Reviewerly/exHarmony huggingface dataset:
ANALISIS TINGKAT KEPUASAN PENGGUNA SISTEM INFORMASI PERPUSTAKAAN MENGGUNAKAN PIECES FRAMEWORK

The use of information systems requires special management and management, so this system is commonly referred to as a management information system. In it there are various activities ranging from system planning, maintenance, to measuring performance. In running a library information system, software and hardware are needed as well as humans as operators. The above components must be activities that are interconnected so that the library can run smoothly. To determine whether the components of the information system are running well, it requires an evaluation process. The purpose of this study is to measure the level of satisfaction of system users and to determine the strengths and weaknesses of the system in the use of library information systems. The method used is the analytical method PIECES Framework, which consists of several points of analysis, namely: Performance, Information and Data, Economics, Control and Security, Efficiency, and Service. Where each analysis point is a reference for evaluation and analysis of information systems. The results of this study are web applications that are able to analyze the level of user satisfaction with the library information system using the PIECES Framework analysis method and equipped with a satisfaction level reporting chart of the system measured in the 2018 period.
A recommender system for heterogeneous and time sensitive environment

The digital game industry has recently adopted recommender systems to deliver the most relevant content and suggest the most suitable activities to players. Because of diverse game designs and dynamic experiences, recommender systems typically operate in highly heterogeneous and time-sensitive environments. In this paper, we describe a recommender system at a digital game company which aims to provide recommendations for a large variety of use-cases while being easy to integrate and operate. The system leverages a unified data platform, standardized context and tracking data pipelines, robust naive linear contextual multi-armed bandit algorithms, and experimentation platform for extensibility as well as flexibility. Several games and applications have successfully launched with the recommender system and have achieved significant improvements.
Recurrent Translation-Based Network for Top-N Sparse Sequential Recommendation

Fulfilling users' needs and increasing the retention rate of recommendation systems are challenging. Most users have consumed a few items in most systems. Translation-based model performs well on sparse datasets. However, a user and only single previous item are considered for the user suggestion of next items. Alternatively, recurrent neural network utilizes sequential dependency but performs poorly on sparse datasets. We unify both and propose Recurrent Translation-based Network (RTN). RTN utilizes sequences of users' consumed items without limiting interactions between items to the most recent one. The results of conducting experiments on real-world datasets show that RTN outperforms other state-of-the-art approaches on sparse datasets.
Tolerant parsing using modified LR(1) and LL(1) algorithms with embedded “Any” symbol

Tolerant parsing is a form of syntax analysis aimed at capturing the structure of certain points of interest presented in a source code. While these points should be well-described in a tolerant grammar of the language, other parts of the program are allowed to be described coarse-grained, thereby parser remains tolerant to the possible variations of the irrelevant area. Island grammars are one of the basic tolerant parsing techniques. “Islands” term is used as the relevant code alias, the irrelevant code is called “water”. Efforts required to write water rules are supposed to be as small as possible. Previously, we extended island grammars theory and introduced a novel formal concept of a simplified grammar based on the idea of eliminating water description by replacing it with a special “Any” symbol. To work with this concept, a standard LL(1) parsing algorithm was modified and LanD parser generator was developed. In the paper, “Any”-based modification is described for LR(1) parsing algorithm. In comparison with LL(1) tolerant grammars, LR(1) tolerant grammars are easier to develop and explore due to solid island rules. Supplementary “Any” processing techniques are introduced to make this symbol easier to use while staying in the boundaries of the given simplified grammar definition. Specific error recovery algorithms are presented both for LL and LR tolerant parsing. They allow one to further minimize the number and complexity of water rules and make tolerant grammars extendible. In the experiments section, results of a large-scale LL and LR tolerant parsers testing on the basis of 9 open-source project repositories are presented.
Resolving Gendered Ambiguous Pronouns with BERT

Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity.This is an important task for natural language understanding and a necessary component of machine translation systems, chat bots and assistants.Neural machine learning systems perform far from ideally in this task, reaching as low as 73% F1 scores on modern benchmark datasets.Moreover, they tend to perform better for masculine pronouns than for feminine ones.Thus, the problem is both challenging and important for NLP researchers and practitioners.In this project, we describe our BERT-based approach to solving the problem of gender-balanced pronoun resolution.We are able to reach 92% F1 score and a much lower gender bias on the benchmark dataset shared by Google AI Language team.
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts

This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes.For the first two steps, we reuse a state-of-theart approach, focusing our contribution on the final entity-linking step.For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data.Our system (relaxed F1: 0.337-0.345)outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.
Personality Traits Recognition in Literary Texts

Interesting stories often are built around interesting characters.Finding and detailing what makes an interesting character is a real challenge, but certainly a significant cue is the character personality traits.Our exploratory work tests the adaptability of the current personality traits theories to literal characters, focusing on the analysis of utterances in theatre scripts.And, at the opposite, we try to find significant traits for interesting characters.Our preliminary results demonstrate that our approach is reasonable.Using machine learning for gaining insight into the personality traits of fictional characters can make sense.
Composite nonlinear multiset canonical correlation analysis for multiview feature learning and recognition

Summary In this paper, we propose a composite nonlinear multiset canonical correlation projections (CNMCPs) framework where orthogonal constraints are imposed in each set. This makes CNMCP capable of learning uncorrelated low‐dimensional features with minimum redundancy in Hilbert space. With the CNMCP framework, we further present a particular algorithm called multikernel multiset canonical correlations or mKMCC, which introduces different weights into multiple nonlinear functions in all views. An alternating iterative optimization is designed for computational solution. Numerous experimental results on practical datasets have demonstrated the effectiveness and robustness of mKMCC, in contrast with existing kernel correlation learning approaches.
A comparative analysis of tools for visualizing association rules: A proposal for visualising fuzzy association rules

Discovering new trends and co-occurrences in massive data is a key step when analysing social media, data coming from sensors, etc. Although, nowadays Data Mining is very useful and widely used for the industry, business and government, the main problem of application of machine learning or data mining in other fields is the interpretability and complexity of obtained results for non-expert users in computer or data science.For this reason in the KDD process one of the most important phases is the interpretation and evaluation.In the case of association rules is essential that results are interpretable for experts.One of the most useful tools for this goal is the visualization, because it clarifies the interpretation of results, being easier to understand in order to take a decision or explaining the behaviour of data.
Going from Analogue to Digital: A Study of Documentation Methods during an Excavation of the Neolithic Flint Mines at Pilbladet, Sweden

The ‘digital turn’ in archaeology has resulted in documentation, analysis, visualization and repository requirements becoming increasingly digital in recent years. However, we are only at the beginning of understanding how the shift from analogue to digital affects archaeological interpretation, as attention has mainly been directed towards technological aspects. However, how archaeology is executed influences the production of archaeological knowledge, and additional research into digital practices and their consequences is needed. During the latest excavation in 2014 of the Neolithic flint mines of Södra Sallerup, in Malmö in southern Sweden, several recording methods were used to document the remains in plan, including hand drawing, digital mapping with GPS and digital photography using a camera mounted on a pole. The records were used to create both a digital plan as well as georeferenced orthophotos from a 3D model and from photomosaic. The aim was to produce a record comparable to previous documentation from decades of archaeological excavations of the flint mines in the area, as well as one that is up-to-date with today’s digital standards. The methods are described and their consequences for the archaeological results are discussed.
Fault Diagnosis of Rolling Bearing Based on SDAE and PSO-DBN

A new fault diagnosis method for rolling bearing based on two-step cascaded system with two deep network is presented in this paper. In view of the low accuracy of traditional diagnosis algorithm, Stacked Denoising Auto-Encoder (SDAE) model as the first network is used to extract the basic and shallow feature of the fault signal; in order to acquire more robust and deep feature representation, Deep Belief Network (DBN) is configured as the second network. However, as for specific fault diagnosis problems, the number of hidden layer nodes, learning rate and momentum factor will directly affect the diagnosis result of DBN model. Therefore, this paper adopts particle swarm optimization (PSO) algorithm to adaptively select the hyper-parameters of DBN to determine the optimal structure of network, finally realizes the classification of multiple faults. Rolling bearing fault simulation and experiments have been conducted under single load condition to verify the effectiveness of the proposed algorithm. Experimental results obviously demonstrate that, from the aspects of generalization capability and classification performance, this algorithm is superior to support vector machine (SVM), back propagation neural network (BPNN) and grey relational analysis (GRA).
Advanced Medical Imaging Analytics in Breast Cancer Diagnosis

Modern imaging technique provides a fast, noninvasive means to study physiologic, metabolic, and molecular processes in the body. Imaging is the primary means in clinical cancer practice to facilitate diagnosis, prognosis, and treatment evaluation. While breast cancer contributes to 25% of morbidity in all cancer and is the second leading cause of cancer death in women, it is also one of the most treatable malignancies if detected early. In this chapter, we present an overview of research using advanced imaging analytics tools on Digital Mammography (DM) to improve the sensitivity and specificity of breast cancer detection. Currently, there are two dominating trends in the advanced imaging analytics field: texture analysis and deep learning. We implement three texture analysis algorithms: Gray Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), and Gabor Filter and one deep learning network: ResNet. Gradient Boosted Tree (GBT) classifier is then developed on the features to diagnose the lesion as malignant vs. benign. The classifier using texture features from each texture analysis algorithm has an accuracy of 0.82, 0.72, and 0.72 for GLCM, LBP, and Gabor, respectively. If the texture features from different texture analysis algorithms are pooled together, the classifier has an accuracy of 0.81. The same classifier using features extracted from ResNet has an accuracy of 0.89 indicating the potentials of deep learning in medical imaging for disease diagnosis.
UPC: Learning Universal Physical Camouflage Attacks on Object Detectors.

In this paper, we study physical adversarial attacks on object detectors inthe wild. Prior arts on this matter mostly craft instance-dependentperturbations only for rigid and planar objects. To this end, we propose tolearn an adversarial pattern to effectively attack all instances belonging tothe same object category (e.g., person, car), referred to as Universal PhysicalCamouflage Attack (UPC). Concretely, UPC crafts camouflage by jointly foolingthe region proposal network, as well as misleading the classifier and theregressor to output errors. In order to make UPC effective for articulatednon-rigid or non-planar objects, we introduce a set of transformations for thegenerated camouflage patterns to mimic their deformable properties. Weadditionally impose optimization constraint to make generated patterns looknatural for human observers. To fairly evaluate the effectiveness of differentphysical-world attacks on object detectors, we present the first standardizedvirtual database, AttackScenes, which simulates the real 3D world in acontrollable and reproducible environment. Extensive experiments suggest thesuperiority of our proposed UPC compared with existing physical adversarialattackers not only in virtual environments (AttackScenes), but also inreal-world physical environments. Codes, models, and demos are publiclyavailable at https://mesunhlf.github.io/index_physical.html.
Improving Usable-Security of Web based Healthcare Management System through Fuzzy AHP

Retracted: After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IJACSA`s Publication Principles. We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
Replaceability and negotiation in a cloud service ecosystem

Abstract Cloud federation is an aggregation of services from different providers in a single pool supporting interoperability and resource migration. In federation, Services are assigned to the consumer’s service access pool as per their specific functional and associated Quality level requirements. The said assignment is based on the advertised features of services. Sometimes, the selected provider fails to provide the committed service or, it fails to fulfill the expected QoS level. As a result, the consumer is being deprived of getting the services at required quality levels, in spite of subscribing and paying. Re-federation i.e. the inclusion of new services from different providers in the resource pool is a solution. This costly and time consuming re-federation process harms the overall harmony, reputation and performance of the existing federation. In this paper, the necessary strategies to make a federation autonomic is proposed. It helps federation to work in a self-adaptive manner by delaying the re-federation process through replacement and negotiation mechanisms. This allows the federation to keep a balanced state in case of failures. The proposed methods are simulated and the claims are substantiated by the preliminary experimental outcomes.
Quantum Random Oracle Model for Quantum Public-Key Encryption

Random oracle model is a general security analysis tool for rigorous security proof and effective cryptographic protocol design. In the quantum world, the attempts of constructing a quantum random oracle (QRO) have been made, such as quantum-accessible random oracle for post-quantum cryptography and quantum random oracle for quantum digital signature. As in the classical circumstance, it is crucial and challenging to design and instantiate the QRO model with an appropriate quantum hash function. In this work, we construct a QRO model for quantum public-key encryption against key-collision attack, due to the near-orthogonality property of the QRO. To explore a feasible instantiation procedure in the quantum setting, we distinctively give two instantiation examples of QRO by means of single-qubit rotation and quantum fingerprinting, and compare the numerical results of their performances under the key-collision attack. As a result, we extend the QRO model to the security analysis of quantum public-key encryption beyond quantum digital signature, and immunity from collision-type attacks.
Examining the Combination of Multi-Band Processing and Channel Dropout for Robust Speech Recognition

A pivotal question in Automatic Speech Recognition (ASR) is the robustness of the trained models.In this study, we investigate the combination of two methods commonly applied to increase the robustness of ASR systems.On the one hand, inspired by auditory experiments and signal processing considerations, multi-band band processing has been used for decades to improve the noise robustness of speech recognition.On the other hand, dropout is a commonly used regularization technique to prevent overfitting by keeping the model from becoming over-reliant on a small set of neurons.We hypothesize that the careful combination of the two approaches would lead to increased robustness, by preventing the resulting model to overrely on any given band.To verify our hypothesis, we investigate various approaches for the combination of the two methods using the Aurora-4 corpus.The results obtained corroborate our initial assumption, and show that the proper combination of the two techniques leads to increased robustness, and to significantly lower word error rates (WERs).Furthermore, we find that the accuracy scores attained here compare favourably to those reported recently on the clean training scenario of the Aurora-4 corpus.
DiaHClust: an Iterative Hierarchical Clustering Approach for Identifying Stages in Language Change

Language change is often assessed against a set of pre-determined time periods in order to be able to trace its diachronic trajectory.This is problematic, since a pre-determined periodization might obscure significant developments and lead to false assumptions about the data.Moreover, these time periods can be based on factors which are either arbitrary or non-linguistic, e.g., dividing the corpus data into equidistant stages or taking into account language-external events.Addressing this problem, in this paper we present a data-driven approach to periodization: 'DiaHClust'.Di-aHClust is based on iterative hierarchical clustering and offers a multi-layered perspective on change from text-level to broader time periods.We demonstrate the usefulness of Di-aHClust via a case study investigating syntactic change in Icelandic, modelling the syntactic system of the language in terms of vectors of syntactic change.
Gaussian Process Models of Sound Change in Indo-Aryan Dialectology

This paper proposes a Gaussian Process model of sound change targeted toward questions in Indo-Aryan dialectology.Gaussian Processes (GPs) provide a flexible means of expressing covariance between outcomes, and can be extended to a wide variety of probability distributions.We find that GP models fare better in terms of some key posterior predictive checks than models that do not express covariance between sound changes, and outline directions for future work.
Design of Mobile Robot Based on Cartographer SLAM Algorithm

Aiming at some problems existing in the development and industrialization of intelligent autonomous mobile robot, the SLAM mobile robot platform is designed and developed in this paper. Cartographer is used as the SLAM algorithm to realize the mapping and localization of the robot in the unknown environment. What’s more, the new hardware design architecture is proposed, and the bottom control module is designed to be applied to the autonomous mobile robot. On the one hand, the design can reduce the workload of the non-algorithm part of the main control unit and improve the real-time of mapping. On the other hand, it solves the problem of communication clogging and enhances the compatibility of the system. Finally, an autonomous test platform is built to test the mapping and localization functions of the robot. The results of mapping, computational complexity and robustness of Cartographer SLAM and Hector SLAM algorithms are analyzed.
Grammar and Meaning: Analysing the Topology of Diachronic Word Embeddings

The paper showcases the application of word embeddings to change in language use in the domain of science, focusing on the Late Modern English period (17-19th century).Historically, this is the period in which many registers of English developed, including the language of science.Our overarching interest is the linguistic development of scientific writing to a distinctive (group of) register(s).A register is marked not only by the choice of lexical words (discourse domain) but crucially by grammatical choices which indicate style.The focus of the paper is on the latter, tracing words with primarily grammatical functions (function words and some selected, polyfunctional word forms) diachronically.To this end, we combine diachronic word embeddings with appropriate visualization and exploratory techniques such as clustering and relative entropy for meaningful aggregation of data and diachronic comparison.
BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer

This paper describes the Cognitive Computation (CogComp) Group’s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop. The final model submitted is a multi-source neural NER system with multilingual BERT embeddings, trained on the concatenation of training data in various Slavic languages (as well as English). The performance of our system on the official testing data suggests that multi-source approaches consistently outperform single-source approaches for this task, even with the noise of mismatching tagsets.
WhatsApp as an Educational Support Tool in a Saudi University

WhatsApp is a widely used social media app, growing in popularity across the Middle East, and the most popular in Saudi Arabia.In this paper, we investigate the usage of WhatsApp as an educational support tool in a Saudi university.An online survey was constructed to ascertain how students and staff feel about and utilize WhatsApp as part of their daily studies.It also aimed to gather their thoughts on other platforms offered by the university such as Blackboard and email.The survey was tested and the results analyzed for frequency distributions, mean score, and standard deviation.Our results from nearly 200 student and staff members reveals that WhatsApp is heavily utilized for a variety of educational support tasks and greatly preferred over the other platforms.We propose that WhatsApp has good potential to support not only student coordination, information dissemination and simple enquiries but also to support formal teaching and out-of-class learning.