Reviewerly/exHarmony huggingface dataset:
An FFT-based visual quality metric robust to spatial shift

Measuring image visual quality is extremely important for many image processing tasks. In the past, several metrics have been proposed for measuring image visual quality such as structural similarity index (SSIM) and visual information fidelity (VIF). Nevertheless, these metrics are not robust to image spatial shifts when the reference and distorted images are misaligned by a few pixels. These metrics generate extremely low metric scores which is undesirable. It is well known that shifting the image by a few pixels does not affect the perceived image quality significantly. In this paper, we modify the SSIM metric to make it more robust to spatial shifts by pre-processing the input images with two-dimensional (2D) Fast Fourier Transform (FFT2). We then use the magnitudes of the Fourier coefficients in the existing metrics since these coefficients are shift-invariant. Experiments show that our proposed novel method is particularly good at measuring the visual quality of 2D images because it is far less complex than the existing methods and it offers better accuracy. Our new method is better than SSIM even when no spatial shifts are introduced to the images.
Analysis of the Tools for Static Code Analysis

Static code analysis tools are being increasingly used to improve code quality. Such tools can statically analyze the code to find bugs, security vulnerabilities, security spots, duplications, and code smell. Various software tools are being developed to support developers in conducting static code analysis. In this paper, three tools to support static code analysis were analyzed and evaluated using the DESMET methodology. The tools were selected by conducting a systematic literature review in the field of static code analysis.
Learning‐free handwritten word spotting method for historical handwritten documents

Word spotting on degraded and noisy historical documents can become a challenging task considering the computational time and memory usage required to scan the entire document image. This paper proposes a new effective technique for multi-language word spotting using a two different feature extraction techniques, Histogram of Oriented Gradients (HOG) and Speeded Up Robust Features (SURF) features. First, regions of interest (ROIs) are extracted using a cross-correlation measure, and the extracted ROIs are re-ranked using feature extraction and matching methods. The algorithm handles two types of scenarios: Segmentation-based and segmentation-free. It also facilitates the search for words that occur once as well as multiple times in the image. Evaluations were conducted on the George Washington and HADARA datasets using a standard evaluation method. The proposed methodology shows improved performance over contemporary technologies currently being used in the word spotting research field.
On Optimal Control of Discounted Cost Infnite-Horizon Markov Decision Processes Under Local State Information Structures

This paper investigates a class of optimal control problems associated with Markov processes with local state information. The decision-maker has only a local access to a subset of a state vector information as often encountered in decentralized control problems in multi-agent systems. Under this information structure, part of the state vector cannot be observed. We leverage ab initio principles and find a new form of Bellman equations to characterize the optimal policies of the control problem under local information structures. The dynamic programming solutions feature a mixture of dynamics associated unobservable state components and the local state-feedback policy based on the observable local information. We further characterize the optimal local-state feedback policy using linear programming methods. To reduce the computational complexity of the optimal policy, we propose an approximate algorithm based on virtual beliefs to find a sub-optimal policy. We show the performance bounds on the sub-optimal solution and corroborate the results with numerical case studies.
Semantic Similarity of Words in RuWordNet Thesaurus and in Psychosemantic Experiment

In the paper we compare the structure of the Russian language thesaurus RuWordNet with the data of a psychosemantic experiment to identify semantically close words. The aim of the study is to find out to what extent the structure of RuWordNet corresponds to the intuitive ideas of native speakers about the semantic similarity of words. The respondents were asked to list synonyms to a given word. The words of the mental sphere were chosen for the experiment. As a result of the experiment, we found that the respondents mainly mentioned not only synonyms but words that are in paradigmatic relations with the stimuli. In 95% of cases, the words characterized in the experiment as semantically close were also close according to the thesaurus. In other cases, additions to the thesaurus were proposed.
Multi-hop Reasoning Question Generation and Its Application

This article focuses on the topic of multi-hop question generation (QG), which aims to generate the questions requiring multi-hop reasoning skills from the given text. These questions are not only syntactically valid but also logically correlated with the answers. Concretely, we first design a basic QG model and customize several techniques to ensure results' syntactic validity. In order to promote the logical correlations, we use a reasoning chain extracted from the text to regularize the results. Considering that different samples have their own characteristics on the aspects of text contextual structure, the type of question, and logical correlation, we propose a new adaptive meta-learner to optimize the basic QG model. Each case and its similar samples are viewed as a pseudo-QG task. The similar structural contexts contained in the same task are used as guidance to fine-tune the model. To measure the similarity of samples' structured inputs, we propose a data-driven multi-level recognizer. The experimental results on two typical data sets in various domains show the effectiveness of the proposed approach. Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements. That demonstrates the capacity of multi-hop QG in facilitating real-world applications.
How Category Selection Impacts Inference Reliability: Inheritance Inference From an Ecological Perspective

Abstract This article presents results from a simulation‐based study of inheritance inference, that is, inference from the typicality of a property among a “base” class to its typicality among a subclass of the class. The study aims to ascertain which kinds of inheritance inferences are reliable, with attention to the dependence of their reliability upon the type of environment in which inferences are made. For example, the study addresses whether inheritance inference is reliable in the case of “exceptional subclasses” (i.e., subclasses that are known to be atypical in some respect) and attends to variations in reliability that result from variations in the entropy level of the environment. A further goal of the study is to show that the reliability of inheritance inference depends crucially on which sorts of base classes are used in making inferences. One approach to inheritance inference treats the extension of any atomic predicate as a suitable base class. A second approach identifies suitable base classes with the cells of a partition (of a preselected size k ) of the domain of objects that satisfies the condition of maximizing the similarity of objects that are assigned to the same class. In addition to permitting more inferences, our study shows that the second approach results in inheritance inferences that are far more reliable, particularly in the case of exceptional subclasses.
Geometric Analysis of Uncertainty Sampling for Dense Neural Network Layer

For model adaptation of fully connected neural network layers, we provide an information geometric and sample behavioral active learning uncertainty sampling objective analysis. We identify conditions under which several uncertainty-based methods have the same performance and show that such conditions are more likely to appear in the early stages of learning. We define riskier samples for adaptation, and demonstrate that, as the set of labeled samples increases, margin-based sampling outperforms other uncertainty sampling methods by preferentially selecting these risky samples. We support our derivations and illustrations with experiments using Meta-Dataset, a benchmark for few-shot learning. We compare uncertainty-based active learning objectives using features produced by SimpleCNAPS (a state-of-the-art few-shot classifier) as input for a fully-connected adaptation layer. Our results indicate that margin-based uncertainty sampling achieves similar performance as other uncertainty based sampling methods with fewer labelled samples as discussed in the novel geometric analysis.
Synchronization and Resource Allocation in Quantum Key Distribution Optical Networks

The following topics are dealt with: Internet of Things; satellite communication; resource allocation; telecommunication network routing; 5G mobile communication; bandwidth allocation; optimisation; probability; software defined networking; telecommunication traffic.
A Fundamental Performance Limit of Cloud-based Control in Terms of Differential Privacy Level

In this paper, we address a privacy issue raised by cloud based control. In a cloud based control framework, a plant typically has no access to the models of the cloud system and other plants connected via the cloud system. Under restricted information, the plant is required to design its local controller for achieving control objectives. As a control objective, we consider a tracking problem, and for constant reference signals, a class of tracking controllers is identified based on Youla parametrization. More importantly, as local tracking controllers are implemented, there is a possibility that the cloud system or other plants connected via the cloud system may be able to identify private information of the plant by using the collected signal from the plant; for example, the reference signal (say, the target production amount) of the plant can be viewed as a piece of private information. In order to evaluate the privacy level of the reference signal, we employ the concept of differential privacy. For the Laplace mechanism induced by the entire system, we show that the differential privacy level cannot be further improved from a ceiling value for any parameters of the local controller. In other words, there is a performance limit in terms of differential privacy level, which is determined by the plant and cloud system only.
A Contemporary Framework Key Based Crypto Method to Enhance Security in Multi Cloud Ambience

Cloud Computing, similitude of Internet is a technological amelioration which is in regime for last two decades because of its Pay-per-Usage policy. Despite its multitudinous benefits, particular aspect which is shuddering its wider adoption throughout the technological society is its Security. In this paper, proposed a security framework Modified Key Based Crypto Method (KBCM) which is an amalgam of Improved Hamming weight-based RSA and Shamir Secret Sharing Scheme. KBCM is designed and implemented such that is enhances the security of data by assuring Confidentiality, Integrity, Availability (CIA) in multi cloud ambiance.
Revisiting NIZK-Based Technique for Chosen-Ciphertext Security: Security Analysis and Corrected Proofs

Non-interactive zero-knowledge (NIZK) proofs for chosen-ciphertext security are generally considered to give an impractical construction. An interesting recent work by Seo, Abdalla, Lee, and Park (Information Sciences, July 2019) proposed an efficient semi-generic conversion method for achieving chosen-ciphertext security based on NIZK proofs in the random oracle model. The recent work by Seo et al. demonstrated that the semi-generic conversion method transforms a one-way (OW)-secure key encapsulation mechanism (KEM) into a chosen-ciphertext secure KEM while preserving tight security reduction. This paper shows that the security analysis of the semi-generic conversion method has a flaw, which comes from the OW security condition of the underlying KEM. Without changing the conversion method, this paper presents a revised security proof under the changed conditions that (1) the underlying KEM must be chosen-plaintext secure in terms of indistinguishability and (2) an NIZK proof derived from the underlying KEM via the Fiat–Shamir transform must have the properties of zero-knowledge and simulation soundness. This work extended the security proof strategy to the case of identity-based KEM (IBKEM) and also revise the security proof for IBKEM of previous method by Seo et al. Finally, this work gives a corrected security proof by applying the new proofs to several existing (IB)KEMs.
The Enslaved Ontology: Peoples of the Historic Slave Trade

We present the Enslaved Ontology (V1.0) which was developed for integrating data about the historic slave trade from diverse sources in a use case driven by historians. Ontology development followed modular ontology design principles as derived from ontology design pattern application best practices and the eXtreme Design Methodology. Ontology content focuses on data about historic persons and the event records from which this data can be taken. It also incorporates provenance modeling and some temporal and spatial aspects. The ontology is available as serialized in the Web Ontology Language OWL, and carries modularization annotations using the Ontology Pattern Language (OPLa). It is available under the Creative Commons CC BY 4.0 license.
A Gray Scale Correction Method for Side-Scan Sonar Images Based on GAN

During the acquisition of a side-scan sonar image, energy loss occurs in the sonar acoustic waves, which lead to gray scale distortion in side-scan sonar images. Therefore, gray scale correction of the side-scan sonar image is necessary before further processing of side-scan sonar images. In this paper, we introduce the causes of gray scale distortion in side-scan sonar images and several commonly used methods of image gray scale correction. Analyze the shortcomings of existing methods, according to the characteristics of the side-scan sonar image, we propose a gray scale correction method based on generative adversarial network(GAN). In addition to presenting a new approach, we introduce a new refined loss function for achieving improved results. The loss function is aimed at reducing artifacts introduced by GAN and achieve better visual quality. Experiments show that the proposed method can effectively correct the gray distortion in the image.
Automation of Message Sending Processes Using Specialized Software

The article discusses a method for automating the sending congratulatory messages and images to relatives in the Whatsapp messenger using its web version "WhatsApp Web" due to the lack of an official api for creating bots. Also, a chat bot, which was written with python and can recognize and respond to commands, was implemented.
Secure Data Transmission in Cyber-Physical Systems Based on the New Approach for Stream Cipher’s Gamma Generation

We suggested a new approach for secure gamma generation in stream ciphers with application to secure data transmission between components of cyber-physical systems (in terms of ensuring confidentiality). Secure gamma generation is based on using a new type of pseudo-random number generator (PRNG) with linear feedback shift registers (LFSR) and fuzzy logic. We used a nonlinear combination function based on fuzzy logic for selecting the best LSFR while generating the output. We adjusted some parameters of fuzzy PRNG (FPRNG) to get the best pseudo-random sequences (gamma), which are very close to white random noise. We also studied the structure of FPRNG to select the best design that better fits the requirements of good gamma generators with application to cyber-physical systems. We showed that the suggested approach allows generating gamma sequences with better quality than many others. It makes possible using this approach in new stream ciphers and applying them to ensure confidentiality of data transmission between different components of cyber-physical systems (CPS).
Exploring Neural Machine Translation on the Russian-Vietnamese Language Pair

State-of-the-art neural machine translation performs very well in translation tasks of high-resource and linguistically close language pairs, such as English-French and English-German. In this work, we tried neural machine translation approach for Russian-Vietnamese which is a low-resource and linguistically distant language pair. We implemented and assessed three different neural machine translation models: recurrent sequence-to-sequence model, convolutional sequence-to-sequence model and transformer model. Experiment results showed the superiority of transformer model compared to other models. In terms of BLEU score, transformer model exceeds the second best model by an impressive nine points.
Dictionary Distribution Based on Number of Characters for Damerau-Levenshtein Distance Spell Checker Optimization

Damerau-Levenshtein Distance is an algorithm that can solve word correction problems. This algorithm changes one word into another word using a specified set of edit operations. In word correction using Damerau-Levenshtein Distance, edit operations that can be performed are: substitution, insertion, deletion and transposition. However, the Damerau-Levenshtein Distance algorithm also has a weakness, which is a long processing time. In order for the system to be able to display word suggestions on the wrong string, the system must calculate the word with each word in the dictionary. The processing time will be longer if the dictionary used is very large, for example, the Indonesian Dictionary has more than 30,000 basic words. So that in this study, a dictionary distribution based on the number of characters to shorten the processing time. The use of a distributed dictionary speeds up the Damerau-Levenshtein Distance algorithm by 29.04 seconds.
Quantum Cryptography for Data Science Security

In today’s world, computing fields are facing many problems of data space and densely parallel because the data on the internet is getting vast day by day. We have to analyze the data for proper management and proper use of data, that’s why we use data science to easily analyze or evaluate the data; and for data analysis, we have to use security, so that the data can be secured with use some mechanics of quantum.” Data Science is a field or it is a multi-disciplinary area that utilizes scientific method, processes, algorithm and systems to extract information and insight from structure and un-structure data”. It is mostly related to data mining and big data. This paper is the review that describes how quantum cryptography (QC) is dealing with data science. It also describes the data security and analysis of protocols using quantum cryptography.
Conditional Adversarial Networks for Multi-Domain Text Classification

In this paper, we propose conditional adversarial networks (CANs), a framework that explores the relationship between the shared features and the label predictions to impose stronger discriminability to the learned features, for multi-domain text classification (MDTC). The proposed CAN introduces a conditional domain discriminator to model the domain variance in both the shared feature representations and the class-aware information simultaneously, and adopts entropy conditioning to guarantee the transferability of the shared features. We provide theoretical analysis for the CAN framework, showing that CAN’s objective is equivalent to minimizing the total divergence among multiple joint distributions of shared features and label predictions. Therefore, CAN is a theoretically sound adversarial network that discriminates over multiple distributions. Evaluation results on two MDTC benchmarks show that CAN outperforms prior methods. Further experiments demonstrate that CAN has a good ability to generalize learned knowledge to unseen domains.
Ensemble Classifiers for Pulsar Detection

A common way of identifying pulsars is based on their emitted radio waves. To allow for automated screening of a large number of radio signals, machine learning methods can be adopted. One challenge here though is that training such methods is hampered by the inherent imbalance in the available data since signals related to actual pulsars are relatively rare. In this chapter, we show that ensemble classification methods that specifically address class imbalance can be successfully employed for pulsar detection. Ensemble classifiers combine several base classifiers to provide more robust and typically improved classification performance, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Our results demonstrate that such dedicated ensembles yield better results compared to methods that do not consider class balance.
Personal Space and Territorial Behavior – Sharing a Tabletop in Collaborative Enterprise Modeling

Tabletops represent a convenient digital workspace that serves the purpose of collaborative enterprise modeling well. The participants of a modeling session must, however, share this workspace. In this paper, we will present a study on personal space and territorial behavior in collaborative modeling sessions of six different teams. We found that areas on the tabletop can be identified that are predominantly occupied by certain individuals. This, however, does not automatically imply the existence of personal territories. In fact, we state that personal territories were less represented than a general territory that seemed to belong to the whole team. The absence of an etiquette, e.g., participants usually did not ask for permission before taking away an item situated near a team colleague, underlines this finding and supports our assumption that the participants feel a collective ownership towards the model.
ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning

Novel Object Captioning is a zero-shot Image Captioning task requiring describing objects not seen in the training captions, but for which information is available from external object detectors.The key challenge is to select and describe all salient detected novel objects in the input images.In this paper, we focus on this challenge and propose the ECOL-R model (Encouraging Copying of Object Labels with Reinforced Learning), a copy-augmented transformer model that is encouraged to accurately describe the novel object labels.This is achieved via a specialised reward function in the SCST reinforcement learning framework (Rennie et al., 2017) that encourages novel object mentions while maintaining the caption quality.We further restrict the SCST training to the images where detected objects are mentioned in reference captions to train the ECOL-R model.We additionally improve our copy mechanism via Abstract Labels, which transfer knowledge from known to novel object types, and a Morphological Selector, which determines the appropriate inflected forms of novel object labels.The resulting model sets new state-of-the-art on the nocaps (Agrawal et al., 2019) and held-out COCO (Hendricks et al., 2016) benchmarks.