Reviewerly/exHarmony huggingface dataset:
SAFe transformation in a large financial corporation

Abstract As agile software development is increasingly adopted in the software industry, the popularity of scaling frameworks supporting adoption in large development contexts is increasing rapidly. While several such frameworks exist, the most popular one at the moment is the Scaled Agile Framework (SAFe). Despite its popularity, there exists limited research on its usage and adoption. In this paper, we contribute by presenting a single case study in a large financial organization, studying the transformation reasons, transformation process, as well as the benefits, and challenges of SAFe adoption. We conducted 24 semi-structured interviews with 27 interviewees and analyzed the transcribed interviews using open and axial coding. We identified 17 reasons for SAFe adoption in this organization, of which the most salient ones were to shorten the time to market, improve collaboration, and use a well-described and comprehensive framework. An industry context-specific reason was the popularity of SAFe in the financial sector. The transformation in the case organization was top-down and proceeded step-wise. The most significant activities during the transformation were piloting, education, coaching, and the forming of agile release trains. Our case also implemented "Scrum tours" to increase the understanding of lean and agile principles. We identified 13 benefits of SAFe, of which improved collaboration, transparency, and shorter time to market were considered the most important. We identified a total of 16 challenges, with the most salient one being aligning the release trains with value streams. Failing with this led to cross-release train dependencies and coordination overhead, inhibiting agility. Further, the organization did not get rid of projects and project managers, which led to priority clashes and coordination overhead.
Visualizing bowling performance in cricket using contour plot

Batting and bowling are widely considered to be the pivotal skills in cricket. Traditionally, cricket has relied on three distinct metrics to measure bowling performance: the bowling average, the bowler's strike rate, and the economy rate. While each of these measures attempts to quantify the performance of bowlers, their respective scopes are unique. Researchers have made several attempts to integrate these measures into a composite index to rank bowlers. However, the integration process inevitably results in the loss of the individual metrics' distinct natures. This paper endeavours to visualize the three measures in a two-dimensional plane using a contour plot. To better illustrate the proximity amongst bowlers concerning the three distinct measures, a few improvements to the plot are proposed. The proposed graphical extension utilizes the career statistics of bowlers across different eras and demonstrates how the style of bowling has evolved as the scoring patterns in cricket change. Additionally, an R program has been developed to facilitate the drawing of said graphical extension.
Synergic Approach in Philosophy and Pedagogy: Interaction and Influence on the Educational Process

This article is devoted to the consideration of synergetic as the basis of a new approach to the functioning of the modern educational system. Various points of view on synergetic as a methodology of modern scientific research are considered. The study “Synergetic approach in philosophy and pedagogy: interaction and impact on the educational process” is a comprehensive analysis of the concept of synergetic in the context of its application in philosophy and pedagogy, as well as an assessment of its potential impact on improving educational processes. This article examines the key aspects of the synergetic approach and its connection with the educational sphere, and also offers practical recommendations for its implementation in teaching practice.
Unambiguous correct discrimination of real quantum states

We investigate the unambiguous correct discrimination strategy of a set of linearly independent equidistant real quantum states and three sets of three linearly independent states. The measurement operators include the success and correct operators, and can gain more information about the input states than by using the conventional unambiguous discrimination strategy. Five examples are given to demonstrate the efficiency of the unambiguous correct discrimination strategy.
Rolling the Crypto Dice: The Interplay of Legal Environments, Market Uncertainty, and Gambling Attitudes on Users’ Behavioral Intentions

The high volatility and inherent high-risk nature of cryptocurrency investments promote the study of the determinants of value perception and the various factors influencing individuals’ intentions regarding whether to adopt, abstain from, or continue their investments in these dynamic cryptocurrency markets. The main aim of this study is to examine the determinants of behavioral intention to continue using cryptocurrencies. In addition, it is aimed at exploring the effect of gambling attitudes on the perceived benefits and legal environment in the cryptocurrency context. An online questionnaire was developed in order to gather data from 258 respondents in the United Arab Emirates (UAE). The research model was assessed, and the hypotheses were tested using PLS-SEM. The outcomes of the PLS analysis revealed that gambling attitudes, perceived benefits, legal environment, and market uncertainty are significant determinants of behavioral intention to continue using cryptocurrencies. This study also revealed a significant effect of gambling attitudes and legal environment on the perceived benefits of cryptocurrencies. This study adds to the body of knowledge on cryptocurrency adoption by providing new insights into the factors that influence consumers’ continued investment. Furthermore, the study has crucial practical implications for cryptocurrency firms in promoting this financial technology to users by increasing policymakers’ understanding of how investors think and get inspired to continue investing in cryptocurrencies.
Data Leaks Using SQL Injection

The security of digital data is paramount in today's interconnected world. Among the various cyber threats, SQL injection attacks represent a significant menace to the confidentiality, integrity, and availability of sensitive information stored within databases. SQL injection is a technique employed by malicious actors to exploit vulnerabilities in web applications that interact with databases, allowing unauthorized access to or manipulation of the data. This paper presents an in-depth analysis of SQL injection attacks, their mechanisms, and the potential risks they pose to organizational data. It examines various preventive measures and best practices to mitigate the vulnerabilities that lead to SQL injection. Techniques such as input validation, parameterized queries, and the use of prepared statements are explored as effective defences against these attacks
The impact of sound and immersive experience on learners when using virtual reality and tablet: A mixed‐method study

Multisensory‐rich VR experiences, which encompass visual, auditory, and haptic stimuli, have the potential to enhance engagement, motivation, and learning. However, extensive sensory stimuli could also compromise learning through sensory overload. In museum settings, visitors who are inundated with excessive stimuli such as unrelated background music or competing visual options may not experience optimal learning. This mixed‐methods study addressed this potential problem by exploring the impact of sound on learning, enjoyment, sense of presence, and the development of interest among museum attendees ( N = 255) who used two different types of technology: high‐immersion VR or a tablet. Results from a one‐way MANOVA revealed that learning and sense of presence were unaffected by the technology used unless sound was added. Using a tablet with sound lowered the sense of presence. Participants in the VR condition with or without sound had significantly higher enjoyment scores than those in either tablet condition. The development of interest was not significantly affected by any condition, regardless of whether sound was used. The research findings have implications for implementing VR for learning in museum settings. For instance, VR can enhance a sense of presence in museum exhibits, but a sense of presence may be significantly diminished when museum visitors use tablets with sound. Additional recommendations for the use of multimodal VR in museums are provided. Practitioner notes What is already known about this topic VR can support an immersive and engaging experience for users. VR is more immersive than tablets. When designed effectively, sound can increase immersion and engagement. When integrated into the visual environment, sound can add authenticity to the learning content. VR can complement museum exhibits as it allows museum visitors to extend their learning experiences. What this paper adds VR enriched with sound can significantly and positively impact sense of presence. Compared with a soundless VR exhibition, VR enriched with sound enhances engagement of museum visitors by drawing their attention to the content of the exhibits. VR with and without sound can support learning and add enjoyment to museum visitor experiences. Implications for practice and/or policy Developers interested in adding sound to VR should ensure that the visual and auditory experiences are directly related to the desired content delivery. Museum exhibit creators and curators should consider the use of VR to provide dynamic learning experiences that increase enjoyment and presence.
Pemanfaatan Aplikasi Microsoft Office Dalam Proses Belajar Mengajar Bagi Guru Di SDK Tilang, Desa Tilang, Kecamatan Nita

This research aims to introduce and train Tilang SDK teachers in utilizing Microsoft Office applications such as Word, Excel and Power Point which provide interesting features as tools in the teaching and learning process because there are some teachers who cannot use and utilize the features provided. It's in Microsoft Office. This training was carried out at SDK Tilang with the main subject being the teachers at the school because based on the results of the review there were still teachers who used monotonous teaching media, namely thematic books. Apart from that, there were several teachers who were not yet able to operate the features in Microsoft Office.This research uses a qualitative descriptive method, with the research subjects being teachers at SDK Tilang.
Elaborating a Human Rights friendly Copyright Framework for Generative AI

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Deep Learning Approaches for End-to-End Modeling of Medical Spatiotemporal Data

For many medical applications, a single, stationary image may not be sufficient for detecting subtle pathology. Advancements in fields such as computer vision have produced robust deep learning (DL) techniques able to effectively learn complex interactions between space and time for prediction. This chapter presents an overview of different medical applications of spatiotemporal DL for prognostic and diagnostic predictive tasks, and how they built on important advancements in DL from other domains. Although many of the current approaches draw heavily from previous works in other fields, adaptation to the medical domain brings unique challenges, which will be discussed, along with techniques being used to address them. Although the use of spatiotemporal DL in medical applications is still relatively new, and lags behind the progress seen from still images, it provides unique opportunities to incorporate information about functional dynamics into prediction, which could be vital in many medical applications. Current medical applications of spatiotemporal DL have demonstrated the potential of these models, and recent advancements make this space poised to produce state-of-the-art models for many medical applications.
Machine Learning Based Miscellaneous Objects Detection with Application to Cancer Images

With the technological developments in artificial intelligence, traditional machine learning methods, and recently Deep Convolutional Neural networks (DCNNs) have accomplished great success in several vision related tasks. For example, object detection, object classification or recognition, and image segmentation are effectively achieved with the aid of aforementioned technologies. To train an effective DCNN model generally needs a big amount of diverse and balanced data. However, the DCNNs are quite often constrained by several aspects, such as (i) data annotation cost, (ii) long-tailed dispersal of data or imbalanced data, and (iii) scarcity of relevant data. Moreover, these days Generative Adversarial Networks (GANs) are widely used in image generation and synthesis related tasks. Object detection, being an important but challenging problem in the field of image processing and computer vision, plays an important role for several applications. While different methods exist to detect objects that appear in an image, a detailed analysis regarding common object detection is still lacking. This chapter pertains to detect objects that appear in an image with complex backgrounds using the Adaptive Boosting Algorithm (ABA). Different from several previously published chapters or research articles that focus on detection of six different objects, such as ships, vehicles, face, or eyes on standard datasets, this chapter focuses on real life objects, which include: (i) Melanoma (actual Malignant Melanoma, Nevus Spilus, and Blue Navy), (ii) license plates, (iii) vehicles, (iv) pedestrians, (v) players, and (vi) football. The ABA is applied on 8525 test images, which include: 251 Melanoma, 2512 license plates, 2400 vehicles, 2502 pedestrians, 590 players/sportsmen, and 270 football images. The chapter also analyses the challenges of current study during investigating real life several objects’ images and proposes a promising research direction, namely supervised learning cloud-based object detection. We are optimistic that this study will be useful for the researchers, practitioners, and beginners as a quick review with better understanding about object detection.
Gamification in education: Using quest technology

The article updates the issue of gamification of education by using quest technology as a particular type of students' research activity, which involves game practices and mechanisms in a non-game context to solve educational tasks. A practical example of the gamification of the educational process is a quest as one of the types of special-organized research activity, which aims to achieve a result by searching for information and solving various tasks (creative, search, intellectual, etc.). It was determined that introducing quest technologies into the educational process contributes to developing students’ analytical abilities and creative and critical thinking, prompts them to make unconventional decisions, and increases their motivation to study. The algorithm of how to make a quest and its main elements are disclosed (methodological commentary on the organization and conduct of the quest, goal, task, legend, equipment, rules, content of locations, results, summary, etc.). A sample of quest technology in working with senior school students in institutions of general secondary education is presented by the example of the quest “Figures of Enlightenment”, which can be partially or fully used in grades 9-11 during art lessons and other disciplines, organizing extracurricular work. It is concluded that the total or partial implementation of game tasks of the quest into the educational process contributes to: the formation of interdisciplinary connections (art, geography, history, Ukrainian literature), expansion of Ukrainian studies and cultural aspects of the lesson (event), school subjects and subject cycles; formation of key and general subject competencies of education seekers; development of critical, analytical and creative thinking, interaction skills, search activity, systematization of information; realization of competence approach (formation of subject, cross-subject, and key competences), person-oriented approach (development of search activity skills, the creative potential of students), activity-based approach (development of the ability to apply acquired knowledge in educational and sociocultural practice), and integrative approach (inter-subject connections with subjects of other academic fields, integration with the sociocultural environment).
CUSTOMS FRAUD DETECTION USING EXTREMELY BOOSTED NEURAL NETWORK (XBNET)

Noticing the vital role of cross-border trade has made Customs plays a crucial role not only in maintaining supply chain but also in securing government revenue from intentional fraud. Given the huge volume of international trade and limited workforce, Customs across the world must implement efficient and effective risk management. This paper proposes XBNet, an ensemble of tree-based algorithms with deep learning algorithms, to detect fraud in import activity. The strength of XBNet is combining gradient-boosted trees with neural networks where the weights, bias, and loss are adjusted simultaneously with the importance features of each tree in each layer. The object of this study is Import Declaration data from four Customs Offices, and the model is set for each Customs office to capture fraud patterns related to their region. We compared the model with two different parameters and concluded the models with learning rates = 1%, number of hidden layers = 2, activation function = sigmoid, and number of epochs = 100 as the most suitable for the Belawan, Merak, and Makassar and model with number of hidden layers = 2, number of epochs = 50 and other parameters are set the same as the most suitable for Tanjung Emas.
NGDCNet: Noise Gating Dynamic Convolutional Network for Image Denoising

Deep convolution neural networks (CNNs) have become popular for image denoising due to their robust learning capabilities. However, many methods tend to increase the receptive field to improve performance, which leads to over-smoothed results and loss of critical high-frequency information such as edges and texture. In this research, we introduce an innovative end-to-end denoising network named the noise gating dynamic convolutional network (NGDCNet). By integrating dynamic convolution and noise gating mechanisms, our approach effectively reduces noise while retaining finer image details. Through a series of experiments, we conduct a comprehensive evaluation of NGDCNet by comparing it quantitatively and visually against state-of-the-art denoising methods. Additionally, we provide an ablation study to analyze the contributions of dynamic convolutional blocks and noise gating blocks. Our experimental findings demonstrate that NGDCNet excels in noise reduction while preserving essential texture information.
Deep learning and transfer learning of earthquake and quarry-blast discrimination: applications to southern California and eastern Kentucky

SUMMARY Discrimination between tectonic earthquakes (EQs) and quarry blasts is important for accurate EQ cataloguing and seismic hazard analysis. However, reliable classification of these two types of seismic events is challenging with no prior knowledge of source parameters. Here, we applied deep learning to perform this classification task in southern California and eastern Kentucky. Since the two regions differ significantly in available labelled data, class imbalance and waveform characteristics, we adopted different strategies for them. We directly trained a convolutional neural network (CNN) for southern California due to its data abundancy. To alleviate the class imbalance, the blast data were augmented by repeated sampling. The model for California yields F1-scores of &amp;gt;83.5 per cent when estimated by individual stations and &amp;gt;98.1 per cent by network average (i.e. averaging the CNN’s outputs on all available stations for each event). As eastern Kentucky has a much smaller data size, we apply transfer learning to the pre-trained California model to fit the Kentucky data. The transfer-learned model yields F1-scores of &amp;gt;86.9 per cent when estimated by individual stations and &amp;gt;96.7 per cent by network average. The transfer-learned model outperforms the model re-trained from scratch for the Kentucky data. Gradient-weighted class activation mapping shows the S onset and the S long-period coda are important to identify EQs and blasts, respectively. By visual inspections of the seismograms, the source locations, the origin time and the P-wave polarities, we verified that most of the events falsely predicted by our models are actually mislabelled by seismic analysts. Our models thus show great potential in helping seismic analysts find those mislabelled events which remain hidden in the historical catalogue. Our results demonstrate that deep learning can achieve high accuracy in seismic event discrimination and that transfer learning is effective and efficient to generalize deep learning models across different regions.
Unconditionally Secure Multiparty Computation for Symmetric Functions with Low Bottleneck Complexity

Bottleneck complexity is an efficiency measure of secure multiparty computation (MPC) introduced by Boyle et al. (ICALP 2018) to achieve load-balancing. Roughly speaking, it is defined as the maximum communication complexity required by any player within the protocol execution. Since it was shown to be impossible to achieve sublinear bottleneck complexity in the number of players n for all functions, a prior work constructed MPC protocols with low bottleneck complexity for specific functions. However, the previous protocol for symmetric functions needs to assume a computational primitive of garbled circuits and its unconditionally secure variant has exponentially large bottleneck complexity in the depth of an arithmetic formula computing the function, which limits the class of symmetric functions the protocol can compute with sublinear bottleneck complexity in n. In this work, we make the following contributions to unconditionally secure MPC protocols for symmetric functions with sublinear bottleneck complexity in n.
LERNA: Secure Single-Server Aggregation via Key-Homomorphic Masking

This paper introduces LERNA, a new framework for single-server secure aggregation. Our protocols are tailored to the setting where multiple consecutive aggregation phases are performed with the same set of clients, a fraction of which can drop out in some of the phases. We rely on an initial secret sharing setup among the clients which is generated once-and-for-all, and reused in all following aggregation phases. Compared to prior works [Bonawitz et al. CCS’17, Bell et al. CCS’20], the reusable setup eliminates one round of communication between the server and clients per aggregation—i.e., we need two rounds for semi-honest security (instead of three), and three rounds (instead of four) in the malicious model. Our approach also significantly reduces the server’s computational costs by only requiring the reconstruction of a single secret-shared value (per aggregation). Prior work required reconstructing a secret-shared value for each client involved in the computation. We provide instantiations of LERNA based on both the Decisional Composite Residuosity (DCR) and (Ring) Learning with Rounding ((R)LWR) assumptions respectively and evaluate a version based on the latter assumption. In addition to savings in round-complexity (which result in reduced latency), our experiments show that the server computational costs are reduced by two orders of magnitude in comparison to the state-of-the-art. In settings with a large number of clients, we also reduce the computational costs up to twenty-fold for most clients, while a small set of “heavy clients” is subject to a workload that is still smaller than that of prior work.