Reviewerly/exHarmony huggingface dataset:
Uncertainty principle and sparse reconstruction in pairs of orthonormal rational function bases

This paper presents theoretical results on the uncertainty principle and the sparse reconstruction of rational transfer functions in a dictionary of two orthonormal rational function (ORF) bases. The uncertainty principle concerning pairs of representations of rational transfer functions in different ORF bases is established. It is shown that a rational transfer function cannot have a sparse representation simultaneously in two different mutually incoherent ORF bases. The uniqueness for the sparse representation is derived as a direct consequence of this uncertainty principle. A reconstruction method for a rational transfer function in a pair of ORF bases is proposed. The sparse reconstruction result shows that, given a rational transfer function with a sufficiently sparse representation in a given dictionary of two ORF bases, the sparse representation can be recovered by solving a linear programming problem. A lower bound is provided on the number of frequency response measurements required to recover the sparse representation with high probability.
From COBOL to Business Rules—Extracting Business Rules from Legacy Code

The reverse engineering project described in this paper is aimed at documenting a 6.4 million lines of code COBOL/IMS/DB2 system for world-wide car leasing. The ultimate goal is to re-implement that system. The system was originally developed in the 1980s with less than 3 million code lines and has since evolved to its current size. It survived the year 2000 date change and the Euro conversion as well as several major company reorganizations to preserve the continuity of the leasing service. Finally, after 30 years of service it is planned to retire the system. However, the first two attemptsSneed, Harry M. to replace it, one by automatically converting it and the other by replacing it with a standard package ended in failure. It is now planned to rewrite the system based on a specification derivedVerhoef, Chris from the current code base. That specification includes among other documents a documentation of the processing rules. The extracted rules are intended to act as guide to those writing the new code.
PHENOMENAL AND INTENTIONAL ASPECTS OF CONSCIOUSNESS

The article discusses the phenomenal and intentional aspects of consciousness. The problem of phenomenal consciousness existence in the physical Universe is stated, since the emergence of non-intentional content contradicts the principle of the epistemological completeness of physics. In this regard, the concepts of representationalism and separatism are compared. Arguments are adduced in favour of the separatist point of view, which asserts the heterogeneous nature of mental experience. It is demonstrated that, firstly, one and the same intentional object can be represented by different phenomenal content and, secondly, one and the same phenomenal content can represent different intentional objects. Hence, the conclusion is made about the non-reducibility of the phenomenal aspect of consciousness to the intentional one and, as a consequence, about the falsity of physicalist ontology.
Fitness-Based Linkage Learning in the Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm

The recently introduced real-valued gene-pool optimal mixing evolutionary algorithm (RV-GOMEA) has been shown to be among the state of the art for solving gray-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem, which often is unknown a priori and has to be learned online. Previously published work on RV-GOMEA, however, demonstrated excellent scalability when the linkage structure is prespecified appropriately. A mutual information-based metric to learn linkage structure online, as commonly adopted in EDA's and the original discrete version of the gene-pool optimal mixing evolutionary algorithm, did not lead to similarly excellent results, especially in a black-box setting. In this article, the strengths of RV-GOMEA are combined with a new fitness-based linkage learning approach that is inspired by differential grouping that reduces its computational overhead by an order of magnitude for problems with fewer interactions. The resulting new version of RV-GOMEA achieves scalability similar to when a predefined linkage model is used, outperforming also, for the first time, the EDA AMaLGaM upon which it is partially based in a black-box setting where partial evaluations cannot be leveraged.
The Use of Nouns in Exhibition Texts at the Islamic Arts Museum Malaysia

Exhibition texts are commonly found alongside exhibitions in a museum. The texts are there to serve specific purposes in the overall context of the museum, and the linguistic features that are found within the texts are assumed to be functional and serve the respective purposes of the museum. This paper investigates the use of one of the linguistic features, i.e nouns, in the texts that accompany displays at the Islamic Arts Museum Malaysia. The nouns are examined in terms of their frequency of use and the extend in which the nouns function in terms of serving the purposes of exhibition texts in the museum context. The findings reveal that nouns pertaining to locations and artefacts are the most frequently used, and they reflect the context of the studied museum which is Islamic history and civilisation. In addition, different categories of nouns, such as names of people, are also found to serve the communicative purpose of the exhibition texts. This study contributes to the linguistic description of the discourse of art and explains the relations between text, museum exhibits and visitors. It may benefit linguistic scholars and practitioners as the findings also suggest that the use of linguistic features can attract visitors to look at the exhibits.
Workplace Document Management System Employing Cloud Computing and Social Technology

This study is focused on implementing a document management system for Romblon State University, Cajidiocan Campus, Romblon, Philippines. To date, most Higher Education Institution like the Romblon State University generates the bulk of documents of several types like feedback reports, minutes of the meeting, syllabi, lesson plans, policies, memos, and other circulars. These documents are often stored in filing cabinets and are used as compiled evidence for accomplishment reports or accreditation purposes. With such, the school is faced with problems like storage space, document security and sharing of documents when needed. For such reasons, this study will support the facilitation of easy access to documents using social technology across the academic organization as well as providing a larger storage space through cloud computing. Furthermore, the study was designed to 1] track, manage and store documents digitally, 2] to keep records of various versions which were created or revised by different users and 3] to employ social technology to increase staff collaboration skills and work efficiency. To construct the study, the researchers made use of several fact-finding techniques and an Agile methodology. It used a total population of purposive sampling to attain 100% confidence in evaluating the performance of the system through user-acceptance testing. Through evaluation, the respondents scored the developed system with a mean of 4.94, which exhibits that the respondents strongly agree with the capabilities of the developed system and that it is responsive to their needs. It was then concluded that the study was able to allow users to manage their documents socially and provide larger space of storage.
Publisher Correction: Cortical encoding of speech enhances task-relevant acoustic information

An amendment to this paper has been published and can be accessed via a link at the top of the paper. The original and corrected figures are shown in the accompanying Publisher Correction.
CLASSIFICATION OF CHILDREN SHORT FILMS FOR MOBILE MOVIE SCREENING BY BIOSCIL

Access to movie is limited for some part of the society due to minimum amount of cinema in remote area or suburban.This limited access is worsen by the limitation of available children films.BIOSCIL (Bioskop Kecil Keliling), a community based organization, concerns about the situation and responds to it.BIOSCIL conducts mobile movie screening for children in remote areas in Yogyakarta.So far, they have at least visited 20 locations in five years.Together with several local movie makers, BIOSCIL tries to collect movies that can be watched by their audience in remote area.However, the limited amount of children movies brings new problem to BIOSCIL because not every movie in their inventory matches their audience in each screening.Thus, it is important to make movie classification for BIOSCIL based on their audience age group, to help them choose the right movie for each screening and audience.This is a participatory action research using FGD, documentary, and participatory observation for data collection.After working together with BIOSCIL for over a year, this research has successfully defined a movie classification for BIOSCIL to serve as a guideline for BIOSCIL to decide the right movie for their audience in each screening.
An Adaptive Consensus Reaching Process Dealing with Comparative Linguistic Expressions in Large-scale Group Decision Making

Nowadays, society often faces decision problems under uncertainty that can hardly be managed by a single expert or a few of them because of their complexity.Under these conditions, large-scale group decision making (LS-GDM) problems are becoming more and more common.The decisions made on these types of problems might affect directly lots of people in which the consensual decisions are better accepted and consensus reaching processes (CRPs) support reaching such consensus.LS-GDM under uncertainty has been solved by using linguistic information but considering only single linguistic terms to represent experts' opinions.Especially in large-scale, this is an important drawback, since the complexity of the problems causes the apparition of experts' hesitancy, which cannot be modeled by single linguistic terms.Concretely, comparative linguistic expression (CLEs) based on hesitant fuzzy linguistic term sets have provided remarkable results in hesitancy modeling.Therefore, this contribution aims at defining a novel adaptive CRP for LS-GDM in which experts' preferences are modeled by CLEs.
Introduction to the Symposium on Blockchain Regulation and Governance

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Improving Pre-Trained Multilingual Models with Vocabulary Expansion

Recently, pre-trained language models have achieved remarkable success in a broad range of natural language processing tasks. However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language. Instead of exhaustively pre-training monolingual language models independently, an alternative solution is to pre-train a powerful multilingual deep language model over large-scale corpora in hundreds of languages. However, the vocabulary size for each language in such a model is relatively small, especially for low-resource languages. This limitation inevitably hinders the performance of these multilingual models on tasks such as sequence labeling, wherein in-depth token-level or sentence-level understanding is essential. In this paper, inspired by previous methods designed for monolingual settings, we investigate two approaches (i.e., joint mapping and mixture mapping) based on a pre-trained multilingual model BERT for addressing the out-of-vocabulary (OOV) problem on a variety of tasks, including part-of-speech tagging, named entity recognition, machine translation quality estimation, and machine reading comprehension. Experimental results show that using mixture mapping is more promising. To the best of our knowledge, this is the first work that attempts to address and discuss the OOV issue in multilingual settings.
Software Development Methods – Properties and Advances

It has taken a period of time for evolution of the field of software development.In order to promote its effectiveness and efficiency propositions of various models and methods has been done.In this paper we are going to discuss the various models and methods, while identifying the latest industry trends and also discuss their impacts..
Feedback-based Self-improving CNN Algorithm for Breast Cancer Lymph Node Metastasis Detection in Real Clinical Environment

Digital pathology can be thought of as a model composed of 3 main elements; classification algorithm, Graphical User Interface (GUI) and the pathologists. Currently there is only a one way interaction from the classification algorithm to the pathologist. This paper, proposes an additional backward path which is a new feedback-based method, aimed to improve the performance of the classification algorithms by utilizing the feedback of the pathologists. The GUI developed for this purpose, is aimed to be simple and adaptive to different classification algorithms. The method showed significant improvement in the classification performance of the applied Convolutional Neural Network (CNN) algorithm. The 25% quantile of the probability score of the predictions increased from 0.48 to 0.89 and the median of the data increased from 0.95 to 0.99.
The Use of Reinforcement Learning in the Task of Moving Objects with the Robotic Arm

The article describes the task of controlling a robotic arm to transfer objects in front of it. To select actions, the reinforcement learning algorithm is used. In conclusion, there are presented the results of experiments in the Gazebo simulation environment with two different inputs: either with information about the position of the hand and the object, or with information about the position of the hand and the image with the camera.
Comparing the Features of the Diffuse Alpha Pattern with the Normal Alpha Pattern using Wavelet-crosscorrelation Analysis

In some individuals, alpha waves appear in many regions of the brain, and this is considered abnormal electroencephalography (EEG). This phenomenon is known as diffuse alpha pattern. This study aims to extract and compare the features of the connectivity between the parts of the brain. The EEGs of healthy individuals and mental disorder patients with the diffuse alpha pattern were analyzed using wavelet-crosscorrelation analysis. Five epochs of 2 seconds were analyzed for each subject. Wavelet-crosscorrelation coefficients were calculated for frequencies in the alpha band for all epochs in each subject. The results showed that the WCC values were higher in the patients with diffuse alpha. The highest wavelet power spectra in the alpha band of the patients occurred at a lower frequency. Our results suggest that the connectivity strength along the sagittal and occipital orientations in the brain of the patients could be stronger than healthy individuals.
UCSYNLP-Lab Machine Translation Systems for WAT 2019

This paper describes the UCSYNLP-Lab submission to WAT 2019 for Myanmar-English translation tasks in both direction. We have used the neural machine translation systems with attention model and utilized the UCSY-corpus and ALT corpus. In NMT with attention model, we use the word segmentation level as well as syllable segmentation level. Especially, we made the UCSY-corpus to be cleaned in WAT 2019. Therefore, the UCSY corpus for WAT 2019 is not identical to those used in WAT 2018. Experiments show that the translation systems can produce the substantial improvements.
[Paper] Disparity Compensation Framework for Light-Field Coding Using Weighted Binary Patterns

We propose a disparity compensation framework for efficient light-field coding. We have proposed a novel lightfield coding scheme of approximating a light field with the sum of weighted binary patterns. This coding scheme achieves comparable performance to the modern video coding standards H.265/HEVC and enables a dramatically simple decoding process, but its computational complexity for encoding is quite high. We have also proposed a progressive coding scheme that progressively encodes the target light field with a small number of weighted binary patterns in a step-by-step manner. The progressive scheme remarkably mitigates the computational complexity, but its rate-distortion performance is degraded. To address the above problems, we design a disparity compensation framework, which can be applied to the existing progressive coding, to improve the rate-distortion performance while keeping feasible computational complexity. Experimental results demonstrate that the proposed method improves the rate-distortion performance of the progressive coding without a computational complexity explosion.
Single Image De-raining Using GAN for Accurate Video Surveillance

Video surveillance is a very crucial area of research. The accuracy is the basic criterion of this type of research works because the video surveillance system ensures security issues. A less accurate surveillance system may lead to mass causalities in recent terrorism threatening the world. However, the surveillance cameras are installed mainly in open areas. Therefore, the acquired data can be suffered from environmental stimuli. The degraded data lowers the performance of the surveillance system. It is needed to restore the surveillance data for accurate analysis. In this paper, we concentrate on video surveillance data affected by rain. In this work, we de-rain degraded rainy surveillance video data using a Generative Adversarial Network (GAN) model. The proposed model performs better than other types of CNN based de-raining algorithm.
On the reliability of computational chaos-based cryptography for information exchange

This paper investigates the use of dynamical chaotic systems to encrypt and exchange images between different devices.Two devices were used to simulate the Cubic Map, having the same set of initial conditions, to generate an encryption key.Although both devices are floatingpoint compliant, the simulations, and consequently the encryption key, turned out to differ from one another.This indicates that many existing chaos-based encryption schemes are just special cases of computational arithmetic properties, in which some characteristics in the construction of the devices coincided.A method to mitigate such flaw was also presented.
On the Detection of Malicious Behaviors against Introspection Using Hardware Architectural Events

The arms race between offense and defense in the cloud impels the innovation of techniques for monitoring attacks and unauthorized activities. The promising technique of virtual machine introspection (VMI) becomes prevalent for its tamper-resistant capability. However, some elaborate exploitations are capable of invalidating VMI-based tools by breaking the assumption of a trusted guest kernel. To achieve a more reliable and robust introspection, we introduce a practical approach to monitor and detect attacks that attempt to subvert VMI in this paper. Our approach combines supervised machine learning and hardware architectural events to identify those malicious behaviors which are targeted at VMI techniques. To demonstrate the feasibility, we implement a prototype named HyperMon on the Xen hypervisor. The results of our evaluation show the effectiveness of HyperMon in detecting malicious behaviors with an average accuracy of 90.51% (AUC).
Need analysis for the development of a microcontroller instructional module programming literacy

In the era of the IR 4.0, the use of information technology among school students is widespread but students are not proficient in computer programming. To compete in the digital world, students need to be exposed to computer programming in order to produce computer programming experts. Integrating computer programming into the school curriculum can improve students literacy of computer programming but adequate computer programming skill among teachers are quite limited. Therefore, the development of microcontroller instructional teaching module which could address this problem is needed. This development aims to develop the module using design and developmental research (DDR) approach. Need Analysis phase in DDR is discussed in this article. The phase consists of identifying the level of knowledge, attitudes and practices of teachers about microcontroller and to obtain the views and opinions of the teachers on the developmental needs of microcontroller teaching modules. The type of microcontroller and the programming language to be used in the microcontroller module also identified.The results of this study are important to ensure that the design and development of an instructional module for microcontroller education are implemented and have a positive impact on increasing the programming literacy level among secondary school children.
Reversible fingerprinting for genomic information

New genome sequencing technologies have simplified the generation of genomic data, making them more common but in turn a likely target of attack. Security strategies have been devised such as restricting the amount of information that can be queried or using new encryption techniques. These solutions might not be enough if the entire file has to be shared, as the recipient might leak the accessible information. This contribution addresses this issue using watermarking. Each read in a genomic file is modified depending on its content and a secret key. This allows generating different watermarked instances of the original file. Each watermark acts as a fingerprint: if a leak occurs, the unique modifications of the instance points to who originated the unauthorized publication. Using the key, the modifications can be undone. This allows sharing a leak-discouraging version with which the relevance of a file can be assessed, and can be reversed to the original if needed.
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild

We propose to improve text recognition from a new perspective by separating the text content from complex backgrounds. As vanilla GANs are not sufficiently robust to generate sequence-like characters in natural images, we propose an adversarial learning framework for the generation and recognition of multiple characters in an image. The proposed framework consists of an attention-based recognizer and a generative adversarial architecture. Furthermore, to tackle the issue of lacking paired training samples, we design an interactive joint training scheme, which shares attention masks from the recognizer to the discriminator, and enables the discriminator to extract the features of each character for further adversarial training. Benefiting from the character-level adversarial training, our framework requires only unpaired simple data for style supervision. Each target style sample containing only one randomly chosen character can be simply synthesized online during the training. This is significant as the training does not require costly paired samples or character-level annotations. Thus, only the input images and corresponding text labels are needed. In addition to the style normalization of the backgrounds, we refine character patterns to ease the recognition task. A feedback mechanism is proposed to bridge the gap between the discriminator and the recognizer. Therefore, the discriminator can guide the generator according to the confusion of the recognizer, so that the generated patterns are clearer for recognition. Experiments on various benchmarks, including both regular and irregular text, demonstrate that our method significantly reduces the difficulty of recognition. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.