Reviewerly/exHarmony huggingface dataset:
Broker-Insights: An Interactive and Visual Recommendation System for Insurance Brokerage

The black box nature of the recommendation systems limits the understanding and acceptance of the recommendation received by the user. In contrast, user interaction and information visualization play a key role in addressing these drawbacks. In the brokerage domain, insurance brokers offer, negotiate and sell insurance products for their customers. Support brokers into the recommendation process can improve the loyalty, profit, and marketing campaign in their client portfolio. This work presents Broker-Insights, an interactive and visualisation-based insurance products recommender system to support brokers into the decision-making (recommendation) at two levels: recommendations for a specific potential customer; and recommendations for a group of customers. Looking for offering personalized recommendations, Broker-Insights provides a tool to manage customers information in the recommendation task and a module to perform customers segmentation based on specific characteristics. With the help of an eye-tracker, we evaluated Broker-Insigths usability with ten naive users on the offline fashion and also performed an evaluation in the wild with three insurance brokers. Results achieved show that data mining methods, while combined with interactive data visualization improved the user experience and decision-making process into the recommendation task, and increased the products recommendation acceptance.
Solution of Multiple Travelling Salesman Problem using Particle Swarm Optimization based Algorithms

Nowadays, the systems that are inspired by biological structures have gained importance and attracted the attention of researchers. The Multiple Travelling Salesman Problem (MTSP) is an extended version of the TSP. The aim in the MTSP is to find the tours for m salesmen, who all start and end at the depot, such that each intermediate node is visited exactly once and the total cost of visiting nodes is minimized. The Particle Swarm Optimization (PSO) algorithm which is a meta-heuristic algorithm based on the social behaviour of birds. In this article, 2 algorithms based on PSO, called APSO and HAPSO, were proposed to solve the MTSP. The APSO algorithm is based on the PSO and 2-opt algorithms, the path-relink and swap operators. While the HAPSO algorithm is based on the GRASP, PSO and 2-opt algorithms, the path-relink and swap operators. In the experiments, 5 TSP instances are used and the algorithms are compared with the GA and ACO algorithms. According to the results, the HAPSO algorithm has the better performance than the other algorithms on the most instances. Moreover the HAPSO algorithm produces more stable results than the APSO algorithm and the performance of the HAPSO algorithm is better in all the MTSP instances. Therefore, the HAPSO algorithm is more robust than the APSO algorithm.
Problems of ontological analysis use for knowledge representation in Wiki-resources

The main direction of the Wiki-technologies use for development of information resources of large volume and complex structure, focused on functioning in the open Web environment is analyzed. We substantiate the demand for intellectualization of the Wiki technology and compare various approaches for its semantic expansion. Such evaluation shows the expediency of the Semantic MediaWiki use to obtain the necessary expressive power to represent the semantics of the main elements of a Wiki resource, their properties and the relations between them. The relations between the representation of knowledge in the Semantic MediaWiki and the standards of the Semantic Web, which are used for integration of the Web-oriented intelligent software and knowledge bases are considered. The examples deal with the online version of the Big Ukrainian Encyclopedia (e-VUE) demonstrate the feasibility of us of ontological analysis and development of ontological model for the formal and unambiguous representatio of the structure and content of the knowledge base of the complex Wiki-resource. Methods and means of application of this ontological model for development of e-VUE content and more efficient retrieval and navigation in this information resource are proposed.
Information-theoretic aspects of the generalized amplitude damping channel

The generalized amplitude-damping channel (GADC) is one of the sources of noise in superconducting-circuit-based quantum computing. It can be viewed as the qubit analog of the bosonic thermal channel, and it thus can be used to model lossy processes in the presence of background noise for low-temperature systems. In this work, we provide an information-theoretic study of the GADC. We first determine the parameter range for which the GADC is entanglement breaking and the range for which it is antidegradable. We then establish several upper bounds on its classical, quantum, and private capacities. These bounds are based on data-processing inequalities and the uniform continuity of information-theoretic quantities, as well as other techniques. Our upper bounds on the quantum capacity of the GADC are tighter than the known upper bound reported recently in Rosati et al., [Nat. Commun. 9, 4339 (2018)] for the entire parameter range of the GADC, thus reducing the gap between the lower and upper bounds. We also establish upper bounds on the two-way assisted quantum and private capacities of the GADC. These bounds are based on the squashed entanglement, and they are established by constructing particular squashing channels. We compare these bounds with the max-Rains information bound, the mutual information bound, and another bound based on approximate covariance. For all capacities considered, we find that a large variety of techniques are useful in establishing bounds.
Flipped on its Head: Deep Learning-Based Saliency Finds Asymmetry in the Opposite Direction Expected for Singleton Search of Flipped and Canonical Targets

A search asymmetry occurs when it is faster for an observer to find some search target A amongst a set of distractors B than when searching for a target B amongst a set of A distractors. A number of search asymmetries are well-established in humans, but the phenomenon is less well studied by research into computational saliency models. Nevertheless, if these algorithsm are truly representing a component of early human visual attention, they should also be able to account for aspects of human attention beyond simply testing prediction performance on free-viewing fixation datasets (Bruce et al., 2015). Leveraging the recently developed Saliency Model Implementation Library for Experimental Research (SMILER), we devise a set of visual search arrays and test whether learned models of saliency exhibit an asymmetry of performance for targets with a novel flipped orientation over canonically oriented targets. Our findings show that the deep learning approach to computational saliency modelling which currently dominates the field consistently displays an asymmetric preference for canonically oriented stimuli. This asymmetry in performance runs counter to the behavioural performance patterns expected of human subjects, and suggests that the pattern-matching nature of deep learning is insufficient to fully account for human judgements of target salience.
Adivinanzas en el aula de ELE: literatura oral, patrimonio e innovación educativa

espanolEste articulo pretende abordar la ensenanza del espanol como lengua extranjera (ELE) valiendonos de textos de la tradicion popular, por su caracter mas cercano a la oralidad, enmarcandolo en el aprendizaje por competencias y, mas en concreto, en la competencia comunicativa que aparece referenciada en las normativas oficiales. Se propone una recuperacion de estos textos patrimoniales para el conocimiento/reconocimiento del patrimonio cultural y, ademas, como base para la escritura creativa, a traves de las tecnologias disponibles en el aula, de textos similares originales con los que conformar una fono/videoteca de aula o centro. EnglishThe purpose of this paper is to address the Teaching of Spanish as a Foreign Language (TSFL) through the use of short traditional or popular texts. These are closer in character to spoken language, thus providing an opportunity to implement a competence-based approach to learning -namely through communicative competence, which is at the centre of standard syllabi today. These popular texts are presented here as a tool for teaching cultural heritage as well as being an optimal base for creative writing workshops. All of which is carried out through the use of new classroom technologies and other, similar texts with which to create an educational audiovisual catalogue for the classroom and/or the school.
HLS Accelerated Noise Reduction Approach Using Image Stacking on Xilinx PYNQ

Eliminating noise from the original signal is still a demanding problem for researchers. There has been a number of published algorithms and each approach has its limitations, advantages, and constraints. Here we present our own contribution on the noise reduction problem by proposing an intermediary step between the image sensor and the post-processing software in the image capturing process. Our accelerated method, compared to other researchers, can be used either as a standalone solution or combined with an existing algorithm to further results improvement. Our solution matches the discrete samples between multiple frames and averages the pixel values. The output image maintains its structural integrity, holds better color accuracy and incurs less noise than others.
SOCIAL MEDIA, HATE SPEECH AND CONFLICT: INTERPLAY OF INFLUENCES

Given the pronouncement of a new bill by the Nigerian senate to douse the increasing hate speech that results in conflict occasionally fuelled by the social media, this work aims at examining the interplay between hate speech, social media and conflict in the society. The design adopted in the study is the Critical Discourse Analyses (CDA). Fifty-three textual documents downloaded from the social media comprising speeches made by some Nigerian personalities were analyzed. Hinged on the spiral of silence theory, the study considers the effect of  hate speech on the formation of public opinion by some people especially those who form their opinions from the dominant view.  While the study found the existence of hate contents on the social media, the extant literature shows that measuring hate speech requires knowing the hate words or hate targets priori and that the description of hate speech tends to be wide, sometimes extending to embody words that are insulting of those in power or minority groups, or demeaning of individuals who are particularly visible in the society. As the study also revealed, while hate speech may be prone to manipulation at critical times such as during election campaigns, accusations of promoting hate speech may be traded among political opponents or used by those in power to curb opposition and criticism, suggesting the need for intermediaries to advance the fight against hate speech because of the tendency of negative opinion formation by those exposed to hate messages given that some efforts are motivated by the impulse to ban hate speech as it can provoke pain, distress, fear, embarrassment and isolation to individuals.
Class Indistinguishability for Outsourcing Equality Conjunction Search

Searchable symmetric encryption (SSE) enables a remote cloud server to answer queries directly over encrypted data on a client’s behalf, therefore, relieves the resource limited client from complicated data management tasks. Two key requirements are a strong security guarantee and a sub-linear search performance. The bucketization approach in the literature addresses these requirements at the expense of downloading many false positives or requiring the client to search relevant bucket ids locally, which limits the applicability of the method. In this paper, we propose a novel approach CLASS to meet these requirements for equality conjunction search while minimizing the client work and communication cost. First, we generalize the standard ciphertext indistinguishability to partitioned data, called class indistinguishability, which provides a level of ciphertext indistinguishability similar to that of bucketization but allows the cloud server to perform search of relevant data and filtering of false positives. We present a construction achieving these goals through a two-phase search algorithm for a query. The first phase finds a candidate set through a sub-linear search. The second phase finds the exact query result using a linear search applied to the candidate set. Both phases are performed by the server and are implemented by plugging in existing search methods. The experiment results on large real-world data sets show that our approach outperforms the state-of-the-art.
Extracting Global Entities Information from News

Author(s): Xia, Chen | Advisor(s): Chang, Kai-Wei | Abstract: There is a ton of news generated every day. However, it is more than anyone can analyze. Since reading every single news is impossible, presenting key information extracted from news can highly improve the efficiency for accessing massive knowledge pieces. And people nowadays analyze news by applying various kind of natural language processing technique both of linguistic approach and machine learning approach. Different methods can interpret news in different ways. Various natural language processing approaches, such as semantic role labeling and coreference resolution, have been applied in information extraction and we applied the technique for analyzing news. We extracted global entities information from news and presented a demo to visualize different aspects of news data. To better extract key information from massive news, we focused on semantic roles in the news data, and the relationship between them in the news flow.
Adaptive Refinements of Pitch Tracking and HNR Estimation within a Vocoder for Statistical Parametric Speech Synthesis

Recent studies in text-to-speech synthesis have shown the benefit of using a continuous pitch estimate; one that interpolates fundamental frequency (F0) even when voicing is not present. However, continuous F0 is still sensitive to additive noise in speech signals and suffers from short-term errors (when it changes rather quickly over time). To alleviate these issues, three adaptive techniques have been developed in this article for achieving a robust and accurate F0: (1) we weight the pitch estimates with state noise covariance using adaptive Kalman-filter framework, (2) we iteratively apply a time axis warping on the input frame signal, (3) we optimize all F0 candidates using an instantaneous-frequency-based approach. Additionally, the second goal of this study is to introduce an extension of a novel continuous-based speech synthesis system (i.e., in which all parameters are continuous). We propose adding a new excitation parameter named Harmonic-to-Noise Ratio (HNR) to the voiced and unvoiced components to indicate the degree of voicing in the excitation and to reduce the influence of buzziness caused by the vocoder. Results based on objective and perceptual tests demonstrate that the voice built with the proposed framework gives state-of-the-art speech synthesis performance while outperforming the previous baseline.
Web-based Application of Agricultural Management Development

Abstract This paper discusses the web application of the agricultural sector, especially food crops and horticulture which is one sector that drives the wheels of regional economy and the income of the people of North Aceh, Aceh Province, Indonesia. Some agricultural products and food crops that are the leading products in North Aceh Regency are peanuts, long beans, mango, lime, nutmeg and candlenut. The North Aceh regional government does not yet have a web-based application to manage superior products in the agricultural sector. The mechanism used in designing and creating web-based applications starts from designing, application coding and application testing. After testing the web application that has been built with black box and white box testing to determine the behavior and robustness of the agricultural web application. The results showed that the accuracy of agricultural web-based application reaching of 93.4%. The application of web-based agricultural management development has been running as it should for mapping superior agricultural commodities and potential agricultural locations in the North Aceh region.
The revolving door for AI and pathologists—docendo discimus?

Dietz and Pantanowitz (1) present a well-explained and informative viewpoint on the history, theory, and science behind, as well as, current and potential future uses and challenges of, artificial intelligence (AI) and machine learning (ML), for pathology. They emphasize the importance of development of a “killer suite” of AI applications whose use is evidence-based, that will accelerate acceptance and integration of digital pathology (DP) into diagnostic practice. This is an invited reflection on their editorial content with reference to findings from other groups.
Learning Transferable Feature Representations Using Neural Networks

Learning representations such that the source and target distributions appear as similar as possible has benefited transfer learning tasks across several applications. Generally it requires labeled data from the source and only unlabeled data from the target to learn such representations. While these representations act like a bridge to transfer knowledge learned in the source to the target; they may lead to negative transfer when the source specific characteristics detract their ability to represent the target data. We present a novel neural network architecture to simultaneously learn a two-part representation which is based on the principle of segregating source specific representation from the common representation. The first part captures the source specific characteristics while the second part captures the truly common representation. Our architecture optimizes an objective function which acts adversarial for the source specific part if it contributes towards the cross-domain learning. We empirically show that two parts of the representation, in different arrangements, outperforms existing learning algorithms on the source learning as well as cross-domain tasks on multiple datasets.
Randomized Nyström Features for Fast Regression: An Error Analysis

We consider the problem of fast approximate kernel regression. Since kernels can map input features into the infinite dimensional space, kernel trick is used to make the algorithms tractable. However on large data set time complexity of $$O(n^2)$$ is prohibitive. Therefore, various approximation methods are employed, such as randomization. A Nyström method (based on a random selection of columns) is usually employed. Main advantage of this algorithm is its time complexity which is reduced to $$O(n m^2 + m^3)$$ . Space complexity is also reduced to O(nm) because it does not require the computation of the entire matrix. An arbitrary number $$m \ll n$$ represents both the size of a random subset of an input set and the dimension of random feature vectors. A Nyström method can be extended with the randomized SVD so that l (where $$l > m$$ ) randomly selected columns of a kernel matrix without replacement are used for a construction of m-dimensional random feature vectors while keeping time complexity linear in n. Approximated matrix computed in this way is a better approximation than the matrix computed via the Nyström method. We will prove here that the expected error of the approximated kernel predictor derived via this method is approximately the same in expectation as the error of the error of kernel predictor. Furthermore, we will empirically show that using the l randomly selected columns of a kernel matrix for a construction of m-dimensional random feature vectors produces smaller error on a regression problem, than using m randomly selected columns.
WordCode using WordTrie

Computers work with text data by assigning a code for each character, called encoding. Character-encoding techniques emerged in the late 1960s, and a similar type of technique is still used to encode text data. Computers can only understand alphabets, not words. In this article, we develop an approach that enables computers to understand words. We introduce a word-based encoding of text data named WordCode. WordCode encodes the most frequent set of characters (i.e., words) found in Internet directories with a dynamic code combination. Although some dictionary-encoding techniques have been proposed, we still tend to use character encoding, such as Unicode, to encode text data. Dictionary-encoding techniques have not been adopted due to the massive size of the code page and the complexity in accessing the code page. In this article, we introduce a customised trie named WordTrie to store words for faster encoding and decoding. We generate the code combination in such a way that the size of the WordCode for a word is always smaller than the total size of the character coding. Our experimental results from encoding text files from the Gutenberg corpus, Canterbury corpus, large corpus, Calgary corpus and Silesia corpus using WordCode show an up to 19.9% reduction in file size with respect to character-based encoding. This smaller file size means that less storage space is needed and results in faster processing and communication of text data.
Control Renewable Energy System and Optimize Performance by Using Weather Data

There is no doubt that renewable energy (RE) is now playing a key role in our daily life and has become more popular thanks to many advantages such as environment friendly, unlimited sources and so on, variable of applications. Obviously, the only one obstacle we must face is the dependence upon weather conditions which have been, so far, the unpredictable variants of most RE systems. Hence, in this paper, we propose a new algorithm using weather forecast and historical weather data in order to optimize performance of RE systems. Basically, this algorithm is not only making full use of weather data base but also helping us decide whether to discharge the load or have it operated on high performance at least in the next three hours. We have already embedded it into our RE system combining with Darius wind turbine and photovoltaic (PV) system, the result is showed below.
Directional Thermodynamic Formalism

The usual thermodynamic formalism is uniform in all directions and, therefore, it is not adapted to study multi-dimensional functions with various directional behaviors. It is based on a scaling function characterized in terms of isotropic Sobolev or Besov-type norms. The purpose of the present paper was twofold. Firstly, we proved wavelet criteria for a natural extended directional scaling function expressed in terms of directional Sobolev or Besov spaces. Secondly, we performed the directional multifractal formalism, i.e., we computed or estimated directional Hölder spectra, either directly or via some Legendre transforms on either directional scaling function or anisotropic scaling functions. We obtained general upper bounds for directional Hölder spectra. We also showed optimal results for two large classes of examples of deterministic and random anisotropic self-similar tools for possible modeling turbulence (or cascades) and textures in images: Sierpinski cascade functions and fractional Brownian sheets.