Reviewerly/exHarmony huggingface dataset:
Acquisition Planning for Regional Indigenous Heritage Languages in Indonesia

Regional indigenous heritage languages (RIHLs) are in decline in Indonesia, and this problem needs attention from language policy and planning. This study explores a subset of the Indonesian language policy, namely, its acquisition planning. Content analysis and doctrinal method were employed. The sample included national legislations and some regional legislations. The results are as follows. As subjects taught in schools, Indonesian is “compulsory” at all levels; RIHLs are “optional” at primary and secondary levels and “absent” at the tertiary level; English is “compulsory” at the secondary level and “optional” at the tertiary level. As the media of instruction, Indonesian is “compulsory” at all levels; RIHLs are “optional” in very limited cases; English is “optional” at the tertiary level. As languages for mass media, Indonesian is “compulsory”; English is “optional” for specific aims or audience; RIHLs are “optional” for local communities. There are possible “incoherences” among various legislations, that is, the Constitution, some national laws and regulations, and some regional bylaws. To implement constitutional mandate, the acquisition planning may need revision. In the revision, RIHLs may need to be included as mandatory subjects, while some RIHLs may need to be used as the media of instruction and in mass media. Further studies for the revision are recommended.
Semi-automatic extraction and validation of concepts in ontology learning from texts in Spanish

The construction of ontologies from texts in Spanish is a challenge since this language lacks conceptual databases to validate abstract ontology structures as concepts and relations between them. The preceding generates the necessity of using manual evaluation by human experts; carrying high expenses that limit the calibration of algorithm parameters and large-scale evaluations. This document presents a proposal to evaluate abstract ontology structures through the task of semantic clustering of documents, without the expensive necessity of using manual evaluation or conceptual databases. The proposal is not only affordable but also applicable to model data and domains that lack structured knowledge resources. The experiments lead to the extraction and validation of the ontology structures from texts in Spanish regarding the domain of the Colombian armed conflict.
Informal Learning Sites and Their Role in Communicating the Nature of Science

There is a widespread agreement on the aspects of the nature of science (NOS) that everyone should understand and belief that informal environments can play a role in that understanding. We first define and consider the nature and purpose of informal science education, particularly in relation to formal science education. We then discuss the potential found in several exemplars of informal science education – learning in museums, learning in the field and learning through biographies – to contribute to learning about the nature of science. These three exemplars are not, of course, intended to be exhaustive. We end with some thoughts and cautions from our own experiences regarding NOS learning outside of schools. Our key conclusion is that informal science learning can indeed help learners to appreciate important aspects of the nature or features of science and act as a sturdy bridge to link the relative brief world of school with lifelong learning. However, this is not an easy task for the informal environment or for the visitor/learner to gain NOS understanding. The key to learning about the nature of science out of school may therefore be learning about it in school; a prior, accurate understanding of aspects of NOS will guide and extend learning for those visiting non-school sites.
Libservices

Containers are commonly used to run the data-intensive applications of different tenants in cloud infrastructures. The storage I/O of the colocated tenants is typically handled by the shared system kernel of the container host. When a data-intensive container competes with a noisy neighbor, the kernel I/O services can cause performance variability and slowdown. This is a challenging problem for which several approaches have already been explored. Although the dynamic resource allocation, kernel structure replication, and hardware-level virtualization are helpful, they incur costs of high implementation complexity and execution overhead. As a realistic cost-effective alternative, we isolate the I/O path of each tenant by running dedicated storage systems at user level on reserved resources. We introduce the libservices as a unified user-level storage abstraction to dynamically provision per tenant container root filesystems, application data filesystems and image repositories. We outline several examples of container storage systems whose clients and servers can be composed from libservices. With an early prototype, we successfully demonstrate that the libservices combine the required efficiency and flexibility to build isolated I/O services on multitenant hosts with superior performance over existing user-level or kernel-level systems.
Effective Vietnamese Sentiment Analysis Model Using Sentiment Word Embedding and Transfer Learning

Sentiment analysis is one of the most popular fields in NLP, and with the development of computer software and hardware, its application is increasingly extensive. Supervised corpus has a positive effect on model training, but these corpus are prohibitively expensive to manually produce. This paper proposes a deep learning sentiment analysis model based on transfer learning. It represents the sentiment and semantics of words and improves the effect of Vietnamese sentiment analysis model by using English corpus. It generated semantic vectors through Word2Vec, an open-source tool, and built sentiment vectors through LSTM with attention mechanism to get sentiment word vector. With the method of sharing parameters, the model was pre-training with English corpus. Finally, the sentiment of the text was classified by stacked Bi-LSTM with attention mechanism, with input of sentiment word vector. Experiments show that the model can effectively improve the performance of Vietnamese sentiment analysis under small language materials.
Food Image Generation and Translation and Its Application to Augmented Reality

Food image recognition has drawn attention because it is important technology to record and manage people's eating habits automatically. In addition, recently, due to the great progress of generative adversarial networks (GAN) and GAN-based image translation, we can generate food images and change the categories of the foods in food images. In addition, food-to-food image translation can be integrated with augmented reality for new eating experiences as well as food entertainments. In this paper, we introduce some works of ours on food image generation and translation and its application to augmented reality (AR). (1) Conditional food image generation. (2) Food image translation system, "MagicalRice-Bowl" and AR-based system for new eating experience, "DeepTaste". (3) Interactive sketch-based ramen image generating system, "RamenAsYouLike" and its extension with a ramen style encoder.
Efficiently Mining Gapped and Window Constraint Frequent Sequential Patterns

Sequential pattern mining is one of the most widespread data mining tasks with several real-life decision-making applications. In this mining process, constraints were added to improve the mining efficiency for discovering patterns meeting specific user requirements. Therefore, the temporal constraints, in particular, those that arise from the implicit temporality of sequential patterns, will have the ability to efficiently apply temporary restrictions such as, window and gap constraints. In this paper, we propose a novel window and gap constrained algorithms based on the well-known PrefixSpan algorithm. For this purpose, we introduce the virtual multiplication operation aiming for a generalized window mining algorithm that preserves other constraints. We also extend the PrefixSpan Pseudo-Projection algorithm to mining patterns under the gap-constraint. Our performance study shows that these extensions have the same time complexity as PrefixSpan and good linear scalability.
Federated Digital Gateway: Methodologies, Tools, and Applications

Federated machine learning (FML) is a new machine learning paradigm that is focused on training distributed models, where data are scattered in different places known as data silos, only necessary modeling information (not raw data) is exchanged, and data privacy and security are protected during the modeling. This research area has been growing fast during the past years, but the vision of making it a practical solution is still not fulfilled. Motivated by this, here we introduce an intelligent architecture, termed Federated Digital Gateway. It is designed to help algorithm engineers to easily deploy FML methods for real-life tasks. It provides different modules such as secure communication tools, database interface, authentication center, account system, and user interface. This architecture has been shown to function smoothly in two real-world applications. Overall, the federated digital gateway is practical and deployable for applying federated learning to solve real-life tasks.
Deep Binarized Convolutional Neural Network Inferences over Encrypted Data

Homomorphic encryption provides a way to perform deep learning over encrypted data and permits the user to encrypt the data before uploading, leaving the control of data on the user side. However, operations on encrypted data based on homomorphic encryption are time-consuming, especially in a deep convolutional neural network (CNN), which incorporates a large number of layers and operations. To speed up deep learning on encrypted data, we binarized the input data and weights of CNN model, while operations including the addition and multiplication in CNN become bit-wise operations. Therefore, the homomorphic evaluation of CNN can be performed in the binary field in a highly efficient way. We also construct an efficient pooling layer by designing circuits to perform comparison operations on the ciphertext. Simulation results clearly show that the convolution operation of the proposed model is at least 6.3 times faster than that of existing schemes. Last, our model exhibits no privacy leakage associated with the data being processed.
TF-IDF Keyword Extraction Method Combining Context and Semantic Classification

Keyword extraction plays the same role as the cornerstone in the field of natural language processing. Text classification, information retrieval, abstract generation and text clustering are all based on keyword extraction. This article takes the research of keyword extraction model as the subject. First, it analyzes the principle and limitations of the traditional keyword extraction model TF-IDF when extracting keywords. Secondly, it focuses on the problem of ignoring context and word polysemy in the keyword extraction model. To improve, introduce the concept of context vector, construct a chain-extensible structure for polysemous words, and propose a new keyword extraction method of TF-IDF that combines context and semantic classification. The specific research contents are as follows:
Attention-Based LSTM for Automatic Evaluation of Press Conferences

We propose an approach to automatically predict the evaluation of the consultant for press conferences, using text only. The proposed approach includes a word representation model and a language model for automatic evaluation. The word representation model consists of token embedding using ELMo and type embedding. The language model we used is an LSTM with a self-attention mechanism. We collected seven publicly available press conference videos, and all the Q&A pairs between the journalists and the speakers were annotated by a professional consultant. As a result, we achieved an average accuracy of 57.6% for the prediction of 11 evaluation criterions.
VDENCLUE: An Enhanced Variant of DENCLUE Algorithm

Density-based algorithms have attracted many researchers due to their ability to identify clusters with arbitrary shapes in noisy datasets. DENCLUE is a density-based algorithm that clusters objects based on a density function instead of proximity measurements within data. DENCLUE is efficient in clustering high-dimensional datasets. However, it has difficulty in discovering clusters with highly varying densities. To overcome this issue, this study proposes an enhanced variant of the DENCLUE algorithm, called VDENCLUE, based on the varying Kernel Density Estimation. The VDENCLUE uses the local features of the data space, so clusters with arbitrary shapes and densities can be identified. In order to demonstrate the effectiveness of its approach, VDENCLUE was empirically evaluated and compared to the DENCLUE algorithm. Experimental results show that in almost all datasets, the VDENCLUE algorithm outperforms the DENCLUE algorithm.
Gas Sensor Drift Compensation by Ensemble of Classifiers Using Extreme Learning Machine

The step by step deteriorated chemically, in the sensor response over long time, when the external conditions are constant, is known as sensor drift. Sensors drift is one of the most grave worsening of bothering gas sensors as in electronic nose. In this respect, drift mitigation is an important issue. Traditional techniques are expensive and difficult because they need to regularly recalibrate referred gases or continually provide data labeling. In this work, software based approach for drift compensation is proposed by not changing the hardware components, which in turn reduces the cost. To verify our proposed approach, gas sensors drift dataset is retrieved from the UCI machine learning repository. The dataset has been collected for three years of six different volatile organic compounds, under tightly controlled operating conditions using an array of 16 metal-oxide gas sensors. In this work, extreme learning machine has been implemented, for very fast and efficient performance. Extreme learing machine is used both as single standalone classifier and ensemble base classifier. It is shown out of these two methods ensemble technique has handled the problem of drift more efficiently and reasonably improved the classification accuracy to compensate the challenge and helps in pattern recognition in compensating the sensor drift.
An Empirical Study of the NTFS Cluster Allocation Behavior Over Time

The amount of data to be handled in digital forensic investigations is continuously increasing, while the tools and processes used are not developed accordingly. This especially affects the digital forensic sub-field of file carving. The use of the structuring of stored data induced by the allocation algorithm to increase the efficiency of the forensic process has been independently suggested by Casey and us. Building on that idea we have set up an experiment to study the allocation algorithm of NTFS and its behavior over time from different points of view. This includes if the allocation algorithm behaves the same regardless of Windows version or size of the hard drive, its adherence to the best fit allocation strategy and the distribution of the allocation activity over the available (logical) storage space. Our results show that space is not a factor, but there are differences in the allocation behavior between Windows 7 and Windows 10. The results also show that the allocation strategy favors filling in holes in the already written area instead of claiming the unused space at the end of a partition and that the area with the highest allocation activity is slowly progressing from approximately 10 GiB into a partition towards the end as the disk is filling up.
Selective liability, regulated digital commerce, and the subversion of product trading bans: the case of elephant ivory

This paper outlines and applies the concept of “selective liability” (i.e., operating as if there was a genuine choice between fault-based and strict liability legal regimes) to examine the apparent plausible deniability pact between online selling platforms and the sellers. Through an analysis of online trading data, this paper considered the issues inherent in prosecuting traders who use online auction platforms to sell ivory in contravention of “CITES” regulations. Three bone euphemisms for ivory (antique bovine bone, antique cow bone, antique Chinese cow bone) were identified to track sales over the course of 90 days. The results showed that sellers are using such euphemisms to sell ivory online and these findings highlighted the contradiction between a firm’s ban on ivory sales and the apparent ease with which ivory is sold through a website.
A Range and Velocity Ambiguity Resolution Method Based on Ambiguity Matrix Completion and Elimination with Low SNR

In space-borne surveillance radar, the medium pulse repetition frequency (MPRF) is preferred to avoid the transmit pulse eclipsing in range domain meanwhile to obtain a large unambiguous Doppler detectable zones, especially for ultra-high speed targets. However, there exist great challenges in acquiring the unambiguous information of targets due to the serious range and velocity ambiguity. In this paper, a new method is proposed to resolve two-dimensional (2D) ambiguity and suppress the ghosts simultaneously. Firstly, a 2D ambiguity matrix consists of all the possible range and velocity information from multiple PRFs is constructed. The element of this matrix is calculated by counting the target number located in the same position. Secondly, the element value related to detected target will be replaced by the updating strategy. Then the accurate range and velocity information can be obtained by calculating the mean range-Doppler value measured from different PRFs. The numerical results show that the proposed algorithm has a lower computation complexity with less ghosts in low-SNR case compared to the 1D clustering method.
Classical Mechanism is Optimal in Classical-Quantum Differentially Private Mechanisms

Differential privacy (DP) is an influential privacy measure and has been studied to protect private data. DP has been often studied in classical probability theory, but few researchers studied quantum versions of DP. In this paper, we consider classical-quantum DP mechanisms which (i) convert binary private data to quantum states and (ii) satisfy a quantum version of the DP constraint. The class of classical-quantum DP mechanisms contains classical DP mechanisms. As a main result, we show that some classical DP mechanism optimizes any information quantity satisfying the information processing inequality. Therefore, the performance of classical DP mechanisms attains that of classical-quantum DP mechanisms.
A model that adopts human fixations explains individual differences in multiple object tracking

In many settings “keep your eye on the ball” is good advice. People fixate important objects to obtain high quality information. Perhaps equally often, however, we engage with multiple important, moving, and unpredictable objects. Where should we fixate in these situations, and where do we? Do we for example appropriately center fixations to manage spatial non-uniformity in our visual system? And do we fixate empty space strategically to gain as much information as possible about multiple objects of interest? We explored these issues in the context of Multiple Object Tracking (MOT), wherein observers track several moving objects (targets) within a larger set of moving objects (nontargets), all the objects physically indistinguishable from one another. Among the features that make MOT an interesting paradigm is that it cannot be accommodated by continuous gaze to one important object, because there are multiple such objects in a given trial. Instead, it demands sustained processing of inputs from an entire display and iterated inferences about target versus nontarget identities. MOT therefore demands a strategic interaction between eye movements and cognition: the observer should seek fixation locations that minimize the aggregate probability of confusing any target with any nontarget. Individuals who meet this fixation challenge should perform the task better than those who meet the challenge less effectively. Here we describe a probabilistic model that implements the basic computations needed to do MOT, estimating the positions of targets, predicting their future positions, and inferring correspondences between new inputs and represented targets. The quality of the input received by the model depends on its fixation location at a given moment. We simulated a group of fifty participants who all performed the same MOT trials, with the model adopting each observer's fixation locations in the respective simulations. The model reliably predicted individual participant tracking performances and their relative rankings within the cohort. The results suggest that an individual's relative capability in this cognitively demanding task is in part determined by his/her utilization of eye fixations to control the quality and relevance of incoming visual input.
Using an ESP8266 Microcontroller to Develop a Learning Game

Abstract This article discusses the possibility of connecting an ESP8266 microcontroller to a learning game developed on the Unity3D game engine. The game is aimed at teaching and developing the skills of students, such as mathematics, physics, programming and the basics of robotics. The paper presents a diagram of the interaction of the microcontroller and the game, as well as a comparative analysis of the Arduino and ESP8266 platforms. Comparison of the Arduino firmware process via USB and ESP8266 over Wi-Fi is carried out. It was possible to establish that the firmware by air takes an average of 2 seconds more, which does not affect the gameplay.
A New Recursive Framework for Trajectory Generation of UAVs

Unmanned aerial vehicles (UAVs) have recently gained increasing attention. Self-positioning and integrated navigation are main aspects during a flight mission and rely on predefined trajectories. Current guidance and control systems provide real-time control laws to track desired trajectories given by the Embedded Flight Management System (E-FMS). Since some UAVs are highly non-linear systems with under-actuation properties from the control point of view, discontinuities in control inputs (coming from trajectories generated by E-FMS) can produce undesired vibrations causing aging and damages in the structure. In this paper, we propose a Recursive Smooth Trajectory generation algorithm (RST) that allows for finding a smooth C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∝</sup> polynomial path, and thus a close form trajectory satisfying any arbitrary dynamic limitations translated into kinematic constraints (e.g. position, velocity, acceleration, etc). Each kinematic constraint is recursively fulfilled, leading to a fast online implementation for the E-FMS. The storage requirements and execution time are therefore discussed in this paper. The effectiveness and suitability of the RST algorithm over a minimum-snap piecewise polynomial approach for highly nonlinear UAV flight is also analyzed.
Crime Prediction for Patrol Routes Generation Using Machine Learning

Citizen security is one of the main objectives of any government worldwide. Security entities make multiple efforts to apply the latest technologies in order to prevent any type of criminal offence. The analysis of a database of the National Police of Ecuador has allowed us generating patrol routes to prevent and reduce the crime rate in the city of Quito, Ecuador. The K-means clustering has been used to determine the points of greatest crime concentration and then linear regression is applied for the prediction of crimes within subgroups of data. Those way-points will allow to generate and optimize police patrol routes. The results obtained in the prediction of crimes is greater than 80%.