Reviewerly/exHarmony huggingface dataset:
Convergent social responsibility as the key to corporate strategic success

Purpose This paper aims to increase corporate social responsibility (CSR), including business acting in an ethical and transparent way, through the prism of strategic conversation with applying the author’s convergent methodology that ensures the integrity, purposefulness and sustainability of development of corporations in the external environment. Design/methodology/approach The methodology is based on the integration of following approaches and methods: international CSR standards, strategic planning, networked virtual collaboration, group cognitive (conceptual) modelling, inverse problem-solving in topological spaces, categories theory, control thermodynamics, big data analysis for cognitive models verification and quantum semantic approach. Findings The convergent methodology helps to visualize the strategic situation in a corporation and gives more chances to get a clear feeling for every employee that his or her social responsibility helps to achieve not only his or her own goals but the goals of the company. This approach helps to accelerate the raising of the level of CSR. Research limitations/implications The strategic situation for raising the level of CSR has to be represented during strategic conversations. The strategic process should cover all the employees of the corporation. Groups can be hierarchically organized. The number of participants of one group can be about 35, and the duration of one conversation is up to 5 hours. Practical implications The convergent methodology was applied many times for creating a high-level CSR atmosphere by the collective building of companies’ strategies. Social implications The convergent approach provides proactive identification of adverse effects of the external environment on the level of CSR and prevention of such impacts. The approach also provides acceleration of corporate strategic planning, processes of network democracy and group decision-making processes. Originality/value The main developing problem, which is beyond the state of the art, is that the CSR situation cannot be described in a clear, logical and formalised way, and a traditional computer model cannot be created for this case with formalised approach. The proposed convergent methodology with cognitive modelling helps to do it.
Mapping Crisp Structural Semantic Similarity Measures to Fuzzy Context: A Generic Approach

Ontology-based similarity measures have received much importance in recent years. In many real-world cases, the domain considered in the ontological similarity assessment consists of uncertainty or incomplete information. Such vagueness has led to the successful implementation of fuzzy ontology (FO)-based similarity measures. Despite various applications of FO-based similarity measures, limited methods have so far been proposed for this purpose. Accordingly, this paper presents a generic model for semantic similarity assessment based on a fuzzy ontology. The proposed approach relies on the broad literature of Crisp Ontology-based Structural Semantic Similarity Measures (CO-SSSM). It provides an approach for mapping CO-SSSMs to fuzzy context. Consequently, the proposed generic model can be applied to various CO-SSSMs to develop their corresponding FO-SSSMs. In this regard, as an empirical investigation, four of the common CO-SSSMs were selected, their equivalent FO-SSSMs were developed by means of the proposed approach, and the accuracy of their similarity assessment was compared with each other. The results show the power of FO-SSSMs in describing the relations between concepts and their superiority over CO-SSSMs.
Steganalysis of Storage-based Covert Channels Using Entropy

A covert channel is a means of transmitting information that is not normally allowed in a network protocol. For instance, unused or insignificant bits of a protocol could be used to transmit secret messages. We present a novel technique to uncover the existence of covert channels in networks. The technique uses entropy estimates, and can not only detect secret messages, but also estimate their size.
Understanding Deep Learning Algorithms for Object Detection and Recognition

In the current state-of-art, computer vision is one of the emerging areas of artificial intelligence. Among the various branches of computer vision, object detection and recognition are the challenging ones which has a wide range of applications in various areas like self-driving cars, facial recognition, pedestrian's detection, and in surveillance system. With great improvement in deep learning algorithms for detection tasks it is been widely used for object detection and recognition tasks. In this survey, we have explained various deep learning algorithms and their applications in various fields. Both classification and regression algorithms for detecting and recognition tasks are discussed elaborately. Finally based upon certain parameter, the algorithms are compared, and the results are inferred.
PCI DSS Applicability

The Payment Card Data Security Standard (PCI DSS) has been designed to be the benchmark standard for the protection of cardholder data and applies to any business IT system or business operation that is involved in the storage, processing, or transmission of this type of sensitive data. The successful application and implementation of the PCI DSS control sets against the card payment operations makes it more difficult for the attackers to steal such data and, as such, provides greater assurance to the Card Brands, Issuers, and Acquirers and ultimately helps protect the consumers from payment card fraud. However, the criminals are opportunists seeking to exploit the misinterpretation/misapplication of the PCI DSS controls, poorly managed PCI DSS oversight, bad practices, or poor risk management processes.
Performance Analysis of a Clustering Model for QoS-Aware Service Recommendation

The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods.
Short Term Prediction of Solar Irradiance Fluctuation Using Image Processing with ResNet

PV power generation has a big challenge in dealing with solar irradiance fluctuation. Several prediction methods have been proposed based on whole sky image processing techniques. This paper presents a combination technique of image processing with a convolution neural network based on ResNet for raising trigger events in advance before the sun cover happens 1 to 2 minutes in advance. The first step is captured sky image, After that image preprocessing. Next step we use Hough transform to finding the Sun position and optical flow to cloud movement tracking. Finally, use a convolution neural network to generate trigger events in advance before the sun cover happens. The results for predict stage show low error of 27.50%.
Predictable Efficiency for Reconfiguration of Service-Oriented Systems with Concerto

Dynamic reconfiguration of distributed software systems is nowadays gaining interest because of the emergence of dynamic IoT and smart applications as well as large scale dynamic infrastructures (e.g., Fog and Edge computing). When quality of service and experience is of prime importance, efficient reconfiguration is necessary, as well as performance predictability to decide when a reconfiguration should occur. This paper tackles the problem of efficient execution of a reconfiguration plan and its predictability with Concerto, a reconfiguration model supporting a high level of parallelism. Evaluation performed on synthetic cases and on two real production scenarios show that Concerto provides better performance than state-of- the-art systems with accurate time estimation.
A Visual Environment for Hypothesis Formation and Reasoning in Studies with fMRI and Multivariate Clinical Data

We present an interactive visual environment for linked analysis of brain imaging and clinical measurements. The environment is developed in an iterative participatory design process involving neur ...
Creating QR-coded identification cards using CardExchange for straightforward identification of implant systems

Detailed record-keeping is essential in implant dentistry. With more than 250 dental implant companies producing implant components, it is difficult to accurately ascertain each system. While online resources such as https://whatimplantisthat.com may assist in identification, accurate clinical and radiographical recognition is still the responsibility of the dentist. Misidentification of an implant system may lead to clinical mishaps and delays. Offering patients quick response (QR)–coded implant identification wallet cards (Fig. 1) is a straightforward, inexpensive, and universal solution to this problem. Here, the CardExchange Producer Premium software program was used. This software program can be purchased online from CardExchange Solutions. Figure 2Phone screenshot after quick response (QR) code scanned. View Large Image Figure Viewer Download Hi-res image
Handwritten Malayalam Character Recognition System using Artificial Neural Networks

This paper presents a system that recognizes Malayalam in handwritten form using artificial neural networks. A system for recognizing handwritten text (HCR) is a technique that is used for recognizing human handwritten text in any language. HCR is one of the research areas of recognition of patterns, which is still very challenging as perfect solutions have not yet been found. For certain foreign languages like English, Japanese, Chinese, etc, HCRs have been developed, which are reasonably good. But it is still premature for languages in India, especially for languages in south India. Because of the large character set, compound characters, presence of modifiers, and the curvature of characters in these languages, the task is quite complicated. This project aims to convert the photograph containing handwritten script into corresponding text. In this approach a trained ANN is used to identify the handwritten characters. The recognition system has been developed in python. The OpenCV library is used for performing different operations on the input image.This paper pertains to the first part of a work where individual characters alone are recognized. The continuation of the work which is ongoing, is to recognize complete sentences.
Cohesive Hybrid Intelligent Multi-Agent System Architecture

The paper is devoted to the development of architecture of a new class of intelligent system, namely a cohesive hybrid intelligent multi-agent system. This intelligent system is designed to model collective problem solving by an expert team at round-table. Like traditional hybrid intelligent multi-agent systems, this system integrates the advantages of the hybrid intelligent system concept and the multi-agent approach. The key difference of the proposed class of intelligent systems is the modeling of expert team cohesion, by coordinating goals, domain models and developing problem-solving protocol by agents. These mechanisms are especially relevant when a hybrid intelligent multi-agent system is built from agents developed by various independent teams. The paper presents the model of cohesive hybrid intelligent multi-agent system, its functional structure, and the architectures of its agents.
A meta-analysis of the effects of using PhET interactive simulations on student’s worksheets toward senior high school students learning result of physics

Abstract The purpose of this study was to analyze the effect of the use of PhET Interactive Simulations student worksheets on students learning outcomes. The method used is the meta-analysis method. Meta-analysis is a technique used to summarize a variety of quantitative research results by finding the value of effect size using a sample of 10 articles in national journals. The results showed average effect size of 1, 117 with a standard deviation of 181,1. The resulting effect size is categorized as medium. Means that the influence of the use of PhET Interactive Simulations student worksheets can improve student learning outcomes by 32,73%.
High capacity information transmission with single-photon in polarization and spatial mode degrees of freedom over a collective noisy channel

High-dimensional quantum states are potential resources to enhance the channel capacity of information transmission over noisy channels. However, the over-consumption of entanglement and joint measurement make previous proposals of information transmission unpractical , taking quantum dense coding as an example. In this paper, we report high-capacity information transmission over a collective noisy channel using single-photon states both in polarization and spatial mode degrees of freedom and quantum channel multiplexing. Our proposal can reach the same channel capacity as quantum dense coding, even over the collective noisy channel. These results will provide an optional and feasible method to high-capacity information transmission and help to promote the practicability of quantum communication.
Secure data hiding scheme using firefly algorithm with hidden compression

The goal of proposed procedure is to make a protected information concealing framework to shroud the incognito information by finding the intense area in the spread picture to such an extent that the created stego picture is unrivaled, furnish high security with a less significant memory use, less computational impulsiveness, high visual feature and can achieve great execution. In RDH the spread picture will be recovered in the wake of removing the secrecy information which was inserted in that picture. This paper shows another method in reversible information concealing strategy dependent on Firefly calculation (FA). The most ideal area to cover up the hidden information that will be established by firefly calculation. The firefly calculation results in the salt and pepper commotion. Picture de-noising consists of the control of the picture data to build an outwardly astounding picture. 2D respective channel is utilized to channel the picture after shrouded the mystery data. In this framework, a hearty computerized shading picture watermarking strategy utilizing Discrete Wavelet Transform(DWT) is projected. DWT is used for implanting the mystery information into spread picture and after that IDWT is connected to recuperate unique picture. Invert procedure is connected for extraction of discharge information. The experimental results demonstrates that proposed strategy accomplished enhanced MSE and higher PSNR. This make the proposed technique increasingly compelling and secure for concealing information.
Reassessing Claims of Human Parity and Super-Human Performance in Machine Translation at WMT 2019

We reassess the claims of human parity and super-human performance made at the news shared task of WMT 2019 for three translation directions: English-to-German, English-to-Russian and German-to-English. First we identify three potential issues in the human evaluation of that shared task: (i) the limited amount of intersentential context available, (ii) the limited translation proficiency of the evaluators and (iii) the use of a reference translation. We then conduct a modified evaluation taking these issues into account. Our results indicate that all the claims of human parity and super-human performance made at WMT 2019 should be refuted, except the claim of human parity for English-to-German. Based on our findings, we put forward a set of recommendations and open questions for future assessments of human parity in machine translation.
Blur kernel estimation of noisy-blurred image via dynamic structure prior

An accurate blur kernel is key to blind image deblurring and kernel estimation heavily relies on strong edges in the observed image [1, 2, 3]. Previous methods [4] [5] leveraging image gradient prior with i.i.d statistics can hardly restrict strong edges in a noisy-blurred image, since both noise and strong edges are presented as strong gradients. In this paper, we propose a dynamic structure prior (DSP) to distinguish strong edges from noise by a non local total variation regularization. Our method measures accumulated intensity change of each pixel along all possible directions within its neighborhood. In this case, the structural contents with less change in certain directions, i.e.strong edges, will be accumulated lower than non-structural contents such as noise. By contrast, the previous image gradient priors only measure the changes in horizontal and vertical directions, which is not capable to present the directional characteristic of image edges. Moreover, in our method, the prior changes the weight dynamically with regard to local structures to impose fewer penalties on pixels with large changes. As a result, strong edges are preserved while fine textures are suppressed by large penalties. At last, a cost function containing the dynamic structure prior and L0 gradient prior that sharpens the preserved edges is proposed to estimate blur kernels from noisy-blurred images. Comprehensive experimental results show that our method outperforms previous methods in both commonly used datasets with various noise levels and real world images, from monochrome image to color image.
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings

This paper investigates the use of unsupervised cross-lingual embeddings for solving the problem of code-mixed social media text understanding. We specifically investigate the use of these embeddings for a sentiment analysis task for Hinglish Tweets, viz. English combined with (transliterated) Hindi. In a first step, baseline models, initialized with monolingual embeddings obtained from large collections of tweets in English and code-mixed Hinglish, were trained. In a second step, two systems using cross-lingual embeddings were researched, being (1) a supervised classifier and (2) a transfer learning approach trained on English sentiment data and evaluated on code-mixed data. We demonstrate that incorporating cross-lingual embeddings improves the results (F1-score of 0.635 versus a monolingual baseline of 0.616), without any parallel data required to train the cross-lingual embeddings. In addition, the results show that the cross-lingual embeddings not only improve the results in a fully supervised setting, but they can also be used as a base for distant supervision, by training a sentiment model in one of the source languages and evaluating on the other language projected in the same space. The transfer learning experiments result in an F1-score of 0.556, which is almost on par with the supervised settings and speak to the robustness of the cross-lingual embeddings approach.
Academia-industry Collaboration for Augmented Reality Application Development

Abstract Due to our previous experience in AR development, with this research project we propose to study how Augmented Reality (AR) can be adopted by an industrial partner and which are the major outcomes from which a company may benefit. For this purpose, we partnered with a forward-looking company willing to embrace the idea of implementing new technologies for industrial purposes. In this research we identified that the most significant impact which AR may have for our industrial partner is in providing remote assistance and for product exploitation and marketing purposes. For the latest we developed a customized AR application which is currently available on Apple’s App Store. By analysing the app analytics data, we found out that this application is a success in terms of marketing and not only. For the remote assistance AR app, we chose to use a commercial solution that can be adapted to fit our industrial partner’s needs. We also tested this application and found out that it greatly improves the communication between remote users thus dismissing the required assistance time.
Graph transform learning

Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitly account for the correlation in the dataset in terms of graph Laplacian. We will give two variants; in the first one the graph is computed from the data and fixed during the operation. In the second, the graph is learnt iteratively from the data during operation. The first technique will be applied for clustering, and the second one for solving inverse problems.
Design and Implementation of UAV Obstacle Avoidance System

Aiming at the obstacle avoidance problem of UAV, a control method of UAV obstacle avoidance based on ultrasonic is proposed. The number of ultrasonic waves is increased to surround a circle based on the traditional ultrasonic obstacle avoidance scheme, and fused the information of multiple ultrasonic sensors, and according to the distance between the UAV and the obstacle and the channel value of the tele controller, the obstacle avoidance flight of the multi-rotor UAV is realized. The experimental results show that the system has the characteristics of low price, simple implementation and reliable security performance, and has a certain reference value.
Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?

Subtitling is becoming increasingly important for disseminating information, given the enormous amounts of audiovisual content becoming available daily. Although Neural Machine Translation (NMT) can speed up the process of translating audiovisual content, large manual effort is still required for transcribing the source language, and for spotting and segmenting the text into proper subtitles. Creating proper subtitles in terms of timing and segmentation highly depends on information present in the audio (utterance duration, natural pauses). In this work, we explore two methods for applying Speech Translation (ST) to subtitling: a) a direct end-to-end and b) a classical cascade approach. We discuss the benefit of having access to the source language speech for improving the conformity of the generated subtitles to the spatial and temporal subtitling constraints and show that length is not the answer to everything in the case of subtitling-oriented ST.