Reviewerly/exHarmony huggingface dataset:
Performance Trade-offs in Design of MimbleWimble Proofs of Reserves

Revelio (CVCBT 2019) is a proof of reserves protocol for MimbleWimble-based cryptocurrencies which provides privacy to a cryptocurrency exchange by hiding the exchange-owned outputs in a larger anonymity set of unspent outputs. A drawback of Revelio is that the proof size scales linearly in the size of the anonymity set. To alleviate this, we design RevelioBP, a Bulletproofs-based proof of reserves protocol with proof sizes which scale logarithmically in the size of the anonymity set. This improvement allows us to use the set of all UTXOs as the anonymity set, resulting in better privacy for the exchange. On the downside, the higher proof generation and verification time of RevelioBP than that of Revelio might affect practical deployment of RevelioBP. Through implementation of RevelioBP, we quantitatively analyse trade-offs in design of MimbleWimble proofs of reserves in terms of scalability and performance. We conclude that unless proof size is a concern for exchanges, Revelio is a marginally better choice for proof of reserves. On the other hand, if an exchange is willing to pay in terms of proof generation time, RevelioBP offers proof sizes significantly smaller than Revelio.
Extended LBP Based Secret Image Sharing with Steganography

We have presented a new (k, n) threshold secret image sharing technique in this paper. The secret image is first encoded under the proposed structure that uses the extended LBP (ELBP) technique. The shares are then created from the encoded data. At the end, ELBP based data hiding (or steganography) strategy has been used to incorporate the shares in the image covers. This scheme ensures that no information will be leaked during the share generation/share transmission, and stego images have good quality.
A Script Knowledge Based Dialogue System for Indoor Navigation

We present an indoor navigation system that is based on natural spoken interaction. The system navigates the user through the University of Ulm based on scripts, supporting three different routes and varying communication styles for the system descriptions. Furthermore, the system is able to cope with incomplete scripts and inconclusive situations by passing the dialogue initiative to the user. In order to create the scripts, a data collection has been carried out in which 97 native speakers described the routes with the help of videos. In the end, the system has been evaluated in a user study with 30 participants. The work is part of the research project “MUNGO—Multi-User Indoor Navigation Using Natural Spoken Dialog and Self-learning Ontologies”, co-funded by the 2018 Google Faculty Research Award.
Development of a bionic interactive interface for Owl robot using stereo vision algorithms

With the requirements for improving life quality, companion robots have gradually become hotspot of application for healthy home living. In this article, a novel bionic human‐robot interaction (HRI) strategy using stereo vision algorithms has been developed to imitate the animal vision system on the Owl robot. Depth information of a target is found via two methods, vergence and disparity. Vergence requires physical tracking of the target, moving each camera to align with a chosen object, and through successive camera movements (saccades) a sparse depth map of the scene can be built up. Disparity however requires the cameras to be fixed and parallel, using the position of the target within the field of view, of a stereo pair of cameras, to calculate distance. As disparity does not require the cameras to move, multiple targets can be chosen to build up a disparity map, providing depth information for the whole scene. In addition, a salience model is implemented imitating how people explore a scene. This is achieved with feature maps, which apply filtering to the scene to highlight areas of interest, for example color and edges, which is purely a bottom‐up approach based on Itti and Koch's saliency model. A series of experiments have been conducted on Plymouth Owl robot to validate the proposed interface.
An Intrinsically-Typed Solution for the List-Machine Benchmark

Formal models are important tools in the programming language research community. However, such models are full of intricacies and, due to that, they are subject to subtle errors. Such failures motivated the usage of tools to ensure the correctness of these formalisms. One way to eliminate such errors is to encode models in a dependently-typed language in order to ensure its "correctness-by-construction". In this paper, we use this idea to build a verified interpreter for the list-machine benchmark in the Agda programming language, comparing the results with formalizations developed by Appel and Leroy. We formalize the 14 tasks of the benchmark using roughly 14%of LOC compared to a Twelf solution, and 47%of LOC compared to a Coq solution, even without the use of proof automation.
Analyzing a variant of Clobber: The game of San Jego

San Jego is a two-player game with perfect information. It is a variation of the games Clobber (2001) and Cannibal Clobber. Given is a rectangular board with black and white pieces on the cells. Multiple pieces which are stacked on each other are called towers. The piece on top of a tower indicates her owner. A move consists of picking an own tower and placing it completely on top of an adjacent tower. If both players can not move anymore, the game ends. Winner is the player with the highest tower. An upper bound for the state-space complexity of San Jego is determined. Furthermore, the game-tree complexity is approximated theoretically and numerically. For small board sizes, the optimal game-theoretic values are calculated and the advantage of the first move is determined.
DEEP framework for deep learning

The DEEP-Hybrid-DataCloud project developed a distributed architecture to leverage intensive computing techniques such as needed for deep learning. The DEEP framework applies a hybrid-cloud approach and provides a set of tools and cloud services to cover the whole machine learning cycle: from models creation, training, validation and testing to model serving and sharing. These tools and services in particular include DEEP API for a web access to machine learning models, Pilot testbed with heterogeneous resources to develop and test models, DEEP Marketplace for easy sharing, DevOps approach with Data Science template and CI/CD pipeline to facilitate development, testing, and delivery of machine learning applications. The project recently published its second software release and platform, code named DEEP Rosetta, and provides distributed training facility for machine learning, artificial intelligence and deep learning via EOSC portal.
The Design of Attack-Defense Training Environments Based on Virtualization

This paper analyses the current situation of information security personnel training and skills training environment in colleges. Through the analysis of the operation principle and function characteristics of KVM virtualization and SDN virtual network, it combines information security training exercise with virtualization technology to provide a highly simulated attack-defense environment for colleges and universities, optimizes the virtual scheduling and operation, and makes the full use of computing resources in colleges. After that, high load pressure test is used to verify the actual effect of the optimization technology.
Teachers' and students' attitudes towards information security: A qualitative study

Abstract The goal of this research study is to examine teenager's and educational staff's attitudes towards the issue of information security (IS). The study focuses on teenagers and on educational staff from high schools from varied socio‐economic groups. The goal of this study is to eventually instigate a cultural change in the area of IS, therefore, it is important to understand the motivations behind end users' actions. The research findings indicate a clear connection between students' and teachers' evaluation of their information sources and the threat they feel by information risks. This, in turn affects their willingness to apply IS measures. In addition, the necessity of integration of IS into the educational curriculum emerged, as it constitutes an indispensable skill in the 21st century.
RGBD-Fusion: Depth Refinement for Diffuse and Specular Objects

The popularity of low-cost RGB-D scanners is increasing on a daily basis and has set off a major boost in 3D computer vision research. Nevertheless, commodity scanners often cannot capture subtle details in the environment. In other words, the precision of existing depth scanners is often not accurate enough to recover fine details of scanned objects. In this chapter, we review recent axiomatic methods to enhance the depth map by fusing the intensity and depth information to create detailed range profiles. We present a novel shape-from-shading framework that enhances the quality of recovery of diffuse and specular objects’ depth profiles. The first shading-based depth refinement method we review is designed to work well with Lambertian objects, however, it breaks down in the presence of specularities. To that end, we propose a second method, which utilizes the properties of the built-in monochromatic IR projector and the acquired IR images of common RGB-D scanners and propose a lighting model that accounts for the specular regions in the input image. In the methods suggested above, the detailed geometry is calculated without the need to explicitly find and integrate surface normals, this allows the numerical implementations to work in real-time. Finally, we also show how we can leverage deep learning to refine depth details. We present a neural network that is trained with the above models and can be naturally integrated as part of a larger network architecture. Both quantitative tests and visual evaluations prove that the suggested methods produce state-of-the-art depth reconstruction results.
Dynamic Re-Configuration of Conversationally Initiated Automated Negotiations

Autonomous agents are well established to assist customers in contract negotiations on electronic markets. Recently, AI-based conversational agents provide more natural customer communication. Yet, on markets with varying negotiation items natural conversation can not be guaranteed to include all possible customer preferences for optimum negotiation results. In this paper, we propose a negotiation model supporting dynamic interactive preference post-specification and present a Hybrid Alternating Offers Protocol for that purpose. We demostrate it for conversationally initiated negotiations in the context of an app-market.
Possibilistic BRISK method for an efficient registration (PBRISK)

This paper aims to present a possibilistic registration method using BRISK. BRISK method is a key point detector and descriptor. It is rotation and scale invariant, but it takes more time to detect the feature points and it suffers from the high number of outliers. The main idea of the proposed method is to apply the theory of possibilities for extracting primitives to obtaining an efficient registration. We explore the suitability of the BRISK method for the task of image registration by limiting the outlier's number. The proposed method uses the semantic aspect of images for features detection as well as matching. This “semantic focussing process” allows reducing the quantity of information, as well as the noise effects during the matching process by the creation of a new space called “Semantic knowledge space” which contains a set of projections of images each presenting a single content called a “possibilistic maps The experiments as well as the comparative study carried out, using medical images, show the efficiency of the proposed method in terms of outliers' reduction, noise robustness, time complexity and precision improved.
Post Connection Attacks in MySQL

This article covers the default security features of MySQL, a very popular open-source database management system, and what types of attacks an attacker can perform after successfully connecting to a database. In this context, it introduces Zephyr, a platform we developed, which automatically performs the types of scanning and attacks caused by incorrect or incomplete security configurations. We demonstrate an analysis of the security of MySQL usage in Turkey over Zephyr.
Predicting Language Endangerment: A Machine Learning Approach

Some languages flourish, while others may decline or eventually turn extinct in due course of time. When there are fewer and fewer people claim a language as their own, the language falls to endangerment; they neither use it nor pass it on to next generation [1]. Identifying actual vitality of a language is subject to many factors, which interact in dynamic ways and; therefore, they are not entirely predictable. However, recognizable trends may be found on careful observation. This study attempts to create a predictive model based on regression using machine learning approach to forecast the timeline as to when a language may become extinct following its ongoing vitality trend. The main datasets have been used from Census of India surveys of 1991, 2001, and 2011. Presently, this model is tested on the languages of Sikkim. However, the model may forecast the vitality prediction of any language.
Application of blockchain technology for electricity supplier change

In the course of digitalization various technologies which could deeply transform present processes are put forth. As such an innovation, blockchain is attributed to have potential to make energy industry procedures more efficient and to simplify them. The aim of this paper is to examine the applicability of this technology using the change of power supplier as an example. Based on Ethereum a proper blockchain solution for the use-case is developed using smart contracts to handle all processes of the example. Beside qualitative evaluation of manipulation security and privacy, a test scenario is built to measure the speed in which the developed solution allows supplier changes.
Uniformity Measurement for French Cinema

French cinema theaters follow strict rules; there are two French standards involved when granting theater operation licenses. One standard is about architectural considerations, the other about movie reproduction parameters. The main goal of these specifications is to ensure that movies are reproduced uniformly in all theaters while matching the intention of the creative team during final check. Another goal is to allow a satisfying perception by the audience, trying to avoid unnecessary physical or audio-visual stress. — These two standards are currently under periodic revision. The specifications listed in the standard about movie reproduction includes a uniformity formula. Counterexamples demonstrate problems with the validity of this uniformity. A new uniformity method of measurement is under study. — The proposed method unifies projection and display systems in the same measurement. The projection vignetting effect is cancelled using current room and screen dimensions. The formula is also independent of the reproduction system's dynamic range. Nine locations on screen are used as points of measurement. — The proposed measurement of uniformity is based on a statistical measurement called the K-S distance. It compares current results with a standard statistical distribution. The type of distribution is chosen based on practical considerations.
An Incentive Mechanism for Improving Energy Efficiency of Colocation Data Centers Based on Power Prediction

Colocation data centers (colocations, for short) are developing rapidly in recent years, resulting in a heavy burden on the power grid and the environment. Due to the special management mode of colocations, even the colocation operators wish to reduce their power demand, they have no authority to control the servers because the servers belong to and are operated by the tenants themselves. To solve the "uncoordinated relationship" issue between operators and tenants, a truthful and feasible incentive mechanism MesPP is proposed in this paper. Different from existing works, MesPP aims at maximizing the energy reduction of colocations with a limited cost budget and can be applied even there is no demand response (DR) program. Meanwhile, power prediction is integrated into MesPP to further improve the energy efficiency of colocations and fairness of the mechanism. To solve the optimization problem, we develop a (1−ε)-approximation algorithm. Simulations are performed and show that MesPP can achieve 12.23% more energy saving compared with existing incentive mechanisms.
IWApriori: An Association Rule Mining and Self-updating Method Based on Weighted Increment

The mining of association rules plays an important role in fault prediction. Many studies have shown that there is an obvious temporal and spatial correlation between the failure records of the cluster system. Therefore, most cluster system failure prediction engines are built based on causal correlation analysis between log events. However, the original system log file usually contains a large number of invalid records (duplicate or non-fault related records), which makes the mining of event correlation extremely difficult and seriously affects the efficiency and accuracy of fault prediction. Therefore, this paper proposes an association rule mining and self-updating method based on weighted increment, named IWApriori (improved weighted Apriori algorithm). The method includes two important steps: 1) log preprocessing; 2) mining and updating of association rules based on improved algorithm IWApriori. This method can effectively improve the rule completeness and realize the efficient mining and updating of rules in the whole life cycle of the system. In addition, we used the real log data set Blue Gene/L to validate our method. The results show that our association rule mining method is better than other methods in terms of time performance, space performance and the effectiveness of mining rules.
Exploring factors affecting renewal and stopping reasons in cross‐session search

Abstract This study analyzes the relationships between search session renewal reasons and stopping reasons for everyday cross‐session search, and their relationships with types of needed information, the number of search sessions, and task performance stage. We present results from an online survey questionnaire distributed on the Amazon Mechanical Turk platform. Our results validate the renewal reasons from Lin and Belkin's MISE model and generalize the stopping reasons found by other empirical studies to a broader population and tasks. We found that participants in different renewal modes more often look for specific information than general information. Sessions which happened early or at the middle of a task were often renewed because a task spawned sub‐problems or transmuted. Sessions renewed near the end or after the task was done often involved looking for updated information or were renewed due to search failure. Our study has implications for predicting reasons that can cause searchers to start or stop a search session during cross session search, and on designing search support tools to provide more targeted, customized help based on the relations among those reasons.
Portraying the Russian-Ukrainian Conflict on the German Facebook Social Network

The article reviews the concept of information war, the functioning of social media and their impact. The newest information war technologies are being used to form the image of the conflict between Russia and Ukraine in Facebook social network and German-speaking community. It examines the methods and objectives influencing and manipulating consumers. The article reveals how the conflict between Russia and Ukraine in Germany’s Facebook social space is represented. The analysis of Facebook pages resulted in an analysis of visual content abundantly found regarding the Russian-Ukrainian conflict.
On the Robustness of Rating Aggregators Against Injection Attacks

For a decade now, Academia has been researching refined techniques to detect fake reviews. In this article, rather than proposing a new detection methodology, we propose to contain the consequences of an attack launched by a fake reviewer who attaches arbitrary scores to the review target. We demonstrate that, by simply changing the score aggregator, the review site can withstands smart and targeted attacks, even carried out for an extended period of time. While experimentation is carried on on real data from a popular e-advice website, our approach is general enough to be applied in any other information service where voting and ratings need to be aggregated.
Refresh Power Reduction of DRAMs in DNN Systems Using Hybrid Voting and ECC Method

A deep neural network (DNN) system typically needs dynamic random access memories (DRAMs) for the data buffering. In this paper, an error-correction-code (ECC)-based technique is proposed to reduce the refresh power of DRAMs in the DNN system by extending the refresh period. By taking advantage of the characteristics of weight data of DNNs, a hybrid voting and ECC (VECC) method is used to protect the weight data from data retention fault caused by the refresh period extension. Analysis results show that the VECC method can achieve about 93% refresh power saving with about 0.5% accuracy loss and smaller than 0.5% check bit overhead on AlexNet, ResNet, and VGG19 convolutional neural network CNN models trained by ImageNet data set.