Reviewerly/exHarmony huggingface dataset:
Deep Clustering with a Dynamic Autoencoder.

In unsupervised learning, there is no obvious straightforward loss functionwhich can capture the major factors of variations and similarities. Sincenatural systems have smooth dynamics, an opportunity is lost if an unsupervisedloss function remains static during the training process. The absence ofconcrete supervision suggests that smooth complex dynamics should be integratedas a substitute to the classical static loss functions to better make use ofthe gradual and uncertain knowledge acquired through self-supervision. In thispaper, we propose Dynamic Autoencoder (DynAE), a new model for deep clusteringthat allows to solve a clustering-reconstruction trade-off by gradually andsmoothly eliminating the reconstruction objective in favor of a constructionone while preserving the space topology. Experimental evaluations on benchmarkdatasets show that our approach achieves state-of-the-art results compared toall the other autoencoder-based clustering methods.
From mathematical axioms to mathematical rules of proof: recent developments in proof analysis

A short text in the hand of David Hilbert, discovered in Göttingen a century after it was written, shows that Hilbert had considered adding a 24th problem to his famous list of mathematical problems of the year 1900. The problem he had in mind was to find criteria for the simplicity of proofs and to develop a general theory of methods of proof in mathematics. In this paper, it is discussed to what extent proof theory has achieved the second of these aims. This article is part of the theme issue ‘The notion of ‘simple proof’ - Hilbert's 24th problem’.
A Deep Learning-Inspired Method for Social Media Satire Detection

In this paper, we put forward an effective approach of segmentation of sentiment in social media texts that may include informal language or pop culture texts. We introduce a method to churn out vector representations from phrase-level sentences. We train a recurrent neural network combining quantitative and qualitative methods with lexical features stored in gold standard array of lexicons. In this work, we extract opinion expression using deep RNNs in the form of a token-level sequence-labeling sentiment from variable length of text corpuses. Furthermore, in this paper, we have introduced a novel approach to determine whether the article is satirical or not via the combination of computational linguistics and machine learning tools. We have compared the performance of our algorithm with respect to the benchmark methods, on satire detection as well, on benchmark datasets, news articles, and social media platforms for better reflection of the experiment, and we yielded competitive and satisfactory results.
The Development of Teaching Materials with Mind Mapping Methods Literary Literacy Oriented and Character Education to Improve Understanding of Literary History

This study aims to develop teaching materials using mind mapping method oriented to literary and character education in a valid, practical, and effective Literature History course. There are three aspects that used to measure the instructional material developed, namely: 1) the content of teaching materials validity will be assessed by experts that adjusted to the supporting theory while construct validity is assessed by experts by paying attention to the relevance and suitability of the developed teaching materials; 2) practicality of the developed teaching material is measured by its implementation in lectures in class by using the instrument of questionnaire responses of students and lecturers; 3) effectiveness of instructional materials was measured using multiple choice tests. The development of this teaching material follows the procedure for product development from Plomp, namely: 1) initial investigation, 2) design, 3) realization/ construction, 4) test, evaluation, and revision; and 5) implementation. Given the time constraints, this study did not arrive at the implementation stage but only arrived at limited trials. From the results of the study, the instructional material developed has met the criteria of valid, practical, and effective. Whereas from the results of limited trials, the instructional material developed effectively increases the understanding of Literature History in PBSI semester I students in the academic year 2018-2019. This is evident by increasing of the percentage of students getting B +, A-, that is 62% in the first cycle become 80% in second cycle. It is already above the percentage that have been achieved and even exceeding the target, that is 70% of students get B +, A-, and A in the beginning of study.
Algoritma Naïve Bayes Untuk Memprediksi Kredit Macet Pada Koperasi Simpan Pinjam

Cooperatives are a forum that can help people, especially small and medium-sized communities. Cooperatives play an important role in the economic growth of the community such as the price of basic commodities which are relatively cheap and there are also cooperatives that offer borrowing and storing money for the community. Constraints that have been felt by this cooperative are that borrowers find it difficult to repay loan installments, causing bad credit. Because the cooperative in conducting credit analysis is carried out in a personal manner, namely by filling out the loan application form along with the requirements and conducting a field survey. Therefore there is a need for an evaluation to be carried out in lending to borrowers. To minimize these problems, it is necessary to detect customer criteria that are used to predict bad loans and to determine whether or not the elites are eligible to take credit using data mining. The data mining technique used is classification with the Naive Bayes method. Based on testing the accuracy of the resulting model obtained accuracy level of 59%, sensitivity (True Positive Rate (TP Rate) or Recall) of 46.80%, specificity (False Negative Rate (FN Rate or Precision) of 69.81%, Positive Predictive Value (PPV) of 57.89%, and Negative Predictive Value (NPV) of 59.67%.
OntoDI: The Methodology for Ontology Development on Data Integration

The implementations of data integration in current days have many issues to be solved.Heterogeneity of data with non-standardization data, data conflicts between various data sources, data with a different representation, as well as semantic aspects problems are among the challenges and still open to research.Semantic data integration using ontology approach is considered as an appropriate solution to deal with semantic aspects problem in data integration.However, most methodologies for ontology development are developed to cover specific purpose and less suitable for common data integration implementation.This research offers an improved methodology for ontology development on data integration to deal with semantic aspects problem, called OntoDI.It is a continuation and improvement of the previous work about ontology development methods on agent system.OntoDI consists of three main parts, namely the pre-development, core-development and postdevelopment, in which every part contains several phases.This paper describes the experiment of OntoDI in the electronic learning system domain.Using OntoDI, the development of ontology knowledge gives simpler phases, complete steps, and clear documentation for the ontology client.In addition, this ontology knowledge is also capable to overcome semantic aspect issues that happen in the sharing and integration process in education area.
A Forest from the Trees: Generation through Neighborhoods

In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors). Although human learning makes seamless use of both learned perceptual features and instance recall, current generative learning paradigms only make use of one of these two components. Take, for instance, flow models, which learn a latent space of invertible features that follow a simple distribution. Conversely, kernel density techniques use instances to shift a simple distribution into an aggregate mixture model. Here we propose multiple methods to enhance the latent space of a flow model with neighborhood information. Not only does our proposed framework represent a more human-like approach by leveraging both learned features and memories, but it may also be viewed as a step forward in non-parametric methods. The efficacy of our model is shown empirically with standard image datasets. We observe compelling results and a significant improvement over baselines.
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling

Gatherings of thousands to millions of people frequently occur for an enormous variety of events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further improves crowd counting accuracy. Our new network architecture performs favorably in comparison with the state-of-the-art. However, our labeling and upsampling techniques are generally applicable to existing crowd counting architectures.
Audiobook- the Paradigm of the Portuguese Publishing Market

Initially, the audiobook was not a product to be marketed, being produced only for libraries, in order to allow access to information for the blind.In 2005, with the appearance of the first specialized audiobook, publishers in Portugal, the audiobooks began to be produced in order to be commercialized.The audiobook market in Portugal is small, having only 2 companies: Boca palavras que alimentam e a Companhia Nacional de Música.The Portuguese companies have very low sales volumes; which do not exceed a hundred thousand euros.The paper presents the business and production models of audiobooks in USA and proposes their adaptation to Portugal.In order to obtain the degree of acceptance of this format among the Portuguese society, two recordings of a book by a renowned Portuguese writer were made.The two records were made according to a set of technical requirements in order to ensure the quality of the audiobook.The two recordings were evaluated through a questionnaire.The results of the questionnaire are presented and analysed.From the analysis of the questionnaire, it seems that the Portuguese market of audiobooks has yet to be explored.It also seems that it worth the effort because Portuguese community is very open to new technologies and easily uses the internet market.Anyhow, it seems that some marketing campaigns are needed in order to change the actual preference of the printed book face to the virtual version.
What's New on the Internetz? Extraction and Lexical Categorisation of Collocations in Computer-Mediated Slovene

The paper focuses on collocations typical of Slovene computer-mediated communication (CMC), which comprises communication via social networks, forums, blogs, etc. The study examines the CMC-specific collocates of the most frequent Slovene nouns, as well as collocates of CMC-typical nouns. Collocations were automatically extracted with procedures based on the Sketch Engine corpus tool. For the identification of CMC-specific collocations, collocates occurring in the CMC and the reference corpus were compared. In the study, the extracted data were categorised as: (a) new vocabulary in relation to existing lexicographical resources; (b) orthographically or lexically non-standard; (c) terminology; (d) topic-/genre-related. The new vocabulary was furthermore explored as per the distinction between new words, new collocations, and new meanings, where semantic shifts are of special interest. The results indicate that comparative collocation extraction provides a good framework for detecting lexical novelties and can thus provide good support for describing contemporary language use.
Let terms choose their own kernels: An intelligent approach to kernel selection for healthcare search

Proximity-based information retrieval models usually pre-define a kernel function on all documents. However, terms, especially professional terms used in healthcare, can exert various levels of influence in different positions. In this paper, we propose an intelligent approach with an elite kernel function for healthcare search. The unique contribution is that the proposed approach assigns a term with the most suitable kernel function at a position, instead of pre-defining a single kernel, such as the Gaussian kernel, over all documents. Furthermore, our approach requires no prior data, external resources, or advance training steps. We first define the elite kernel and present its existence in a kernel space. Then, we optimize the kernel by maximizing the probability using the Poisson process. Finally, we use a kernel-based weighting function to evaluate the effectiveness of the intelligent approach on three standard CLEF healthcare domain data sets and two non-healthcare domain data sets. Our approach shows great advantages in terms of both healthcare domain and non-healthcare domain data.
Image De-Noising For Salt and Pepper Noise by Introducing New Enhanced Filter

When an image is formed, factors such as lighting (spectra, source, and intensity) and camera characteristics (sensor response, lenses) affect the appearance of the image. Therefore, the prime factor that reduces the quality of the image is noise. It hides the important details and information of images. In order to enhance the qualities of the image, the removal of noises become imperative and that should not at the cost of any loss of image information. Noise removal is one of the pre-processing stages of image processing. In this paper a new method for the enhancement of grayscale images is introduced, when images are corrupted by fixed valued impulse noise (salt and pepper noise). The proposed methodology ensures a better output for the low and medium density of fixed value impulse noise as compared to the other famous filters like Standard Median Filter (SMF), Decision Based Median Filter (DBMF) and Modified Decision Based Median Filter (MDBMF) etc. The main objective of the proposed method was to improve peak signal to noise ratio (PSNR), visual perception and reduction in the blurring of the image. The proposed algorithm replaced the noisy pixel by trimmed mean value. When previous pixel values, 0s, and 255s are present in the particular window and all the pixel values are 0s and 255s then the remaining noisy pixels are replaced by mean value. The gray-scale image of mandrill and Lena were tested via the proposed method. The experimental result shows better peak signal to noise ratio (PSNR), mean square error values with better visual and human perception.
Normalization for planar string diagrams and a quadratic equivalence algorithm

In the graphical calculus of planar string diagrams, equality is generated by exchange moves, which swap the heights of adjacent vertices. We show that left- and right-handed exchanges each give strongly normalizing rewrite strategies for connected string diagrams. We use this result to give a linear-time solution to the equivalence problem in the connected case, and a quadratic solution in the general case. We also give a stronger proof of the Joyal-Street coherence theorem, settling Selinger's conjecture on recumbent isotopy.
An Efficient Identity-Based Homomorphic Broadcast Encryption

Broadcast encryption (BE) allows a sender to broadcast its message to a set of receivers in a single ciphertext.However, in broadcast encryption scheme, ciphertext length is always related to the size of the receiver set.Thus, how to improve the communication of broadcast encryption is a big issue.In this paper, we proposed an identity-based homomorphic broadcast encryption scheme which supports an external entity to directly calculate ciphertexts and get a new ciphertext which is the corresponding result of the operation on plaintexts without decrypting them.The correctness and security proofs of our scheme were formally proved.Finally, we implemented our scheme in a simulation environment and the experiment results showed that our scheme is efficient for practical applications.
Developing Foreign Language Regional Competence of Future Foreign Language Teachers: Modeling of the Process

The article considers modeling of the process aimed at developing the future foreign language teacher regional competence. It is noted that modeling is one of the practice-oriented methods for description and generalization of research results. The process of model development has revealed a number of contradictions in the systems of secondary and higher education. The structure of the regional competence development model comprises the following linked components: theoretical-methodological, purpose, contentful, process-operational, and evaluative-result. Methodology of the obtained model is based on the intercultural, regional, competence, professionally-oriented, and personal-activity approaches. The process of the experimental verification of the developed model has revealed a complex of professional knowledge and pedagogical skills that have a direct link with the foreign language learning process in the context of intercultural communication at the regional level. The experiment is conducted by employing experimental and control groups of students, as well as invariant and varied learning conditions. The result of the study is the assessment of the regional competence of future foreign language teachers in terms of six criteria. The research outcomes prove the purposefulness of incorporating materials with a regional component of native and foreign-speaking society in the process of foreign-language training of future foreign language teachers.
$$\mathsf {ELSA}$$ : Efficient Long-Term Secure Storage of Large Datasets

An increasing amount of information today is generated, exchanged, and stored digitally. This also includes long-lived and highly sensitive information (e.g., electronic health records, governmental documents) whose integrity and confidentiality must be protected over decades or even centuries. While there is a vast amount of cryptography-based data protection schemes, only few are designed for long-term protection. Recently, Braun et al. (AsiaCCS’17) proposed the first long-term protection scheme that provides renewable integrity protection and information-theoretic confidentiality protection. However, computation and storage costs of their scheme increase significantly with the number of stored data items. As a result, their scheme appears suitable only for protecting databases with a small number of relatively large data items, but unsuitable for databases that hold a large number of relatively small data items (e.g., medical record databases). In this work, we present a solution for efficient long-term integrity and confidentiality protection of large datasets consisting of relatively small data items. First, we construct a renewable vector commitment scheme that is information-theoretically hiding under selective decommitment. We then combine this scheme with renewable timestamps and information-theoretically secure secret sharing. The resulting solution requires only a single timestamp for protecting a dataset while the state of the art requires a number of timestamps linear in the number of data items. We implemented our solution and measured its performance in a scenario where 12 000 data items are aggregated, stored, protected, and verified over a time span of 100 years. Our measurements show that our new solution completes this evaluation scenario an order of magnitude faster than the state of the art.
‘Stop: think forensic’ – developing a framework for contamination minimization for police and forensic staff

The Victoria Police Forensic Services Department (VPFSD) has developed a single framework for both forensic science staff and operational police to cover procedures and training in Contamination Minimization.
A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine

Unlike other languages, the Arabic language has a morphological complexity which makes the Arabic sentiment analysis is a challenging task. Moreover, the presence of the dialects in the Arabic texts have made the sentiment analysis task is more challenging, due to the absence of specific rules that govern the writing or speaking system. Generally, one of the problems of sentiment analysis is the high dimensionality of the feature vector. To resolve this problem, many feature selection methods have been proposed. In contrast to the dialectal Arabic language, these selection methods have been investigated widely for the English language. This work investigated the effect of feature selection methods and their combinations on dialectal Arabic sentiment classification. The feature selection methods are Information Gain (IG), Correlation, Support Vector Machine (SVM), Gini Index (GI), and Chi-Square. A number of experiments were carried out on dialectical Jordanian reviews with using an SVM classifier. Furthermore, the effect of different term weighting schemes, stemmers, stop words removal, and feature models on the performance were investigated. The experimental results showed that the best performance of the SVM classifier was obtained after the SVM and correlation feature selection methods had been combined with the uni-gram model.
Reinforcement Learning Without Backpropagation or a Clock

Coagent policy gradient algorithms (CPGAs) are reinforcement learning algorithms for training a class of stochastic neural networks called coagent networks. In this work, we prove that CPGAs converge to locally optimal policies. Additionally, we extend prior theory to encompass asynchronous and recurrent coagent networks. These extensions facilitate the straightforward design and analysis of hierarchical reinforcement learning algorithms like the option-critic, and eliminate the need for complex derivations of customized learning rules for these algorithms.
Hierarchical Design of Highway Merging Controller Using Navigation Vector Fields Under Bounded Sensing Uncertainty

This paper presents a hierarchical control design for the motion of an autonomous car (ego-vehicle) through traffic on a highway. The ego-vehicle is assumed to sense the position and speed of the surrounding cars with bounded errors, and its objective is to move safely among traffic. The design is composed of a low-level tracking controller and a high-level decision-making process: the low-level controller is based on a navigation vector field and a velocity controller that safely drive the ego-car to selected merging points. The merging points are decided based upon a cost function capturing the traffic conditions. Simulation results demonstrate the efficacy of the proposed algorithm.
HTML5 Based Ajax and SSE Technology with Server Intelligent Instant Messaging to Meet the Needs of Transactional Websites

With the continuous development of science and technology, Internet technology has been quietly affect the mode of transmission, traditional web site information data only through the web page to load when it is open, need to update the web page information can only be accomplished by way of a page refresh. In today's efficient informatization environment, has highlighted its problems, restricted information updated timely. In this paper, the new Internet programming language of HTML5 is combined with Ajax and SSE technology to study intelligent instant messaging and realize the new application mode.