Reviewerly/exHarmony huggingface dataset:
Enhancing the robustness of vision transformer defense against adversarial attacks based on squeeze-and-excitation module

Vision Transformer (ViT) models have achieved good results in computer vision tasks, their performance has been shown to exceed that of convolutional neural networks (CNNs). However, the robustness of the ViT model has been less studied recently. To address this problem, we investigate the robustness of the ViT model in the face of adversarial attacks, and enhance the robustness of the model by introducing the ResNet- SE module, which acts on the Attention module of the ViT model. The Attention module not only learns edge and line information, but also can extract increasingly complex feature information; ResNet-SE module highlights the important information of each feature map and suppresses the minor information, which helps the model to perform the extraction of key features. The experimental results show that the accuracy of the proposed defense method is 19.812%, 17.083%, 18.802%, 21.490%, and 18.010% against Basic Iterative Method (BIM), C&W, DeepFool, DI2FGSM, and MDI2FGSM attacks, respectively. The defense method in this paper shows strong robustness compared with several other models.
Non-residual unrestricted pruned ultra-faster line detection for edge devices

Line detection with deep learning is a popular visual task that focuses mostly on lane detection. It requires quicker inference speed and lower consumption, especially for high-speed edge device applications. Based on the UFAST, we propose the Non-Residual Unrestricted Pruned Ultra-faster (NRUPU) line detection via a novel model compression method including non-interference structural reconstruction (NISR), shallow channel priority reservation (SCPR) pruning and non-residual equivalent transformation (NRET). NISR is a structure reconstruction scheme allocating residual branches into each layer to solve the cross-layer channel interference in ResNet-18. SCPR pruning directly uses the factors of BN layers to build channel importance evaluation for backbone and designs channel selection method for head based on data distribution consistency, reducing the parameters of each layer independently. Then NRET losslessly converts the multi-branch model to a single-branch one containing only convolution, linear, and relu, which reduces implementation complexity on edge devices. These designs follow the theoretical foundations: the data distribution transformation trend and effect of gradient back-propagation on model learning ability. Compared with previous pruning methods, our method optimizes not only the parameters of the model but also the structure of the model. We train NRUPU in RTX2080Ti and deploy tests on edge devices NVIDIA Jetson Xavier NX (NJXN) and Atlas 200 DK (A2DK). Extensive experiments are conducted on the dataset TuSimple, CULane and our belt dataset with 11,894 data. Results show that NRUPU achieves over 96% speed increase and over 66% parameter reduction on all datasets within 0.7% accuracy loss. The FPS can reach 749, 665 and 783 on RTX2080Ti, 133, 117 and 143 on NJNX, 178, 161 and 183 on A2DK respectively. The code is released at https://anonymous.4open.science/r/NRUPU-29B0.
Eliminating Massive Martian Dust Storms from Images of Tianwen-1 via Deep Learning

Abstract Dust storms may remarkably degrade the imaging quality of Martian orbiters and delay the progress of mapping the global topography and geomorphology. To address this issue, this paper presents an approach that reuses the image dehazing knowledge obtained on Earth to resolve the dust-removal problem on Mars. In this approach, we collect remote-sensing images captured by Tianwen-1 and manually select hundreds of clean and dusty images. Inspired by the haze formation process on Earth, we formulate a similar visual degradation process on clean images and synthesize dusty images sharing a similar feature distribution with realistic dusty images. These realistic clean and synthetic dusty image pairs are used to train a deep model that inherently encodes the irrelevant features of dust and decodes them into dust-free images. Qualitative and quantitative results show that dust storms can be effectively eliminated by the proposed approach, leading to obviously improved topographical and geomorphological details of Mars.
DC-FUDA: Improving deep clustering via fully unsupervised domain adaptation

By transferring knowledge from a source domain, the performance of deep clustering on an unlabeled target domain can be greatly improved. When achieving this, traditional approaches assume that an adequate amount of labeled data are available in the source domain. However, this assumption is not always satisfied in practice. First, it cannot be guaranteed that rich labeled samples are readily available in the selected source domain. Second, the noisy data in the source domain may lead to negative transferring. In this paper, we propose a novel transfer learning framework to improve deep clustering via fully unsupervised domain adaptation, called DC-FUDA. Specifically, to select reliable instances in the source domain for transferring, we propose a novel adaptive threshold algorithm to select low entropy instances. To transfer important features of the selected instances, we propose a feature-level domain adaptation network (FeatureDA) that cancels an unstable instance generation process. With extensive experiments, we validate that our method effectively improves deep clustering. Besides, without using any labeled data in the source domain, our method achieves competitive results, compared to the state-of-the-art methods using labeled data in the source domain.
Adaptive hypergraph superpixels

Superpixel segmentation, which amounts to partitioning an image into a number of superpixels each of which is a set of pixels sharing common visual meanings, requires specific needs for different computer vision tasks. Graph based methods, as a kind of popular superpixel segmentation method, regard an image as a weighted graph whose nodes correspond to pixels of the image, and partition all pixels into superpixels according to the similarity between pixels over various feature spaces. Despite their improvement of the performance of segmentation, these methods ignore high-order relationship between them incurred from either locally neighboring pixels or structured layout of the image. Moreover, they measure the similarity of pairwise pixels using Gaussian kernel where a robust radius parameter is difficult to find for pixels which exhibit multiple features (e.g., texture, color, brightness). In this paper, we propose an adaptive hypergraph superpixel segmentation (AHS) of intensity images for solving both issues. AHS constructs a hypergraph by building the hyperedges with an adaptive neighborhood scheme, which explores an intrinsic relationship of pixels. Afterwards, AHS encodes the relationship between pairwise pixels using characteristics of current two pixels as well as their neighboring pixels defined by hyperedges. Essentially, AHS models the relationship of pairwise pixels in a high-order group fashion while graph based methods evaluate it in a one-vs-one fashion. Experiments on four datasets demonstrate that AHS achieves higher or comparable performance compared with state-of-the-art methods.
Brain Principles Programming

The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence”, referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP) – the formalization of universal mechanisms (principles) of the brain’s work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with this information. In the paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin’s Theory of Functional Brain Systems, Eleonor Rosh’s prototypical categorization theory, Bob Rehter’s theory of causal models and “natural” classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithm’s operation.
Monte Carlo Bias Correction in Q-Learning

The Q-learning algorithm suffers from overestimation bias due to the maximum operator appearing in its update rule. Other popular variants of Q-learning, like double Q-learning, can on the other hand cause underestimation of the action values. In many stochastic environments both underestimation and overestimation can lead to sub-optimal strategies. In this paper, we present a variation of Q-learning that uses elements from Monte-Carlo Reinforcement Learning to correct for the overestimation bias. Our method 1) makes no assumptions on the distributions of the action values or the rewards, 2) does not require extensive hyperparameter tuning unlike other popular variants proposed to deal with the overestimation bias and 3) requires storing only two estimators, similar to double Q-learning, along with the most recent episode. Our method is shown to effectively control for the overestimation bias in a number of simulated stochastic environments leading to better policies with higher cumulative rewards and action values that are closer to the optimal ones, as compared to a number of well-established approaches.
Elements of Active Continuous Learning and Uncertainty Self-awareness: A Narrow Implementation for Face and Facial Expression Recognition

Reflection on one’s thought process and making corrections to it if there exists dissatisfaction in its performance is, perhaps, one of the essential traits of intelligence. However, such high-level abstract concepts mandatory for Artificial General Intelligence can be modelled even at the low level of narrow Machine Learning algorithms. Here, we present the self-awareness mechanism emulation in the form of a supervising artificial neural network (ANN) observing patterns in activations of another underlying ANN in a search for indications of the high uncertainty of the underlying ANN and, therefore, the trustworthiness of its predictions. The underlying ANN is a convolutional neural network (CNN) ensemble employed for face recognition and facial expression tasks. The self-awareness ANN has a memory region where its past performance information is stored, and its learnable parameters are adjusted during the training to optimize the performance. The trustworthiness verdict triggers the active learning mode, giving elements of agency to the machine learning algorithm that asks for human help in high uncertainty and confusion conditions.
Statistical Analysis of Trace Element Content in Neolithic, Eneolithic and Bronze Age Metal Artefacts from Central and South-Eastern Europe

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Transforming dispositions towards automation

Speaking to debates on how digital automation is changing people's capacities, my commentary affirms the value of surveying the myriad dispositions to automation that are muffled by the force of dominant boosterist or dystopian narratives. However, rather than assuming strong ‘reactions’ to automated objects or interfaces, I speculate that interactions with automation might be better characterised as ‘non-encounters’ for many. I explain how the non-encounters of being lulled, cajoled, and swayed can be evaluated as politically troubling, but that responses to heighten awareness of automation's politics may not necessarily result in people caring more about it. My commentary proposes a more indeterminate understanding of how people immanently evaluate automation which invites a more transitional and low-intensity understanding of the dispositions involved.
DBCC2: an improved difficulty-based cooperative co-evolution for many-modal optimization

Abstract Evolutionary multimodal optimization algorithms aim to provide multiple solutions simultaneously. Many studies have been conducted to design effective evolutionary algorithms for solving multimodal optimization problems. However, optimization problems with many global and acceptable local optima have not received much attention. This type of problem is undoubtedly challenging. In this study, we focus on problems with many optima, the so-called many-modal optimization problems, and this study is an extension of our previous conference work. First, a test suite including additively nonseparable many-modal optimization problems and partially additively separable many-modal optimization problems is designed. Second, an improved difficulty-based cooperative co-evolution algorithm (DBCC2) is proposed, which dynamically estimates the difficulties of subproblems and allocates the computational resources during the search. Experimental results show that DBCC2 has competitive performance.
A Comparative Study of YOLOv5 models on American Sign Language Dataset

Sign language is the most common way of communication for people with hearing and speech difficulties. One of the biggest problems for sign language user is that most people does not understand sign language. The most promising solution to this problem is a sign language detection system using object detection algorithm. YOLOv5 is state-of-art one-stage object detection algorithm and is available in wide range of model complexity, ranging from simplest YOLOv5n to most complex YOLOv5x. To achieve efficient communication, the sign language detection system needs to be fast and reliable. However, many previous studies only used the most complex model without considering the time needed for the system to run. As more complex model tends to performs better at the cost of computational time, the most optimal model for sign language detection system is a model that performs well while maintaining fast inference time. In this study, we compare the inference time and performance of every YOLOv5 model available, trained on American Sign Language dataset to find the most optimal model of YOLOv5 for sign language detection. The experiment results shows that while YOLOv5x has slightly better performance than other models with mAP of 0.88 and F1 score of 0.91, it required twice the amount of time to detect the sign language with inference time of 26.2 ms. The same can be said to YOLOv5m and YOLOv5l, both with mAP of 0.88 and F1 score of 0.88 and 0.90, while require inference time of 16.2 ms and 19.1 ms respectively. YOLOv5n is the fastest model at inference time of 7.2 ms, but the performance is considerably worse with mAP of 0.79 and F1 score of 0.88. In conclusion, YOLOv5s is the most optimal model with mAP of 0.88, F1 score of 0.90, and inference time of 10.6 ms.
Exploring Customer Experience with Service Robots in Hospitality and Tourism: Activity Theory Perspective

Abstract Addressing a call for theoretical development in human-robot interaction research, this study introduces activity theory to the field of service robots (SRs) in hospitality and tourism. Activity theory was used as the foundation for the conceptual analysis of in-depth interviews with hospitality customers. The results of content analysis of the interviews and future research directions are presented based on each of the service activity system’s components: object (customer experience (CE) with SRs in a hospitality unit), subject (customers), technology (SRs), rules (implementation procedures of SRs), community (customers, other customers, and employees), division of labor (a division of service), outcome (satisfaction, overall experience with hospitality establishments, and behavioral intentions), and context. The study provides future research directions in using activity theory in studies on human-robot interaction and CE with SRs in hospitality and tourism. Robot developers and hospitality professionals can use the data analysis framework proposed in this study to evaluate CE with SRs.
Analysis of Information Technology Governance on Process Management Services and Management of Information Technology Security Using COBIT 2019 (Case Study: PT XYZ)

The development of disruptive technology in the modern era is the basis for the important role of Information Technology (IT) for every component, even in an organization, to achieve its targets and goals. At PT XYZ, there are some stages which include general administration, accounting, and finance that cannot be separated from the application system usage. Management must measure whether the application system is safe from any risk, mainly from security issues, frequently issues that management receives are web-based cyber-attacks, and the recovery process often impedes the course of business activity. Management’s efforts to reduce the obstacles that occur begin with knowing the ability of information technology governance, so governance and processes are needed to analyze. This study aims to provide recommendations related to the management of IT services and IT security, which refers to the COBIT 2019 framework by focusing on the domain processes of APO13 Managed Security, BAI06 Manage IT Changes, and DSS02 Managed Service Requests and Incidents at PT XYZ. The process of this research begins by collecting data through documentation analysis methods, interviews, and distributing questionnaires with an analysis of the ideal conditions desired by the company so that it will produce a gap analysis by COBIT 2019 and COBIT 2019 Implementation. Furthermore, recommendations will be made to improve services in the aspects of people, process, and technology based on the results of the questionnaire analysis, such as offering several choices of software or applications and drafting documents regarding work instructions for fulfilling IT services and handling IT problems.
Speeded Up Robust Features Algorithm on Augmented Reality for Sirah Nabawiyah Learning Media

Based on the survey results, 85% of young children do not know about the history of the prophet or the miracles that were revealed to the prophet. Studying the history of the prophet is an embodiment of the 4th pillar of faith, namely Faith in the Prophet. Given the urgency, several learning media in various forms have been created and marketed to introduce Sirah nabawiyah, one of which is Augmented Reality. This study aims to build an application to recognize the history of the prophet using Augmented Reality. The Speeded Up Robust Features (SURF) algorithm is applied to detect AR markers. Based on the effectiveness test results, the application can display objects with an average time of 0.17 seconds for a distance of 10 cm, 0.23 seconds for a distance of 20 cm, and 0.34 seconds for a distance of 30 cm. This shows that the algorithm can function well in detecting objects.
Implementation of Code Integrity Check Module for RISC-V Architecture with AES

Embedded applications are commonly used in different fields. Different kinds vulnerabilities can occur in embedded systems because of various reasons such as coding bugs or security attacks. Most of security vulnerabilities can occur during coding phase of an embedded application. During the design phase of a program, different types of coding bugs can be inserted to executable. On the other hand, even if the executable program is very well coded, many different kinds of vulnerabilities can also occur at runtime. In this thesis, we aim to provide a security application which controls integrity of running program in computer. Executable binary of a program consists of data and text sections. CPU fetches and executes instructions in the text section of a program. Each instruction block is firstly placed into instruction cache of CPU. Instruction block hashes are generated after compiler phase of a program. Then, during the runtime phase instruction block hashes are recalculated and compared with compiler phase hash. We aim to prevent running of a corrupted or manipulated program during runtime phase.
Features of Metaheuristic Algorithm for Integration with ANFIS Model

In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification problems. In the Adaptive Neuro-fuzzy inference system(ANFIS), many researchers used the adaption of metaheuristic algorithms with ANFIS to propose the best estimation model. However, many researchers only focused on the experiment without the demonstration mathematical or indicating which characteristic of optimization algorithm, during the run, affect and settable in coordination with ANFIS. The paper provides an adaption of metaheuristic algorithms with ANFIS which has been performed by considering accuracy parameters in layer 1 and layer 4 for the estimation problem. It is integrated six well-known metaheuristic algorithms and extracting the characteristic of them. In the experiment, the metaheuristic algorithms based on the evolutionary computation have been demonstrated more stable than swarm intelligence methods in tuning parameters of ANFIS.
Development of a Covid-19 Information Dashboard to Access the Health Care Resources and Requirements Online

With the emergence of coronavirus and the rapidly increasing number of cases, we observed that people were suffering from a lack of information about where to source critical requirements such as oxygen cylinders, beds, ambulance service, and ventilators. In this research paper and project, the authors have designed and developed an interactive information dashboard to access the availability of these resources and requirements at different locations across India from myriad sources. The information dashboard will show information for all the states across India. The dashboard visualizes the number of corona cases, hospital beds, blood bank, etc. The end-users and the COVID-19 support team can imagine all the requirements in the dashboard that is factual and credible within their vicinity from multiple information sources. The user need not search various information sites to search for different conditions. The project collates the data of other requirements from primary sources of information like Twitter and government websites and lists them on the dashboard. The authors used open-source programming and database technologies to display the data per user requirements. The dashboard was helpful to the end-users during COVID-19 times in terms of convenient accessibility and efficiency.