Reviewerly/exHarmony huggingface dataset:
Crafting Linked Open Data to Enhance the Discoverability of Institutional Repositories on the Web

Institutional repositories are archives for collecting and disseminating digital copies of the intellectual output of institutions. Linked open data is to expose and connect pieces of data, information, and knowledge on the Semantic Web. This paper studies how BIBFRAME 2.0 can be used to describe objects in institutional repositories, with the goal of bringing together efforts within two communities devoted to openness. We examine a sample of mappings and conversions from Dublin Core to BIBRAME 2.0 ontology to see if BIBFRAME 2.0 will increase visibility of local digital collections on the Web.
Wrangling in the Power of Code Pointers with ProxyCFI

Despite being a more than 40-year-old dark art, control flow attacks remain a significant and attractive means of penetrating applications. Control Flow Integrity (CFI) prevents control flow attacks by forcing the execution path of a program to follow the control flow graph (CFG). This is performed by inserting checks before indirect jumps to ensure that the target is within a statically determined valid target set. However, recent advanced control flow attacks have been shown to undermine prior CFI techniques by swapping targets of an indirect jump with another one from the valid set. In this article, we present a novel approach to protect against advanced control flow attacks called ProxyCFI. Instead of building protections to stop code pointer abuse, we replace code pointers wholesale in the program with a less powerful construct – pointer proxies. Pointer proxies are random identifiers associated with legitimate control flow edges. All indirect control transfers in the program are replaced with multi-way branches that validate control transfers with pointer proxies. As pointer proxies are uniquely associated with both the source and the target of control-flow edges, swapping pointer proxies results in a violation even if they have the same target, stopping advanced control flow attacks that undermine prior CFI techniques. In all, ProxyCFI stops a broad range of recently reported advanced control flow attacks on real-world applications with only a 4% average slowdown.
An Experimental Trial of a Novel Ticketing System Using Biometrics

To prevent ticket scalping, we implemented fast identity online (FIDO) technology to authenticate and ensure the correct ticket holder identities at a basketball game held in the city of Tsukuba, Ibaraki, Japan. This paper describes an overview of our system and describes the advantages of our method compared with other ticketing systems. The system was implemented using an open-source electronic commerce platform. The addition of the FIDO module to the platform enables the biometric authentication function. Moreover, we carried out a simple evaluation of the service by asking participants to respond to a questionnaire. We also present the results of the evaluation analysis.
Reflection segmentation for potential augmentation - Geometric approach

In this article, we have consider the given image as the input. And subdivide the image that is input in the required form after applying proposed CIRSQUAREC algorithm constructed with three different cases. This approach provides the optimal segmentation of the input in small parts to get an optimized level to Future Enhancement. This approach will provide more application in medical image processing.
The virtual library tours

The virtual tours make the new type of library information products borrowed from the tourist and museum practice. The author examines theoretical aspects of designing a virtual tour: Its essential components, tour types, technologies to be used. Qualitative features which allow to expect their rapid growth and implementation in many spheres, in particular, in libraries, are revealed. Library virtual tours are specified and characterized based on the study of different types of libraries. For this purpose, the www-sites of 236 libraries were reviewed. Significant divergence in interpreting the term of “virtual tour” in the museum and library spheres is revealed. The most popular technologies of designing such tours are described. The key objects of the library virtual tours are discussed, i. e. library’s history and current situation, individual divisions, or the most valuable units (old manuscripts, rare collections, etc.).
A Quantitative Study of Attribute Based Correlation in Micro-databases and Its Effects on Privacy

Preserving the privacy associated with publicly released micro-databases is an active area of research since an adversary can mine sensitive information about the database respondents from them. The work in this paper establishes a working model for quantitatively estimating the attribute based correlation present among multiple micro-databases. In this study, we have introduced an information-theoretic metric termed as Correlation Degree $$(\rho )$$ which estimates the amount of correlated information present among two micro-databases and accordingly assigns a cumulative score in the range [0, 1]. The design of our proposed metric is based on the fact that correlation among multiple datasets exists due to the presence of both overlapping and implicitly dependent attributes. We have also established a functional association between $$\rho $$ and the general notion of privacy during the execution of an adversarial linking attack. Finally, we have empirically validated our work by estimating the value of $$\rho $$ and the resulting privacy loss for the Adult micro-database on the backdrop of two well-established privacy preservation models.
An Efficient Method for Frequent Itemset Mining on Temporal Data

Frequent itemset mining (FIM) is a data mining idea with extracting frequent itemset from a database. Finding frequent itemsets in existing methods accept that datasets are static or steady and enlisted guidelines are pertinent all through the total dataset. In any case, this isn't the situation when information is temporal which contains time-related data that changes data mining results. Patterns may occur during all or at specific interims, to limit time interims, frequent itemset mining with time cube is proposed to manage time arranges in the mining technique. This is how patterns are perceived that happen occasionally, in a period interim, or both. Thus, this paper mostly centres around developing up a productive calculation to mine frequent itemsets and their related time interval from a value-based database by expanding from the earlier calculation dependent on support and density as another edge. Density is proposed to deal with the overestimated timespan issue and to ensure the authenticity of the patterns found. As an extension from the current framework, here the density rate and minimum threshold is dynamically generated which is user determined parameter previously. Likewise, an analysis concerning time is made between dataset with partitioning and without apportioning the dataset, which shows computation time is less on account of partitioning technique.
Unsupervised Absent Multiple Kernel Extreme Learning Machine

Learning from the absent multiple sources data is very challenging, especially when lacking label information. Although existing multiple kernel learning methods with kernel imputing achieved remarkable performance, they may fail in practical issues due to hardly tracking the data with huge missing samples or easily fall into a local optimum. Thus, we propose an unsupervised absent multiple kernel extreme learning machine to effectively deal with absent data. It firstly extracts information from absent multiple sources data via multiple kernels to structure a new data space named K-space. Then, the optimal multiple kernel combination coefficients are learned in the K-space. Finally, we establish a seamlessly integrated object function to complete the absent kernel matrices and acquire the clustering information simultaneously by a well-designed optimization strategy within finite steps. As evidenced by comprehensive experiments, the proposed method provides significantly better clustering performance.
Physical and Information Theoretical Limits of the Single-Shot 3D Movie Camera

The last chapter introduced a concept for a single-shot 3D movie camera based on multi-line triangulation. The feasibility has been proven by first measurements with a prototype. The demonstrated measurements are single-shot and display a high 3D data density.
Extreme Latent Representation Learning for Visual Classification

Representational learning using Extreme Learning Machine (ELM) theory has aroused lots of interest. Motivated by recent advance in ELM, this paper presents a novel Extreme Latent Representation (ELR) learning model to seamlessly connect original perception data and the corresponding high-level semantics. Specifically, ELM and ELM based auto-encoder (ELM-AE) are formulated in a unified learning model with both classification and reconstructive ability of the representation considered. ELR inherits the merits of ELM and ELM-AE, and discriminative and compact representation can be learnt with data information well preserved. Furthermore, an efficient algorithm based on alternating direction method of multipliers (ADMM) is developed to solve the resulting ELR model. The performance of ELR is verified on two visual classification tasks, and encouraging results have are achieved.
Scheme of lensless optical encryption with spatially incoherent illumination

Lensless encryption scheme with spatially incoherent illumination is presented. Encoding diffraction optical element was designed to form desired light distribution in photosensor plane under spherically diverging illumination. Validity is supported by results of optical experiments.
On the Anonymity of Multi-Receiver Identity-Based Encryption Based on Fujisaki–Okamoto Transformation

In 2010, Fan et al. first introduced the concept of anonymous multi-receiver identity-based encryption (AMRIBE). They used the concept of Fujisaki–Okamoto transformation (F.O. transformation) to support their proof in random oracle models. Recently, many works on this topic have been proposed. Most of them adopted Fujisaki–Okamoto transformation to achieve the indistinguishability of encryptions under multi-ID, chosen-ciphertext attacks (IND-MID-CCA) security. However, we find out that these schemes do not gain anonymity against insider attacks. In this manuscript, we use a sequence of games to prove that any AMRIBE scheme will fail to gain anonymity under chosen-ciphertext attacks and chosen-plaintext attacks if the IND-MID-CCA security is proven using Fujisaki–Okamoto transformation.
Multimodal Swarm Algorithm Based on the Collective Animal Behavior (CAB) for Circle Detection

In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global) are known, the implementation can be quickly switched to another solution without much interrupting the design process. This chapter presents a swarm multimodal optimization algorithm named as the Collective Animal Behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact to each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi circle detection, achieving satisfactory results.
IAAS Service in the Public Domain: Impact of Various Security Components on Trust

Cloud computing a glorified paradigm that has seen a sharp rise in the last few decades and comes up as the fastest growing IT segment. This computing technology has stretched the prevailing capabilities of computing resources by offering them on a sharing basis. Shared resources have made it affordable for medium and small-scale business to use this latest technology and upgrade themselves in term of technology at par with big business houses. Through this technology, an individual or group can use the latest IT tools/technologies/platforms without any huge investment, licensing and installation glitches, and training and administration concerns. As more and more individuals and societies are joining this stream, the question has become more relevant is the cloud safe? The biggest promises made by the technology become counterfeit when data leakage incidences surfaced. By keeping in mind the utility of the technology, more and more researchers are trying to address the concerns faced by the technology. Important challenges for the technology are security, privacy, and trust. These issues are interrelated. The present study is an inspection to find the impact of various security components on trust. To achieve the stated objective, a study of concerned literature was conducted followed by the primary survey between free and paid cloud consumers through an online questionnaire. Collected responses are analyzed through SPSS to find out the impact of each component on trust.
A Robust and Dynamically Enhanced Neural Predictive Model for Foreign Exchange Rate Prediction

In today’s highly interlinked international economy, accurate and timely real-time predictions of foreign exchange (fx) market offer tremendous business, social and political values to our community. In this work, an effective and accurate neural predictive model based on recurrent neural network and Regularised Extreme Learning Machine is developed. Hidden layer of the developed model is designed to map complex financial input patterns into a higher dimensional hidden feature space where input patterns are represented is a clear and delicate way. Output feedback loops and regularisation are implemented in the model to facilitate strong robustness and capability of capturing complex dynamical characteristics of the fx system. Experimental results show superior performance of the developed model comparing to benchmark models under varying market conditions.
Generating Virtual Worlds for Collaborative Innovation Activities: A Responsive Templating Approach

Virtual worlds possess great potentials to support distant collaboration for business teams involved in innovation activities developing solutions for end-users. However, not all users have the technical and programming skills to create custom virtual worlds on the fly that would be suited for their innovation sessions and managing processes within such sessions. Also, it is important that the created virtual world supports such sessions to make users more productive, hence the need to ensure that virtual world characteristics and affordances are used to benefit users rather than impede their effectiveness. This research proposes a responsive templating approach that allows users (facilitators) to generate virtual worlds for collaborative innovation and manage processes within the world, thereby exploring virtual world affordances. We propose that this approach would benefit users enabling easier generation of collaborative innovation spaces and more effective collaboration.
Physics Props Development based on Personal Desk Laboratory System to Improve Creative Thinking Ability and Students’ Scientific Attitude

This research aims to determine the feasibility of a Personal Desk Laboratory (PDL)-based physics teaching aids system, as well as to find out whether PDL-based physics teaching aids can improve students' creative thinking skills and scientific attitudes. The method used in this study uses the Research and Development method. Data obtained from the results of observations, the results of the questionnaire responses, and the results of the pretest&post-test scores. The products are in the form of PDL-based physical teaching aids systems and supporting devices in the form of Student Worksheets on static fluid material, lesson plan, and practicum guidelines. Validation results calculated using CVR scores and CVI scores stated that PDL-based physics teaching aids are feasible to be tested in the field.
A Comparative Analysis of Classification and Regression Models for Energy-Efficient Clouds

Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason, it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a con-solidation strategy strongly depends on the forecast of the VMs resource needs. This paper presents the experimental evaluation of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. Migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting classification and regression models and shows good benefits in terms of energy saving.
IUID in Practice—Industrial Development

First, aspects of the procedural approach of IUID in practice are presented. Subsequently, the essential phases (requirements analysis and concept development, design elements and implementation, evaluation and test) of the process for the development of intercultural user interfaces (IUID) with regard to cultural differences to the established development process models (V-model and agile methods as well as U-CD and U-CD) are examined in more detail.
CO-LEELM: Continuous-Output Location Estimation Using Extreme Learning Machine

Indoor positioning is a key technology enabler for various smart systems that require location-based optimization and automation. In this paper, we present CO-LEELM, a continuous-output location fingerprinting method that combines two existing location fingerprinting methods to produce better accuracy in a dynamic environment where training data and reference devices are sparsely-distributed. The proposed method incorporates the use of Extreme Learning Machine (ELM) to improve the training speed which is a crucial factor that affects the scalability of the method.
A Quantum-Behaved Neurodynamic Approach for Nonconvex Optimization with Constraints

This paper presents a quantum-behaved neurodynamic swarm optimization approach to solve the nonconvex optimization problems with inequality constraints. Firstly, the general constrained optimization problem is addressed and a high-performance feedback neural network for solving convex nonlinear programming problems is introduced. The convergence of the proposed neural network is also proved. Then, combined with the quantum-behaved particle swarm method, a quantum-behaved neurodynamic swarm optimization (QNSO) approach is presented. Finally, the performance of the proposed QNSO algorithm is evaluated through two function tests and three applications including the hollow transmission shaft, heat exchangers and crank–rocker mechanism. Numerical simulations are also provided to verify the advantages of our method.
Two New Module-Code-Based KEMs with Rank Metric

In this paper, we use a class of module codes to construct a suite of code-based public-key schemes—Piglet, which includes a new IND-CPA-secure public-key encryption scheme Piglet-1.CPAPKE and an IND-CCA-secure key encapsulation mechanism (KEM for short) Piglet-1.CCAKEM by applying the KEM variant of Fujisaki-Okamoto transform to Piglet-1.CPAPKE. We also put a new IND-CPA-secure KEM Piglet-2.CPAKEM into Piglet. Then, we present the parameters comparison between our schemes and some code-based NIST submissions. The results show that our schemes are good long-term-secure candidates for post-quantum cryptography.
Monte Carlo Analysis of Local Cross–Correlation ST–TBD Algorithm

The Track–Before–Detect (TBD) algorithms allow the estimation of the state of an object, even if the signal is hidden in the background noise. The application of local cross–correlation for the modified Information Update formula improves this estimation for extended objects (tens of cells in the measurement space) compared to the direct application of the Spatio–Temporal TBD (ST–TBD) algorithm. The Monte Carlo test was applied to evaluate algorithms by using a variable standard deviation of the additive Gaussian noise. The proposed solution does not require prior knowledge of the size or measured values of the object. Mean Absolute Error for the proposed algorithm is much lower, close to zero to about 0.8 standard deviation, which is not achieved for the ST–TBD.
Detecting Fraudulent Bookings of Online Travel Agencies with Unsupervised Machine Learning

Online fraud poses a relatively new threat to the revenues of companies. A way to detect and prevent fraudulent behavior is with the use of specific machine learning (ML) techniques. These anomaly detection techniques have been thoroughly studied, but the level of employment is not as high. The airline industry suffers from fraud by parties such as online travel agencies (OTAs). These agencies are commissioned by an airline carrier to sell its travel tickets. Through policy violations, they can illegitimately claim some of the airline’s revenue by offering cheaper fares to customers. This research applies several anomaly detection techniques to detect fraudulent behavior by OTAs and assesses their strengths and weaknesses. Since the data is not labeled, it is not known whether fraud has actually occurred. Therefore, unsupervised ML is used. The contributions of this paper are, firstly, to show how to shape the online booking data and how to engineer new and relevant features. Secondly, this research includes a case study in which domain experts evaluate the detection performance of the considered ML methods by classifying a set of 75 bookings. According to the experts’ analysis, the techniques are able to discover previously unknown fraudulent bookings, which will not have been found otherwise. This demonstrates that anomaly detection is a valuable tool for the airline industry to discover fraudulent behavior.
Automated Multi-objective Refactoring Based on Quality and Code Element Recentness

Search-Based Software Engineering (SBSE) has been used to automate various aspects of the software development cycle. One particular case is refactoring, especially to improve software quality. However, often there are other factors that influence the refactoring process. One such factor, the recentness of the code elements, is identified in this paper as important. The paper describes the use of a multi-objective genetic algorithm to automate software refactoring based on a metric function for software quality and a second objective to measure the recentness of the code elements being refactored. The recentness measure is calculated from data on previous versions of the software. The multi-objective setup refactors the input program to improve its quality using the quality objective, while also focusing on the recentness of the code elements inspected. The approach is implemented in a tool, MultiRefactor and validated using a set of six open source Java programs. An experiment is described that compares the multi-objective approach against an alternative mono-objective approach that uses only the quality function. The results show that the multi-objective approach gives significantly better recentness scores without greatly degrading improvements in the quality score.