Reviewerly/exHarmony huggingface dataset:
Synthesis of a Leadership Model for DevOps Adoption

The first decade of DevOps-orientation in software-intensive organizational environments is often characterized with an emerging set of skills that support DevOps practice adoption, targeting a cross-functional collaborative culture; with an aim of achieving a shift in mindset, skillset, and toolset. We investigate DevOps adoption constructs to facilitate development of a formative measurement model to support leadership throughout the DevOps transitional journey. The model and its constructs are designed and validated with a multi-method approach. Initially an exploratory study of a survey is conducted with 250 respondents 76% of whom possess leadership roles, 93% work in Europe and Middle East, and two-thirds are practicing as DevOps practitioners. Pertinent model indicators are produced and grouped under constructs based on survey results and validated using PLS-SEM. The formative structural model is presented and validated in three separate focus group sessions, comprising of respectively seven (7), five (5), and seven (7) participants all of whom had held leadership positions; from countries including USA, UK, The Netherlands, UAE, Greece, Georgia, Switzerland. Seventeen (17) focus group participants provided additional responses through a focus group in-session survey, which allowed feedback on specific model constructs. Results indicate that a set of practices, a set of skills, a set of metrics, DevOps adoption planning and the existence of the DevOps adoption leader roles, should be part of organizational aspirations in the definition of leadership in a DevOps transition path.
Creation and Detection of German Voice Deepfakes

Synthesizing voice with the help of machine learning techniques has made rapid progress over the last years [1]. Given the current increase in using conferencing tools for online teaching, we question just how easy (i.e. needed data, hardware, skill set) it would be to create a convincing voice fake. We analyse how much training data a participant (e.g. a student) would actually need to fake another participants voice (e.g. a professor). We provide an analysis of the existing state of the art in creating voice deep fakes and align the identified as well as our own optimization techniques in the context of two different voice data sets. A user study with more than 100 participants shows how difficult it is to identify real and fake voice (on avg. only 37% can recognize a professor’s fake voice). From a longer-term societal perspective such voice deep fakes may lead to a disbelief by default.
Quantum speed-ups in reinforcement learning

As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.
The BRAIINS AI for Kids Platform

BRAIINS (BRingAIIN Schools) 2020–2023 Erasmus+ project is first and foremost a bottom-up educational alliance between schools in EU with different backgrounds, expertise and focus and among students. Although already permeating our world, AI is not explicitly taught in most schools and has not found an appropriate place in curricula yet. While some platforms for kids and professional tools can be found, BRAIINS is targeting 15–18 year olds with a peer-to-peer approach. This paper explains how students with technical background provide online-learning materials and a ready to use website for their peers from the partner schools using ML4Kids, KNIME, the ML Kit for Firebase and robotic platforms such as Misty, Nao, Botball. This will give them the opportunity to learn from each other, collaborate to enhance the website with self-written exercises and examples as well as use it for their discussions about ethics and creative mindsets.
Construction and Application of Knowledge Graph of Teaching Arrangement for Cross-disciplinary Majors

With the rapid development of science and technology and the explosive growth of knowledge, cross-disciplinary majors have become a common choice in colleges and universities. In cross-disciplinary majors, the teaching content is rich, the demand changes rapidly, and the foundation of existing teachers is limited. These characteristics make the professional teaching arrangement of cross-disciplinary face great challenges. Based on the above problems and challenges, combined with deep learning, the paper proposes the automatic construction of knowledge point system (knowledge graph). It matches the existing teacher and assists scholars and teaching managers to develop teaching arrangement methods for cross-disciplinary professional courses.
A Modified APSODEE for Large Scale Optimization

The balance of exploration and exploitation of particle swarm optimization (PSO) is still a great challenge in large scale optimization. In our previous work, an adaptive particle swarm optimizer with decoupled exploration and exploitation (APSODEE) has been proposed to solve this problem. However, it still shows room for further improvements. First, APSODEE guides particles with the best individual in each sub-swarm, which is adverse to swarm diversity preservation especially in case of large scale optimization. Second, APSODEE fails to lead particles moving to sparse areas which are also potentially promising. To address these issues, two modifications are incorporated into APSODEE including a partial updating strategy and a quality restrained local sparseness diversity measurement. The former is proposed to further enhance the algorithm's swarm diversity preservation ability while the latter is designed to help guiding updated particles moving towards the areas which are both sparse and potentially promising, resulting in the modified APSODEE. The experiments are conducted based on CEC 2013 benchmarks with 1000 dimensionality. The results show the competitiveness of the proposed algorithm.
Physics-guided machine learning: A new paradigm for scientific knowledge discovery

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Exploring the Perceptions of Faculty members and Students on Cloud Computing Adoption in Higher Educational Institutions of Bangladesh

The purpose of this paper is to explore on the perceptions of faculty members and students of tertiary institutions of Bangladesh regarding adoption of cloud computing in teaching and learning. The qualitative study used purposive sampling technique for selecting the 10 faculty members and 10 students from four private universities and four public universities of the capital city of Bangladesh. The researchers conducted a semi-structured interview with the participants. The data were coded, categorised and analysed inductively for a qualitative thematic pattern. The findings revealed that the faculty members and students agreed that using cloud computing was completely beneficial for them. However, the participants also opined that for some of them, technophobia towards adopting an innovation, the complexity of using technology, pop-up advertisements and lack of institutional infrastructure and trained staff discouraged them from using cloud computing and adhere to the traditional mode of imparting and receiving knowledge. In addition, lack of awareness and knowledge about cloud computing also hindered them from adopting it. The study is the first initiative to explore the perceptions among faculty members and students in private and public universities in Bangladesh.
Restricted stacks as functions

The stack sort algorithm has been the subject of extensive study over the years. In this paper we explore a generalized version of this algorithm where instead of avoiding a single decrease, the stack avoids a set T of permutations. We let sT denote this map. We classify for which sets T the map sT is bijective. A corollary to this answers a question of Baril, Cerbai, Khalil, and Vajnovszki about stack sort composed with s{σ,τ}, known as the (σ,τ)-machine. This fully classifies for which σ and τ the preimage of the identity under the (σ,τ)-machine is counted by the Catalan numbers. We also prove that the number of preimages of a permutation under the map sT is bounded by the Catalan numbers, with a shift of indices. For T of size 1, we classify exactly when this bound is sharp. We also explore the periodic points and maximum number of preimages of various sT for T containing two length 3 permutations.
A Multi-Objective Approach for Software Quality Improvement

Abstract Software industry always demands high-quality software that must be reliable, robust, flexible, reusable, effective, and extendable. To improve software quality refactoring is an efficient and frequently opted technique. Most of the refactoring work has been done on the source code level less work has been done on model-based. Model base refactoring is more difficult to estimate. In this research paper, the multi-objective evolutionary algorithm based on decomposition (MOEA/D) is applied to find how software quality is affected by refactoring technique. It impacts the software quality in both positive as well as negative way.
Ghost Routing to Enable Oblivious Computation on Memory-centric Networks

With offloading of data to the cloud, ensuring privacy and securing data has become more important. However, encrypting data alone is insufficient as the memory address itself can leak sensitive information. In this work, we exploit packetized memory interface to provide secure memory access and support oblivious computation in a system with multiple memory modules interconnected with a multi-hop, memory-centric network. While the memory address can be encrypted with a packetized memory interface, simply encrypting the address does not provide full oblivious computation since coarse-grain memory access patterns can be leaked. In this work, we first propose a scalable encryption microarchitecture with source-based routing where the packet is only encrypted once at source and latency overhead in intermediate routers is minimized. We then define secure routing in memory-centric networks to enable oblivious computation such that memory access patterns across the memory modules are completely obfuscated. We explore different naive secure routing algorithms to ensure oblivious computation but they come with high performance overhead. To minimize performance overhead, we propose ghost packets that replace dummy packets with existing network traffic. We also propose Ghost routing that batches multiple ghost packets together to minimize bandwidth loss from naive secure routing while exploiting random routing.
Face Images Classification using VGG-CNN

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image. The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).
Domain-driven Correlation-aware Recombination and Mutation Operators for Complex Real-world Applications

Evolutionary algorithms are a very general method for optimization problems that allow adaption to many different use cases. Application to real-world problems usually comes with features as constraints, dependencies and approximations. When a multidimensional search space comes with strings attached- namely dependencies between its dimensions- an expression in two ways is possible: Restrictive-as equalities or inequalities- or vague-as correlations between dimensions, for example. Correlations between dimensions are not as easy to grasp as constraints. Therefore, well-known techniques as death penalty or penalty functions do not apply directly. We propose new mutation and recombination operators that incorporate domain knowledge to increase the offspring fraction that adheres to these correlations. We evaluate our approach with several benchmark functions and different assumptions on the dependencies of the search space. We compare the likelihood of valid (in terms of adhering correlations) outcomes of algorithms using standard mutation and recombination operators to those with the proposed operators. We find that the correlation-aware operators preserve population's features in terms of dependencies.
Adaptive Teacher Finetune: Towards high-performance knowledge distillation through adaptive fine-tuning

Abstract Knowledge distillation is a widely used method to transfer knowledge from a large model to a small model. Traditional methods use pre-trained large models to supervise the training of small models, called Offline Knowledge Distillation, However, the structural gap between teachers and students limits its performance. After that, Oneline Knowledge Distillation retrained the teacher-student network from the beginning and the method of echo teaching greatly improved the performance. But there is very little work to explore the difference between the two. In this paper, we first point out that the essential difference between Offline and Oneline Knowledge Distillation is actually whether the weight of the teacher-student network has a process of mutual adaptation. If they adopt the teacher network and the student network jointly train to implement Offline Knowledge Distillation, there is no obvious difference in the final performance, no matter whether it is a joint distillation training. This shows that teacher-student network adaptation is important for Knowledge Distillation. Then, we propose an Adaptive Teacher Finetune (ATF) to adapt the teacher model to the student network. It will use student model information for Tinetune during the Offline Knowledge Distillation process. With normalized logical distribution and alpha-divergence, the performance improvement of ATF clearly exceeds the existing Offline and Oneline Knowledge Distillation method. Extensive experiments conducted on cifar and ImageNet support our aforementioned analysis and conclusions. With the newly introduced ATF, we obtained state-of-the-art performance on ResNet 18 on ImageNet.
Is Algebraic Differential Evolution Really a Differential Evolution Scheme?

The Algebraic Differential Evolution (ADE) is a recently proposed combinatorial evolutionary scheme which mimics the behaviour of the classical Differential Evolution (DE) in discrete search spaces which can be represented as finitely generated groups. ADE has been successfully applied to both permutation and binary optimization problems. However, in the previous works, the relationship between ADE and the classical continuous DE has been only intuitively sketched without any theoretical or experimental proof. Here, we fill this gap by providing both theoretical and experimental justifications proving that ADE is a full-fledged generalization of DE which works across different search spaces. First, we formally prove that there exists a concrete implementation of ADE's algebraic operations converging to the classical vector operations of DE, then we propose a real-vector implementation of ADE and we experimentally prove that its behaviour is statistically equivalent to DE. As conclusion, we also pave the way for further applications of the original DE idea to mixed discrete/continuous search spaces.
The co-construction of data in-time: collaborative pedagogical encounters of golf instructors and students with data

Data capture technologies are increasingly incorporated into teaching and learning settings in an effort to offer personalized learning opportunities. Despite the general enthusiasm for these initiatives, concerns persist as to the effective and ethical collection and use of that data. In response, this article offers a complimentary account of teaching and learning with data through the use of ethnographic methods in a novel setting – a Golf Teaching and Research Program (GTRP) – at a large U.S. university. This research illustrates how golf coaches and students leverage their bodies to create embodied re-creations of data. The analysis shows how the GTRP instructors use their bodies in-the-moment during pedagogical encounters to help students understand and feel their golf swing data. This article concludes by suggesting that these collaborative in-the-moment embodied encounters with data offer opportunities for teachers and learners to collectively analyze and interrogate their data through the process of data stewardship.
Security Framework for Delivery of Training, Using VR Technology

With rapid advances in industrialisation and informalisation methodologies being attributed to industry 4.0, there is no surprise that Education 4.0 would want to harness those capabilities for its own use. Immersive ecosystems for teaching, utilising the benefits brought by augmented and virtual reality, offer a new horizon in learning, but also unlock a plethora of associated security and privacy issues. This is where the motivation to look for a security framework that would encompass, not only the learning environment, but also the process from manufacturer to the endpoint user. Blockchain, once the enabling power behind bitcoin's distributed ledger, has expanded its applicability to a number of areas, in the context of this paper this is primarily for the enforcement of smart contracts. This is not to say that this couldn't be extended into a way to protect the data that is moved across the learning schematic.
HBONext: HBONet with Flipped Inverted Residual

The top-performing deep CNN (DCNN) architectures are presented every year based on their compatibility and performance ability on the embedded edge applications, significantly for image classification. There are many obstacles in making these neural network architectures hardware friendly due to the limited memory, lesser computational resources, and the energy requirements of these devices. The addition of Bottleneck modules has further helped this classification problem, which explores the channel interdependencies, using either depthwise or groupwise convolutional features. The classical inverted residual block, a well-known design methodology, has now gained more attention due to its growing popularity in portable applications. This paper presents a mutated version of Harmonious Bottlenecks (DHbneck) with a Flipped version of Inverted Residual (FIR), which outperforms the existing HBONet architecture by giving the best accuracy value and the miniaturized model size. This FIR block performs identity mapping and spatial transformation at its higher dimensions, unlike the existing concept of inverted residual. The devised architecture is tested and validated using CIFAR-10 public dataset. The baseline HBONet architecture has an accuracy of 80.97% when tested on CIFAR-10 dataset and the model's size is 22 MB. In contrast, the proposed architecture HBONext has an improved validation accuracy of 88.30% with a model reduction to a size of 7.66 MB.
DES-HyperNEAT: Towards Multiple Substrate Deep ANNs

Neuroevolution (NE), inspired by the natural evolution of biological brains, may be applied to the evolution of both weights and the topology of Artificial Neural Networks (ANNs). DES-HyperNEAT, proposed herein, further extends existing NE algorithms to enable evolvable substrate topologies with non-fixed node positions - a desirable feature for optimisation of deep ANNs. DES-HyperNEAT builds on HyperNEAT and ES-HyperNEAT whilst adding beneficial features from NEAT and MSS-HyperNEAT. The preliminary experiments in this work consider potential variants of DES-HyperNEAT, evaluating such variants in terms of performance and efficiency on the Iris, Wine and Retina datasets. Further, DES-HyperNEAT, ES-HyperNEAT, HyperNEAT and NEAT are compared to highlight whether the DES-HyperNEAT extension has merit in terms of performance and efficiency.
Data Management Tools and Privacy by Design and by Default

Data Management Tools (DMTs) play a crucial role in today’s data-driven society, but they also entail certain problems related to compliance with the regulation on personal data protection. With regard to this matter, in the past years, the European legislator has initiated a process, which is still ongoing, of profound innovation, which will continue to evolve in terms of data governance. This contribution provides some critical observations on the application of Article 25 GDPR, in the matter of privacy by design and by default, on DMTs, highlighting some problem areas the regulation in question includes in terms of application.
Deep Recommendation Based on Dual Attention Mechanism

The recommendation model is committed to filtering massive information and providing users with valuable information. Although the recommendation model based on matrix factorization is widely used, it does not perform well in the case of sparse data. Comments are based on the recommendation model and use comments to extract user preferences and item functions. Although it can alleviate the problem of data sparseness, the interaction between users and items is not a good performance. To this end, we propose a model that can consider the user's personalized preferences Deep recommendations based on dual attention mechanisms (DRDA). The model is based on the user's comment text, through the interaction of user comments and project comments, a deep neural network framework with attention factors is obtained to learn the user's personalized representation. A large number of experiments on the Amazon data set and Yelp data set show that the performance of the DRDA model is better than the traditional baseline model. Further experiments show that the dual attention factor does make a huge contribution to the model.