Reviewerly/exHarmony huggingface dataset:
Teaching students of TPU energy &amp; power engineering school to read technical texts in a foreign language: Psycholinguistic aspects and engagement principles

The article is devoted to the problem of engaging students of TPU Energy & Power Engineering School in reading technical texts in a foreign language. Regarding this problem, the authors present the results of their research on the psycholinguistic aspects and engagement principles that should be taken into account when choosing teaching methods and tasks.
The Einstein<i>A</i>and<i>B</i>coefficients

Abstract The Einstein A and B coefficients for atom–photon reactions are defined: they describe absorption, stimulated emission, and spontaneous emission. We calculate B and consequently find A by the Einstein trick. The procedure is validated by application of detailed balance. Separating out frequencies permits introduction of the spectral lineshape function (normalized line profile). A reformulation describes transitions involving single atomic states and single field modes. This points to a link with quantum electrodynamics.
A Multi-layer Feature Parallel Processing Method for Image Captioning

Most image captioning methods based on neural networks use high-level features extracted by CNNs, but it is difficult for high-level features to retain the information of small objects, so the generated description cannot meet more fine-grained requirements. To solve the above problems, we propose a multi-layer feature parallel processing method for image captioning, which feeds each layer of features to each stacked layer of the decoder in a certain order, thereby using multi-feature expression to generate a more fine-grained description. We provide two design schemes for the proposed multi-layer feature parallel processing method: Sequential Parallel Connection(SPC) and Reverse Parallel Connection(RPC). This work focuses on exploring a more effective and robust model connection method that can generate finer-grained descriptions. Extensive experiments in the COCO dataset show that our connection method can generate better quality sentences.
Rewiev of current text representation technics for semantic relationship extraction

Article provides review on current most popular text processing technics; sketches their evolution and compares sequence and dependency models in detecting semantic relationship between words.
Scientific Application and Research of Computer Technology in Sports Training

Abstract With the development of economy, the role of computer technology in the field of education has gradually become prominent, especially the application of computer technology represented by multimedia technology has greatly promoted the quality of teaching. Computer virtual reality technology also provides a new teaching method for college physical education and promotes the improvement of college physical education. Based on computer vision technology, this paper constructs a systematic and complete virtual reality technology framework for modern physical education teaching, analyses in detail the realization of each key step in the framework, and provides technical guidance for the practical application of virtual reality technology in physical education.
A Multi -Role Graph Attention Network for Knowledge Graph Alignment

Entity alignment aims to automatically align entities which are equivalent in the real world from different knowledge graphs (KGs). The task is challenged by the diversity of KGs, the multi-relational graph structures and limited seed entity pairs. Most existing GNN-based models do not distinguish the types or the directions of relations while the existing translation-based models merely rely on local semantics. To tackle the challenges, we propose a Multi-Role Graph Attention network (MRGA), which incorporates local semantics in triples with global structural information in graphs and exploits rich relational information effectively. MRGA devises a GAT-based layer which regards each entity as a combination of roles that it plays in different triples. Multifaceted entity semantics (i.e. roles), associated with different triples, are aggregated to enrich entity embeddings. An additional attention mechanism is applied to capture the correlation in relations of different KGs. The model is evaluated on three publicly available datasets. The results show the superior performance of MRGA compared with baseline methods and demonstrate the effectiveness of each component in MRGA via ablation study.
Leveraging Dual Blockchain to Solve Security Issues in Electronic Health Records

Developing Electronic Health Records using Blockchain technology is the most discussed research topic for recent years. Blockchain seems to be structuring business models and handling large scale data. It has emerged absolutely beneficial with the EHR system by securing direct access to critical data, yet previous researches failed to address the storage, interoperability and three level security model. This paper ensures security in EHR by using Dual Blockchain Architecture technology to create a user-focused electronic health record whilst maintaining a single true version of the user’s data and enables the user to give healthcare professionals access to their personal health data with a decentralized platform that enables secure, fast and transparent exchange and usage of medical data. Also, records interactions with this data in an auditable, transparent and secure way using cloud and distributed ledger. Lastly, EHR has to be a platform for others to use to build applications that complement and improve the user experience. Users will be able to leverage their medical data to power a plethora of applications and services.
An Ontology-Based Indigenous Knowledge Management Portal for Subfertility of Females

Subfertility in humans is the major problem in the technological world mostly by their habits and foods. There are many treatment methods for the subfertility of females, but the documentation in this field is mostly available in the local language, which cannot be understood by others, and time by time, this knowledge towards the future generation is diminished. The authors have depicted the knowledge using ontology and thereafter the knowledge management portal (KMP) for the indigenous knowledge was developed using the modeled ontology. They believe that the indigenous knowledge management portal (IKMP) will help future generations to get knowledge easily by using this system. They also strongly believe that the IKMP will serve as the experience-sharing tool for the subfertility-related indigenous knowledge.
Application of the LSA Technique to Determine the Priority of Alerts from a Command and Control Center

It is essential to determine the alert level in a command-and-control center when someone calls an operator for an emergency since life may be in danger. The alert level is determined based on the evaluation that the operator can perceive during the call. Sometimes, cases can be similar to those previously attended. Therefore, it is helpful that this knowledge can be rescued and applied to new cases. In this context, the Latent Semantic Analysis (LSA) technique can determine the alert level and find the most representative words in each case. Thus, when a new alarm is triggered, the system can recommend the alert level with which it is rated. Consequently, a solution based on previous knowledge has been stated. This solution leads to the following methodological process: i) data preprocessing, ii) topic analysis and iii) classification. When this proposal was applied, the results revealed an accuracy greater than 60% in predicting the type of alert based on the text.
A Novel Fuzzy Based Weighted Differential Evolution Algorithm for Clustering Gene Expression Data

Invention of microarray DNA technology has paved the way of clustering gene expression data. It helps to examine and study different gene expression patterns at other instances of time points. To monitor such gene expression distribution, the fuzzy clustering algorithm is used commonly. This paper proposes an assimilated form of fuzzy clustering with a Weighted Differential Evolution (WDE) optimization algorithm for clustering gene data. Moreover, WDE algorithm based fuzzy clustering can regress efficient evolutionary search procedures. Through the simulation study, it is observed that the WDE algorithm that has been proposed for solving numerical optimization problem can also cluster real-life gene expression data effectively. Different cluster validity indexes are used to validate clustering solutions. Comparative performance analysis and experiments are conducted on real-life datasets and gene expression datasets numerically and graphically.
Trust, but Verify: Alleviating Pessimistic Errors in Model-Based Exploration

We propose trust-but-verify (TBV) mechanism, a new method which uses model uncertainty estimates to guide exploration. The mechanism augments graph search planning algorithms with the capacity to deal with learned model's imperfections. We identify certain type of frequent model errors, which we dub false loops, and which are particularly dangerous for graph search algorithms in discrete environments. These errors impose falsely pessimistic expectations and thus hinder exploration. We confirm this experimentally and show that TBV can effectively alleviate them. TBV combined with MCTS or Best First Search forms an effective model-based reinforcement learning solution, which is able to robustly solve sparse reward problems.
Attribution of authorship in instant messaging software applications, based on similarity measures of the stylometric features’ vector

This paper describes the issue of authorship attribution based on the content of conversations originating from instant messaging software applications. The results presented in the paper refer to the corpus of conversations conducted in Polish. On the basis of a standardised model of the corpus of conversations, stylometric features were extracted, which were divided into four groups: word and message length distributions, character frequencies, tf-idf matrix and features extracted on the basis of turns (conversational features). The vectors of users’ stylometric features were compared in pairs by using Euclidean, cosine and Manhattan metrics. CMC curves were used to analyse the significance of the feature groups and the effectiveness of the metrics for identifying similar speech styles. The best results were obtained by the group of features being the tf-idf matrix compared with the use of cosine distance and the group of features extracted on the basis of turns compared with the use of the Manhattan metric.
Invertible Manifold Learning for Dimension Reduction

Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information. In the context of manifold learning, we define that the representation after information-lossless DR preserves the topological and geometric properties of data manifolds formally, and propose a novel two-stage DR method, called invertible manifold learning (inv-ML) to bridge the gap between theoretical information-lossless and practical DR. The first stage includes a homeomorphic sparse coordinate transformation to learn low-dimensional representations without destroying topology and a local isometry constraint to preserve local geometry. In the second stage, a linear compression is implemented for the trade-off between the target dimension and the incurred information loss in excessive DR scenarios. Experiments are conducted on seven datasets with a neural network implementation of inv-ML, called i-ML-Enc. Empirically, i-ML-Enc achieves invertible DR in comparison with typical existing methods as well as reveals the characteristics of the learned manifolds. Through latent space interpolation on real-world datasets, we find that the reliability of tangent space approximated by the local neighborhood is the key to the success of manifold-based DR algorithms.
Resilient Boot

As security continues to permeate embedded systems, it remains largely at odds with other performance metrics such as speed. Time-critical computing systems need to meet strict timing requirements and often cannot afford to add security checks. The impact of these checks is particularly evident before the systems are configured to run at full speed, or during booting. This paper describes a method that allows to speed up the boot process by relying on seemingly unorthodox infrastructure in nonvolatile memories. The additional infrastructure requires positive confirmation—which can be deferred and come after booting— for the system to continue performing application-specific actions. In the event of failure to provide confirmation, the infrastructure initiates a recovery process.
Handling Transitive Relations in First-Order Automated Reasoning

Abstract We present a number of alternative ways of handling transitive binary relations that commonly occur in first-order problems, in particular equivalence relations , total orders , and transitive relations in general. We show how such relations can be discovered syntactically in an input theory, and how they can be expressed in alternative ways. We experimentally evaluate different such ways on problems from the TPTP, using resolution-based reasoning tools as well as instance-based tools. Our conclusions are that (1) it is beneficial to consider different treatments of binary relations as a user, and that (2) reasoning tools could benefit from using a preprocessor or even built-in support for certain types of binary relations.
Channel Interaction with Local Enhancement for Few-Shot Semantic Segmentation

State-of-the-art semantic segmentation algorithms are based on deep convolutional neural networks, which aim to exploit a great quantity of labeled samples to predict the class label for each pixel in an image. Few-shot segmentation alleviate the data labeling task, learning a model given only a few labeled samples, which adapts well to unseen classes. Previous works only extract spatial pixels relationship to build the guidance, which ignores the important feature channel information that contain abundant local semantic structure in the images. In this paper, we propose a novel channel interaction network (CINet) for few-shot semantic segmentation. It mainly consists of three novel steps: a local enhancement module that aggregates the spatial pixels connection in both query images and support images; a channel interaction module that captures the similar channel from relationship between each pair of images for segmentation guidance; and a residual attention module that exploiting residual connection of feature scales to alleviate spatial inconsistency between query images and corresponding example images. Extensive experiments on PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> dataset demonstrate that our model outperforms state-of-the-art methods without parameters increase sharply.
Method of Intelligent Choice of Parameters of Swarm Intelligence Algorithms to Increase Effectiveness of Processes Control in Complex Educational Systems

The core of the proposed method is to take into account the personal and psychological qualities of learners and the requirements of employers in the process of intelligent choice of parameters of swarm intelligence algorithms when managing processes in complex educational systems. The method allows to perform intelligent tuning of the parameters of swarm intelligence algorithms upon receipt of new data on learners’ course results and upon change of employers’ requirements. A distinctive feature of the proposed method is the integrated use of cluster analysis for the intelligent distribution of learners into groups, of the T. Saati’s method for determining the importance for employers of personal and psychological qualities, and the knowledge, skills and abilities of learners formed in the training process, as well as dynamic intelligent tuning of the parameters of swarm intelligence algorithms as new data on the results of learners training and environment factors become available.
Surveying for man-made objects in photographic images

Surveying for man-made objects in photographic images is of utmost importance for various military and civilian applications. In this paper, we present two supervised approaches for classifying a photographic image as containing either dominant natural or man-made regions. The first approach has low-complexity where features are extracted from a statistical model of multi-scale sub-band coefficients of natural scenes. The second approach is based on traditional robust feature extraction along with recent deep methods. We evaluate the performance of these approaches on two popular image databases composed of a mixture of man-made and natural scene photographic images. We compare their performance in terms of classification accuracy as well as computational complexity. While the traditional robust feature based classification approach appears to be an obvious choice for such a task, we conclude that low-complexity approaches cannot be discounted for real-time applications. Finally, we also construct a likelihood map for the man-made regions for quick localisation of man-made regions within mixed image that could help in speeding up the detection process.
Research on E-commerce Marketing and Enterprise Performance Based on Cloud Service Platform

Through the investigation and analysis of domestic mainstream cloud service providers' cloud service supply chain operation mode, the established operation model is tested and revised by means of explanatory case study. Then, the measurement scale of IT technology capability based on cloud computing is developed, and the realization of business value of cloud computing is studied from the perspective of business process. The business process oriented research model of cloud computing business value realization is established, and the research hypothesis is verified and analyzed. Finally, we use factor analysis to evaluate the financial performance of the sample cloud service e-commerce enterprises, and on this basis, we use quantile regression model to make regression analysis on the influencing factors of financial performance, and draw the conclusion that e-commerce companies should improve the company size, optimize the capital structure and promote financial performance.
Commercial ERP Chatbots : Conversational Intelligence Agents' Performance Analysis, User experience Benchmarks, and Quality Standards

A key goal of this paper is to investigate commercial applications for chatbots and propose several evaluation metrics for evaluating an embodied conversational agent's performance, usability, and overall quality. Our assessment is based on metrics that include a) size of market share &amp; reach, b) range of users and c) likelihood of being the most advanced commercial chatbot created by their creators. Our study examines several aspects of the function of embodied conversational agents, including their visual appearance, implementation of web sites, speech synthesis unit, built-in knowledge base (with general and specialized information), presentation of knowledge and additional functionality, emergency responses in unexpected situations, and user ratings. An evaluation of any commercial chatbot deployment requires a multidimensional assessment and the current state of Conversational Intelligence in the commercial virtual assistant market.
Generate and Revise: Reinforcement Learning in Neural Poetry

Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems.
Human Interaction with Embedded Systems

All of us are very comfortable to power on our laptop, install and execute any software. The software guides us in executing the next operation to some extent. In case you do some wrong operations, the software suggests a way to recover back. In most of embedded systems, you have no screen or a mouse but certain physical interfaces like buttons, sliders, rotating knobs, sticks, etc. as the interaction devices. If the end user is not conversant with the system and does a wrong operation, it may be very difficult to recover. The design needs careful operator interaction to prevent them to do faults. There are no default interactions on embedded devices. Interaction with an embedded device should work in any harsh environment. The device must be operable by variety of operators, young and old people, experts, and novices. The device should adapt to the operator’s capabilities. To summarize, embedded systems need effective interaction satisfying human needs. This needs formal methodologies to be studied and adapted. Good interface helps in operating the system safely, effectively with minimal operations and the user enjoys operating the system. The quality of interface is measured in terms of “Usability” of the product. Basic mistake we do is by making an assumption that “all users are alike and they are like the designer.” Evaluating the user’s physical and cognitive capabilities is essential. Human user’s physiology, their capabilities, and limitations in sensing through different channels, memory, cognitive, and motoring capabilities have to be studied as explained in Sect. 9.3 before interface design. Section 9.4 details certain physical interfaces used in the embedded systems. The interaction between the human user and the system is to be designed with user in mind and not the other way. Section 9.5 describes the concept of interaction. Interaction models help us to understand how the interaction between user and system is progressing. Section 9.6 reviews recent paradigms in computer interaction. Section 9.7 covers rules for interface design for maximum usability. Section 9.8 covers methods of interface evaluation using cognitive, heuristic, and user participation methods. To summarize that the ultimate factor for success of a product is the usability.