Reviewerly/exHarmony huggingface dataset:
Dual subaperture stitching for large flat mirror testing

The Ritchey–Common test is a widely used method for testing flat mirrors with a larger reference spherical mirror. However, with the increase in the size of the flat mirror, the fabrication of the spherical mirror becomes more time consuming and expensive. In this study, we developed a novel technique to test a large flat mirror with a smaller reference sphere using a dual subaperture stitching (DSS) method. One part of the DSS technique is a modified Ritchey–Common test, which uses a reference sphere smaller than the flat mirror. The other part involves scanning along the centerline of the flat mirror. The former can be used to determine the surface form error (SFE), except for rotationally symmetric components, such as power and spherical aberrations, which can be measured by the latter. To perform the stitching process using a smaller reference sphere, the flat mirror was repeatedly rotated by a fixed angular step. One of the advantages of the rotation of the flat mirror is that it can be used to identify the errors resulting from the reference sphere, which do not vary during the rotation of the flat mirror. We verified the DSS method using a 152 mm diameter optical flat mirror and determined the root-mean-square (rms) measurement error to be only 0.2 nm, which was comparable to the error of the full-aperture interferometric measurement. In addition, we tested a 1.2 m diameter flat mirror with a reference sphere with an aperture of 0.8 m and measured its SFE to be <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>11.9</mml:mn> </mml:mrow> <mml:mo>±<!-- ± --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>0.5</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">n</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:math> rms.
Quantifying Explainability of Saliency Methods in Deep Neural Networks

One way to achieve eXplainable artificial intelligence (XAI) is through the use of post-hoc analysis methods. In particular, methods that generate heatmaps have been used to explain black-box models, such as deep neural network. In some cases, heatmaps are appealing due to the intuitive and visual ways to understand them. However, quantitative analysis that demonstrates the actual potential of heatmaps have been lacking, and comparison between different methods are not standardized as well. In this paper, we introduce a synthetic data that can be generated adhoc along with the ground-truth heatmaps for better quantitative assessment. Each sample data is an image of a cell with easily distinguishable features, facilitating a more transparent assessment of different XAI methods. Comparison and recommendations are made, shortcomings are clarified along with suggestions for future research directions to handle the finer details of select post-hoc analysis methods.
Security of Blockchain Distributed Ledger Consensus Mechanism in Context of the Sybil Attack

In this paper, the susceptibility of blockchain network to the Sybil attack in which the creation of false identities is used was checked. We performed series of simulations using implemented environment, the cryptocurrency process was simulated using the proof of work consensus mechanism and limited network parameters, such as network computing power expressed in the number of computed hash results, difficulty in extracting the block and the number of users participating in the block in progress. The first of the examined situations was the one in which network nodes have an evenly distributed power allocation and the distribution of block output by individual units is checked. Another tested situation concerned moment of random allocation of computing power to each of the nodes in the network. Research was concluded with statistical analysis of simulation cases of the block mining process due to several parameters.
FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding

Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such as Computer Vision, the progress of FSL in Nature Language Processing (NLP) is much slower. One of the key reasons for this is the lacking of public benchmarks. NLP FSL researches always report new results on their own constructed few-shot datasets, which is pretty inefficient in results comparison and thus impedes cumulative progress. In this paper, we present FewJoint, a novel Few-Shot Learning benchmark for NLP. Different from most NLP FSL research that only focus on simple N-classification problems, our benchmark introduces few-shot joint dialogue language understanding, which additionally covers the structure prediction and multi-task reliance problems. This allows our benchmark to reflect the real-word NLP complexity beyond simple N-classification. Our benchmark is used in the few-shot learning contest of SMP2020-ECDT task-1. We also provide a compatible FSL platform to ease experiment set-up.
Enhanced Evolutionary Computing Assisted K-Means Clustering Algorithm for BigData Analytics

The exponential rise in internet technologies and allied applications has given rise to the technology named Big Data that intend to process gigantically huge data to assist real-time analytics or decision purposes.However, high pace increasing data heterogeneity, non-linearity, multi-dimensional features and unannotated data characteristics forces classical approaches to undergo huge computational overheads and limited accuracy that confines its suitability for major Big Data analytics purposes.With this motivation, in this paper a robust Big Data analytics model has been developed by incorporating Min-Max normalization, Dual Phased Feature Selection (DPFS) and enhanced Adaptive Genetic Algorithm (AGA) assisted K-Means clustering.Here, the use of Min-Max normalization helps alleviating key issues like data heterogeneity, data imbalance and pre-mature convergence during computation.Unlike classical feature selection approaches, DPFS exploited the efficacy of both Pearson correlation assisted significant test as well as T-Test analysis that ensure optimal feature selection for further computation.In addition, the use of AGA assisted K-Means clustering algorithm has accomplished computationally efficient and reliable clustering for efficient Big Data analytics purposes.Noticeably, the use of adaptive fitness sensitive GA parameter selection has strengthened our proposed system to exhibit better performance without imposing computational overheads.The computational efficacy of AGA-K-Means can strengthen MapReduce to be used for real-time Big Data analytics applications.
Finite representability of semigroups with demonic refinement

Composition and demonic refinement $\sqsubseteq$ of binary relations are defined by \begin{align*} (x, y)\in (R;S)&\iff \exists z((x, z)\in R\wedge (z, y)\in S) R\sqsubseteq S&\iff (dom(S)\subseteq dom(R) \wedge R\restriction_{dom(S)}\subseteq S) \end{align*} where $dom(S)=\{x:\exists y (x, y)\in S\}$ and $R\restriction_{dom(S)}$ denotes the restriction of $R$ to pairs $(x, y)$ where $x\in dom(S)$. Demonic calculus was introduced to model the total correctness of non-deterministic programs and has been applied to program verification. We prove that the class $R(\sqsubseteq, ;)$ of abstract $(\leq, \circ)$ structures isomorphic to a set of binary relations ordered by demonic refinement with composition cannot be axiomatised by any finite set of first-order $(\leq, \circ)$ formulas. We provide a fairly simple, infinite, recursive axiomatisation that defines $R(\sqsubseteq, ;)$. We prove that a finite representable $(\leq, \circ)$ structure has a representation over a finite base. This appears to be the first example of a signature for binary relations with composition where the representation class is non-finitely axiomatisable, but where the finite representations for finite representable structures property holds.
AUDiaL: A Natural Language Interface to Make Statistical Charts Accessible to Blind Persons

Abstract This paper discusses the design and evaluation of AUDiaL (Accessible Universal Diagrams through Language). AUDiaL is a web-based, accessible natural language interface (NLI) prototype that allows blind persons to access statistical charts, such as bar and line charts, by means of free-formed analytical and navigational queries expressed in natural language. Initial evaluation shows that NLIs are an innovative, promising approach to accessibility of knowledge representation graphics, since, as opposed to traditional approaches, they do not require of additional software/hardware nor user training while allowing users to carry out most tasks commonly supported by data visualization techniques in an efficient, natural manner.
An Empirical Analysis on the Usability and Security of Passwords

Security and usability are two essential aspects of a system, but they usually move in opposite directions. Sometimes, to achieve security, usability has to be compromised, and vice versa. Password-based authentication systems require both security and usability. However, to increase password security, absurd rules are introduced, which often drive users to compromise the usability of their passwords. Users tend to forget complex passwords and use techniques such as writing them down, reusing them, and storing them in vulnerable ways. Enhancing the strength while maintaining the usability of a password has become one of the biggest challenges for users and security experts. In this paper, we define the pronounceability of a password as a means to measure how easy it is to memorize - an aspect we associate with usability. We examine a dataset of more than 7 million passwords to determine whether the usergenerated passwords are secure. Moreover, we convert the usergenerated passwords into phonemes and measure the pronounceability of the phoneme-based representations. We then establish a relationship between the two and suggest how password creation strategies can be adapted to better align with both security and usability.
Binary Imbalanced Data Classification Based on Modified D2GAN Oversampling and Classifier Fusion

Binary imbalance problem refers to such a classification scenario where one class contains a large number of samples while another class contains only a few samples. When traditional classifiers face with imbalanced datasets, they usually bias towards majority class resulting in poor classification performance. Oversampling is an effective method to address this problem, yet how to conduct diversity oversampling is a challenge. In this article, we proposed a diversity oversampling method based on a modified D2GAN model, and on the basis of diversity oversampling, we also proposed a binary imbalanced data classification approach based on classifier fusion by fuzzy integral. Extensive experiments are conducted on 8 data sets to compare the proposed methods with 7 state-of-the-art methods on 5 aspects: MMD-score, Silhouette-score, F-measure, G-means, and AUC-area. The 7 methods include 4 SMOTE related approaches and 3 GAN related approaches. The experimental results demonstrate that the proposed methods are more effective and efficient than the compared approaches.
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design

The rapid development and wide utilization of object detection techniques have aroused attention on both accuracy and speed of object detectors. However, the current state-of-the-art object detection works are either accuracy-oriented using a large model but leading to high latency or speed-oriented using a lightweight model but sacrificing accuracy. In this work, we propose YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design. A novel block-punched pruning scheme is proposed for any kernel size. To improve computational efficiency on mobile devices, a GPU-CPU collaborative scheme is adopted along with advanced compiler-assisted optimizations. Experimental results indicate that our pruning scheme achieves 14$\times$ compression rate of YOLOv4 with 49.0 mAP. Under our YOLObile framework, we achieve 17 FPS inference speed using GPU on Samsung Galaxy S20. By incorporating our proposed GPU-CPU collaborative scheme, the inference speed is increased to 19.1 FPS, and outperforms the original YOLOv4 by 5$\times$ speedup. Source code is at: \url{https://github.com/nightsnack/YOLObile}.
Optimization of Online Course Content According to Users Activity Statistics

Digitalization of modern education leads to a change in the style of the educational activities of students and professional work of higher school teachers. The professional standard of education workers oblige educators not only to develop educational content discipline, but also apply ICT to manage student’s training activity. The aim of the research was to study the capacity of the standard user statistics tool in LMS MOODLE for course optimization on three parameters – content presentation, temporary course structure and quality of educational measurement tools. These options, according to the authors, are the main factors for users’ motivation to consistent use of online courses during the semester. The study of statistical characteristics of student activity in e-course “Informatics” based on LMS MOODLE has been carried out during three years. The experiment covered the first-year students enrolled in the “Electricity and Electrical Equipment”. The results of the content optimization and presentation of the discipline have shown a positive change in the dynamics of the student’s activity during the semester. The findings of this study have a number of practical implications. Optimization of the learning process with the use of online course requires designing of a course with week module structure and singled out micro-targets. The learning material should be presented in various formats taking into account students’ preference for infographics. This contributes to raising the academic performance on the whole. Statistical analysis of test tasks differentiating capacity will substantially improve the quality of tools for educational measurement. It is shown that the use of a MOODLE statistics tool to estimate the user activity makes it possible the documented testing the effectiveness of innovations in pedagogical design of e-course.
Bias Field Poses a Threat to DNN-based X-Ray Recognition

The chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19. More recently, many works construct deep neural networks (DNNs) for chest X-ray images to realize automated and efficient diagnosis of lung diseases. However, bias field caused by the improper medical image acquisition process widely exists in the chest X-ray images while the robustness of DNNs to the bias field is rarely explored, which definitely poses a threat to the X-ray-based automated diagnosis system. In this paper, we study this problem based on the recent adversarial attack and propose a brand new attack, i.e., the adversarial bias field attack where the bias field instead of the additive noise works as the adversarial perturbations for fooling the DNNs. This novel attack posts a key problem: how to locally tune the bias field to realize high attack success rate while maintaining its spatial smoothness to guarantee high realisticity. These two goals contradict each other and thus has made the attack significantly challenging. To overcome this challenge, we propose the adversarial-smooth bias field attack that can locally tune the bias field with joint smooth & adversarial constraints. As a result, the adversarial X-ray images can not only fool the DNNs effectively but also retain very high level of realisticity. We validate our method on real chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and MobileNet, and show different properties to the state-of-the-art attacks in both image realisticity and attack transferability. Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.
Learned Low Precision Graph Neural Networks

Deep Graph Neural Networks (GNNs) show promising performance on a range of graph tasks, yet at present are costly to run and lack many of the optimisations applied to DNNs. We show, for the first time, how to systematically quantise GNNs with minimal or no loss in performance using Network Architecture Search (NAS). We define the possible quantisation search space of GNNs. The proposed novel NAS mechanism, named Low Precision Graph NAS (LPGNAS), constrains both architecture and quantisation choices to be differentiable. LPGNAS learns the optimal architecture coupled with the best quantisation strategy for different components in the GNN automatically using back-propagation in a single search round. On eight different datasets, solving the task of classifying unseen nodes in a graph, LPGNAS generates quantised models with significant reductions in both model and buffer sizes but with similar accuracy to manually designed networks and other NAS results. In particular, on the Pubmed dataset, LPGNAS shows a better size-accuracy Pareto frontier compared to seven other manual and searched baselines, offering a 2.3 times reduction in model size but a 0.4% increase in accuracy when compared to the best NAS competitor. Finally, from our collected quantisation statistics on a wide range of datasets, we suggest a W4A8 (4-bit weights, 8-bit activations) quantisation strategy might be the bottleneck for naive GNN quantisations.
Practical and Provably Secure Distributed Aggregation: Verifiable Additive Homomorphic Secret Sharing

Often clients (e.g., sensors, organizations) need to outsource joint computations that are based on some joint inputs to external untrusted servers. These computations often rely on the aggregation of data collected from multiple clients, while the clients want to guarantee that the results are correct and, thus, an output that can be publicly verified is required. However, important security and privacy challenges are raised, since clients may hold sensitive information. In this paper, we propose an approach, called verifiable additive homomorphic secret sharing (VAHSS), to achieve practical and provably secure aggregation of data, while allowing for the clients to protect their secret data and providing public verifiability i.e., everyone should be able to verify the correctness of the computed result. We propose three VAHSS constructions by combining an additive homomorphic secret sharing (HSS) scheme, for computing the sum of the clients’ secret inputs, and three different methods for achieving public verifiability, namely: (i) homomorphic collision-resistant hash functions; (ii) linear homomorphic signatures; as well as (iii) a threshold RSA signature scheme. In all three constructions, we provide a detailed correctness, security, and verifiability analysis and detailed experimental evaluations. Our results demonstrate the efficiency of our proposed constructions, especially from the client side.
Learning Graph Normalization for Graph Neural Networks

Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations in each layer are computed through propagating and aggregating the neighboring node features with respect to the graph. By stacking to multiple layers, GNNs are able to capture the long-range dependencies among the data on the graph and thus bring performance improvements. To train a GNN with multiple layers effectively, some normalization techniques (e.g., node-wise normalization, batch-wise normalization) are necessary. However, the normalization techniques for GNNs are highly task-relevant and different application tasks prefer to different normalization techniques, which is hard to know in advance. To tackle this deficiency, in this paper, we propose to learn graph normalization by optimizing a weighted combination of normalization techniques at four different levels, including node-wise normalization, adjacency-wise normalization, graph-wise normalization, and batch-wise normalization, in which the adjacency-wise normalization and the graph-wise normalization are newly proposed in this paper to take into account the local structure and the global structure on the graph, respectively. By learning the optimal weights, we are able to automatically select a single best or a best combination of multiple normalizations for a specific task. We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task. Source code is released here https://github.com/cyh1112/GraphNormalization.
Exploring the Product Owner Role Within SAFe Implementation in a Multinational Enterprise

Abstract [Context] Agile development methods are highly popular across software organizations. To leverage benefits in larger enterprises, Agile development methods have to be scaled. Scaled Agile Framework (SAFe) is the most commonly used scaling framework. Performing of the Product Owner role has been identified as crucial in project success in large-scale environments. Staffing the right Product Owner is one of the challenges of adopting SAFe. [Motivation] Research papers focused on Product Owner in SAFe are scarce. Our study outcomes help enterprises to understand the Product Owner role in SAFe and therefore contribute to the removal of challenges with finding the right Product Owners. Additionally, we aim to improve the research community’s understanding of the Product Owner role within the context of SAFe. [Method] Qualitative data were collected through three semi-structured interviews and analyzed using deductive content analysis. [Results] This paper presents the initial results of a single case study. We found out that many activities identified for Product Owners in previous research are not carried out by Product Owners in this particular SAFe implementation.
Teacher Education in Higher Education Systems during Pandemic and the Synergy of Digital Technology

In the modern world of education, the level of digital technology development plays a decisive role in the competitiveness of educational institutions. The transition to digital education is considered a key driving force for the development of any university. In recent years, pedagogical universities have made tangible progress in many areas of digital development. However, the introduction of digital technologies (DT) in the educational process reached its peak during the pandemic. This study aims to identify the distinctive features in the work of a teacher at a pedagogical university during the pandemic. The study revealed the following: the doubts of the teachers with no e-learning experience; didactically effective digital technologies in the educational process, the combined use of which ensures the synergistic effectiveness of e-learning; students’ attitude towards the quality of the e-learning process during the pandemic. Given the identified problems and positive aspects, the results of the study can be used to further develop a strategy for the digitalization of teacher education. The study proved that effective digitalization of the educational process is possible under the condition of cooperative and systematic work of all participants.