area-Efficient Processor for Elliptic Curve Point Multiplication Over Prime Field Along with RSA Comparison

Creating a high-speed elliptic curve cryptographic (ECC) processor capable of performing fast point Multiplication with low hardware utilisation is a critical requirement in cryptography and network security. This paper describes the implementation of a high-speed, field-programmable gate array (FPGA) in this paper. A high-security digital signature technique is implemented using Edwards25519, a recently approved twisted Edwards’s curve. For point addition and point doubling operations on the twisted Edwards curve, advanced hardware configurations are developed in which each task involves only 516 and 1029 clock cycles, respectively. As an observation the ECC processor presented in this paper begins with the process which takes 1.48 ms of single-point multiplication to be performed. The comparison of key size and its ratio which shows the impact on processing of each processor is shown for ECC processor and RSA processor. The delay and number of slices used for the ECC processor is shown and this is a developed solution saves time by providing rapid scalar multiplication with low hardware consumption without compromising on security.
Provable Security for Public Key Cryptosystems

In the early years after the invention of public key cryptography by Diffie and Hellman in 1976, the design and evaluation of public key cryptosystems has been done merely in ad-hoc manner based on trial and error. The public key cryptosystem said to be secure as long as there is no successful cryptanalytic attack on it. But due to various successful attacks on the cryptosystems after development, the cryptographic community understood that this ad-hoc approach might not be good enough. The paradigm of provable security is an attempt to get rid of ad hoc design. The goals of provable security are to define appropriate models of security on the one hand, and to develop cryptographic designs that can be proven to be secure within the defined models on the other. There are two general approaches for structuring the security proof. One is reductionist approach and other is game-based approach. In these approaches, the security proofs reduce a well known problem (such as discrete logarithm, RSA) to an attack against a proposed cryptosystem. With this approach, the security of public key cryptosystem can be proved formally under the various models viz. random oracle model, generic group model and standard model. In this chapter, we will briefly explain these approaches along with the security proofs of well known public key cryptosystems under the appropriate model.