Reviewerly/exHarmony huggingface dataset:
Determining Movement of a 2-DOF Motion Chair using Optical Flow for Realistic Roller Coaster Ride using 360° Video

Virtually created roller coaster rides are computer generated scenario that mimic real life roller coasters. But most virtual roller coaster rides are dependent on artificially made environments that seem fake and are not at all realistic. This study focuses on the use of motion chair, 360° roller coaster videos, and the use of optical flow to create a roller coaster system that is more realistic. Using optical flow to determine movements in the 360° video that is sent to the created motion chair, while the video is played on the connected android phone for viewing of the video. The solution proved that acquiring 3D movements of roller coaster in 360° video is possible through the proposed method by the researchers but provides the problem of level of accuracy of movements. It was also proven that the created motion chair can execute the calculated movements of the program. Finally, the realism of the created system is evaluated by respondents that have previously experienced riding rollercoaster.
A tetrachotomy of ontology-mediated queries with a covering axiom

Our concern is the problem of efficiently determining the data complexity of answering queries mediated by description logic ontologies and constructing their optimal rewritings to standard database queries. Originated in ontology-based data access and datalog optimisation, this problem is known to be computationally very complex in general, with no explicit syntactic characterisations available. In this article, aiming to understand the fundamental roots of this difficulty, we strip the problem to the bare bones and focus on Boolean conjunctive queries mediated by a simple covering axiom stating that one class is covered by the union of two other classes. We show that, on the one hand, these rudimentary ontology-mediated queries, called disjunctive sirups (or d-sirups), capture many features and difficulties of the general case. For example, answering d-sirups is Π2p-complete for combined complexity and can be in or L-, NL-, P-, or coNP-complete for data complexity (with the problem of recognising FO-rewritability of d-sirups being 2ExpTime-hard); some d-sirups only have exponential-size resolution proofs, some only double-exponential-size positive existential FO-rewritings and single-exponential-size nonrecursive datalog rewritings. On the other hand, we prove a few partial sufficient and necessary conditions of FO- and (symmetric/linear-) datalog rewritability of d-sirups. Our main technical result is a complete and transparent syntactic /NL/P/coNP tetrachotomy of d-sirups with disjoint covering classes and a path-shaped Boolean conjunctive query. To obtain this tetrachotomy, we develop new techniques for establishing P- and coNP-hardness of answering non-Horn ontology-mediated queries as well as showing that they can be answered in NL.
Cyber Insurance Market in Israel – What is the Official Policy?

Since 1997, with the first Internet security liability policy (Granato and Polacek, 2019), the cyber insurance market has evolved. But despite the importance of cyber insurance as one of the tools for organizations to manage their cyber risks, there are still problems relating to this market which have persisted over the years, mainly in aspects of the lack of information and knowledge that affect market maturity and the willingness to use it. These problems have been addressed by academic research. This study seeks to examine Israel's policy on cyber insurance based upon official policy documents and academic papers on Israeli cyberspace, while emphasizing that in Israel, as in other countries, cyber insurance is still in the process of implementation. The conclusion of the study is that the Israeli cyber insurance market is in its early development and action must be taken to make the relevant information accessible and to create activities of guidance and regulation by relevant government agencies. However, the study indicates that the relevant agencies do understand the importance of this market and its current problem, and already initiated several initiatives to develop and promote the cyber insurance market in Israel.
SQL Generation from Natural Language Using Supervised Learning and Recurrent Neural Networks

Databases store a vast amount of today’s data and information, and to access that data users are required to have command over SQL or equivalent interface language. Hence, using a system that can convert a natural language to equivalent SQL query would make the data more accessible. In this sense, building natural language interfaces to relational databases is an important and challenging problem in natural language processing (NLP) and a widely studied field, and found recently momentum again due to the introduction of large-scale Datasets. In this paper, we present our approach based on word embedding and Recurrent Neural Networks (RNN), precisely on Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) cells. We present also the DataSet used for training and testing our models, based on WikiSQL, and finally we show where we arrived in terms of accuracy.
Performance Enhancement of Speech Scrambling Techniques Based on Many Chaotic Signals

scrambling method structure consists of three main components: scrambling, noisy channel and descrambling. The scrambling and descrambling processes. On the basis of chaotic signals, a speech scrambling technique is an essential work to upgrade and improve data protection in secure communication applications, especially military applications, and the main idea is immunity against eavesdropper to achieve security. The aim of uses the key encrypted in the Quadrature Amplitude Modulation-Orthogonal Frequency Division Multiplexing QAM OFDM system to improve performance of the Bits Error Rates BER on the receiving side that is robust against the effects of the Additive White Gaussian Noise AWGN. This paper has been published a speech authentication model using five separate chaotic maps / flows: Logistic maps LM, Baker maps BM, Henon map HM, Rössler system RÖS and Lorenz system LS based on 16-64 QAM_OFDM technique. The evaluation measures included six different tests: Segmental spectral signal to ratio of noise SSSNR, Log spectral distance d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LOG</sub> , Linear predict coding distance d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPC</sub> , cepstral distance d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CD</sub> , Frequency Weighted log Spectral distance dFWLOG and Bit error rate BER. Objective tests to measure the residual intelligibility R.I. of speech between the original and scrambling/ descrambling signal for proposed models: d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LOG</sub> = 14.5415, d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPC</sub> = 0.9741, d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CD</sub> = 8.8503, SSSNR= -26.506, and dFWLOG= 20.9976, While the BER between the original signal and recovered signal at 10 dB about 10-03, Which is big enough to protect the brute force attack encryption signal. We conducted a series of unpredictable map / flow randomness tests: Lyapunov exponent test and Auto correlation.
Study of League Championship Algorithm Efficiency for Global Optimization Problem

The article objective is to study a new League Championship Algorithm (LCA) algorithm efficiency by its comparing with the efficiency of the Particle Swarm optimization (PSO) algorithm. The article presents a brief description of the terms used in the League Championship algorithm, describes the basic rules of the algorithm, on the basis of which the iterative process for solving the global optimization problem is built. Gives a detailed description of the League Championship algorithm, which comprises a flowchart of the algorithm, as well as a formalization of all its main steps. Depicts an exhaustive description of the software developed to implement the League Championship algorithm to solve global optimization problems. Briefly describes the modified particle swarm algorithm. Presents the values of all free parameters of the algorithm and the algorithm modifications, which make it different from the classical version, as well. The main part of the article shows the results of a great deal of computational experiments using two abovementioned algorithms. All the performance criteria, used for assessment of the algorithms efficiency, are given. Computational experiments were performed using the spherical function, as well as the Rosenbrock, Rastrigin, and Ackley functions. The results of the experiments are summarized in Tables, and also illustrated in Figures. Experiments were performed for the vector dimension of the variable parameters that is equal to 2, 4, 8, 16, 32, and 64. An analysis of the results of computational experiments involves a full assessment of the efficiency of the League Championship algorithm, and also provides an answer about expediency for further algorithm development. It is shown that the League Championship algorithm presented in the article has a high development potential and needs further work for its study.
Single Image Reflection Removal Based on GAN with Gradient Constraint

When we take a picture through glass windows, the photographs are often degraded by undesired reflections. To separate reflection layer and background layer is an important problem for enhancing image quality. However, single-image reflection removal is a challenging process because of the ill-posed nature of the problem. In this paper, we propose a single-image reflection removal method based on generative adversarial network. Our network is an end-to-end trained network with four types of losses. It includes pixel loss, feature loss, adversarial loss and gradient constraint loss. We propose a novel gradient constraint loss in order to separate the background layer and the reflection layer clearly. Gradient constraint loss is applied in a gradient domain and it minimize the correlation between the background and reflection layer. Owing to the novel loss and our new synthetic dataset, our reflection removal method outperforms state-of-the-art methods in PSNR and SSIM, especially in real world images.
Information Technology for Decision Making Support and Monitoring in Man-Machine Systems for Managing Complex Technical Objects of Critical Application

The information technology of decision support and monitoring in distributed man-machine systems for managing complex technical objects of critical application is proposed, which complements the theory and methods for solving the problems of increasing the functional sustainability and efficiency of the operational management of the working modes, due to introducing the complex of modern monitoring, evaluation, and analysis means and making relevant decisions to ensure trouble-free operation. The development of this information technology on the example of management of a multilevel regional electrical power network is considered. In calculating the criteria characterizing the degree of workability and technical condition of the electrical power network equipment, in addition to the data obtained from control devices of the monitoring system and diagnostic modules, modern mathematical probabilistic methods and fuzzy models based on the theory of expert evaluation were used. To evaluate the modes of operation, and maintain the full functional sustainability of the regional power network, a Bayesian confidence network was built, with the help of which, based on the experts’ knowledge, the probability of the state of both individual components and a complex evaluation of its functioning were performed. For practical substantiation of the obtained results, an experiment was conducted, the results of which confirmed the practical value of the proposed information technology, which can be used in distributed man-machine systems for managing complex technical objects of critical application.
Piecemeal crosslinguistic influence and multilingualism

Saussure proposed the division language/parole and argued that language can be studied as a formal system. Fifty years later Chomsky declared competence the core interest of linguistics. Although for years Generative second language acquisition (GenSLA) has adopted this view, a number of recent publications poke holes into the competence bubble. Westergaard’s article is among those that pushes the boundaries of Generative Grammar (GG). In our commentary, we propose that Westergaard’s Micro-cue Model (McM) and the Linguistic Proximity Model (LPM) may actually be closer to a Usage-Based Approach (UbA) to language development than to the original spirit of GG, and that Westergaard’s sound, evidence-based proposals face some drag by being presented under the aegis of GG. Specifically, the assumption that all learning derives from general cognitive processes—hence, no essential difference between L1, L2, and Ln; the use of cues that are emergent and acquired piecemeal, and the idea that language development proceeds from the specific to the general, are all hallmarks of the UbA. We believe Westergaard’s contribution is important and timely and should encourage a better appreciation of the work being done in other domains as well as an understanding of how the different approaches complement each other.
Deep Learning of Robust Representations for Multi-instance and Multi-label Image Classification

In multi-instance problems (MIL), an arbitrary number of instances is associated with a class label. Therefore, the labeling of training data becomes simpler (since it is done together, instead of individually) with the disadvantage that a weakly supervised database is produced [9]. In the PCRY, each restaurant is represented by a set of images that share the attribute label(s) of that establishment. This paper explores the use of previously learned attribute extractors, trained in 3 different databases that are similar and complementary to the PCRY database.
A Microservices-Based Framework for Smart Design and Optimization of PV Installations

The design of photovoltaic (PV) installations mostly relies on rule-of-thumb criteria and on gross estimates of the shading patterns, and the few optimized approaches are generally focused on the problem of identifying the most suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This article proposes a framework to address the design and the optimization of PV installations through a set of microservices focusing on the different variables of the design: identification of the target surfaces, elaboration of weather data, modeling of the PV panel, and floorplanning of the panel on the surface. The microservices architecture ensures extensibility and generality, as the user may execute only a subset of the proposed services or provide novel algorithms to extend the existing ones. Additionally, the framework provides a set of built-in models that allow sensitivity to the distribution of shades and accurate modeling of the power production over time. We show the many benefits of the proposed framework on two different use cases.
Toward an Integrated Framework of Corporate Knowledge Portals Vulnerabilities: Resolving KM Security Challenges

Recently, knowledge management (KM) has been considered as a strategic weapon in a competitive environment. Corporate knowledge portals are one of the popular KM mechanisms for managing organisational knowledge and implementing knowledge management systems (KMSs) which use the communication networks. Since these portals are one of the main sources of organisational knowledge, their security becomes more essential. Despite the importance of the knowledge portal security, there are few studies to identify and evaluate the vulnerabilities of knowledge portals. The main purpose of this study is to offer an integrated framework to recognise, categorise and prioritise the key knowledge portal vulnerabilities based on the ITU-TX-805 architecture in order to enhance the security of KMSs. To identify the knowledge portal vulnerabilities, related studies were reviewed and then using the survey method, the main categories and related items were weighted by KM experts. The results proposed a new framework for identifying and prioritising knowledge portal vulnerabilities by using ITU-TX-805 architecture. The framework contains three main categories of vulnerabilities such as network components, network activities, and network security dimensions. In KM initiatives implementation, knowledge sharing along with knowledge protection must be considered. The proposed framework identifies and prioritizes the main vulnerabilities of knowledge portals which must be considered to make secure knowledge sources. It can assist knowledge manager officers (CKOs) to identify vulnerabilities, strengthen the security of knowledge portals, and protect knowledge assets.
Features and Behaviours Mapping In Model-based Testing in Software Product Line

Abstract Testing in Software Product Line (SPL) is very hard task due to the high degree of variability that existed in products. Nowadays, many testing approaches have concern on reusability of technique. Feature Model (FM) is used to represent variability and commonality of products in SPL. The components of FM that represented as symbols caused the needs of mapping with other models to represent their semantics. In this paper, there are concise definitions that relates with mapping approaches between FM and behaviour model. Model definitions presented in our algorithms is used for automation mapping process based on traceability link created. The advanced query mechanism proposed to automate the process of mapping between models. Based on the experimental result, it shows that our proposed algorithm can help tester in automate searches for accurate mapping of features and requirements.
Thresholding binary coding for image forensics of weak sharpening

Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations.
TRANSMISSION OF VERIFICATION

Abstract This paper clarifies, revises, and extends the account of the transmission of truthmakers by core proofs that was set out in chap. 9 of Tennant (2017). Brauer provided two kinds of example making clear the need for this. Unlike Brouwer’s counterexamples to excluded middle, the examples of Brauer that we are dealing with here establish the need for appeals to excluded middle when applying, to the problem of truthmaker-transmission, the already classical metalinguistic theory of model-relative evaluations.
How to Label? Combining Experts’ Knowledge for German Text Classification

A supervised machine learning classifier can only be as good as the labeled training data. For this reason, there is a need for explicit human expert knowledge inside the workflow. Existing data collections often consist of classes different to the ones which are necessary for an individual application. Therefore, generating a new data set based on a predefined labeling guideline is mandatory. The aim of this work is to increase the quality of labeled data sets during their creation. We present a workflow for the labeling of unsorted data by a group of experts, including subsequent classifier training and evaluation. Even if combined with standard methods for feature extraction and classification, a performance improvement was achieved with the proposed labeling method. Furthermore, we offer access to our data set (German newspaper articles) including the labeling guideline as contribution to the research community.
Ambiguity, Robust Statistics, and Raiffa's Critique

I show that ambiguity-averse decision functionals matched with the multiple-prior learning model are more robust to model misspecification than the standard expected utility with Bayesian learning. However, these criteria may fail to deliver robust decisions because the multiple-prior learning model inherits the same fragility of Bayesian learning. There are misspecified learning problems in which an ambiguity-averse DM optimally chooses a sequence of ambiguous acts over a sequence of risky acts that would deliver a strictly higher average utility.
CUT-FREE COMPLETENESS FOR MODULAR HYPERSEQUENT CALCULI FOR MODAL LOGICS K, T, AND D

We investigate a recent proposal for modal hypersequent calculi. The interpretation of relational hypersequents incorporates an accessibility relation along the hypersequent. These systems give the same interpretation of hypersequents as Lellman's linear nested sequents, but were developed independently by Restall for S5 and extended to other normal modal logics by Parisi. The resulting systems obey Dosen's principle: the modal rules are the same across different modal logics. Different modal systems only differ in the presence or absence of external structural rules. With the exception of S5, the systems are modular in the sense that different structural rules capture different properties of the accessibility relation. We provide the first direct semantical cut-free completeness proofs for K, T, and D, and show how this method fails in the case of B and S4.