Reviewerly/exHarmony huggingface dataset:
Does Good Governance Matter more for Energy Investment? Evidence from Sub-Saharan Africa

Abstract This paper examines the relationship between three global priorities: access to energy, good governance, and financing for development. Using the World Governance Indicators (WGI), it finds that while governance matters for raising domestic revenues, its effect on external financing sources is mixed. Good governance, except for political stability, does not appear to matter much for attracting foreign direct investment (FDI) to oil exporting countries but is positively associated with FDI to oil importing countries. In contrast, official development finance (ODA) is positively associated with good governance. The bigger bang for improving governance is at home in the form of increased tax revenues (excluding resource rents). The paper also uses the newly developed Regulatory Indicators for Sustainable Energy (RISE) and finds that improved governance is associated with increased private investment and ODA to the energy sector. In contrast, Chinese investment to the sector appears not to be responsive to changes in governance.
Don’t Panic: Recursive Interactions in a Miniature Metaworld

Metaworld is a new recursive interaction paradigm for virtual reality, where a miniature display (or 3D map) of the virtual world is presented to the user as a miniature model that itself lives inside the virtual world. The miniature model is interactive and every action which occurs on the miniature world similarly occurs to the greater virtual world and vice-versa. We implemented the metaworld concept in the virtual reality application MetaCity, a city designing sandbox where users can reach into a miniature model and move the cars and skyscrapers. Design considerations of how to display and interact with the miniature model are presented, and a technical implementation of the miniature world is described. The metaworld concept was informally and playfully tested in the MetaCity which revealed a number of novel interactions that enable the user to navigate quickly through large spaces, re-scale objects in the world and manipulate the very fabric of the world itself. These interactions are discussed within the context of four major categories – Experiential Planning, Interdimensional Transformations, Power of the Gods and Self Manipulation.
Blockchain Based Smart Contract Applications in Tourism Industry

The main aim of the study is to design the bases of the blockchain system and the creation of smart contracts and to generate a new blockchain in the financial payment system. In the scope of the study, the technology under the blockchain system and smart contracts examined. In light of the information obtained, a reliable, transparent, accountable simplified blockchain data structure as a result of intensive use of data and goods can be used in smart contracts in the financial payment system in tourism enterprises. In this context, utilizing the smart contract system consisting of continuous growth and structured data, it will be possible to dispose of the intermediary institutions in the transfer fees that can be used as a priority in the financial payments system. Content analysis was used within the scope of the research and examples of smart contract projects in the tourism sector were examined.
ROI based Medical Image Compression using DWT and SPIHT Algorithm

In an image the spatial region always assumes higher priority compared to other regions. Different image compression methods have the capability of providing higher reconstruction quality of important parts of the image. In case of medical images, small portions of the image may be diagnostically significant, but if incorrectly interpreted, it can have serious ramifications. To circumvent this issue, Region-Based Coding (RBC) is an important technique for transmission and compression of an image that inspires diagnostic significance. Lossless compression schemes possess a safe mechanism of transmission, which in turn play a vital role in telemedicine applications which further research and diagnosis with higher degrees of accuracy. Our research involves employment of lossless scalable RBC for Digital Imaging and Communications in Medicine (DICOM) images by Discrete Wavelet Transform (DWT) and with distortion limiting compression technique for remaining portions of the image. The primary aim of our work is to eradicate the noisy content in the background and resurrect the portions of image in lossless manner. As a part of the Region of Interest (ROI) compression technique, an algorithm is developed using DWT and Set Partitioning in Hierarchical Trees (SPIHT) algorithm. Based on parameters like Mean Square Error (MSE), Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR), we have carried out the entire analysis. ROI compression techniques have outperformed conventional compression schemes as they enhance the quality of ROI with lower MSE and PSNR.
Financial Fraudulent Statements Detection through a Deep Dense Artificial Neural Network

A very important issue in the financial field is to identify and reliably predict Fraudulent Financial Statements (FFS). For this purpose, several Machine Learning models have been developed that identify the issues that are directly related to FFS. In this paper, we present a new predictive model for fraudulent detection through a deep dense artificial neural network. Specifically, a new forecasting model was tested experimentally using data from Greek companies. The obtained results showed that the proposed scheme is robust and promising.
Authentically Distorted Image Quality Assessment by Learning From Empirical Score Distributions

Most existing works on image quality assessment (IQA) focus on predicting a scalar quality score (SQS) based on the assumption that people can reach a consensus on the judgment of image quality. However, assigning a single scalar fails to reveal the subjective diversity that an image will probably receive divergent opinion scores from different subjects. This is particularly true for real-world authentically distorted images which usually involve composite mixtures of multiple distortions. To characterize such an property, this letter proposes to use a more informative vectorized label called empirical score distribution (ESD) to build an ESD-aided deep neural network (DNN) for authentically distorted image quality prediction. Our proposed network contains two streams: ESD prediction stream and SQS prediction stream. The whole DNN is optimized end-to-end with a combined loss so that both of the supervision information from ESD and SQS can be fully utilized in the training process. Experiments on two public authentically distorted image databases verify the superiority of our method.
Fooling the Sense of Cross-Core Last-Level Cache Eviction Based Attacker by Prefetching Common Sense

Cross-core last-level cache (LLC) eviction based side-channel attacks are becoming practical because of the inclusive nature of shared resources (e.g., an inclusive LLC), that creates back-invalidation-hits at the private caches. Most of the cross-core eviction based side-channel attack strategies exploit the same for a successful attack. The fundamental principle behind all the cross-core eviction attack strategies is that the attacker can observe LLC access time differences (in terms of latency differences between events such as hits/misses) to infer about the data used by the victim. We fool the attacker (by providing LLC hits to the addresses of interest) through a back-invalidation-hits triggered hardware prefetching technique (BITP). BITP is an L2 cache level hardware prefetcher that prefetches the back-invalidated block addresses and refills the LLC (along with the L2) before the attacker's observation/access, efficiently nullifying inferences due to differences in access latencies. We show that BITP can fool the attacker with various security metrics related to LLC side-channel. BITP provides zero probability of success in terms of attacker's probability of success for Evict+Time, Evict+Reload, and Prime+Probe attacks. We also show the effectiveness of BITP in terms of performance by simulating SPEC CPU 2006, PARSEC, and CloudSuite benchmarks and find that, on average, BITP improves system performance marginally by 1.1%. Overall, BITP is a simple, practical, and yet powerful technique in mitigating various cross-core LLC eviction-based side-channel attacks. Compared to the state-of-the-art policies, BITP does not require support from software writer, operating system (OS), and runtime systems. Overall, BITP provides marginal improvement in system performance, providing security with no hardware and performance overhead, which makes BITP readily-implementable.
SentiRusColl: Russian Collocation Lexicon for Sentiment Analysis

Most sentiment lexicons include individual words rather than collocations. However, the use of collocations can improve the performance of sentiment analysis since the meaning of some collocations cannot be derived from the meaning of their constituents, for example, " " ("kick the bucket") or " " ("it is impossible to take one's eyes off something"). In our study, we create sentiment collocation lexicons for ten domains – reviews of books, movies, music, cars, computers, house appliances, phones, banks, hotels and restaurants. The lexicons are built on the basis of a semi-automatic approach using the corpora of reviews. What is more, we form a universal SentiRusColl lexicon with the help of union of created domain-oriented lexicons. We demonstrate the possibility of using the generated lexicon for various domains. In addition, we reveal the improved performance of sentiment analysis when union of SentiRusColl and existing lexicon – RuSentiLex – is used.
Quantum circuits synthesis using Householder transformations

The synthesis of a quantum circuit consists in decomposing a unitary matrix into a series of elementary operations. In this paper, we propose a circuit synthesis method based on the QR factorization via Householder transformations. We provide a two-step algorithm: during the first step we exploit the specific structure of a quantum operator to compute its QR factorization, then the factorized matrix is used to produce a quantum circuit. We analyze several costs (circuit size and computational time) and compare them to existing techniques from the literature. For a final quantum circuit twice as large as the one obtained by the best existing method, we accelerate the computation by orders of magnitude.
Effect of Fault Tolerance in the Field of Cloud Computing

Cloud Computing has enabled the availability of various software, platforms and infrastructural resources as scalable services on demand over the internet. However, the performance of cloud computing services is hampered due to their inherent vulnerability to failures owing to the scale at which they operate. Fault tolerance is a major concern to guarantee availability and reliability of critical services as well as application execution. In order to minimize failure impact on the system and application execution, failures should be anticipated and proactively handled. This paper presents a comprehensive overview of fault tolerance-related issues in cloud computing; emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods. The objective is to provide insights into the existing fault tolerance approaches as well as challenges yet required to be overcome.
Spread-n-share

Traditional batch job schedulers adopt the Compact-n-Exclusive (CE) strategy, packing processes of a parallel job into as few compute nodes as possible. While CE minimizes inter-node network communication, it often brings self-contention among tasks of a resource-intensive application. Recent studies have used virtual containers to balance CPU utilization and memory capacity across physical nodes, but the imbalance in cache and memory bandwidth usage is still under-investigated.
simFIDO

WebAuthn as part of FIDO2 is a new standard for two-factor and even password-less user authentication to web-services. Leading browsers, like Google Chrome, Microsoft Edge, and Mozilla Firefox, support the WebAuthn API. Unfortunately, the availability of hardware authenticators that support FIDO2 authentication is still focused heavily on desktop computers, while for mobile devices, only a limited choice of suitable authenticators is available to users (few roaming authenticators with wireless connectivity and even fewer built-in platform authenticators on mobile devices). This creates a void for users, in particular users of older device generations that lack platform authenticators and the right connectivity, to authenticate themselves with WebAuthn to web-services. In this poster, we present the idea ofsimFIDO, a FIDO2 setup using a recently developed simTPM as (platform) authenticator for mobile devices and even as roaming authenticator offered by mobile devices to connected computers. The move-ability property of the key storage of simTPM makes the users' lives easier for credential portability between devices. In particular, a seamless integration of simTPM with non-mobile devices through phones will help to create a kind of universal authentication setup using FIDO2. Although we present the concrete design and implementation of a SIM card-based FIDO2 authenticator, we hope this poster will contribute to the discussion about how and in which form hardware authenticators can be made available to users.
On Continuously Matching of Evolving Graph Patterns

An evolving pattern graph is defined by an initial pattern graph and a graph update stream consisting of edge insertions and deletions. Identifying and monitoring evolving graph patterns in the data graph is important in various application domains such as Cyberthreats surveillance. This motivates us to explore matching patterns with evolvement, and the investigation presents a novel algorithm \incepg for continuously matching of evolving patterns. Specially, we propose a concise representation \Index of partial matching solutions, and its execution model allows fast incremental maintenance. We also conceive an effective model for estimating step-wise cost of pattern evaluation to drive the matching process. Extensive experiments verify the superiority of \incepg.
Multi-Level Fusion of the Multi-Receptive Fields Contextual Networks and Disparity Network for Pairwise Semantic Stereo

In this paper, we propose a multi-level fusion framework to address the pairwise semantic stereo issue. For disparity estimation, we adopt the pyramid stereo matching network. For semantic segmentation, the single segmentation network is proposed with respect to the left image, along with the disparity fusion segmentation network for the combination of semantic features and disparity features. Specifically, the multi-receptive fusion block is designed and employed to fully extract and fuse the contextual information. Finally, the refined segmentation result is obtained via yet another fusion of the multi-model results. The proposed method achieved a mean intersection over union (mIoU) of 79.05%, an average endpoint error (EPE) of 1.3966, and an mIoU-3 of 77.75%, ranking first in the Pairwise Semantic Stereo Challenge of the 2019 IEEE GRSS Data Fusion Contest [1],[2].
Polarimetric SAR Image Super-Resolution VIA Deep Convolutional Neural Network

In order to solve the problem of full-polarimetric SAR image degradation, this paper proposes a full-polarimetric SAR image super-resolution reconstruction method combined with a convolutional neural network and residual compensation. Through the advantages of the deep convolutional neural network for nonlinear model fitting, this paper performs super-resolution reconstruction on low-resolution full-polarimetric SAR images, and then applies residual compensation to network reconstruction results, using low-resolution image information to the network. The super-resolution reconstruction results are corrected to obtain a high-resolution full-polarimetric SAR image. Compared with the traditional full-polarimetric SAR image super-resolution reconstruction method, the proposed method shows excellent results in both visual and quantitative evaluation indicators, especially the reconstruction of detailed information.
Modelling Mismatch and Noise Statistics Uncertainty in Linear MMSE Estimation

Standard filtering techniques, such as Kalman, sigma-point or particle filters, assume a perfect knowledge of the system. This implies that both process and measurement functions, and the system noise statistics, are assumed known and fit the reality. Regarding the noise statistics this involves knowing not only the distributions but also their parameters. In this contribution, we explore the impact of system model mismatch and uncertain noise statistics parameters into linear minimum mean square error estimators for linear discrete state-space models. Illustrative examples are shown to support the discussion.
Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multi-fuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.
Reinventing Museums in 21st Century

The forthcoming Industry 4.0 is expected to change not only manufacturing and industrial services, but will rearrange how services are offered in a variety of sectors, including museum's services. Museums will inevitably be led to more digital (VR &amp; AR) and promoting (Social Media) paths. A forthcoming “digital convergence” between VR &amp; AR technologies and social media's promoting logic could enlarge museums' potentialities in attracting more visitors, younger visitors, while new patterns for connecting learning effects and amusement should be established. This chapter contributes to the following: • Presenting existing theoretical and empirical research on Virtual Reality and Augmented Reality technological implementation in Museums. • Presenting current tensions on social media's usage from cultural organizations. • Exploring how VR &amp; AR applications can incorporate various elements coming from social media operational logic.
Laplace Input and Output Perturbation for Differentially Private Principal Components Analysis

With the widespread application of big data, privacy-preserving data analysis has become a topic of increasing significance. The current research studies mainly focus on privacy-preserving classification and regression. However, principal component analysis (PCA) is also an effective data analysis method which can be used to reduce the data dimensionality, commonly used in data processing, machine learning, and data mining. In order to implement approximate PCA while preserving data privacy, we apply the Laplace mechanism to propose two differential privacy principal component analysis algorithms: Laplace input perturbation (LIP) and Laplace output perturbation (LOP). We evaluate the performance of LIP and LOP in terms of noise magnitude and approximation error theoretically and experimentally. In addition, we explore the variation of performance of the two algorithms with different parameters such as number of samples, target dimension, and privacy parameter. Theoretical and experimental results show that algorithm LIP adds less noise and has lower approximation error than LOP. To verify the effectiveness of algorithm LIP, we compare our LIP with other algorithms. The experimental results show that algorithm LIP can provide strong privacy guarantee and good data utility.
Visualizing and Interacting with Hierarchical Menus in Immersive Augmented Reality

Graphical User Interfaces (GUIs) have long been used as a way to inform the user of the large number of available actions and options. GUIs in desktop applications traditionally appear in the form of two-dimensional hierarchical menus due to the limited screen real estate, the spatial restrictions imposed by the hardware e.g. 2D, and the available input modalities e.g. mouse/keyboard point-and-click, touch, dwell-time etc. In immersive Augmented Reality (AR), there are no such restrictions and the available input modalities are different (i.e. hand gestures, head pointing or voice recognition), yet the majority of the applications in AR still use the same type of GUIs as with desktop applications.
CNN Based Extraction of Panels/Characters from Bengali Comic Book Page Images

Peoples nowadays prefer to use digital gadgets like cameras or mobile phones for capturing documents. Automatic extraction of panels/characters from the images of a comic document is challenging due to the wide variety of drawing styles adopted by writers, beneficial for readers to read them on mobile devices at any time and useful for automatic digitization. Most of the methods for localization of panel/character rely on the connected component analysis or page background mask and are applicable only for a limited comic dataset. This work proposes a panel/character localization architecture based on the features of YOLO and CNN for extraction of both panels and characters from comic book images. The method achieved remarkable results on Bengali Comic Book Image dataset (BCBId) consisting of total 4130 images, developed by us as well as on a variety of publicly available comic datasets in other languages, i.e. eBDtheque, Manga 109 and DCM dataset.
Recommender System Using Sequential and Global Preference via Attention Mechanism and Topic Modeling

Deep neural networks improved the accuracy of sequential recommendation approach which takes into account the sequential patterns of user logs, e.g., a purchase history of a user. However, incorporating only the individual's recent logs may not be sufficient in properly reflecting global preferences and trends across all users and items. In response, we propose a self-attentive sequential recommender system with topic modeling-based category embedding as a novel approach to exploit global information in the process of sequential recommendation. Our self-attention module effectively leverages the sequential patterns from the user's recent history. In addition, our novel category embedding approach, which utilizes the information computed by topic modeling, efficiently captures global information that the user generally prefers. Furthermore, to provide diverse recommendations as well as to prevent overfitting, our model also incorporates a vector obtained by random sampling. Experimental studies show that our model outperforms state-of-the-art sequential recommendation models, and that category embedding effectively provides global preference information.