Miracle-dz/UniFAR-FTUs huggingface dataset:
ROD-Revenue: Seeking Strategies Analysis and Revenue Prediction in Ride-on-Demand Service Using Multi-Source Urban Data

Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand, and to improve service responsiveness and quality. Despite this, on the drivers’ side, dynamic pricing creates a new problem: how to seek for passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies, and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product-form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability–to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers’ revenue. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers’ revenue by offering useful guidance.
PENGEMBANGAN MOTION GRAPHIC PEMBELAJARAN MATA PELAJARAN PENDIDIKAN KEWARGANEGARAAN KELAS I SEKOLAH DASAR

Penelitian ini merupakan penelitian dan pengembangan yang bertujuan untuk menghasilkan motion graphic pembelajaran mata pelajaran Pendidikan Kewarganegaraan pada kelas 1 Sekolah Dasar yang layak dijadikan sebagai sumber belajar. Model penelitian yang digunakan adalah Borg & Gall (1983) dan Dick & Carey (2005). Hasil sebagai berikut. (1) Kelayakan produk pada aspek pembelajaran dan aspek materi/isi dari ahli materi menunjukkan skor 3,56 (baik), aspek media/teknis dari ahli media I menunjukkan skor 3,57 (baik) dan aspek media/teknis ahli media II menunjukkan skor 3,62 (baik). Data yang diperoleh melalui uji coba pada siswa pada uji coba satu-satu menunjukkan skor 75%, melalui uji coba kelompok kecil menunjukkan skor 81%, dan melalui uji coba lapangan menunjukkan skor 90%. (2) Hasil belajar siswa pada saat pretes rerata menunjukkan skor 62, sedangkan hasil belajar siswa pada posttes rerata menunjukkan skor 76. Sehingga menghasilkan N-gain dengan skor 36. Berdasarkan N-gain yang tergolong sedang, maka motion graphic pembelajaran yang dikembangkan efektif dan dapat melatih keterampilan berpikir kritis siswa.
Programming with Scratch in primary school, indicators related to effectiveness of education process and analysis of these indicators in terms of various variables

Since programming processes involve different thinking skills and different fields of knowledge, it is especially important for children to acquire 21st-century skills. Even though the programming education activities are being intensively applied, it can be said that there is a gap in quantitative researches supporting the effort to reveal the direct or indirect effectiveness of the learning–teaching processes for the programming education. This study, which was done to fill this gap, aims to examine the degree to which students learn programming concepts (PC) and to identify effective variables in that process with a developed curriculum for gifted students studying in the second–third–fourth grade in primary schools. For this purpose, a 15-week application was carried out and each student developed an individual project. In the study, a criterion list, observation forms and peer evaluations were used based on PC to examine projects and learning process. The scores obtained from these tools were used to examine the application of each participant, to comment on the effective variables and the adequacy of the teaching process. The evidence from this study intimates that female participants obtained higher scores than male ones in programming education. Those scores are higher in 9 and 10 age group of students than others. Those who haven’t had Internet access, who have never used computer or have had access to Internet as well as who haven’t had any computer courses had lower scores than others. The upshot of this is that previous computer technology experiences of students may have affected the scores obtained programming education process.
Integrating TTF and IDT to evaluate user intention of big data analytics in mobile cloud healthcare system

ABSTRACT With the rapid development of mobile technology and cloud computing, observers have recognised the vast potential for mobile cloud healthcare systems in individualised preventive healthcare. Using a mobile cloud healthcare system and big data analysis, this study aids young users in preventive healthcare against diabetes. It also integrates the Task-Technology Fit (TTF) and Innovation Diffusion Theory (IDT) models to evaluate user intentions to use the system, and tests this model using data collected from 423 young people. Results show that task-technology fit is significantly affected by task characteristics and technology characteristics, and also user intention of using the mobile cloud healthcare system is affected by task-technology fit, complexity, and relative benefits. However, observability has no significant effect on user intentions of using the mobile cloud healthcare system. These findings provide some interesting theoretical insights into the usage of the mobile cloud healthcare system. The direct effects of TTF and IDT on young users′ intention of using the mobile cloud healthcare system are shown. This study thus makes an important contribution by highlighting the role that TTF and IDT may have in affecting use of the mobile cloud healthcare system.
Rumor Detection by Exploiting User Credibility Information, Attention and Multi-task Learning

In this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. We incorporate the user credibility information into the rumor detection layer, and we also apply attention mechanism in the rumor detection process. The attended information include not only the hidden states in the rumor detection layer, but also the hidden states from the stance detection layer. The experiments on two datasets show that our proposed model outperforms the state-of-the-art rumor detection approaches.
Applying qualitative trajectory Calculus to human motion fnflysis: a case study towards robot social path planning

Qualitative Trajectory Calculus (QTC) offers a powerful set of tools towards selectable-granularity abstraction of relative trajectories of moving entities, while preserving essential aspects of their interaction. In this paper, we present a case study of an application of QTC towards analyzing human motion and interaction patterns in a shopping mall. The ultimate purpose of this study is to use the derived results towards tuning human-aware social path planning algorithms for robots cohabitating and interacting with humans in malls, and in other public spaces. This is increasingly important given the rapid rise of service robots and the need for human-aware navigation which maximizes the safety and comfort of humans while preserving social norms such as proxemics and personal spaces.
Evaluation of NoSQL databases for DIRAC monitoring and beyond

Nowadays, many database systems are available but they may not be optimized for storing time series data. Monitoring DIRAC jobs would be better done using a database optimised for storing time series data. So far it was done using a MySQL database, which is not well suited for such an application. Therefore alternatives have been investigated. Choosing an appropriate database for storing huge amounts of time series data is not trivial as one must take into account different aspects such as manageability, scalability and extensibility. We compared the performance of Elasticsearch, OpenTSDB (based on HBase) and InfluxDB NoSQL databases, using the same set of machines and the same data. We also evaluated the effort required for maintaining them. Using the LHCb Workload Management System (WMS), based on DIRAC as a use case we set up a new monitoring system, in parallel with the current MySQL system, and we stored the same data into the databases under test. We evaluated Grafana (for OpenTSDB) and Kibana (for ElasticSearch) metrics and graph editors for creating dashboards, in order to have a clear picture on the usability of each candidate. In this paper we present the results of this study and the performance of the selected technology. We also give an outlook of other potential applications of NoSQL databases within the DIRAC project.
Some remarks on dynamic output feedback control of non uniformly observable systems

This paper presents results on dynamic output feedback control for a class of systems which are in cascade form and a priori are not uniformly observable. These results are based on previous studies on observer design for the considered class of systems on the one hand, and similar arguments as in the case of uniformly observable systems on the other hand. Some further extensions on this basis are also discussed.
DRAWING UP A MEDICAL SYLLABUS BY INTEGRATING THE GAMIFIED BLENDED MODULE OF L2 ENGLISH LEARNING

Introduction: Nowadays, mobile-mediated learning has become a primary concern of scholars applying the blended method of L2 teaching and learning. Meanwhile, prevailing game apps on mobile devices, namely m-games, have broadened the educators' pedagogical outlook. In view of that, this study endeavored to compare the efficacy of team - (vs. individual) teaching on learning L2 through the medium of m-game - mediated blended learning method.Methods: This quasi-experimental study embraced triangulation research design. To that end, 86 male and female students from Ahvaz JondiShapur University of Medical Sciences were selected through stratified sampling method. Afterwards, 76 students were homogenized through the application of Vocabulary Levels Test (VLT) to learn L2 vocabulary and reading comprehension in the individual- or team-teaching situations. The instrumentation included VLT, pre-research questionnaire and interview as well as formative assessment. The reliability of the instrumentation was calculated through KR-21 method and Cronbach's alpha. Likewise, their validity was authenticated by five TEFL experts. In addition to debriefing the participants through questionnaire and interview, tracing participants' performance through paper-andpencil exercises, m-games (native vs. prefabricated), and also tailor-made tests over 18 m-game-based blended sessions prepared the ground for embarking on the triangulation design. For analyzing the data, SPSS version 21 along with descriptive and inferential statistics were employed.Results: In the end, the t-tests analyses showed that a range of situational and social factors were found to affect participants' L2 learning. In the same vein, team-teaching situation was of palpable interest to participants to expedite their L2 learning via m-game based blended module (p Conclusion: In effect, welding native m-game into the proper pedagogical situations culminate in students' outperformance.
Connection Setup in a Quantum Network

Near-term quantum networks will grow to form a Noisy, Intermediate- Scale Quantum Internet (NISQI). Connection setup will require adapting behavior along the path to the noise levels of individual elements. In this proposal, path creation is triggered by an application at the Initiator, information is accumulated node-by-node on an outbound pass in a series of QCap (quantum capability) blocks, then the RuleSets are created at the Responder. RuleSets are installed at the individual nodes on the return pass. This document describes the architecture of connection setup in a network. Details of the RuleSets and QCaps, addressing architecture, link protocols, routing, resource allocation (multiplexing), extension of this setup procedure to an internetwork, and extension to multiparty communications are beyond the scope of this document.
Towards Incremental Learning of Word Embeddings Using Context Informativeness

In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way. We focus on the notion of ‘informativeness’, that is, the idea that some content is more valuable to the learning process than other. We further highlight the challenges of online learning and argue that previous systems fall short of implementing incrementality. Concretely, we incorporate informativeness in a previously proposed model of nonce learning, using it for context selection and learning rate modulation. We test our system on the task of learning new words from definitions, as well as on the task of learning new words from potentially uninformative contexts. We demonstrate that informativeness is crucial to obtaining state-of-the-art performance in a truly incremental setup.
Practical Lock-Free Dynamic Memory Allocation

Dynamic memory allocation is an important component of most applications. Nowadays, due to the use of thread-level parallelism by applications, an important requirement in a memory allocator implementation is the ability to be thread safe. To guarantee thread safety, current state-of-the-art memory allocators use lock-based data structures. The use of locks has implications in terms of program performance, availability, robustness and flexibility. As such, current memory allocators attempt to minimize the effects of locking by using fine-grained locks, by reducing the frequency of lock operations and by providing synchronization-free allocations as much as possible through the use of thread-specific caches. An interesting but challenging alternative is to use lock-free data structures and atomic operations to guarantee thread safety. We present LRMalloc, a lock-free memory allocator that leverages lessons of modern memory allocators and combines them with a lock-free scheme. Current state-of-the-art memory allocators possess good performance but lack desirable lock-free properties, such as, guarantees of systemwide progress, priority inversion tolerance, kill-tolerance availability, and/or deadlock and livelock immunity. LRMalloc’s purpose is to show the feasibility of lock-free memory management algorithms, without sacrificing competitiveness in comparison to commonly used state-of-the-art memory allocators, especially for concurrent multithreaded applications. We also present a lock-free best-fit mechanism, capable of lock-free coalescing and splitting of blocks. To the best of our knowledge, our mechanism is the first lock-free general coalescing mechanism that supports coalescing and splitting of blocks with arbitrary sizes and does not put restrictions on how coalescing can be performed. Our mechanism ultimately aims to be used as a lower-level allocator for LRMalloc or other lock-free memory allocators, allowing them to manage memory without the assistance of the operating system, potentially improving performance by reducing the number of system calls.
Use of semantic, syntactic and sentiment features to automate essay evaluation

Manual grading of essays by humans is time-consuming and likely to be susceptible to inconsistencies and inaccuracies. Mostly performed within an academic institution, the task at hand is to grade hundreds of submitted essays and the major hurdle is the homogeneous assessment from the first till the last. It can take hours or sometimes even days to finish the assessment. Automating this tedious manual task is not only a relief to the teachers but also assures the students of consistent markings throughout. The challenge in automatizing is to recognize crucial aspects of natural language processing (NLP) which are vital for accurate automated essay evaluation. NLP is a subset of the field of artificial intelligence which deals with making computers understand the language used by humans for expression and then further process it. Since essays are a written textual form of expression and idea exchange, automating the essay assessment process through a computer system leverages progress from NLP field and automates one of the biggest manual tasks of educational systems. In recent years, an abundance of research has been done to automate essay evaluation processes, yet little has been done to take into consideration the syntax, semantic coherence and sentiments of the essay’s text together. Our proposed system incorporates not just the rule-based grammar and surface level coherence check but also includes the semantic similarity of the sentences. We propose to use graph-based relationships within the essay’s content and polarity of opinion expressions. Semantic similarity is determined between each statement of the essay to form these graph-based spatial relationships. Our algorithm uses 23 salient features with high predictive power, which is less than the current systems while considering every aspect to cover the dimensions that a human grader focuses on. Fewer features help us get rid of the redundancies of the data so that the predictions are based on more representative features and are robust to noisy data. The prediction of the scores is done with neural networks using the data released by the ASAP competition held by Kaggle. The resulting agreement between human grader’s score and the system’s prediction is measured using Quadratic Weighted Kappa (QWK). Our system produces a QWK of 0.793. Our results are repeatable and transparent, and every feature is very well explained as compared to other existing systems where authors have not explained the methodologies and feature extraction to a similar extent for the results to be
Effects of explicit instruction in previewing, thinking aloud, and text structure on Egyptian University students' reading ability

The present study examines the effect of explicit instruction in three selective reading strategies on reading comprehension in English as a foreign language (EFL). The study addresses the following questions: A) Does explicit instruction in text structure help students identify discourse organization patterns? B) Does explicit instructional reading strategy in EFL develop reading comprehension? C) Is there a direct correlation between discourse organization knowledge and reading development? D) Does text structure knowledge develop EFL reading comprehension? Participants in the study are 60 first year Egyptian University students studying English as a foreign language. Results of the study indicated that knowledge of discourse organization patterns and reading strategies developed students' reading comprehension. They also showed that the students who were trained in text organization outperformed those who did not receive similar training. Moreover, the results revealed a statistically significant and positive correlation between text structure knowledge and reading comprehension. Finally, the paper provides suggestions for further research and implications for teachers and learners of English as a foreign language.
Modeling Extreme Events in Time Series Prediction

Time series prediction is an intensively studied topic in data mining. In spite of the considerable improvements, recent deep learning-based methods overlook the existence of extreme events, which result in weak performance when applying them to real time series. Extreme events are rare and random, but do play a critical role in many real applications, such as the forecasting of financial crisis and natural disasters. In this paper, we explore the central theme of improving the ability of deep learning on modeling extreme events for time series prediction. Through the lens of formal analysis, we first find that the weakness of deep learning methods roots in the conventional form of quadratic loss. To address this issue, we take inspirations from the Extreme Value Theory, developing a new form of loss called Extreme Value Loss (EVL) for detecting the future occurrence of extreme events. Furthermore, we propose to employ Memory Network in order to memorize extreme events in historical records.By incorporating EVL with an adapted memory network module, we achieve an end-to-end framework for time series prediction with extreme events. Through extensive experiments on synthetic data and two real datasets of stock and climate, we empirically validate the effectiveness of our framework. Besides, we also provide a proper choice for hyper-parameters in our proposed framework by conducting several additional experiments.
SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures

The processing of speech corrupted by interfering overlapping speakers is one of the challenging problems with regards to today's automatic speech recognition systems. Recently, approaches based on deep learning have made great progress toward solving this problem. Most of these approaches tackle the problem as speech separation, i.e., they blindly recover all the speakers from the mixture. In some scenarios, such as smart personal devices, we may however be interested in recovering one target speaker from a mixture. In this paper, we introduce SpeakerBeam, a method for extracting a target speaker from the mixture based on an adaptation utterance spoken by the target speaker. Formulating the problem as speaker extraction avoids certain issues such as label permutation and the need to determine the number of speakers in the mixture. With SpeakerBeam, we jointly learn to extract a representation from the adaptation utterance characterizing the target speaker and to use this representation to extract the speaker. We explore several ways to do this, mostly inspired by speaker adaptation in acoustic models for automatic speech recognition. We evaluate the performance on the widely used WSJ0-2mix and WSJ0-3mix datasets, and these datasets modified with more noise or more realistic overlapping patterns. We further analyze the learned behavior by exploring the speaker representations and assessing the effect of the length of the adaptation data. The results show the benefit of including speaker information in the processing and the effectiveness of the proposed method.
SphereRE: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings

Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.