Reviewerly/exHarmony huggingface dataset:
Towards a Taxonomy of Autonomous Systems

In this paper, we present a precise and yet concise characterisation of autonomous systems. To the best of our knowledge, there is no similar work, which through a mathematical definition of terms provides a foundation for describing the systems of the future: autonomous software-intensive systems and their architectures. Such systems include robotic taxi as an example of 2D mobility, or even drone/UAV taxi, as an example in the field of 3D urban air mobility. The presented terms lead to a four-level taxonomy. We describe informally and formally the taxonomy levels and exemplarily compare them to the degrees of automation as previously proposed by the SAE J3016 automotive standard.
Path and Action Planning in Non-uniform Environments for Multi-agent Pickup and Delivery Tasks

Although the multi-agent pickup and delivery (MAPD) problem, wherein multiple agents iteratively carry materials from some storage areas to the respective destinations without colliding, has received considerable attention, conventional MAPD algorithms use simplified, uniform models without considering constraints, by assuming specially designed environments. Thus, such conventional algorithms are not applicable to some realistic applications wherein agents have to move in a more complicated and restricted environment; for example, in a rescue or a construction site, their paths and orientations are strictly restricted owing to the path width, and the sizes of agents and materials they carry. Therefore, we first formulate an N-MAPD problem, which is an extension of the MAPD problem for a non-uniform environment. We then propose an N-MAPD algorithm, the path and action planning with orientation (PAPO), to effectively generate collision-free paths meeting the environmental constraints. The PAPO is an algorithm that considers not only the direction of movement but also the orientation of agents as well as the cost and timing of rotations in our N-MAPD formulation by considering the agent and material sizes, node sizes, and path widths. We experimentally evaluated the performance of the PAPO using our simulated environments and demonstrated that it could efficiently generate not optimal but acceptable paths for non-uniform environments.
Synthetic Aperture Radar Image Compression Based on a Variational Autoencoder

Given the uniqueness of synthetic aperture radar (SAR) images, traditional optical image compression algorithms cannot fully exploit their redundant information. To improve SAR image compression in terms of rate–distortion performance and visual perception, an end-to-end SAR image compression convolutional neural network (CNN) model based on a variational autoencoder is proposed. The proposed CNN model consists of a main autoencoder and a hyper autoencoder. To reduce dependencies in latent space, a joint transform of linear CNN and nonlinear generalized divisive normalization (GDN) activation is applied in the main autoencoder. Moreover, residual blocks are combined with the transforms to boost the efficiency of feature learning and make use of subpixels to improve the quality of reconstructed images. Instead of a fixed entropy model, a conditioned entropy model that works with a hyperprior network is used to learn the distribution of latents, which helps to further improve the compression quality. During training, the model is optimized by evaluating the rate–distortion performance. The experimental results show that the proposed method can achieve better distortion performance than JPEG, JPEG2000, and the available CNN-based method in terms of objective evaluation criteria and human vision perception quality.
Attention Mask-Based Network With Simple Color Annotation for UAV Vehicle Re-Identification

Vehicle re-identification (VeID) has attracted a growing research interest in recent years, and excellent performance has been shown with fixed traffic cameras. However, vehicle ReID in aerial images taken by unmanned aerial vehicles (UAVs), possessing both variable locations and special viewpoints, is still under-explored. Recent works tend to extract meaningful local features by careful annotation, which are effective but time-consuming. In order to extract discriminative features and avoid tedious annotating work, this letter develops an attention mask (AM)-based network with simple color annotation for object enhancement and background reduction. The network makes full use of deep features obtained by a pretrained color classification network and then utilizes principal component analysis (PCA) as a mapping function to achieve AMs without partial annotation. Besides, we introduce weighted triplet loss (WTL) function to deal with the problem of great similarity between classes caused by overlook views of UAVs. The loss function concentrates more on negative pairs to facilitate the identification ability of network. Rich experiments are conducted on both UAV dataset and surveillance dataset, and our method achieves competitive performance compared with recent ReID works.
An Energy-Efficient FPGA-based Convolutional Neural Network Implementation

Convolutional Neural Networks (CNNs) are a very popular class of artificial neural networks. Current CNN models provide remarkable performance and accuracy in image processing applications. However, their computational complexity and memory requirements are discouraging for embedded realtime applications. This paper proposes a highly optimized CNN accelerator for FPGA platforms. The accelerator is designed as a LeNet CNN architecture focusing on minimizing resource usage and power consumption. Moreover, the proposed accelerator shows more than 2x higher throughput in comparison with other FPGA LeNet accelerators with reaching up 14 K images/sec. The proposed accelerator is implemented on the Nexys DDR 4 board and the power consumption is less than 700 mW which is 3x lower than the current LeNet architectures. Therefore, the proposed solution offers higher energy efficiency without sacrificing the throughput of the CNN.
Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity.Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs.In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets.Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications.Bag of words is an approach used in natural language processing to retrieve information and features from written documents.For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy.For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets.Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques.The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms.The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset.
LoL-V2T: Large-Scale Esports Video Description Dataset

Esports is a fastest-growing new field with a largely online-presence, and is creating a demand for automatic domain-specific captioning tools. However, at the current time, there are few approaches that tackle the esports video description problem. In this work, we propose a large-scale dataset for esports video description, focusing on the popular game "League of Legends". The dataset, which we call LoL-V2T, is the largest video description dataset in the video game domain, and includes 9,723 clips with 62,677 captions. This new dataset presents multiple new video captioning challenges such as large amounts of domain-specific vocabulary, subtle motions with large importance, and a temporal gap between most captions and the events that occurred. In order to tackle the issue of vocabulary, we propose a masking the domain-specific words and provide additional annotations for this. In our results, we show that the dataset poses a challenge to existing video captioning approaches, and the masking can significantly improve performance. Our dataset and code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Cloud-Based Computing Framework for Artificial Intelligence Innovation in Support of Multidomain Operations

The DoD’s artificial intelligence (AI) strategy requires the delivery of transformative and disruptive capabilities that impact the “character of the future battlefield and the pace of threats” that US forces must be prepared to handle. Candidate frameworks must also address key mission areas while enabling partnerships with the private sector, academia, and global allies. To meet these challenges, a flexible, cost-effective, and scalable computing infrastructure that incorporates cutting edge technologies and complies with stringent information assurance requirements is necessary. The DoD AI strategy mandates the agile employment of innovative AI capabilities that “rapidly and iteratively” execute experimentation with new operating concepts, and leverage lessons learned in subsequent experiments. Using cloud computing, we present a flexible approach to solve complex systems problems. Promoting “rapid experimentation” and collaboration on problems such as recursive algorithm implementation, deep learning, and inference in neural networks has enabled inherent advantages over existing computing frameworks. Leveraging the cloud to implement shared responsibility security models, serverless architectures, and high-performance virtual machines, aspects of the AI lifecycle including build, deploy, and monitor have resulted in an adaptable and scalable computing framework that is not only disruptive to the current computing paradigm but also promotes enhanced and productive collaboration.
Text-independent Speaker Recognition using Deep Neural Networks

In this paper, a deep neural network is proposed for Speaker Verification (SV) system. SV is an authentication process, which involves the examination of knowing, if the speaker is the target person or not? The human voice is a biometric-like fingerprint which is an authentication tool. Neural networks are one of the successful methods to learn from unstructured data such as speech signals. Deep neural networks show plausible results in many applications of speaker verification. In this paper, we propose a Dense neural network trained on the free-spoken digit dataset (FSDD). Simulation results demonstrate the effectiveness of the proposed approach, despite of the simple deep neural network, we achieved a good accuracy of about 93%.
The development and construction of an <scp>AR</scp>‐guided learning model with focused learning theories

Abstract Background Augmented reality (AR) has been incorporated in context‐aware ubiquitous learning (CAUL) designs to guide learners but few of the designs adopt specific learning theories. Furthermore, there are no CAUL design models that align learning theories to CAUL effectiveness. In addition, CAUL review papers have not documented CAUL developments since AR technology has become normalized. Objectives A phenomenological research method with expert design input was adopted to build theoretically informed CAUL design models and provide guidelines for AR‐driven CAUL. Methods The researchers reviewed 38 empirical AR studies in education from 2016 to 2019 in seven high‐impact journals of educational technology using a taxonomy based on CAUL learning theories. Seven domain experts reviewed the findings and provided insights into current developments, patterns of variables, and the proposed AR‐guided CAUL model. The expert feedback was used to refine the CAUL models. Results and conclusions Research into humanity‐related subjects are growing due to the features of AR, and the majority of studies were in primary education as AR corresponds well with lower‐level cognitive learning tasks. Many AR functions cooccurred with situated learning theory, inquiry‐based learning theory, and collaborative learning theory. A macro‐design model and three micro‐design models of AR‐guided CAUL with specific learning theories was proposed. Major takeaways This study offers a set of AR‐guided CAUL design models aligned with established learning theories. The variables in the CAUL studies are linked with learning theories to clearly show how AR guides learning. The models can guide instructors to meet their students' needs.
UCloD: Small Clock Delays to Mitigate Remote Power Analysis Attacks

This paper presents UCloD, a novel random clock delay-based robust and scalable countermeasure against recently discovered remote power analysis (RPA) attacks. UCloD deploys very small clock delays (in the picosecond range) generated using the tapped delays lines (TDLs) to mitigate RPA attacks. UCloD provides the most robust countermeasures demonstrated thus far against RPA attacks. RPA attacks use delay sensors, such as Time to Digital Converters (TDC) or Ring Oscillators (ROs) to measure voltage fluctuations occurring in power delivery networks (PDNs) of Field Programmable Gate Arrays (FPGAs). These voltage fluctuations reveal secret information, such as secret keys of cryptographic circuits. The only countermeasure proposed thus far activates ROs to consume significant power and has managed to secure Advanced Encryption Standard (AES) circuits for up to 300,000 encryptions. Using TDLs available in FPGAs, UCloD randomly varies the clock to the cryptographic circuits under attack to induce noise in the adversary's delay sensor(s). We demonstrate correlation power analysis (referred to as CPA) attack resistance of UCloD AES implementations for up to one million encryptions. Compared to an unprotected AES circuit, UCloD implementations have minimal overheads (0.2% Slice LUT overhead and 4.8% Slice register overhead for Xilinx implementations and 0.5% LogicCells overhead for Lattice Semiconductor implementations).
Estimating quantum steering and Bell nonlocality through quantum entanglement in two-photon systems

Quantum entanglement, quantum steering and Bell nonlocality, as significant quantum resources in the field of quantum information science, can achieve variously valuable quantum information tasks. Among of them, quantum entanglement and Bell nonlocality are the weakest and strongest nonlocal correlations, respectively. One can capture the quantum steering and Bell nonlocality via violating steering inequality and Bell inequality, respectively. In general, the detections of quantum steering and Bell nonlocality are strictly harder than entanglement detection. Here, based on steering inequality test and quantum state tomography, we attain various nonlocal correlations and experimentally demonstrate that the estimations of quantum steering and Bell nonlocality can be realized according to the quantum entanglement of the prepared two-photon test states. The estimated efficiency of quantum steering is stronger than the one of Bell nonlocality in this scenario, i.e., more steerable two-photon test states can be verified through quantum entanglement. In addition, quantum steering and Bell nonlocality are bounded by the corresponding upper and lower bounds, and these bounds cannot be punctured by all prepared two-photon states in experiment. These results are conducive to understand the relations among these nonlocal correlations.
Verbose exponence: Integrating the typologies of multiple and distributed exponence

Multiple exponence is the multiple marking of the same feature or category within a single word. Distributed exponence is the occurrence of morphological structure such that providing a precise interpretation of a category can only be determined after considering more than one morphological formative. I propose the term verbose exponence to capture the common ground between these phenomena, i.e. all situations involving multiple morphological formatives sensitive to some common information. From this definition, a typology based on informational overlap emerges with types of verbose exponence corresponding to types of set interactions. The typology incorporates known related phenomena, such as overlapping exponence, while providing a sophisticated framework for describing previously unnamed phenomena. This typology is naturally cast in set-theoretic terms and formal definitions are provided for all assumptions and points in the typology. Such an approach provides precise articulations of the typological space but also of morphological redundancy, a property often associated with multiple exponence.
Head Node Selection Algorithm in Cloud Computing Data Center

Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.
Evolution of Activation Functions: An Empirical Investigation

The hyper-parameters of a neural network are traditionally designed through a time-consuming process of trial and error that requires substantial expert knowledge. Neural Architecture Search algorithms aim to take the human out of the loop by automatically finding a good set of hyper-parameters for the problem at hand. These algorithms have mostly focused on hyper-parameters such as the architectural configurations of the hidden layers and the connectivity of the hidden neurons, but there has been relatively little work on automating the search for completely new activation functions, which are one of the most crucial hyperparameters to choose. There are some widely used activation functions nowadays that are simple and work well, but nonetheless, there has been some interest in finding better activation functions. The work in the literature has mostly focused on designing new activation functions by hand or choosing from a set of predefined functions while this work presents an evolutionary algorithm to automate the search for completely new activation functions. We compare these new evolved activation functions to other existing and commonly used activation functions. The results are favorable and are obtained from averaging the performance of the activation functions found over 30 runs, with experiments being conducted on 10 different datasets and architectures to ensure the statistical robustness of the study.
Domain Adaptation Deep Attention Network for Automatic Logo Detection and Recognition in Google Street View

Signboards are important location landmarks that provide services to a local community.Non-disabled people can easily understand the meaning of a signboard based on its special shape; however, visually impaired people who need an assistive system to guide them to destinations or to help them understand their surroundings cannot.Currently, designing accurate assistive systems remain a challenge.Computer vision struggles to recognize signboards due to the diverse designs that combine text and images.Moreover, there is a lack of datasets to train the best model and reach good results.In this paper, we propose a novel framework that can automatically detect and recognize signboard logos.In addition, we utilize Google Street View to collect signboard images from Taiwan's streets.The proposed framework consists of a domain adaptation that not only reduces the loss function between source-target datasets, but also represents important source features adopted by the target dataset.In our model, we add nonlocal blocks and attention mechanisms called deep attention networks to achieve the best final result.We perform extensive experiments on both our dataset and public datasets to demonstrate the superior performance and effectiveness of our proposed method.The experimental results show that our proposed method outperforms state-of-the-art methods across all evaluation metrics.
Authentication Mechanisms and Classification: A Literature Survey

The Security must be considered from the need of two nodes to communicate with each other, through the moment of confirmed authentication to the secured establishment. It is an essential component of each authentication. In general, nodes communicate over different communication channels, which can be in private or in public networks. The methods and complexity of authentication vary depending on the parties involved in communication. In this paper, we are surveying the various methods for authentication, based on particular criteria, like the possibility to remember passwords; different graphical ways; body specifics (fingerprints, face); combining various techniques (certificates, PINs, digital signatures, one-time passwords, hash-chains, blockchains); QR codes (also with the involvement of augmented reality) for transmitting long passwords, etc. We aim to do as detailed as possible a survey over the literature, and to classify the ways for authentication, and different authenticators.
Longitudinal Impact of Preference Biases on Recommender Systems' Performance

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