Reviewerly/exHarmony huggingface dataset:
Path Optimization for Ground Vehicles in Off-Road Terrain

We present a method for path optimization for ground vehicles in off-road environments at high speeds. This path optimization considers the kinematic constraints of the vehicle. By thinking in the actuator space we can represent such constraints as limits in the space rather than derived properties of the path. In this paper we present an actuator space approach to path optimization for off-road ground vehicles. This is done by representing the path as a list of steering angles over the path length. This transforms the set of kinematic constraints into constraints on the steering angle. We then put this path into a gradient descent solver. This produces paths that are kinematically feasible and optimized in accordance with our cost function. Finally, we tested the system both in simulation and on an off-road vehicle at speeds of 5 m/s.
Proposal to Integrate Security Practices into the ISO/IEC 29110 Standard to Develop Mobile Apps

In recent years, smartphones have accessed to personal information and manage sensitive data, defined as a wide quantity of information encompassing the individual’s personal life. Thus, mobile apps security has become a major area of interest for everyone involved in the development of these. It is important to consider that most of the applications security issues are introduced in the development process. In addition, according to A. Semeney, Founder of Dev Team Space, mobile applications are generally developed by small teams or software development Very Small Entities (VSEs). In this context, there is a need for models, standards or methodologies suitable for VSEs to help in the development of secure mobile applications. In the software development industry, there are few standards suitable for VSEs that integrate security practices. Therefore, the article aims is to present a proposal to integrate security practices into the ISO/IEC 29110 standard for use in the development of secure mobile applications in Very Small Entities.
Anti-theft security hidden alert system based on IoT

The Internet of things (IoT) describes any electronic thing connected to data with other devices and systems over the Internet. Prosperity and Stability of any scene or zone is a main key factor in life. This paper provides a revolutionary model for a thief detection device based on cloud computing and GSM technology. The pressure sensor is certified to detect the login of strangers. When the sensor registers the login signal at the security site, the system stores the location of the fracture areas in the server. Mobile devices also send GPS coordinates for violated areas to the control center. The advantage of this system is that the sensors and detectors are hidden. This feature solves the problem of early detection of alarm devices by intruders or experts in detecting organized crime. Every day, experiments are being conducted to enable theft detection devices, which are matched by special experiments to detect these systems. As a result, unobserved systems are difficult to disclose.
Online Recommendation-based Convolutional Features for Scale-Aware Visual Tracking

In this paper, we develop an online learning-based visual tracking framework that can optimize the target model and estimate the scale variation for object tracking. We propose a recommender-based tracker, which is capable of selecting the representative convolutional neural network (CNN) layers and feature maps autonomously. In addition, the proposed recommender computes the weights of these layers and feature maps. A discriminative target percept of each recommended layer is reconstructed by the weighted sum of the recommended feature maps. Then the target model of the correlation filter is updated by the weighted sum of the target percepts. Thus, a sub-network is extracted from the pre-trained CNN backbone for the tracking process of a specific target. To deal with scale changes, we propose a spatiotemporal-based min-channel method to estimate the target size variation directly from CNN features. Experimental results on 50 benchmark datasets and video data from a rescue drone demonstrate that the proposed tracker is quite competitive with the state-of-the-art CNN-based trackers in terms of accuracy, scale adaptation, and robustness for UAV-related applications.
Collaborative Augmented Digital Twin: A Novel Open-Source Augmented Reality Solution for Training and Maintenance Processes in the Shipyard of the Future

Large companies use a lot of resources on workshop operator training and industrial machinery maintenance since the lack of this practice or its poor implementation increases the cost and risks of operating and handling sensitive and/or hazardous machinery. Industrial Augmented Reality (IAR), a major technology in the Industry 4.0 paradigm that may enhance worker performance, minimize hazards and improve manufacturing processes, could be beneficial in this situation. This paper presents an IAR solution that allows for visualizing and interacting with the digital twin of a critical system. Specifically, the augmented digital twin of an industrial cooler was developed. The proposed IAR system provides a dynamic way to perform operator training with a full-size model of the actual equipment and to provide step-by-step guidance so that maintenance processes can be performed more safely and efficiently. The proposed system also allows several users to use devices at the same time, creating a new type of collaborative interaction by viewing the model in the same place and state. Performance tests with many simultaneous users have been conducted, with response latency being measured as the number of connected users grows. Furthermore, the suggested IAR system has been thoroughly tested in a real-world industrial environment.
Local and global context-based pairwise models for sentence ordering

Sentence Ordering refers to the task of rearranging a set of sentences into the appropriate coherent order. For this task, most previous approaches have explored global context-based end-to-end methods using Sequence Generation techniques. In this paper, we put forward a set of robust local and global context-based pairwise ordering strategies, leveraging which our prediction strategies outperform all previous works in this domain. Our proposed encoding method utilizes the paragraph's rich global contextual information to predict the pairwise order using novel transformer architectures. Analysis of the two proposed decoding strategies helps better explain error propagation in pairwise models. This approach is the most accurate pure pairwise model and our encoding strategy also significantly improves the performance of other recent approaches that use pairwise models, including the previous state-of-the-art, demonstrating the research novelty and generalizability of this work. Additionally, we show how the pre-training task for ALBERT helps it to significantly outperform BERT, despite having considerably lesser parameters. The extensive experimental results, architectural analysis and ablation studies demonstrate the effectiveness and superiority of the proposed models compared to the previous state-of-the-art, besides providing a much better understanding of the functioning of pairwise models.
An Improved Q-learning Approach with Kalman Filter for Self-balancing Robot Using OpenAI

A two-wheeled self-balancing robot (SBR) is a typical example in control systems that works on the principle of an inverted pendulum. In this paper, we experiment to see how the learning and stability performance varies based on Kalman filter introduction for IMU noise filtering and controlling the robot using reinforcement learning. All the implementation is performed in ROS and Gazebo, and Q-learning is implemented using OpenAI (toolkit for development of Reinforcement learning) for ROS, i.e., Openai_ros package. Our work deals with a novel approach of providing the angular output from IMU to Kalman filter and passing it to the input of Q-learning for balancing control. Finally, we analyze the results with and without using Kalman filter from the output of IMU before passing it to Q-learning and evaluate the performance based on robot’s learning behavior and its robustness.
Overview of Information Hiding Algorithms for Ensuring Security in IoT Based Cyber-Physical Systems

Cyber-physical systems are one of the key technological trends in the modern world. However, their use is associated with the need to counter a variety of cyber threats. This review is devoted to information hiding methods and algorithms designed to ensure security in cyber physical systems. This application of embedding methods is fairly new, but it has attracted the attention of many researchers. The main contribution of our review consists in a new classification of methods for embedding information into data transmitted in cyber-physical systems. We show that these methods can be divided into four broad groups. The main feature for this division is the type of data used to embed additional information. Our review shows that the methods of data hiding used in cyber-physical systems have already formed a whole area that markedly differs from classical digital steganography and digital watermarking.
Quantum Random Number Generators for Cryptography: Design and Evaluation

In this article, quantum circuit-based secure communication architecture has been envisioned. Herein, all the proposed circuits and architecture are verified by IBM Qiskit and established on the quantum nanostructure. The salient goal of this hardware-based cryptographic structure in the quantum domain is to attain a diversified invulnerable quantum communication arrangement through a commendable post-CMOS technology. This architecture consists of a novel quantum random number generator (QRNG) and swap gate-based quantum shuffler. In our intended framework for cryptographically secured communication model as well as its implementation through a novel quantum encryption–decryption prototype by the random bits extracted from quantum Hadamard gates, rotation gate is (Rz) postulated on QRNG.
Optimal Cyber-Insurance Contract Design for Dynamic Risk Management and Mitigation

With the recent growing number of cyberattacks and the constant lack of effective defense methods, cyber risks have become ubiquitous in enterprise networks, manufacturing plants, and government computer systems. Cyber insurance provides a valuable approach to transfer the cyber risks to insurance companies and further improve the security status of the insured. The designation of effective cyber-insurance contracts requires considerations from both the insurance market and the dynamic properties of the cyber risks. To capture the interactions between the users and the insurers, we present a dynamic moral-hazard type of principal–agent model incorporated with Markov decision processes, which are used to capture the dynamics and correlations of the cyber risks as well as the user’s decisions on the protections. We study and fully analyze a case with a two-state two-action user under linear coverage insurance and further show the risk compensation, Peltzman effect, linear insurance contract principle, and zero-operating profit principle in this case. Numerical experiments are provided to verify our conclusions and further extend to cases of a four-state three-action user under linear coverage insurance and threshold coverage insurance.
A New Public Alsat-2B Dataset for Single-Image Super-Resolution

Recently, deep learning methods dominate the proposed solutions for image super-resolution due to their powerful properties. However, for remote sensing benchmarks, it is very expensive to obtain high spatial resolution images. Most of the super-resolution methods use down-sampling techniques to simulate low and high spatial resolution pairs and construct the training samples. As an alternative, the paper introduces a novel public remote sensing dataset (Alsat-2B) of low and high spatial resolution images (10m and 2.5m respectively) where the high-resolution images are obtained through pansharpening. Besides, the performance of some state-of-the-art methods is assessed based on common criteria. The obtained results reveal that the proposed scheme is promising and highlight the challenges in the dataset which show the need for advanced methods to grasp the relationship between the low and high-resolution patches.
PROBA-V-REF: Repurposing the PROBA-V Challenge for Reference-Aware Super Resolution

The PROBA-V Super-Resolution challenge distributes real low-resolution image series and corresponding high-resolution targets to advance research on Multi-Image Super Resolution (MISR) for satellite images. However, in the PROBA-V dataset the low-resolution image corresponding to the high-resolution target is not identified. We argue that in doing so, the challenge ranks the proposed methods not only by their MISR performance, but mainly by the heuristics used to guess which image in the series is the most similar to the high-resolution target. We demonstrate this by improving the performance obtained by the two winners of the challenge only by using a different reference image, which we compute following a simple heuristic. Based on this, we propose PROBA-V-REF a variant of the PROBA-V dataset, in which the reference image in the low-resolution series is provided, and show that the ranking between the methods changes in this setting. This is relevant to many practical use cases of MISR where the goal is to super-resolve a specific image of the series, i.e. the reference is known. The proposed PROBA-V-REF should better reflect the performance of the different methods for this reference-aware MISR problem.
Compression-Aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations

Convolutional neural networks (CNNs) achieve remarkable performance in a wide range of fields. However, intensive memory access of activations introduces considerable energy consumption, impeding deployment of CNNs on resource-constrained edge devices. Existing works in activation compression propose to transform feature maps for higher compressibility, thus enabling dimension reduction. Nevertheless, in the case of aggressive dimension reduction, these methods lead to severe accuracy drop. To improve the trade-off between classification accuracy and compression ratio, we propose a compression-aware projection system, which employs a learnable projection to compensate for the reconstruction loss. In addition, a greedy selection metric is introduced to optimize the layer-wise compression ratio allocation by considering both accuracy and #bits reduction simultaneously. Our test results show that the proposed methods effectively reduce 2.91×~5.97× memory access with negligible accuracy drop on MobileNetV2/ResNet18/VGG16.
Ensemble Learning For Mega Man Level Generation

Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying data with a single model. In this paper, we investigated the use of ensembles of Markov chains for procedurally generating Mega Man levels. We conduct an initial investigation of our approach and evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.
Provisioning Differentiated Last-Level Cache Allocations to VMs in Public Clouds

Public cloud providers offer access to hardware resources and users rent resources by choosing among many VM sizes. While users choose the CPU core count and main memory size per VM, they cannot specify last-level cache (LLC) requirements. LLC is typically shared among all cores of a modern CPU causing cache contention and performance interference among co-located VMs. Consequently, a user's only way to avoid this interference is purchasing a full-server VM to prevent co-tenants. Although researchers have studied LLC partitioning and despite its availability in commodity processors, LLC QoS has not been offered to public cloud users today. Existing techniques rely mostly on performance profiling, which is not feasible in public cloud settings with opaque VMs. Moreover, prior work does not address how to deliver differentiated LLC allocations at scale.
Automatic first-arrival picking workflow by global path tracing

It can be challenging to pick high-quality first arrivals on noisy seismic data sets. The stability and smoothness criteria of the picked first arrival are not satisfied for data sets with shingles and interference from unexpected and backscattered events. To improve first-arrival picking, we have adopted an automatic first-arrival picking workflow using global path tracing (GPT) to find a global solution for first-arrival picking with the condition of smoothness of the traced path. Our methodology is composed of data preconditioning, GPT, and a final addition of traced and piloted traveltimes to compute the total picked traveltime. We propose several ways to precondition the data set, including the use of the amplitude and amplitude ratio with and without a pilot. A 2D GPT is composed of two steps, namely, accumulation of energy on the potential path and backtracking of the optimal path with a strain factor for smoothness. For higher dimensional data sets, two strategies were adopted. One was to split the higher dimension data into subdomains of two dimensions to which 2D GPT was applied. The alternative method was to smooth the preconditioned data set in directions except for the one used to trace the path before applying 2D GPT. We discuss the importance of choosing the proper parameters for data preconditioning and for constraining GPT. We demonstrate the robustness and stability of our automatic first-arrival picking via GPT using synthetic and field data examples.
Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation, driving high-end Information Retrieval (IR) systems.The Semantic Web (SW) can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SW base system to provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model: data processing, ontology creation, SW processing, and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis, discovery, IR, and decision making.In addition to that, it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology's quality has improved by 39.28% in terms of completeness, and structural complexity has decreased by 45.29%, 11% and 37.7% in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D's ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.
Exploring Contextual-Aware Representation and Linguistic-Diverse Expression for Visual Dialog

Visual dialog is a fundamental vision-language task where an AI agent holds a meaningful dialogue about visual content with humans in nature. However, this task remains challenging, since there is still no consensus way to capture rich visual contextual information contained in the environment rather than only focusing on visual objects. Furthermore, conventional methods suffer from the single-answer learning strategy, where it only accepts one correct answer without considering the diverse expressions of the language (i.e., one identical meaning but multiple expressions via rephrasing or adopting synonyms etc). In this paper, we introduce Contextual-Aware Representation and linguistic-diverse Expression (CARE), a novel plug-and-play framework with contextual-based graph embedding and curriculum contrastive learning to solve the above two issues. Specifically, the contextual-based graph embedding (CGE) module aims to integrate the environmental context information with visual objects to improve the answer quality. In addition, we propose a curriculum contrastive learning (CCL) paradigm to imitate the learning habits of humans when facing a question with multiple correct answers sharing the same meaning but with diverse expressions. To support CCL, a CCL loss is designed to progressively strengthen the model's ability in identifying the answers with correct semantics. Extensive experiments are conducted on two benchmark datasets, and our proposed method outperforms the state-of-the-arts by a considerable margin on VisDial V1.0 (4.63% NDCG) and VisDial V0.9 (1.27% MRR, 1.74% [email protected], 0.87% [email protected], 1.28% [email protected], 0.26 Mean.
Niche-based cooperative co-evolutionary ensemble neural network for classification

Recently, artificial neural networks have been widely used for classification. It is important to optimize the weight parameters and topological structure of the neural network simultaneously. These two tasks are interdependent and should be solved at the same time to achieve a better result. However, existing works cannot balance the accuracy and diversity of neural networks very well. In this paper, a cooperative co-evolutionary algorithm is proposed to simultaneously evolve artificial neural network topology, neuron attributes, and connection weights. In the proposed algorithm, two effective strategies are proposed. First, the niche-based strategy is used in the evolutionary and cooperative process to refine the local search ability. In this way, a set of candidate networks with a higher level of output diversity is obtained. Second, a two-step comparison scheme is designed to acquire a compact ensemble network. Moreover, a fully connected weights matrix crossover scheme is used to avoid destroying the network structure. The proposed algorithm is tested on the benchmark classification problems in the UCI machine learning repository and compared with other state-of-the-art methods. The experimental results show that the proposed niche-based cooperative co-evolutionary ensemble neural network has a higher capability of generalization compared with other methods in six of nine kinds of classification problems. Furthermore, the proposed ensemble neural network has relatively low complexity.
Hierarchical Multi-Task Learning for Diagram Question Answering with Multi-Modal Transformer

Diagram question answering (DQA) is an effective way to evaluate the reasoning ability for diagram semantic understanding, which is a very challenging task and largely understudied compared with natural images. Existing separate two-stage methods for DQA are limited in ineffective feedback mechanisms. To address this problem, in this paper, we propose a novel structural parsing-integrated Hierarchical Multi-Task Learning (HMTL) model for diagram question answering based on a multi-modal transformer framework. In the proposed paradigm of multi-task learning, the two tasks of diagram structural parsing and question answering are in the different semantic levels and equipped with different transformer blocks, which constituents a hierarchical architecture. The structural parsing module encodes the information of constituents and their relationships in diagrams, while the diagram question answering module decodes the structural signals and combines question-answers to infer correct answers. Visual diagrams and textual question-answers are interplayed in the multi-modal transformer, which achieves cross-modal semantic comprehension and reasoning. Extensive experiments on the benchmark AI2D and FOODWEBS datasets demonstrate the effectiveness of our proposed HMTL over other state-of-the-art methods.