Reviewerly/exHarmony huggingface dataset:
Debiasing Word Embeddings Improves Multimodal Machine Translation

In recent years, pretrained word embeddings have proved useful for multimodal neural machine translation (NMT) models to address the shortage of available datasets. However, the integration of pretrained word embeddings has not yet been explored extensively. Further, pretrained word embeddings in high dimensional spaces have been reported to suffer from the hubness problem. Although some debiasing techniques have been proposed to address this problem for other natural language processing tasks, they have seldom been studied for multimodal NMT models. In this study, we examine various kinds of word embeddings and introduce two debiasing techniques for three multimodal NMT models and two language pairs -- English-German translation and English-French translation. With our optimal settings, the overall performance of multimodal models was improved by up to +1.93 BLEU and +2.02 METEOR for English-German translation and +1.73 BLEU and +0.95 METEOR for English-French translation.
CATI: An Active Learning System for Event Detection on Mibroblogs’ Large Datasets

Today, there are plenty of tools and techniques to perform text- or image-based classification of large datasets, targeting different levels of user expertise and abstraction. Specialists usually collaborate in projects by creating ground truth datasets and do not always have deep knowledge in Information Retrieval. This article presents a full platform for assisted binary classification of very large textual and text and image composed documents. Our goal is to enable human users to classify collections of several hundred thousand documents in an assisted way, within a humanly acceptable number of clicks. We propose a graphical user interface, based on several classification assistants: text- and image-based event detection, Active Learning (AL), search engine and rich visual metaphors to visualize the results. We also propose a novel query strategy in the context of Active Learning, considering the top unlabeled bi-grams and duplicated (e.g. re-tweeted) content in the target corpus to classify. These contributions are supported not only by a tool whose code is freely accessible but also by an evaluation of the impact of using the aforementioned methods on the number of clicks needed to reach a stable level of accuracy.
Augmented Semantic Explanations for Collaborative Filtering Recommendations

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the rating for unseen items with high accuracy. However, they fail to justify their output. The main objective of this paper is to present a novel approach that employs Semantic Web technologies to generate explanations for the output of black box recommender systems. The proposed model significantly outperforms state-of-the-art baseline models in terms of the error rate. Moreover, it produces more explainable items than all baseline approaches.
A novel method of text detection in radiographic images

With the rapid development of industries such as nuclear power and shipbuilding, radiographic testing (RT) is widely used in these fields as an important means of weld inspection. It also produces a large number of radiographic films, which consume a great deal of manpower and material resources. It is therefore beneficial for the radiographic film to be digitised for storage and archiving. Text detection in RT weld images is an important prerequisite for the archiving of digitised films. This paper proposes a novel text detection method that employs mask convolution and frequency-domain filtering, which can detect text at different positions, with different fonts and of different sizes in RT weld images. The method is evaluated using 366 different images and shows significant efficacy for text detection in RT weld images, with the precision value reaching 96%. The method used in this paper is also compared with other methods that are commonly used in other fields and the results show that the proposed method gives improved results compared to state-of-the-art methods.
Active Learning and User Segmentation for the Cold-start Problem in Recommendation Systems

Recommendation systems, which are employed to mitigate the information overload faced by e-commerce users, have succeeded in aiding customers during their online shopping experience. However, to be able to make accurate recommendations, these systems require information about the items for sale and information about users’ individual preferences. Making recommendations to new customers, with no prior data in the system, is therefore challenging. This scenario, called the “cold-start problem,” hinders the accuracy of recommendations made to a new user. In this paper, we introduce the popular users personalized predictions (PUPP) framework to address cold-starts. In this framework, soft clustering and active learning is used to accurately recommend items to new users. Experimental evaluation shows that the PUPP framework results in high performance and accurate predictions. Further, focusing on frequent, or so-called “popular,” users during our active-learning stage clearly benefits the learning process.
Contact Pattern of Alveolar Consonants in the Malay Consonants of Paralysis Subject using Electropalatography

Articulation is a combination between respiratory organs (lungs, throat, vellum, mouth, and nose) and speech organs or articulators (lips, tongue, soft palate, hard palate and teeth) in order to produce speech sound [1].During speech, human produce many types of sound due to the characteristic of each sound.The place and manner of articulation, including the articulator are important elements to generate different speech sounds.In Malay language, there are six types of manner of articulation, and six types of place of articulation [2].The manners of articulation are plosive, nasal, affricate, fricative, approximant and lateral approximant while the places of articulation are, alveolar, postalveolar, velar and glottal [3].Speech is one of the essential elements in communication.Failure of speech may cause communicatio n breakdown.The failure of speech is caused by a number of problems, commonly health problem.Wendy et al. (2018) in their study stated that, there are differences in hearing and speech difficulties as well as facial appearances for the people Abstract: Place of articulation plays an important part to produce different sounds.Besides the place of articulation, tongue is also an active articulator during a continuous speech.During the speech, the tongue moves around creating different sounds when it is placed at different place of articulation.The movement of tongue is controlled by muscles.The lack of muscle movement will produce inactive tongue movement.Paralysis is an example of the muscle weakness in a person resulting in difficulties to move.Paralysis may occur due to several factors including stroke and spinal cord injury (SCI).One of the indirect effects of paralysis is slurred speech and difficulty in speaking.This study aims to determine the contact pattern of five paralysed subjects during speech production of alveolar consonants in the Malay Language.The subjects had paralysis due to different aetiologies and with different medical history backgrounds.All participants were required to produce five single consonants; /d/, /t/, /l/, /n/ and /s/.The data recording was done in a studio laboratory with a soundproof system.The device used for detecting the tongue and hard palate contact in this study was electropalatography (EPG).Subjects were required to wear the artificial palate consists of 62 sensors to detect the tongue and hard palate contact.The speech contact was analysed using Articulate Assistant 1.18 TM .The results were then compared with the average contact pattern of Malay speaker which had been obtained in the previous study.In conclusion, the subjects who had frequent treatments produced better articulation and the subjects with positive attitudes produced better articulation during the treatment process.
An Augmented Cell Segmentation in Fluorescent in Situ Hybridization Images

Fluorescence in situ hybridization (FISH) surpass previously available technology to become a foremost biological assay, which can provide reliable imaging biomarkers to diagnose cancer and genetic disorders in the cellular level. In order to guarantee the validity of the quality analysis in cell images, it is significant to accurately segment the cell touching regions. We previously structured a mini-U-net to precisely capture cell regions, but this method sometimes can not separate multiple cells that are attached to each other. This work aims to solve this matter by applying cell identification results to provide more accurate prior information for the watershed to describe the cell boundaries. Validation results on 458 cells showed that Dice coefficients and intersection over union were improved from 81.92% to 83.98% and from 68.34% to 73.83% (p=0.03), respectively. The improved results indicated that cell identification is an effective means to handie the cell touching and produce more accurate cell segmentation.
Analysis of rule-based machine translation and neural machine translation approaches for translating portuguese to LIBRAS

In this paper, we propose a rule-based machine translator for Brazilian Portuguese to Brazilian Sign Language translation. This translator was implemented using the part of speech tagging and lemmatization techniques with implementations in Aelius and CoGrOO, respectively. Then, we developed a convolutional translator seeking to replicate the rule-based translation with a sintetic corpus. In this corpus, preprocessing techniques such as substitution of names, numbers and spelling errors by symbols were applied to improve processing. The translator were tested on two corpus, Bosque e OpenSub (extracted from a site of subtitles), of 69 and 36.858 lines respectively, and compared with translations generated by interpreters and translations generated by the application VLibras (LAVID-UFPB).
Safe Triplet Screening for Distance Metric Learning

Distance metric learning has been widely used to obtain the optimal distance function based on the given training data. We focus on a triplet-based loss function, which imposes a penalty such that a pair of instances in the same class is closer than a pair in different classes. However, the number of possible triplets can be quite large even for a small data set, and this considerably increases the computational cost for metric optimization. In this letter, we propose safe triplet screening that identifies triplets that can be safely removed from the optimization problem without losing the optimality. In comparison with existing safe screening studies, triplet screening is particularly significant because of the huge number of possible triplets and the semidefinite constraint in the optimization problem. We demonstrate and verify the effectiveness of our screening rules by using several benchmark data sets.
Cloudlet Scheduling Based Load Balancing on Virtual Machines in Cloud Computing Environment

Management of cloud computing resources is critical, especially when several cloudlets are submitted simultaneously to cloud computing. Therefore, it is very important to use high efficient cloudlet scheduling techniques to guarantee efficient utilization of computing resources. This paper presents a two-phase approach, called SAAC , for scheduling cloudlets onto Virtual Machines ( VMs ) of cloud computing environment to balance workload on the available VMs and minimize makespan (i.e., the completion time at the maximum loaded VM ). In the first phase, the SAAC approach applies the Simulated Annealing ( SA ) to find a near optimal scheduling of the cloudlets. While, in the second phase, the SAAC approach improves the cloudlets distribution by applying the Ant Colony Optimization ( ACO ) considering the solution obtained by the SA as the initial solution. The SAAC approach overcomes the computational time complexity of the ACO algorithm and low solutions quality of the SA . The proposed approach is evaluated by using the CloudSim, and the results are compared with that obtained by the most recent algorithms in terms of schedule length, load balancing, and time complexity.
Implementation of a REDCap-Based Research Data Collection System in Cameroon.

Implementing electronic data collection for health research can be challenging in resource-limited settings, where electricity, Internet access, and study staff with computer training may be limited. Our team has established a successful research data infrastructure using the REDCap software at three HIV clinics and one coordinating center in Cameroon. We describe our recommended network architecture and guidance for study data teams working in similar settings.
Redundancy elimination for overlapping point clouds based on two-dimensional corresponding point pair constraints between adjacent camera stations in a grating projection rotation measurement system

A grating projection shape measurement system has been a commonly used method in the field of three-dimensional (3D) reconstruction in recent years, and global point cloud registration is a key step in this method. However, in the registration process, a large amount of low-precision overlapping redundant data (ORD) is generated between adjacent camera stations, which will seriously affect the speed and accuracy of later modeling. Therefore, how to eliminate these low-precision ORD is a major problem to be solved at present. Determining all overlapping 3D point pairs between two adjacent stations and deleting the points with low precision in the point pairs is the key to solving this problem. Therefore, based on an omnidirectional rotation measurement system, combined with the constraint relationships between the projection space and the acquisition space in the global registration process and the stereo-matching method of space conversion, an elimination algorithm for ORD with a two-dimensional (2D) phase constraint and a 2D pixel constraint is proposed. The experimental results show that the proposed algorithm can faster locate overlapping 3D point pairs between adjacent stations, with a higher elimination rate, and the accuracy of the overall point cloud is higher after the redundancy elimination.
The Computer Assisted Instruction Model Based on a Combination of Tutorial Model and Drill and Practice Model in the Instructional Design of Database Systems in Information Technology Colleges

The traditional face-to-face teaching methods in the class that emphasize teaching-centered learning systems caused teachers to be unable to deliver teaching material thoroughly because of the limited time.The Computer Assisted Instruction (CAI) model presents learning by using a variety of media, both in the form of text, images, and in the way of interactive videos, so that it helps the learning process effectively and helps students to set the pace of learning independently anytime and anywhere.Interconnection of the CAI model with online-based technology also helps students improve their learning creativity, because diverse learning resources support it.This paper presents CAI conceptual models based on a combination of Tutorial models and Drill and Practice models, which are supported by online technology to provide conceptual understanding at the beginning of learning and offer exercises to develop creativity and test students' abilities in the final stages of learning Database Systems.The evaluation results on limited trials show that the CAI model design based on a combination of Tutorial models and Drill and Practice models much motivates students to learn and improve mastery of competencies in the Database System.Another finding in this paper is the need to provide adequate guidance for the application of CAI in its use, and the obligation to provide more effective designs when CAI applications are designed to connect to systems that offer to teach resources online, so students really can use CAI as an auxiliary media active learning for independent learning.
A 2-Component Laplace Mixture Model: Properties and Parametric Estimations

Mixture distributions have received considerable attention in life applications.This paper presents a finite Laplace mixture model with two components.We discuss the model properties and derive the parameters estimations using the method of moments and maximum likelihood estimation.We study the relationship between the parameters and the shape of the proposed distribution.The simulation study discusses the effectiveness of parameters estimations of Laplace mixture distribution.
Evaluating Face Tracking for Political Analysis in Japanese News Over a Long Period of Time

TV news is the major source of political information for most of the people, exercising a strong influence on public opinion. Japanese news media try to carefully balance politicians’ representation, but it is important to empirically examine this balance longitudinally. However, it is a tedious, and in many cases impossible task to achieve manually, especially when the news archive covers over a decade. We therefore rely on automatic procedures to do it computationally rather than manually. In this paper, we compare the two face tracking methods as well as a traditional text-based method by using the same dataset of Japanese broadcasting news spanning over a decade. We evaluate the three methods against a manually curated random sample of NHK’s News 7, the flagship news program of the Japanese public broadcasting. The first tracking method is inherited from previous works used on the same dataset and based on traditional Viola-Jones detections and VGGFace for embeddings. Our second method uses modern deep learning techniques with MTCNN for face detection, and ResNet50 trained with VGGFace2 for embeddings. We not only demonstrate that our modern implementation outperforms the two other methods, but also discuss implications and application for social scientific studies.
The role of a linguistic club in the foreign language communicative competency development

The article deals with the process of foreign language communicative competency development at a technical university within the work of the linguistic club "Intelligent" of Lenin Ivanovo State Power Engineering University. The pedagogic conditions and methodological principles are explained. It is proved that the structure and variation of the linguistic club activities promotes development of the key components of foreign language communicative competency: linguistic, verbal, sociolinguistic, sociocultural, strategic, discursive competences. It is concluded that the linguistic club is an important component of teaching a foreign language at a technical university as it gives the opportunity for improvement for students of different levels, contributes to students' raising foreign language learning motivation and creative potential realisation. During the preparation for the events students' professional and personal competences are also developed.
Laser Printing of a Nano-Imager to Perform Full Optical Machine Learning

Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning [1], are transforming several fields ranging from clinical medicine to optical computing. Integrating full-optical neuromorphic architectures in opto-electronic devices (Fig. 1a) will lead to the near-term availability of clinically and industrially relevant applications such as real-time features detection and classification, image processing and optical implementation of computational intensive tasks such as matrix multiplication with low-power consumption, high-accuracy and ultra-fast processing speed [2].
Self-Supervised Correlational Monocular Depth Estimation using ResVGG Network

Self-supervised monocular depth estimation (SMDE) has recently received significant attention in computer vision. Leveraging the development of deep learning approaches, SMDE provides a solution to the applications of automation, navigation, and scene understanding. In this paper, we propose a novel training objective and learning network to perform a single image depth estimation in our convolutional neural network without the ground truth depth data. The proposed training objective enables the learning network to learn the stereo image correlation in training and estimates the image depth from a single input image in prediction. The proposed learning network ResVGG is a hybrid structure of Resnet50 and VGG-16. The proposed ResVGG has a similar performance as Resnet50 but needs much less computational costs. We demonstrate that our proposed method has competitive accuracy comparing to the current state-of-the-art on KITTI dataset and achieves the frame rates of 33 frame per second (FPS) in prediction using a single NVIDIA GTX 1080 GPU. Furthermore, the proposed method can potentially support visual odometry depth estimation.
Fostering Undergraduate Students’ Reading Performance in English Discourse through Problem-Based Learning Model

Choosing and applying appropriate models or strategies in teaching and learning reading comprehension may lead to enhance the quality of both teacher and students’ performance or achievement. Fostering students’ reading performance is the primary endeavor to master other English skills. This investigation has done in five main stages which cover pre-test, planning, acting, observing, reflecting, and evaluation of the results. This recent study expects to reveal and describe the application of Problem-Based Learning (PBL) in fostering the students’ reading English discourses. The instruments used in gathering the data were the observation sheets, questionnaire, and tests. There were 32 participants chosen to form the fourth-semester students which majoring in English education program of UIN Syarif Hidayatullah Jakarta. The data were analyzed qualitatively and quantitatively. The results show that the students’ scores since the pre-action test, first cycle up to the second cycle undergo increasing which tends to ‘positive category’ and ended with ‘good level’ (78 scores or high qualification category). It could be summarized that the application of PBL model towards EFL undergraduate students is effective in teaching and learning of English discourse reading comprehension.
Blockchain: A Software Engineering Perspective

Blockchain is intended to help enhance the trustworthiness of data and transactions, by chaining and distributing blocks of the same data so that they may become more transparent, traceable, and immutable. With such a great potential, blockchain is increasingly being explored in many applications, especially in the presence of (potential) dis-/mis-/un-trust among organizations and individuals, well beyond its original use, namely, for bitcoin. However, an ad hoc approach to adopting blockchain may lead to not better, but worse, situations and results, and with severe disappointments. In this talk, I will describe Fides - a framework for systematically utilizing blockchain towards enhancing business processes with trustworthiness. Fides takes a goal-oriented, software engineering approach, in which trust-related concerns are explicitly represented as (soft)goals to be achieved, problems for achieving the goals are diagnosed, and then alternatives are explored in terms of business processes for eliminating or alleviating the problems, while at the same time achieving the goals. Finally, a selection is made among the alternatives that best utilize blockchain. In this approach, business processes are modelled, rules are formulated at the requirements level for beneficially transitioning from as-is to to-be, and transformed into smart contracts. At the end, I will describe a couple of applications of Fides, for an initial assessment of its strengths and weaknesses.