Reviewerly/exHarmony huggingface dataset:
Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth

Graph Neural Networks (GNNs) have been studied through the lens of expressive power and generalization. However, their optimization properties are less well understood. We take the first step towards analyzing GNN training by studying the gradient dynamics of GNNs. First, we analyze linearized GNNs and prove that despite the non-convexity of training, convergence to a global minimum at a linear rate is guaranteed under mild assumptions that we validate on real-world graphs. Second, we study what may affect the GNNs' training speed. Our results show that the training of GNNs is implicitly accelerated by skip connections, more depth, and/or a good label distribution. Empirical results confirm that our theoretical results for linearized GNNs align with the training behavior of nonlinear GNNs. Our results provide the first theoretical support for the success of GNNs with skip connections in terms of optimization, and suggest that deep GNNs with skip connections would be promising in practice.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si8.svg"><mml:mrow><mml:mn>3</mml:mn><mml:mi>D</mml:mi></mml:mrow></mml:math> object recognition through a size function resulting from an invariant topological feature

• The 3 D object is described by a size function. • The size function resulted from a measure function that encompasses invariance to affine transformations, articulations, and torsion. • The proposed method tackles with the problem of partial matching of 3D objects. In this paper, a critical points based descriptor for 3 D objects recognition is presented. It is based on the topological invariant provided by the critical points of the 3 D object. The critical points and the links between them are represented by a size function resulting from a measure function that captures the surface displacement along the 3 D object, and that encompasses invariance to affine transformations, articulations and torsions. In order to tackle the problems of partial matching of the 3 D objects, a well-suited metric learning method is used to weight the matchings according to their relevance. The proposed method’s performance was validated by different collections of 3 D objects. The obtained scores are favorably comparable to the related work.
Quantum Energy Landscape and VQA Optimization

We study the effects of entanglement and control parameters on the energy landscape and optimization performance of the variational quantum circuit. Through a systematic analysis of the Hessian spectrum, we characterize the local geometry of the energy landscape at a random point and along an optimization trajectory. We argue that decreasing the entangling capability and increasing the number of circuit parameters have the same qualitative effect on the Hessian eigenspectrum. Both the low-entangling capability and the abundance of control parameters increase the curvature of non-flat directions, contributing to the efficient search of area-law entangled ground states as to the optimization accuracy and the convergence speed.
Content‐augmented feature pyramid network with light linear spatial transformers for object detection

As one of the prevalent components, feature pyramid network (FPN) is widely used in current object detection models for improving multi-scale object detection performance. However, its feature fusion mode is still in a misaligned and local manner, thus limiting the representation power. To address the inherited defects of FPN, a novel architecture termed content-augmented feature pyramid network (CA-FPN) is proposed in this paper. Firstly, a global content extraction module (GCEM) is proposed to extract multi-scale context information. Secondly, lightweight linear spatial Transformer connections are added in the top-down pathway to augment each feature map with multi-scale features, where a linearized approximate self-attention function is designed for reducing model complexity. By means of the self-attention mechanism in Transformer, it is no longer needed to align feature maps during feature fusion, thus solving the misaligned defect. By setting the query scope to the entire feature map, the local defect can also be solved. Extensive experiments on COCO and PASCAL VOC datasets demonstrated that the CA-FPN outperforms other FPN-based detectors without bells and whistles and is robust in different settings.
Topological Conditional Separation

Pearl's d-separation is a foundational notion to study conditional independence between random variables. We define the topological conditional separation and we show that it is equivalent to the d-separation, extended beyond acyclic graphs, be they finite or infinite.
Exploiting Reused-Based Sharing Work Opportunities in Big Data Multiquery Optimization with Flink

Multiquery optimization is fundamental to retrieve data from different sources in a specific time frame to fulfill the credibility of the big data applications. To avoid high-cost inputs or outputs operations over large-scale data, exploiting sharing opportunities including (join, aggregation, and sort) is helpful to improve the performance of the multiple queries. Furthermore, considering the in-memory big data platforms such as Flink is essential to enhance the performance of the multiple queries. We have extended our previous proposed system, Multi-Query Optimization using Tuple Size and Histogram (MOTH), to exploit sharing work among multiquery, including join, aggregation, and sort. The comprehensive system, called the Join-Aggregation-Sort (JAS)-MOTH system, is used to minimize the data in-network movement time, that is, shuffle time needed to transfer intermediate data. The proposed system is introduced by developing two additional modules to investigate the sharing work; query explorer and JAS-MOTH optimizer, including the sort exploiter module. The JAS-MOTH system can exploit the shared explicit and implicit sorts among multiple sorts and aggregation queries. The proposed system refines the pipelined multiway join execution for multiple join queries by considering coarse-grained sharing data, join ordering, joining pipelining, and shared implicit sorts. Furthermore, it introduces an end-to-end multiway join optimizer over Flink. An elaborated experimental comparison of the naive and state-of-art techniques among a broader class of queries (i.e., join, aggregation, and sort) is presented. Given the consideration of the findings based on Flink, the proposed system has improved the query execution time regarding the naive and state-of-art techniques by 47% and 30%, respectively. For intermediate data size, the JAS-MOTH system has reduced the intermediate data size reduction concerning the naive and state-of-art techniques by 50% and 31% on average, respectively, over Hadoop-like infrastructures.
Rapidly-Exploring Random Graph Next-Best View Exploration for Ground Vehicles

In this paper, a novel approach is introduced which utilizes a Rapidly-exploring Random Graph to improve sampling-based autonomous exploration of unknown environments with unmanned ground vehicles compared to the current state of the art. Its intended usage is in rescue scenarios in large indoor and underground environments with limited teleoperation ability. Local and global sampling are used to improve the exploration efficiency for large environments. Nodes are selected as the next exploration goal based on a gain-cost ratio derived from the assumed 3D map coverage at the particular node and the distance to it. The proposed approach features a continuously-built graph with a decoupled calculation of node gains using a computationally efficient ray tracing method. The Next-Best View is evaluated while the robot is pursuing a goal, which eliminates the need to wait for gain calculation after reaching the previous goal and significantly speeds up the exploration. Furthermore, a grid map is used to determine the traversability between the nodes in the graph while also providing a global plan for navigating towards selected goals. Simulations compare the proposed approach to state-of-the-art exploration algorithms and demonstrate its superior performance.
Sharing the Science of Agricultural Practice: Information Transfer via the Annual Reports of the United States Department of Agriculture, 1862–1868

The information work at the U.S. Department of Agriculture not only altered the kind and amount of information farmers had access to, but effectively sought to redefine who the experts were: through the production and dissemination of research conducted by scientists at the Department, or work by others filtered through the institution. One vehicle for this information transfer was the annual report. This study identifies and examines the information work of the agency from 1862–1868 and situates it within the context of the emergence of the modern state and the history of information.It is conceded on all hands that the farming interest is the basis of all other interests and the primary source of national prosperity. The outlines of the rise and decay of the Roman empire could have been written in the fields which environed the capitol as well as in her libraries amid historical records.Report of Mr. Owen Lovejoy (IL) from the House Committee on Agriculture, February 11, 1862. (Rives et al., 1862 Rives, J. C., Blair, F. P., Rives, F., & Bailey, G. A. (1862). The Congressional Globe. Blair & Rives. [Google Scholar], p. 856)[The American farmer] belongs to a class of citizens who hold in their hands five-sixths of the wealth of the country and its entire political power; and the hands which have wrought this wealth are able to defend the Constitution which makes us one people.Isaac Newton, Commissioner of Agriculture, January 1, 1863. (United States Department of Agriculture, 1863 United States Department of Agriculture. (1863). Report of the Commissioner of Agriculture for the year 1862. U.S. Government Printing Office. [Google Scholar], p. 14)
Interactive Cross-language Code Retrieval with Auto-Encoders

Cross-language code retrieval is necessary in many real-world scenarios. A major application is program translation, e.g., porting codebases from an obsolete or deprecated language to a modern one or re-implementing existing projects in one’s preferred programming language. Existing approaches based on the translation model require large amounts of training data and extra information or neglects significant characteristics of programs. Leveraging cross-language code retrieval to assist automatic program translation can make use of Big Code. However, existing code retrieval systems have the barrier to finding the translation with only the features of the input program as the query. In this paper, we present BigPT for interactive cross-language retrieval from Big Code only based on raw code and reusing the retrieved code to assist program translation. We build on existing work on cross-language code representation and propose a novel predictive transformation model based on auto-encoders. The model is trained on Big Code to generate a target-language representation, which will be used as the query to retrieve the most relevant translations for a given program. Our query representation enables the user to easily update and correct the returned results to improve the retrieval process. Our experiments show that BigPT outperforms state-of-the-art baselines in terms of program accuracy. Using our novel querying and retrieving mechanism, BigPT can be scaled to the large dataset and efficiently retrieve the translation.
Categorising and clustering knowledge in fuzzy cognitive maps

The literature on managing environmental hazards in complex human-natural systems increasingly acknowledges the importance of integrating diverse stakeholder mental models into decision-making. Participatory Fuzzy Cognitive Mapping (FCM) provides an effective tool in this process, as it allows representation of mental models as complex causal networks that aid in the study of knowledge and understandings. While most participatory FCM research has studied mental model variation using graph theory and other structural metrics, our goal is to demonstrate a generalisable approach for analysing perspectives and content. We use a novel method of knowledge categorisation to identify variation among stakeholder mental models and explore its implications for social learning and collaboration. In our case study of flood managers in Boston, Massachusetts, our findings include identification of knowledge gaps, differing priorities among individuals and across jurisdictional scales and opportunities for learning and collaboration.
Classification of Breast Tumor Using Ensemble Learning

Breast Cancer is a common cancer occuring among women throughout the entire world. This attributes to a majority of deaths and cases in the global statistics, which makes it a significant health problem in our society. This paper showcases the classification of breast cancer tumors and the different boosting/ensemble techniques which are used for a more accurate classification. This research presents the working and comparison between classifiers such as Bagging Meta-Estimator, ADA-Boost and XGBM. We will also be showcasing the comparison between the results of these classifiers/techniques and conclude which one is the most accurate in the classification process.
Investigating into phishing risk behaviour among healthcare staff: The phish or the patient? (Preprint)

<sec> <title>BACKGROUND</title> Through phishing attacks, the memory of the negative impact of security breaches in healthcare will remain with the inflicted for long. The healthcare sector is mostly associated with time sensitivity and the urgency to access patients’ records, especially during emergency situations. This creates a sense of force for management to opt for a ransom payment. Phishing is one of the less difficult ways to circumvent sophisticated technical security measures and it is used to exploit the psychological and other factors of the healthcare system users to succeed in the ransomware attack. </sec> <sec> <title>OBJECTIVE</title> Motivated by these, this study empirically examined the phishing susceptibility level among healthcare staff in Ghana. The study seeks to determine phishing appraisal threat levels among healthcare staff as well as their ability to resist phishing baits. Such knowledge would help hospital authorities to adopt better strategies to improve upon the security practice of the healthcare staff in a way towards mitigating real potential attacks </sec> <sec> <title>METHODS</title> Guided with the state-of-the-arts in phishing simulated study in healthcare and after deeply assessing the ethical dilemmas, an SMS-based phishing simulation was conducted among healthcare workers in Ghana. The study adopted an in-the-wild study approach alongside quantitative and qualitative surveys. </sec> <sec> <title>RESULTS</title> From the state-of-the-art studies, the in-the-wild study approach was commonly used as compared to laboratory-based experiments and statistical surveys because its findings are generally reliable and effective. The attack results showed that more than half of the targeted healthcare staff (61%) were susceptible to the attack. A survival bias phone call made to the participants showed that some of the healthcare staff were not victims. They prioritized patient care whereas other staff were victims because they were distracted by patient care. This raised a vital question as to what caused the susceptibility; is it the patient or the bait in the phishing? The intended phishing behavior risk of healthcare workers was generally lower than their actual behavior. A correlation between perception and work factor variables showed that perceived barrier was a predictor of self-reported intended phishing behaviour among healthcare staff, workload significantly predicted self-efficacy risk (r=0.494, p-value=0.05) and work emergency predicted perceived barrier risk in the reverse direction at a significant level of r=-0.401, p-value=0.05. </sec> <sec> <title>CONCLUSIONS</title> Based on the results, the study outlined various study implications including phishing security training, awareness and incentives with future directions for the scientific community to explore. </sec>
Exploration of Ontological Representations for Evolutionary Computation

This research explores the utility of ontological representations using object-oriented (OO) design principles, such that characteristics of the problem domain are directly mapped onto the representation of individuals. A comparison against more traditional representations is performed in two problem domains of differing complexity: (i) Tangram, a simple geometric puzzle; and (ii) EvoRecSys, an evolutionary recommender system for health and well-being advice. We show that OO representations aid research and development as naturally decoupled components can be more easily modified and extended, which can in turn lead to the discovery of better solutions.
Modeling cryptographic algorithms validation and developing block ciphers with electronic code book for a control system at nuclear power plants

Nuclear power plants have recognized the importance of nuclear cybersecurity. Based on regulatory guidelines and security-related standards issued by regulatory agencies around the world including IAEA, NRC, and KINAC, nuclear operating organizations and related systems manufacturing organizations, design companies, and regulatory agencies are considering methods to prepare for nuclear cybersecurity. Cryptographic algorithms have to be developed and applied in order to meet nuclear cybersecurity requirements. This paper presents methodologies for validating cryptographic algorithms that should be continuously applied at the critical control system of I&C in NPPs. Through the proposed schemes, validation programs are developed in the PLC, which is a critical system of a NPP’s I&C, and the validation program is verified through simulation results. Since the development of a cryptographic algorithm validation program for critical digital systems of NPPs has not been carried out, the methodologies proposed in this paper could provide guidelines for Cryptographic Module Validation Modeling for Control Systems in NPPs. In particular, among several CMVP, specific testing techniques for ECB mode-based block ciphers are introduced with program codes and validation models.
The Moral Discourse of Free Speech: A Virtual Ethnographic Study

Freedom of speech has long been considered an essential value in democracies. However, its boundaries concerning hate speech continue to be contested across many social and political spheres, including governments, social media websites, and university campuses. Despite the recent growth of so-called free speech communities online and offline, little empirical research has examined how individuals embedded in these communities make moral sense of free speech and its limits. Examining these perspectives is important for understanding the growing involvement and polarization around this issue. Using a digital ethnographic approach, I address this gap by analyzing discussions in a rapidly growing online forum dedicated to free speech (r/FreeSpeech subreddit). I find that most users on the forum understand free speech in an absolutist sense (i.e., it should be free from legal, institutional, material, and even social censorship or consequences), but that users differ in their arguments and justifications concerning hate speech. Some downplay the harms of hate speech, while others acknowledge its harms but either focus on its epistemic subjectivity or on the moral threats of censorship and authoritarianism. Further, the forum appears to have become more polarized and right-wing-dominated over time, rife with ideological tensions between members and between moderators and members. Overall, this study highlights the variation in free speech discourse within online spaces and calls for further research on free speech that focuses on first-hand perspectives.
Evaluation Model of Teaching Quality based on Algorithm of Stepwise Regression Analysis

The traditional AHP-based evaluation model of teacher teaching quality is unreasonable for the weight distribution of evaluation factors of teaching quality. The evaluation model of teacher teaching quality based on algorithm of stepwise regression analysis is constructed. According to the idea of the stepwise regression analysis, the correlation coefficient augmentation matrix, the calculation variance contribution, the proposed variables and the introduced variables are calculated. The key factors affecting the teaching quality are analyzed to find an effective way to improve the teaching quality. On this basis, the Logistic regression model is applied to classify the teaching quality and calculate the probability that teachers reach different levels to achieve the evaluation of teachers' teaching quality. The experimental results show that the constructed model can make rapid and accurate evaluation of the teaching quality of university teachers, and provides reference for the improvement of teachers' teaching quality.
Towards Quantum Scalable Data for Heterogeneous Computing Environments

Data scalability is a crucial characteristic in various classical applications. In this study, we introduce the concept of quantum scalable data. A hybrid quantum-classical architecture is proposed to support quantum scalable data, which includes scalable data definition, associated metadata, and operations in both classical and quantum parts. As a proof of concept, experiments of quantum machine learning are carried out with quantum scalable data. It is found that when more data layers are retained, classification performance will be increased. Also, scalability implementation of quantum data is much more straightforward than that of classical data. This could be considered an advantage of quantum data compared to classical data. To the best of our knowledge, this is the first work that introduces and experimentally proves the concept of quantum scalable data.
DeepPicarMicro: Applying TinyML to Autonomous Cyber Physical Systems

Running deep neural networks (DNNs) on tiny Micro-controller Units (MCUs) is challenging due to their limitations in computing, memory, and storage capacity. Fortunately, recent advances in both MCU hardware and machine learning software frameworks make it possible to run fairly complex neural networks on modern MCUs, resulting in a new field of study widely known as TinyML. However, there have been few studies to show the potential for TinyML applications in cyber physical systems (CPS).In this paper, we present DeepPicarMicro, a small self-driving RC car testbed, which runs a convolutional neural network (CNN) on a Raspberry Pi Pico MCU. We apply a state-of-the-art DNN optimization to successfully fit the well-known PilotNet CNN architecture, which was used to drive NVIDIA’s real self-driving car, on the MCU. We apply a state-of-art network architecture search (NAS) approach to find further optimized networks that can effectively control the car in real-time in an end-to-end manner. From an extensive systematic experimental evaluation study, we observe an interesting relationship between the accuracy, latency, and control performance of a system. From this, we propose a joint optimization strategy that takes both accuracy and latency of a model in the network architecture search process for AI enabled CPS.