Reviewerly/exHarmony huggingface dataset:
Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP

As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multi-dimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.
The Virtual reality electrical substation field trip: Exploring student perceptions and cognitive learning

<p style="text-indent:20px;">COVID19 has disrupted many higher education's learning experiences, including those related to work integrated learning. This included the cancelling of the annual electrical engineering field trip to a local electrical substation. Field trips provides students an opportunity to connect their classroom learning with industry relevant engaging experiences. While virtual reality (VR) alternatives to electrical substations have been implemented and researched, the focus has been on the innovation and not on the educational benefits. The impact on learning is not well documented and understood. To address this gap an experimental study is conducted on fifty electrical engineering students at the University of Wollongong to determine if a VR replica of an electrical substation can provide an equal or better learning and student experience compared to traditional methods. A successful finding would provide confidence to implement such alternatives for situations that include: addressing COVID disruptions; for students that miss the field trip; and for providers that don't have the funds or resources to visit a substation. It was found that the VR substation simulation provided a comparable student experience and stronger cognitive learning benefits than traditional methods. Further research is needed to explore learning impact beyond the cognitive domain.
Hierarchical Decomposed-Objective Model Predictive Control for Autonomous Casualty Extraction

In recent years, several robots have been developed and deployed to perform casualty extraction tasks.However, the majority of these robots are overly complex, and require teleoperation via either a skilled operator or a specialised device, and often the operator must be present at the scene to navigate safely around the casualty.Instead, improving the autonomy of such robots can reduce the reliance on expert operators and potentially unstable communication systems, while still extracting the casualty in a safe manner.There are several stages in the casualty extraction procedure, from navigating to the location of the emergency, safely approaching and loading the casualty, to finally navigating back to the medical assistance location.In this paper, we propose a Hierarchical Decomposed-Objective based Model Predictive Control (HiDO-MPC) method for safely approaching and manoeuvring around the casualty.We implement this controller on ResQbot -a proof-of-concept mobile rescue robot we previously developed -capable of safely rescuing an injured person lying on the ground, i.e. performing the casualty extraction procedure.HiDO-MPC achieves the desired casualty extraction behaviour by decomposing the main objective into multiple sub-objectives with a hierarchical structure.At every time step, the controller evaluates this hierarchical decomposed objective and generates the optimal control decision.We have conducted a number of experiments both in simulation and using the real robot to evaluate the proposed method's performance, and compare it with baseline approaches.The results demonstrate that the proposed control strategy gives significantly better results than baseline approaches in terms of accuracy, robustness, and execution time, when applied to casualty extraction scenarios.
How fragmentation can undermine the public health response to Covid-19

Responses to COVID-19 have largely been led by local, national, and international public health agencies, who have activated their pandemic plans and opened the epidemiological toolkit of modelling, testing, isolation and movement restrictions, surveillance, and contact tracing. In the contemporary tech-heavy world, many assumed that the common manual process of human investigators and phone calls could or should be replaced by digital solutions. But it's not as simple as "add more technology" - the complex way in which users and societies interact with the technology has significant impacts on effectiveness. When efforts are not well co-ordinated, fragmentation in system design and user experience can negatively impact the public health response. This article briefly covers the journey of how contact tracing registers and digital diaries evolved in New Zealand during the COVID-19 pandemic, the initial poor outcomes caused by the lack of central co-ordination, and the later improvement.
Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm

Abstract Magnetorheological (MR) fluid is among the smart materials that can change its default properties with the influence of a magnetic field. Typical application of an MR fluid based device involves an adjustable damper which is commercially known as an MR fluid damper. It is used in vibration control as an isolator in vehicles and civil engineering applications. As part of the device development process, proper understanding of the device properties is essential for reliable device performance analysis. This study introduce an accurate and fast prediction model to analyse the dynamic characteristics of the MR fluid damper. This study proposes a new modelling technique called Extreme Learning Machine (ELM) to predict the dynamic behaviour of an MR fluid damper hysteresis loop. This technique was adopted to overcome the limitations of the existing models using Artificial Neural Networks (ANNs). The results indicate that the ELM is extremely faster than ANN, with the capability to produce high accuracy prediction performance. Here, the hysteresis loop, which represents the relationship of force-displacement for the MR fluid damper, was modelled and compared using three different activation functions, namely, sine, sigmoid and hard limit. Based on the results, it was found that the prediction performance of ELM model using the sigmoid activation functions produced highest accuracy, and the lowest Root Mean Square Error (RMSE).
Neural Networks with Improved Extreme Learning Machine for Demand Prediction of Bike-sharing

Abstract Accurate demand prediction of bike-sharing is an important prerequisite to reducing the cost of scheduling and improving the user satisfaction. However, it is a challenging issue due to stochasticity and non-linearity in bike-sharing systems. In this paper, a model called pseudo-double hidden layer feedforward neural networks is proposed to approximately predict actual demands of bike-sharing. Specifically, to overcome limitations in traditional back-propagation learning process, an algorithm, an extreme learning machine with improved particle swarm optimization, is designed to construct learning rules in neural networks. The performance is verified by comparing with other learning algorithms on the dataset of Streeter Dr bike-sharing station in Chicago.
Quantum Free-Start Collision Attacks on Double Block Length Hashing with Round-Reduced AES-256

Recently, Hosoyamada and Sasaki (EUROCRYPT 2020), and Xiaoyang Dong et al. (ASIACRYPT 2020) proposed quantum collision attacks against AES-like hashing modes AES-MMO and AES-MP. Their collision attacks are based on the quantum version of the rebound attack technique exploiting the differential trails whose probabilities are too low to be useful in the classical setting but large enough in the quantum setting. In this work, we present dedicated quantum free-start collision attacks on Hirose’s double block length compression function instantiated with AES-256, namely HCF-AES-256. The best publicly known classical attack against HCF-AES-256 covers up to 9 out of 14 rounds. We present a new 10-round differential trail for HCF-AES-256 with probability 2−160, and use it to find collisions with a quantum version of the rebound attack. Our attack succeeds with a time complexity of 285.11 and requires 216 qRAM in the quantum-attack setting, where an attacker can make only classical queries to the oracle and perform offline computations. We also present a quantum free-start collision attack on HCF-AES-256 with a time complexity of 286.07 which outperforms Chailloux, Naya-Plasencia, and Schrottenloher’s generic quantum collision attack (ASIACRYPT 2017) in a model when large qRAM is not available.
Special Issue on Digital Pathology, Tissue Image Analysis, Artificial Intelligence, and Machine Learning: Approximation of the Effect of Novel Technologies on Toxicologic Pathology

For decades, it has been postulated that digital pathology is the future. By now it is safe to say that we are living that future. Digital pathology has expanded into all aspects of pathology, including human diagnostic pathology, veterinary diagnostics, research, drug development, regulatory toxicologic pathology primary reads, and peer review. Digital tissue image analysis has enabled users to extract quantitative and complex data from digitized whole-slide images. The following editorial provides an overview of the content of this special issue of Toxicologic Pathology to highlight the range of key topics that are included in this compilation. In addition, the editors provide a commentary on important current aspects to consider in this space, such as accessibility of publication content to the machine learning-novice pathologist, the importance of adequate test set selection, and allowing for data reproducibility.
Fully neural object detection solutions for robot soccer

Abstract RoboCup is one of the major global AI events, gathering hundreds of teams from the world’s best universities to compete in various tasks ranging from soccer to home assistance and rescue. The commonality of these three seemingly dissimilar tasks is that in order to perform well, the robot needs to excel at the all major AI tasks: perception, control, navigation, strategy and planning. In this work, we focus on the first of these by presenting what is—to our knowledge—the first fully neural vision system for the Nao robot soccer. This is a challenging task, mainly due to the limited computational capabilities of the Nao robot. In this paper, we propose two novel neural network architectures for semantic segmentation and object detection that ensure low-cost inference, while improving accuracy by exploiting the properties of the environment. These models use synthetic transfer learning to be able to learn from a low number of hand-labeled images. The experiments show that our models outperform state-of-the-art methods such as Tiny YOLO at a fraction of the cost.
Effective entanglement recovery via operators

Abstract Entanglement is one of essential quantum resources in quantum information processing, playing an important role during quantum communication and computation. While any system unavoidably interacts with its surrounding environment, this thus will result in decoherence or dissipation effect. With this in mind, a natural question arises: how to recover quantum entanglement under decoherence inducing by the environmental noises. In this work, our attempt is to propose an effective strategy to optimally achieve entanglement recovery. Specifically, we derive a nontrivial condition satisfied by any operation U , i.e. <?CDATA ${\left(U\otimes \mathbb{I}\right)}^{{\dagger}}\left(\tilde {U}\otimes \mathbb{I}\right)=f\left(U\right)\left(\mathbb{I}\otimes \mathbb{I}\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>U</mml:mi> <mml:mo>⊗</mml:mo> <mml:mi mathvariant="double-struck">I</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mo>†</mml:mo> </mml:mrow> </mml:msup> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mo>̃</mml:mo> </mml:mover> </mml:mrow> <mml:mo>⊗</mml:mo> <mml:mi mathvariant="double-struck">I</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:mi>f</mml:mi> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi>U</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mi mathvariant="double-struck">I</mml:mi> <mml:mo>⊗</mml:mo> <mml:mi mathvariant="double-struck">I</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:math> , which will definitely recover the entanglement of an arbitrary two-qubit system, and more importantly offer an explicit physical explanation towards the intrinsic mechanics of the entanglement recovery. By means of derivations, we obtain the appropriate operational strength to realize optimal entanglement recovery through taking advantage of our proposed methods. As illustrations, we apply some local operations complying with our presented strategy on validly protecting entanglement, including <?CDATA $\mathcal{PT}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="script">PT</mml:mi> </mml:math> -symmetry operation, quantum weak measurement and quantum reversal measurement.
A Turkish Question Answering System Based on Deep Learning Neural Networks

In the domain of Natural Language Processing (NLP), despite the progress made for some common languages, difficulties persist for many others for the completion of particular NLP tasks.In this scope, the current study aims to explore these challenges by proposing a question answering (QA) system in the Turkish language.In particular, the system will generate the best answers in terms of content and length from questions that are based on a set of documents related to the banking sector.In order to achieve this goal, the system utilizes advanced artificial intelligence algorithms and large data sets.More specifically, BERT algorithm is used for the generation of the language model, followed by a fine-tuning procedure for performing a machine reading for question answering (MRQA) task.In this work, various experiments were conducted using original and translated data sets in an effort to solve the challenges that arise from morphologically complex languages as Turkish.Finally, the system achieved a performance that overall is applicable to a wider range than any other QA system in the Turkish language.The proposed methodology is not only proper to the Turkish language, but can also be adapted to any other language for performing various NLP tasks.
Deep Network Quantization via Error Compensation

For portable devices with limited resources, it is often difficult to deploy deep networks due to the prohibitive computational overhead. Numerous approaches have been proposed to quantize weights and/or activations to speed up the inference. Loss-aware quantization has been proposed to directly formulate the impact of weight quantization on the model’s final loss. However, we discover that, under certain circumstances, such a method may not converge and end up oscillating. To tackle this issue, we introduce a novel loss-aware quantization algorithm to efficiently compress deep networks with low bit-width model weights. We provide a more accurate estimation of gradients by leveraging the Taylor expansion to compensate for the quantization error, which leads to better convergence behavior. Our theoretical analysis indicates that the gradient mismatch issue can be fixed by the newly introduced quantization error compensation term. Experimental results for both linear models and convolutional networks verify the effectiveness of our proposed method.
Machine learning approach to vulnerability detection in OAuth 2.0 authentication and authorization flow

Abstract Technologies for integrating enterprise web applications have improved rapidly over the years. The OAuth framework provides authentication and authorization using the users’ profile and credentials in an existing identity provider. This makes it possible for attackers to exploit any vulnerability arising from exchange of data with the provider. Vulnerability in OAuth authorization flow allows an attacker to alter the normal flow sequence of the OAuth protocol. In this paper, a machine learning-based approach was applied in the detection of potential vulnerability in the OAuth authentication and authorization flow by analyzing the relationship between changes in the OAuth parameters and the final output. This research models the OAuth protocol as a supervised learning problem where seven classification models were developed, tuned and evaluated. Exploratory Data Analytics (EDA) techniques were applied in the extraction and analysis of specific OAuth features so that each output class could be evaluated to determine the effect of the identified OAuth features. The models developed in this research were trained, tuned and tested. A performance accuracy above 90% was attained for detection of vulnerabilities in the OAuth authentication and authorization flow. Comparison with known vulnerability resulted in a 54% match.
The last CPU

Since the end of Dennard scaling and Moore's Law have been foreseen, specialized hardware has become the focus for continued scaling of application performance. Programmable accelerators such as smart memory, smart disks, and smart NICs are now being integrated into our systems. Many accelerators can be programmed to process their data autonomously and require little or no intervention during normal operation. In this way, entire applications are offloaded, leaving the CPU with the minimal responsibilities of initialization, coordination and error handling.
Predicting Weather Forecasting State Based on Data Mining Classification Algorithms

Weather forecasting is the process of predicting the status of the atmosphere for certain regions or locations by utilizing recent technology. Thousands of years ago, humans tried to foretell the weather state in some civilizations by studying the science of stars and astronomy. Realizing the weather conditions has a direct impact on many fields, such as commercial, agricultural, airlines, etc. With the recent development in technology, especially in the DM and machine learning techniques, many researchers proposed weather forecasting prediction systems based on data mining classification techniques. In this paper, we utilized neural networks, Naïve Bayes, random forest, and K-nearest neighbor algorithms to build weather forecasting prediction models. These models classify the unseen data instances to multiple class rain, fog, partly-cloudy day, clear-day and cloudy. These model performance for each algorithm has been trained and tested using synoptic data from the Kaggle website. This dataset contains (1796) instances and (8) attributes in our possession. Comparing with other algorithms, the Random forest algorithm achieved the best performance accuracy of 89%. These results indicate the ability of data mining classification algorithms to present optimal tools to predict weather forecasting.
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution

Event coreference resolution is an important research problem with many applications.Despite the recent remarkable success of pretrained language models, we argue that it is still highly beneficial to utilize symbolic features for the task.However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain errors.Also, depending on the specific context, some features can be more informative than others.Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features.Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016. 1
Quantum Coarse Graining for Extreme Dimension Reduction in Modeling Stochastic Temporal Dynamics

Stochastic modelling of complex systems plays an essential, yet often computationally intensive role across the quantitative sciences. Recent advances in quantum information processing have elucidated the potential for quantum simulators to exhibit memory advantages for such tasks. Heretofore, the focus has been on lossless memory compression, wherein the advantage is typically in terms of lessening the amount of information tracked by the model, while -- arguably more practical -- reductions in memory dimension are not always possible. Here we address the case of lossy compression for quantum stochastic modelling of continuous-time processes, introducing a method for coarse-graining in quantum state space that drastically reduces the requisite memory dimension for modelling temporal dynamics whilst retaining near-exact statistics. In contrast to classical coarse-graining, this compression is not based on sacrificing temporal resolution, and brings memory-efficient, high-fidelity stochastic modelling within reach of present quantum technologies.
Attention Calibration for Transformer in Neural Machine Translation

Attention mechanisms have achieved substantial improvements in neural machine translation by dynamically selecting relevant inputs for different predictions.However, recent studies have questioned the attention mechanisms' capability for discovering decisive inputs.In this paper, we propose to calibrate the attention weights by introducing a mask perturbation model that automatically evaluates each input's contribution to the model outputs.We increase the attention weights assigned to the indispensable tokens, whose removal leads to a dramatic performance decrease.The extensive experiments on the Transformer-based translation have demonstrated the effectiveness of our model.We further find that the calibrated attention weights are more uniform at lower layers to collect multiple information while more concentrated on the specific inputs at higher layers.Detailed analyses also show a great need for calibration in the attention weights with high entropy where the model is unconfident about its decision 1 .