Reviewerly/exHarmony huggingface dataset:
A Flexible Supervised Term-Weighting Technique and its Application to Variable Extraction and Information Retrieval

Successful modeling and prediction depend on effective methods for the extraction of domain-relevant variables. This paper proposes a methodology for identifying domain-specific terms. The proposed methodology relies on a collection of documents labeled as relevant or irrelevant to the domain under analysis. Based on the labeled document collection, we propose a supervised technique that weights terms based on their descriptive and discriminating power. Finally, the descriptive and discriminating values are combined into a general measure that, through the use of an adjustable parameter, allows to independently favor different aspects of retrieval such as maximizing precision or recall, or achieving a balance between both of them. The proposed technique is applied to the economic domain and is empirically evaluated through a human-subject experiment involving experts and non-experts in Economy. It is also evaluated as a term-weighting technique for query-term selection showing promising results. We finally illustrate the applicability of the proposed technique to address diverse problems such as building prediction models, supporting knowledge modeling, and achieving total recall.
The design and implementation of an Arabic pronunciation application for early childhood

Learning is not limited to a certain age group or a formal study environment. However, the first eight years, and particularly the first three years, are most important, as the effects of the care and attention provided to the child during this period will continue throughout his or her life. A child’s early learning processes develop their educational abilities and skills, and fosters their academic achievements.Preschool can be very benefit to development and learning for children, but there are differences between countries in this area. It can reduce the repetition in the classroom and can raise achievement scores for students through the preschool programs of high quality (Jacobson and Linda 2008). There are many applications in the Middle East that teach kids, but there is didn't link between the vowels of the Arabic alphabet characters and verbally voice for each character and training to select correct format of Arabic alphabet like (fatha, dama and kasra). This study aims to develop an educational application for children from the ages of 3 to 5 to help them improve their skills and abilities. The application will also prepare them for school by teaching them the Arabic letters, pronunciation and the short vowels (fatha, dama and kasra). The application also aims to help children communicate and interact more effectively with their external environment by employing several theories about learning skills, such as Piaget’s Theory of Cognitive Development, Edger Dale’s Cone of Learning and Bloom's Taxonomy of Cognitive Goals. The application will focus on the first three levels of Bloom's Taxonomy Pyramid which are ‘Remember’, ‘Understand’ and ‘Apply’. Furthermore, the application will be designed to aid children with their Arabic pronunciation, and aural and oral skills, and train and educate them in preparation for attending school. The expected result is that it will be considerably better than educational books for children’s education. Combining education and play in e-learning applications is an important way to attract children.
English students’ experience of reframing narrative stories from a critical literacy perspective

This article reports a study investigating the English teacher’s strategies for assisting secondary school students of English to be critically literate through an analysis of narrative texts. In this study, Luke and Freebody’s (1999) four resources model of critical literacy was used as a framework for English instruction situated in an Indonesian secondary school. Data were collected from classroom observations, students’ reflective journals, and an interview with secondary school students. The findings showed that the four resources model of critical literacy, to some extent, helped students to become critically literate especially at the stage of meaning-making-of and criticizing the text. The implication of the study is that there is the possibility of implementing critical literacy-oriented English classrooms in Indonesian and Asian secondary schools.
Breast Cancer Classification using Global Discriminate Features in Mammographic Images

Breast cancer has become a rapidly prevailing disease among women all over the world.In term of mortality, it is considered to be the second leading cause of death.Death risk can be reduced by early stage detection, followed by a suitable treatment procedure.Contemporary literature shows that mammographic imaging is widely used for premature discovery of breast cancer.In this paper, we propose an efficient Computer Aided Diagnostic (CAD) system for the detection of breast cancer using mammography images.The CAD system extracts largely discriminating features on the global level for representation of target categories in two sets: all 20 extracted features and top 7 ranked features among them.Texture characteristics using cooccurrence matrices are calculated via the single offset vector.Multilayer perceptron neural network with optimized architecture is fed with individual feature sets and results are produced.Data division corresponds as 60%, 20%, and 20% is used for training, cross-validation, and test purposes, respectively.Robust results are achieved and presented after rotating the data up to five times, which shows higher than 99% accuracy for both target categories, and hence outperform the existing solutions.
Evaluation of radial basis function neural network minimizing L-GEM for sensor-based activity recognition

Abstract Sensor-based activity recognition involves the automatic recognition of a user’s activity in a smart environment using computational methods. The use of wearable devices and video-based approaches have attracted considerable interest in ubiquitous computing. Nevertheless, these methods have limitations such as issues with privacy invasion, ethics, comfort and obtrusiveness. Environmental sensors are an increasingly promising consideration in the ubiquitous computing domain for long-term monitoring, as these devices are non-invasive to inhabitants, yet certain challenges remain with activity recognition in sensorised environments, for example, addressing the challenge of intraclass variation between activities and reasoning from low-level uncertain information. In an effort to address these challenges, this paper proposes and evaluates the performance of a Radial Basis Function Neural Network approach for activity recognition with environmental sensors. The model is trained using the Localized Generalization Error and focuses on the generalization ability by considering both the training error and stochastic sensitivity measure. This measures the network output fluctuation with respect to the minor perturbation of input, to address the tolerance of the low-level uncertain sensor data. This approach is compared with three benchmark Neural Network approaches, including a popular deep learning approach using an Autoencoder, and it is evaluated with a simulated dataset as well as a number of publicly available datasets. The proposed method has shown advantages over the other models for all four evaluated datasets. This paper provides insights into the importance of model generalization abilities and an initial analysis of the limitation of deep Neural Networks with respect to sensor-based activity recognition.
Joint Task Difficulties Estimation and Testees Ranking for Intelligence Evaluation

In this paper, we study the testing tasks evaluation and testees ranking problem, in which tasks have different difficulty levels, and testees have different capabilities.We assume that a testee may have a probability to pass a certain task so as to allow certain uncertainty. The goal of this problem is to simultaneously determine the relative difficulty level of each testing task and the relative capability of every testee, purely based on the test outcome. We design two models to solve this problem. The first one assumes that the test outcome follows a certain Bernoulli distribution; while the second one assumes that the test outcome follows a certain Bernoulli distribution with the beta distribution-type a priori knowledge. Then, we form the original problem into likelihood estimation problems and solve them by using coordinate descent algorithms. We show that the beta distribution-type a priori knowledge is needed, when we only carry out a limited number of tests due to time and financial budgets. All these findings are useful to intelligence tests. Finally, we discuss how to extend this statistical learning model for more general cases as well as in a specific case in the field of Computational Social Systems like artificial social cognition evaluation.
McTAR: A Multi-Trigger Checkpointing Tactic for Fast Task Recovery in MapReduce

Cloud computing and big data technologies have gained great popularity in recent years. MapReduce is still one of the most efficient and well-adopted computing paradigms for providing big data services. MapReduce applications need to be executed on cloud platform where failures are inevitable. Hadoop is the de facto implementation of MapReduce, but it deploys a coarse grained and unsatisfactory fault tolerant services. The failed tasks are rescheduled from scratch to re-execute from the very beginning, which apparently brings amount of overload for failure recovery, and the whole job would be heavily delayed as failures happen. In this paper, we propose a novel multi-trigger checkpointing approach for fast recovery of MapReduce tasks, named a Multi-trigger Checkpointing Tactic for fAst TAsk Recovery (McTAR). As a finer-grained and better fault tolerance tactic, our McTAR employs multi-trigger checkpoint generation, push-pull combined intermediate data distribution and optimized failure task prediction techniques together to make the recovery task attempt be able to start at a specific progress according to the valid checkpoint for intermediate data. In this way, McTAR could effectively speed up the recovery process of MapReduce jobs and highly reduce the task recovery delay.
Task-aware flow scheduling with heterogeneous utility characteristics for data center networks

With the continuous enrichment of cloud services, an increasing number of applications are being deployed in data centers. These emerging applications are often communication-intensive and data-parallel, and their performance is closely related to the underlying network. With their distributed nature, the applications consist of tasks that involve a collection of parallel flows. Traditional techniques to optimize flow-level metrics are agnostic to task-level requirements, leading to poor application-level performance. In this paper, we address the heterogeneous task-level requirements of applications and propose task-aware flow scheduling. First, we model tasks' sensitivity to their completion time by utilities. Second, on the basis of Nash bargaining theory, we establish a flow scheduling model with heterogeneous utility characteristics, and analyze it using Lagrange multiplier method and KKT condition. Third, we propose two utility-aware bandwidth allocation algorithms with different practical constraints. Finally, we present Tasch, a system that enables tasks to maintain high utilities and guarantees the fairness of utilities. To demonstrate the feasibility of our system, we conduct comprehensive evaluations with real-world traffic trace. Communication stages complete up to 1.4× faster on average, task utilities increase up to 2.26×, and the fairness of tasks improves up to 8.66× using Tasch in comparison to per-flow mechanisms.
Improved single-random-phase holographic encryption using double-phase method

An improved single-random-phase holographic encryption (SRPHE) is proposed by using double-phase method and demonstrated by a spatial light modulator (SLM). Different from the conventional SRPHE using Fresnel diffraction and the bidirectional error diffusion method, the proposed SRPHE employees the angular spectrum diffraction and double-phase method to generate a phase-only hologram, and the phase-only hologram is encrypted into ciphertext by multiplying a single random phase mask. When the ciphertext is modulated by the conjugate of the random phase mask and loaded to a SLM, the decrypted image can be achieved successfully. The improved SRPHE has two advantages of better quality of the decrypted image, and lower time-cost of hologram generation, which is compared with the conventional SRPHE. Numerical simulations and optical reconstruction results have been carried out to verify the validity of the proposed SRPHE.
Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification

The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.
Medical assertion classification in Chinese EMRs using attention enhanced neural network

Electronic medical records (EMRs), such as hospital discharge summaries, contain a wealth of information only expressed in natural language. Automated methods for extracting information from these records must be able to recognize medical concepts in text and their semantic context. A contextual property critical to reason on information from EMRs is the doctor's belief status or assertion of the patient's medical problem. Research on the medical assertion classification (MAC) can establish the foundation for various health data analyses and clinical applications. However, previous MAC studies are mainly based on traditional machine learning methods which mostly require manually constructed features and the original unlabeled data cannot be easily and effectively applied to classification or classification tasks. Furthermore, external medical knowledge such as various medical dictionary bases, which provides rich explain and definition information about medical entity, is rarely utilized in existing neural network models of medical information extraction. In this study, we propose a deep neural network architecture enhanced by medical knowledge attention layer through combining GRU neural network with CNN model to classify the assertion type of medical problem such as disease and symptom in Chinese EMRs. The attention layer in the model is applied to integrate entity representations learned from medical dictionary bases as query for encoding. Experimental results on own manually annotated corpus indicate our approach achieves better performance compared to existing methods.
Multi-context systems in dynamic environments

Multi-Context Systems (MCSs) are able to formally model, in Computational Logic, distributed systems composed of heterogeneous sources, or “contexts”, interacting via special rules called “bridge rules”. In this paper, we consider how to enhance flexibility and generality in bridge-rules definition and use. In particular, we introduce and discuss some formal extensions of MCSs aimed to their practical application in dynamic environments, and we provide guidelines for implementations.
Knowledge Graphing Git Repositories: A Preliminary Study

Knowledge Graph, being able to connect information from a variety of sources, has become very famous in recent years since its creation in 2012 by Google. Researchers in our community have leveraged Knowledge Graph to achieve various purposes such as improving API caveats accessibilities, generating answers to developer questions, and reasoning common software weaknesses, etc. In this work, we would like to leverage the knowledge graph concept for helping developers and project managers to comprehend software repositories. To this end, we design and implement a prototype tool called GitGraph, which takes as input a Git repository and constructs automatically a knowledge graph associated with the repository. Our preliminary experimental results show that GitGraph can correctly generate knowledge graphs for Git projects and the generated graphs are also useful for users to comprehend the projects. More specifically, the knowledge graph, on one hand, provides a graphic interface that users can interactively explore the integrated artefacts such as commits and changed methods, while on the other hand, provides a convenient means for users to search for advanced relations between the different artefacts.
Mole Messenger

Mole Messenger is a pair of connected creatures that help children share and send messages to their loved ones who may be far away. Each Mole Messenger box houses a pushable mole. When one is pushed, the other pops up, as if traversing to the other side of the globe. The mole can be tapped to carry different color messages when pushed and can be used to play physically engaged games across distances. This paper describes the design and implementation of the system, explores various approaches within the design space, engages with the context and user experience, and meditates on the potential for positive psychological impact. Inspired by one family's story of separation and how they helped their children stay connected, we hope Mole Messenger can become an important tool for communication and healthy emotional development in children.
A Cross-Center Smoothness Prior for Variational Bayesian Brain Tissue Segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.
Somatosensory Loss Influences the Adoption of Self-Centered Versus Decentered Perspectives

The body and the self are commonly experienced as forming a unity. Experiencing the external world as distinct from the self and the body strongly relies on adopting a single self-centered perspective which results in integrating multisensory sensations into one egocentric body-centered reference frame. Body posture and somatosensory representations have been reported to influence perception and specifically the reference frame relative to which multisensory sensations are coded. In the study reported here, we investigated the role of somatosensory and visual information in adopting self-centered and decentered spatial perspectives. Two deafferented patients who have neither tactile nor proprioceptive perception below the head and a group of age-matched control participants performed a graphesthesia task, consisting of the recognition of ambiguous letters (b, d, p, and q) drawn tactilely on head surfaces. To answer which letter was drawn, the participants can adopt either a self-centered perspective or a decentered one (i.e., centered on a body part or on an external location). The participants' responses can be used, in turn, to infer the way the left-right and top-bottom letters' axes are assigned with respect to the left-right and top-bottom axes of their body. In order to evaluate the influence of body posture, the ambiguous letters were drawn on the participants' forehead, left, and right surfaces of the head, with the head aligned or rotated in yaw relative to the trunk. In order to evaluate the role of external information, the participants completed the task with their eyes open in one session and closed in another one. The results obtained in control participants revealed that their preferred perspective varied with body posture but not with vision. Different results were obtained with the deafferented patients who overall do not show any significant effect of their body posture on their preferred perspective. This result suggests that the orientation of their self is not influenced by their physical body. There was an effect of vision for only one of the two patients. The deafferented patients rely on strategies that are more prone to interindividual differences, which highlights the crucial role of somatosensory information in adopting self-centered spatial perspectives.
Estimation of global solar radiation, sunshine hour distribution and clearness index in Enugu, Nigeria

The concept of solar energy and its applications in this present day world would come to be one of the solutions to our present Nigerian problem of instability and epileptic power supply.In the study, the baseline data for mean monthly sunshine hours ( ) S and global solar radiation ( ) H for Enugu (6.7 0 N, 7.6 0 E) were obtained from the Nigeria Metrological Agency (NIMET), Oshodi Lagos Nigeria and spanned 1996 to 2010.The data for global solar radiation were measured with a Gunn-Bellani radiometer.The average sunshine hour for the period of study was estimated to be 5.67 hours; the Angstrom constants a and b of Angstrom-type correlation used in estimating monthly average global solar radiation was estimated to be 0.27 and 0.58 respectively; a linear regression correlation model was developed for Enugu.The result of global solar radiation was found to be 14.44 1 2 --day MJm thereafter subjected to statistical tests [ ] MPE RMSE MBE , , and proved to be good estimates with previous literatures reviewed.The value of clearness index ( ) T
Prototype adjustment for zero shot classification

Zero shot classification addresses the problem of classifying unseen classes with seen class samples. Current zero shot learning methods mostly focus on learning the mapping function from image feature space to semantic space which is extremely important. However, these methods assume the seen and unseen class prototypes are fixed. A class prototype is referred to the semantic representation of a class. The semantic representation is represented by the attributes or word vectors which may be inaccurate and not discriminative. We attempt to find new prototypes that are more accurate for the zero shot classification tasks. In this paper, we proposed a Prototype adjustment method for the zero shot classification tasks (PAZSC) by adjusting the prototypes and learning the mapping function from image feature space to semantic space, simultaneously. The adjusted prototypes are more separable and discriminative for the zero shot classification tasks. A joint optimization function is proposed to learn the new prototypes and the mapping function. What is more, there is a domain shift problem in zero shot classification tasks caused by the disjointed seen and unseen images. We further learn a more generalizable mapping function to alleviate the domain shift problem. We have experimented on the state-of-the-art zero shot learning datasets, demonstrating that our PAZSL method has good performance.