Reviewerly/exHarmony huggingface dataset:
Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective

Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions.
Development of a formal algorithm for the formulation of a dual linear optimization problem

The rigorous formal algorithm for formulating a dual problem for different forms (general, basic, standard, and canonical) of a primal linear programming problem is proposed. First, definitions of a pair of dual problems for standard form of primal linear programming are given. This approach is based on the fact that such a pair was noted first, since it had substantial interpretation.The economic interpretation of the standard problem is profit maximization in the production and sale of some types of products. Such an approach substantially indicates the existence of the primal problem (I) and the strictly corresponding dual (conjugate) (II). The problem of cost minimization is accompanying to the primal problem.The basic concept of the duality theory in linear programming problems is the fact that a pair of problems are mutually conjugate — obtaining dual of dual leads to a primal problem.The rigorous approach to obtaining an algorithm for formulating a dual problem is based on the statement that the dual problem of dual is a primal (original) problem. This approach is used in the paper. For different pairs of dual problems, this statement is rigorously proved.The existing schemes of primal to dual conversion are substantial. Given this, the algorithm of the general approach to formulating pairs of conjugate problems is proposed and rigorously proved.Formalization of the developed scheme makes it easy to get pairs of known dual problems. This allowed for the first time to propose and validate the algorithm for constructing a dual problem for an arbitrary form of the primal problem.
Towards Model Checking Product Lines in the Digital Humanities: An Application to Historical Data

Rapid development in computing techniques and databases’ systems have aided in the digitization of, and access to, various historical (big) data, with significant challenges of analysis and interoperability. The Death and Burial Data, Ireland project aims to build a Big Data interoperability framework loosely based on the Knowledge Discovery Data (KDD) process to integrate Civil Registration of Death data with other data types collated in Ireland from 1864 to 1922. For our project, we resort to a Document Type Description (DTD) product line to represent and manage various representations and enrichments of the data. Well-formed documents serve as contracts between a provider (of the data set) and its customers (the researchers that consult them). We adopt the Context-Free Modal Transition Systems as a formalism to specify product lines of DTDs. The goal is to then proceed to product line verification using context-free model checking techniques, specifically the M3C checker of [14] to ensure that they are fit for purpose. The goal is to later implement and manage the corresponding family of data models and processes in the DIME framework, leveraging its flexible data management layer to define and efficiently manage the interoperable historical data framework for future use. The resulting hierarchical product line verification will allow our technical platform to act as a high-quality service provider for digital humanities researchers, providing them with a wide range of tailored applications implementing the KDD process, whose essential business rules are easily checked by a standard DTD checker.
A Deep Learning-based Ranking Approach for Microblog Retrieval

Today, Twitter has become one of the most popular micro-blogging service with a large amount of information on various topics produced by millions of users every day. When searching for useful information in Twitter, users need to assess high quality content that meets their needs. However, the study of effective information retrieval in such microblog is still a challenge because there is a large difference in the quality level of relevant tweets returned in the search results for a given query. Therefore, looking for an effective microblog retrieval requires distinguishing the high-quality tweet content among thousands of results. Existing works are based on hand-crafted features (e.g., number of re-tweets, number of followers, etc.) using hard-hand-engineering to indicate the quality of tweets. In this paper, we focus on the problem of ranking tweets, and particularly on retrieving high quality content. We propose a ranking approach based on k-means clustering to distinguish high quality from low quality tweets. The clustering algorithm is based on learning features from deep learning autoencoder and hand-crafted features from tweets’ content and authors’ profiles. We used information gain as a feature importance measure to find the optimal set of features having stronger power in clustering the data. By conducting a pilot feature analysis study, we demonstrate the impact of the learned features to identify tweets’ quality in the clustering process. Our experimental results show that the integration of learned features has shown significant improvement in the quality of clustering and especially on the ranking performance compared to the use of hand-crafted features only.
Blockchains 2019 in e-HRM: Hit or Hype?

Blockchains, also known as “distributed ledger technologies” (DLT) are perhaps the emerging innovation that, in the years leading up to and including 2019, is raising the highest expectations for HRM in the 4.0 business environment. In essence, a blockchain is a very specific type of database, with characteristics that made it the ideal application for cryptocurrencies like Bitcoin. Within the context of digital- or e-HRM, there is potential to improve human resource management (HRM) processes using blockchains for employment screening, credential and educational verification, worker contracts and payments, among others, notwithstanding questions about its efficiency vis-à-vis conventional alternatives (Maurer, 2018; Zielinski, 2018). The research questions examined in this chapter include the following: What are the main characteristics of blockchains? Will they be adopted in a widespread form, specifically by HRM departments? Constructs from Diffusion of Innovations (DOI) theory (Rogers, 2003) are used to inform the Human Resources scholarly and practitioner communities; this robust theory may help companies allocate resources (e.g., budgets, personnel, managerial time, etc.) in an evidence-informed manner. As of this writing, very few blockchain applications, such as credential verification and incident reporting, seem to hold a strong potential for adoption.
Smart Buildings IoT Networks Accuracy Evolution Prediction to Improve Their Reliability Using a Lotka–Volterra Ecosystem Model

Internet of Things (IoT) is the paradigm that has largely contributed to the development of smart buildings in our society. This technology makes it possible to monitor all aspects of the smart building and to improve its operation. One of the main challenges encountered by IoT networks is that the the data they collect may be unreliable since IoT devices can lose accuracy for several reasons (sensor wear, sensor aging, poorly constructed buildings, etc.). The aim of our work is to study the evolution of IoT networks over time in smart buildings. The hypothesis we have tested is that, by amplifying the Lotka–Volterra equations as a community of living organisms (an ecosystem model), the reliability of the system and its components can be predicted. This model comprises a set of differential equations that describe the relationship between an IoT network and multiple IoT devices. Based on the Lotka–Volterra model, in this article, we propose a model in which the predators are the non-precision IoT devices and the prey are the precision IoT devices. Furthermore, a third species is introduced, the maintenance staff, which will impact the interaction between both species, helping the prey to survive within the ecosystem. This is the first Lotka–Volterra model that is applied in the field of IoT. Our work establishes a proof of concept in the field and opens a wide spectrum of applications for biology models to be applied in IoT.
Implementing Binary Search Tree Concept for Image Cryptography

So many cryptographic systems are secure and in use today, such as AES, 3DES, Blowfish, RC4, etc., however, new ideas and innovations are still highly required due to the ever-growing threats to data security.This paper presents a novel idea for symmetric image encryption utilizing binary search tree mechanism for generating the encryption/decryption key.This key consists of local and global parts with free user controlled key length, hence, it provides good flexibility for high security, which is manifested in the encrypted images.For every single byte of the encrypted image, the substitution and transposition operations involve all the image contents, fulfilling Shannon's diffusion and confusion concept.Experimental computations of the proposed encryption method have shown satisfactory visual and image energy distribution histogram.Besides, the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Absolute Error (NMAE) measurement results were comparable with the widely used cryptosystems such as DES and AES.On the other hand, the proposed method has flexible key length and shorter execution time by 10% or more depending on the image contents.
Fault Section Location Method Based on Random Forest Algorithm for Distribution Networks with Distribution Generations

The fault information collected by the feeder terminal units is easily distorted under the undesirable environment. The traditional fault section location methods of the distribution network are facing problems of poor fault tolerance and low accuracy. In this paper, a novel fault section location method for distribution networks is proposed based on the random forest algorithm. Considering the high-dimension and unbalanced characteristics of the original fault sample sets, the SMOTE oversampling method and the learning-model-based feature selection method are utilized to preprocess the original data. Furthermore, for establishing the fault location prediction model, the relevance between the fault attributes and the fault section is mined by the random forest algorithm. The simulation results show that the proposed method can achieve the single and multiple fault section location in distribution networks. Meanwhile, compared to the traditional methods, the proposed method has two significant advantages, strong fault tolerance to distorted information and high prediction accuracy.
Multiple Object Tracking: Case of Aircraft Detection and Tracking

Tracking has recently become one of the most interesting and active research fields. Tracking object consists in estimating the state of the object in function of time. This operation is based on largely developed filtering techniques. It is possible to classify tracking into Single Object Tracking and Multiple Object Tracking. This paper discusses the problem of multiple tracking. The aim is to encode a multiple object tracking algorithm to track aerial maneuvering aircraft by using the Interacting Multiple Model (IMM) combined with the assignment Global Nearest Neighbor (GNN) algorithm. The GNN represents the combinatorial optimization solution based on the comparison of different assignments.
REFLECTION OF LABOR MARKET INTERESTS AND EXPECTATIONS IN EDUCATIONAL ORDER FOR CONTEMPORARY SCHOOL

Purpose of the study: The main objective of the study was the analysis of key labor market request content for the secondary education system, the level of employers' expectations compliance and actual learning outcomes, the research of resources and partnership interaction limitations of all interesting subjects in the process of an educational order development.&#x0D; Methodology: The main method was the questionnaire survey of experts (individual entrepreneurs, the heads of industrial and structural divisions) of Moscow (N = 316). The results of the expert survey showed the interest of employers in close cooperation with school in order to reflect the interests of the labor market in the educational order adequately.&#x0D; Results: The most motivated group is a small business, the leaders of the lowest level in the sphere of trade and service, who directly interact with the school graduates in the process of their early start of labor activity. The experts noted the problems in the development of the social competencies among schoolchildren: excessive self-esteem, an excessive level of claims, low level of willingness to work in a team, the lack of such qualities as responsiveness and responsibility. The cooperation between school and employers is limited to traditional sponsorship practices. The interaction of employers and educational institutions is quite fragmentary, it depends on a variety of random factors (personal and situational motives), which does not ensure the stability and the effectiveness of social partnership.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: In this research, the model of Reflection of Labor Market Interests and Expectations in Educational Order for Contemporary School is presented in a comprehensive and complete manner.
Generating Greenberger-Horne-Zeilinger states with squeezing and postselection

Many quantum state preparation methods rely on a combination of dissipative quantum state initialization followed by unitary evolution to a desired target state. Here we demonstrate the usefulness of quantum measurement as an additional tool for quantum state preparation. Starting from a pure separable multipartite state, a control sequence, which includes rotation, spin squeezing via one-axis twisting, quantum measurement, and postselection, generates highly entangled multipartite states, which we refer to as projected squeezed (PS) states. Through an optimization method, we then identify parameters required to maximize the overlap fidelity of the PS states with the maximally entangled Greenberger-Horne-Zeilinger (GHZ) states. The method leads to an appreciable decrease in the state preparation time of GHZ states for successfully postselected outcomes when compared to preparation through unitary evolution with one-axis twisting only.
Development of Court Practice in Cases Involving Human Genomics: World Experience and Russia

The legal regulation of the area of human genomics covering both research in the area of human genome and activities carried out to implement their results in life, is today in the stage of development and is tightly linked to scientific progress in the area under consideration. In this regard, the analysis of judicial practice is an important indicator that allows the author to determine problematic practical issues of formation and development of legal regulation in the area under consideration. The best international jurisprudence and case law in the field of the human genome is sporadic and embodies the diversity of approaches to the legal regulation of these issues in different legal systems. If in the USA and Europe such judicial practice began to develop since the end of the twentieth century, in Russia the first case involving the genome was considered in the middle of the current decade, which generally corresponds to the catch-up dynamics of development of Russian law in the area in question. The paper is aimed at the study of the best jurisprudence in the world concerning genomic studies and implementation of their results, including the legal positions of the ECHR and the US courts, as well as the emerging practice of Russian courts. The paper covers judicial practice both in the field of genetic engineering and in the field of genomic information circulation. The paper analyzes specific cases in the area of human genomics dealt with in the course of judicial proceedings on both sides of the Atlantic. It also covers the issues of reflecting the genomic research in the practice of Russian courts, e.g. in Prosecutor Kemerovo v. IK-22. In addition, an attempt is made, on the basis of performed analysis, to identify differences in approaches to legal regulation in different legal systems.
Building Mongolian TTS Front-End with Encoder-Decoder Model by Using Bridge Method and Multi-view Features

In the context of text-to-speech systems (TTS), a front-end is a critical step for extracting linguistic features from given input text. In this paper, we propose a Mongolian TTS front-end which joint training Grapheme-to-Phoneme conversion (G2P) and phrase break prediction (PB). We use a bidirectional long short-term memory (LSTM) network as the encoder side, and build two decoders for G2P and PB that share the same encoder. Meanwhile, we put the source input features and encoder hidden states together into the Decoder, aim to shorten the distance between the source and target sequence and learn the alignment information better. More importantly, to obtain a robust representation for Mongolian words, which are agglutinative in nature and lacks sufficient training corpus, we design specific multi-view input features for it. Our subjective and objective experiments have demonstrated the effectiveness of this proposal.
Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks.But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes.In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset.In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively.To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes.Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset.The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard).
GRET: Global Representation Enhanced Transformer

Transformer, based on the encoder-decoder framework, has achieved state-of-the-art performance on several natural language generation tasks. The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence. These hidden states usually correspond to the input words and focus on capturing local information. However, the global (sentence level) information is seldom explored, leaving room for the improvement of generation quality. In this paper, we propose a novel global representation enhanced Transformer (GRET) to explicitly model global representation in the Transformer network. Specifically, in the proposed model, an external state is generated for the global representation from the encoder. The global representation is then fused into the decoder during the decoding process to improve generation quality. We conduct experiments in two text generation tasks: machine translation and text summarization. Experimental results on four WMT machine translation tasks and LCSTS text summarization task demonstrate the effectiveness of the proposed approach on natural language generation1.
Identification of Sarcasm in Textual Data: A Comparative Study

Abstract Purpose Ever increasing penetration of the Internet in our lives has led to an enormous amount of multimedia content generation on the internet. Textual data contributes a major share towards data generated on the world wide web. Understanding people’s sentiment is an important aspect of natural language processing, but this opinion can be biased and incorrect, if people use sarcasm while commenting, posting status updates or reviewing any product or a movie. Thus, it is of utmost importance to detect sarcasm correctly and make a correct prediction about the people’s intentions. Design/methodology/approach This study tries to evaluate various machine learning models along with standard and hybrid deep learning models across various standardized datasets. We have performed vectorization of text using word embedding techniques. This has been done to convert the textual data into vectors for analytical purposes. We have used three standardized datasets available in public domain and used three word embeddings i.e Word2Vec, GloVe and fastText to validate the hypothesis. Findings The results were analyzed and conclusions are drawn. The key finding is: the hybrid models that include Bidirectional LongTerm Short Memory (Bi-LSTM) and Convolutional Neural Network (CNN) outperform others conventional machine learning as well as deep learning models across all the datasets considered in this study, making our hypothesis valid. Research limitations Using the data from different sources and customizing the models according to each dataset, slightly decreases the usability of the technique. But, overall this methodology provides effective measures to identify the presence of sarcasm with a minimum average accuracy of 80% or above for one dataset and better than the current baseline results for the other datasets. Practical implications The results provide solid insights for the system developers to integrate this model into real-time analysis of any review or comment posted in the public domain. This study has various other practical implications for businesses that depend on user ratings and public opinions. This study also provides a launching platform for various researchers to work on the problem of sarcasm identification in textual data. Originality/value This is a first of its kind study, to provide us the difference between conventional and the hybrid methods of prediction of sarcasm in textual data. The study also provides possible indicators that hybrid models are better when applied to textual data for analysis of sarcasm.