Reviewerly/exHarmony huggingface dataset:
Probabilistic modelling of deception-based security framework using markov decision process

Existing studies using deception are ad-hoc attempts and few theoretical models have been designed to plan and integrate deception. We theorise that a pre-planning stage should be a fundamental part to obtain information about the attackers’ behaviours and the attack process by analysing known attacks. This will help plan and take defence actions by actively interacting with the attackers and predicting their actions using a probabilistic approach. This paper proposes a framework that provides a theoretical understanding to plan and integrate deception systematically and strategically. We also present probabilistic modelling to predict attack actions by formalising a real case of attacks captured on simulated Internet of Things devices as an Markov Decision Process (MDP) and verifying related properties using Probabilistic Symbolic Model Checker (PRISM). MDP’s properties verification results reveal that the associated cost for defence actions can be decreased by successfully predicting attackers’ probable actions. Moreover, we identify several quantification metrics (e.g. cost, reward, trust, incentive and penalty) to evaluate the performance of actions performed by attackers and defenders.
Empirical study of future image prediction for image-based mobile robot navigation

Recent image-based robotic systems use predicted future state images to control robots. Therefore, the prediction accuracy of the future state image affects the performance of the robot. To predict images, most previous studies assume that the camera captures the entire scene and that the environment is static. However, in real robot applications, these assumptions do not always hold. For example, if a camera is attached to a mobile robot, its view changes from time to time. In this study, we analyzed the relationship between the performance of the image prediction model and the robot’s behavior, controlled by an image-based navigation algorithm. Through mobile robot navigation experiments using front-faced and omni-directional cameras, we discussed the capabilities of the image prediction models and demonstrated their performance when applied to the image-based navigation algorithm. Moreover, to adapt to the dynamic changes in the environment, we studied the effectiveness of directing the camera to the ceiling. We showed that robust navigation can be achieved without using images from cameras directed toward the front or the floor, because these views can be disturbed by moving objects in a dynamic environment. • Empirical evaluation and comparison of image prediction models on a mobile robot. • Action-conditioned LSTM-based prediction model performs well across camera types. • Omni-directional camera is effective for image prediction models. • Omni-directional camera is effective for image-based mobile robot navigation tasks.
NeoFlow: A Flexible Framework for Enabling Efficient Compilation for High Performance DNN Training

Deep neural networks (DNNs) are increasingly deployed in various image recognition and natural language processing applications.The continuous demand for accuracy and high performance has led to innovations in DNN design and a proliferation of new operators.However, existing DNN training frameworks such as PyTorch and TensorFlow only support a limited range of operators and rely on hand-optimized libraries to provide efficient implementations for these operators.To evaluate novel neural networks with new operators, the programmers have to either replace the holistic new operators with existing operators or provide low-level implementations manually.Therefore, a critical requirement for DNN training frameworks is to provide high-performance implementations for the neural networks containing new operators automatically in the absence of efficient library support.In this paper, we introduce NeoFlow, which is a flexible framework for enabling efficient compilation for high-performance DNN training.NeoFlow allows the programmers to directly write customized expressions as new operators to be mapped to graph representation and low-level implementations automatically, providing both high programming productivity and high performance.First, NeoFlow provides expression-based automatic differentiation to support customized model definitions with new operators.Then, NeoFlow proposes an efficient compilation system that partitions the neural network graph into subgraphs, explores optimized schedules, and generates high-performance libraries for subgraphs automatically.Finally, NeoFlow develops an efficient runtime system to combine the compilation and training as a whole by overlapping their execution.In the experiments, we examine the numerical accuracy and performance of NeoFlow.The results show that NeoFlow can achieve similar or even better performance at the operator and whole graph level for DNNs compared to deep learning frameworks.Especially, for novel networks training, the geometric mean speedups of NeoFlow to PyTorch, TensorFlow, and CuDNN are 3.16X, 2.43X, and 1.92X, respectively.
Localized Text-Free User Interfaces

User interface is an essential element of an information system from the user perspective.The use of text in the user interface presents a challenge to some users, such as illiterate, elderly, non-local language speakers, or users with any disabilities.Numerous studies have proposed text-free user interfaces based on non-textual aids such as pictograms, audio, video, or specialized hardware.Such rich elements in the user interface have implications for an information system's storage and processing requirements.We propose using concept hierarchies to present a dynamic interface to a user based upon user characteristics.A drill-down approach may present more specific interface elements to a user, and a roll-up scheme can identify more generalized interface elements.The proposed scheme can be used to manage internal data of large user interfaces with a rich set of features more effectively as it is computationally inexpensive than sequential processing techniques.We also explore various physical and psychological characteristics of elements and evaluate user preference for them.The results of a survey of 200 respondents show that users consider the use of various colors, alignments, mental models, and look & feel to be essential criteria in interface design.We also conclude that size of interface elements and the use of characters for expressing emotions are significantly important characteristics in interface design.The future work will apply the proposed scheme to a full spectrum of applications ranging from enterprise systems to mobile applications.
A Self-Supervised Sampler for Efficient Action Recognition: Real-World Applications in Surveillance Systems

The common paradigm of CNN-based action recognition modelsis to simply use the average of the dense predictions from every frame. However, these dense predictions are inefficient since all frames are evenly utilized regardless of the existence of the action. In real-time action recognition applications where the input video is untrimmed, the dense prediction is even more severely inefficient. Instead of these dense predictions, we propose a lightweight CNN-based sampler that selects frames that are relevant to the action in the video. The proposed sampler is trained by a self-supervised learning method that mixes one irrelevant frame in an input video clip and finds that irrelevant frame. With the trained sampler, we select frames to be used as inputs to the action recognition model. Our sampler can be combined with any action recognition models and works independently of them. Through various experiments, we demonstrate that the proposed sampler is not only time and memory-efficient but also significantly increases performance in the action recognition benchmarks, including HMDB51 and UCF101. Finally, we applied the self-supervised sampler to the real-time surveillance system in the subway station, and improved the action recognition performance by 7% with 80% reduction in computation. Code and dataset are available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/seominseok0429/SelSupSampler</uri> and <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://aihub.or.kr/aidata/34124</uri> .
The Role of Smart Contracts in the Transaction Networks of Four Key DeFi-Collateral Ethereum-Based Tokens

We analyse the transaction networks of four representative ERC-20 tokens that run on top of the public blockchain Ethereum and can be used as collateral in DeFi: Ampleforth (AMP), Basic Attention Token (BAT), Dai (DAI) and Uniswap (UNI). We use complex network analysis to characterize structural properties of their transaction networks. We compute their preferential attachment and we investigate how critical code-controlled nodes (smart contracts, SC) executed on the blockchain are in comparison to human-owned nodes (externally owned accounts, EOA), which are be controlled by end users with public and private keys or by off-blockchain code. Our findings contribute to characterise these new financial networks. We use three network dismantling strategies on the transaction networks to analyze the criticality of smart contract and known exchanges nodes as opposed to EOA nodes. We conclude that smart contract and known exchanges nodes play a structural role in holding up these networks, theoretically designed to be distributed but in reality tending towards centralisation around hubs. This sheds new light on the structural role that smart contracts and exchanges play in Ethereum and, more specifically, in Decentralized Finance (DeFi) networks and casts a shadow on how much decentralised these networks really are. From the information security viewpoint, our findings highlight the need to protect the availability and integrity of these hubs.
Semantic Representation as a Key Enabler for Blockchain-Based Commerce

Decentralized applications (in short, DApps) built on blockchains are disrupting the digital commerce foundations by pursuing new business models based on trustless, decentralized transactions, where intermediaries and central authorities are discarded. One of those emerging means are the digital tokens, certificates emitted and exchanged on the blockchain to provide digital representations of assets, which grant to the owners specific rights that are publicly verifiable by smart automatic contractual types of arrangement called smart contracts. Due to the increasing complexity of commercial mechanisms, a clear unambiguous description of commercial participants and of their in and out of blockchain activities, on top of which trustworthy and affordable ecosystems are constructed, is demanded. To face the challenges that digital commerce nowadays poses on realizing such ecosystems, the ONTOCHAIN consortium has funded a third-party research and development project named POC4COMMERCE. In this paper, we discuss the advancements of the project, giving insights into the approach and the best practices adopted by the developing team, to build a suite of ontologies modelling representative entities of the digital commerce ecosystem such as commercial participants and assets traded leveraging the Ethereum blockchain.
DEVIATE: A Deep Learning Variance Testing Framework

Deep learning (DL) training is nondeterministic and such nondeterminism was shown to cause significant variance of model accuracy (up to 10.8%). Such variance may affect the validity of the comparison of newly proposed DL techniques with baselines. To ensure such validity, DL researchers and practitioners must replicate their experiments multiple times with identical settings to quantify the variance of the proposed approaches and baselines. Replicating and measuring DL variances reliably and efficiently is challenging and understudied.We propose a ready-to-deploy framework DEVIATE that (1) measures DL training variance of a DL model with minimal manual efforts, and (2) provides statistical tests of both accuracy and variance. Specifically, DEVIATE automatically analyzes the DL training code and extracts monitored important metrics (such as accuracy and loss). In addition, DEVIATE performs popular statistical tests and provides users with a report of statistical p-values and effect sizes along with various confidence levels when comparing to selected baselines.We demonstrate the effectiveness of DEVIATE by performing case studies with adversarial training. Specifically, for an adversarial training process that uses the Fast Gradient Signed Method to generate adversarial examples as the training data, DEVIATE measures a max difference of accuracy among 8 identical training runs with fixed random seeds to be up to 5.1%.Tool and demo links: https://github.com/lin-tan/DEVIATE
Using t-SNE to explore Misclassification

t-SNE is a tool for dimensional reduction. The goal of t-SNE is to get the best representation of high-dimensional space on a lower dimensional space. In our project, we used four different MNIST dataset with altered features to observe how t-SNE displays misclassification and if there is any correlation between clusters, accuracy scores, and KL divergence. We construct multiple experiments to find optimal parameters for displaying our different datasets.
Motion Sickness Prediction in Self-Driving Cars Using the 6DOF-SVC Model

Drivers who assign the driving task to a self-driving car switch to a passive role to work or enjoy leisure time like a traditional passenger. Consequently, the risk of developing motion sickness (MS) symptoms increases significantly. Adapting one’s own driving behavior, e.g. by choosing an alternative route or decreasing the velocity, offers future intelligent vehicles a way to independently prevent MS. Accurate predictions help to improve journey’s planning of the vehicle and make correct decisions so as to minimize disruption to the traffic flow. In the present study our contribution is as follow: We conduct two studies by focusing on real-world driving under self-driving conditions and induced MS symptoms in passengers. We simulated driving parameters of the conducted studies to extract simulated driving dynamics and contrasted them with recorded driving dynamics. A well-known model of MS, namely the six-degrees-of-freedom subjective vertical conflict model (6DOF-SVC model) was utilized to predict motion sickness incidence (MSI) for both studies. In order to do so, we implemented a customized Human-Vehicle-Model to map the car’s dynamics to the head, which is crucial to apply the 6DOF-SVC model. We evaluated different Human-Vehicle-Model conditions and optimized the parameters of the 6DOF-SVC model to increase prediction accuracy in the case of our experiments. Note that our modeling approach enabled capture effects of missing visual anticipation and cognitive distraction that we present in our experiment. It is concluded that the 6DOF-SVC model is applicable in realistic driving scenarios as the ones used in our study.
Using Neural Networks for Two Dimensional Scientific Data Compression

Continual advances in high-performance computing have enabled the development of higher resolution and more realistic simulations of a wide variety of scientific phenomena. As a result, many computational science communities are increasingly constrained by the massive volumes of data produced, for example, strict storage constraints often force reductions in the number of output variables, data output frequency, or simulation length. Accordingly, modelers across many scientific domains are beginning to adopt purpose-built scientific data compression techniques as an effective mitigation for these challenges. The origins of scientific data compression tools every so often lie in image and video compression. Recently, compression researchers have achieved state-of-the-art performance using neural networks for natural image compression, but this achievement has yet to be adapted to scientific data. This paper assesses the performance of an existing autoencoder neural network compression algorithm on two sets of two-dimensional floating-point scientific data. Compared to state-of-the-art scientific data compression algorithms SZ and ZFP, this out-of-the-box neural network achieves higher peak signal-to-noise ratios at low bit rates, and remains competitive in controlling maximum point-wise error. This preliminary assessment paves the way for future research into neural network compression on floating-point scientific data.
Deep Reinforcement Learning based Adaptive Real-Time Path Planning for UAV

Real-time path planning typically aims to obtain a collision-free and shorter path with lower computational complexity for UAVs in unknown environment. Apart from the above basic objective, kinematic constraints and the smoothness of path should be further considered especially for fixed-wing UAVs restricted by their maneuverability. In this paper, we propose an adaptive real-time path planning method based on Deep Reinforcement Learning. Taking the sensor data of obstacles nearby and the target’s position relative to the UAV as the decision information, and designing the action satisfying kinematic constraints of fixed-wing UAV, the proposed method can plan a feasible path for fixed-wing UAV in real-time. Experimental results show that the adaptive action devised combining with greedy reward, granularity reward and smoothness reward can accelerate the convergence speed of the algorithm and enhance the smoothness of the planned path.
Heterogeneous Federated Meta-Learning With Mutually Constrained Propagation

Federated learning is a popular framework that guarantees privacy security. Thereinto, heterogeneity is a challenging barrier. We propose a meta-based federated method with mutually constrained propagation (2MFed) method to cope with this. First, 2MFed gives each client the right to choose the most appropriate model from server-side parallel models as its local model, which tolerates hardware heterogeneity. Second, the server mutually exchanges the information of parallel models under the consistency and disparity constraints to improve robustness, thus eliminating data heterogeneity. Third, we use the power of the meta-based label propagation algorithm to treat the federated training as the federated few-shot problem, which removes the model heterogeneity. Finally, extensive experiments confirm the effectiveness of the proposed method. In addition, we evaluate the proposed method on few-shot tasks and demonstrate its excellent performance further.
AspectEmo: Multi-Domain Corpus of Consumer Reviews for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a text analysis method that categorizes data by aspects and identifies the sentiment assigned to each aspect. Aspect-based sentiment analysis can be used to analyze customer opinions by associating specific sentiments with different aspects of a product or service. Most of the work in this topic is thoroughly performed for English, but many low-resource languages still lack adequate annotated data to create automatic methods for the ABSA task. In this work, we present annotation guidelines for the ABSA task for Polish and preliminary annotation results in the form of the AspectEmo corpus, containing over 1.5k consumer reviews annotated with over 63k annotations. We present an agreement analysis on the resulting annotated corpus and preliminary results using transformer-based models trained on AspectEmo.
Learning Foreground-Background Segmentation from Improved Layered GANs

Deep learning approaches heavily rely on high-quality human supervision which is nonetheless expensive, time-consuming, and error-prone, especially for image segmentation task. In this paper, we propose a method to automatically synthesize paired photo-realistic images and segmentation masks for the use of training a foreground-background segmentation network. In particular, we learn a generative adversarial network that decomposes an image into foreground and background layers, and avoid trivial decompositions by maximizing mutual information between generated images and latent variables. The improved layered GANs can synthesize higher quality datasets from which segmentation networks of higher performance can be learned. Moreover, the segmentation networks are employed to stabilize the training of layered GANs in return, which are further alternately trained with Layered GANs. Experiments on a variety of single-object datasets show that our method achieves competitive generation quality and segmentation performance compared to related methods.
GreedyFool: Multi-factor imperceptibility and its application to designing a black-box adversarial attack

Adversarial examples are well-designed input samples that include perturbations that are imperceptible to the human eye but easily mislead the output of deep neural networks (DNNs). Existing studies synthesize adversarial examples by leveraging simple metrics to penalize perturbations that lack sufficient consideration of the human visual system (HVS), producing noticeable artifacts. To explore why these perturbations are visible,four primary factors that affect the perceptibility of human eyes are summarized in this paper. Based on this investigation, we design a multi-factor metric MulFactorLoss for measuring the perceptual loss between benign examples and adversarial examples. To test the imperceptibility of the multi-factor metric, we propose a novel black-box adversarial attack that is referred to as GreedyFool. GreedyFool applies differential evolution to evaluate the effects of perturbed pixels on the confidence of a target DNN and introduces greedy approximation to automatically generate adversarial perturbations. We conduct extensive experiments on the ImageNet and CIFRA-10 datasets and a comprehensive user study with 60 participants. The experimental results demonstrate that MulFactorLoss is a more imperceptible metric than the existing pixelwise metrics, and GreedyFool achieves a 100% success rate in a black-box manner.
Subsequent Embedding in Targeted Image Steganalysis: Theoretical Framework and Practical Applications

Steganalysis is a collection of techniques used to detect whether secret information is embedded in a carrier using steganography. Most of the existing steganalytic methods are based on machine learning, which typically requires training a classifier with "laboratory" data. However, applying machine-learning classification to a new source of data is challenging, since there is typically a mismatch between the training and the testing sets. In addition, other sources of uncertainty affect the steganlytic process, including the mismatch between the targeted and the true steganographic algorithms, unknown parameters -- such as the message length -- and even having a mixture of several algorithms and parameters, which would constitute a realistic scenario. This paper presents subsequent embedding as a valuable strategy that can be incorporated into modern steganalysis. Although this solution has been applied in previous works, a theoretical basis for this strategy was missing. Here, we cover this research gap by introducing the "directionality" property of features with respect to data embedding. Once this strategy is sustained by a consistent theoretical framework, new practical applications are also described and tested against standard steganography, moving steganalysis closer to real-world conditions.