Reviewerly/exHarmony huggingface dataset:
Economics of Resource Sharing via Library Consortia

The chapter seeks to discuss the role played by library consortia in resource sharing among libraries, media organizations, and archives. It will outline the existing models that are functioning effectively especially in the developing countries and discuss the best practices. The target audience being libraries, archives, and media. While unravelling the economics of resources sharing among the information players, the chapter will address the challenges and opportunities in supporting research, teaching, and learning. The value of the book chapter is that it will give an insight into the development of library consortia and their significance as opportunities to provide equal access to research in libraries, archives, and media. The chapter will also provide a comprehensive comparison between library consortia in developing countries and developed countries highlighting how resource sharing in developing countries has helped to bridge the gap of information access, thereby showing progressive development.
DeepKey: Towards End-to-End Physical Key Replication from a Single Photograph

This paper describes DeepKey, an end-to-end deep neural architecture capable of taking a digital RGB image of an ‘everyday’ scene containing a pin tumbler key (e.g. lying on a table or carpet) and fully automatically inferring a printable 3D key model. We report on the key detection performance and describe how candidates can be transformed into physical prints. We show an example opening a real-world lock. Our system is described in detail, providing a breakdown of all components including key detection, pose normalisation, bitting segmentation and 3D model inference. We provide an in-depth evaluation and conclude by reflecting on limitations, applications, potential security risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images covering a few test keys with bounding boxes, pose and unaligned mask data.
Marketing Semiotics in the Digital Age

The nature of communication has begun to change dramatically, as face-to-face communication has gradually been replaced by online communication. People's need to express their emotions and thoughts more visually than ever before has triggered an evolution in communication, from verbal to visual. In this sense, the importance of symbols and icons in communication is continuously increasing. Furthermore, this change can be closely related to the marketing discipline. Therefore, this chapter focuses on semiotics as the field of research that studies symbols as an essential part of marketing communication in the digital age.
Sentimental Analysis in Various Business Applications

Data mining has given many solutions to various data- and information-related issues that directly reflect in advancements in science and technologies and so affect the society in multiple ways. Sentiment analysis is one branch of data mining that deals with opinions of customer and tries to use these opinions in the growth of the business. In this chapter, the authors discuss the use of sentiment analysis in various types of businesses and how this analysis helps these businesses to grow along with the challenges along the way. This will help readers to know why sentiment analysis has become popular in the business world.
A preliminary geometric structure simplification for Principal Component Analysis

Real world data are commonly geometrically nonlinear and thus are not easy to be processed by the traditional linear methods. Many existing techniques for nonlinear dimensionality reduction need careful parameter tuning and cannot be applied to real data stably and consistently. In this article we propose an efficient data preprocessing algorithm, called Curve Straightening Transformation (CST), to flatten the nonlinear geometric structure of data. Then Principal Component Analysis (PCA) and other linear projection methods are adequate to perform the dimensionality reduction task in most cases. In this aspect, the proposed CST algorithm can be regarded as a geometric preprocessing step tailored for PCA. The comprehensive experiments on both artificial and real datasets demonstrate that the proposed preprocessing algorithm is able to simplify the nonlinear geometric structures, and the flattened data are suitable for further dimensionality reduction by linear methods such as PCA.
Noise Reduction and Recovery Algorithm for Underwater Distorted Image

Han, P. and Han, Y., 2018. Noise reduction and recovery algorithm for underwater distorted image. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 720–723. Coconut Creek (Florida), ISSN 0749-0208.Due to some unavoidable internal factors of digital cameras and other imaging equipment, as well as human and environmental external factors, the underwater photographing images will be distorted, thus affecting the recognition effect of optical character recognition. The traditional noise reduction algorithm has low efficiency and short time-consuming. Therefore, in this paper, a document image distortion recognition algorithm based on full automatic image splicing is proposed. Firstly, two distorted images captured from different angles for the same document page are processed by feature extraction, image registration, transformation and so on, and then spliced into a nearly non-deformation document image. The experimental results show that the algorithm proposed in this paper can effectively deal with the problem of document image distortion recognition, and has a very high development prospect.
Autonomous Return Path Planning Method for Small Underwater Robots

Song, W.; Wang, W.; Guo, X., and Jiang, F., 2018. Autonomous return path planning method for small underwater robots. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 184–187. Coconut Creek (Florida), ISSN 0749-0208.The traditional underwater robot return path planning is vulnerable to interference, resulting in poor planning accuracy. In order to solve the problem, the fuzzy rules based research method is used to plan the autonomous return path for multiple small underwater robots and analyze the composition of the control system for path planning of underwater robot. According to the environmental information acquisition, the fuzzy rule is constructed. On this basis, the input and output variables are selected, and the selection results are made the fuzzy processing, so as to design the dynamic planning path. According to the planning process, the specific return path is determined. Through the experiment results, the highest accuracy of the planning method can reach 98%, and it has good effect on path planning.
Festival of Youth Enterprise - Tayside Fringe Event

Tayside’s Fringe event as a part of the Festival of Youth Enterprise is a day of inspirational speakers & workshops to support S1-S3 students in Tayside
Çizge Tabanlı WordNet Ağı Üzerinde Anlamsal İlişki Ağırlıklarının Tespiti

Determination of semantic relatedness between two textual items is one of the crucial phases in many Natural Language Processing applications. In this study, a new approach to lexicon based semantic relation determination methods was experienced using WordNet 3.0 and Men’s real-life similarity dataset. Men’s test collection was used for the determination of the relation weights and determined weights were used in semantic relatedness computation. RG65 similarity dataset was used for a benchmark of the proposed method and Spearman correlation 0.81 was gained, taking into account that retrieving the relations weight using a large scale dataset and testing them with another real-life dataset promises new perspectives to the determination of the relations weight and to the relatedness computation .
Snapshot of the Status Quo of Selected Zimbabwe School Libraries

The aim of this chapter is to discuss the challenges and opportunities faced by selected school libraries in Zimbabwe. It is based on the observation that the school's library system in Zimbabwe, including rural schools, is plagued by a plethora of challenges ranging from neglect in relation to funding and policy issues. It is from this perspective that this chapter seeks to deeply understand and address the challenges and opportunities faced by Zimbabwe's school libraries. The study adopted interpretivism approach and was qualitative in nature. It is a multiple case study with purposively eight schools being selected to be part of the research. Some of the findings noted were that expensive schools mainly frequented by the elites had modern libraries which are manned by qualified librarians with most of them being well remunerated. This was not the case with rural schools in which there were no libraries whilst those with libraries had few outdated and irrelevant books and there were no trained librarians in those schools.
A video quality improvement technique for a fast combined watermark embedding method

The use of watermarks to embed user information is expected to deter the unauthorized use of video content in VOD (Video On-Demand) streaming services.This requires that watermark embedding and video encoding for each video must be finished so rapidly as to not create excessive VOD delay between the user's video request and video download to the user.This paper describes a watermark embedding and encoding process for a fast combined watermark embedding method, and proposes a new encoding technique that minimizes video quality degradation.
Creating Characters for Various Interfaces

This chapter is focused on the theme of creating characters for visual storytelling discussed in practical, theoretical, and historical terms. The description includes a discussion of artistic forms acting as characters for telling stories, various meanings conveyed by characters in semiotic terms, the creating of characters by drawing, and then a set of learning projects follows, on creating characters for various interfaces.
Pattern-Based Cloud Migration

To migrate on-premises business systems to the cloud environment faces challenges: the complexity, diversity of the legacy systems, cloud, and cloud migration services. Consequently, the cloud migration faces two major problems. The first one is how to select cloud services for the legacy systems, and the second one is how to move the corresponding workload from legacy systems to cloud. This chapter presents a total cloud migration solution including cloud service selection and optimization, cloud migration pattern generation, and cloud migration pattern enforcement. It takes the pattern as the core, and unifies the cloud migration request, the cloud migration service pattern, and the cloud migration service composition. A cloud migration example of blockchain system shows that the proposed approach improves the cloud service selection, cloud migration service composition generation efficiency, migration process parallelization, and enables long transaction support by means of pattern reuse.
System Level Attacks &amp; Countermeasures

This chapter describes system-level attacks, with specific focus on software attacks that exploit functional or side-channel bugs in hardware. It presents SoC design flow, including definition of security architecture and design review to ensure security. It also describes security policy-based solutions that govern confidentiality, integrity, and availability of security assets in an SoC to provide effective countermeasures against these attacks.
Company Acquisition Relations Extraction From Web Pages

In this chapter, we study the problem of extracting company acquisition relation from huge amounts of webpages, and propose a novel algorithm for a company acquisition relation extraction. Our algorithm considers the tense feature of Web content and classification technology of semantic strength when extracting company acquisition relation from webpages. It first determines the tense of each sentence in a webpage, where a CRF model is employed. Then, the tense of sentences is applied to sentences classification so as to evaluate the semantic strength of the candidate sentences in describing company acquisition relation. After that, we rank the candidate acquisition relations and return the top-k company acquisition relation. We run experiments on 6144 pages crawled through Google, and measure the performance of our algorithm under different metrics. The experimental results show that our algorithm is effective in determining the tense of sentences as well as the company acquisition relation.
Risk Analysis of ICT Assets

Risk analysis is required in all companies to help the business owners or top managers make decisions about risk management strategy, which itself provides an organization with a roadmap for information and information infrastructure protection aligned to business goals and the organization's risk profile. This chapter identifies information assets including network, electricity, hardware, service, software, and human resources in the ICT department of a health insurance company and their relevant risks. To determine the risks, the level of confidentiality, level of integrity, level of availability, the likelihood of threat occurrence, and intensity of vulnerability have been assessed and rated. Assessment is done based on the opinions of 30 experts in the field of information security. According to the results, the highest information security risk is on the network.
Random walks generated by the Ewens distribution on the symmetric group

This paper studies Markov chains on the symmetric group S n where the transition probabilities are given by the Ewens distribution with parameter θ>1. The eigenvalues are identified to be proportional to the content polynomials of partitions. We show that the mixing time is bounded above by a constant depending only on the parameter if θ is fixed. However, if it agrees with the number of permuted elements (θ=n), the sequence of chains has a total variation cutoff at logn log2.
Principality and approximation under dimensional bound

We develop an algebraic and algorithmic theory of principality for the recently introduced framework of intersection type calculi with dimensional bound. The theory enables inference of principal type information under dimensional bound, it provides an algebraic and algorithmic theory of approximation of classical principal types in terms of computable bases of abstract vector spaces (more precisely, semimodules), and it shows a systematic connection of dimensional calculi to the theory of approximants. Finite, computable bases are shown to span standard principal typings of a given term for sufficiently high dimension, thereby providing an approximation to standard principality by type inference, and capturing it precisely for sufficiently large dimensional parameter. Subsidiary results include decidability of principal inhabitation for intersection types (given a type does there exist a normal form for which the type is principal?). Remarkably, combining bounded type inference with principal inhabitation allows us to compute approximate normal forms of arbitrary terms without using beta-reduction.
Modular materialisation of Datalog programs

Answering queries over large datasets extended with Datalog rules plays a key role in numerous data management applications, and it has been implemented in several highly optimised Datalog systems in both academic and commercial contexts. Many systems implement reasoning via materialisation, which involves precomputing all consequences of the rules and the dataset in a preprocessing step. Some systems also use incremental reasoning algorithms, which can update the materialisation efficiently when the input dataset changes. Such techniques allow queries to be processed without any reference to the rules, so they are often used in applications where the performance of query answering is critical. Existing materialisation and incremental reasoning techniques enumerate all possible ways to apply rules to the data in order to derive all relevant consequences. This, however, can be inefficient because derivations of rules commonly used in practice are redundant; for example, rules axiomatising a binary predicate as symmetric and transitive can have a cubic number of applications, yet they can derive at most a quadratic number of facts. Such redundancy can be a significant source of overhead in practice and can prevent Datalog systems from successfully processing large datasets. To address this issue, in this paper we present a novel framework for modular materialisation and incremental reasoning. Our key idea is that, for certain combinations of rules commonly used in practice, all consequences can be derived using specialised procedures that do not necessarily enumerate all possible rule applications. Thus, our framework supports materialisation and incremental reasoning via a collection of modules. Each module is responsible for deriving consequences of a subset of the program, by using either standard rule application or proprietary algorithms. We prove that such an approach is complete as long as each module satisfies certain properties. Our formalisation of a module is very general, and in fact it allows modules to keep arbitrary auxiliary information. We also show how to realise custom procedures for four types of modules: transitivity, symmetry–transitivity, chain rules, and sequencing elements of a total order. Finally, we demonstrate empirically that using our custom procedures can speed up materialisation and incremental reasoning by several orders of magnitude on several well-known benchmarks. Thus, our technique has the potential to significantly improve the scalability of Datalog reasoners.
Towards Benefiting Both Cloud Users and Service Providers Through Resource Provisioning

Cloud users expect high Quality of service (QoS) levels within their budget and the cloud service providers (CSPs) to maximize their profits, always strive for the cost and energy minimization and better resource utilization. Any error in the management of resources causes Service Level Agreement (SLA) violations, high penalties, low customer satisfaction, and long-term losses. The objective of this article is to present a literature review on various resource provisioning strategies and also to present a novel cluster-based resource provisioning (CB-RP) technique that satisfies the needs of both cloud users and CSP. CB-RP employs a heart algorithm to cluster the arriving requests based on its characteristics. The CB-RP technique aims to analyze the requests and provision the resources according to the request category. Simulation results show that our technique produces significant improvements in terms of cost savings, resource utilization and turnaround time compared with state of art technique.
IDR Privacy Protection Based on Database Digital Watermarking

Background: In smart grid, a flexible demand response management mechanism is used to achieve the purpose of stabilizing the power grid, optimizing the power market, and rationally allocating resources. There are two types of demand response management in the demand response management mechanism: Price-based Demand Response (PDR) and Incentive-based Demand Response (IDR). Methods: The paper studied the problem of privacy protection in IDR, and proposed a method based on database digital watermark to protect user privacy. Segment the time, and then embed watermarks in the user’s consumption data of each time segment. At the end of each billing period, extract the watermarks from the data of each segment time, and then send the total consumption data of the user of this billing period to the power supply company. The power supply company only knows the total consumption data of the user, the company does not have any information regarding the users consumption data which can prevent them from snooping the user privacy. The proposed digital watermarking algorithm is based on K-Means clustering and wavelet transform, the K-Means algorithm is used to cluster the database tuple data, and then wavelet transform is carried out on the available attribute values within the clusters, and the watermark is embedded in the transformed attribute values. Results: The experimental results show that the proposed method is more robust when the database is under subset deletion attacks, subset substitution attacks and subset addition attacks. Besides, the computational cost is very low. Conclusion: The proposed digital watermark algorithm can embed the watermarks more decently and overcome the burden of watermark embedding caused by statistical feature control. Besides, the proposed method can protect the user privacy better than the other two methods.
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.
Artificial Intelligence Applied

The purpose of the chapter is to present some real applications of the most advanced information technologies in complex adaptive systems like for-profit companies and organizations. In particular, the authors present the application of machine learning and artificial intelligence to support some of the activities that are strategic for an effective management of human resources. The tools have been applied to analyze the professional profiles (competencies, skills, knowledge, and activities), to evaluate the candidates for hiring and selection, to assess the competences in order to obtain a certification, or to prove the results of a training course. For each project, the authors provide a description of 1) the context, 2) the problem, 3) the solution implemented, 4) an analysis of the advantages and the limits of the solution. All these cases offer quantitative and qualitative data to sustain the thesis: artificial intelligence is a tool that can help humans managing the complexity levels of the so-called Anthropocene era we live in.
On Spectral Design Methods for Quasi-Cyclic Codes

A method is provided for constructing upper triangular square matrices over the univariate polynomial ring over a finite field, under certain constraints on the eigenvalues of the matrices. In some cases of interest, the degree of the determinant of such matrices is shown to be the smallest possible. The method is then applied to construct generator polynomial matrices of quasi-cyclic codes for correcting phased burst errors. Finally, an interpolation-based list decoding algorithm is presented for these codes, which, for a wide range of code parameters, is shown to outperform existing list decoding schemes.