Reviewerly/exHarmony huggingface dataset:
Technology for Predicting the Lithology Cube Using Kolmogorov Neural Networks

Summary For the prediction of the lithofacies cube, it is proposed to use new age full-functional Kolmogorov neural networks. These three-layer neural networks, which can be positioned as a new generation of neural networks, have a high degree of freedom comparable to deep multi-layer neural networks. For a more accurate lithofacies cube, it is suggested to perform the forecast in two stages. At the first stage, a separate forecast of each lithofacie is made in the form of a probability cube. At the second stage, the connection of such cubes into one lithofacies cube is based on the principle of maximum probability.
Knowledge-based Residual Learning

Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it. We prove that KRL is guaranteed to improve over pure domain knowledge model and pure neural net model under certain loss functions. Extensive experiments have shown the superior performance of KRL over baselines. In addition, several case studies have explained how the domain knowledge can assist the prediction.
Liability for artificial intelligence

Regulation of liability for artificial intelligence (AI) is one of the challenges of our time. In Europe, the EU is currently considering changes to the Product Liability Directive, while Member States are assessing national tort law regimes. Their task is hard, as there are many controversial questions, such as who of the involved actors should be held responsible and whether an AI should itself be responsible. These questions are relevant not only for the establishment of a proper liability regime but they also construct a part of the foundation on which the public policy of the EU and its Member States will be created in this field. If the new rules turn out to be too restrictive, they will disincentivize manufacturers from further innovations. On the other hand, the new rules should also provide sufficient protection to the injured parties. In the first part, this chapter will address the current legal framework relevant to liability in the EU and its Member States. The second part will address the challenges that come from the peculiar nature of AI that lawmakers have to take into consideration. The last part of the chapter will place focus on the current proposals and suggestions.
BLUE SCREEN VIDEO FORGERY DETECTION AND LOCALIZATION USING AN ENHANCED 3-STAGE FOREGROUND ALGORITHM

The availability of easy to use video editing software has made it easy for cyber criminals to combine different videos from different sources using blue screen composition technology. This, makes the authenticity of such digital videos questionable and needs to be verified especially in the court of law. Blue Screen Composition is one of the ways to carry out video forgery using simple to use and affordable video editing software. Detecting this type of video forgery aims at revealing and observing the facts about a video so as to conclude whether the contents of the video have undergone any unethical manipulation. In this work, we propose an enhanced 3-stage foreground algorithm to detect Blue Screen manipulation in digital video. The proposed enhanced detection technique contains three (3) phases, extraction, detection and tracking. In the extraction phase, a Gaussian Mixture Model (GMM) is used to extract foreground element from a target video. Entropy function as a descriptive feature of image is extracted and calculated from the target video in the detection phase. The tracking phase seeks to use Minimum Output Sum of Squared Error (MOSSE) object tracking algorithm to fast track forged blocks of small sizes in a digital video. The result of the experiments demonstrates that the proposed detection technique can adequately detect Blue Screen video forgery when the forged region is small with a true positive detection rate of 98.02% and a false positive detection rate of 1.99%. The result of this our research can be used to
Optimal Lp filtering for discrete‐time non‐Gaussian dynamic systems

This paper investigates the L p filtering problem for linear dynamic systems. The main objective is to discuss the optimal filter for non-Gaussian systems. The filter structure is obtained by extension of the maximum a posteriori estimation problem for general norm exponential probability density functions. The obtained filter has a linear structure, and two different algorithms are proposed for 1 ≤ p < ∞ and p = ∞ . In the following, the choice of p has been discussed based on the asymptotic distribution and the central limit theorem as well as the kurtosis of error probability density function. Both of them confirm that the L 2 filter is optimal for Gaussian systems, and the L p filter, for p < 2 , has the best performance for the systems with leptokurtic error distributions, and for p > 2 , it is the best for the systems with platykurtic error distributions. In this regard, it is shown that the L 1 and L ∞ filters have the best MSE performance for the systems with Laplacian and uniform error probability distribution functions, respectively. Simulation results verify the superior performance of the proposed filtering approach for linear time-invariant and time-varying systems.
The Presumption of Innocence as a Source for Universal Rules on Digital Evidence — The guiding principle for digital forensics in producing digital evidence for criminal investigations

Article The Presumption of Innocence as a Source for Universal Rules on Digital Evidence — The guiding principle for digital forensics in producing digital evidence for criminal investigations was published on June 1, 2021 in the journal Computer Law Review International (volume 22, issue 3).
The Ethics of Datasets: Moving Forward Requires Stepping Back

Machine learning research culture is driven by benchmark datasets to a greater degree than most other research fields. But the centrality of datasets also amplifies the harms associated with data, including privacy violation and underrepresentation or erasure of some populations. This has stirred a much-needed debate on the ethical responsibilities of dataset creators and users. I argue that clarity on this debate requires taking a step back to better understand the benefits of the dataset-driven approach. I show that benchmark datasets play at least six different roles and that the potential harms depend on the roles a dataset plays. By understanding this relationship, we can mitigate the harms while preserving what is scientifically valuable about the prevailing approach.
An Operative Application of Distributed Ledger Technology for Banking Domain

Banking systems have been using a centralised network over several years. Any attack on the centralised unit would risk a whole lot of banking data. To avoid this, Blockchain is an approach, which is more appropriate to hold the large data in a safe manner as it is a decentralised network. The purpose of this paper is to provide better understanding about using blockchain for the banking sector by outlining the opportunities, benefits and challenges of this technology. We propose a technique of banking using blockchain which would make things simpler, safer and transparent. Also, at some point in time, we can even put restrictions to limit the transaction amount, day limit, credit limit or other restrictions depending upon the banking rules. Through these restrictions, attempts towards hacking or misuse could be prevented. This work would benefit the banking sectors and modify the direction of Finance.
Stacked Attention Networks for Referring Expressions Comprehension

Referring expressions comprehension is the task of locating the image region described by a natural language expression, which refer to the properties of the region or the relationships with other regions.Most previous work handles this problem by selecting the most relevant regions from a set of candidate regions, when there are many candidate regions in the set these methods are inefficient.Inspired by recent success of image captioning by using deep learning methods, in this paper we proposed a framework to understand the referring expressions by multiple steps of reasoning.We present a model for referring expressions comprehension by selecting the most relevant region directly from the image.The core of our model is a recurrent attention network which can be seen as an extension of Memory Network.The proposed model capable of improving the results by multiple computational hops.We evaluate the proposed model on two referring expression datasets: Visual Genome and Flickr30k Entities.The experimental results demonstrate that the proposed model outperform previous state-of-the-art methods both in accuracy and efficiency.We also conduct an ablation experiment to show that the performance of the model is not getting better with the increase of the attention layers.
Orbital angular momentum uncertainty relations of entangled two-photon states

Abstract The inseparability of quantum correlation requires that the particles in the composite system be treated as a whole rather than treated separately, a typical example is the Einstein–Podolsky–Rosen (EPR) paradox. In this paper, we provide a theoretical study on the uncertainty relations of two kinds of bipartite observables in two-photon orbital angular momentum (OAM) entanglement, that is, the relative distance and centroid of the two photons at azimuth. We find that the uncertainty relations of the bipartite observables holds in any two-photon state, and they are separable in two-photon OAM entanglement. In addition, the entangled state behaves as a single particle in the bipartite representation. Finally, we find that the uncertainty relations of the bipartite observables can be used to manipulate the degree of the entanglement of an EPR state. Graphic abstract
Analyzing Security Testing Tools for Web Applications

A web application makes use of web browsers to perform Internet tasks. Lots of businesses utilize the Internet as a profitable means of communication. Currently, the business world is facing multiple cyber breaches and attacks. Conducting a penetration test on any website or information system is, therefore, essential to enhance cybersecurity. Choosing the most effective tool for penetration testing is more important. This paper aims to examine two vital tools that are employed in testing the vulnerabilities of a chosen test website. The two tools, ZAP and Acunetix, are used to identify the weaknesses of the website. Besides, the usefulness and the purpose of each tool are shown and described. The comparison of these tools is intended to identify the effectiveness of all penetration testing tools in information systems and the difference they may depict depending on a given tool. The results show that the ZAP tool seems to be more effective compared with the other format.
ASSESSING STUDENTS’ ABILITY ABOUT 16 ACTIVE VOICE TENSES USING QUIZIZZ APPLICATION

Assessing students’ ability about 16 active voice tenses and giving a treatment about 16 active voice tenses are two main points for this study. This study is conducted to know how is the effectiveness of using the Top grammar method in improving students’ understanding the 16 active voice tenses, and how is the result of the students’ ability when assessed by using the Quizizz application. The research was used quantitative analysis. After analyzing the data, the researcher found that there was a significant improvement in students' understanding the 16 active voice tenses, and the students’ score was also improved in the post-test.
Remittances and health outcomes in Sub-Saharan African countries: Understanding the role of financial development and institutional quality

Sub-Saharan Africa (SSA) is one of the highest recipients of remittances; however, this is inconsistent with the region?s growth and the state of its weak healthcare systems. This paper therefore analyses the effect of remittances on health outcomes for 39 selected SSA countries over the period 1996 to 2016. It considers the channels through which remittances affect health outcomes, including financial development and institutional quality. Using dynamic panel estimation, we find that remittances sustain health outcomes, while both financial development and institutional quality complement remittances in this respect. SSA countries should therefore continue to improve their financial sectors and develop the quality of institutions to an adequate level. Achieving sound financial systems and institutions would both allow and attract a substantial amount of remittances, benefitting human capital and health outcomes and alleviating poverty.
Predictive Analysis for daASD Using Population based Incremental Learning

Autism is a clinically defined behavioral syndrome that initially appears in childhood and reflects the underlying neurodevelopmental irregularities.The main aim is to define the clinical trajectories of dyslipidemia-associated ASD (daASD) is to take the massive data integration has revealed an emerging ASD subtype characterized by dyslipidemia.However, the clinical course of this subtype remains unknown.To analysis of Electronic Health Record (EHR), data identifying the pattern sequence of medical events experienced by people with daASD.Within these data, individuals with daASD will be identified as those having at least one ASD diagnosis and dyslipidemia diagnosis or repeated blood lipid test results that are outside the normal range.Comparisons will be made between individuals with daASD, ASD with no dyslipidemia (ASD+/dyslipidemia-), dyslipidemia with no ASD (ASD-/dyslipidemia+), and individuals with no history of neuro-developmental disorders or abnormal lipid levels (ASD-/dyslipidemia-). Matching will be based on age, gender, ethnicity and address/zip code, as a proxy of socioeconomic status.The parents of each member of the above groups will be identified and their data will be collected.The direct comparisons of daASD with other ASD subtypes are required diagnosis for comorbid epilepsy/recurrent seizures and comorbid Fragile X syndrome using population based incremental learning.In this regard, Random Forest approach is on daASD for classification and semi-supervised learning is a process for daASD.The daASD experimental result is based on Electronic Health Records of Least Square Estimation Regression with Predict the Disease.
Properties of Composition of Fuzzy Relations and its Verifications

This paper, deals with fundamental notions in pure and applied sciences, i.e., basic operations related to fuzzy relations. The composition of fuzzy relations are defined in two ways such as max-min composition and max-product composition with suitable example. This paper also introduces the properties of composition of fuzzy relations. The newly introduced properties inculcates zero, identity, equal, not-equal, subset, associative, union, intersection and distributive fuzzy relations. Finally, the paper verifies the properties of composition of fuzzy relations using some numerical values for 2x2 order of matrix. Also gives some exercise problems related to the above concepts with accurate answer keys.
It&amp;#8217;s Not What You Said, it&amp;#8217;s How You Said it: Discriminative Perception of Speech as a Multichannel Communication System

People convey information extremely effectively through spoken interaction using multiple channels of information transmission: the lexical channel of what is said, and the non-lexical channel of how it is said. We propose studying human perception of spoken communication as a means to better understand how information is encoded across these channels, focusing on the question 'What characteristics of communicative context affect listener's expectations of speech?'. To investigate this, we present a novel behavioural task testing whether listeners can discriminate between the true utterance in a dialogue and utterances sampled from other contexts with the same lexical content. We characterize how perception - and subsequent discriminative capability - is affected by different degrees of additional contextual information across both the lexical and non-lexical channel of speech. Results demonstrate that people can effectively discriminate between different prosodic realisations, that non-lexical context is informative, and that this channel provides more salient information than the lexical channel, highlighting the importance of the non-lexical channel in spoken interaction.
From an Emerging Economy Perspective: Central Bank Digital Currency Analysis of Turkey

Digitalization of the world brought about a change in many aspects, interactions switched to digital technologies from the analogue ones. Businesses have been reshaped and new value-producing opportunities have occurred. For sure money, as one of the most important realities of life, is enforced to change. New forms of money have been developed in time and money has adapted itself to the changing world since the time it was invented.Even though fundamental functions of money do not change, the form of money evolves to respond to the needs of individuals over time. Today, money is expected to be more convenient for everyone and digitalization enables this change.
Documenting evidence of a reuse of ‘a systematic study of the class imbalance problem in convolutional neural networks’

We report here the reuse of oversampling, and modifications to the basic approach, used in a recent TSE ’21 paper by YedidaMenzies. The method reused is the oversampling technique studied by Buda et al. These methods were studied in the SE domain (specifically, for defect prediction), and extended by Yedida & Menzies.
Image Denoising by Discrete Wavelet Transform with Edge Preservation

Digital images are a big part of today's life and science. It is important to have a good quality images which is not always a case due to the different reasons. One of the common problems with digital images is presence of the various types of noise. Removing noise from digital images is an important research field widely studied in the past decades. In this paper, we combined three successful methods applied in the wavelet domain with the aim to improve the quality of the denosining. The discrete wavelet transformation was used to enable image processing in frequency domain. In order to remove noise, soft thresholding technique was combined with the median filter. To preserve the image sharpness, edge coefficients were kept and not affected by the denoising process. The proposed method was tested on four standard benchmark images. In the comparison to other methods from literature and in term of peak-signal-to-noise-ratio the proposed method achieved better results. Based on the structure similarity index measure, we can conclude that the proposed method is efficient for removing Gaussian noise.
Improved Bounds for Two Query Adaptive Bitprobe Schemes Storing Five Elements

In this paper, we study two-bitprobe adaptive schemes storing five elements. For these class of schemes, the best known lower bound is $$\varOmega (m^{1/2})$$ due to Alon and Feige [1]. Recently, it was proved by Kesh [9] that two-bitprobe adaptive schemes storing three elements will take at least $$\varOmega (m^{2/3})$$ space, which also puts a lower bound on schemes storing five elements. In this work, we have improved the lower bound to $$\varOmega (m^{3/4})$$. We also present a scheme for the same that takes $$\mathcal {O}(m^{5/6})$$ space. This improves upon the $$\mathcal {O}(m^{18/19})$$-scheme due to Garg [7] and the $$\mathcal {O}(m^{10/11})$$-scheme due to Baig et al. [5].
Robust Semisupervised Deep Generative Model Under Compound Noise

Semisupervised learning has been widely applied to deep generative model such as variational autoencoder. However, there are still limited work in noise-robust semisupervised deep generative model where the noise exists in both of the data and the labels simultaneously, which are referred to as outliers and noisy labels or compound noise. In this article, we propose a novel noise-robust semisupervised deep generative model by jointly tackling the noisy labels and outliers in a unified robust semisupervised variational autoencoder randomized generative adversarial network (URSVAE-GAN). Typically, we consider the uncertainty of the information of the input data in order to enhance the robustness of the variational encoder toward the noisy data in our unified robust semisupervised variational autoencoder (URSVAE). Subsequently, in order to alleviate the detrimental effects of noisy labels, a denoising layer is integrated naturally into the semisupervised variational autoencoder so that the variational inference is conditioned on the corrected labels. Moreover, to enhance the robustness of the variational inference in the presence of outliers, the robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> -divergence measure is employed to derive the novel variational lower bound, which already achieves competitive performance. This further motivates the development of URSVAE-GAN that collapses the decoder of URSVAE and the generator of a robust semisupervised generative adversarial network into one unit. By applying the end-to-end denoising scheme in the joint optimization, the experimental results demonstrate the superiority of the proposed framework by the evaluating on image classification and face recognition tasks and comparing with the state-of-the-art approaches.
Multi-party Video Conferencing System with Gaze Cues Representation for Turn-Taking

In a multi-party video conference, it is more difficult to achieve smooth turn-taking than in face-to-face communication. This is probably because gaze cues are not shared. In this paper, we propose a system for facilitating turn-taking through the sharing of gaze cues in multi-party video conferences. We implemented video conferencing systems that use arrows and modification of the video window size to share gaze cues the same as in face-to-face communication. We also conducted an experiment to investigate the effect of the system on turn-taking. The results suggested that our system could facilitate turn-taking and communication.
Correlation-Based Out-of-Plane Displacement Measurement for Optical Fiber Material

Due to the monitoring requirement of optical fiber industrial production, an out-of-plane displacement measurement method is proposed. Firstly, the in-plane displacements between two consecutive images, captured through a microscope with a CCD camera, are estimated by the Digital Speckle Correlation Method (DSCM). Subsequently, the out-of-plane displacement of optical fiber material can be obtained by the wedge model. Finally, the effectiveness of the method is verified experimentally by comparing the measurement data of routine practice with its theoretical values. Simulation and experimental results indicate that the absolute errors and the relative errors of the measurement by the optical microscope with a magnification of 50× are less than ±0.2 μm and 5%, respectively. The new method only needs two images obtained by the microscopic imaging system with a single camera to accomplish the measurement, which can significantly reduce the measurement time and complexity of the arrangement. Further, the method needs neither frequency domain conversion nor phase unwrapping operation, therefore, it is especially suitable for dynamic out-of-plane displacement measurement. The proposed method has been applied to the industrial uniformity and micro–nano-scale deformation monitoring of optical fiber image transmission materials.