Reviewerly/exHarmony huggingface dataset:
Using Social Network Analysis to Investigate the Collaboration Between Architects and Agile Teams: A Case Study of a Large-Scale Agile Development Program in a German Consumer Electronics Company

Abstract Over the past two decades, agile methods have transformed and brought unique changes to software development practice by strongly emphasizing team collaboration, customer involvement, and change tolerance. The success of agile methods for small, co-located teams has inspired organizations to increasingly use them on a larger scale to build complex software systems. The scaling of agile methods poses new challenges such as inter-team coordination, dependencies to other existing environments or distribution of work without a defined architecture. The latter is also the reason why large-scale agile development has been subject to criticism since it neglects detailed assistance on software architecting. Although there is a growing body of literature on large-scale agile development, literature documenting the collaboration between architects and agile teams in such development efforts is still scarce. As little research has been conducted on this issue, this paper aims to fill this gap by providing a case study of a German consumer electronics retailer’s large-scale agile development program. Based on social network analysis, this study describes the collaboration between architects and agile teams in terms of architecture sharing.
Software Artefacts Consistency Management towards Continuous Integration: A Roadmap

Software development in DevOps practices has become popular with the collaborative intersection between development and operations teams.The notion of DevOps practices drives the software artefacts changes towards continuous integration and continuous delivery pipeline.Subsequently, traceability management is essential to handle frequent changes with rapid software evolution.This study explores the process and approaches to manage traceability ensuring the artefact consistency towards CICD in DevOps practice.We address the key notions in traceability management process including artefact change detection, change impact analysis, consistency management, change propagation and visualization.Consequently, we assess the applicability of existing change impact analysis models in DevOps practice.This study identifies the conceptualization of the traceability management process, explores the state-of-art solutions and suggests possible research directions.This study shows that the lack of support in heterogeneous artefact consistency management with welldefined techniques.Most of the related models are limited with the industry-level applicability in DevOps practice.Accordingly, there is inadequate tool support to manage traceability between heterogeneous artefacts.This study identifies the challenges in managing software artefact consistency and suggests possible research directions that can be applied to manage the traceability in the process of software development in DevOps practice.
Zircon U-Pb geochronology, geochemistry, Sr-Nd isotopic compositions, and tectonomagmatic implications of Nay (NE Iran) postcollisional intrusives in the Sabzevar zone

The mafic to felsic intrusive rocks of Nay (IRN) are located in the northeast of the central Iranian block. In this study, we present new major and trace element geochemistry, U-Pb zircon ages, and Sr-Nd isotopic data to discuss the origin of the IRN postcollisional units. The oldest units in the Nay area belong to Paleocene?early Eocene volcanic and pyroclastic series including basalt-andesite, latite, dacite, and tuff. These series are crosscut by subvolcanic and granitoid rocks with lithological composition varying from quartz gabbro to K-feldspar granite. The youngest igneous activity is represented by quartz monzodiorite dikes. Hornblende-biotite quartz monzonite from Nay granitoids was dated at 40 Ma (zircon U-Pb). The IRN rocks are metaluminous to peraluminous with high-K calc-alkaline and shoshonitic affinities. They display enrichment in light REEs [(La/Yb)N = 3.79?8.71] and LILEs (such as Ba, Th, Rb, U, and K), with depletion in HFSEs (such as Nb, Zr, Y, and Ti). All rocks have negative Eu anomalies [(Eu/Eu*)N = 0.17?0.88] and relatively flat heavy REE patterns [(Gd/Yb)N = 1.12?1.69]. Granitoids have initial 87Sr/86Sr values from 0.7053 to 0.7061 and ?Nd values from ?1.65 to ?0.02 calculated at 40 Ma. The geochemical composition of IRN rocks along with the low ISr and positive ?Nd values and mantle model ages of 0.6?0.8 Ga indicate that two end-members, enriched mantle and a continental crust, were involved in the magma generation. We argue that the Eocene IRN magmatism occurred as a postcollisional product by asthenospheric upwelling owing to the convective removal of the lithosphere during an extensional collapse of the central Iranian block.
Siremis: Sistem Informasi Rekam Medis Puskesmas Kecamatan Matraman Jakarta

&#x0D; The Matraman Sub-district Health Center, which was established on July 3, 1977 in DKI Jakarta, is one of the puskesmas that always records and searches its patient data in Medical Records. This Puskesmas requires a Medical Record Information System so that the recording of the patient's medical history can be stored and arranged in a good mechanism. The development of the patient data collection system in Medical Records uses the Waterfall method in developing the software. While the process of collecting data by conducting interviews with the relevant parts. Depictions of the running system can be seen in the form of Activity Diagrams and proposed systems can be seen in the Use Case Diagram, ERD and LRS. Research produces a Medical Record Information System that helps related parts in recording and searching for patient data needed quickly and precisely so that medical action can be done immediately.
Code Smells Analysis Mechanisms, Detection Issues, and Effect on Software Maintainability

Software evolution is an inevitable need in most of the modern businesses, software that doesn’t accommodate changes is hard to survive the market needs. Also, software changes can affect the overall design of the software and sometimes in a corrupting way, affecting the maintainability and evolvability of the software, which introduces technical debt that needs to be solved by continuous refactoring and restructuring of software. Code smells are useful indicators to identify the parts of the code to be refactored to improve the overall maintainability of the software. We present an overview of software code smells, detection and analysis mechanisms and difficulties. Also, we address the effect of refactoring on software maintainability and error-proneness of software.
Development of Smart Content Model-based Augmented Reality to Support Smart Learning

Augmented Reality (AR) is an optical technology that combines virtual objects or worlds into real worlds like in real time and increases user perceptions and interactions with the real world. Information conveyed by virtual objects helps users carry out activities (tasks) in the real world. The convenience offered makes AR technology can be used for various fields, including education, such as the development of materials or learning media. Augmented reality in its development is more comfortable, cheaper, and can be widely implemented in various multimedia needs. This research is a study of the development of multimedia based on augmented reality to produce dynamic learning materials or media in supporting the concept of smart learning. This research provides multimedia models of mathematics for circles, ellipses, parabola, and hyperbole based on augmented reality to create more dynamic learning as smart learning. Multimedia is produced according to operational standards and meets content standards based on media experts, content, and users. Multimedia-based augmented reality math is easy to operate, helps, and increases understanding and increases student motivation.
VLSI Implementation of Restricted Coulomb Energy Neural Network with Improved Learning Scheme

This paper proposes a restricted coulomb energy neural network (RCE-NN) with an improved learning algorithm and presents the hardware architecture design and VLSI implementation results. The learning algorithm of the existing RCE-NN applies an inefficient radius adjustment, such as learning all neurons at the same radius or reducing the radius excessively in the learning process. Moreover, since the reliability of eliminating unnecessary neurons is estimated without considering the activation region of each neuron, it is inaccurate and leaves unnecessary neurons extant. To overcome this problem, the proposed learning algorithm divides each neuron region in the learning process and measures the reliability with different factors for each region. In addition, it applies a process of gradual radius reduction by a pre-defined reduction rate. In performance evaluations using two datasets, RCE-NN with the proposed learning algorithm showed high recognition accuracy with fewer neurons compared to existing RCE-NNs. The proposed RCE-NN processor was implemented with 197.8K logic gates in 0.535 mm 2 using a 55 nm CMOS process and operated at the clock frequency of 150 MHz.
Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors

Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference descriptors is proposed. First, the motion magnitude of each frame is estimated by optical flow, and the foreground region is determined according to each motion magnitude of the pixels; the trajectories are only extracted from behavior-related foreground regions. Second, in order to better describe the relative temporal information between different actions, a motion difference descriptor is introduced to describe the foreground trajectory, and the direction histogram of the motion difference is constructed by calculating the direction information of the motion difference per unit time of the trajectory point. Finally, a Fisher vector (FV) is used to encode histogram features to obtain video-level action features, and a support vector machine (SVM) is utilized to classify the action category. Experimental results show that this method can better extract the action-related trajectory, and it can improve the recognition accuracy by 7% compared to the traditional dense trajectory method.
Convergence Analyses of Online ADAM Algorithm in Convex Setting and Two-Layer ReLU Neural Network

Nowadays, online learning is an appealing learning paradigm, which is of great interest in practice due to the recent emergence of large scale applications such as online advertising placement and online web ranking. Standard online learning assumes a finite number of samples while in practice data is streamed infinitely. In such a setting gradient descent with a diminishing learning rate does not work. We first introduce regret with rolling window, a new performance metric for online streaming learning, which measures the performance of an algorithm on every fixed number of contiguous samples. At the same time, we propose a family of algorithms based on gradient descent with a constant or adaptive learning rate and provide very technical analyses establishing regret bound properties of the algorithms. We cover the convex setting showing the regret of the order of the square root of the size of the window in the constant and dynamic learning rate scenarios. Our proof is applicable also to the standard online setting where we provide the first analysis of the same regret order (the previous proofs have flaws). We also study a two layer neural network setting with ReLU activation. In this case we establish that if initial weights are close to a stationary point, the same square root regret bound is attainable. We conduct computational experiments demonstrating a superior performance of the proposed algorithms.
Potential of Cognitive Management and Value-communicative Nature of University Education

The article provides an overview of foreign and national research on the possibilities of using cognitive management in the educational environment of higher education institution. The authors emphasize the potential of cognitive management principles application in the educational organization, the direct correlation between cognitive management tasks and the general purposes of educational process. The possible approaches to knowledge management in educational organization are described. The authors dwell on the general methodological, economic and functional aspects of cognitive management at the university, argue that the value-communicative essence of university education enables to manage the knowledge base of the university. On the example of Ukhta State Technical University the article considers the possibilities of students’ project activity and project self-government. Firstly, it ensures the increment and intensification of the value-communicative flow of knowledge within the University and in its interaction with the external environment. Secondly, it fosters students to master the role of a cognitive manager. Thirdly, it encourages students to develop their meta-abilities which can be viewed as a form of cognitive management outcomes in higher educational institution.
Gender Equality, Agriculture and Rural Development: Evidence from Nyamasheke Coffee Production in Rwanda

Gender Equality, Agriculture and Rural Development: Evidence from Nyamasheke Coffee Production in Rwanda Rachel Bayisenge(PhD), Prof. Hu Shengde, Yves Harimana, Jean Bosco Karega, Muhammad Nasrullah, Diogene Tuyiringire Abstract This study examines and proves how women contribute to gender equality and rural development through cash crops. Although, women in agriculture sector have a greater role to everyplace in the world, which differs from country to country, state to state, work to work, society to society and region to region. Combination of the quantitative and qualitative survey methods was used. The analysis targeted on primary data by using observation and interview. Based on previous culture in Rwanda; most of women were not used to participate in cash crops, thought that was for men; so far, it was identified that women who intervene in cash crops prove their capability in it, and contribute more to gender equality as one way of empowering them economically. This study will help rural women farmers from rural area who were not participating yet in cash crop to change their minds, and be able to participate in cash crops as men do. The context is essential and policies can be founded on sound materials and gender analysis. Full Text: PDF DOI: 10.15640/ijgws.v7n1a4
WebMTD: Defeating Cross-Site Scripting Attacks Using Moving Target Defense

Existing mitigation techniques for cross-site scripting attacks have not been widely adopted, primarily due to imposing impractical overheads on developers, Web servers, or Web browsers. They either enforce restrictive coding practices on developers, fail to support legacy Web applications, demand browser code modification, or fail to provide browser backward compatibility. Moving target defense (MTD) is a novel proactive class of techniques that aim to defeat attacks by imposing uncertainty in attack reconnaissance and planning. This uncertainty is achieved by frequent and random mutation (randomization) of system configuration in a manner that is not traceable (predictable) by attackers. In this paper, we present WebMTD, a proactive moving target defense mechanism that thwarts various kinds of cross-site scripting (XSS) attacks on Web applications. Relying on built-in features of modern Web browsers, WebMTD randomizes values of certain attributes of Web elements to differentiate the application code from the injected code and disallow its execution; this is done without requiring Web developer involvement or browser code modification. Through rigorous evaluation, we show that WebMTD has very a low performance overhead. Also, we argue that our technique outperforms all competing approaches due to its broad effectiveness, transparency, backward compatibility, and low overhead.
Learning Smooth Representation for Unsupervised Domain Adaptation

Typical adversarial-training-based unsupervised domain adaptation methods are vulnerable when the source and target datasets are highly-complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to unsupervised domain adaptation and usually perform poorly on large-scale datasets. In this paper, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of unsupervised domain adaptation. A connection between them is built and an illustration of how Lipschitzness reduces the error bound is presented. A \textbf{local smooth discrepancy} is defined to measure Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of unsupervised domain adaptation, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension and batchsize of samples indeed greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.
Developing Jemparingan Tradition-Based and Android-Assisted Learning Media for Improving the Graphic and Vector Representation Ability

&lt;p class="0abstract"&gt;&lt;strong&gt;­&lt;/strong&gt;This study aimed to (1) develop a suitable learning device of Jemparingan Tradition-based (JEMASIK) Android-assisted (2) to know the effect of JEMASIK-based media to improve the representation ability of graphical and vector in sub-chapter of parabolic motion vector. The total sample on field test trial were 61 students, consisted of 29 control class students and 31 experimental class students. The research results showed that there were differences between experimental classes using JEMASIK and control classes using Power Point media. The difference was proved by multivariate test score of 0.000 less than 0.05. The effectiveness of JEMASIK media was viewed by the gain score of pretest and posttest. The gain score of control class was 0.54 in graphical representation and 0.49 in vector representation. Meanwhile, the gain score of experimental class was 0.75 in graphical representation and 0.60 in vector representation.&lt;/p&gt;
Copy move forgery detection using key point localized super pixel based on texture features

The most important barrier in the image forensic is to ensue a forgery detection method such can detect the copied region which sustains rotation, scaling reflection, compressing or all. Traditional SIFT method is not good enough to yield good result. Matching accuracy is not good. In order to improve the accuracy in copy move forgery detection, this paper suggests a forgery detection method especially for copy move attack using Key Point Localized Super Pixel (KLSP). The proposed approach harmonizes both Super Pixel Segmentation using Lazy Random Walk (LRW) and Scale Invariant Feature Transform (SIFT) based key point extraction. The experimental result indicates the proposed KLSP approach achieves better performance than the previous well known approaches.
DAVE: A Deep Audio-Visual Embedding for Dynamic Saliency Prediction

This paper studies audio-visual deep saliency prediction. It introduces a conceptually simple and effective Deep Audio-Visual Embedding for dynamic saliency prediction dubbed ``DAVE" in conjunction with our efforts towards building an Audio-Visual Eye-tracking corpus named ``AVE". Despite existing a strong relation between auditory and visual cues for guiding gaze during perception, video saliency models only consider visual cues and neglect the auditory information that is ubiquitous in dynamic scenes. Here, we investigate the applicability of audio cues in conjunction with visual ones in predicting saliency maps using deep neural networks. To this end, the proposed model is intentionally designed to be simple. Two baseline models are developed on the same architecture which consists of an encoder-decoder. The encoder projects the input into a feature space followed by a decoder that infers saliency. We conduct an extensive analysis on different modalities and various aspects of multi-model dynamic saliency prediction. Our results suggest that (1) audio is a strong contributing cue for saliency prediction, (2) salient visible sound-source is the natural cause of the superiority of our Audio-Visual model, (3) richer feature representations for the input space leads to more powerful predictions even in absence of more sophisticated saliency decoders, and (4) Audio-Visual model improves over 53.54\% of the frames predicted by the best Visual model (our baseline). Our endeavour demonstrates that audio is an important cue that boosts dynamic video saliency prediction and helps models to approach human performance. The code is available at https://github.com/hrtavakoli/DAVE
Quantum‐Behaved Particle Swarm Optimization Algorithm Based on the Two‐Body Problem

The present study proposes an improved Quantum-behaved particle swarm optimization algorithm based on the two-body problem model (QTPSO) for solving the problem that other quantum-behaved particle swarm optimization algorithms easily converge on local optimal solutions when solving complex nonlinear problems. In the proposed QTPSO algorithm, particles are categorised as core particles and edge particles. Once the position of the core particle is determined, the edge particle appears in the vicinity of the attractor exhibiting a high probability, and the attractor is obtained through the random weighted sum of the core particle and the optimal mean position. Through simulation of the motion of these two particles by applying the interaction of the particles in the two-body problem, this mechanism not only improves the diversity of the population, but also enhances the local search capacity. To validate the proposed algorithm, three groups of experimental results were obtained to compare the proposed algorithm with other swarm intelligence algorithms. The experimental results indicate the superiority of the QTPSO algorithm.
Mathematical Document Retrieval System based on Signature Hashing

Scientific documents and magazines involve large number of mathematical expressions and formulas along with text. The continuous growth of such documents necessitates the requirement of developing specialized tools and techniques, which could handle and analyse mathematical expressions and formulas. Mathematical expressions and formulae are highly structured and quite different from traditional text. Due to which conventional text retrieval system performs poorly in retrieving scientific documents based on mathematical expression formulated as a query. Mathematical information retrieval is concerned with finding information in documents that include mathematics. To address the challenges posed by mathematical formulae as compared to text, this paper aims to construct a math aware search engine, which can retrieve relevant scientific documents based on a mathematical query. A novel signature based hashing scheme to index raw mathematical web documents is proposed in this paper, which can also take mathematical notational equivalences into account. The proposed system demonstrates better precision and stability of the ranked results when compared with other related state-of-the-art math aware search engines.