Reviewerly/exHarmony huggingface dataset:
From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery

This paper proposes a novel paradigm for the unsupervised learning of object landmark detectors.Contrary to existing methods that build on auxiliary tasks such as image generation or equivariance, we propose a self-training approach where, departing from generic keypoints, a landmark detector and descriptor is trained to improve itself, tuning the keypoints into distinctive landmarks.To this end, we propose an iterative algorithm that alternates between producing new pseudo-labels through feature clustering and learning distinctive features for each pseudo-class through contrastive learning.With a shared backbone for the landmark detector and descriptor, the keypoint locations progressively converge to stable landmarks, filtering those less stable.Compared to previous works, our approach can learn points that are more flexible in terms of capturing large viewpoint changes.We validate our method on a variety of difficult datasets, including LS3D, BBCPose, Human3.6M and PennAction, achieving new state of the art results.Code and models can be found at https://github.com/malldimi1/KeypointsToLandmarks.
Design and Analysis of RSA and Paillier Homomorphic Cryptosystems Using PSO-Based Evolutionary Computation

Non-lattice based homomorphic encryption schemes usually involve huge modular exponentiation operations. Thus, improving the efficiency of modular exponentiation for large exponents is a real-world issue. Modular exponentiation can be computed by a series of modular multiplications. However, performing a series of modular multiplications is computationally expensive. This paper proposes hardware /software co-design for efficient modular exponentiation. This article first explores the use of particle swarm optimization (PSO) for the modular exponentiation in software, and present an efficient hardware co-design utilizing FPGA. Our findings reveal that the suggested PSO approach surpasses all other deterministic and non-deterministic approaches already in use. Further, we also demonstrate a comprehensive analysis for the optimal performance and parameter selection of our proposed PSO approach. Finally, we implement homomorphic encryption schemes, such as RSA and Paillier, using our PSO-based hardware /software co-design. Our approach gains resource savings for 1024-bit as follows: RSA encryption/decryption - 60.7% (area) and 65.3% (DSP); Paillier encryption - 46.3% (area) and 40% (DSP) and Paillier decryption - 73.7% (area) and 66.6% (DSP). We have obtained area-time improvements of 1024-bit as follows: RSA encryption/decryption - 2.7x; Paillier encryption - 2x and Paillier decryption - 4.6x using Xilinx Virtex-7 FPGAs.
Exploring Diverse Feature Extractions for Adversarial Audio Detection

Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks. In the audio domain, malicious audio examples generated by adversarial attacks can cause significant performance degradation and system malfunctions, resulting in security and safety concerns. However, compared to recent developments in the audio domain, the properties of the adversarial audio examples and defenses against them still remain largely unexplored. In this study, to provide a deeper understanding of the adversarial robustness in the audio domain, we first investigate traditional and recent feature extractions in terms of adversarial attacks. We show that adversarial audio examples generated from different feature extractions exhibit different noise patterns, and thus can be distinguished by a simple classifier. Based on the observation, we extend existing adversarial detection methods by proposing a new detection method that detects adversarial audio examples using an ensemble of diverse feature extractions. By combining the frequency and self-supervised feature representations, the proposed method provides a high detection rate against both white-box and black-box adversarial attacks. Our empirical results demonstrate the effectiveness of the proposed method in speech command classification and speaker recognition.
LiDA: Language-Independent Data Augmentation for Text Classification

Developing a high-performance text classification model in a low-resource language is challenging due to the lack of labeled data. Meanwhile, collecting large amounts of labeled data is cost-inefficient. One approach to increase the amount of labeled data is to create synthetic data using data augmentation techniques. However, most of the available data augmentation techniques work on English data and are highly language-dependent as they perform at the word and sentence level, such as replacing some words or paraphrasing a sentence. We present Language-independent Data Augmentation (LiDA), a technique that utilizes a multilingual language model to create synthetic data from the available training dataset. Unlike other methods, our approach worked on the sentence embedding level independent of any particular language. We evaluated LiDA in three languages on various fractions of the dataset, and the result showed improved performance in both the LSTM and BERT models. Furthermore, we conducted an ablation study to determine the impact of the components in our method on overall performance. The source code of LiDA is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yest/LiDA</uri> .
Motion Planning for Dynamic Scenario Vehicles in Autonomous-Driving Simulations

Scenario vehicles are an important part of the dynamic environment utilized in autonomous-driving simulations. They are required to meet the demands of traffic-scenario diversity and form a larger coverage scale in the road network. However, the current motion planning of scenario vehicles either adheres to the classical microscopic traffic-flow model or follows a predefined path; thus, interacting with the vehicle under test in a dynamic bidirectional fashion is difficult. This study researches a motion-planning method for a broader category of unmanned vehicles and proposes a motion-planning method for scenario vehicles based on Pontryagin’s minimal principle, used in optimal control theory and the closed-form solution of the minimum snap method. The study reclassifies actions received from the behavior layer according to the boundary conditions and final times and derives an analytical solution for each of them. The analytical solution is then experimentally verified. The proposed method not only accomplishes efficient motion planning but also exhibits variant driving styles, which provides a practical solution for the motion planning of scenario vehicles in simulations.
Support towards emergency event processing via fine-grained analysis on users' expressions

Purpose With the rapid development of social media, the occurrence and evolution of emergency events are often accompanied by massive users' expressions. The fine-grained analysis on users' expressions can provide accurate and reliable information for event processing. Hence, 2,003,814 expressions on a major malignant emergency event were mined from multiple dimensions in this paper. Design/methodology/approach This paper conducted finer-grained analysis on users' online expressions in an emergency event. Specifically, the authors firstly selected a major emergency event as the research object and collected the event-related user expressions that lasted nearly two years to describe the dynamic evolution trend of the event. Then, users' expression preferences were identified by detecting anomic expressions, classifying sentiment tendencies and extracting topics in expressions. Finally, the authors measured the explicit and implicit impacts of different expression preferences and obtained relations between the differential expression preferences. Findings Experimental results showed that users have both short- and long-term attention to emergency events. Their enthusiasm for discussing the event will be quickly dispelled and easily aroused. Meanwhile, most users prefer to make rational and normative expressions of events, and the expression topics are diversified. In addition, compared with anomic negative expressions, anomic expressions in positive sentiments are more common. In conclusion, the integration of multi-dimensional analysis results of users' expression preferences (including discussion heat, preference impacts and preference relations) is an effective means to support emergency event processing. Originality/value To the best of the authors' knowledge, it is the first research to conduct in-depth and fine-grained analysis of user expression in emergencies, so as to get in-detail and multi-dimensional characteristics of users' online expressions for supporting event processing.
Weighted ensemble classifier for malicious link detection using natural language processing

Purpose The internet has completely merged into contemporary life. People are addicted to using internet services for everyday activities. Consequently, an abundance of information about people and organizations is available online, which encourages the proliferation of cybercrimes. Cybercriminals often use malicious links for large-scale cyberattacks, which are disseminated via email, SMS and social media. Recognizing malicious links online can be exceedingly challenging. The purpose of this paper is to present a strong security system that can detect malicious links in the cyberspace using natural language processing technique. Design/methodology/approach The researcher recommends a variety of approaches, including blacklisting and rules-based machine/deep learning, for automatically recognizing malicious links. But the approaches generally necessitate the generation of a set of features to generalize the detection process. Most of the features are generated by processing URLs and content of the web page, as well as some external features such as the ranking of the web page and domain name system information. This process of feature extraction and selection typically takes more time and demands a high level of expertise in the domain. Sometimes the generated features may not leverage the full potentials of the data set. In addition, the majority of the currently deployed systems make use of a single classifier for the classification of malicious links. However, prediction accuracy may vary widely depending on the data set and the classifier used. Findings To address the issue of generating feature sets, the proposed method uses natural language processing techniques (term frequency and inverse document frequency) that vectorize URLs. To build a robust system for the classification of malicious links, the proposed system implements weighted soft voting classifier, an ensemble classifier that combines predictions of base classifiers. The ability or skill of each classifier serves as the base for the weight that is assigned to it. Originality/value The proposed method performs better when the optimal weights are assigned. The performance of the proposed method was assessed by using two different data sets (D1 and D2) and compared performance against base machine learning classifiers and previous research results. The outcome accuracy shows that the proposed method is superior to the existing methods, offering 91.4% and 98.8% accuracy for data sets D1 and D2, respectively.
Rendered Tile Reuse Scheme Based on FoV Prediction for MEC-Assisted Wireless VR Service

Interactive real-time wireless Virtual Reality (VR) has become increasingly popular because it provides an immersive experience for VR users anytime, anywhere. Introducing multi-access edge computing (MEC) to wireless VR service can solve the problem that VR devices having insufficient rendering power. However, the problem that some tiles in the overlapping field of views (FoVs) may be rendered repeatedly usually be ignored. Repeated rendering would waste the computing resources of edge nodes and damage users' quality of experience (QoE). This paper proposes a rendered tile reuse scheme based on FoV prediction and 3C (caching, computing, and communication) optimization for the MEC-assisted VR service. Firstly, we model the above scheme as an optimization problem that aims to maximize the total users' QoE value under the motion to photons (MTP) delay constraints. Secondly, we use the recurrent neural network model with gated recurrent unit (GRU) architecture to dynamically predict the users' FoV in the next time slot. Thirdly, we use the proximal policy optimization (PPO)to learn the question's solution iteratively based on the results of FoV prediction. The simulation results show that our proposed algorithm is superior to other algorithms in improving the value of total users' QoE and reducing the MTP delay.
Deep Embedded Clustering Framework for Mixed Data

Deep embedded clustering (DEC) is a representative clustering algorithm that leverages deep-learning frameworks.DEC jointly learns low-dimensional feature representations and optimizes the clustering goals but only works with numerical data.However, in practice, the real-world data to be clustered includes not only numerical features but also categorical features that DEC cannot handle.In addition, if the difference between the soft assignment and target values is large, DEC applications may suffer from convergence problems.In this study, to overcome these limitations, we propose a deep embedded clustering framework that can utilize mixed data to increase the convergence stability using soft-target updates; a concept that is borrowed from an improved deep Q learning algorithm used in reinforcement learning.To evaluate the performance of the framework, we utilized various benchmark datasets composed of mixed data and empirically demonstrated that our approach outperformed existing clustering algorithms in most standard metrics.To the best of our knowledge, we state that our work achieved state-of-the-art performance among its contemporaries in this field.
Engineering graduate attribute attainment measurement models

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A new quantum multi-party signature protocol based on SNOP states without arbitrator

As an important part of quantum signature, quantum multi-party signature solves the practical signing problem of requiring multiple signers or multiple verifiers. However, most of the existing multi-party signature protocols focus on the situation of multiple signers. Meanwhile the arbitrator is usually introduced to ensure their security. In this paper, we propose a new quantum multi-party signature protocol without arbitrator where multiple signers and multiple verifiers simultaneously appear in it. During the protocol, the strongly nonlocal orthogonal product (SNOP) states are applied to generate the signed messages. According to our analysis, it can be seen that this protocol is resistant to denial and forgery attacks, even if some of the participants collude. We hope the ideas and methods may make positive effects to the further research of quantum signature.
Artificial intelligence based image recognition system

Data are abundant in the pharmaceutical and medical industries. No human agent can compete with the robust and extremely clever technology, the so-called Artificial Intelligence, when it comes to evaluating this data, patient classification, picture analysis, genetic profiling, drug research and sales, and other tasks. The majority of products on the market often have barcodes, which speed up data gathering and interpretation. However, products used in pharmaceutical applications, such as tablets, syrups, eye drops, and so on, do not typically have barcodes. A human agent does each step manually, which is slow and can result in incorrect data interpretation. So the plan is to swap out the aforementioned equipment for an effective artificial intelligence-based optical character recognition system. The entire procedure in the pharmaceutical industry is made simple and quicker by developing a system that can analyze the product and gather the necessary data. Convolutional neural networks and Python are utilized to put this concept into practice.
CFGM: An algorithm for closed frequent graph patterns mining

The extraction of frequent subgraphs is a basic and well studied operation on graphs. Thus, mining frequent graph patterns and problems associated with it is very important. However, the number of frequent subgraphs is potentially exponential while mining large graph patterns. This issue can be partly overcome using closed frequent graphs mining. Instead of mining all frequent subgraphs, it is more efficient to enumerate only the closed frequent graphs. Thus, in this paper, we propose a novel closed frequent subgraph mining algorithm: CFGM. In this algorithm, a stack-based architecture is used to enumerate the frequent graph represented by the depth-first search. Moreover, this algorithm defines a strict partial order among frequent graphs. We demonstrate that, with respect to this strict partial order, only maximal elements (frequent graphs) need to be discovered. A pruning strategy is developed based on this strict partial order that dramatically reduces unnecessary frequent subgraphs to be enumerated. Computational results show our algorithm displays excellent performance, especially for some large asymmetric frequent graph patterns.
Extracting tactics learned from self-play in general games

Local, spatial state-action features can be used to effectively train linear policies from self-play in a wide variety of board games. Such policies can play games directly, or be used to bias tree search agents. However, the resulting feature sets can be large, with a significant amount of overlap and redundancies between features. This is a problem for two reasons. Firstly, large feature sets can be computationally expensive, which reduces the playing strength of agents based on them. Secondly, redundancies and correlations between features impair the ability for humans to analyse, interpret, or understand tactics learned by the policies. We look towards decision trees for their ability to perform feature selection, and serve as interpretable models. Previous work on distilling policies into decision trees uses states as inputs, and distributions over the complete action space as outputs. In contrast, we propose and evaluate a variety of decision tree types, which take state-action pairs as inputs, and provide various different types of outputs on a per-action basis. An empirical evaluation over 43 different board games is presented, and two of those games are used as case studies where we attempt to interpret the discovered features.
Providing command and control agility: A software product line approach

Command and Control (C2) is a broad concept that encompasses the coordination of individuals and organizations towards achieving a goal. However, dynamic and uncertain scenarios, such as military and disaster relief operations, present an inherent challenge to C2 activities. In such situations, plans often need to be changed in the face of unforeseen problems, and even coordination processes may be subject to variation. This dynamism increases the complexity of resource management and requires C2 Agility—i.e., the ability to respond to change in a timely and suitable fashion. Nonetheless, there is a lack of solutions to provide C2 Agility to cope with dynamic contexts. To address this problem, this work proposes a computational model of C2 Agility for a team of autonomous agents. This model describes how to combine reconfiguration of individual team members and of coordination approaches to adapt to context changes. The proposed approach leverages a typed-parameterized extension of a channel system to define the coordinating roles and responsibilities of team members. Each member is modeled as a dynamic software product line, with the inherent ability to reconfigure itself. To assess this model, a team of Unmanned Aerial Vehicles (UAV) performing a reconnaissance mission was simulated. The simulation showed that the proposed model was suitable for dealing with dynamic contexts. Particularly, metrics for the agile approach suggest improved system resilience in the face of induced perturbations, compared to non-agile C2. The obtained results with the proposed software-based simulations showed that the proposed model is useful in providing C2 Agility to the studied scenarios, making the behavior of the entities specified in the model capable of dealing with context changes.
Cross-domain multi-style merge for image captioning

Multi-style image captioning has attracted wide attention recently. Existing approaches mainly rely on style synthetics within a single domain. They cannot deal with multiple styles combination since various styles naturally cannot be included in a uniform dataset. This paper is the first one to investigate the cross-domain multi-style merge for image captioning. Specifically, we propose a novel image caption model with a multi-style gated transformer block to fit the cross-domain caption generation task. Conventional generative adversarial learning for language methods may suffer from the distribution distortion problem, since real datasets do not contain captions with style combinations. Therefore, we devise a multi-stage self-learning framework for the proposed image caption model to exploit real corpus with pseudo styles gradually. Comprehensive experiments and ablation studies demonstrate the effectiveness of our proposed method on the multi-style merge for image captioning.
CGBO-Net: Cruciform structure guided and boundary-optimized lymphoma segmentation network

Lymphoma segmentation plays an important role in the diagnosis and treatment of lymphocytic tumor. Most current existing automatic segmentation methods are difficult to give precise tumor boundary and location. Semi-automatic methods are usually combined with manually added features such as bounding box or points to locate the tumor. Inspired by this, we propose a cruciform structure guided and boundary-optimized lymphoma segmentation network(CGBS-Net). The method uses a cruciform structure extracted based on PET images as an additional input to the network, while using a boundary gradient loss function to optimize the boundary of the tumor. Our method is divided into two main stages: In the first stage, we use the proposed axial context-based cruciform structure extraction (CCE) method to extract the cruciform structures of all tumor slices. In the second stage, we use PET/CT and the corresponding cruciform structure as input in the designed network (CGBO-Net) to extract tumor structure and boundary information. The Dice, Precision, Recall, IOU and RVD are 90.7%, 89.4%, 92.5%, 83.1% and 4.5%, respectively. Validate on the lymphoma dataset and publicly available head and neck data, our proposed approach is better than the other state-of-the-art semi-segmentation methods, which produces promising segmentation results.