Reviewerly/exHarmony huggingface dataset:
COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking

Multitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.
Privacy in Public: A Democratic Defense

Abstract Traditional arguments for privacy in public suggest that intentionally public activities, such as political speech, do not deserve privacy protection. In this article, I develop a new argument for the view that surveillance of intentionally public activities should be limited to protect the specific good that this context provides, namely democratic legitimacy. Combining insights from Helen Nissenbaum’s contextualism and Jürgen Habermas’s theory of the public sphere, I argue that strategic surveillance of the public sphere can undermine the capacity of citizens to freely deliberate in public and therefore conflicts with democratic self-determination.
Revocable and Linkable Ring Signature

In this paper, we construct a revocable and linkable ring signature (RLRS) scheme, which enables a revocation authority to revoke the anonymity of the real signer in linkable ring signature scheme under any circumstances. In other words, the revocability of RLRS is mandatory. The proposed RLRS scheme inherits the desired properties of group signature (anonymity revocation) and linkable ring signature (spontaneous group formation and linkability). In addition, we proved the security of our scheme in the random oracle model. We also provided a revocable ring confidential transaction protocol based on our RLRS scheme, which embedded the revocability in ring confidential transaction protocol.
Persistent quantum walks: Dynamic phases and diverging timescales

Persistence in quantum walks is introduced by allowing a choice of two different step lengths and the last step length taken is repeated with probability $p$. It is found that the mean square displacement dependence with time varies with $p$.
Korean-Vietnamese Neural Machine Translation with Named Entity Recognition and Part-of-Speech Tags

Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.
Smart home platform supporting decentralized adaptive automation control

Smart Home is an exemplar application domain of the Internet of Things (IoT). In particular, smart home automation processes that manage the interactions with the home devices and integrate all their (possibly interfering) services, introduce new challenges and call for modern engineering practices, software platforms, and computational intelligence. A key example is the need to achieve a more efficient integration between design and runtime aspects in the automation process, taking into account that unanticipated situations can occur in the home environment and require a smart home system to deal with them at runtime. In this paper, using an application example, we foresee how to architect future smart home systems in order to endow them with self-adaptation capabilities allowing decentralized control and device interconnection. Namely, we propose to exploit feedback control loop architectures (e.g., composite MAPE feedback loops) to design and manage the home automation processes, as alternative software architectural solutions to those of smart home platforms currently available.
Interdisciplinary Coordination in Historical-Theoretical and Music-Performing Training of Future Musical Art Teachers

The article investigates the basic blocks of future musical art teachers' training, which include historical-music, theoretical-music, and music-performing disciplines. In this regard, the didactic potential of their interdisciplinary coordination is revealed, and ways of its realization are suggested. The methodological basis of the study includes the phenomenological approach in which music is positioned as a distinctive phenomenon, an ideal substance that comprehends the immense and creates a sensual picture of the world. It is shown that, in contrast to the competence approach, which directs the efforts of researchers to the formation of professional and personal qualities of the teacher, the interdisciplinary approach primarily involves optimization of the curriculum. Interdisciplinary coordination is considered as flexible and consistent use of all its components in a real learning process. Interdisciplinary coordination, based on the allocation of related elements in the content of subjects of historical-theoretical and music-performing blocks, provides formation of common knowledge and skills in future musical art teachers, ability to use their various activities (educational, performing and didactic) independently, flexibly and creatively. Essential characteristics of future musical art teacher’s music-performing training are presented, which consist of voice training, choral conducting and music-instrumental training, aimed at interpreting composers’ works. The latter requires harmonization of cognitive and emotional components of the experience. For all types of music-performing training (piano, violin, accordion, vocal, choral conducting, etc.), it is important to analyze the performance means of expressiveness, technical difficulties, differences between ideal auditory-figurative representations and real sound.
Reliability of using high-resolution aerial photography (red, green and blue bands) for detecting available soil water in agricultural land

The need for irrigation water is influenced by soil water content or more precisely by available water (pF 2.5 and pF 4.2). There is a need for technological breakthroughs in using Unmanned Aerial Vehicle (UAV) to identify water content quickly and broadly and accurately. The study was conducted in an area of ±18 hectares in the Sisim Sub Watershed in September 2019 at 09.00 a.m. Aerial photographs were taken at an altitude of 100 m with DJI Phantom Pro 3.0. The number of observation points was 75 points, where 15 points for validation were calculated based on the map scale. Photo processing was made using Agisoft. The Digital Elevation Model (DEMNAS) with 8.2 m resolution was used to compare the red, green and blue bands. The analysis used was Co-Kriging Geo Statistics Analysis, the compilation of algorithms based on the regression equation and ten index formulations. Validation was done by correlation continued with the regression or paired t-test if the parameter relationship was close. The available water measured in the field ranged from 5.16-48.28%. The results showed that the formulation of soil water content could be run on the Red, Green, and Blue bands, Intensity index, TGI index, ExGreen index and DEMNAS with a weak correlation (below 0.5), where TGI had the highest value (r=0.32). A test of t-pairing was not done because of a weak correlation. The highest estimation of pF 4.2 is DEMNAS (r=0.35), and pF 2.5 was on the TGI index (r=0.4).
Neural Architecture Generator Optimization

Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art performance through the discovery of new architecture patterns, without human intervention. An over-reliance on expert knowledge in the search space design has however led to increased performance (local optima) without significant architectural breakthroughs, thus preventing truly novel solutions from being reached. In this work we 1) are the first to investigate casting NAS as a problem of finding the optimal network generator and 2) we propose a new, hierarchical and graph-based search space capable of representing an extremely large variety of network types, yet only requiring few continuous hyper-parameters. This greatly reduces the dimensionality of the problem, enabling the effective use of Bayesian Optimisation as a search strategy. At the same time, we expand the range of valid architectures, motivating a multi-objective learning approach. We demonstrate the effectiveness of this strategy on six benchmark datasets and show that our search space generates extremely lightweight yet highly competitive models.
Feature-Level Sentiment Analysis Based on Rules and Fine-Grained Domain Ontology

Mining product reviews and sentiment analysis are of great significance, whether for academic research purposes or optimizing business strategies. We propose a feature-level sentiment analysis framework based on rules parsing and fine-grained domain ontology for Chinese reviews. Fine-grained ontology is used to describe synonymous expressions of product features, which are reflected in word changes in online reviews. First, a semiautomatic construction method is developed by using Word2Vec for fine-grained ontology. Then, feature-level sentiment analysis that combines rules parsing and the fine-grained domain ontology is conducted to extract explicit and implicit features from product reviews. Finally, the domain sentiment dictionary and context sentiment dictionary are established to identify sentiment polarities for the extracted feature-sentiment combinations. An experiment is conducted on the basis of product reviews crawled from Chinese e-commerce websites. The results demonstrate the effectiveness of our approach.
Noise Removal Techniques and Quality analysis of X-ray Images

To detection of any types of anomalies in the medical images, the quality of the image is very important. To improve the quality of the images pre-processing steps are used. This includes the image enhancement and noise removal. Removing of the noise is done by various methods. This paper elaborates on different types of noise in the X-ray Dicom images and different filtering methods. Image improvement is calculated using PSNR and MSE.
A MIMD Interpreter for Genetic Programming

Most Genetic Programming implementations use an interpreter to execute an individual, in order to obtain its outcome. Usually, such interpreter is the main bottleneck of the algorithm, since a single individual may contain thousands of instructions that must be executed on a dataset made of a large number of samples. Although one can use SIMD (Single Instruction Multiple Data) intrinsics to execute a single instruction on a few samples at the same time, multiple passes on the dataset are necessary to calculate the result. To speed up the process, we propose using MIMD (Multiple Instruction Multiple Data) instruction sets. This way, in a single pass one can execute several instructions on the dataset. We employ AVX2 intrinsics to improve the performance even further, reaching a median peak of 7.5 billion genetic programming operations per second in a single CPU core.
Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths

Knowledge graphs' incompleteness has motivated many researchers to propose methods to automatically infer missing facts in knowledge graphs. Knowledge graph embedding has been an active research area for knowledge graph completion, with great improvement from the early TransE to the current state-of-the-art ConvKB. ConvKB considers a knowledge graph as a set of triples, and employs a convolutional neural network to capture global relationships and transitional characteristics between entities and relations in the knowledge graph. However, it only utilizes the triple information, and ignores the rich information contained in relation paths. In fact, a path of one relation describes the relation from some aspect in a fine-grained way. Therefore, it is beneficial to take relation paths into consideration for knowledge graph embedding. In this paper, we present a novel convolutional neural network-based embedding model PConvKB, which improves knowledge graph embedding by incorporating relation paths locally and globally. Specifically, we introduce attention mechanism to measure the local importance of relation paths. Moreover, we propose a simple yet effective measure DIPF to compute the global importance of relation paths. Experimental results show that our model achieves substantial improvements against state-of-the-art methods.
SentiInc: Incorporating Sentiment Information into Sentiment Transfer Without Parallel Data

Sentiment-to-sentiment transfer involves changing the sentiment of the given text while preserving the underlying information. In this work, we present a model SentiInc for sentiment-to-sentiment transfer using unpaired mono-sentiment data. Existing sentiment-to-sentiment transfer models ignore the valuable sentiment-specific details already present in the text. We address this issue by providing a simple framework for encoding sentiment-specific information in the target sentence while preserving the content information. This is done by incorporating sentiment based loss in the back-translation based style transfer. Extensive experiments over the Yelp dataset show that the SentiInc outperforms state-of-the-art methods by a margin of as large as $$\sim $$11% in G-score. The results also demonstrate that our model produces sentiment-accurate and information-preserved sentences.
HKA: A Hierarchical Knowledge Attention Mechanism for Multi-Turn Dialogue System

Generating informative responses by incorporating external knowledge into dialogue system attracts more and more attention. Most previous works facilitate single-turn dialogue system on generating such responses. However, few works focus on incorporating knowledge for multi-turn system, since the hierarchy of knowledge, from the words and utterances in context, is ignored. Motivated by this, we propose a novel hierarchical knowledge attention (HKA) mechanism for open-domain multi-turn dialogue system in this paper, which utilizes both word and utterance level attention jointly. Experiments demonstrate that the proposed HKA can incorporate more appropriate knowledge and make the state-of-the-art models generate more informative responses. Further analysis shows that our HKA can improve the model's ability of dialogue state management, especially when the number of dialogue turns is large.
El marketing educativo como estrategia para la satisfacción de alumnos universitarios

In a globalized and competitive world, the need for the Higher Education Institutions to have a competitive advantage and exceed the expectations of the learners becomes more and more imperative, although the fundamental part of the Institutions is also true in addition to capturing enrollment is to retain it. and to retain it, and to achieve the above, it requires student satisfaction, so the objective of this research was to propose educational marketing as a strategy for student satisfaction at the Polytechnic University of Francisco I, Madero (UPFIN). The study has a quantitative approach of a descriptive type, it was applied to a sample of 361 UPFIN students and some results were that in order to achieve student satisfaction, variables such as: teaching videos, digital library service, linkage with microenterprises, extra classes for those who fail, overtime teaching second language, psychological and social guidance, study guide to present exams, classrooms and laboratories in good condition, plan according to the race, congresses and symposium in different careers, student involvement in research projects, friendly treatment of administrative staff, scholarships for student tickets, demonstrations and training with microentrepreneurs and the general public of what is done in the university.
CRITERIA AND INDICATORS FOR ASSESSING THE LEVEL OF PROFESSIONAL TRAINING OF FUTURE TEACHERS OF VOCATIONAL TRAINING AT A TRAINING MODULE

Now, in the education system, the main focus is given to the interests of the trainee. Orientation to the formation of a professional personality means the restructuring of the educational process from passive mastering of knowledge into the active process of forming the skills of their application in the process of life activity. In solving this problem, intensive training technologies aimed at optimizing, updating, systematizing, humanizing and comprehensively acquiring knowledge play an important role. The maximal consideration of the individual characteristics of the individual, as well as the activity of the individual in the process of obtaining professional education, come to the fore. The article is dedicated to increasing the quality of training of future teachers of vocational training; disclosed the epistemological, axiological and praxeological components; defned criteria and indicators for assessing the level of training of the future teachers of vocational education. It offers an introduction to the practice of modular technology education, promoting greater autonomy of educational work of students, ﬂexibility and multilevel.
Learning Hidden States in a Chaotic System: A Physics-Informed Echo State Network Approach

We extend the Physics-Informed Echo State Network (PI-ESN) framework to reconstruct the evolution of an unmeasured state (hidden state) in a chaotic system. The PI-ESN is trained by using (i) data, which contains no information on the unmeasured state, and (ii) the physical equations of a prototypical chaotic dynamical system. Non-noisy and noisy datasets are considered. First, it is shown that the PI-ESN can accurately reconstruct the unmeasured state. Second, the reconstruction is shown to be robust with respect to noisy data, which means that the PI-ESN acts as a denoiser. This paper opens up new possibilities for leveraging the synergy between physical knowledge and machine learning to enhance the reconstruction and prediction of unmeasured states in chaotic dynamical systems.
Parallel Hardware Implementation of Efficient Embedding Bit Rate Control Based Contrast Mapping Algorithm for Reversible Invisible Watermarking

This paper presents an improved reversible contrast mapping (RCM) algorithm for reversible invisible watermarking (RIW) in both software and hardware platforms. Based on well-known parameters for RIW like distortion, embedding bit rate, payload size and data hiding capacity, an efficient embedding bit rate control based contrast mapping (EBCRCM) algorithm is proposed. An adaptive linear contrast mapping on pixel intensity value is asserted with RCM that controls the embedding bit rate without changing the embedding capacity to maintain distortion. Xilinx system generator (XSG) and VIVADO tool construct the novel VLSI architecture that needs 173.362 ns latency for 100 MHz clock with throughput 46.146 Mbps and 5.8 ns critical path for single cycle of embedding process. The proposed algorithm is verified in MATLAB tool based software platform by taking different types of multimedia data like gray-scale images, color images and video signals. Implementation of low hardware resources based VLSI architecture through zed-board in real time field programmable gate array (FPGA) platform confirms the capability of high speed, low cost and real-time use. 100% agreement is observed from software simulations and hardware platforms.
SlideImages: A Dataset for Educational Image Classification

In the past few years, convolutional neural networks (CNNs) have achieved impressive results in computer vision tasks, which however mainly focus on photos with natural scene content. Besides, non-sensor derived images such as illustrations, data visualizations, figures, etc. are typically used to convey complex information or to explore large datasets. However, this kind of images has received little attention in computer vision. CNNs and similar techniques use large volumes of training data. Currently, many document analysis systems are trained in part on scene images due to the lack of large datasets of educational image data. In this paper, we address this issue and present SlideImages, a dataset for the task of classifying educational illustrations. SlideImages contains training data collected from various sources, e.g., Wikimedia Commons and the AI2D dataset, and test data collected from educational slides. We have reserved all the actual educational images as a test dataset in order to ensure that the approaches using this dataset generalize well to new educational images, and potentially other domains. Furthermore, we present a baseline system using a standard deep neural architecture and discuss dealing with the challenge of limited training data.
Connecting Higher-Order Separation Logic to a First-Order Outside World

Abstract Separation logic is a useful tool for proving the correctness of programs that manipulate memory, especially when the model of memory includes higher-order state: Step-indexing, predicates in the heap, and higher-order ghost state have been used to reason about function pointers, data structure invariants, and complex concurrency patterns. On the other hand, the behavior of system features (e.g., operating systems) and the external world (e.g., communication between components) is usually specified using first-order formalisms. In principle, the soundness theorem of a separation logic is its interface with first-order theorems, but the soundness theorem may implicitly make assumptions about how other components are specified, limiting its use. In this paper, we show how to extend the higher-order separation logic of the Verified Software Toolchain to interface with a first-order verified operating system, in this case CertiKOS, that mediates its interaction with the outside world. The resulting system allows us to prove the correctness of C programs in separation logic based on the semantics of system calls implemented in CertiKOS. It also demonstrates that the combination of interaction trees + CompCert memories serves well as a lingua franca to interface and compose two quite different styles of program verification.