Reviewerly/exHarmony huggingface dataset:
Fault Detection in Cryptographic Systems

Even a single data bit fault in an encryption or decryption circuit can spread quickly, and result in a totally scrambled output. Furthermore, deliberately injecting faults into a cryptographic device and observing the corresponding outputs can help an attacker gain access to the secret key. For these reasons, fault detection is needed in cryptography. We start this chapter with a brief overview of symmetric key and public key cryptography, and describe the fault injection attacks that can be mounted against them. We then present techniques that can be used to detect the injected faults in an attempt to foil such attacks.
6DoF Pose Estimation for Similar Objects Using Spatial Relationship of Part-Affordance

In this paper, we propose a method to estimate 6D pose (3D position and pose) of the everyday objects, especially Kitchen or DIY tools, even if there are not the same 3D model of the target object. We assume that the tools that belong to same category have common spatial relation in part-affordances, the role of each part. The proposed method uses this assumption for pose estimation. By minimizing two types of consistencies, the spatial relationship of part-affordances and the shapes, we have confirmed that the proposed method achieved higher performance compared with traditional pose estimation method.
HORBoVF—A Novel Three-Level Image Classifier Using Histogram, ORB and Dynamic Bag of Visual Features

Every country has its own currency in terms of coins and paper notes. Each of the currency of Individual County has its unique features, colors, denominations, and international value. Though it is easy for us to identify the denomination but for the blind people, it is a not at all possible to do this! Especially when size of currency of different denominations is same, it becomes almost impossible for them to do this and there are chances that they might got cheated by others. This paper proposes and discusses a novel three-stage image classifier algorithm for the same purpose to help the blind people in identifying denomination more accurately and to check if the currency is real or fake.
Using Boundary Objects in the Methodology of TEFL at the Tertiary Level of Education

Boundary objects were first defined in the 1989 article “‘Translations’ and Boundary Objects: Amateurs and Professionals in Berkeley’s Museum of Vertebrate Zoology 1907–39” by Susan Leigh Star and Jason Griesemer. Since then the term has become extremely popular. It refers to “an analytical concept that belongs to two boundary worlds and fulfills the communicative needs of each of these social worlds” (Bukalska, 2015, p. 94). The concept of “mobile technologies as boundary objects in the hands of pre-service language teachers” was, however, first coined and described by Elżbieta Gajek in her 2016 article “Mobile Technologies as Boundary Objects in the Hands of Student Teachers of Languages Inside and Outside the University”. The following article focuses on the particular teaching strategies adapted to the usage of mobile phones in the English language classroom in order to show their potential as boundary objects. The author’s aim is to show that learners and teachers who use mobile devices inside the institution cross not only linguistic and cultural boundaries but also build the bridges over social and generation gaps in order to mediate the meaning. The case study will concern the SWPS University English Philology students.
Intercultural Tele-Improvisation: Multi-idiomatic Approaches

This chapter defines intercultural tele-improvisation and situates it among other improvisatory and networked music practices. It describes the multiple idiomatic approaches employed by networked musicians and the challenges they face in negotiating first online encounters. It highlights the role of culture in online performers’ expression in, and interpretation of, tele-improvised interaction and related issues of appropriation and hybridity in a framework of distributed authorship. The chapter concludes by outlining approaches to understanding interaction in intercultural tele-improvisatory performance.
Improving image similarity estimation via global distance distribution information

Estimating the similarity between two images or image patches is at the heart of many computer vision problems including content-based image retrieval, image registration, and scene recognition. However, commonly used distance-based similarity estimation is not always reliable due to the limitations in both image understanding techniques and distance metrics. In this paper, we present a scheme to improve the similarity estimation under image search scenario. To this end, we explore the discriminative capability underlying global distance distribution obtained by querying an auxiliary image dataset in an unsupervised manner. According to the results of motivational experiments, we discover that global distance distributions have the desired capability in distinguishing inter-class images which can be applied to enhance the original distance metric. Following this finding, we propose a novel approach to incorporate the global distance distribution into the original distance metric to improve the reliability of the similarity estimation. One key novelty of this approach is to model the global distance distribution as Rayleigh distribution and then represent the difference between two distributions by the relative entropy. In this way, the difference between two global distance distributions can be calculated in an extremely efficient way. We also demonstrate that Rayleigh distribution leads to consistent performance compared to the real distribution. Extensive experiments on three public datasets with various image representations and distance metrics show that the enhanced similarity estimation remarkably outperforms the original one. Furthermore, the proposed approach shows the desired scalability for handling large-scale image search scenarios.
A Trio Approach Satisfying CIA Triad for Medical Image Security

Medical image security attains a great appeal due to the challenges in transmitting them through an open network channel. Even a small change in medical information leads to a wrong diagnosis. Hence, methods to prevent the attacks and tampering on the medical images in the open channel have a great demand. This work proposes such an attractor-assisted medical image watermarking and double chaotic encryption which preserves the confidentiality, integrity and authenticity of the medical image. Patient diagnosis details are compressed using lossless compression approach and embedded in the pixels of patient’s DICOM image. Attractor-based selective watermarking is implemented using integer wavelet transform. Encryption will be carried out by employing chaotic maps using confusion and diffusion operations. The effectiveness of the work will be evaluated using standard analyses namely MSE, PSNR, SSIM, entropy, correlation, histogram and key space.
Designing an IMS-LD Model for Communication Space of Learning Management System

The context of this work is that of designing an IMS-LD model for communication space of a Learning Management System (LMS). Our work is specifically in the field or seeking to promote, by means of information technology from a distance. Our approach is to first think about the conditions for creating a real communication space for LMS between learners, and designing the IT environment that supports this space. In this paper, we try to adapt the IMS-LD model with a communication model for LMS based on the social constructivism. This adaptation will go through three stages. Firstly, the development of an LMS model. Secondly, the study of correspondence between the developed model and IMS-LD model. Finally, their transformation to IMS-LD model.
Structured Search in Annotated Document Collections

In this work, we demonstrate structured search capabilities of the GYANI indexing infrastructure. GYANI allows linguists, journalists, and scholars in humanities to search large semantically annotated document collections in a structured manner by supporting queries with regular expressions between word sequences and annotations. In addition to this, we provide support for attaching semantics to words via annotations in the form of part-of-speech, named entities, temporal expressions, and numerical quantities. We demonstrate that by enabling such structured search capabilities we can quickly gather annotated text regions for various knowledge-centric tasks such as information extraction and question answering.
On Fail-Stop Signature Schemes with &lt;i&gt;H&lt;/i&gt;-EUC Security

Fail-Stop Signature (FSS) scheme is a signature scheme which satisfies unforgeability even against a forger with super-polynomial computational power (i.e. even against a forger who can compute acceptable signatures) and non-repudiability against a malicious signer with probabilistic polynomial time computational power (i.e. a PPT malicious signer). In this paper, under some settings, the equivalence relation has been derived between a set of security properties when single FSS scheme is used singly and a security property called Universally Composable (UC) security when plural FSS schemes are concurrently used. Here, UC security is a security property guaranteeing that even when plural schemes are concurrently used, security properties of each scheme (for single scheme usage) are preserved. The above main settings are as follows. Firstly, H-EUC (Externalized UC) security is introduced instead of “conventional” UC security, where a new helper functionality H is constructed appropriately. It is because that we can derive “conventional” UC security cannot hold for FSS schemes when malicious parties (e.g. a forger and a malicious signer) have super-polynomial computational power. In the environment where the above helper functionality H is used, all parties are PPT, but only a forger may compute acceptable signatures by obtaining some additional information from H. Secondly, the definition of unforgeability (in a set of security properties for single FSS scheme usage) is revised to match the above environment. The above equivalence relation derived under the above settings guarantees that even when plural FSS schemes are concurrently used, those security properties for single scheme usage are preserved, provided that some conditions hold. In particular, the equivalence relation in this paper has originality in terms of guaranteeing that unforgeability is preserved even against a forger who is PPT but may compute acceptable signatures. Furthermore, it has been firstly proved in this paper that H-EUC security holds for an existing instantiation of an FSS scheme by Mashatan et al. From this, it can be said that the equivalence relation shown in this paper is practical.
How Perceived Real-World Danger Affects Virtual Reality Experiences

Since current VR system blocks user’s view (vision) of the real world as well as all the wires and physical objects around him/her, major VR manufactures such as HTC suggest securing a large space before experiencing the immersive virtual environment. There are possibilities that these “potential danger” elements could induce a negative effect on their virtual reality experiences. Exploring a relationship between user’s percieved danger of the real world and immersion in the virtual reality is the main topic of this paper. In particular, we wanted to see the level of perceived danger when a user in the immersive virtual environment encountered a dangerous situation from the objects in the “real world”.
The Various Graphs in Graph Computing

The world is full of relationships, and graph is the most evident representation for them. With the increasing of data scale, graphs become larger and have encountered a new world of analyzing. What can we learn from a graph? How many kinds of graphs are there? How different is graph from one area to that from another? All these questions need answers, but previous research on graph computing mainly focused on computing frameworks and systems, paying little attention to graph itself. In this paper, we studied graphs of different kinds, different scales and different mining methods, trying to give a sketcher and classification of graph categories. Besides, we studied characters and analyzed algorithms in each category. We researched public graph datasets to show current graph scale and its trend for future infrastructure.
Bayesian parameter estimation of Euler-Bernoulli beams

This paper develops a statistical signal processing algorithm for parameter estimation of Euler-Bernoulli beams from limited and noisy measurement. The original problem is split into two reduced-order sub-problems coupled by a linear equation. The first sub-problem is cast as an inverse problem and solved by using Bayesian approximation error analysis. The second sub-problem is cast as a forward problem and solved by using the finite element technique. An optimal solution to the original problem is then obtained by coupling the solutions to the two sub-problems. Finally, a statistical signal processing algorithm for adaptive estimation of the optimal solution is developed. Computer simulation shows the effectiveness of the proposed algorithm.
Neural Network Based Image Registration Using Synthetic Reference Image Rotation

Typical image registration techniques use a set of features from a target and reference images and search in the affine transformation space using a similarity metric. Neural Networks typically have employed two choices—geometric transformations to find correlation between images and a similarity metric. In this paper, however, we have proposed and employed a simple and effective method for image registration using neural networks. The image registration has been formulated as a classification problem. By generating and learning exhaustive synthetic reference image transformations appropriate re-transformation for target image is computed for effective registration. The proposed work is tested on satellite imagery.
Fast and Scalable Bilinear-Type Conversion Method for Large Scale Crypto Schemes

Bilinear-type conversion is to translate a cryptographic scheme designed over symmetric bilinear groups into one that works over asymmetric bilinear groups with small overhead regarding the size of objects concerned in the target scheme. In this paper, we address scalability for converting complex cryptographic schemes. Our contribution is threefold. Investigating complexity of bilinear-type conversion. We show that there exists no polynomial-time algorithm for worst-case inputs under standard complexity assumption. It means that bilinear-type conversion in general is an inherently difficult problem. Presenting a new scalable conversion method. Nevertheless, we show that large-scale conversion is indeed possible in practice when the target schemes are built from smaller building blocks with some structure. We present a novel conversion method, called IPConv, that uses 0-1 Integer Programming instantiated with a widely available IP solver. It instantly converts schemes containing more than a thousand of variables and hundreds of pairings. Application to computer-aided design. Our conversion method is also useful in modular design of middle to large scale cryptographic applications; first construct over simpler symmetric bilinear groups and run over efficient asymmetric groups. Thus one can avoid complication of manually allocating variables over asymmetric bilinear groups. We demonstrate its usefulness by somewhat counter-intuitive examples where converted DLIN-based Groth-Sahai proofs are more compact than manually built SXDH-based proofs. Though the early purpose of bilinear-type conversion is to save existing schemes from attacks against symmetric bilinear groups, our new scalable conversion method will find more applications beyond the original goal. Indeed, the above computer-aided design can be seen as a step toward automated modular design of cryptographic schemes.
EZW, SPIHT and WDR Methods for CT Scan and X-ray Images Compression Applications

Scanning rate of medical image tools has been significantly improved owing to the arrival of CT, MRI and PET. For medical imagery, storing in less area and not losing its details are vital. So, an efficient technique is necessary for storing in a cost-effective way. In this paper, wavelet is employed to perform decomposition, and image is compressed using Embedded Zero-Tree Wavelet (EZW), Set Partitioning in Hierarchical Trees (SPIHT) and Wavelet Difference Reduction (WDR) algorithms. These algorithms are applied to compress X-ray and CT images, and compared using performance metrics. From results, it is seen that compression ratio is better in WDR for all the wavelets than SPHIT and EZW. High compression ratio, 82.47, is obtained with Haar and WDR combination for CT scan, whereas this is 32.89 for Biorthogonal and WDR combination for X-ray. The main objective of this paper is to find the optimal combination of wavelets and image compression techniques.
A Renewable Energy Driven Approach for Computational Sprinting

Computational Sprinting, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with certain phase-changing materials, has proven to be an effective way to boost the computing performance for bursty workloads. However, extra power available for sprinting is constrained by existing power distribution infrastructures. Using batteries alone to provide the additional power to achieve performance target not only limits the effectiveness of sprinting, but also negatively impacts the lifetime of the batteries. Leveraging renewable power supply in a green data center provides an opportunity to make full use of Computational Sprinting. However, the intermittent nature of renewable energy, along with limited cooling capacity, makes it very challenging. In this paper, we propose GreenSprint, a renewable energy driven approach that enables a data center to boost its computing performance efficiently by conducting computational sprinting under the intermittent and time-varying nature of renewable energy supply. Three basic strategies are designed to determine the core count and frequency level for sprinting based on current power supply. Furthermore, we propose a Hybrid strategy that combines reinforcement learning to dynamically determine the optimal server setting, targeting at both the power provision safety and the quality of service. In consideration of practical cooling conditions, we also present a thermal-aware sprinting strategy Hybrid-T. Finally, we build an experimental prototype to evaluate GreenSprint on a cluster of 10 servers with a simulated solar power generator. The results show that renewable energy by itself can sustain different duration lengths of sprinting when its supply is sufficient and can improve performance by up to 4.8x for representative interactive applications. We also show the effectiveness of core-count and frequency scaling in the presence of varied renewable power and limited battery energy.
Sarcasm Detection in Twitter Data

Posting sarcastic messages on social media like Twitter, Facebook, WhatsApp, etc., became a new trend to avoid direct negativity. Detecting this indirect negativity in the social media text has become an important task as they influence every business organization. In the presence of sarcasm, detection of actual sentiment on these texts has become the most challenging task. An automated system is required that will be capable of identifying actual sentiment of a given text in the presence of sarcasm. In this chapter, we proposed an automated system for sarcasm detection in social media text using six algorithms that are capable to analyze the various types of sarcasm occurs in Twitter data. These algorithms use lexical, pragmatic, hyperbolic and contextual features of text to identify sarcasm. In the contextual feature, we mainly focus on situation, topical, temporal, and historical context of the text. The experimental results of proposed approach were compared with state-of-the-art techniques.
<i>ATAR</i>

In recent years, we have witnessed a rapid increase of text content stored in digital archives such as newspaper archives or web archives. With the passage of time, it is however difficult to effectively perform search within such collections due to vocabulary and context change. In this paper, we present a system that helps to find analogical terms across temporal text collections by applying non-linear transformation. We implement two approaches for analog retrieval where one of them allows users to also input an aspect term specifying particular perspective of a query. The current prototype system permits temporal analog search across two different time periods based on New York Times Annotated Corpus.
Conclusion: Intercultural Tele-Improvisatory Interaction: Applications and Contexts

This chapter describes how the findings in this book can be used to enhance knowledge of tele-collaborative engagement across a range of disciplines and contexts. It highlights examples in HCI, education, musicology, and games design to show how tele-improvisatory practices contribute useful perspectives to these fields. Additional findings pertaining to technical experiences of software and hardware installation, network configurations and the ramifications of connectivity problems on performers’ creativity are discussed. The chapter also outlines how the methodologies used in the research can themselves, be advanced, and extended to new areas of enquiry. It begins with an overview of the book to illustrate insights into intercultural collaborative interaction in distributed and digitally mediated environments provided by each chapter. The final discussion focuses on future work and the development of a taxonomy of tele-improvisatory interaction that integrates a sizeable number of world cultures, musical traditions, instruments, technologies and performer experiences.
A Mobile Augmented Reality System for Enhancing Electrical Machine Supervision

Electrical machines are used almost everywhere, and our daily life depends on them. For this reason, it is important to articulate mechanisms to control, supervise, and perform proper maintenance of these machines, especially those used in critical industrial process. The SCADA protocol is one of the technologies that eases the operation and supervision of electrical machines. However, the absence of a spatial connection between the SCADA signals and the machines being supervised suggests the use of augmented reality (AR) to fill this void. This chapter describes SIMARA: A Mobile AR application based on a dual computer-vision system (QR-codes and fiducial markers). SIMARA provides a robust client application for the integration of AR and SCADA signals by means of virtual panels shown on top of real SCADA machines. An authoring tool is also provided in order for users to customize the application to their particular needs, allowing to create, by means of web services, customized virtual panels, and links between SCADA signals and the virtual information shown in the AR application.