Reviewerly/exHarmony huggingface dataset:
Interpreting Neural CWI Classifiers’ Weights as Vocabulary Size

Complex Word Identification (CWI) is a task for the identification of words that are challenging for second-language learners to read.Even though the use of neural classifiers is now common in CWI, the interpretation of their parameters remains difficult.This paper analyzes neural CWI classifiers and shows that some of their parameters can be interpreted as vocabulary size.We present a novel formalization of vocabulary size measurement methods that are practiced in the applied linguistics field as a kind of neural classifier.We also contribute to building a novel dataset for validating vocabulary testing and readability via crowdsourcing.
Variational level set image segmentation model coupled with kernel distance function

One of the crucial challenges in the area of image segmentation is intensity inhomogeneity. For most of the region-based models, it is not easy to completely segment images having severe intensity inhomogeneity and complex structure, as they rely on intensity distributions. In this work, we proposed a firsthand hybrid model by blending kernel and Euclidean distance metrics. Experimental results on some real and synthetic images suggest that our proposed model is better than models of Chan and Vese, Wu and He, and Salah et al.
Automated Conversation Review to Surface Virtual Assistant Misunderstandings: Reducing Cost and Increasing Privacy

With the rise of Intelligent Virtual Assistants (IVAs), there is a necessary rise in human effort to identify conversations containing misunderstood user inputs. These conversations uncover error in natural language understanding and help prioritize and expedite improvements to the IVA. As human reviewer time is valuable and manual analysis is time consuming, prioritizing the conversations where misunderstanding has likely occurred reduces costs and speeds improvement. In addition, less conversations reviewed by humans mean less user data is exposed, increasing privacy. We present a scalable system for automated conversation review that can identify potential miscommunications. Our system provides IVA designers with suggested actions to fix errors in IVA understanding, prioritizes areas of language model repair, and automates the review of conversations where desired.Verint - Next IT builds IVAs on behalf of other companies and organizations, and therefore analyzes large volumes of conversational data. Our review system has been in production for over three years and saves our company roughly $1.5 million in annotation costs yearly, as well as shortened the refinement cycle of production IVAs. In this paper, the system design is discussed and performance in identifying errors in IVA understanding is compared to that of human reviewers.
Numerical detection of Gaussian entanglement and its application to the identification of bound entangled Gaussian states

We present a numerical method for solving the separability problem of Gaussian quantum states in continuous-variable quantum systems. We show that the separability problem can be cast as an equivalent problem of determining the feasibility of a set of linear matrix inequalities. Thus, it can be efficiently solved using existent numerical solvers. We apply this method to the identification of bound entangled Gaussian states. We show that the proposed method can be used to identify bound entangled Gaussian states that could be simple enough to be producible in quantum optics.
Building a X-ray Database for Mammography on Vietnamese Patients and automatic Detecting ROI Using Mask-RCNN

This paper describes the method of building a X-ray database for Mammography on Vietnamese patients that we collected at Hanoi Medical University Hospital. This dataset has 4664 images (Dicom) corresponding to 1161 standard patients with uniform distribution according to BIRAD from 0 to 5. This paper also presents the method of detecting Region of Interest (ROI) in mammogram based on Mask R-CNN architecture. The method of determining the ROI for accuracy mAP@0.5 = 0.8109 and the accuracy of classification BIRAD levels is 58.44%.
Differentially Private Contextual Dynamic Pricing

In this paper, we design differentially private algorithms for the contextual dynamic pricing problem. In contextual dynamic pricing, the seller sells heterogeneous products to buyers that arrive sequentially. At each time step, a buyer arrives with interests in purchasing a product. Each product is represented by a set of product features, i.e., the context, and the buyer's valuation for the product is a function of the product features and the buyer's private preferences. The goal of contextual dynamic pricing is to adjust the price over time to learn how to set the optimal price for the population from interacting with individual buyers. In the meantime, this learning process creates potential privacy concerns for individual buyers. A third-party agent might be able to infer the information of individual buyers from how the prices change after the participation of a particular buyer. In this work, using the notion of differential privacy as our privacy measure, we explore the design of differentially private dynamic pricing algorithms. The goal is to maximize the seller's payoff, or equivalently, minimize the regret with respect to the optimal policy when knowing the distribution of buyers' preferences while ensuring the amount of privacy leak of individual buyers' valuations is bounded. We present an algorithm that is e-differentially private and achieves expected regret O (√dT over e), where d is the dimension of product features and T is the time horizon.
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention

Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature.These may not explicitly refer to entities or canonical concept forms occurring in a fact-based knowledge source, e.g. the UMLS ontology.Moreover, hidden associations between related concepts meaningful in the current context, may not exist within a single document, but across documents in the collection.Predicting semantic concept tags of documents can therefore serve to associate documents related in unseen contexts, or categorize them, in information filtering or retrieval scenarios.Thus, inspired by the success of sequence-to-sequence neural models, we develop a novel sequence-to-set framework with attention, for learning document representations in a unique unsupervised setting, using no human-annotated document labels or external knowledge resources and only corpus-derived term statistics to drive the training.This can effect term transfer within a corpus for semantically tagging a large collection of documents.Our sequence-to-set modeling approach to predict semantic tags , gives to the best of our knowledge, the state-of-theart for both, an unsupervised query expansion (QE) task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25based document retrieval system; and also over the MLTM system baseline (Soleimani and Miller, 2016), for both supervised and semi-supervised multi-label prediction tasks with del.icio.usand Ohsumed datasets.We make our code and data publicly available 1 .
Trustless Technology within Trust-Based Systems; A Comparative Study of the Big Four’s Approach to Blockchain Adoption and its Future Prospects

Technological developments and industrial adaptations are leading to a fundamental change in how industries and strategy function in the world today. Disruptive technologies are forcing us to reconsider the way we make decisions, and the models that were previously in place for delivering products and services. Blockchain in particular has demonstrated its ability to completely upend industry as we know it. Its main strengths lie in the fact that it is decentralized, unchangeable, anonymous and auditable. In this paper, we present a comparative study on the Big Four accounting firms’ approach to Blockchain development and adoption. We first give an overview of the blockchain technology and key characteristics of the blockchain and its applications. Furthermore, we discuss some existing approaches for blockchain development and application in the Big Four accounting firms and highlight the opportunities and future prospects of blockchain technology that can be utilized by the four professional services conglomerates.
A Generative Framework of Generativity

Several frameworks exist to describe how procedural content can be understood, or how it can be used in games. In this paper, we present a framework that considers generativity as a pipeline of successive data transformations, with each transformation either generating, transforming, or pruning away information. This framework has been iterated through repeated engagement and education interactions with the game development and generative art communities. In its most recent refinement, it has been physically instantiated into a deck of cards, which can be used to analyze existing examples of generativity or design new generative systems. This Generative Framework of Generativity aims to constructively define the design space of generative pipelines.
A Multi-Task Approach to Open Domain Suggestion Mining (Student Abstract)

Consumer reviews online may contain suggestions useful for improving the target products and services. Mining suggestions is challenging because the field lacks large labelled and balanced datasets. Furthermore, most prior studies have only focused on mining suggestions in a single domain. In this work, we introduce a novel up-sampling technique to address the problem of class imbalance, and propose a multi-task deep learning approach for mining suggestions from multiple domains. Experimental results on a publicly available dataset show that our up-sampling technique coupled with the multi-task framework outperforms state-of-the-art open domain suggestion mining models in terms of the F-1 measure and AUC.
Reasoning About Strong Inconsistency in ASP

The last decade has witnessed remarkable improvements in the analysis of inconsistent formulas, namely in the case of Boolean Satisfiability (SAT) formulas. However, these successes have been restricted to monotonic logics. Recent work proposed the notion of strong inconsistency for a number of non-monotonic logics, including Answer Set Programming (ASP). This paper shows how algorithms for reasoning about inconsistency in monotonic logics can be extended to the case of ASP programs, in the concrete case of strong inconsistency. Initial experimental results illustrate the potential of the proposed approach.
Pedagogical practices based on areas of knowledge: Reflections on the technology use

The appropriation of technologies in education must consider the specificities of each knowledge field and propose the demand for its pedagogical use. In distance learning (DL), which relies on the use of technologies, the reflection on the use of this resource becomes fundamental. Processes, methodologies and materials are usually standardised due to time and production costs. It is necessary to identify an intermediate point in this standardisation that takes into account the specificities of the knowledge fields. This study aims to discuss the pedagogical practices in DL considered appropriate to the knowledge fields, in the appropriate combination of contents, technologies and pedagogical practices. The methodology adopted is qualitative, using survey as a data collection tool. In conclusion, this article indicates that there are, actually, more effective teaching practices for each knowledge field. There are still recommendations in order to study deeply the real learning in each teaching practice.&#x0D; Keywords: Higher education, distance learning, pedagogical practices, education technology, TPACK.
Appraising the Laws Governing the Control of Cybercrime in Nigeria

Appraising the Laws Governing the Control of Cybercrime in Nigeria Ufuoma V. Awhefeada Ph.D; Ohwomeregwa Ogechi Bernice Abstract The paper analyses the legal regime for the control of cybercrime in Nigeria by examining the legal and institutional frameworks for cyber security in Nigeria. The definitions and effects of cybercrime in Nigeria were adumbrated. An attempt was made to compare the laws governing the control of cybercrimes in Nigeria in tandem with the Budapest Convention on Cybercrime as well as with some selected jurisdictions. A finding made was that Nigeria’s laws governing the control of cybercrimes were inadequate in the fight against cybercrimes and that there is need to ratify and domesticate the Budapest Convention into our laws to be in line with the global best practices in the control of cybercrime. The study makes a consummate call to the legislature to amend the provisions of these laws, particularly the principal Act (Cybercrime Prohibition, Prevention Act, 2015) such as implementing the public/private partnership provision in the Act towards achieving cyber security. Full Text: PDF DOI: 10.15640/jlcj.v8n1a3
Bitcoin’s APIs in Open-Source Projects: Security Usability Evaluation

Given the current state of software development, it does not seem that we are nowhere near vulnerability-free software applications, due to many reasons, and software developers are one of them. Insecure coding practices, the complexity of the task in hand, and usability issues, amongst other reasons, make it hard on software developers to maintain secure code. When it comes to cryptographic currencies, the need for assuring security is inevitable. For example, Bitcoin is a peer-to-peer software system that is primarily used as digital money. There exist many software libraries supporting various programming languages that allow access to the Bitcoin system via an Application Programming Interface (API). APIs that are inappropriately used would lead to security vulnerabilities, which are hard to discover, resulting in many zero-day exploits. Making APIs usable is, therefore, an essential aspect related to the quality and robustness of the software. This paper surveys the general academic literature concerning API usability and usable security. Furthermore, it evaluates the API usability of Libbitcoin, a well-known C++ implementation of the Bitcoin system, and assesses how the findings of this evaluation could affect the applications that use Libbitcoin. For that purpose, the paper proposes two static analysis tools to further investigate the use of Libbitcoin APIs in open-source projects from a security usability perspective. The findings of this research have improved Libbitcoin in many places, as will be shown in this paper.
MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT

In this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of candidates in Regional Elections in Indonesia based on data taken from General Election Commission (KPU). The data consist of composition of political parties that support each candidate. The purpose of this research is to develop a Machine Learning model based on verified data provided by official institution to predict the victory of each candidate in a Regional Election instead of using social media data as in previous studies. The prediction itself simply a classification task between two classes, i.e. ‘win’ and ‘lose’. Several Machine Learning algorithms were applied to find the best model, i.e. k-Nearest Neighbors, Naïve Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest performance. We found in this research that Neural Networks with Multilayer Perceptron is the best model with 74.20% of accuracy.
Determining the Articles Acceptance Using Logic of Fuzzy Inference System Tsukamoto

Publication of scientific article is now getting bigger, but the process of the scientific article acceptance takes a long time. The longest stage in the process, especially, is in the review process. The process about article assessment consists of many criteria which cause a very high level of subjectivity. Computerized system on the assessment of the scientific article acceptance apply a reasoning scheme using Fuzzy Inference Tsukamoto Logic; therefore, by using the logic, the duration issue in the assessment process can be handled fast.
The Effects of Meta-Discussion Strategies Toward Low English Proficiency Students in Oral English Performance

The ability to speak proficiently is very important among English as a second language (ESL) students.Educators should employ teaching approaches to make teaching and learning more effective.This paper discusses the effects of teaching meta-discussion strategies on low English profeciency students' achievement in oral English.This study used a quasi-experimental design using a non-equivalent control-group design.The sample population was 177 students comprising of 94 students in the experimental group and 83 students in the control group.The students in the experimental group were exposed to metadiscussion strategies for seven weeks under the guidance of two instructors.This study used the indicators of teaching and learning process questionnaire (InTLPQ) to measure the respondents' level of meta-cognitive awareness, meta-attention, meta-comprehension and meta-cognitive reflection.The independent variables in this study are meta-cognitive awareness, meta-attention and meta-comprehension.The data were analysed using descriptive and inferential statistics.The results of the MANOVA revealed that there was a statistically significant difference in the post-test score between the treatment and control groups with the value p = 0.00; p < 0.01.The Stepwise multiple regression analysis indicated both that the three independent variables are correlated and that they contributed positively to the achievement of the respondents' oral English.In conclusion, with constant practice of these strategies, students could enhance their oral ability by overcoming their weaknesses during the learning process.
Pairwise-Constraints Based Semi-Supervised Fuzzy Clustering with Entropy Regularization

Compared with traditional unsupervised clustering, semi-supervised clustering is a more powerful computing method and plays a vital role in pattern analysis and machine learning. The reason for these is that semi-supervised approaches can take advantage of semi-supervised information, which can significantly boost the performance of clustering. Existing methods for semi-supervised fuzzy c-means clustering (FCM) suffer from the following issues: it is generally uneasy to assign the appropriate membership degree value based on traditional entropy regularization using semi-supervised information involved in their objective function. To address this problem, we systematically propose a novel Fuzzy Symmetric Relative Entropy Clustering with Pairwise-Constraints (FSREC-PC) by introducing entropy regularization into the objective function. Moreover, FSREC-PC introduces symmetric relative entropy as a regularized term in its objective function such that its resulting formulas have the clear membership meaning compared with the other semi-supervised FCM algorithms. Further experiments conducted in UCI data show that the proposed clustering algorithm can derive a better performance.
Didactic Opportunities of Information and Communication Technologies in Graphic Training of Future Technology Teachers

Purposeful use of information and communication technologies (ICT) in drawing lessons provides an opportunity for the complex attraction of various forms of sensual and rational cognition for comprehensive study and thorough assimilation of the essence of the studied objects and phenomena; formation of the scientific style of thinking of students; development of creative abilities of future specialists; improving the efficiency of the educational process, etc.The purpose of the article is to explore the didactic capabilities of ICT tools in the process of graphic training of future technology teachers in drawing lessons.It is established that the effectiveness of using ICT in the process of graphic preparation of students is achieved only with the use of special software.In this regard, according to various classification features (didactic goals; basic architecture; branches of scientific knowledge; functional purpose) the analysis and systematization of the software the tools that can be used in the process of graphic training of future technology teachers in drawing lessons.It presents the practical experience of systematic use of didactic capabilities of software of various types in graphic preparation of students (educational presentations, electronic databases, electronic directories, controlling software) as well as the general characteristics of modelling software, including computer-aided design systems (ArchiCad, AutoCad, SolidWorks, T-Flex Cad, COMPASS), which have become the most widespread in graphic preparation of students.The authors reveal functional possibilities and didactic significance of the COMPASS program in the process of studying geometrical, projection, machine-building, schematic and construction drawings.The article presents the results of the implementation of ICT tools (including the COMPASS program) in the process of graphic training of future teachers of technologies.
A Novel Rule based Data Mining Approach towards Movie Recommender System

The proposed research work is an effort to provide accurate movie recommendations to a group of users with the help of a rule-based content-based group recommender system. The whole approach is categorized into 2 phases. In phase 1, a rule- based approach has been proposed which considers the users’ viewing history to provide the Rule Base for every individual user. In phase 2, a novel group recommendation system has been proposed which considers the ratings of the movies as per the rule base generated in phase 1. Phase 2 also considers the weightage of every individual member of the group to provide the accurate movie recommendation to that particular group of users. The results of experimental setup also establish the fact that the proposed system provides more accurate outcomes in terms of precision and recall over other rule learning algorithms such as C4.5.
A MAP OF KNOWLEDGE AND ITS IMPORTANCE IN THE LIFE CYCLE OF A CONSTRUCTIOM OBJECT

In the modern world, knowledge is treated as another resource necessary for the development of civilization. An important element is information, thanks to which you can update knowledge and thus create a compendium of knowledge. Knowledge and its organization is particularly useful in construction. The article proposes a knowledge map, which is a record of domain knowledge. The fields were designated there, and the fields were assigned elements of importance in the life cycle of the building.