Reviewerly/exHarmony huggingface dataset:
Multiphoton and Side-Channel Attacks in Mistrustful Quantum Cryptography

New types of attacks on practical implementations of mistrustful quantum cryptographic schemes are identified, showing that countermeasures previously believed to ensure security in fact can leave schemes completely vulnerable.
Modality-wise relational reasoning for one-shot sensor-based activity recognition

Deep learning concepts have been successfully transferred from the computer vision task to that of wearable human activity recognition (HAR) over the last few years. However, deep learning models require a large volume of annotated samples to be efficiently trained, while adding new activities results in training the whole network from scratch. In this paper, we study the use of one-shot learning techniques based on high-level features extracted by deep neural networks that rely on convolutional layers. Using these feature vectors as input we measure the similarity of two activities by computing their Euclidean distance, cosine similarity or applying self-attention to perceive the relations between the signals. We evaluate four different one-shot learning approaches using two publicly available HAR datasets, by keeping out of the training set several activity classes. Our results demonstrate that the model relying on modality-wise relational reasoning surpasses the other three, achieving 94.8% and 84.41% one-shot accuracy on UCL and PAMAP2 dataset respectively, while we demonstrate the model’s sensitivity on fusing sensor modalities and provide explainable attention maps to display the modality-wise similarities.
Privacy risk in contact tracing systems

For over a century, contact tracing has been an integral public health strategy for infectious disease control when there is no pharmaceutical treatment. Contact tracing for the coronavirus disease COVID-19 introduced a variety of automated methods deployed across several countries. The present paper examines privacy risk to infected persons and their physical contacts in contact tracing systems. Automated contact tracing systems implemented during the early months of COVID-19 are compared to conventional manual methods. Solove’s taxonomy of privacy is applied to examine privacy risks in both conventional and automated contact tracing systems. As a method of epidemiological surveillance, all contact tracing systems inherently incur privacy risk. However, compared to conventional methods, automated contact tracing systems amplify privacy risk with pre-emptive data collection on all app users, regardless of exposure to an infectious disease; continuous, granular data collection on all users’ location and proximity contacts; insecurities in proximity app technologies and interconnectivity; and in many cases, the use of centralised systems. Reducing these risk factors can reduce privacy harms, such as identification, distortion, secondary use, stigma, and social control.
A Survey of Correlated High Utility Pattern Mining

Pattern mining is an unsupervised data mining approach aims to find interesting patterns that can be used to support decision-making. High Utility Pattern Mining (HUPM) aims to extract patterns having high utility or importance which has broad applications in domains such as market basket analysis, product recommendation, bioinformatics, e-learning, text mining, and web click stream analysis. However, it has several limitations on real life scenarios; as a consequence, many extensions of HUPM appeared in the literature such as Correlated High Pattern Mining, Incremental Utility Mining, On-Shelf High Utility Pattern Mining, and Concise Representations of High Utility Patterns. The Correlated High Utility Pattern Mining aims to extract interesting high utility patterns by utilizing both Utility and Correlation measures. Several algorithms have been proposed to mine the correlated high utility patterns. These algorithms differ in the measures used to evaluate the interestingness of the patterns, data structures and pruning properties which they use to improve the mining performance. This paper presents a detailed survey on correlated high utility pattern mining, their methods, measures, data structures and pruning properties.
Colour-Balanced Edge-Guided Digital Inpainting: Applications on Artworks

The virtual inpainting of artworks provides a nondestructive mode of hypothesis visualization, and it is especially attractive when physical restoration raises too many methodological and ethical concerns. At the same time, in Cultural Heritage applications, the level of details in virtual reconstruction and their accuracy are crucial. We propose an inpainting algorithm that is based on generative adversarial network, with two generators: one for edges and another one for colors. The color generator rebalances chromatically the result by enforcing a loss in the discretized gamut space of the dataset. This way, our method follows the modus operandi of an artist: edges first, then color palette, and, at last, color tones. Moreover, we simulate the stochasticity of the lacunae in artworks with morphological variations of a random walk mask that recreate various degradations, including craquelure. We showcase the performance of our model on a dataset of digital images of wall paintings from the Dunhuang UNESCO heritage site. Our proposals of restored images are visually satisfactory and they are quantitatively comparable to state-of-the-art approaches.
Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing

(2021). Super Resolution for Multi-Sources Image Stream Data Using Smooth and Sparse Tensor Completion and Its Applications in Data Acquisition of Additive Manufacturing. Technometrics. Ahead of Print.
The origin of misconceptions in inorganic chemistry and their correction by computer modelling

Abstract The paper goal was to analyse the typical mistakes in the learning of the university course of inorganic chemistry, determine the origin of misconceptions and estimate the effectiveness of the use of computer simulations to correct false chemical concepts. Ten problems that are the most typical for students were revealed. One of the leading causes of their occurrence is the failure of many students to form mental relationships between different levels of representation of chemical knowledge - microscopic, macroscopic and symbolic. Other reasons include an insufficient understanding of the material, the incompleteness of the knowledge of the microscopic basis of processes, and inability to work with different models, including misunderstanding of their purpose and constraints. NetLogo programming environment was used for students’ self-administering tests to study gas laws. Scope for the usage of NetLogo models was estimated in correcting of incorrectly formed conceptions of the chemical knowledge. Independent work with NetLogo models facilitates the formation of stable relationships between multiple levels of representation of chemical information. It improves an understanding of the studied topic fundamentally, and this holds for all students practically independently of their grounding level in chemistry. The introduction of computer simulation into the practice of teaching chemical subjects shows promise. Still, it requires the solution of several scientific, methodological, logistical and organisational issues.
Chinese UMUX: Cross-Cultural Adaptation for Perceived Usability Measurement

A new Usability Metric for User Experience (UMUX) is translated and validated for native Chinese speakers in this study. The forward-backward translation method is applied to translate the UMUX. The results are optimized through structure-back interviews to obtain a final, unambiguous UMUX version. The Chinese version of the UMUX questionnaire is proven to have high reliability, sensitivity, and effectiveness. The correspondence between the Chinese UMUX version, UMUX-LITE, and SUS is also investigated. The results of this work may provide Chinese usability practitioners with a standardized scale after rigorous testing, as well as a closer understanding of the relationship between the three questionnaires in assisting users to make sound choices.
Investigating potential rooftop solar energy generated by Leased Federal Airports in Australia: Framework and implications

Australia is facing an energy crisis, yet the solar resource is still underutilized. Government's support to small-scale residential solar photovoltaic (PV) systems undermines large-scale rooftop PV systems which are relatively overlooked, and their fully potential remains unexplored. This paper addresses the importance of implementing large-scale rooftop PV systems at airports precinct for energy planning and policy making. This paper aims to investigate the potential of electricity production of rooftop solar PV at 21 Leased Federal Airports (LFAs) in Australia. Specifically, we demonstrated a reproducible methodological framework including: (i) a low-cost data collection of 3D rooftop, (ii) a user-friendly solar radiation modeling in GIS, and (iii) accurate estimation of electricity potential from large-scale airport rooftop solar PV arrays. In addition, a comparison of rooftop solar PV energy productions between the LFAs and a local government area, City of Greater Bendigo, was conducted. The results show that estimated annual electricity potential from large-scale rooftop PV systems at 21 LFAs is 466.68 GWh, and nearly 10 times greater than the annual electricity generated with nearly 17,000 installed residential solar rooftop PV panels in the local government area. If the potential area of LFAs rooftops, currently 2.61 km2 in total, were considered for solar PV, Australia could mitigate the energy crisis significantly to supply the equivalent of 136,000 homes and offset 151.6 kt of GHG emission annually. The outcome of this study not only opens the possibilities for more large-scale solar rooftop PV projects but also aids the authorities in their drive to lessen carbon emissions in the country.
Evaluating Guided Policy Search for Human-Robot Handovers

We evaluate the potential of Guided Policy Search (GPS), a model-based reinforcement learning (RL) method, to train a robot controller for human-robot object handovers.Handovers are a key competency for collaborative robots and GPS could be a promising approach for this task, as it is data efficient and does not require prior knowledge of the robot and environment dynamics.However, existing uses of GPS did not consider important aspects of human-robot handovers, namely large spatial variations in reach locations, moving targets, and generalizing over mass changes induced by the object being handed over.In this work, we formulate the reach phase of handovers as an RL problem and then train a collaborative robot arm in a simulation environment.Our results indicate that GPS is limited in the spatial generalizability over variations in the target location, but that this issue can be mitigated with the addition of local controllers trained over target locations in the high error regions.Moreover, learned policies generalize well over a large range of end-effector masses.Moving targets can be reached with comparable errors using a global policy trained on static targets, but this results in inefficient, high-torque, trajectories.Training on moving targets improves trajectories, but results in worse worst-case performance.Initial results suggest that lower-dimensional state representations are beneficial for GPS performance in handovers.
A method to improve the computational efficiency of the Chan-Vese model for the segmentation of ultrasound images

Advanced image segmentation techniques like the Chan-Vese (CV) models transform the segmentation problem into a minimization problem which is then solved using the gradient descent (GD) optimization algorithm. This study explores whether the computational efficiency of CV can be improved when GD is replaced by a different optimization method. Two GD variants from the literature (Nesterov accelerated, Barzilai-Borwein) and a newly developed hybrid variant of GD were used to improve the computational efficiency of CV by making GD insensitive to local minima. One more variant of GD from the literature (projected GD) was used to address the issue of maintaining the constraint on boundary evolution in CV which also increases computational cost. A novel modified projected GD (Barzilai-Borwein projected GD) was also used to overcome both problems at the same time. The effect of optimization method selection on processing time and the quality of the output was assessed for 25 musculoskeletal ultrasound images (five anatomical areas). The Barzilai-Borwein projected GD method was able to significantly reduce computational time (average(±std.dev.) reduction 95.82 % (±3.60 %)) with the least structural distortion in the delineated output relative to the conventional GD (average(±std.dev.) structural similarity index: 0.91(±0.06)). The use of an appropriate optimization method can substantially improve the computational efficiency of CV models. This can open the way for real-time delimitation of anatomical structures to aid the interpretation of clinical ultrasound. Further research on the effect of the optimization method on the accuracy of segmentation is needed.
Causal Mechanism Transfer Network for Time Series Domain Adaptation in Mechanical Systems

Data-driven models are becoming essential parts in modern mechanical systems, commonly used to capture the behavior of various equipment and varying environmental characteristics. Despite the advantages of these data-driven models on excellent adaptivity to high dynamics and aging equipment, they are usually hungry for massive labels, mostly contributed by human engineers at a high cost. Fortunately, domain adaptation enhances the model generalization by utilizing the labeled source data and the unlabeled target data. However, the mainstream domain adaptation methods cannot achieve ideal performance on time series data, since they assume that the conditional distributions are equal. This assumption works well in the static data but is inapplicable for the time series data. Even the first-order Markov dependence assumption requires the dependence between any two consecutive time steps. In this article, we assume that the causal mechanism is invariant and present our Causal Mechanism Transfer Network (CMTN) for time series domain adaptation. By capturing causal mechanisms of time series data, CMTN allows the data-driven models to exploit existing data and labels from similar systems, such that the resulting model on a new system is highly reliable even with limited data. We report our empirical results and lessons learned from two real-world case studies, on chiller plant energy optimization and boiler fault detection, which outperform the existing state-of-the-art method.
Bi-Tier Differential Privacy for Precise Auction-Based People-Centric IoT Service

With the fast proliferation of device sensing and computing, crowed sensing has become the building block of the Internet of things.Consequently, various data collection and incentive mechanisms are investigated for people-centric services.In this paper, we have investigated the problem of privacyaware people-centric IoT service based on a tailored auction approach.We applied a bi-tier differential privacy methodology on the data collected from crowdsensing IoT devices.A corresponding pricing scheme is also proposed to ensure the property of incentive compatibility, precise service data, and anonymized query results.Comparing to traditional privacy-aware auction schemes which only focus on the cost, our corresponding precise privacy-aware auction scheme provides a tailored IoT service based on the customers' request.The proposed trial query technique is able to provide a precise assessment of service quality, thus improves the efficiency of the people-centric IoT service.The customer could enjoy the convenience of service evaluation before making a bid, while the actual service data is anonymized to guarantee the service providers' interests.We evaluate the proposed bi-tier differential privacy schema for auction-based service by conducting extensive simulations.The experimental results show that our proposed method yields higher data utility and accuracy for the IoT service customers with privacy concerns.
Embodying Pre-Trained Word Embeddings Through Robot Actions

We propose a promising neural network model with which to acquire a grounded representation of robot actions and the linguistic descriptions thereof. Properly responding to various linguistic expressions, including polysemous words, is an important ability for robots that interact with people via linguistic dialogue. Previous studies have shown that robots can use words that are not included in the action-description paired datasets by using pre-trained word embeddings. However, the word embeddings trained under the distributional hypothesis are not grounded, as they are derived purely from a text corpus. In this letter, we transform the pre-trained word embeddings to embodied ones by using the robot's sensory-motor experiences. We extend a bidirectional translation model for actions and descriptions by incorporating non-linear layers that retrofit the word embeddings. By training the retrofit layer and the bidirectional translation model alternately, our proposed model is able to transform the pre-trained word embeddings to adapt to a paired action-description dataset. Our results demonstrate that the embeddings of synonyms form a semantic cluster by reflecting the experiences (actions and environments) of a robot. These embeddings allow the robot to properly generate actions from unseen words that are not paired with actions in a dataset.
Sentiment Analysis on Twitter using Neural Network: Indonesian Presidential Election 2019 Dataset

Abstract Due to the utilization of Twitter by Indonesian politicians ahead of the Indonesian presidential election 2019, Indonesian people have given diverse responses and sentiments to the politicians. This study aims to classify sentiment on Indonesian presidential election 2019 tweet data by using neural network algorithms and obtain the best algorithm. In our study, we train our dataset using some variants of deep neural network algorithms, including Convolutional Neural Network (CNN), Long short-term memory (LSTM), CNN-LSTM, Gated Recurrent Unit (GRU) -LSTM and Bidirectional LSTM. Moreover, as a comparison with our deep learning model, we also train our dataset using other traditional machine learning algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR) and Multinomial Naïve Bayes (MNB). Our experiments showed that Bidirectional LSTM achieved the best performance with the accuracy of 84.60%.
Exploring Open Source Information for Cyber Threat Intelligence

The cyberspace is one of the most complex systems ever built by humans, the utilization of cyber-technology resources are used ubiquitously by many, but sparsely understood by the majority of the users. In the past, cyber attacks were usually orchestrated in a random pattern of attack to lure unsuspecting targets. More evidence has demonstrated that cyber attack knowledge is shared among individuals and hacker forums in the virtual ecosystem. This paper proposes using open source intelligence from the surface web (Twitter) and deep web hacker forums to identify texts related to cyber threats.Our model can provide cybersecurity experts and law enforcement agencies reliable information that can be adopted in developing control and containment strategies for cyberattacks with 82% accuracy. The proposed methodology combines information extracted from the deep web and technical indicators of threats from the surface web. In this paper, we analyzed more than 10 billion records breached in over 8,000 reported cases between 2005 - 2019 in the United States that were obtained from the Privacy Rights Clearinghouse (PRC) Chronology of Data Breaches. Finally, we propose a future research direction on risk profiling for cyberattacks using geo-spatial techniques.
PDA: Proxy-based domain adaptation for few-shot image recognition

Learning from limited supervision is a challenging problem that has recently attracted wide attention in the machine learning community. With scarce annotated samples available in target categories, so-called few-shot image recognition aims to transfer basic knowledge from a large-scale image set to recognize unseen classes. Many existing approaches tend to learn a general source data representation and apply it to address the few-shot task by building a target classifier on scare support features, which performs favorably only if source and target data distributions are similar. We argue that ignoring the distribution gap and directly leveraging frozen representations lead to a sub-optimal solution. Taking domain shift into consideration, we explore an efficient task adaptation strategy that can jointly achieve task and domain transfer. Accordingly, we propose a simple yet effective method, called proxy-based domain adaptation (PDA), to optimize the pre-trained representation and a target classifier simultaneously. PDA can be characterized as: (1) a source-data-independent approach that only leverages few support data from the target domain (2) a non-parametric adaptation method that performs model adaptation by minimizing a designed loss without involving any parametric modules additionally. We extensively conduct experiments on multiple few-shot image recognition benchmarks, highlighting the superiority of PDA over many SOTA methods. Besides, careful ablation studies verify each component's effectiveness in our method and demonstrate the significance of domain adaptation in few-shot image recognition.
Sediment colour as recorder of climate and tectonics: Cenozoic continental red beds of the Himalayan foreland basin in NW India

The Cenozoic Himalayan foreland basin sedimentary facies are widely studied by researchers as they preserve record of complex interplay between tectonics, sedimentation and climate. Palaeoclimate reconstructions from these sediments have been done using various proxy tools. Colour of sedimentary facies is considered as one of the diagnostic palaeoclimate/climate indicator and the red colour is commonly associated to be with hot, arid climate. The Himalayan foreland basin (HFB) sediments from the oldest to the youngest, comprise almost all red and grey shades and the bright red facies greatly fascinate Earth Scientists. These red to grey coloured Himalayan foreland basin sediments preserve a record of global to regional climatic cycles and tectonic pulses. Studies pertaining to red colour and climate, specifically its association with hot, arid climate, are overwhelming. In general, studies focusing on continental red beds with specific climate are contradictory and confusing till date. Paradoxically, we now know more about red colour and climate than about red color and tectonics. Therefore, representative Himalayan foreland basin red beds of different times in the NW Himalaya have been selected for the present study. The HFB red beds of Oligocene, middle Miocene and upper Pliocene age have been investigated to find out any specific relationship of tectonics and/or particular climate cycles with red colour of these sediments on the basis of compilation of morphological, micromorphological, sub-microscopic and geochemical studies. It has been found that the bright red and grey colour shades are reliably shown by finer facies, particularly palaeosols, and coarser facies of the HFB sediments respectively, whether formed in cool, humid conditions or warm, arid conditions. This suggests greater role of tectonics during foreland basin sedimentation for preservation of sediment colour. Also, the purpose of this study is to highlight the need for more such investigations at various temporal and spatial scales in foreland basin sediments so that the colour of sediment can be appropriately used in future investigations dealing with sediment colour as an indicator of specific climate.