Reviewerly/exHarmony huggingface dataset:
Optimising Layered Video Content Delivery Based on Satellite and Terrestrial Integrated 5G Networks

Multimedia delivery over satellite network may be considered as a promising service in the emerging 5G cellular system. However, content delivery over the satellite link poses an enormous challenge to the service provider due to its long latency. The objective of this paper is to develop a video streaming framework over satellite and terrestrial integrated 5G content delivery infrastructure, which is investigated in the EU 5GPPP phase 2 SaT5G Project where satellite link is used as an additional channel as 5G backhaul. In this work, clients send SVC (Scalable Video Coding) layer download request to the Multi-access Edge Computing (MEC) server, which is able to dynamically select a backhaul link (Satellite or terrestrial) for each layer download request according to specific contexts. The purpose is to achieve optimised content load distribution between the two types of 5G backhaul links. Experiments conducted reveal that the proposed framework is able to achieve a good traffic offloading percentage without compromising with QoE of the end-user.
Scalable Machine Learning in the R Language Using a Summarization Matrix

Big data analytics generally rely on parallel processing in large computer clusters. However, this approach is not always the best. CPUs speed and RAM capacity keep growing, making small computers faster and more attractive to the analyst. Machine Learning (ML) models are generally computed on a data set, aggregating, transforming and filtering big data, which is orders of magnitude smaller than raw data. Users prefer “easy” high-level languages like R and Python, which accomplish complex analytic tasks with a few lines of code, but they present memory and speed limitations. Finally, data summarization has been a fundamental technique in data mining that has great promise with big data. With that motivation in mind, we adapt the $$\varGamma $$ (Gamma) summarization matrix, previously used in parallel DBMSs, to work in the R language. $$\varGamma $$ is significantly smaller than the data set, but captures fundamental statistical properties. $$\varGamma $$ works well for a remarkably wide spectrum of ML models, including supervised and unsupervised models, assuming dimensions (variables) are either dependent or independent. An extensive experimental evaluation proves models on summarized data sets are accurate and their computation is significantly faster than R built-in functions. Moreover, experiments illustrate our R solution is faster and less resource hungry than competing parallel systems including a parallel DBMS and Spark.
Model View Controller in iOS mobile applications development

Due to the increased number of mobile applications and their popularity, many software developers have begun to focus on mobile platforms.While this focus has positive effects (e.g. a larger developer community, new open source projects, new tools), it also has a down side.With the migration of developers from different software development areas, where they have used other programming paradigms or architectural approaches, the topic of software architecture on mobile platforms become more trending and hype in the mobile development communities.Even though several new architectural solution were proposed for solving some of the issues which arise from using the classical architectural patterns popularised by the creators of the mobile platforms, we want to emphasise the principles of software architecture in mobile computing, why they have to be respected and how their adoption impacts the development process.Therefore, this paper focuses on showing that the Model View Controller (MVC) -one of the most common classical architectural patterns -can be used successfully for building mobile applications and the problems which might arise are by products of the wrong usage of the pattern rather than pattern issues.We show that by analysing the most common architectural misuses of the MVC pattern in both open-source and private projects and offers solutions to those problems.
Perancangan Flowchart dan Pseudocode Pembelajaran Mengenal Angka dengan Animasi untuk Anak PAUD Rambutan

The number learning process is a effort that can be made from early childhood to children with age up to six years as a stimulation of education to facilitate the physical and spiritual development neededin further education.The development of smartphone technology rapildly spreading to the public has produced many negative effects.However, if used in the right waysbyrightpeople, smartphone can be a medium that can actually produce positive effectson student learning.The purpose of this study is to designa number learning application in a smartphone for early childhood so that they can makea positive use of smartphones for their development.Based on the result of the research, there are 2 application designs, namely the one using the flowchart algorithm and the one using Pseudocode,which have been adjusted to the needs of PAUD Rambutan.There are 2 menus, namely: learn and write numbers.
Teaching English to Young Learners in Indonesia (Pros and Cons)

Teaching English for young learners in Indonesia still invites the controversies among the educators and psycholinguists that concern on learners’ development in thinking and communication. Because teaching English for young learners in Indonesia seems to force them in knowing the rule and forms of the sentence. In addition, some of the teachers who teach English for young learners have not the appropriate educational background that can teach the younger as well as the objective wish. Actually, there is not any regulation or decisions that obligate the school to teach English to young learners. It only depends on the school itself. In this paper, the writer would like to describe the teaching English to young learners in Indonesia in the side of its implementation and also the pros and cons based on the second language acquisition experts.
Multiple Choice Question Answering in the Legal Domain Using Reinforced Co-occurrence

Nowadays, the volume of legal information available is continuously growing. As a result, browsing and querying this huge legal corpus in search of specific information is currently a tedious task exacerbated by the fact that data presentation does not usually meet the needs of professionals in the sector. To satisfy these ever-increasing needs, we have designed an appropriate solution to provide an adaptive and intelligent solution for the automatic answer of questions of legal content based on the computation of reinforced co-occurrence, i.e. a very demanding type of co-occurrence that requires large volumes of information but guarantees good results. This solution is based on the pattern-based methods that have been already successfully applied in information extraction research. An empirical evaluation over a dataset of legal questions seems to indicate that this solution is promising.
SENIOR TOURISTS’ PERCEPTIONS OF BUS STOP ENVIRONMENTS USED IN COLLABORATIVE DESIGN

Presently, universal accessibility is seen as an attribute of sustainable cities and territories, as it contributes to the quality of built environments, friendly environmental behaviors and social inclusion.Accessible built environments are required; so urban spaces, buildings, transport vehicles, information technology communication and services must include the process of "Age Sensitive Design" that attends to ageing and considers the perceptions of elderly people, who tend to have mobility, vision, hearing and cognitive issues or problems.Active ageing requires that there be an accessible tourism strategy, where the conditions related with elderly peoples' mobility are important.The research project Accessibility for All in Tourism (ACCES4ALL) focuses on modal interfaces that are designed to be age friendly.Its main objective is to develop a pilot study: an accessible, smart and sustainable bus stop to be located at Faro International Airport.The layout is beyond the consideration of international technical norms and considers human diversity.The design process is collaborative, focusing on the users' needs.It is a humanized process that requires an integrated team of civil engineering, architecture, geography, design, information, and communication and tourism professionals, who consider senior tourists' perceptions of bus stop environments.A questionnaire was developed for the elderly tourist aged 60 or over, about their mobility and perceptions of bus stop environments, and the importance of some attributes for their mobility.This paper describes some results of the ACCES4ALL project that indicate elderly tourists have the perception of an accessible bus stop's attributes, mainly in urbanistic aspects: sidewalks and bus stops with barrier-free space, non-slip surfaces, well lit, with soft ramps.They consider it important to have shelters with places to rest.Most consider that tactile and chromatic markings, QR (quick response) codes and near field communication (NFC) technology are not important for their mobility.
False positive rate control for positive unlabeled learning

Learning classifiers with false positive rate control have drawn intensive attention in applications over past years. While various supervised algorithms have been developed for obtaining low false positive rates, they commonly require the coexistence of both positive and negative samples in data. However, the scenario studied in positive unlabeled (PU) learning is more pervasive in practice. Namely, at inception, most of the data may not have known labels, and the data with known labels may only represent one type of samples. To tackle this challenge, in this paper we propose a new positive unlabeled learning classifier with false positive rate control. In particular, we first prove that in this context employing oft-adopted convex surrogate loss functions, such as the hinge loss function, begets a redundant penalty for false positive rates. Then, we present that the non-convex ramp loss surrogate function can overcome this barrier and show a concave-convex procedure can solve the associated non-convex optimization problem. Finally, we demonstrate the effectiveness of the proposed method through extensive experiments on multiple datasets.
Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision

Jianhao Yan, Lin He, Ruqin Huang, Jian Li, Ying Liu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Attention Based Semi-Supervised Dictionary Learning for Diagnosis of Autism Spectrum Disorders

Diagnosis of Autism spectrum disorder (ASD) has rapidly developed by using technologies of neuroimaging and machine learning. However, there are still two crucial issues that degrades the performance of existing diagnosis methods of ASD. On one hand, the potential relationships between different samples (e.g., patients and health controls) are ignored. The other challenge is that there are usually a limited number of labeled training samples in practical applications. In order to resolve above problems, in this paper we propose a novel attention based semi-supervised dictionary learning (ASSDL) for diagnosis of ASD by exploring the discrimination embedded in the unlabeled data. In the proposed ASSDL, the discriminative relationship hidden in the samples is exploited by learning a class-specific dictionary with designed classcommonality and sample-particularity representation regularizations; while for the unlabeled data, an attention scheme incorporating class-specific reconstruction error is proposed in the label estimation. A good class estimation of unlabeled data and a discriminative dictionary learning will benefit to each other, making the dictionary based classifier better and better. We present an efficient algorithm to minimize the derived objective function and the proposed method shows the promising performance on the four publicly available autism datasets.
Improve Language Modelling for Code Completion by Tree Language Model with Tree Encoding of Context (S)

In last few years, using a language model such as LSTM to train code token sequences is the state-of-art to get a code generation model.However, source code can be viewed not only as a token sequence but also as a syntax tree.Treating all source code tokens equally will lose valuable structural information.Recently, in code synthesis tasks, tree models such as Seq2Tree and Tree2Tree have been proposed to generate code and those models perform better than LSTMbased seq2seq methods.In those models, encoding model encodes user-provided information such as the description of the code, and decoding model decodes code based on the encoding results of user-provided information.When applying decoding model to decode code, current models pay little attention to the context of the already decoded code.According to experiments, using tree models to encode the already decoded code and predicting next code based on tree representations of the already decoded code can improve the decoding performance.Thus, in this paper, we propose a novel tree language model (TLM) which predicts code based on a novel tree encoding of the already decoded code (context).The experiments indicate that the proposed method outperforms state-of-arts in code completion.
A Survey of Segmentation, Annotation, and Recommendation Techniques in Micro Learning for Next Generation of OER

With the fast development of Internet technologies and mobile devices, space-time boundaries of people's daily activities become blurred. This makes people utilizing their fragmented time become possible. In recent years, micro learning, which aims to make good use of people's fragmented time and deliver micro-format of open education resource to learners, has drawn wider attention. The generation and delivery of micro learning materials are two essential steps for the micro learning service. This paper first discusses the characteristics of three significant stages of a sophisticated micro learning system: segmentation, annotation, and recommendation, for learning materials. Then various state-of-the-art techniques for different processing stages are reviewed in this survey. Different segmentation and annotation strategies based on different information sources (such as content and users' interaction) are demonstrated and analysed. Soft computing, transfer learning, reinforcement learning, and context-aware techniques are also compared and discussed for solving different difficulties in recommending scenarios. We contribute this paper as the first work focusing on the three-phased techniques in micro learning.
Low-rank tensor completion with fractional-Jacobian-extended tensor regularization for multi-component visual data inpainting

Several low-rank tensor completion methods have been integrated with total variation (TV) regularization to retain edge information and promote piecewise smoothness. In this paper, we first construct a fractional Jacobian matrix to nonlocally couple the structural correlations across components and propose a fractional-Jacobian-extended tensor regularization model, whose energy functional was designed proportional to the mixed norm of the fractional Jacobian matrix. Consistent regularization could thereby be performed on each component, avoiding band-by-band TV regularization and enabling effective handling of the contaminated fine-grained and complex details due to the introduction of a fractional differential. Since the proposed spatial regularization is linear convex, we further produced a novel fractional generalization of the classical primal-dual resolvent to develop its solver efficiently. We then combined the proposed tensor regularization model with low-rank constraints for tensor completion and addressed the problem by employing the augmented Lagrange multiplier method, which provides a splitting scheme. Several experiments were conducted to illustrate the performance of the proposed method for RGB and multispectral image restoration, especially its abilities to recover complex structures and the details of multi-component visual data effectively.
Learning Behavior Recognition and Analysis by Using 3D Convolutional Neural Networks

Due to the diversity of human behavior, scene noise, camera view angle and other characteristics, the difficulty of recognizing human behavior has increased. Nowadays, there are more and more problems, such as low learning efficiency or excessive mental pressure. To a large extent, these problems are caused by an unreasonable distribution of work and rest. Therefore, this paper proposes a learning behavior recognition method based on the structure of 3D convolutional neural networks (CNN). This involves taking several continuous frames of video as a group, through effective training, after the convolution and pooling operation to extract the action information in the features. Finally, the recognition and classification results are obtained through the full connection layer and the classifier. The experimental results show that this method is accurate and fast. Taking the KTH dataset as an example, the methods of improving test accuracy under different parameters were compared and discussed, which provided a theoretical basis for learning behavior dataset recognition.
Populations of spiking neurons for reservoir computing: Closed loop control of a compliant quadruped

Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing framework. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.
Locally differentially private continuous location sharing with randomized response

With the growing popularity of fifth-generation-enabled Internet of Things devices with localization capabilities, as well as on-building fifth-generation mobile network, location privacy has been giving rise to more frequent and extensive privacy concerns. To continuously enjoy services of location-based applications, one needs to share his or her location information to the corresponding service providers. However, these continuously shared location information will give rise to significant privacy issues due to the temporal correlation between locations. In order to solve this, we consider applying practical local differential privacy to private continuous location sharing. First, we introduce a novel definition of [Formula: see text]-local differential privacy to capture the temporal correlations between locations. Second, we present a generalized randomized response mechanism to achieve [Formula: see text]-local differential privacy for location privacy preservation, which obtains the upper bound of error, and serve it as the basic building block to design a unified private continuous location sharing framework with an untrusted server. Finally, we conduct experiments on the real-world Geolife dataset to evaluate our framework. The results show that generalized randomized response significantly outperforms planar isotropic mechanism in the context of utility.
A Hierarchical Missing Value Imputation Method by Correlation-Based K-Nearest Neighbors

Missing value is a common occurrence in the real-world dataset, and many methods have been proposed to solve it. Among those methods, KNN imputation attracts a lot of attention due to the simple realization, easy understanding, and relatively high accuracy. However, it ignores the influence of correlations between attributes on the similarity of records. In this paper, we take the correlations into consideration when selecting the nearest neighbors, and impute the incomplete records successively according to the number of missing values in each record. During the imputation, the correlation coefficients are calculated by the complete records and updated with the union of complete records and imputed records. Therefore, the correlations between attributes are more accurate with the improvement of data utilization, which makes the selected nearest neighbors more appropriate. Experimental results demonstrate that the improved method is more effective in missing value imputation.
A Blockchain-Enabled Decentralized Time Banking for a New Social Value System

Blockchain technology is reshaping the the traditional economies. People may have more trust than ever before as the transaction is immutable and transparent. Success in crypto-currency and other technical areas highlights many attractive features of the blockchain technology that can benefit more aspects of modern society. Time Banking is a generalized exchange economy not based on money, but values everyone's contribution on the same scale, the time expended. Time banking is a noble idea with great potential, but the security and trust issues are not well addressed. In this paper a BLockchain-ENabled Decentralized Time Banking System (BlendTBS)is proposed to build a trustful, dynamic and respectful community. People in this community are encouraged to be engaged in mutual serving relationships. For this purpose, the BlendTBS is designed to reward the residents who commit in socially beneficial activities. An initial prototype is implemented on a permissioned blockchain network and a small scale study is planned to examine the utility of BlendTBS to a traditional community on the island of Aneityum, Republic of Vanuatu. Within a selected community in the village of Analgahuat, deeper insights will be explored by observing the trust enabled by Blockchain technology that allows peer to peer service exchanges between any two individuals. Authors hope this position paper may inspire more interests in the roles that blockchain technology can play in modern society.
STUDENTS’ DIFFICULTIES IN WRITING RECOUNT TEXT AT INCLUSION CLASSES

Writing skill is already taught in school and colleges. The students of Junior High School should retells something that already happened (Recount text). The objective of the study is to identify the students’ difficulties in writing recount text in inclusion classes. The study belongs to a mixed-method research involving 30 students of 8th grade inclusion class. The data collecting techniques was testing. There were several steps to analyze the data: analyzing the students’ work, calculating the average score by using descriptive statistic and the last step is descriptive analysis. The result of the study shows that the students’ difficulties in writing recount text are related to content aspect, organization aspect, grammar aspect, mechanic aspect and generic structure of recount text aspect especially in orientation and reorientation.