Reviewerly/exHarmony huggingface dataset:
Impact of Web Based Learning and Teaching in Higher Education in India

Since independence, education sector visualized an unremarkable increase in its educational policies as the years passed on. In the development countries like India, the number of Government, Government aided and private Universities/institutions have increased. And, due to the revolution of Internet the teaching methodologies are also affected. The Government agencies like UGC, AICTE etc. are determining of standards in educational Universities/Institutions. The Government is also focusing on new technological resources up to a large extent to increase the education ration and quality of education. The Government of India is providing grants to UGC, AICTE, IITs, IIMs, Universities, Institutions and individuals for the growth of teaching, learning, research and innovations. Information technology (IT) plays a significant role in one’s daily life style, whose growing rate is very wide.
Focus Areas, Themes, and Objectives of Non-Functional Requirements in DevOps: A Systematic Mapping Study

Non-functional requirements of software address a multitude of objectives, expectations, and even liabilities that must be considered during its development and operation. Typi-cally, these non-functional requirements originate from different domains and their concrete scope, notion, and demarcation to functional requirements is often ambiguous. We conducted a systematic mapping study, including 142 selected primary studies, extracted the focus areas, and synthesized the themes and objectives of the described NFRs. In order to also examine non-engineering-focused aspects related to non-functional requirements in DevOps, we added a backward snowballing step and included 17 primary studies. Our analysis revealed 7 recurrent focus areas and 41 themes that characterize NFRs in DevOps, along with typical objectives for these themes. Overall, the focus areas and themes of NFRs in DevOps are very diverse and reflect the different perspectives required to align software engineering with technical quality, business, compliance, and organizational considerations. The lack of methodological support for specifying, measuring, and evaluating fulfillment of these NFRs in DevOps-driven projects offers ample opportunities for future research in this field. Particularly, there is a need for empirically validated approaches for operationalizing non-engineering-focused objectives of software.
Moving HCI Outdoors: Lessons Learned from Conducting Research in the Wild

Researchers are increasingly adopting ‘research in the wild’ approaches to design and evaluate prototypes in public spaces and to understand how city dwellers interact with them. Although ‘in the wild’ studies can provide more ecologically valid findings compared with typical performance measures collected in the lab, there are challenges associated with designing for and deploying in emergent contexts. A huge amount of work is involved, especially navigating the range of pragmatic and logistical concerns that inevitably arise. It is messy, expensive, time consuming, and unpredictable where often things don’t go according to plan. However, such practicalities are rarely discussed in published studies. The importance of the ‘behind the scenes’ work in making research in the wild happen should not be underestimated. In this chapter, we focus on three case studies where technology prototypes were evaluated in the wild. These are discussed in terms of: technology and design, space and place, social factors, and sustainability. Finally, we provide a set of practical recommendations for researchers and practitioners in the field.
Chapter 5. Using lengthwise scaling to compare feature frequencies across text lengths on Reddit

Texts of different lengths can be difficult to compare using quantitative methods. This is particularly true if many of the texts are extremely short, as is commonly the case with social media comments, where the median text length may be only a few dozen words. In this paper, I explore lengthwise scaling, that is, scaling applied to each text length separately, as a possible approach for getting around some of the statistical problems caused by different text lengths and short texts. I describe two implementations of this family of methods, lengthwise rarity scaling and lengthwise quantile scaling. I show in an exploratory analysis that these scaling methods support earlier results in terms of register differences between Reddit subreddits.
Analysis of the Effects of Different types of Noises and Wavelets used in Denoising of an Image using Wavelet Transform

An image is often contaminated by some noise, which brings about changes in the originality of the image. Denoising of such images is done in order to obtain the less-contaminated version by suppressing noise. In this paper, we are using a recent method of image denoising with the help of Wavelet Transform. Denoising carried out using Wavelet Transform has its own set of challenges. There are two main factors which largely affect the end result - types of wavelets, and types of noises. In this paper, the degree by which these factors affect the image is studied and detailed analysis is given so as to select the correct parameters while carrying out Denoising by Wavelet Transform.
An Extended Knowledge Representation Learning Approach for Context-Based Traceability Link Recovery: Extended Abstract

Software requirements traceability links have been widely recognized as an essential means for effective system evolution when requirements change. However, creating and maintaining traceability links is not an easy task in practice, especially when faced time pressure. Therefore, an automatic and accurate traceability link recovery approach is needed. Existing methods usually recover the traceability links through information retrieval models [1], which calculate text similarity among software artifacts. Although the text-similarity plays an essential role in correlating software artifacts, we argue that the context of software artifacts also renders important clues for establishing the traceability links among software artifacts. For example, for each software use case, its includes/extends use cases can be seen as its context information, contributing to comprehensively profiling the use case. The collection of software artifacts can be modeled as a graph structure through a variety of explicit relationships. Description-Embodied Knowledge Representation Learning (DKRL) [2] is a widely accepted method, which can effectively capture the structural information of explicit relationship and description information of entities. By effectively and precisely embedding such a graph, the context information can be meaningfully represented, contributing to the identification of requirements traceability links. In this paper, we propose a Traceability Link Recovery-Knowledge Representation Learning (TLR-KRL) to recover requirements traceability links between use cases and code based on DKRL. This work has been accepted in The 32nd International Conference on Software Engineering& Knowledge Engineering. TLR-KRL can comprehensively characterize software artifacts by embedding both text information and structural relationships, an overview of which is shown in Fig. 1. Specifically, we follow a systematic process to extend the DKRL model, improving its negative sampling method and thus minimizing false-negative samples generated by the original DKRL model. In such a way, we are able to obtain more precise embedding of software artifacts. Such meaningful embeddings are then used to train traceability link classifiers by using supervised machine learning algorithms. All traceability link candidates obtained from the classifier will be further screened by using triple classification in order to retrieve more correct traceability links. To verify the effectiveness of our method, we have carried out experiments on four datasets, including eTour, EAnci, Clinic, and ITrust. The evaluation results on each of the datasets have all shown that our approach can outperform existing work.
An Optimized Multi-sensor Fused Object Detection Method for Intelligent Vehicles

An accurate and efficient environment perception system is crucial for intelligent vehicles. This study proposes an optimized 2D object detection method utilizing multi-sensor fusion to improve the performance of the environment perception system. In the sensor fusion module, a depth completion network is used to predict dense depth map, so both dense and sparse RGB-D images can be obtained. Then, an efficient object detection baseline is optimized for intelligent vehicles. This method is verified by KITTI 2D object detection dataset. The experimental results show that the proposed method can be more accurate than many latest methods on KITTI leaderboard. Meanwhile, this method consumes less inference time and shows its high efficiency.
Biogenicity of Spicular Geyserite from Te Kopia, New Zealand: Integrated Petrography, High-Resolution Hyperspectral and Elemental Analysis

Hyperspectral and micro X-ray fluorescence (μXRF) imagery were used to derive maps of mineralogy and elemental chemistry from a sample of a siliceous hot spring deposit, or sinter, collected from a landslide breccia deposit at the base of the Paeroa fault, which bounds the eastern Taupo Rift at Te Kopia, Taupo Volcanic Zone, New Zealand. The sample is of a known biogenic sinter layer from a paleo-vent area of a recently extinct alkali chloride hot spring. The aim of the study was to distinguish it from other horizons derived from nonbiogenic sources, which is of relevance to early and extraterrestrial life research, specifically to help assess the potential reliability of morphology as an indicator of biology in the geological record. In particular, the distribution of opal, a common mineral in hot springs deposits that is known to preserve microbial features, and the relative abundances of Al-OH clay and water (OH and H2O) were mapped from hyperspectral imagery and element distributions defined by μXRF element mapping. Layers within the sinter sample composed of spicular geyserite-a type of micro-columnar stromatolite-showed contrasting mineralogy and water content in comparison with interspicular clastic sediment. Whereas clay was found to be concentrated in the interspicular sediment, high water contents characterized the spicules. μXRF imagery also showed differences in the composition of the two components of the spicule-bearing layers, with interspicular sediment being enriched in K, Ti, Fe, and Rb relative to the spicules, which are enriched in Ga. The contrasting nature of the mapped components highlights the detailed upward-branching nature of the spicules, identical to those found in living microstromatolites. These discriminants show that the spicular component can be discerned from the geological background through hyperspectral and μXRF mapping and used to define morphological features that may survive burial diagenesis and metamorphism as a biosignature in deep time rocks.
5G-NR Cross Layer Rate Adaptation for VoIP and Foreground/Background Applications in UE

The recommended bit rate (RBR) is assigned by gNodeB to the user equipment (UE) using MAC control element (CE) entity to provide bit rate information in 5G New Radio (NR). At the UE, the bit rate information is passed on to the upper layers; i.e., transport or application, for a specific logical channel either in uplink or downlink. However, based on specific application rate of target, UE does not know how to efficiently utilize and distribute RBR/throughput in lower and upper layer, respectively considering the specific logical channel. To address these problems, we propose a cross layer rate adaptation (CLRA) mechanism for UE. CLRA consists of two parts. In the first part, CLRA utilizes RBR received from gNodeB to compute throughput at lower layer. In the second part, CLRA distributes the throughput in upper layer received from lower layer based on specific applications rate of target. CLRA provides an intelligent mechanism to distribute throughput among foreground/ background applications and voice over internet protocol (VoIP) application considering a learning based codec adaptation. We conduct experiments with Samsung Galaxy S8 device and simulations to validate CLRA mechanism for applications in 5G NR.
Modelling Security Aspects with ArchiMate: A Systematic Mapping Study

Enterprise Architecture (EA) allows organizations to align their business goals and processes to information technology. This alignment is delineated using modelling languages, one of which is ArchiMate. However, current modelling languages experience a lack of elements to model security - one of the most decisive aspects for organizations nowadays. The objective of this paper is to identify the state of the art surrounding incorporation of security aspects into EA modelling languages, with particular interest in the micromobility context, which inherits critical security concerns from the Internet-of-Things and Cyberphysical systems domains. A systematic mapping study was carried out identifying lack of research concerning the intersection of EA modelling languages, security and micromobility. Only 14 primary papers were found; the vast majority highlight limitations in the existing security modelling, with ArchiMate being the most commonly used, but also the most criticized. The results reinforce a lack of research related to EA, security and micromobility. We can conclude that the current modelling alternatives still present shortcomings related to the standardization, complexity and completeness. An extension of ArchiMate to represent security aspects needs to be investigated, adding new elements to achieve the required representation that allow security requirements to be considered.
WIKA: A Projected Augmented Reality Workbench for Interactive Kinetic Art

Iterative artistic exploration, mechanism building, and interaction programming are essential processes of prototyping interactive kinetic art (IKA). However, scattered tools and interwoven workflows across digital and physical worlds make the task difficult. We present WIKA, an integrated environment supporting the whole creation process of IKA in the form of a layered picture frame in a single workspace. A projected AR system with a mobile device efficiently makes an interactive tabletop. The projected information connected with physical components (e.g. sensors and motors) enables the programming and simulation on the workspace. Physical components are applied from the initial phase of prototyping using an AR plate, and this supports the iterative trial-and-error process by bridging the workflow. A user study shows that WIKA enabled non-experts to create diverse IKA with their ideas. A tangible interaction and projected information enable the iterative and rapid creation. The method that integrates the hardware and software in the physical environment can be applied to other prototyping tools that support the creation of interactive and kinetic elements.
Analysis of Image Classification for Text Extraction from Bills and Invoices

Optical Character Recognition (OCR) technology offers a complete alphanumeric recognition of printed or handwritten characters from pictures such as scanned bills and invoices. Intelligent extraction and storage of text in structured document serves document analytics. The current research attempts to find a methodology through which any information from the printed invoice can be extricated. The intermediate image is passed over using an OCR engine for further processing. Segmentation extracts written text in various fonts and languages. Image classification helps in making a decision based on the classification results. This paper surveys these techniques and compares them in terms of metrics, algorithm and results.
Predicting high school graduates using Naive Bayes in State University Entrance Selections

Competition to entering the best and major state universities that are suitable for student interest is a problem for some high school graduates. There is three entrance selection system to the state university, they call National Selection for Entering State University (SNMPTN), Joint Selection for Entering State University (SBMPTN), and University Self Selection. SNMPTN becomes a favorite admission for students because it does not require written exams. SNMPTN is carried out before the other admissions are conducted, therefore the SNMPTN admission had very high competition. Students will be proud when they are accepted in the favorite state university with the major of their interest. This research was conducted to predict the high school students majoring in science to entering the state university via SNMPTN admission at first choice using Naive Bayes and SMOTE methods. SMOTE is used to overcome the imbalance class of data. The results showed that using SMOTE can increase the accuracy of the Naive Bayes method. The performance of Naive Bayes showed an accuracy rate of 90.1%, a precision of 52,23%, and the recall of 51,66%. While, the performance of Naive Bayes combined with SMOTE method gave 5.39% improvement of the accuracy rate, 43.3% of the precision, and 43.83% of recall.
A Framework for DRL Navigation With State Transition Checking and Velocity Increment Scheduling

To train a mobile robot to navigate using end-to-end approach which maps sensors data into actions, we can use deep reinforcement learning (DRL) method by providing training environments with proper reward functions. Although some studies have shown the success of DRL in navigation task for mobile robots, the method needs appropriate hyperparameter settings such as the environment's timestep size and the robot's velocity range to produce a good navigation policy. The previous existing DRL framework has proposed the use of odometry sensor to generate dynamic timestep size in the environment to solve the mismatch problem between the timestep size and the robot's velocity. However, the framework lacks a procedure for checking terminal conditions which may occur during action executions resulting inconsistency in the environment and poor navigation policies. In the case of navigation task, terminal conditions may happen when the robot achieves the navigation goal position or collides with obstacles while performing an action in one timestep. To cope with this problem, we propose a state transition checking method in the DRL environment which is specific for navigation task that leverages odometry and laser sensor to ensure that the environment follows Markov Decision Process with dynamic timestep size. We also introduce a velocity increment scheduling to stabilize the mobile robot during training. Our experiment results show that state transition checking along with the velocity increment scheduling are able to make the robot navigate faster with higher success rate compared to other existing DRL frameworks.
Attentive preference personalized recommendation with sentence-level explanations

Personalized recommendation mostly employs users’ historical data to improve their user profiles, and these profiles are then used as the bases for recommendations. Because reviews can contain a large amount of information regarding user preferences and item features, they can be naturally into recommender systems (RSs) as contextual information, thus solving the problem of data sparsity and helping to provide personalized recommendations. The existing technology mainly extracts latent representations of users or items in an independent and static manner. We argue that static embedding cannot fully capture a user's preferences. Indeed, a user will have different preferences corresponding to different items. This type of review-based recommendation model cannot provide a personalized, and complete semantic explanation of a candidate recommendation item to a user. In this paper, we introduce an attention mechanism to explore the importance of specific sentences in reviews for different users and propose a novel attentive preference personalized recommendation with sentence-level explanations (APSE). The APSE employs the latent features of users and items and the latent factors of their pairwise interactions to obtain review representations. Then, the APSE uses probability matrix factorization to model additional high-level feature interactions based on these user-item pairs for rating prediction. We implement review feature learning in the APSE to exploit review data in which an attentive mechanism is used to highlight the influences of words and sentences to achieve focused paragraph embedding. Finally, the APSE also employs an explanation sentence judgment mechanism that implements the user-item pair interaction method to extract comments or statements that pertain to user preferences as recommendation interpretations. Experiments are performed on real-world datasets for validation. Additionally, we show the important words and sentences highlighted by the attentive mechanism. At the end of the experiment, a specific item explanation for a user is produced and compared with the user's existing comments. The results show that the performance of the APSE can exceed that of various recommended models when the available ratings are limited.
A Novel Method for the Synthetic Generation of Non-I.I.D Workloads for Cloud Data Centers

Cloud data center workloads have time- dependencies and are hence non-i.i.d (independent and identically distributed). In this paper, we propose a new model-based method for creating synthetic workload traces for cloud data centers that have similar time characteristics and cumulative distributions to those of the actual traces. We evaluate our method using the actual resource request traces of Azure collected in 2019 and the well-known Google cloud trace. Our method enables generating synthetic traces that can be used for a more realistic evaluation of cloud data centers.
UPON: User Profile Transferring across Networks

User profiling has very important applications for many downstream tasks, such as recommender system, behavior prediction and market strategy. Most existing methods only focus on modeling user profiles of one social network with plenty of data. However, user profiles are difficult to acquire, especially when the data is scarce. Modeling user profiles under such conditions often leads to poor performance. Fortunately, we observed that not only user attributes but also user relationships are useful for user profiling and benefit the results. Meanwhile, similar users have similar behavior in different social networks. Finding user dependencies between social networks will help to infer user profiles. Motivated by such observations, in this paper, we for the first time propose to study the user profiling problem from the transfer learning perspective. We design an efficient User Profile transferring acrOss Networks (UPON) framework, which transfers knowledge of user relationship from one social network with plenty of data to facilitate the user profiling on the other social network with scarce data. In UPON, we first design a novel graph convolutional networks based characteristic-aware domain attention model (GCN-CDAM) to find user dependencies within and between domains (referring to social networks). We then design a dual-domain weighted adversarial learning method to solve the domain shift problem existing in the transferring procedure. Experimental results on Twitter-Foursquare dataset demonstrate that UPON outperforms the state-of-the-art models.