Reviewerly/exHarmony huggingface dataset:
Modeling Distributed Agile Software Development Utilizing Cloud Computing: A Holistic Framework

In distributed agile software development, to conquer the problems raised by rapid development of complex products with ever changing technological advances and risks, there is a need of a model with specific tools and technology supported approaches to manage them. Distributed environment has challenges and complexities in many ways of over collocated teams. However, they seem to enhance the basic principles in which agile methodology thrives on. Agile projects and distributed projects are compatible and therefore practices, principles, tools, and techniques for distributed agile software development are introduced to the distributed teams to ensure that the agile projects become successful. Although cloud computing is well established for software system developments, its effect on agile methodology has not been extensively studied. This study outlines how agile projects can be modeled in the cloud-computing era, namely, seamless agile-cloud integration. We propose a model to improve Agile Methodology in distributed computing platform. This model has uncovered a gap between the distributed agile development phases that are overemphasized and the phases that require actual attention. In addition to this, emphasis on appropriate phases would help build desired software that can exploit the benefits of cloud computing in numerous ways.
Robust face hallucination via locality-constrained multiscale coding

Face hallucination (FH) is to produce face images with High Resolution (HR) from Low Resolution (LR) observations. Unfortunately, most existing FH methods fail to make full use of the local geometrical information, especially when the LR images are corrupted by noise. Inspired by the observation that regions with large scales can provide much useful information, in this paper we propose a Robust Locality-constrained Multiscale Coding (RLMC) based method to forecast HR face images while suppressing noise and outliers. In RLMC, a weight vector is used in the loss function to ease the effect of outliers in data representation. Furthermore, inspired by the observation that abundant local information can be exploited by jointly representing overlapping patches with multiple scales. Simultaneously encoding multiple scale patches encourages different scales to share complementary information, which admits the proposed method to generate more appropriate coefficients for super-resolution reconstruction. Experimental results verified the effectiveness of the proposed method in terms of both quantitative measurements and visual impressions.
Implementation and evaluation of an effective computational method that promotes the conceptualization of Newton’s laws of motion

Abstract A computational method that contributes to the understanding and conceptualization of Newton’s laws of motion, through the use of a novel environment of virtual experimentation of physical systems, was implemented in the students of the Faculty of Engineering and Basic Sciences of the Universidad del Magdalena. This article investigates the level of learning achieved by implementing the physical experiences in the mechanical laboratory and the virtual laboratories developed in the Unity platform, which can be used by the user to work in web environment and mobile devices, when downloaded as an application in Google Play and App Store. Conceptual tests were administered to assess students comprehension before, during and after instruction. The results revealed that the experimental and virtual conditions were equally effective in promoting student’s understanding of the concepts in the domain of Newton’s laws of motion; therefore, virtual manipulation, at least in a context such as that of the present study, reveals an important method in the learning of physics.
Facilitating Application-aware Bandwidth Allocation in the Cloud with One-step-ahead Traffic Information

Bandwidth allocation to virtual machines (VMs) has a significant impact on the performance of communication-intensive big data applications hosted in VMs. It is crucial to accurately determine how much bandwidth to be reserved for VMs and when to adjust it. Past approaches typically resort to predicting the long-term network demands of applications for bandwidth allocation. However, lacking of prediction accuracy, these methods lead to the unpredictable application performance. Recently, it is conceded that the network demands of applications can only be accurately derived right before each of their execution phases. Hence, it is challenging to timely allocate the bandwidth to VMs with limited information. In this paper, we design and implement AppBag, an Application-aware Bandwidth guarantee framework, which allocates the accurate bandwidth to VMs with one-step-ahead traffic information. We propose an algorithm to allocate the bandwidth to VMs and map them onto feasible hosts. To reduce the overhead when adjusting the allocation, an efficient Lazy Migration (LM) algorithm is proposed with bounded performance. We conduct extensive evaluations using real-world applications, showing that AppBag can handle the bandwidth requests at run-time, while reducing the execution time of applications by 47.3 percent and the global traffic by 36.7 percent, compared to the state-of-the-art methods.
Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data. However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results. Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement. Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process. Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake. Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM , to validate the feasibility and advantages of our proposed recommendation approach. The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.
A Clustering Based Transfer Function for Volume Rendering Using Gray-Gradient Mode Histogram

Volume rendering is an emerging technique widely used in the medical field to visualize human organs using tomography image slices. In volume rendering, sliced medical images are transformed into attributes, such as color and opacity through transfer function. Thus, the design of the transfer function directly affects the result of medical images visualization. A well-designed transfer function can improve both the image quality and visualization speed. In one of our previous paper, we designed a multi-dimensional transfer function based on region growth to determine the transparency of a voxel, where both gray threshold and gray change threshold are used to calculate the transparency. In this paper, a new approach of the transfer function is proposed based on clustering analysis of gray-gradient mode histogram, where volume data is represented in a two-dimensional histogram. Clustering analysis is carried out based on the spatial information of volume data in the histogram, and the transfer function is automatically generated by means of clustering analysis of the spatial information. The dataset of human thoracic is used in our experiment to evaluate the performance of volume rendering using the proposed transfer function. By comparing with the original transfer function implemented in two popularly used volume rendering systems, visualization toolkit (VTK) and RadiAnt DICOM Viewer, the effectiveness and performance of the proposed transfer function are demonstrated in terms of the rendering efficiency and image quality, where more accurate and clearer features are presented rather than a blur red area. Furthermore, the complex operations on the two-dimensional histogram are avoided in our proposed approach and more detailed information can be seen from our final visualized image.
Chinese Zero Pronoun Resolution

Semantic information that has been proven to be necessary to the resolution of common noun phrases is typically ignored by most existing Chinese zero pronoun resolvers. This is because that zero pronouns convey no descriptive information, which makes it almost impossible to calculate semantic similarities between the zero pronoun and its candidate antecedents. Moreover, most of traditional approaches are based on the single-candidate model, which considers the candidate antecedents of a zero pronoun in isolation and thus overlooks their reciprocities. To address these problems, we first propose a neural-network-based zero pronoun resolver ( NZR ) that is capable of generating vector-space semantics of zero pronouns and candidate antecedents. On the basis of NZR , we develop the collaborative filtering-based framework for Chinese zero pronoun resolution task, exploring the reciprocities between the candidate antecedents of a zero pronoun to more rationally re-estimate their importance. Experimental results on the Chinese portion of the OntoNotes 5.0 corpus are encouraging: Our proposed model substantially surpasses the Chinese zero pronoun resolution baseline systems.
MFMAP: Learning to Maximize MAP with Matrix Factorization for Implicit Feedback in Recommender System

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks.To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP).In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches.We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.
Need of Internet of Things for Smart Cities

The world is moving forward at a fast hop, and the credit goes to ever growing technology.One such idea is IOT (Internet of things) with which automation is no longer a virtual reality.The Internet of Things will seamlessly incorporate a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of an overabundance of digital services.Building a wide-ranging architecture for IoT is required because of the extremely large variety of devices but it is a very complex task, link layer technologies, and services that may be involved in such a system.In this paper we emphasis specifically to an urban IoT systems that, while still being quite a broad category, are characterized by their specific application domain.Urban IoTs, in fact, are designed to support the Smart City vision, which aims at take advantage of the most advanced communication technologies to support addedvalue services for the administration of the city and for the citizens.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH SQ3R (SURVEY, QUESTION, READ, RECITE AND REVIEW) TECHNIQUE

This research entitled “Improving Students’ Reading Comprehension Through SQ3R Technique”. This research has purpose to figure out the use of SQ3R strategy in improving students’ reading comprehension. The population of this research is students of SMA Negeri 1 Cipongkor including eleventh grade of IPS class and IPA class. The number of population is 60 students. The sample of this research is only one class that is IPS class consists 30 students. The quantitative research method is used in this research, and specifically the one group pre-test post-test design as research design. The instrument of this researches is pretest and posttest and the writers also use t-test formula for collecting data. The result of the research shows that tobs is 2.42, the ttable with degrees of freedom (df) 29 and level of significance at 0.05 was 1,69. Based on the data, the t-test result was 2.42, it was higher that t-table (2.42 &gt; 1.69). It means the alternative hypothesis was accepted. According to the data analysis which is collected, it can be concluded that the test is reliable. And the research showed that teaching students’ reading comprehension using SQ3R technique is very useful. That is why, the SQ3R strategy is suitable in improving students’ reading comprehension.Keywords: Reading, Reading Comprehension, SQ3R Strategy
Vehicle Verification Using Deep Learning for Connected Vehicle Sharing Systems

Information sharing in connected vehicle systems helps each participating vehicle to have a more complete and expanded sensing range beyond its own sensing capability. When sharing visual traffic information among vehicle nodes, it is of great significance to identify overlapping components and associate objects in common to create an accurate and complete surrounding scene. This paper Extends FusionEye, a study of perception sharing, by exploring deep learning approaches for real time vehicle verification tasks. We propose two deep neural network architectures inspired by ResNet and train the neural networks using FusionEye's dataset. Preliminary results show that when learning from vehicle's appearances and kinematic information, the verification accuracy reaches $92%$, which provides possible solution for real time system.
Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization

Consumers read online reviews for insights which help them to make decisions.Given the large volumes of reviews, succinct review summaries are important for many applications.Existing research has focused on mining for opinions from only review texts and largely ignores the reviewers.However, reviewers have biases and may write lenient or harsh reviews; they may also have preferences towards some topics over others.Therefore, not all reviews are equal.Ignoring the biases in reviews can generate misleading summaries.We aim for summarization of reviews to include balanced opinions from reviewers of different biases and preferences.We propose to model reviewer biases from their review texts and rating distributions, and learn a bias-aware opinion representation.We further devise an approach for balanced opinion summarization of reviews using our bias-aware opinion representation.
Exploration and Reflection on the Teaching Mode of Academic English for Postgraduates in the New Situation

The teaching of postgraduate specialized English has changed from the purely professional vocabulary learning or the imparting of professional technical knowledge to the application ability of academic English.In order to adapt to this change, based on the investigation and analysis of the current situation of professional English teaching and the study characteristics of the graduate students, this paper discusses the main problems existing and builds a more efficient professional English learning class.A series of reform and exploration ideas are put forward from the aspects of strengthening professional pertinence, giving full play to students' independent learning ability and carrying out cooperative teaching.
Extensible Implementation of Reliable Pixel Art Interpolation

Abstract Pixel art is aesthetics that emulates the graphical style of old computer systems. Graphics created with this style needs to be scaled up for presentation on modern displays. The authors proposed two new modifications of image scaling for this purpose: a proximity-based coefficient correction and a transition area restriction. Moreover a new interpolation kernel has been introduced. The presented approaches are aimed at reliable and flexible bitmap scaling while overcoming limitations of existing methods. The new techniques were introduced in an extensible. NET application that serves as both an executable program and a library. The project is designed for prototyping and testing interpolation operations and can be easily expanded with new functionality by adding it to the code or by using the provided interface.
Determining Optimal Multi-layer Perceptron Structure Using Linear Regression

This paper presents a novel method to determine the optimal Multi-layer Perceptron structure using Linear Regression. Starting from clustering the dataset used to train a neural network it is possible to define Multiple Linear Regression models to determine the architecture of a neural network. This method work unsupervised unlike other methods and more flexible with different datasets types. The proposed method adapt to the complexity of training datasets to provide the best results regardless of the size and type of dataset. Clustering algorithm used to impose a specific analysis of data used to train the network such us determining the distance measure, normalization and clustering technique suitable with the type of training dataset used.
Hardware Random Number GeneratorUsing FPGA

Random numbers are employed in wide range of cryptographic applications. Output of an asynchronous sampling of ring oscillators can be used as the source of randomness and Linear Hybrid CellularAutomata is used to improve the quality of random data. FPGA is an ideal platform for the implementation of random number generator for cryptographic applications. The circuit described in this paper has been implemented on a highly efficient FPGA board which generated a 32-bit random number at a frequency of 125 MHz. The generated sequence of random numbers were subjected to Diehard test and NIST test for testing randomness and found to pass these tests. These tests are a battery of statistical tests for measuring the quality of a random number generator.
Gesture Control Robot using Arduino

According to the World Federation of the Deaf, only 10 percent of the world's Deaf population receives any education, and only 3 percent receives this education in sign language.Another problem these deaf and face is inability to communicate with a person who does not understand the sign language.This project aims to reduce these problems by presenting a Sign Language.The project uses a hepatic glove to acquire signals corresponding to various hand gestures.The glove is interfaced with robot using an Arduino.Accelerometers are used to measure the angular displacement of human hand motion .The accelerometer controls the movement of the robot.Device is made of mainly two parts, one is RF transmitter and another is RF receiver.The RF transmitter will transmit the signal according to the position of accelerometer attached on your hand and the RF receiver will receive the signal and make the robot move in respective direction.
Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning

Gamification is frequently employed in learning environments to enhance learner interactions and engagement. However, most games use pre-scripted dialogues and interactions with players, which limit their immersion and cognition. Our aim is to develop a semantic matching tool that enables users to introduce open text answers which are automatically associated with the most similar pre-scripted answer. A structured scenario written in Dutch was developed by experts for this communication experiment as a sequence of possible interactions within the environment. Semantic similarity scores computed with the SpaCy library were combined with string kernels, WordNet-based distances, and used as features in a neural network. Our experiments show that string kernels are the most predictive feature for determining the most probable pre-scripted answer, whereas neural networks obtain similar performance by combining multiple semantic similarity measures.
The Impact of Typical Cemetery on Groundwater Using both Geophysical Techniques and Physicochemical Analyses of Water in South-South, Nigeria

This research work focuses on the impact of cemetery on the groundwater with respect to time. It was carried out using Joint Geophysical Methods and Hydro physicochemical analysis. In addition to these methods this study went further to apply Multivariate Statistical Analyses (Water Quality Index, Principle Component Analysis, Cluster Analysis) in the investigation. This research work was done in Third Cemetery, New Benin, Benin City. Though there are three major cemeteries in Benin Metropolis Third Cemetery was selected for this purpose. In Benin City and Nigeria in general, the major cemeteries are located close to human residential areas and virtually all the populace within this locality depends on groundwater as the primary water source for various domestic purposes. The spread of electrodes reaches a maximum of 230m which covers the entire length of the cemetery. Electrodes (41) were needed in this research to generate data and the result compared with the physico-chemical analysis of groundwater. The field work covered a time lapse of six months (June, 2017 to December, 2017). Toxic chemicals that may be released into groundwater include substances that were used in embalming and burial practices as well as varnishes, sealers and preservatives and metal component of ornaments used on wooden coffins (Jonker and Olivier, 2012).Wood preservatives and paints used in coffin construction contain compounds such as copper, naphthalene and ammoniac or chromated copper arsenate (Spongberg and Becks, 2000). Paints contain lead, mercury, cadmium, and chromium; arsenic is used as a pigment, wood preservative and anti-fouling ingredient while barium is used as a pigment and a corrosion inhibitor (Katz and Salem, 2005; Huang et al., 2010; Jonker and Olivier, 2012).
Managing Software Security Knowledge in Context: An Ontology Based Approach

In the setting of software development, knowledge can be both dynamic and situation specific, and the complexity of knowledge usually exceeds the capacity of individuals to solve problems by themselves. Software developers not only require knowledge about the general security concepts but also about the context for which software is being developed. With traditional security knowledge formats, which are usually organized in a security-centric way, it is difficult for knowledge users to retrieve the desired security information to fulfill the requirements of their working context. In order to effectively regulate the operation of security knowledge and be an essential part of practical software development practices, we argue that security knowledge must first incorporate additional features, that is, to first specify which contextual information is to be handled, and then represent the security knowledge in a format that is understandable and acceptable to the individuals. This study introduces a novel ontology approach for modeling security knowledge in a context-sensitive manner where the security knowledge can be retrieved while taking the context of the application in hand into consideration. In this paper, we present our security ontology with the design concepts and the evaluation process.