Reviewerly/exHarmony huggingface dataset:
Designing on Western Arrarnta Country: The Ntaria Digital Drawings

Communication design in the Aboriginal community of Ntaria is situated and mediated by Western Arrarnta Country, which encourages a deeper exploration of the ways culture, knowledge, and identity are intertwined and shape ideas of what “design” might be from many centers. This visual narrative presents the outcomes of a communication design education program with Western Arrarnta young adults, part of a four-year participatory project. For the Ntaria students, design is a tool for telling stories, asserting identity, caring for Country, and intergenerational knowledge sharing. Their digital drawing outcomes reveal how Western Arrarnta communication design practice exists in relation to local ecologies and ontologies. By sharing a distinct Western Arrarnta imagining, these digital drawings contribute to dialogs that are reshaping current understandings of what it means to design; to explore the value and meaning of place, relationships, and community.
Computer-aided diagnosis for early cancer detection using Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering from mammogram images

Early detection of breast cancer is major issue that must be addressed globally. Segmentation process play key role in detection. We propose a new hybrid approach called Adaptive Kernel Based Fuzzy Cuckoo Search Optimization Clustering to segment mammogram images. It is diversified into two stages. A kernel-based fuzzy c-means algorithm with cuckoo search optimization is used to obtain optimal cluster centers and obtained optimum centers act as centers to the KFCM clustering process to get different segmented regions in pre processing stage. In post processing stage, a level set method is employed to minimize boundary leakages. The simulation was performed on RIDER data set. The proposed approach is validated by calculating similarity indexing parameters. The simulation was performed on 12 distinct images from the RIDER mammography database. The suggested method's experimental results were compared to existing level set approaches such as IVC2010, IVC2013, and ESA2021.
Distributed Inference in Resource-Constrained IoT for Real-Time Video Surveillance

Advances in communication technologies and computational capabilities of Internet of Things (IoT) devices enable a range of complex applications that require ever increasing processing of sensors' data. An illustrative example is real-time video surveillance that captures videos of target scenes and process them to detect anomalies using deep learning (DL). Running deep learning models requires huge processing and incurs high computation delay and energy consumption on resource-constraint IoT devices. In this article, we introduce methods for distributed inference over IoT devices and edge server. Two distinct algorithms are proposed to split the deep neural network layers computation between IoT device and an edge server; the early split strategy (ESS) for battery powered IoT devices and the late split strategy (LSS) for IoT devices connected to regular power source. The evaluation shows that both the ESS and LSS schemes achieve the target inference delay deadline when tested over VGG16 and MobileNet_V2 CNN models. In terms of computational load, the ESS scheme achieves nearly 15–20% reduction whereas LSS scheme achieves up to 60% reduction. The gains in energy saving of IoT devices for both the ESS and LSS schemes are nearly 18% and 52%, respectively.
Accurate Glass Insulators Defect Detection in Power Transmission Grids Using Aerial Image Augmentation

Insulator is one of the most critical components of power transmission lines and its timely and accurate defect detection is considered important to ensure reliable and safe operation of transmission grids. This paper proposes an efficient augmentation method of aerial images captured using the unmanned aerial vehicle for the accurate detection of insulators with self-detonation defects. Through the adoption of the improved Resnet-18 model with the insulator edge features and the Grad-CAM based saliency map generation, the proposed solution can well maintain the vital regions for fine-grained classification in the augmentation process. The proposed solution is extensively assessed in comparison with the CNN-based benchmark methods through experiments. The numerical results indicate that the improved ResNet-18 model with the augmented images outperform the existing solutions and can identify the self-detonation defects with an accuracy of 95.1%.
Petroleum Pipeline Interface Recognition and Pose Detection Based on Binocular Stereo Vision

Liquified natural gas (LNG) manipulator arms have been widely used in natural gas transportation. However, the automatic docking technology of LNG manipulator arms has not yet been realized. The first step of automatic docking is to identify and locate the target and estimate its pose. This work proposes a petroleum pipeline interface recognition and pose judgment method based on binocular stereo vision technology for the automatic docking of LNG manipulator arms. The proposed method has three main steps, including target detection, 3D information acquisition, and plane fitting. First, the target petroleum pipeline interface is segmented by using a color mask. Then, color space and Hu moment are used to obtain the pixel coordinates of the contour and center of the target petroleum pipeline interface. The semi-global block matching (SGBM) algorithm is used for stereo matching to obtain the depth information of an image. Finally, a plane fitting and center point estimation method based on a random sample consensus (RANSAC) algorithm is proposed. This work performs a measurement accuracy verification experiment to verify the accuracy of the proposed method. The experimental results show that the distance measurement error is not more than 1% and the angle measurement error is less than one degree. The measurement accuracy of the method meets the requirements of subsequent automatic docking, which proves the feasibility of the proposed method and provides data support for the subsequent automatic docking of manipulator arms.
SEMMI: Multi-party security decision-making scheme for linear functions in the internet of medical things

In the Internet of Medical Things(IoMT), developing models using machine learning algorithms can detect and assist users effectively in identifying health issues. Due to the risk of private user information being leaked during the machine learning application process, the widespread application and development of IoMT applications are hampered. Encrypting data is a good way to protect user privacy. However, given the participants’ limited resources, processing and analyzing ciphertext data presents a significant challenge. As a result, this paper proposes a secure and efficient assisted decision-making scheme (SEMMI) that is appropriate for the IoMT applications. SEMMI performs a thorough analysis of each participant’s resource constraints, divides the data circulation process into four stages, and constructs a data circulation and ciphertext calculation protocol. Data transmission security is ensured through the use of stream encryption and homomorphic encryption. Each participant sends the ciphertext to the cloud, and the cloud calculates the ciphertext data, effectively relieving each participant’s computational load. The security of the final result is guaranteed by matching the result with pre-decryption. The scheme’s security and efficiency are demonstrated experimentally. The results indicate that the accuracy loss of each data set under the ciphertext is no more than 3% at most and that the cloud performs most of the calculations for each participant. Finally, SEMMI is applied to the FedAvg algorithm, demonstrating the scheme’s universality.
Engineering Grover Adaptive Search: Exploring the Degrees of Freedom for Efficient QUBO Solving

Quantum computers have the potential to solve Quadratic Unconstrained Binary Optimization (QUBO) problems with lower computational complexity than classical ones. Considering the current limitations of quantum hardware, the joint use of classical and quantum paradigms could exploit both advantages. Quantum routines can make some complex tasks for classical computers feasible. For example, in the Grover Adaptive Search (GAS) procedure, the problem cost function is classically shifted iteratively, whenever a negative value is found through the quantum Grover Search (GS) algorithm, until the minimum is achieved. This quantum-classical approach is characterized by many degrees of freedom, e.g. the number of GS iterations in each call and the stop condition of the algorithm, which should be appropriately tuned for an effective and fast convergence to the optimal solution. The availability of software routines could permit the best management of the GAS parameters. This work proposes new mechanisms for GAS parameters management and compares them with the existing ones, like one available in the Qiskit framework. The proposed mechanisms can automatically arrange the parameters according to the algorithm evolution and their previous experience, thus ensuring a more frequent and faster achievement of the optimal solution. Even though these strategies can be further improved, the results are encouraging. The analysis is done to identify the best policy for different problems. It lays the foundation for designing an automatic toolchain for QUBO solving, which can obtain the best possible implementation of the GAS algorithm for each submitted problem.
A Hybrid Deep Dependency Parsing Approach Enhanced With Rules and Morphology: A Case Study for Turkish

Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is low-resourced and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on the IMST-UD Treebank and the results suggest that integration of explicit knowledge about the target language to a neural parser through a rule-based parsing system and morphological analysis leads to more accurate annotations and hence, increases the parsing performance in terms of attachment scores. The proposed methods are developed for Turkish, but can be adapted to other languages as well.
Path Planning of Mobile Robot Using Reinforcement Learning

In complex planning and control operations and tasks like manipulating objects, assisting experts in various fields, navigating outdoor environments, and exploring uncharted territory, modern robots are designed to complement or completely replace humans. Even for those skilled in robot programming, designing a control schema for such robots to carry out these tasks is typically a challenging process that necessitates starting from scratch with a new and distinct controller for each task. The designer must consider the wide range of circumstances the robot might encounter. This kind of manual programming is typically expensive and time consuming. It would be more beneficial if a robot could learn the task on its own rather than having to be preprogrammed to perform all these tasks. In this paper, a method for the path planning of a robot in a known environment is implemented using Q-Learning by finding an optimal path from a specified starting and ending point.
On the hardness of quadratic unconstrained binary optimization problems

We use exact enumeration to characterize the solutions of quadratic unconstrained binary optimization problems of less than 21 variables in terms of their distributions of Hamming distances to close-by solutions. We also perform experiments with the D-Wave Advantage 5.1 quantum annealer, solving many instances of up to 170-variable, quadratic unconstrained binary optimization problems. Our results demonstrate that the exponents characterizing the success probability of a D-Wave annealer to solve a quadratic unconstrained binary optimization correlate very well with the predictions based on the Hamming distance distributions computed for small problem instances.
Training of Deep Learning Models Using Synthetic Datasets

In order to solve increasingly complex problems, the complexity of Deep Neural Networks also needs to be constantly increased, and therefore training such networks requires more and more data. Unfortunately, obtaining such massive real world training data to optimize neural networks parameters is a challenging and time-consuming task. To solve this problem, we propose an easy-to-use and general approach to training deep learning models for object detection and instance segmentation without being involved in the generation of real world datasets. In principle, we generate and annotate images with open-source software and 3D models that mimic real life objects. This approach allows us significantly reduce the effort required to gather pictures as well as automatize data tagging. It is worth noting that such synthetic datasets can be easily manipulated, e.g. to reduce the texture bias that often occurs in the resulting trained convolutional networks. Using the Mask R-CNN instance segmentation model as an example, we demonstrate that a network trained on the synthetic dataset of kitchen facilities shows remarkable performance on the validation dataset of real-world human-annotated photos. We show that our approach helps to bridge the domain gap between pre-trained models and their specific applications. In summary, such synthetic datasets help overcome the problem of acquiring and tagging thousands of images, while reducing the time and labor costs associated with the preparation of an appropriate real dataset.
Toward feature space adversarial attack in the frequency domain

Recent researchers have shown that deep neural networks (DNNs) are vulnerable to adversarial exemplars, making them unsuitable for security-critical applications. Transferability of adversarial examples is crucial for attacking black-box models, which facilitates adversarial attacks in more practical scenarios. We propose a novel adversarial attack with high transferability. Unlike existing attacks that directly modify the input pixels, our attack is executed in the feature space. More specifically, we corrupt the abstract features by maximizing the feature distance between the adversarial example and clean images with a perceptual similarity network, inducing model misclassification. In addition, we apply a spectral transformation to the input, thus narrowing the search space in the frequency domain to enhance the transferability of adversarial examples. The disruption of crucial features in a specific frequency component achieves greater transferability. Extensive evaluations illustrate that our approach is easily compatible with many existing frameworks for transfer attacks and can significantly improve the baseline performance of black-box attacks. Moreover, we can obtain a higher fooling rate even if the model has a defense technique. We achieve a maximum black-box fooling rate of 61.70% on the defense model. Our work indicates that existing pixel space defense techniques are difficult to guarantee the robustness of the feature space, and the feature space from a frequency perspective is promising for developing more robust models.
Hand Gesture and Human-Drone Interaction

Human computer Interaction is a wide domain which includes different ways of interaction i.e., using hand gestures and body postures. Gestures Detection relate to non-verbal ways to deliver information to the system for control. The aim of gesture recognition is first recording the gestures and then these gestures are read and interpreted by a camera. Gesture recognition has wide range of applications. It can be used by disabled persons to communicate. This paper focuses on detailed research of controlling drones with hand gestures. The presented system is made of three main blocks i.e. (1) the detection of gestures, (2) translating the gestures and (3) controlling the drone. Deep learning algorithm is used in the first module to detect the real-time gestures of hands. Secondly gesture translator uses some image processing techniques to identify gestures. Control signals are then generated for controlling the drone. Preliminary validation results are promising.
Commentary on Biological Assets Cataloging and AI in the Global South

The Global South is rich in biodiversity, and with that richness of Biological Assets, the continued discovery of new agricultural products, pharmaceuticals, and other industrially beneficial bio-resources is possible. However, this biodiversity can also be a source of biologically dangerous materials. Key to gaining the ability to discover, sort, utilize, and properly evaluate these resources at pace with the Global North will require highly efficient means. This will likely rely on 4th Industrial Revolution technologies. One that is gaining traction is artificial intelligence. An under discussed topic is how the Global South is treating the intersection of their assets with AI, along with the capability to address this intersection. It is possible that vulnerabilities in cataloging endanger efforts which could obscure, misrepresent, or slow discovery and management of resources apparent. Herein, a commentary is provided on this potential, and possibly extant threat of academic distributed denial of service attacks (DDOS attacks), robbing the world of valuable insight and time in defending against novel threats.
AI based monitoring violent action detection data for in-vehicle scenarios

With the evolution of technology associated with mobility and autonomy, Shared Autonomous Vehicles will be a reality. To ensure passenger safety, there is a need to create a monitoring system inside the vehicle capable of recognizing human actions. We introduce two datasets to train human action recognition inside the vehicle, focusing on violence detection. The InCar dataset tackles violent actions for in-car background which give us more realistic data. The InVicon dataset although doesn't have the realistic background as the InCar dataset can provide skeleton (3D body joints) data. This datasets were recorded with RGB, Depth, Thermal, Event-based, and Skeleton data. The resulting dataset contains 6 400 video samples and more than 3 million frames, collected from sixteen distinct subjects. The dataset contains 58 action classes, including violent and neutral (i.e., non-violent) activities.
GNGraph: Self-Organizing Maps for Autonomous Aerial Vehicle Planning

The present letter tackles the problem of planning a collision-free path in a known environment from a general point of view. We address the problem by using an unsupervised learning algorithm to generate a sparse graph representing the topological structure of the environment and use it for planning paths in 3D spaces. We propose GNGraph, an integrated solution combining the Growing Neural Gas algorithm to generate the sparse graph, a stop criterion to guarantee the graph's connectivity and a collision check to assess the edges and nodes validity. The proposed solution has been tested on simulated and real environment maps, and compared against a state-of-the-art graph planning algorithm among other global planning methods.
CRMRL: Collaborative Relationship Meta Reinforcement Learning for Effectively Adapting to Type Changes in Multi-Robotic System

Multi-agent reinforcement learning methods have been widely used for multi-robotic systems, and meta-learning methods are also applied to help robots reuse prior experiences to guide new tasks learning. But in some multi-robotic tasks, the robot types cannot be determined in advance or may dynamically change in practical application, bringing urgent challenges. When the robot types change, the prior experiences may be outdated and useless for new robots, even leading to negative transfer. It significantly limits the performance of most existing meta reinforcement learning methods. We get inspiration from the structure of bees, ants, and neurons. These colonies can overcome the interference caused by individual replacement, and always maintain dynamic stability. We find the relationship among individuals is more important than individuals themselves in these colonies, and propose a collaborative relationship meta reinforcement learning method (CRMRL). It concentrates on the relationship among robots, and reuses the collective knowledge to alleviate the interference caused by robot changes, dealing with the challenge of different combinations and dynamic changes in multi-robotic systems. The experiments are carried out on the StarCraft II platform and Webots simulator. The extensive results indicate that our method has noticeable improvement on many indicators compared with the traditional meta reinforcement learning methods.
Background knowledge attacks in privacy-preserving data publishing models

Massive volumes of data are being generated at every moment through various sources in the cyber-physical world. While storing as well as facilitating these data for business or individual requirements, data disclosure, sensitive data leakage, and privacy breaches are important concerns to both service providers and service consumers. Many privacy-preserving data publishing models came into existence to protect data security and privacy from disclosure. Background knowledge has been an important data source to the adversary and has become a potential threat to many privacy-preserving data publishing models. Background knowledge allows the adversary to reveal sensitive information of an individual from the published data. In this paper, we formalize background knowledge by defining different knowledge sets. We present a privacy model against the given background knowledge. We analyze the conventional privacy-preserving data publishing models such as k-anonymity, l-diversity, and t-closeness against the background knowledge attacks and show that all these privacy models fail to preserve privacy against the comprehensive background knowledge adversarial model, which we formalized in this work. Comprehensive background knowledge attacks in privacy-preserving data publishing models are practical in many real-world applications, and we believe that the privacy model presented in this work advances the research findings in the area.
Decoding Quadratic Residue Codes Using Deep Neural Networks

In this paper, a low-complexity decoder based on a neural network is proposed to decode binary quadratic residue (QR) codes. The proposed decoder is based on the neural min-sum algorithm and the modified random redundant decoder (mRRD) algorithm. This new method has the same asymptotic time complexity as the min-sum algorithm, which is much lower than the difference on syndromes (DS) algorithm. Simulation results show that the proposed algorithm achieves a gain of more than 0.4 dB when compared to the DS algorithm. Furthermore, a simplified approach based on trapping sets is applied to reduce the complexity of the mRRD. This simplification leads to a slight loss in error performance and a reduction in implementation complexity.