Reviewerly/exHarmony huggingface dataset:
An approach to automatically update the Spanish DBpedia using DBpedia Databus

The Linked Data based project called DBpedia is publishing big amounts of data from Wikipedia semantically. Like Wikipedia, DBpedia also gives its datasets in different international languages. Spanish being one of these languages and the Spanish DBpedia as the second biggest DBpedia after the English one, collecting information from Wikipedia and serving it based on the user’s requirements. Aimed at keeping its data updated, the DBpedia foundation has created DBpedia Databus, an alternative to DBpedia Live and a way that simplifies working with data. This Master’s final project will be focused on an approach to automatically update the Spanish DBpedia, from DBpedia Databus using the Virtuoso OpenLink Software, and potentially the solutions provided, can help with any other DBpedia chapter to update it’s resources.
Totally and Partially Ordered Hierarchical Planners in PDDL4J Library

In this paper, we outline the implementation of the TFD (Totally Ordered Fast Downward) and the PFD (Partially ordered Fast Downward) hierarchical planners that participated in the first HTN IPC competition in 2020. These two planners are based on forward-chaining task decomposition coupled with a compact grounding of actions, methods, tasks and HTN problems.
An Autonomous Driving Framework for Long-term Decision-making and Short-term Trajectory Planning on Frenet Space

In this paper, we present a hierarchical framework for decision-making and planning on highway driving tasks. We utilized intelligent driving models (IDM and MOBIL) to generate long-term decisions based on the traffic situation flowing around the ego. The decisions both maximize ego performance while respecting other vehicles' objectives. Short-term trajectory optimization is performed on the Frenet space to make the calculations invariant to the road's three-dimensional curvatures. A novel obstacle avoidance approach is introduced on the Frenet frame for the moving obstacles. The optimization explores the driving corridors to generate spatiotemporal polynomial trajectories to navigate through the traffic safely and obey the BP commands. The framework also introduces a heuristic supervisor that identifies unexpected situations and recalculates each module in case of a potential emergency. Experiments in CARLA simulation have shown the potential and the scalability of the framework in implementing various driving styles that match human behavior.
Episodic Self-Imitation Learning with Hindsight

Episodic self-imitation learning, a novel self-imitation algorithm with a trajectory selection module and an adaptive loss function, is proposed to speed up reinforcement learning. Compared to the original self-imitation learning algorithm, which samples good state-action pairs from the experience replay buffer, our agent leverages entire episodes with hindsight to aid self-imitation learning. A selection module is introduced to filter uninformative samples from each episode of the update. The proposed method overcomes the limitations of the standard self-imitation learning algorithm, a transitions-based method which performs poorly in handling continuous control environments with sparse rewards. From the experiments, episodic self-imitation learning is shown to perform better than baseline on-policy algorithms, achieving comparable performance to state-of-the-art off-policy algorithms in several simulated robot control tasks. The trajectory selection module is shown to prevent the agent learning undesirable hindsight experiences. With the capability of solving sparse reward problems in continuous control settings, episodic self-imitation learning has the potential to be applied to real-world problems that have continuous action spaces, such as robot guidance and manipulation.
An analysis of TIMSS 2015 science reading demands

This study investigated the reading demands of restricted-use items administered to South African grade 9 learners as part of the Trends in International Mathematics and Science Study (TIMSS) 2015. The method proposed by Mullis, Martin and Foy (2013) was used to categorise items into low, medium and high readability groups. The ‘knowing’ domain contained mostly low readability items; the ‘applying’ domain was almost equally medium and high readability items; with the ‘reasoning’ domain containing mostly high readability items. Results show significant differences between the percentage of correct answers between the low and high categories and between the medium and high categories. However, the impact of reading demand on performance cannot be fully analysed without cross-reference to English proficiency. Nevertheless, the higher the readability, the greater the chance is for learners to answer incorrectly. A continued expected low performance for most South African learners is implied.
Application of Deep Learning in Vehicle Driverless Technology

Abstract In recent years, due to the rapid development of deep learning, the application of deep learning in the field of unmanned vehicle driving has achieved good results through a large number of experiments. First of all,this paper introduces the development of driverless technology at home and abroad, then it introduces the theoretical basis of deep learning. The aim is to give readers a quick look at the history and current status of driverless technology and readers can understand the importance of deep learning in the field of driverless technology at the same time. Secondly, this paper introduced different intelligent scene recognition and compared them to find out which technology is more suitable for the current situation. Finally, the paper proposes the optimization scheme that can be improved or improved according to the shortcomings of the intelligent scene recognition technology and the problems and the development prospects of intelligent vehicles.
Improved Testing of AI-Based Anomaly Detection Systems Using Synthetic Surveillance Data

Major transportation surveillance protocols have not been specified with cyber security in mind and therefore provide no encryption nor identification. These issues expose air and sea transport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fake surveillance messages to dupe controllers and surveillance systems. There has been growing interest in conducting research on machine learning-based anomaly detection systems that address these new threats. However, significant amounts of data are needed to achieve meaningful results with this type of model. Raw, genuine data can be obtained from existing databases but need to be preprocessed before being fed to a model. Acquiring anomalous data is another challenge: such data is much too scarce for both the Automatic Dependent Surveillance&ndash;Broadcast (ADS-B) and the Automatic Identification System (AIS). Crafting anomalous data by hand, which has been the sole method applied to date, is hardly suitable for broad detection model testing. This paper proposes an approach built upon existing libraries and ideas that offers ML researchers the necessary tools to facilitate the access and processing of genuine data as well as to automatically generate synthetic anomalous surveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of the approach by discussing work in progress that includes the reproduction of related work, creation of relevant datasets and design of advanced anomaly detection models for both domains of application.
Self-Adaptive Systems Planning with Model Checking using MAPE-K

This paper presents a model checking-based approach to support the autonomous planning of adaptation actions in Self-Adaptive Systems, designed in consonance with the MAPE-K reference architecture. We evaluated our approach with a case-study aiming at verifying self-healing and self-organizing properties in a distributed and decentralized traffic monitoring system. Results show that our approach is able to generate adaptation plans satisfying the goals for all expected scenarios in such a case-study, providing a flexible formal framework where adaptation strategies and goals can be inserted/removed.
Pengaruh Penggunaan Metode Scramble Terhadap Keterampilan Membaca Permulaan Pada Anak TK Kelompok B

This study were aimed: (1) to know the effect of using scramble method in basic reading skill of the kindergarden students group B. (2) to know the effectiveness of the scramble method in basic reading skill of the kindergarden students group B. The type of research that used was quasi experimental study, with Pre-Test Post-Test Control Group Design. The population of this study were all students of group B TK Pertiwi Selong and the sample were student of groups B1 and B2. The technique of data collection were using interview, validation of research instrument and observation. The data analysis in this study was used an independent sample t test with a significance level of 0.05. The results showed that there were the differences in the final results after being given treatment between the results of the control class and the experimental class with a percentage of 70.83% in the experimental class and 56.49% in the control class. The t-test results showed p <0.05, namely p = 0.024, so it can be concluded that the scramble method is effective to use in the beginning of reading skills of group B children.
The internet-community response to the Mexican Earthquake of February 1, 2019

Signs of the threat of the Mexican earthquake of February 1, 2019, are presented according to estimates of the recurrence of query words in Internet electronic publications. These estimates (information signals) have been determined using the Internet search engines. Before the earthquake, an increase in the recurrence of query words semantically related to seismic-hazard precursors was revealed in electronic publications relative to the seismically hazardous region. After the earthquake, the activity of the Internet community decreases.
Minimizing the Total Tardiness of a Game Project Considering the Overlap Effect

There has long been a custom that game development is not the mainstream of engineering and its tardiness brings little or even no harm to this industry. Nowadays, the pendulum of industrial development has swung to another side. A game project may involve hundreds of developers, thousands of jobs, and millions of costs. Situations would be worse if jobs overlap with others. Any delay of a large game project can cause a heavy penalty. Consequently, in this study, we propose two scheduling algorithms to reduce the total tardiness of a game project. If the problem size is small, a branch-and-bound algorithm is employed to provide the optimal schedules; otherwise a genetic algorithm is used to generate near-optimal schedules. The experimental results show that the proposed algorithms reduce the total tardiness significantly.
SIMF: Single-Instruction Multiple-Flush Mechanism for Processor Temporal Isolation

Microarchitectural timing attacks are a type of information leakage attack, which exploit the time-shared microarchitectural components, such as caches, translation look-aside buffers (TLBs), branch prediction unit (BPU), and speculative execution, in modern processors to leak critical information from a victim process or thread. To mitigate such attacks, the mechanism for flushing the on-core state is extensively used by operating-system-level solutions, since on-core state is too expensive to partition. In these systems, the flushing operations are implemented in software (using cache maintenance instructions), which severely limit the efficiency of timing attack protection. To bridge this gap, we propose specialized hardware support, a single-instruction multiple-flush (SIMF) mechanism to flush the core-level state, which consists of L1 caches, BPU, TLBs, and register file. We demonstrate SIMF by implementing it as an ISA extension, i.e., flushx instruction, in scalar in-order RISC-V processor. The resultant processor is prototyped on Xilinx ZCU102 FPGA and validated with state-of-art seL4 microkernel, Linux kernel in multi-core scenarios, and a cache timing attack. Our evaluation shows that SIMF significantly alleviates the overhead of flushing by more than a factor of two in execution time and reduces dynamic instruction count by orders-of-magnitude.
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments

We are interested in learning models of non-stationary environments, which can be framed as a multi-task learning problem. Model-free reinforcement learning algorithms can achieve good asymptotic performance in multi-task learning at a cost of extensive sampling, due to their approach, which requires learning from scratch. While model-based approaches are among the most data efficient learning algorithms, they still struggle with complex tasks and model uncertainties. Meta-reinforcement learning addresses the efficiency and generalization challenges on multi task learning by quickly leveraging the meta-prior policy for a new task. In this paper, we propose a meta-reinforcement learning approach to learn the dynamic model of a non-stationary environment to be used for meta-policy optimization later. Due to the sample efficiency of model-based learning methods, we are able to simultaneously train both the meta-model of the non-stationary environment and the meta-policy until dynamic model convergence. Then, the meta-learned dynamic model of the environment will generate simulated data for meta-policy optimization. Our experiment demonstrates that our proposed method can meta-learn the policy in a non-stationary environment with the data efficiency of model-based learning approaches while achieving the high asymptotic performance of model-free meta-reinforcement learning.
On some of the basic trends in Russian higher education in the context of transition to education for sustainable development

The paper discusses the basic trends in the system of Russian higher education in the light of transition to the model of education for sustainable development. Special attention is given to some tendencies in language teaching in institutions of higher education, humanitarian focus of higher education being emphasized.
Design and Development of Dyscalculia Checklist Instrument

The purpose of this study is to design and develop Dyscalculia Checklist Instrument (DCI) for dyscalculic pupils in primary schools.Dyscalculia is a learning disability in mathematics.Unlike dyslexia, there is still no checklist available in our country to detect these dyscalculic pupils.If they are not being detected, these pupils will continue being left out and labelled, with no proper intervention or diagnosis given to them.This study will employ Design and Development Research (DDR) approach.During phase one, needs analysis will be carried out to 90 teachers and 90 parents.This is to identify whether there is a need to develop a checklist for dyscalculic pupils in primary schools.During phase two, Fuzzy Delphi Method (FDM) will be carried out by inviting 11 experts in the fields of dyscalculia, special education, and instrumentation.During phase three, Nominal Group Technique (NGT) will be implemented.The experts will vote and rank for the constructs and items in DCI.Purposive sampling method will be employed in this study.The data will be analysed using descriptive and inferential statistics.T-test will be used to compare the data of needs to develop DCI between teachers and parents.ANOVA will be used to compare the data of needs to develop DCI between Malay-Medium National School (SK), National-Type Chinese Primary School (SJKC), and National-Type Tamil Primary School (SJKT).Then, the researcher will obtain the threshold, d value and experts' consensus value to design, develop and evaluate the constructs and items in DCI.
Purification and Entanglement Routing on Quantum Networks

We present an approach to purification and entanglement routing on complex quantum network architectures, that is, how a quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We explore how network parameters influence the performance of path-finding algorithms necessary for optimizing routing and, in particular, we explore the interplay between the bandwidth of a quantum channels and the choice of purification protocol. Finally, we demonstrate multi-path routing on various network topologies with resource constraints, in an effort to inform future design choices for quantum network configurations. Our work optimizes both the choice of path over the quantum network and the choice of purification schemes used between nodes. We consider not only pair-production rate, but optimize over the fidelity of the delivered entangled state. We introduce effective heuristics enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network, with performance comparable to that of a computationally-expensive brute-force path search.
Cloud-Based Video Streaming Services: A Survey

Video streaming, in various forms of video on demand (VOD), live, and 360 degree streaming, has grown dramatically during the past few years. In comparison to traditional cable broadcasters whose contents can only be watched on TVs, video streaming is ubiquitous and viewers can flexibly watch the video contents on various devices, ranging from smart-phones to laptops and large TV screens. Such ubiquity and flexibility are enabled by interweaving multiple technologies, such as video compression, cloud computing, content delivery networks, and several other technologies. As video streaming gains more popularity and dominates the Internet traffic, it is essential to understand the way it operates and the interplay of different technologies involved in it. Accordingly, the first goal of this paper is to unveil sophisticated processes to deliver a raw captured video to viewers' devices. In particular, we elaborate on the video encoding, transcoding, packaging, encryption, and delivery processes. We survey recent efforts in academia and industry to enhance these processes. As video streaming industry is increasingly becoming reliant on cloud computing, the second goal of this survey is to explore and survey the ways cloud services are utilized to enable video streaming services. The third goal of the study is to position the undertaken research works in cloud-based video streaming and identify challenges that need to be obviated in future to advance cloud-based video streaming industry to a more flexible and user-centric service.
A Survey of Deep Learning Approaches for OCR and Document Understanding

Documents are a core part of many businesses in many fields such as law, finance, and technology among others. Automatic understanding of documents such as invoices, contracts, and resumes is lucrative, opening up many new avenues of business. The fields of natural language processing and computer vision have seen tremendous progress through the development of deep learning such that these methods have started to become infused in contemporary document understanding systems. In this survey paper, we review different techniques for document understanding for documents written in English and consolidate methodologies present in literature to act as a jumping-off point for researchers exploring this area.
ClimaText: A Dataset for Climate Change Topic Detection

Climate change communication in the mass media and other textual sources may affect and shape public perception. Extracting climate change information from these sources is an important task, e.g., for filtering content and e-discovery, sentiment analysis, automatic summarization, question-answering, and fact-checking. However, automating this process is a challenge, as climate change is a complex, fast-moving, and often ambiguous topic with scarce resources for popular text-based AI tasks. In this paper, we introduce \textsc{ClimaText}, a dataset for sentence-based climate change topic detection, which we make publicly available. We explore different approaches to identify the climate change topic in various text sources. We find that popular keyword-based models are not adequate for such a complex and evolving task. Context-based algorithms like BERT \cite{devlin2018bert} can detect, in addition to many trivial cases, a variety of complex and implicit topic patterns. Nevertheless, our analysis reveals a great potential for improvement in several directions, such as, e.g., capturing the discussion on indirect effects of climate change. Hence, we hope this work can serve as a good starting point for further research on this topic.
TANTANGAN PEMBELAJARAN DARING DI INDONESIA

This article contains the challenges faced by the Indonesian education world in the implementation of online learning. The method used is literature study by collecting various sources that discuss the challenges of online learning in Indonesia. The literature search results show that there are two main challenges to online learning in Indonesia, namely the quality of teachers and infrastructure. Teacher quality is related to the ability of teachers to use information technology in the learning process which is considered to be very low, influenced by age and laptop ownership. Facilities and infrastructure include internet networks that have not yet reached rural areas, unstable internet networks, and costs to buy internet quotas.
BayesTime: Bayesian Functional Principal Components for Sparse Longitudinal Data

Modeling non-linear temporal trajectories is of fundamental interest in many application areas, such as in longitudinal microbiome analysis. Many existing methods focus on estimating mean trajectories, but it is also often of value to assess temporal patterns of individual subjects. Sparse principal components analysis (SFPCA) serves as a useful tool for assessing individual variation in non-linear trajectories; however its application to real data often requires careful model selection criteria and diagnostic tools. Here, we propose a Bayesian approach to SFPCA, which allows users to use the efficient leave-one-out cross-validation (LOO) with Pareto-smoothed importance sampling (PSIS) for model selection, and to utilize the estimated shape parameter from PSIS-LOO and also the posterior predictive checks for graphical model diagnostics. This Bayesian implementation thus enables careful application of SFPCA to a wide range of longitudinal data applications.
Sociopragmatics Analysis of Politeness Strategy of the Main Character's Dialogues of Tinker Bell in Secret of the Wings Movie

This study investigated the types and functions of politeness strategy used by the main character of Tinker Bell in Secret of the Wings movie. The study was a descriptive qualitative approach. The instrument of this study was document analysis. It focused on analyzing and interpreting recorded documents. To analyze the data, four interactional procedures were applied. Triangulation was used to justify the trustworthiness of data. The findings showed that the interaction between the main character and her addressee realized all politeness strategies. There were 39 data found in the conversation. Each politeness strategy's frequency was positive politeness in the first grade, with 51.3%, followed by a bald-on record with 33.3%. Negative politeness and 'off record' were the same positions with 7.7 %, %. Second, the researchers reported two politeness strategy functions indicated by the transactional view with 74.4 % and interactional view with 25.6%. Last, the main character applied the politeness strategy to have a close relationship, know each other, and avoid the addressee's imposition. This politeness strategy had implications on using politeness strategies of a language in communication aspects for the community.