Reviewerly/exHarmony huggingface dataset:
From External to Internal: Structuring Image for Text-to-Image Attributes Manipulation

Manipulating visual attributes of an image through a natural language description, known as text-to-image attributes manipulation (T2AM), is a challenging task. However, existing approaches tend to search the whole image to manipulate the target instance indicated by a description, thus they often fail to locate and manipulate the accurate text-relevant regions, and even disturb the text-irrelevant contents, e.g. texture and background. Meanwhile, the model efficiency needs to be improved. To tackle the above issues, we introduce a novel yet simple GAN-based approach, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>S</u>tructuring <u>I</u>mage for <u>M</u>anipulating</i> (SIMGAN), to narrow down the optimization areas from external to internal. It consists of two major components: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">External Structuring</i> (ExST), a pretrained segmentation network, for recognizing and separating the target instances and background from an image; and 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Internal Structuring</i> (InST) for seeking out and editing the text-relevant attributes of the target instances based on the given description and masked hierarchical image representations from ExST. Specifically, the InST structures target instances from outline to detail by firstly drawing the sketch and colors underpainting of instances with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Outline-Oriented Structuring</i> (OuST), and then enhancing the text-relevant attributes and elaborating on details with a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Detail-Oriented Structuring</i> (DeST). Extensive experiments on benchmark datasets demonstrate that our framework significantly outperforms state-of-the-art both quantitatively and qualitatively. Compared with the state-of-the-art method ManiGAN, our approach reduces the training time by 88%, while the inferring time is three times faster. In addition, our approach is easily extended to solve the instance-level image-to-image translation problem, and the results exhibit the versatility and effectiveness of our approach. This code is released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qikizh/SIMGAN</uri> .
Further Cryptanalysis of a Type of RSA Variants

To enhance the security or the efficiency of the standard RSA cryptosystem, some variants have been proposed based on elliptic curves, Gaussian integers or Lucas sequences. A typical type of these variants which we called Type-A variants have the specified modified Euler’s totient function $$\psi (N)=(p^2-1)(q^2-1)$$ . But in 2018, based on cubic Pell equation, Murru and Saettone presented a new RSA-like cryptosystem, and it is another type of RSA variants which we called Type-B variants, since their scheme has $$\psi (N)=(p^2+p+1)(q^2+q+1)$$ . For RSA-like cryptosystems, four key-related attacks have been widely analyzed, i.e., the small private key attack, the multiple private keys attack, the partial key exposure attack and the small prime difference attack. These attacks are well-studied on both standard RSA and Type-A variants. Recently, the small private key attack on Type-B variants has also been analyzed. In this paper, we make further cryptanalysis of Type-B variants, that is, we propose the first theoretical results of multiple private keys attack, partial key exposure attack as well as small prime difference attack on Type-B variants, and the validity of our attacks are verified by experiments. Our results show that for all three attacks, Type-B variants are less secure than standard RSA.
Communication Compression for Distributed Nonconvex Optimization

In this article, we consider distributed nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for distributed algorithms as agents iteratively communicate with neighbors, we propose three distributed primal–dual algorithms with compressed communication. The first two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error. We show that the proposed distributed algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms with exact communication. Specifically, we show that they can find first-order stationary points with sublinear convergence rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(1/T)$</tex-math></inline-formula> when each local cost function is smooth, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> is the total number of iterations, and find global optima with linear convergence rate under an additional condition that the global cost function satisfies the Polyak–Łojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.
An Improvement on “CryptCloud$^{+}$: Secure and Expressive Data Access Control for Cloud Storage”

Recently, Ning et al. proposed the “CryptCloud <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{+}$</tex-math></inline-formula> : Secure and Expressive Data Access Control for Cloud Storage” in <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Transaction on Services Computing</b> . This work provided two versatile ciphertext-policy attribute-based encryption (CP-ABE) schemes to achieve flexible access control on encrypted data, namely ATER-CP-ABE and ATIR-CP-ABE, both of which have attractive advantages, such as white-box malicious user traceability, semi-honest authority accountability, public auditing and user revocation. However, we find a bug of access control in both schemes, i.e., a non-revoked user with attribute set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> can decrypt the ciphertext <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ct$</tex-math></inline-formula> encrypted under any access policy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(A,\rho )$</tex-math></inline-formula> , regardless of whether <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$S$</tex-math></inline-formula> satisfies <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(A,\rho )$</tex-math></inline-formula> or not. This paper carefully analyzes the bug, and makes an improvement on Ning's pioneering work, so as to fix it.
Graphing the Future: Activity and Next Active Object Prediction Using Graph-Based Activity Representations

We present a novel approach for the visual prediction of human-object interactions in videos. Rather than forecasting the human and object motion or the future hand-object contact points, we aim at predicting (a) the class of the on-going human-object interaction and (b) the class(es) of the next active object(s) (NAOs), i.e., the object(s) that will be involved in the interaction in the near future as well as the time the interaction will occur. Graph matching relies on the efficient Graph Edit distance (GED) method. The experimental evaluation of the proposed approach was conducted using two well-established video datasets that contain human-object interactions, namely the MSR Daily Activities and the CAD120. High prediction accuracy was obtained for both action prediction and NAO forecasting.
SCUT-CAB: A New Benchmark Dataset of Ancient Chinese Books with Complex Layouts for Document Layout Analysis

Ancient books are the cultural heritage of human civilization, among which there are quite a few precious collections in China. However, compared to modern documents, the absence of large-scale historical document layout datasets makes the digitalization of ancient books still in its infancy and awaiting excavation and decryption. To this end, this paper proposes a large-scale dataset named SCUT-CAB for layout analysis of ancient Chinese books with complex layouts. The dataset is established by manually annotating 4000 images of ancient books, including 31,925 layout element annotations, which contains different binding forms, fonts, and preservation conditions. To facilitate the multiple tasks involved in document layout analysis, the dataset is segregated into two subsets: SCUT-CAB-Physical for physical layout analysis and SCUT-CAB-Logical for logical layout analysis. SCUT-CAB-Physical contains four categories, whereas SCUT-CAB-Logical contains 27 categories. Furthermore, the SCUT-CAB dataset comprises the labeling of the reading order. We compare various layout analysis methods for SCUT-CAB, i.e., methods based on object detection, instance segmentation, Transformer, and multi-modality. Extensive experiments reveal the challenges of layout analysis for ancient Chinese books. To the best of our knowledge, SCUT-CAB may be the first large-scale public available dataset for ancient Chinese document layout analysis. The dataset will be made publicly at https://github.com/HCIILAB/SCUT-CAB_Dataset_Release .
Towards an Evaluation Framework for Autonomous Systems

Despite the active and proficuous research in autonomous and self-adaptive systems of the last years, an evaluation framework to assess abilities related to adaption and to provide guidance to make a system smarter is still missing. In this paper, we perform the first steps towards an evaluation framework for autonomous systems to (i) make an assessment of a system from the perspective of its capacity to adapt and learn over time to handle new and unexpected conditions, (ii) explore and identify the possible pathways of improvement of the smart abilities (e.g., decisional autonomy, adaptability, perception, interaction, etc.) of a system, (iii) make a re-assessment when the improvement has been performed.
ANN-based Representation Learning in a Lifelong Open-ended Learning Cognitive Architecture

The frontier in robot autonomy is currently Lifelong Open-Ended Autonomy (LOLA). Within these settings, a robot must be able to operate and learn in domains that are unknown at design time as well as reuse knowledge learnt in one domain to facilitate learning in others throughout its lifetime. Achieving LOLA goes beyond learning specific algorithms and puts us squarely in the realm of cognitive architectures; however, most cognitive architectures were not built to address the LOLA problem, and thus, lack components and capabilities that would be required for it. In fact, even though there is a growing literature on learning representations, especially in the framework of reinforcement and deep learning, hardly any cognitive architecture considers the issue of autonomously learning representations, which is a crucial problem to be able to efficiently learn and abstract information when seeking LOLA. This paper provides a vision of the general requirements in terms of learning knowledge representations within cognitive architectures geared towards LOLA and addresses a specific problem in this context: the problem of learning representations that facilitate obtaining world and utility models and deciding on actions in situations where multiple goals can be activated. The work is carried out in the framework of the development of the e-MDB cognitive architecture for real robots operating autonomously in real domains.
An Implementation of Automatic Dart Code Verification for Mobile Application Programming Learning Assistance System Using Flutter

The popularity of smartphone devices has rapidly increased in recent years and many people utilize smartphones for various needs. The development of mobile applications has been aimed at various fields that make the demand for mobile application programmers increase. Recently, Flutter has become a software development kit for cross-platform applications development, including Android and iOS, so many software developers have adopted it. To provide a self-learning system for studying mobile programming with Flutter, we propose a learning assistance system with an automatic Dart code verification feature. Based on our previous studies in Android Programming Learning Assistance System (APLAS), the automatic code verification process can adopt software testing process for Android applications. The learning model provides learning materials for studying and practicing by solving an assignment. A learning topic of developing a simple application is prepared for the proposed system evaluation. 40 university students in IT department have been appointed to study Flutter and solve the assignment. Finally, they can solve the assignment correctly and give positive opinions about using this system.
Improving Marine Radar Odometry by Modeling Radar Resolution and Exploiting Additional Temporal Information

Radar odometry may provide valuable input for surface vessels in several marine applications. The vulnerability of global positioning satellite systems to jamming and spoofing motivates the search for alternatives. In this work, we investigate the feasibility of W-band frequency modulated continuous wave radars in marine settings for odometry. A method to model radar resolution is presented and is further extend to include multiple radar frames. Numerical implementation relies on concepts from Lie theory. The proposed methods were evaluated on datasets collected from a ferry in a harbour area, where the average relative translation error was reduced to 3.07% compared to 14.2% of a baseline method.
Draw Me a Flower: Processing and Grounding Abstraction in Natural Language

Abstract Abstraction is a core tenet of human cognition and communication. When composing natural language instructions, humans naturally evoke abstraction to convey complex procedures in an efficient and concise way. Yet, interpreting and grounding abstraction expressed in NL has not yet been systematically studied in NLP, with no accepted benchmarks specifically eliciting abstraction in NL. In this work, we set the foundation for a systematic study of processing and grounding abstraction in NLP. First, we deliver a novel abstraction elicitation method and present Hexagons, a 2D instruction-following game. Using Hexagons we collected over 4k naturally occurring visually-grounded instructions rich with diverse types of abstractions. From these data, we derive an instruction-to-execution task and assess different types of neural models. Our results show that contemporary models and modeling practices are substantially inferior to human performance, and that model performance is inversely correlated with the level of abstraction, showing less satisfying performance on higher levels of abstraction. These findings are consistent across models and setups, confirming that abstraction is a challenging phenomenon deserving further attention and study in NLP/AI research.
Git-Truck: Hierarchy-Oriented Visualization of Git Repository Evolution

The assessment of repository evolution is particularly complex when one needs to analyze both 1) how the codebase is organized hierarchically and 2) how this codebase evolves over time. To address this problem, we developed Git Truck, a visualization tool that includes multiple views for the analysis of the evolution of hierarchically organized Git repositories. We conducted a preliminary user evaluation with 18 participants who, using a remote and asynchronous method, installed Git Truck, used it, and filled in a questionnaire to report their experience and impressions. We learned that participants consider particularly useful views that help them with understanding the contribution level of team members and views that highlight the parts of the system that change the most. The participants see Git Truck as a highly specialized tool; not for daily use but rather for a lower frequency of use.
Design and implementation of access monitoring system for banks using OpenCV and Django

This paper presents a system which configures a web- based access monitoring system for banks. The banking system plays a very significant role in society. There is a need for monitoring the people entering the bank as a part of the procedure for providing security. The traditional monitoring system only records the video, it does not recognize the people entering. This work implements a system for recognizing the people entering the bank using a computer vision technology. With the ongoing development of image processing algorithms, the computer vision discipline of artificial intelligence launching a new era by teaching computers to comprehend and interpret the visual environment. This project proposes an efficient way to keep track of the people entering the banks using face detection. Feature extraction of the images of the employees and the customers is done. The database of the face images of employees of the bank and customers is created and stored in the system. Viola jones algorithm which uses HAAR feature extraction and cascade classifiers is used for the face detection. The face features detected is compared with the existing database and classified as employee, customer and unknown. After the face recognition the information is displayed in a webpage with date and time of entry. This webpage can be very useful in case of Security breach in banks. The face detection algorithm is implemented using OpenCV on a Raspberry pi processor with Linux operating system.
sGrid++: Revising Simple Grid Based Density Estimator for Mining Outlying Aspect

In this paper, we address the problem of outlying aspect mining, which aims to identify a set of features (subspace(s) a.k.a aspect(s)) where a given data object stands out from the rest of the data. To detect the most outlying aspect of a given data object, outlying aspect mining algorithms need to compare and rank subspaces with different dimensionality. Thus, they require a fast and dimensionally unbias scoring measure. Existing measures use density or distance to compute the outlyingness of the query in each subspace. Density and distance are dimensionally bias, i.e. density decreases as the dimension of subspace increases. To make them comparable (dimensionally unbias), Z-score normalization is used in the previous works. However, to compute Z-score normalization, we need to compute the outlyingness of each data point in each subspace, which adds significant computational overhead on top of the already expensive density or distance computation. Recently developed measure called sGrid is a simple and efficient density estimator which allows a fast systemic search. While it is efficient compared to other distance and density-based measures, it is also a dimensionally bias measure and it requires to use Z-score normalization to make it dimensionality unbiased, which makes it computationally expensive. In this paper, we propose a simpler version of sGrid called sGrid++ that is not only efficient and effective but also dimensionality unbiased. It does not require Z-score normalization. We demonstrate the effectiveness and efficiency of the proposed scoring measure in outlying aspect mining using synthetic and real-world datasets.
Research on Dynamic Obstacle Avoidance Path Planning Strategy of UAV

In order to make the Unmanned Aerial Vehicle (UAV) avoid moving obstacles during operation, improve the survivability of the UAV in low-altitude dynamic and complex airspace, and ensure the safe operation of the UAV, a dynamic obstacle avoidance path planning strategy for UAV is proposed. When the UAV detects a moving obstacle, the UAV motion model is established considering the moving speed, moving direction and UAV turning radius of the UAV and the obstacle. Under the constraints of keeping the minimum safe distance between the UAV and moving obstacles as the premise, by changing the UAV turning radius, changing the UAV heading, solving the UAV minimum deviation distance, reducing the UAV obstacle avoidance space, so that the obstacle avoidance effect of the UAV can be optimized. The simulation results show that the calculated minimum deviation distance is about 35% smaller than the maximum deviation distance within the changing range of turning radius. Changing the turning radius of the UAV can reduce the obstacle avoidance space of the UAV and improve the utilization of airspace resources. Using this model, the optimal dynamic obstacle avoidance path of the UAV can be solved.
Adversarial Attacks Against IoT Identification Systems

While machine learning (ML)-based solutions for Internet of Things (IoT) device identification are effective in identifying IoT devices connected to the network, they may be vulnerable to adversarial attacks. Conventional approaches for crafting adversarial attacks from the computer vision (CV) domain are not applicable in the computer network domain as they interfere with the meaningful communication of devices. In this research, we evaluate the robustness of payload-based IoT device identification solutions against adversarial examples generated using our new approach for the real-time crafting of adversarial examples; this approach, which is also applicable to the computer network domain, uses heatmaps generated by class activation mapping (CAM) and Grad-CAM++. We evaluate the effectiveness of adversarial examples generated using the above two methods against six variants of an existing payload-based ML IoT identification solution in a real-time end-to-end scenario. We show that in many cases, an adversarial example crafted using a heatmap can fool the examined variants of an existing payload-based IoT device identification solution with up to 100% accuracy.
Spam Email Assessment Using Machine Learning and Data Mining Approach

Electronic mail is a crucial means of communication, used in both formal and informal ways. Unnecessary communications sent in bulk over the Internet or through any electronic messaging system are known as spam. Some illegal use of spam includes unethical behaviors, phishing, and various other scams. We found that spam emails constitute at least eighty percent of the mail received by the recipients. The key objective of this paper is to provide information for the detection of spam electronic mail. Popular machine learning techniques like K-Nearest Neighbor (KNN), Logistic regression (LR), Cat-Boost, Ada-Boost, Gradient Boosting, Extreme Gradient Boosting (XG-Boost) Light Gradient Boosting Machine (Light GBM), Support Vector Machine (SVM), Decision Tree, Gaussian Naïve Bayes, and Random Forest were implemented on selected features from the data set. Cat-Boost outperformed all the other machine learning algorithms and achieved an accuracy of 0.9699 and an Fl score of 0.97 for spam email classification.