Reviewerly/exHarmony huggingface dataset:
Training Heterogeneous Features in Sequence to Sequence Tasks: Latent Enhanced Multi-filter Seq2Seq Model

In language processing, training data with extremely large variance may lead to difficulty of language model’s convergence. It is difficult for the network parameters to adapt sentences with largely varied semantics or grammatical structures. To resolve this problem, we introduce a model that concentrates the each of the heterogeneous features in the input sentences. Build upon the encoder-decoder architecture, we design a latent-enhanced multi-filter seq2seq model (LEMS) that analyzes the input representations by introducing a latent space transformation and clustering. The representations are extracted from the final hidden state of the encoder and lie in the latent space. A latent space transformation is applied for enhancing the quality of the representations. Thus the clustering algorithm can easily separate samples based on the features of these representations. Multiple filters are trained by the features from their corresponding clusters, the heterogeneity of the training data can be resolved accordingly. We conduct two sets of comparative experiments on semantic parsing and machine translation, using the Geo-query dataset and Multi30k English-French to demonstrate the enhancement our model has made, respectively.
ChartStory: Automated Partitioning, Layout, and Captioning of Charts  into Comic-Style Narratives

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline which characterizes charts by their properties and similarity, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.
Interview with Luc Beaudoin

Luc P. Beaudoin is co-founder of CogSci Apps Corp, CogZest and Somnolence+. He is also adjunct professor of Cognitive Science and of Education at Simon Fraser University. From an integrative design-oriented perspective, he has written two Cognitive Productivity books about using knowledge and hypertext to become profoundly effective, co-designed gStudy and nStudy, and designs the Hook productivity, Hookmark and mySleepButton® apps. Luc has a Ph.D. in Cognitive Science (thesis on goal processing in autonomous agents). He is based in Metro-Vancouver, Canada. More information about Luc can be found at https://cogzest.com/about/founder/
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection

Systems like Voice-command based conversational agents are characterized by a pre-defined set of skills or intents to perform user specified tasks.In the course of time, newer intents may emerge requiring retraining.However, the newer intents may not be explicitly announced and need to be inferred dynamically.Thus, there are two important tasks at hand (a).identifying emerging new intents, (b).annotating data of the new intents so that the underlying classifier can be retrained efficiently.The tasks become specially challenging when a large number of new intents emerge simultaneously and there is a limited budget of manual annotation.In this paper, we propose MNID (Multiple Novel Intent Detection) which is a cluster based framework to detect multiple novel intents with budgeted human annotation cost.Empirical results on various benchmark datasets (of different sizes) demonstrate that MNID, by intelligently using the budget for annotation, outperforms the baseline methods in terms of accuracy and F1-score.
Performance Evaluation of Newly Developed Generalized Correlations for the Prediction of Solar Diffuse Fraction for Various Climatic Regions

Abstract The objective of this work is to develop empirical correlations describing Diffuse Fraction (DF) as a function of (1) Sunshine Fraction (SF), (2) Clearness Index (CI) and (3) both SF and CI. Four years instantaneously measured data was changed to monthly data at five locations belonging to five different climatic regions in Pakistan which was used as training dataset and nine correlations for each location (a total of forty-five) were formulated and their performance was assessed. Moreover, nine general empirical models were developed using the entire dataset (11 years) for five locations which were termed as Generalized Correlations (GCs). These GCs were validated by applying them to five other locations and comparing the generated results with measured results for those locations (validation dataset). The best model among GCs were found as GC8 which was, then applied to compute DF for five more locations for which short term (8 months) measured data was also available and thus a reasonable comparison could be made. Results showed that (1) new models were better than literature models, (2) GCs correlations were found in good agreement and (3) 2nd degree multivariate polynomial models are the best performance models with minimum errors e.g. Mean Absolute Biased Error (MABE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Sum of Square of Relative Error (SSRE) and Standard Relative Error (SRE) for GC8 were estimated as 0.018, 6.397, 0.021, 0.006 and 0.022 respectively (all values for Karachi).
An Italian lexicon-based sentiment analysis approach for medical applications

Sentiment analysis aims at extracting opinions and or emotions mainly from written text. The most popular problem in sentiment analysis certainly is polarity detection, which falls into the broader class of Natural Language Processing (NLP) problems of text classification. To date, state-of-the-art approaches to text classification use neural language models built on popular architectures such as Transformers. However, these approaches are difficult to apply in low-resource languages and domains, as for instance the Italian language or small clinical trials. Motivated by this, this paper presents VADER-IT, a lexicon-based algorithm for polarity prediction in written text, that is an adaptation to the Italian language of the popular VADER. Unlike VADER, our system also predicts a polarity class (i.e. positive, negative or neutral). The system was tested on a dataset of 5495 healthcare related reviews from QSalute https://www.qsalute.it/, reaching a micro averaged F1--score = 81% and a micro averaged Jaccard - score = 73%.
Towards Efficient Discovery of Periodic-Frequent Patterns in Dense Temporal Databases Using Complements

Periodic-frequent pattern mining involves finding all periodically occurring patterns in a temporal database. Most previous studies found these patterns by storing the temporal occurrence information of an item in a list structure. Unfortunately, this approach makes pattern mining computationally expensive on dense databases due to increased list sizes. With this motivation, this paper explores the concept of complements, and proposes an efficient algorithm that records non-occurrence information of an item to find all desired patterns in a dense database. Experimental results demonstrate that our algorithm is efficient.
Educational effect of Disital-storytelling technique : A Systematic Review

최근 IT기술발전과 웹 서비스 형태 변화에 따라 교육 소비자의 새로운 요구가 증가되고 있다. 최첨단 디지털 컨텐츠를 접목한 교육환경이 트랜드로 자리잡은 가운데, 교육요소 중 디지털 스토리텔링 기법이 선호되고 있으며 디지털스토리텔링 기법을 적용한 연구 역시 최근 10년간 양적 증가추세에 있다. 그러나 국내에서는 아직 이에 대한 체계적인 논의가 이루어 지지 않아 실험연구를 통한 효과검증이 미비한 상태이다. 따라서 웹3.0 시대에 콘텐츠 이용행태인 지능화 된 웹과 개인별 맞춤 서비스 제공이라는 새로운 지평에 부합하는 교육환경 조성을 위해 디지털스토리텔링의 교육적 효과를 체계적으로 분석하여 변수 별 효과검증 통해 학문간 융합과 광범위한 적용을 목적으로 방향성을 제시하고자 한다. 이에 본 연구는, 디지털스토리텔링의 교육적 효과를 검증한 국내 실험연구 12편을 체계적으로 분석하여 해외사례와의 비교, 웹3.0시대에 준비해야 발전방안을 제언하였다. As the development of IT technology and Web services have changed recently, education environment combined with high-tech digital content is becoming a new trend with the demands of educational consumers. In Korea, however, there is no systematic discussion on the issue, and there is not enough evidence to verify the effectiveness of the research. Therefore, we will systematically analyze the educational effects of Digital Storytelling to create an educational environment that meets the new horizon of intelligent web and personalized service provision in the Web3.0 era, In this study, 12 domestic experimental studies that verify the educational effects of digital storytelling were systematically analyzed, compared with overseas cases, and proposed development plans in the Web3.0 era.
A t-out-of-n Redactable Signature Scheme

A redactable signature scheme allows removing parts of a signed message without invalidating the signature. Currently, the need to prove the validity of digital documents issued by governments and enterprises is increasing. However, when disclosing documents, governments and enterprises must remove privacy information concerning individuals. A redactable signature scheme is useful for such a situation. In this paper, we introduce the new notion of the t-out-of-n redactable signature scheme. This scheme has a signer, n redactors, a combiner, and a verifier. The signer designates n redactors and a combiner in advance and generates a signature of a message M. Each redactor decides parts that he or she wants to remove from the message and generates a piece of redaction information. The combiner collects pieces of redaction information from all redactors, extracts parts of the message that more than t redactors want to remove, and generate a redacted message. We consider the one-time redaction model which allows redacting signatures generated by the signer only once. We formalize the one-time redaction t-out-of-n redactable signature scheme, define security, and give a construction using the pairing based aggregate signature scheme in the random oracle model.
Twitter sentiment analysis for price and transaction volume changes in the cryptocurrency market

Cryptocurrencies are rising in importance as an investment option and alternative currency.Thus, investors are keen on finding timely market movement insights.One such source is Twitter due to its live feed of information on cryptocurrencies and emotional information from investors expressing their sentiments.This article examines the extent to which Twitter sentiments can be used to predict price and transaction volume changes for the nine largest cryptocurrencies for the period of June 2021 to September 2021.This study was conducted using a lexicon-based approach through the VADER algorithm for sentiment analysis, while applying the Granger causality method to analyze the two-way predictive capabilities of each cryptocurrency's sentiments toward their respective price and transaction volume changes.Past studies have shown that sentiment analysis may work for several cryptocurrencies, while this study only found predictive capabilities in transaction volume changes and not in price movement.
Artificial Intelligence-based Voice Assistants as Social Media Platform: anticipated Effect on Brands During Shopping

Artificial intelligence (AI) technologies enable voice assistants (VA) such as Amazon Alexa and Alibaba Genie to perform activities that resemble cognitive functions associated with the human mind (Norvig, 2011). While performing complex tasks with consumers, VAs become more human-like exchange partners. As these AI-enabled devices learn consumer preferences and habits, they introduce biases and heuristics likely to affect individual and collective shopping behavior while posing new opportunities and threats for managers. Why should managers monitor the rapid diffusion of this novel ‘social media’ platform? What are the anticipated consequences for brands in the shopping context? This interpretative study uses in-depth interviews with elite informants (N = 31) to address these questions. Executives and experts predict that VAs assume a central relational role in the consumer market and progressively mediate market interactions. Simultaneously, they expect VA’s intrinsic social nature to influence consumption decisions increasingly. These fast-changing market dynamics within the context of voice commerce may have severe consequences for consumer brands. The main identified threats for managers and their implications are discussed.
Research on an improved SURF matching algorithm for door handle images

Aiming at the requirement of fastness and accuracy of the feature extraction and matching of the door handle image, an improved SURF algorithm is proposed. The algorithm mainly improves the problem that the image is more difficult to extract a large number of information points at smooth area.increases the edge detection algorithm, obtains the edge information of the image and then performs the morphological processing, And obtains the key edge information of the door handle image by the dilation operation and the Opening Operation, and then extracts the key points, and obtains more obvious information. Compared with the original SURF algorithm, the accuracy of the improved SURF algorithm is better than that of the original SURF algorithm at smooth area and the matching accuracy is improved obviously. Because the total number of extracted feature points decreases, so the matching time is greatly saved, thus enhancing the real-time performance of the algorithm.
The Effect of Servant Leadership on Follower’s Organizational Commitment and Job Performance - Focusing on the Mediating Effect of Naval Leadership Core Competency -

The purpose of this study is to examine the effectiveness of servant leadership in military organizations where top-down decision making is a norm. Servant leadership in the military has not been thoroughly studied compared to that in the government organizations and companies. A few previous studies concerning servant leadership in the military only focused on the army and marine corps. This study is the first research conducted in the navy, analyzing the survey answers of 626 subjects from different personnel groups such as officers, noncommissioned officers, and civilian employees in the navy. To examine how the servant leadership affects subordinates’ organizational commitment and job performance, the survey answers of participants were analyzed in view of each group’s core competencies of leadership as well as common core competencies of leadership such as professionalism, responsibility, cooperative communication and pride. The results suggest that leaders with servant leadership enhanced subordinates’ organizational commitment and job performance. Besides, subordinates’ common core competencies of leadership were also affected positively and were proved to mediate between servant leadership and subordinates’ organizational commitment and job performance. Based on the findings, this study offers practical implications and concludes by discussing the limitations of this study and the future research directions.
Knowledge Authoring with Factual English

Knowledge representation and reasoning (KRR) systems represent knowledge as collections of facts and rules. Like databases, KRR systems contain information about domains of human activities like industrial enterprises, science, and business. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. One solution could be to extract knowledge from English text, and a number of works have attempted to do so (OpenSesame, Google's Sling, etc.). Unfortunately, at present, extraction of logical facts from unrestricted natural language is still too inaccurate to be used for reasoning, while restricting the grammar of the language (so-called controlled natural language, or CNL) is hard for the users to learn and use. Nevertheless, some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. In this paper, we address this issue by transplanting the KALM framework to a neural natural language parser, mStanza. Here we limit our attention to authoring facts and queries and therefore our focus is what we call factual English statements. Authoring other types of knowledge, such as rules, will be considered in our followup work. As it turns out, neural network based parsers have problems of their own and the mistakes they make range from part-of-speech tagging to lemmatization to dependency errors. We present a number of techniques for combating these problems and test the new system, KALMFL (i.e., KALM for factual language), on a number of benchmarks, which show KALMFL achieves correctness in excess of 95%.
Improving Variational Quantum Algorithms performance through Weighted Quantum Ensembles

Due to considerable error rate and insufficient qubits in supporting full-scale quantum error correction, the Variational Quantum Algorithm (VQAs), which adopts a classical optimizer to train a parameterized quantum circuit, stands out as one of the most promising algorithms for Noisy Intermediate-Scale Quantum (NISQ) devices.
QCCC 2022 Keynote Talk: Hybrid Quantum / Classical Algorithms for Machine Learning

In this talk I will provide a new approach to quantum machine learning that involves using classical machine learning to learn a representation for a dataset that can be embedded in a quantum computer. We will then consider applying this strategy to train a generative model for groundstates of chemistry Hamiltonians that will allow us to predict groundstates given data through a classically learnt representation that converts nuclear positions into weights for a quantum neural network that generates the state. This work shows that quantum / classical Hybrid methods can be a powerful way to learn how to generate groundstates and potentially even give a cheaper alternative to approximate groundstate preparation than phase estimation provides in some settings.
Medical image encryption based on hyper-chaotic system

Medical images contain a large amount of private information like patient's lesions. Theft or destruction of medical images will cause irreparable losses to patients and medical institutions. How to realize the secure transmission of information in the process of telemedicine has become a research hotspot. In this paper, an encryption algorithm for hyper-chaotic systems based on DNA coding is proposed. First, a four-dimensional chaotic system is constructed by adding a dimension to the three-dimensional chaotic system, which improves the dynamic performance of the system, increases the parameters of the chaotic system, and improves the key space. Second, the algorithm complexity is increased by the added DNA encoding. The experimental results show that the key space encrypted by the method in this paper is 2469, and the key sensitivity can reach 10-16. At the same time, in the analysis of histogram, adjacent correlation and information entropy, the algorithm in this paper can achieve good security performance.
Deep learning optical image denoising research based on principal component estimation

High-quality denoising of optical interference images usually requires preliminary prediction of the noise level. Although blind denoising can filter the image at the pixel level without noise prediction, it inevitably loses a significant amount of phase information. This paper proposes a fast and high-quality denoising algorithm for optical interference images that combines the merits of a principal component analysis (PCA) and residual neural networks. The PCA is used to analyze the image noise and, in turn, establishes an accurate mapping between the estimated and true noise levels. The mapping helps to select a suitable residual neural network model for image processing, which maximizes the retention of image information and reduces the effect of noise. In addition, a comprehensive evaluation factor to account for the time complexity and denoising effect of the algorithm is proposed, since time complexity can be a dominant concern in some cases of actual measurement. The performance of the denoising algorithm and the effectiveness of the evaluation criterion are demonstrated to be high by processing a set of optical interference images and benchmarking with other denoising algorithms. The proposed algorithm outperforms the previously reported counterparts in a specific area of optical interference image preprocessing and provides an alternative paradigm for other denoising problems of optics, such as holograms and structured light measurements.
Smart Village Health System IoT to Envisage Chronical Disease Using Artificial Neural Network

A Smart Village Health System (SVHS) understands the infrastructure, facilities, and schemes open to its villager.The Internet of Things (IoT) transforms a village health system into an SVHS using ANN that includes schools, highways, environment, and globalization.It has been designed with the intension to provide basic healthcare facilities to the inhabitant.It also gives information about the chronic diseases by employing Artificial Neural Network (ANN).A SVHS model based on HCV dataset is proposed in this paper.The system is built with cloud and IoT to enable data by means of the patient's input parameters to be collected, indexed and visualized in a smart village.The Levenberg-Marquardt (LM), Bayesian Regularization (BR) and the Scale Conjugate Gradient (SCG) algorithms are implemented with ANN-based approach named as " IoT enabled Smart Village Health System to Predict Chronical Disease empowered with Artificial Neural Network (ToVHS)" in order to develop an efficient and smart CHP model.The evaluation of the proposed method indicates that the SCG algorithm achieves promising results with respect to accuracy and miss rates.The predicted accuracy of the proposed model shows 90.39% performance of CHP on the given factors.