Reviewerly/exHarmony huggingface dataset:
Building a Production Model for Retrieval-Based Chatbots

Response suggestion is an important task for building human-computer conversation systems. Recent approaches to conversation modeling have introduced new model architectures with impressive results, but relatively little attention has been paid to whether these models would be practical in a production setting. In this paper, we describe the unique challenges of building a production retrieval-based conversation system, which selects outputs from a whitelist of candidate responses. To address these challenges, we propose a dual encoder architecture which performs rapid inference and scales well with the size of the whitelist. We also introduce and compare two methods for generating whitelists, and we carry out a comprehensive analysis of the model and whitelists. Experimental results on a large, proprietary help desk chat dataset, including both offline metrics and a human evaluation, indicate production-quality performance and illustrate key lessons about conversation modeling in practice.
Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls

Humans can set suitable subgoals to achieve certain tasks. They can also set sub-subgoals recursively if required. The depth of this recursion is apparently unlimited. Inspired by this behavior, we propose a new hierarchical reinforcement learning architecture called RGoal. RGoal solves the Markov Decision Process (MDP) in an augmented state-action space. In multitask settings, sharing subroutines between tasks makes learning faster. A novel mechanism called thought-mode is a type of model-based reinforcement learning. It combines learned simple tasks to solve unknown complicated tasks rapidly, sometimes in zero-shot time.
Comparison of Gait Speed Estimation of Multiple Sensor-Based Technologies

In light of our aging population, there is an immediate need for non-obtrusive, continuous, and ubiquitous health monitoring technologies that will enable our population to age with a higher quality of life and independence. Research has demonstrated that gait indicators, such as walking speed, can reflect cognitive and physical functioning. However, gradual changes in such indicators usually go undetected until critical problems arise; being able to detect changes in indicators, such as gait deterioration, of older adults while in their home environments would enable clinicians to tailor more effective and personalized interventions by better understanding user behaviour in real-world settings. Real-world data is essential to enabling our healthcare system to act where patients most need help and to optimize the effect of designed eHealth solutions.
Elderly Action Recognition System with Location and Motion Data

The elderly population is experiencing high growth in most countries. In Malaysia, a majority of the households are dual-earner families, leaving their elderly parents at home. Without caretakers to look after them, aging parents may be susceptible to accidents. Therefore, an elderly action recognition system which can automatically identify their actions and whereabouts at home is needed. In this paper, the development and testing of a wearable device with motion detection and indoor positioning based on Random Forest Classifier is presented. The experiment was conducted in a home environment (MMU Digital Home Lab). The action recognition system utilizes a gyroscope and an accelerometer to detect different types of motion which are walking, sitting, standing, sitting-down, standing-up, falling, and sleeping. The action recognition system can measure these motions with a 97.6% accuracy. On the other hand, the indoor positioning uses Bluetooth Low Energy beacon and scanners to pinpoint five locations which are living room, bedroom, kitchen, office and toilet with a 97.25% accuracy. The results from the measured motions and indoor locations are pushed to an online Internet of Things (IoT) platform for action recognition, data logging and analytics.
On the Students’ Misconceptions in Object-Oriented Language Constructs

Analyze the Object-oriented (OO) source code developed by students provides useful formative tips to instructors. According to this, it is essential to understand the student’s real difficulties allowing instructors to shape effective courses. To provide run-time feedback to students and to study and analyze the evolution of their performances offline and over time we designed a framework and developed a tool. It allows to identify students’ misconceptions analysing source code and to create personalized student reports automatically. In this paper, we present an empirical study, conducted using our toolchain, that involves 1627 projects extracted from the multi-institution Blackbox dataset. We identified a violation model for Java language constructs based on established results in the computing education community. Afterwards, we grouped such violations in categories and analyzed the relations among them. Our contributions might be helpful in delivering formative feedback and supporting instructors who teach Java and object-oriented programming in general.
Your Opinions Let us Know: Mining Social Network Sites to Evolve Software Product Lines

Software product lines (SPLs) are complex software systems by nature due to their common reference architecture and interdependencies.Therefore, any form of evolution can lead to a more complex situation than a single system.On the other hand, software product lines are developed keeping long-term perspectives in mind, which are expected to have a considerable lifespan and a long-term investment.SPL development organizations need to consider software evolution in a systematic way due to their complexity and size.Addressing new user requirements over time is one of the most crucial factors in the successful implementation SPL.Thus, the addition of new requirements or the rapid context change is common in SPL products.To cope with rapid change several researchers have discussed the evolution of software product lines.However, for the evolution of an SPL, the literature did not present a systematic process that would define activities in such a way that would lead to the rapid evolution of software.Our study aims to provide a requirements-driven process that speeds up the requirements engineering process using social network sites in order to achieve rapid software evolution.We used classification, topic modeling, and sentiment extraction to elicit user requirements.Lastly, we conducted a case study on the smartwatch domain to validate our proposed approach.Our results show that users' opinions can contain useful information which can be used by software SPL organizations to evolve their products.Furthermore, our investigation results demonstrate that machine learning algorithms have the capacity to identify relevant information automatically.
Distance transform and template matching based methods for localization of barcodes and QR codes

Visual codes play an important role in automatic identification, which became an inseparable part of industrial processes. Thanks to the revolution of smartphones and telecommunication, it also becomes more and more popular in everyday life, containing embedded web addresses or other small informative texts. While barcode reading is straightforward in images having optimal parameters (focus, illumination, code orientation, and position), localization of code regions is still challenging in many scenarios. Every setup has its own characteristics, therefore many approaches are justifiable. Industrial applications are likely to have more fixed parameters like illumination, camera type and code size, and processing speed and accuracy are the most important requirements. In everyday use, like with smartphone cameras, a wide variety of code types, sizes, noise levels and blurring can be observed, but the processing speed is often not crucial, and the image acquisition process can be repeated in order for successful detection. In this paper, we address this problem with two novel methods for localization of 1D barcodes based on template matching and distance transformation, and a third method to detect QR codes. Our proposed approaches can simultaneously localize several different types of codes. We compare the effectiveness of the proposed methods with several approaches from the literature using public databases and a large set of synthetic images as a benchmark. The evaluation shows that the proposed methods are efficient, having 84.3% Jaccard accuracy, superior to other approaches. One of the presented approaches is an improvement on our previous work. Our template matching based method is computationally more complex, however, it can be adapted to specific code types providing high accuracy. The other method uses distance transformation, which is fast and gives rough regions of interests that can contain valid visual code candidates.
Pupil detection and tracking for AR 3D under various circumstances

Accurate pupil position detection and tracking of a driver plays an important role and is a prerequisite for augmented reality 3-dimensional head-up display (AR 3D HUD) systems of a vehicle. This paper aims to develop a robust, automated algorithm and system for detection and tracking of pupil centers of a user using a single visual camera and near-infrared (NIR) leds under dynamic driving circumstances. Our proposed pupil tracker consists of eyenose detection, keypoint alignment, and tracking with NIR led on/off control depending on illumination conditions. Eye-nose detection, which utilizes an error reinforcement learning method for selecting best learning DB, generates facial sub-region boxes including eyes and nose. The error reinforcement learning method uses only a small fraction (less than 5%) of the training DB, while improving the detection rate. Based on the detected region, eyenose alignment including pupil centers are then processed by Supervised Descent Method (SDM) with Scale-invariant Feature Transform (SIFT). Then, the pupil centers are tracked with Support Vector Machine (SVM) classifier and SIFT feature based tracking checker, which guarantees the aligned results contains pupil centers. This can be considered a different feature space and a strong classifier used in the eye-nose detection stage, such as Local Binary Pattern (LBP) and a set of weak classifiers (Adaboosting). However, the proposed pupil tracker cannot be applied under low light conditions. To achieve this, we add NIR leds and S/W functions to control the intensity of the NIR. After recognizing the contents of the pupil image, such as low light conditions, wearing eyeglasses or sunglasses, corresponding designated aligners are applied. We achieve fairly high detection rate (98%) and precise eye alignment (average error 2mm) even with challenging conditions, such as day and nighttime driving conditions.
HARPP: HARnessing the Power of Power sets for Mining Frequent Itemsets

Modern algorithms for mining frequent itemsets face noteworthy deterioration of performance when minimum support tends to decrease, especially for sparse datasets. Long-tailed itemsets, frequent itemsets found at lower minimum support, are significant for present-day applications such as recommender systems. In this study, we have developed a novel power set based method named as HARnessing the Power of Power sets (HARPP) for mining frequent itemsets. HARPP iteratively generates power sets to make combinations of overlapping varying-sized subsets of I, where I is a set of items in a large database. Intrinsic feature of creating power sets along with the use of set data structure ensures the agility of HARPP because most of its operations take constant running time. Without storing it entirely in memory, HARPP scans the dataset only once and mines frequent itemsets on the fly. In contrast to state-of-the-art, efficiency of HARPP increases with decrease in minimum support that makes it a viable technique for mining long-tailed itemsets. Performance study shows that HARPP is efficient and scalable, and is faster up to two orders of magnitude than FP-Growth algorithm at lower minimum support particularly when datasets are sparse.
Smartphone Utilization and Satisfaction in Community Dwelling Elderly

Background: Gerontechnology aims to give older people a healthier, more independent, and more sociallyengaged life. In contrast to the widespread use of smartphones by young people, the older generation use only a few functions of their smartphones. Therefore, this study aims to investigate smartphone utilization by older people, and identifies the relationship between the participant’s characteristics, satisfaction, and the utilization of smartphones. Method: This study was conducted as cross-sectional descriptive research. Data was collected from 104 elderly people with smartphones registered at their community service centers, in both rural and urban areas, from March 16th to 23rd. Pearson’s correlation coefficient was calculated to identify the relationship between participant’s characteristics, satisfaction, and the utilization of smartphones. Results: 4.28 functions of smartphones, including texting and taking pictures were used. The elderly within the younger age bracket (r=-.44, p.001), better physical function (r=.36, p.001), and better perceived health status (r=.24, p=.014) used a larger variety of smartphone functions. Conclusion: This study showed that the elderly mainly used smartphones for communication with others. As we consider smartphone’s various uses in the health and welfare areas, further studies investigating the barrier of smartphone usage will be required.
Solution to the slope-height ambiguity problem in phase measuring deflectometry based on a co-axial telecentric optical path

In recent years, phase measuring deflectometry (PMD) has become a popular method to measure the shapes of specular surfaces. However, the inherent slope-height ambiguity problem, which indicates the phase of the recorded fringe pattern is determined by both the height and slope of the test surface, causes considerable ambiguity errors in the measurement results. Existing error reduction methods usually require the initial shape information of the test surface or additional equipment to assist the measurement. These requirements make measurement inflexible in some situations. In this paper, the slope-height ambiguity problem in PMD is modelled and analyzed analytically for the first time, and the system geometry requirements of minimizing the ambiguity error are carried out accordingly. To meet these requirements, a co-axis optical path setup with a telecentric imaging system is proposed. In our simulation and experiment, this system reduces the phase and height errors by up to two orders of magnitude, compared to those from the normally employed system geometry setup.
Deep Semantic Role Labeling for Tweets using 5W1H: Who, What, When, Where, Why and How

In natural language understanding, Semantic Role Labeling (SRL) is considered as one of the important tasks and widely studied by the research community. State-of-the-art lexical resources have been in existence for defining the semantic role arguments with respect to the predicates. However, such lexical resources are complex in nature which is difficult to understand. Therefore, instead of the classical semantic role arguments, we adopted the concept of 5W1H (Who, What, When, Where, Why and How) for SRL. The 5W1H concept is widely used in journalism and it is much simpler and easier to understand as compared to the classical SRL lexical resources. In the recent years, recurrent neural networks (RNN) based end-to-end SRL systems have gained significant attention. However, all recent works have been developed for formal texts. This paper reports on the implementation of a deep neural network using the attention mechanism for extracting the 5W1H from tweets. Our implementation reports an F-1score of 88.21 which outperforms other recent Twitter SRL system by 28.72.
A PROPOSAL TO IMPLEMENT INTEGRATED GIS SYSTEM IN DISSEMINATING SPATIAL INFORMATION FOR MALAYSIA DEVELOPMENT APPROVAL (STAKEHOLDERS AND APPLICANTS) – A CONCEPT PAPER

Abstract. Every year, World Bank Group (WBG) is conducting assessment for 190 countries and produces a ranking report based on Doing Business performance – an initiative to measure benchmark efficiency on business regulation and their enforcement by a sample study (e.g. for Malaysia is Kuala Lumpur City Hall - DBKL). One of the ranking categories is based on Dealing with Construction Permits (DCP) which made us proud as announce Malaysia ranks for 2019 is #3 (based on 2018 assessment). This achievement was based on continues efforts and studies from the last bottom of #137 (since, 2007), #113 (2012) and continues with positive results (straight improvement) respectively till this year. Maintaining the top three ranks is not an easy task; however, is not impossible either. Lots of study in innovations, suggestions and implementation should be carried out to maintain and improve Malaysian DCP ranks. As per DCP of this year assessment, Work Bank has suggested all countries with 5 new suggestions (new additional marking schema). One of them is to engage Geographic Information System (GIS) for land use, cadastre, water, electricity and sewage related datasets. This paper propose an initial study (concept paper) towards engaging GIS in DCP activity as a response (first action) suggested by World Bank Group; crucial in maintaining DCP rank for 2020 as well as to provide better business regulations and enforcements for DBKL or other local authority. Later, an impact study will be conducted, utilizing new integrated GIS system publishing service (GeoMap) and the output of the study will indicate whether Malaysia Productive Corporation (MPC) should engage or ignore the 5th suggestion by World Bank Group, or to enhance with other initiatives.
Statistical Analysis for Detection of Sensitive Data Using Hadoop Clusters

The omnipresence of internet technology and the advent of smart devices accumulate varieties of voluminous, viscous real-time data in a network from varieties of sources and also facilitates a way for criminal, intruders to attack the network which persuades information theft, financial loss, cyber-attack, and cyberwar. It is the major challenge for researchers to determine sensitive data from large scale real-time data so that the right action can be taken at the right time. Therefore, it is important to purpose a framework to handle massive data and to detect sensitive data from big data. In this regard, the collected data from the network are stored in cloud drives, other storage devices and then transferred to Hadoop distributed file system and processed with MapReduce processing using distributed computing concepts, Machine learning algorithms, and advanced statistical methods. Statistical analytical tools like Descriptive Analysis, Regression, ANOVA, Parametric Levene's test, Pearson Correlation, and Kruskal-Wallis Test present that recent big data analytical tools are effective than the traditional method for retrieval of sensitive data from Big Data.
Digital Watermark System with an Ability of its Extraction from Hard Copies of Data

In this paper it is presented Digital Watermark System for color images. The main feature of this system is an ability to extract digital watermarks even after printing and following scanning of watermarked images. There is a description of algorithms for embedding and extracting of additional information. These methods are based on the usage of spread-spectrum signals in the frequency domain. Furthermore, there is described algorithms of distortion correction after printing out and following scanning the paper copies of digital data. The results of the experimental research on evaluation of a possible embedding volume and the reliability after extraction of the embedded data are also presented.
Performance Evaluation of a Generative Adversarial Network for Deblurring Mobile-phone Cervical Images

Visual examination forms an integral part of cervical cancer screening. With the recent rise in smartphone-based health technologies, capturing cervical images using a smartphone camera for telemedicine and automated screening is gaining popularity. However, such images are highly prone to image corruption, typically out-of-focus target or camera shake blur. In this paper, we applied a generative adversarial network (GAN) to deblur mobile-phone cervical (MC) images, and we evaluate the deblur quality using various measures. Our evaluation process is three-fold: first, we calculate the peak signal to noise ratio (PSNR) and the structural similarity (SSIM) of a test dataset with ground truth availability. Next, we calculate the perception based image quality evaluator (PIQE) score of a test dataset without ground truth availability. Finally, we classify a dataset of blurred and the corresponding deblurred images into normal/abnormal MC images. The resulting change in classification accuracy was our final assessment. Our evaluation experiments show that deblurring of MC images can potentially improve the accuracy of both manual and automated cancerous lesion screening.
Locking the Design of Building Blocks for Quantum Circuits

The research community expects that quantum computers will give economical results for particular problems on which the classical computers break down. Examples include factoring of large numbers, searching in a big database, or simulating chemical reactions to design new drugs. Attempts are ongoing to build up a practical quantum computer. Users (clients) can implement quantum circuits to run on these quantum computers. However, before running the quantum circuit on the quantum computer, the users (clients) should compile, optimize, decompose, and technology map the quantum circuit. In the current embodiment, the resulting quantum circuit runs on a remote and untrusted quantum computer server -- introducing security risks. This study explores the risk of outsourcing the quantum circuit to the quantum computer by focusing on quantum oracles. Quantum oracles are pivotal building blocks and require specialized expertise and means to design. Hence, the designer may protect this proprietary quantum oracle intellectual property (IP) and hide his/her private information. We investigate how to manage that on a quantum computer server using the IBM project QX quantum computer and Qiskit tools as an exemplar.
Conjugate Priors and Posterior Inference for the Matrix Langevin Distribution on the Stiefel Manifold

Directional data emerges in a wide array of applications, ranging from atmospheric sciences to medical imaging.Modeling such data, however, poses unique challenges by virtue of their being constrained to non-Euclidean spaces like manifolds.Here, we present a unified Bayesian framework for inference on the Stiefel manifold using the Matrix Langevin distribution.Specifically, we propose a novel family of conjugate priors and establish a number of theoretical properties relevant to statistical inference.Conjugacy enables translation of these properties to their corresponding posteriors, which we exploit to develop the posterior inference scheme.For the implementation of the posterior computation, including the posterior sampling, we adopt a novel computational procedure for evaluating the hypergeometric function of matrix arguments that appears as normalization constants in the relevant densities.