Reviewerly/exHarmony huggingface dataset:
Software Development Estimation Techniques in Industrial Contexts: An Exploratory Multiple Case-Study

Software Effort Estimation is one of the most challenging aspects in the software development life cycle. Recent empirical studies in the area of software development estimation indicate the presence of two models for effort estimation: (i) Formal, and (ii) Expert Based (Informal). The IT sector in Palestine is one of the most promising and constantly growing sectors. Nonetheless, studies addressing effort estimation approaches and techniques within the Palestinian IT sector are still highly missing. Therefore, we were motivated to conduct a qualitative study to increase our understanding about how industrial teams approach software effort estimation and to explore the challenges they are facing. Our investigation started with a survey that targeted software professionals, and then we conducted multiple-case study approach involving four different software development companies in Palestine. Results show that: (i) around 25% of cost overrun in software projects is due to inaccurate estimations; (ii) expert based estimation models are the mostly applied models especially within agile environments; (iii) a potential advantage can be achieved when formalizing the process of expert based models by having guidelines and checklists; (iv) accuracy of effort estimation is largely affected by team experience, domain knowledge, and requirements clarity; (v) companies working with outsourcing model do have better effort estimation accuracy than companies working in local market. Based on our findings, we highlight areas that require further investigation.
Motivation and Academic Performance : A SEM Approach

Many models in educational have tried to clarify the causal relationships of motivation variables on student performance, by presenting hypothesized models, exploratory factor analysis (EFA) and co ...
‘창의적문제해결방법론’ 교과목의 플립러닝 수업 설계에 관한연구

The purpose of this study is to develop instructional design model of flipped learning suitable for engineering education field and to draw out...
MFS fading regularization method for the identification of boundary conditions from partial elastic displacement field data

A method is proposed to solve an inverse problem in two-dimensional linear isotropic elasticity. The inverse problem consists of the determination of both the entire displacement field and the boundary conditions inaccessible to the measurement from the partial knowledge of the displacement field. The algorithm is based on a fading regularization method (FRM) and is numerically implemented using the method of fundamental solutions (MFS). The inverse technique is first validated with synthetic data and is then applied to the interpretation of experimental measurements obtained by digital image correlation (DIC).
Comparative Summarisation of Rich Media Collections

The goal of this thesis is to develop techniques for comparative summarisation of multimodal document collections. Comparative summarisation is extractive summarisation in comparative settings, where documents form two or more groups, e.g. articles on the same topic but from different sources. Comparative summarisation involves, not only, selecting representative and diverse samples within groups, but also samples that highlight commonalities and differences between the groups. We posit that comparative summarisation is a fruitful problem for diverse use cases, such as comparing content over time, authors, or distinct view points. We formulate the problem of comparative summarisation by reducing it to binary classification problem and define objectives to incorporate representativeness, diversity and comparativeness. We design new automatic and crowd-sourced evaluation protocols for summarisation evaluation that scales much better than the evaluations requiring manually created ground truth summaries. We show the efficacy of the approach in a newly curated datasets of controversial news topics. We plan to develop new collection comparison methods for multimodal document collections.
A Survey of Content Based Image Retrieval Using Color and Texture Features

Content based image retrieval (CBIR) system that works on the basis of low level image semantics cannot be directly related to the expressive semantics that is used by humans for deciding image similarities. The low-level semantic of the image consists of color, texture, and shape of the object inside an image. Nowadays, one type of feature extraction technique cannot provide complete result, so now a combination of different feature techniques like color, texture and shape features are being used. There is a generous increase in retrieval precision when combinations of these techniques are used in an effective way. In this paper, we propose a comparison of CBIR system using different feature extraction methods; three features based on color (i.e. HSV Histogram, Color Moment) and other two features computed by applying the texture feature using Gabor Wavelet and Wavelet Transform of the image. For similarity matching between the query image and database images, Manhattan distance or City Block or L1 distance is used. The experimental results on WANG database show higher retrieval efficiency in terms of precision when compared with existing methods using color and texture features.
Domain Partitioning Network

Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game. However, even if the players converge to an equilibrium, the generator may only recover a part of the target data distribution, in a situation commonly referred to as mode collapse. In this work, we present the Domain Partitioning Network (DoPaNet), a new approach to deal with mode collapse in generative adversarial learning. We employ multiple discriminators, each encouraging the generator to cover a different part of the target distribution. To ensure these parts do not overlap and collapse into the same mode, we add a classifier as a third agent in the game. The classifier decides which discriminator the generator is trained against for each sample. Through experiments on toy examples and real images, we show the merits of DoPaNet in covering the real distribution and its superiority with respect to the competing methods. Besides, we also show that we can control the modes from which samples are generated using DoPaNet.
Limit Learning Equivalence Structures

While most research in Gold-style learning focuses on learning formal languages, we consider the identification of computable structures, specifically equivalence structures. In our core model the learner gets more and more information about which pairs of elements of a structure are related and which are not. The aim of the learner is to find (an effective description of) the isomorphism type of the structure presented in the limit. In accordance with language learning we call this learning criterion InfEx-learning (explanatory learning from informant). Our main contribution is a complete characterization of which families of equivalence structures are InfEx-learnable. This characterization allows us to derive a bound of $\mathbf{0''}$ on the computational complexity required to learn uniformly enumerable families of equivalence structures. We also investigate variants of InfEx-learning, including learning from text (where the only information provided is which elements are related, and not which elements are not related) and finite learning (where the first actual conjecture of the learner has to be correct). Finally, we show how learning families of structures relates to learning classes of languages by mapping learning tasks for structures to equivalent learning tasks for languages.
Four Square Writing Method as A Technique to Teach Descriptive Writing

The objective of this study was to improve students’ skill in writing descriptive text utilizing Four Square Writing Method (FSWM). Thirty students of Junior High School participated in this classroom action research which took place in two cycles. Two sorts of instruments were used in this study; observation sheet and writing test. While the first was utilized to obtain the data on students’ responses and behavior, the latter was to achieve the data on students’ writing improvement. The implementation of the method in the first cycle was able to increase students’ mean score from 48.82 to 66.05. In the second cycle, more than 70% of the students successfully reached the score greater or equal to 72.95. This shows that the FSWM is one of alternative techniques in teaching descriptive text.
Image Processing Using Machine Learning

We start this chapter by examining a few of the most widely used image processing algorithms, then move on to machine learning implementation in image processing. The chapter at a glance is as follows:
Implementation of Psychotest System Application Selection of KKL STMIK Participants in Purwokerto Amikom Using DISC (Dominance-Influence-Steadiness-Compliance)

Kuliah Kerja Lapangan abbreviated as KKL is one of the chosen programs for STMIK Amikom Purwokerto students, where this program can be taken after taking 5 semesters. There are two stages of selection for KKL participants which include registration and psychological test exams. The psychological test was conducted to determine the personality type to form a group, a tool used to measure personality types using the DISC (Dominance - Influence - Steadiness - Compliance) theory. Each DISC character has a different response to the pressure he experiences in the environment, because of his direct involvement with the community, a system is needed to help the formation of groups based on psychological results. The researcher made a web-based psychological test application to facilitate prospective field work participants in carrying out exams and help organizers of field work in determining groups based on psychological results. The researcher collected data using observation, interviews, questionnaires, and literature studies. The system development method used is the waterfall method.
Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks

The behavior of self-driving cars must be compatible with an enormous set of conflicting and ambiguous objectives, from law, from ethics, from the local culture, and so on. This paper describes a new way to conveniently define the desired behavior for autonomous agents, which we use on the self-driving cars developed at nuTonomy. We define a as a pre-ordered set of rules, each akin to a violation metric on the possible outcomes (realizations). The rules are partially ordered by priority. The semantics of a rulebook imposes a pre-order on the set of realizations. We study the compositional properties of the rulebooks, and we derive which operations we can allow on the rulebooks to preserve previously-introduced constraints. While we demonstrate the application of these techniques in the self-driving domain, the methods are domain-independent.
Analysis of macrocytosis in a tertiary care hospital of North India

Analysis of macrocytosis in a tertiary care hospital of North India - IJPO- Print ISSN No: - 2394-6784 Online ISSN No:- 2394-6792 Article DOI No:- 10.18231/2394-6792.2019.0004, Indian Journal of Pathology and Oncology-Indian J Pathol Oncol
Geothermal and Volcanic Evaluation of Harrat Rahat, Northwestern Arabian Peninsula (Saudi Arabia)

Saudi Arabia offers a strong potential for geothermal energy, but only a limited amount of exploration efforts has taken place. We conducted a preliminary, low-cost, reconnaissance level evaluation of the western volcanic areas (harrats) based on mapped geology and existing seismic data. The emphasis is on the Harrat Rahat volcanic area in western Saudi Arabia although a survey of other major harrats is conducted using geologic data. The goal is to test the procedure and identify areas for future, more intensive study.
The Weak Call-By-Value {\lambda}-Calculus is Reasonable for Both Time and Space

We study the weak call-by-value $\lambda$-calculus as a model for computational complexity theory and establish the natural measures for time and space -- the number of beta-reductions and the size of the largest term in a computation -- as reasonable measures with respect to the invariance thesis of Slot and van Emde Boas [STOC~84]. More precisely, we show that, using those measures, Turing machines and the weak call-by-value $\lambda$-calculus can simulate each other within a polynomial overhead in time and a constant factor overhead in space for all computations that terminate in (encodings) of 'true' or 'false'. We consider this result as a solution to the long-standing open problem, explicitly posed by Accattoli [ENTCS~18], of whether the natural measures for time and space of the $\lambda$-calculus are reasonable, at least in case of weak call-by-value evaluation. Our proof relies on a hybrid of two simulation strategies of reductions in the weak call-by-value $\lambda$-calculus by Turing machines, both of which are insufficient if taken alone. The first strategy is the most naive one in the sense that a reduction sequence is simulated precisely as given by the reduction rules; in particular, all substitutions are executed immediately. This simulation runs within a constant overhead in space, but the overhead in time might be exponential. The second strategy is heap-based and relies on structure sharing, similar to existing compilers of eager functional languages. This strategy only has a polynomial overhead in time, but the space consumption might require an additional factor of $\log n$, which is essentially due to the size of the pointers required for this strategy. Our main contribution is the construction and verification of a space-aware interleaving of the two strategies, which is shown to yield both a constant overhead in space and a polynomial overhead in time.
Integrating digital tools for teaching of writing expression in Turkish language

Web 2 is one of the most important tools among the internet users. In this paper, the answer is given to a question “how can it be integrated social web tools into education?” and it’s focused on written expression of Turkish teaching for adults Students are selected from different profession departments. Each week they focused on a different subject. There are 2 hours regular, a few practical lectures. Students were asked to voluntarly use integrated applications. A few students were refused to use some tools for different reasons. Privacy setting is offered students as follows;1. To share their writing expression as a public, 2. To share their writing expression as inside the friends, 3.To share their writing expression as assistants and instructor for evaluation, 4.To share their comment and likes about friends’ writing expression, 5. To share their opinion in class but it is too limited because of not enough time. Keywords: Turkish Language, writing expression, motivation, learning strategies, self-regulation.
Deep Learning Based Deep Level Tagger for Malayalam

Background: POS tagging is the process of identifying the correct grammatical category of words based on its meaning and context in a text document. It is one of the preliminary steps in the processing of natural language text. If any error happens in POS tagging the same will be propagated to whole NLP applications. Hence it must be handled in a genuine and precise way. Aim: The purpose of this study is to develop a deep level tagger for Malayalam which indicates the semantics of nouns and verbs in a text document. Methods: The proposed model is a two-tier architecture consisting of deep learning as well as rulebased approaches. The first tier consists of a tagging model, which is trained by a tagged corpus of 287,000 words. To improve the depth of tagging a suffix stripper is also used which can provide morhological features to the shallow machine learning model. Results: The system is trained on 2,30,000 words and tested on 57,000 words. The accuracy of tagging for the phase-1 architecture is 92.03%. Similarly the accuracy of phase-2 architecture is 98.11%. The overall accuracy of tagging is 91.82%. Conclusion: The exclusive feature of the proposed tagger is its depth in tagging the noun words. This deep level information can be used in various semantic processing applications of the natural language text like anaphora resolution, text summarization, machine translation, etc.
Snap! A Look at 5 Years, 250,000 Users and 2 Million Projects

Snap! is blocks-based programming language that inherits a design and vocabulary from Scratch, but has been extended with support for user-defined functions, and robust built-in libraries. The Snap! Cloud software has been running for over 5 years and recently passed 250,000 users and 2 million projects of which nearly 600,000 are shared publicly. We have been to do an exploratory analysis the database of public projects to better understand how students are programming an using Snap!. This analysis is being conducted using a cleaned up XML-based representation of Snap! projects, which allows querying for data using SQL to extract information from project files. So far, we've seen that projects in Snap! computationally more complex than those found in Scratch, and make use of Snap!'s advanced features. Combined with growing use, we hope to give context to the ways which Snap! (and other) blocks-based languages are being used in classrooms. While there has been a discussion of code smells in Scratch, we haven't seen any discussions using Snap! and hope to show its additional functionality helps write clean code. Finally, we are interested in presenting a clear story of growing use for Snap! that is a compelling case to teachers (and subsequently their students) that blocks-based languages can be quite popular.
Embedded Implementation of MOD Algorithm with Image Fail-Safe for Autonomous Vehicles

This paper aims to implement a fail-safe function based on the MOD (Moving Object Detection) algorithm on an embedded board. When the input image quality of the camera deteriorates due to a change in the driving environment, it is necessary to automatically detect the input image quality and provide a warning that the MOD function cannot be operated normally. When the vehicle equipped with a camera without the auto expose function drives in a dark area, the threshold value is determined by analyzing the brightness value of the input image, and a Laplacian filter based method is used to evaluate the occurrence of fail due to image distortion in the rain. In addition, we propose a weight reduction method for implementing the Moving Object Detection (MOD) algorithm executed in a PC environment as an embedded environment. The performance of the proposed fail-safe method was evaluated using the confusion matrix. The obtained precision and recall rates were 94.79% and 95.35%, respectively. The MOD algorithm compares the GT (Ground Truth) recorded in 15,216 images obtained in an actual vehicle driving environment and the performance realized in the PC environment by building the stored image database and carrying out the performance evaluation. In the embedded environment, the performance is realized at an average speed of 15 FPS or greater. This proved the applicability of the proposed method for realistic car operation.
An improved differential power analysis against random process interrupts

In this paper, we propose an improved differential power analysis (DPA) attack method for use against the 128-bit Advanced Encryption Standard (AES-128) with random process interrupt (RPI) protection applied on a field-programmable gate array (FPGA). The misalignment caused by RPIs is undermined by the new signals processed by our proposed method. Our method uses compressed energy entropy and the sliding window technique to process the power consumption curves (PCCs). The results of our experiments show that by gathering the scattered information, our method has excellent performance on the DPA attacks. Compared with existing methods, our proposed method shows better performance in increasing the amplitude of the maximum spike of the correlation curve. While achieving the same success rates, the number of PCCs applied in our method has decreased by 35%.
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning

In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization. To do so, we first interpret momentum as solving an $\ell_2$-regularized minimization problem to learn the offsets between arbitrary two successive model parameters. We call this {\em time-delay momentum} because the model parameter is updated after a few iterations towards finding the minimizer. We then propose our learning algorithm, \ie stochastic gradient descent (SGD) with time-delay momentum. We show that our algorithm can be interpreted as solving a sequence of strongly convex optimization problems using SGD. We prove that under mild conditions our algorithm can converge to a stationary point with rate of $O(\frac{1}{\sqrt{K}})$ and generalization error bound of $O(\frac{1}{\sqrt{n\delta}})$ with probability at least $1-\delta$, where $K,n$ are the numbers of model updates and training samples, respectively. We demonstrate the empirical superiority of our algorithm in deep learning in comparison with the state-of-the-art deep learning solvers.
Varieties of Blockchains

In this chapter, we address the manifold varieties of blockchains by presenting a design taxonomy that defines dimensions and categories for classifying blockchains and ways of using them in systems. Our taxonomy captures major architecturally relevant characteristics of blockchains and indicates their support for various quality attributes. This includes performance and quality attributes of blockchain-based applications, as well as core concerns of blockchains like decentralization and the data structure used.
A Study on the Students’ School Life Satisfaction of the General and Innovative School

The purpose of this study is to compare the quality of the school life and the quality of the school. The results of this study are as follows. First, innovative high school(IHS) students" school life satisfaction was higher than general high school(GHS) in extracurricular activities. Second, students in the median score and lower score have higher life satisfaction than those in general schools. However, there was no difference between the students with the highest grades. The third, innovation schools in Jeolla-do area were more than satisfied with life in general schools in all regions. In Busan, however, general schools were more satisfied than innovative schools. The fact that the school is built by the spontaneity of the teachers and the students` individuality is lower than the general school in Busan should be examined both in the school environment, structure and contents.