Reviewerly/exHarmony huggingface dataset:
Learning to Read in Multilingual Malaysia: A Focus on Bahasa Melayu, Tamil and Chinese

Learning to read fluently is an extremely important skill for all children to acquire. The current article focuses on learning to read in the most widely spoken languages of Malaysia, namely, the national language Bahasa Melayu, Tamil and Chinese. These three unrelated languages have quite distinct writing systems. Bahasa Melayu uses alphabetic Rumi or Roman script, Tamil has an alphasyllabary, and Chinese has a logographic or morphosyllabic writing system. Moreover, many of these children are learning to speak and read in more than one language. When we consider the task of these biscriptal learners, a complex picture emerges, as they may have to learn to map different phonological and orthographic systems. Furthermore, many children in Malaysia have the additional challenge of learning English as a second language. First, a brief review of the characteristics of the three main languages and their orthographies is given. Subsequently, research on phonological awareness, an important skill associated with success in reading, is reviewed. Initially, phonological awareness and reading in single language studies is examined prior to reviewing some research on bilingual learners. As these three languages have rich morphological systems, we will also briefly examine some research on morphological awareness and reading. A review of the literature reveals that children who speak a language with a similar orthography to a second language may have some advantage when learning to read that second language in comparison to children whose first and second languages and orthographies are unrelated.
Multisensory Plucked Instrument Modeling in Unity3D: From Keytar to Accurate String Prototyping

Keytar is a plucked guitar simulation mockup developed with Unity3D that provides auditory, visual, and haptic feedback to the player through a Phantom Omni robotic arm. Starting from a description of the implementation of the virtual instrument, we discuss our ongoing work. The ultimate goal is the creation of a set of software tools available for developing plucked instruments in Unity3D. Using such tools, sonic interaction designers can efficiently simulate plucked string prototypes and realize multisensory interactions with virtual instruments for unprecedented purposes, such as testing innovative plucked string interfaces or training machine learning algorithms with data about the dynamics of the performance, which are immediately accessible from the machine.
Descriptive Statistical Analysis in the Process of Educational Data Mining

The paper describes the process of descriptive statistical analysis of a training set of educational data extracted from distributed sources of blended learning environments. The determined variability in values has indicated the use of the histogram discretization method at the preprocessing phase. Estimation of classification models created over a discretized educational data set has confirmed the importance of descriptive statistical analysis in the case of creating a training set of data from distributed sources.
Educating the Information Integration Using Contextual Knowledge and Ontology Merging in Advanced Levels

This paper defines the methods of educating the information integration by the use of ontologies. For this there are two various architecture are central and peer-to-peer data integration. A ciis generally has a worldwide mapping, which gives the client a uniform interface to get to data put away in the information sources. Conversely, in piis, there are no worldwide purposes of control on the information sources. Such systems enable developers to develop an integrated hybrid contextual based system and new concepts to be introduced. This enables the retrival of the information is easier and faster. The two most significant methodologies for structure an information integration framework is global as view &amp; local as view (lav). In the gav method, each substance in the worldwide pattern is related nearby outline. In this paper we use various ontology languages like xml, rdf, daml+oil, owl etc.
A Probabilistic Framework for Base Level Context Awareness of a Mobile or Wearable Device User

A framework for determining probabilistic context awareness of a mobile or wearable device user with single or multiple sensors is presented. The functionality of this module or subsystem consists of acquisition of raw data from single or multiple, homogeneous or heterogeneous sensors and processing it to derive contextual information for the device user. The framework takes into consideration the base level context of continuous motion and voice activities of the device user and spatial environment surrounding the user along with other deterministic input. The output of that framework is posteriorgram i.e. a posteriori probabilities of each class of each descriptor of context. The framework has several advantages of being expressive, simple, scalable and extendable along with providing output in probabilistic form. The usefulness of such a framework lies in the fact that providing base level context awareness as input gives flexibility to develop various types of application programming interfaces (APIs) and its adaptability to increase or decrease the descriptors of context to be inferred, without change in the base level framework of context awareness. The base level context awareness comprising of deterministic and probabilistic context can be used to derive meta level context awareness of various kinds.
The Impact of Leadership Practices on Total Quality Management and Organizational Performance in the UAE Interior Ministry

This study examines the moderating impact of leadership practices (LP) in TQM and organizational performance (OP). Through literature review, four hypotheses were derived which are tested in the UAE to better understand the nature of the relationship between TQM and LP as well as the impact of TQM on OP in the public sector departments in the UAE. Data were collected by distributing questionnaire to 230, data we analyzed by using descriptive and inferential statistics. The research showed the impact of LP on TQM and OP.
Student Perceptions of the Use of Android-Based Learning Media in the Production Ecrite Intermediaire Course

Based on the author's experience, French Language Study Program students have difficulties in learning Wrting at Production Ecrite Intermédiaire course. The author develops an Android-based mobile application as a learning medium to help students learn writing and increase student motivation for independent learning. This study aims to determine the perceptions of French Language study program students about the use of Android-based mobile applications as learning media in the Production Ecrite Intermediare course. This research is in the form of quantitative descriptive research with survey method. The population studied was the 3rd semester students of the French Department of Medan State University who were taking Production Ecrite Intermediare courses in 2019. The sample in this study amounted to 30 people. Data collection is done through the distribution of questionnaires to respondents. The results of this study indicate that students like learning media in the form of mobile applications. This is shown from the results of a survey which stated that 86.% of students had very good perceptions. More specifically, the use of Android based mobile applications as learning media makes students feel motivated to study Production Ecrite Intermediaire independently with a percentage of 86.1% in the agreed category
Aiming for the Goal with SInE

The Sumo INference Engine (SInE) is a well-established premise selection algorithm for first-order theorem provers, routinely used, especially on large theory problems. The main idea of SInE is to start from the goal formula and to iteratively add other formulas to those already added that are related by sharing signature symbols. This implicitly defines a certain heuristical distance of the individual formulas and symbols from the goal. In this paper, we show how this distance can be successfully used for other purposes than just premise selection. In particular, biasing clause selection to postpone introduction of input clauses further from the goal helps to solve more problems. Moreover, a precedence which respects such goal distance of symbols gives rise to a goal sensitive simplification ordering. We implemented both ideas in the automatic theorem prover Vampire and present their experimental evaluation on the TPTP benchmark.
Privacy-Preserving News Recommendation Model Training via Federated Learning.

News recommendation aims to display news articles to users based on their personal interest. Existing news recommendation methods rely on centralized storage of user behavior data for model training, which may lead to privacy concerns and risks due to the privacy-sensitive nature of user behaviors. In this paper, we propose a privacy-preserving method for news recommendation model training based on federated learning, where the user behavior data is locally stored on user devices. Our method can leverage the useful information in the behaviors of massive number users to train accurate news recommendation models and meanwhile remove the need of centralized storage of them. More specifically, on each user device we keep a local copy of the news recommendation model, and compute gradients of the local model based on the user behaviors in this device. The local gradients from a group of randomly selected users are uploaded to server, which are further aggregated to update the global model in the server. Since the model gradients may contain some implicit private information, we apply local differential privacy (LDP) to them before uploading for better privacy protection. The updated global model is then distributed to each user device for local model update. We repeat this process for multiple rounds. Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
Finding the Sweet Spot: Batch Selection for One-Class Active Learning

Active learning methods collect annotations in the form of class labels, often from human experts, to improve upon some classification task. In many cases, one can collect annotations for a batch of observations at a time, e.g., when several annotators are available. This can make the annotation process more efficient, both regarding human and computational resources. However, selecting a good batch is difficult. It requires to understand several trade-offs between the costs of classifier training, batch selection, annotation effort, and classification accuracy. For one-class classification, a very important application of active learning, batch selection has not received any attention in the literature so far. In this article, we strive to find a sweet spot between the costs of batch-mode learning and classification accuracy. To this end, we first frame batch selection as an optimization problem. We then propose several strategies to identify good batches, discuss their properties, and evaluate them on real-world data. A core result is that a sweet spot indeed exists, with active learning costs reduced by up to an order of magnitude compared to a sequential procedure, without sacrificing accuracy.MSC codesone-class classificationactive learningbatch queries
An Identity Based-Identification Scheme With Tight Security Against Active and Concurrent Adversaries

Identification schemes are used by machines to securely authenticate the identity of other machines or their users over computer networks. As conventional public key schemes require a trusted third party (TTP) or a public file to ensure the corresponding public key matches with the identity, identity-based cryptosystems emerged as a form of certificate-free system. The entity's identity is the public key itself, therefore eliminating the need for a TTP. The identity-based identification (IBI) scheme introduced by Kurosawa and Heng using their transform in 2004 remains as the only IBI derived from the Boneh-Lynn-Shacham (BLS) short signature scheme which has the advantage of shorter keys. We show tight security reduction against active and concurrent attackers (imp-aa/ca) on our scheme that is obtained from the same transform. As the transform will only produce schemes that are only secure against passive attackers (imp-pa), security against imp-aa/ca scheme relies on a strong One-More interactive assumption and therefore resulted in weak security. While the OR-proof method allows schemes secure against imp-pa to be secure against imp-aa/ca, the resulting security against imp-aa/ca will suffer from loose bounds in addition to the user secret keys being doubled in size. Our work avoids both OR-proof and strong interactive assumptions by showing an ad-hoc proof for our construction which utilizes the weaker well-studied co-computational Diffie-Hellman assumption and yet still has tight security against imp-aa/ca. We demonstrate the tight security of our scheme which allows usage of even shorter key sizes.
Comparison of Automated Analyzer Generated Red Blood Cell Parameters and Histogram with Peripheral Smear in the Diagnosis of Anaemia

Introduction: Red blood cell (RBC) indices and histogram obtained from automated analyzer give an idea about morphological changes in anaemia.Along with the peripheral smear, they can be used to interpret the cause of anaemia.The aim of the present study was to correlate typing of anaemia based on RBC indices and histograms obtained from automated analyzer with peripheral smear examination and to give a better approach in the diagnosis of anaemia and analyse their limitations.Material and methods: Blood samples collected from 1575 anaemic patients, over a period of one year, in the Hematology laboratory, were typed for anaemia, first by parameters obtained by haematology analyzer (RBC indices with red cell distribution width) and then by peripheral blood smear findings.The correlation between the diagnosis made by peripheral smear versus RBC indices and histogram were analysed to find out the significance of the mean difference in various parameters.Results: Microcytic hypochromic anaemia (50.86%) was the most common followed by normocytic normochromic (25.14%), macrocytic (2.29%), dimorphic (15.43%) and haemolytic anaemia (6.29%) as diagnosed on peripheral smear examination.Analysis by RBC indices showed 59.43%, 32%, 3.43%, 2.86% and 2.29% of microcytic hypochromic, normocytic normochromic, macrocytic, dimorphic and haemolytic anaemia respectively.Out of 801 (50.86%) cases of microcytic hypochromic anaemia on smear, 513(32.5%)revealed left shift while 288 (18.3%) showed broad-based curve on the histogram.In normocytic normochromic anaemia, anormal bell-shaped curve was seen in 378 cases (25%) while 18 cases (1.1%) showed the broad curve.Histograms in macrocytic anaemia show a right shift in 27 cases (1.7%) while 9 cases (0.6%) showed left shift.In dimorphic anaemia, the histogram was bimodal only in 2.9% of cases whereas a majority (11.4%) showed broad-base histogram. Conclusion:Histogram and RBC indices give valuable information, but peripheral smear examination remains the important diagnostic tool in haematological disorders.
Data-driven assessment of structural evolution of RDF graphs

Since the birth of the Semantic Web, numerous knowledge bases have appeared. The applications that exploit them rely on the quality of their data through time. In this regard, one of the main dimensions of data quality is conformance to the expected usage of the vocabulary. However, the vocabulary usage (i.e., how classes and properties are actually populated) can vary from one base to another. Moreover, through time, such usage can evolve within a base and diverge from the previous practices. Methods have been proposed to follow the evolution of a knowledge base by the observation of the changes of their intentional schema (or ontology); however, they do not capture the evolution of their actual data, which can vary greatly in practice. In this paper, we propose a data-driven approach to assess the global evolution of vocabulary usage in large RDF graphs. Our proposal relies on two structural measures defined at different granularities (dataset vs update), which are based on pattern mining techniques. We have performed a thorough experimentation which shows that our approach is scalable, and can capture structural evolution through time of both synthetic (LUBM) and real knowledge bases (different snapshots and updates of DBpedia).
ENHANCING THE PERFORMANCE OF THE WALL-FOLLOWING ROBOT BASED ON FLC-GA

Determination of the improper speed of the wall-following robot will produce a wavy motion. This common problem can be solved by adding a Fuzzy Logic Controller (FLC) to the system. The usage of FLC is very influential on the performance of the wall-following robot. Accuracy in the determination of speed is largely based on the setting of the membership function that becomes the value of its input. So manual setting on membership function can still be enhanced by approaching the certain optimization method. This paper describes an optimization method based on Genetic Algorithm (GA). It is used to improving the ability of FLC to control the wall-following robot controlled by FLC. To provide clarity, the wall-following robot that controlled using an FLC with manual settings will be simulated and compared with the performance of wall-following robots controlled by a fuzzy logic controller optimized by a Genetic Algorithm (FLC-GA). According to comparative results, the proposed method has been showing effectiveness in terms of stability indicated by a small error.
Medical Image Denoising Based on Biquadratic Polynomial With Minimum Error Constraints and Low-Rank Approximation

To improve the visual quality of noisy medical images acquired by low radiation dose imaging, medical image denoising is highly desirable for clinical disease diagnosis. In this paper, a geometric regularization method is proposed for medical image denoising. To the best of our knowledge, this is the first work aiming at reconstructing surface by minimizing the gradient error and approximation error of the surface to suppress the noise in medical images. The proposed denoising method consists of two stages: one is to output a basic estimate and the other is for the residual noise reduction. Specifically, the method first exploits a biquadratic polynomial surface to generate an initial estimate of the noise-free image. The surface is constructed by dividing its coefficients into two groups. With the reconstruction error constraint, one group is used to minimize the gradient of the surface, and the other is to minimize the approximation accuracy of the surface. Then the residual noise in the initial result is further reduced by using the singular value thresholding mechanism, which exploits the self-similarity of medical images and the intrinsic low-rank property. Unlike the traditional truncated singular value thresholding scheme, the proposed singular value thresholding is derived by optimizing an objective function with a constraint. Experimental results on a real clinical data set demonstrate the effectiveness of the proposed denoising method, especially in detail-preserving. Compared with several widely used denoising methods, our method can achieve a better performance in terms of both quantitative metrics and subjective visual quality.
Generalized shift–rotation absolute measurement method for optical surface shapes with polygonal apertures based on migration recognition by Radon transform

A generalized shift–rotation absolute measurement method for optical surface shapes with polygonal apertures based on migration recognition by Radon transform is proposed. The rotation angles and translation distances of the test surface, measured three times, are calculated through migration recognition. The absolute shape of the test surface with the polygonal aperture is fitted by orthogonal Zernike polynomials. Compared to the existing absolute measurement method for polygonal apertures, our method ensures test surface measurement accuracy without high-precision attitude control and repeated adjustments. The measurement is simple and coherent, which reduces the measurement time and improves the efficiency.
“Perimeter”: Measuring the Ability to Design Activity with a Group Assessment Tool

The article describes a tool for assessing the ability to design activity in the process of group work when group members must coordinate to produce a scheme for their joint work. This new game-based technique was developed on the basis of N.Ach’s and Vygotsky-Sacharov’s integrated methods as well as on V. Rubtsov’s and Y.Gromyko’s methodology. It follows the principles of activity approach. The feature of this technique is that it employs the criteria for assessing communication and coordination in group task solving that were specially developed by us for these purposes. The tool can be applied for assessing the outcomes of design activity and developmental levels of design competencies in adolescents and young people in the course of school and university education. Also, this technique can be used for assessing project teams both in education and in other professional areas. The technique is currently being tested.
Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a variational deep image prior network for joint demosaicing and denoising which can be trained on a single patterned image and works for patterned images with different levels of noise. We also propose a new RGB color filter array (CFA) which works better with the proposed network than the conventional Bayer CFA. Mathematical justifications of why the variational deep image prior network suits the task of joint demosaicing and denoising are also given, and experimental results verify the performance of the proposed method.
Improved de-interleaving algorithm of radar pulses based on dual fuzzy vigilance ART

As a core part of the electronic warfare (EW) system, de-interleaving is used to separate interleaved radar signals. The de-interleaving algorithm based on the fuzzy adaptive resonance theory (fuzzy ART) is plagued by the problems of premature saturation and performance improving dilemma. This study proposes a dual fuzzy vigilance ART (DFV-ART) algorithm to address these problems and make the following improvements. Firstly, a correction method is introduced to prevent the network from prematurely saturating; then, the fuzzy vigilance models (FVM) are constructed to replace the conventional vigilance parameter, reducing the error probability in the overlapping region; finally, a dual vigilance mechanism is introduced to solve the performance improving dilemma. Simulation results show that the proposed algorithm could improve the clustering accuracy (quantization error dropped 60%) and the de-interleaving performance (clustering quality increased by 10%) while suppressing the excessive proliferation of categories.