Reviewerly/exHarmony huggingface dataset:
DFAN: Dual-Branch Feature Alignment Network for Domain Adaptation on Point Clouds

Unsupervised domain adaptation (UDA) significantly reduces the gap between the source domain and the target domain in machine learning and computer vision tasks. Most UDA approaches are applied to images and videos, and only a few methods implement domain adaptation on 3-D computer vision problems. The existing UDA approaches operating on point clouds try to extract domain-invariant features in different domains for feature alignment. However, higher commonality brings less diversity and results a loss of detailed information. In this article, we propose a novel dual-branch feature alignment network (DFAN) architecture for domain adaptation on point cloud visual tasks to better exploit the respective characteristics of local and global features. Our approach specializes in the extraction and alignment of global and local features with different strategies in each branch to complement each other. We also introduce a hierarchical alignment strategy for local feature alignment and a distribution alignment strategy for global feature alignment. Experiments on the PointDA-10 and PointSegDA datasets show that our approach achieves state-of-the-art performance on the UDA of point cloud classification and segmentation tasks. The ablation study demonstrates the effectiveness of the dual-branch design and the feature alignment strategies.
Dynamic Collision and Deadlock Avoidance for Multiple Robotic Manipulators

A flexible operation of multiple robotic manipulators operating in a dynamic environment requires online trajectory planning to ensure collision-free trajectories. In this work, we propose a real-time capable motion control algorithm, based on nonlinear model predictive control, which accounts for static and dynamic obstacles. The proposed algorithm is realized in a distributed scheme, where each robot optimizes its own trajectory with respect to the related objective and constraints. We propose a novel approach for collision avoidance between multiple robotic manipulators, where each robot accounts for the predicted movement of the neighboring robots. Additionally, we propose a method to reliably detect and resolve deadlocks occurring in a setup of multiple robotic manipulators. We validate our approach on pick and place scenarios involving multiple robotic manipulators operating in a common workspace in a realistic simulation environment set up in Gazebo. The robots are controlled using the Robot Operating System. Our approach scales up to 4 manipulators and computes a path for each robot in a simultaneous pick and place operation in 94&#x0025; of all investigated cases without deadlock detection and 100 &#x0025; of cases with the proposed deadlock resolution algorithm. In contrast, the investigated conventional path planners, such as PRM, PRM&#x002A;, CHOMP and RRT-Connect, successfully plan a trajectory in at most 54&#x0025; of all investigated cases for a simultaneous operation of 4 robotic manipulators hindering their application in setups of multiple manipulators.
Monitoring Hyperproperties with Circuits

This paper presents an extension of the safety fragment of Hennessy-Milner Logic with recursion over sets of traces, in the spirit of Hyper-LTL. It then introduces a novel monitoring setup that employs circuit-like structures to combine verdicts from regular monitors. The main contribution of this study is the definition of the monitors and their semantics, as well as a monitor-synthesis procedure from formulae in the logic that yields ‘circuit-like monitors’ that are sound and violation complete over a finite set of infinite traces.
AEC3D: An Efficient and Compact Single Stage 3D Multiobject Detector for Autonomous Driving

This paper proposes a computationally fast and accurate framework to identify natively in 3D point clouds from LIDAR for autonomous driving. Speed is significant and detection is an indispensable component of safety. Many of the approaches are. However, most of them remain massive computation due to high dimensionality and densely of point clouds. We therefore investigate and carefully design an enhanced single-stage keypoint 3D multi-object detection framework to balance high accuracy and real-time efficiency. Experiments on the challenging KITTI dataset show that AEC3D outperforms other projection-based performances by a noteworthy margin. In addition, this is an embedded system-friendly approach, the inference times on TITAN Xp and Jetson AGX Xavier Developer Kit are 48 FPS and 14 FPS, respectively.
A Reinforcement Learning System for Generating Instantaneous Quality Random Sequences

Random numbers are essential to most computer applications. Still, producing high-quality random sequences is a big challenge. Inspired by the success of artificial neural networks and reinforcement learning, we propose a novel and effective end-to-end learning system to generate pseudorandom sequences that operates under the upside-down reinforcement learning framework. It is based on manipulating the generalized information entropy metric to derive commands that instantaneously guide the agent toward the optimal random behavior. Using a wide range of evaluation tests, the proposed approach is compared against three state-of-the-art accredited pseudorandom number generators (PRNGs). The experimental results agree with our theoretical study and show that the proposed framework is a promising candidate for a wide range of applications.
Hyperspectral Image Restoration via Auto-Weighted Nonlocal Tensor Ring Rank Minimization

Hyperspectral Imagery(HSI) restoration is a fundamental problem as a preprocessing step. In this letter, we present a novel Auto-weighted Nonlocal Tensor Ring Rank Minimization (ANTRRM) to reduce noise in HSI. Firstly, Nonlocal Cuboid Tensorization(NCT), built by similar grouping cuboids in HSI data, exploits the nonlocal self-similarity and the spatial-spectral correlation simultaneously. Then, the proposed model introduces Nuclear Norm (NN) regularization via nonlocal tensor ring with mode-&#x007B;<i>d, l</i>&#x007D; unfolding. An auto-weighted optimization is employed to represent the different importance of TR unfolding. Finally, the Alternating Direction Method of Multipliers(ADMM) scheme is employed to solve the proposed model efficiently. Experiments on two simulation HSIs datasets and a real HSI dataset were carried out, compared with representative approaches in both visual and quantitative comparison. The proposed ANTRRM method is superior except in a few cases.
Topological Tracking for Mobility Support Robots Based on Multi-scale Batch Learning Growing Neural Gas

Recently, the basic concept of cyber-physical systems has been extended to various research fields such as cyber-physical-social systems and cyber-physical-human systems. Thanks to the rapid progress of network technology, we can realize real-time tracking in a wide area. We can use features and structures based on graph theory and topology to predict the future state based on real-time tracking. For big data problems, growing neural gas (GNG) can cope well with hidden topological logic features. In this paper, we propose an analysis method of topological tracking based on GNG. First, we apply multi-scale batch learning GNG (MS-BL-GNG) to extract the topological structure for tracking. The proposed method can be used to support the navigation task of the robot, and the proposed method can be used to analyze the topological tracking in motion. We conducted simulation experiments for the proposed method. The experimental results fully demonstrate the effectiveness of the proposed method.
Sequentially Environment-Aware and Recursive Multiscene Image Enhancement for IoT-Enabled Smart Services

To maintain image quality under changing weather conditions, complex deep networks have been widely used for image enhancement. Owing to the restricted computing power of Internet of Things enabled smart cameras, existing lightweight solutions using deep neural networks are prone to degrading image quality. Thus, our study incorporates recursive and attention-based enhancement into a lightweight residual network that uses high-efficiency convolution operations as a basic building block. With this modular design on a recurrent neural network, the resulting neural network can be easily optimized according to the hardware specifications of a smart camera. In addition, to adapt to the various weather conditions, we design a sequential environment-aware model deployer that detects continuous weather changes so that the most appropriate image-enhanced model can be deployed. Comparison of experimental results for the proposed approach with our previous study (also the latest study) and other recent research indicate that the performance of image enhancement in rainy, hazy, and snowy scenes can be slightly improved, and the operation speed (seconds per frame) can be increased by 60%. The detection error rate for the model deployer dropped from 1% to 0.03% (improved by 97%). The image-enhanced results achieved a 4% improvement in accuracy, demonstrating the method's effectiveness and benefits for realizing edge intelligence.
Hierarchical pattern matching for anomaly detection in time series

As companies rely on an ever increasing number of connected devices for their day to day operations, a need arises for automated anomaly detectors to constantly observe crucial device metrics in real time to prevent downtime and data loss. As production environments tend to monitor a huge amount of these metrics, it prevents current state-of-the-art techniques to be deployed as the required computational resources is too high. This paper proposes a lightweight anomaly detection method that can be deployed in these environments without a reduction in accuracy. The approach works fully online, and does not require an extensive history set to be kept in memory. The method is benchmarked on the publicly available Numenta dataset, as well as a network monitoring dataset from different environments provided by a network management solution vendor. These benchmarks show the proposed technique to be very competitive with the current state-of-the-art and exceeding it in production applicability.
Suspicious Human Behaviour Detection Focusing on Campus Sites

Safety and security are the need of the hour for every person in this century. Many countries have acquired and adopted surveillance systems using high-definition CCTV cameras to secure their environment. Thus, automated CCTV surveillance systems can serve as security providers for identifying the behaviour of suspicion humans or intruders based on their actions from previous stages through the CCTV footages is a challenging task. This can be achieved using suspicious behaviour models that use machine learning approaches to a limited extent. It has been found that many of earlier approaches have made use of deep learning, machine learning, IoT and fuzzy logic techniques. The proposed suspicious behaviour detection model (SHBDM) effectively uses CNN model pre-trained on the ImageNet dataset, known as Inception V3 (VGG-16), for image feature extraction. This system was implemented in Python on an open-source platform. The use of Inception V3 + LSTM resulted in an improved precision accuracy of 88.8% when compared with VGG-16 + LSTM and simple CNN model.
Enhancing Aspect-Based Sentiment Classification with Local Semantic Information

The research goal of aspect-level sentiment analysis is to analyze the sentiment polarity expressed by a given sentence according to its specific aspect. The Graph Convolutional Network (GCN) model based on attention mechanism performs better among the existing solutions. This kind of method uses syntactic dependency information and semantic information to adjust and optimize attention. Still, the experimental results show that this kind of model does not perform well in complex sentences. We consider that attention mechanism is good at capturing global feature information, while Convolutional Neural Networks can effectively utilize local features. In this work, we study how to use attention mechanism and Convolutional Neural Networks to extract sentiment features better and propose a Graph Convolutional Network Model (DWGCN) of Depthwise separable convolution. Moreover, the basic design idea is to obtain syntactic dependency information utilizing graph convolution network learning and use the corresponding attention mechanisms to interact syntactic features with contextual information about word order to get semantic information. Then, we extract the emotional statement about the given Aspect-term from the semantic information. Therefore, we design a Feature extraction module based on a Depthwise separable convolution network and GLU (FMDG). To verify the effectiveness of the model, we test it on five benchmark datasets. The experimental results show that the proposed model outperforms the current relevant work in classification accuracy and generalization ability.
A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network

Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.
Recent Advancements on Recommendation Systems in Healthcare-Assisted System

Present recommendation schemes such as content grounded filtering and collaborative filtering practice dissimilar databases to create references. Content-based filtering creates recommendations built on customer favorites for product types. Collaborative filtering mimics user-to-user recommendations. Both approaches have limits. Content-based filtering recommends a new item but requests more data of customer preference to include the finest match. Like, collaborative filtering wants a huge dataset with lively customers who valued a product before to create precise predictions. The arrangement of these dissimilar recommendation schemes is named hybrid systems. These schemes can blend the topographies of the item itself and the favorites of other customers. This paper reviews recent advances in recommendation approaches and their findings.
Object Detection Using Microsoft HoloLens by a Single Forward Propagation CNN

In this paper, the application of HoloLens mixed reality device developed by Microsoft based on machine learning is demonstrated for an enhanced object detection task. The most important objective of this implementation is to enhance object recognition for AR (Augmented Reality) using a machine learning algorithm on HoloLens. This system helps the HoloLens user to recognize and detect the objects in real world. In order to proper object detection, the single forward propagation convolutional neural networks (YOLO) algorithm is performed. Based on the definition of YOLO (You Only Look Once), prediction step in the whole scene and image is performed in a single algorithm run. The system delivers the footnote for detected object and shows bounding boxes via HoloLens. It can also identify the new-found position of shifting object in few milli-seconds. Results demonstrate an increased rate of object detection comparable with other mentioned methods.
Dual Relation Network for Scene Text Recognition

Local visual and long-range contextual features yield two complementary cues for human reading text in natural scene. Existing scene text recognition methods mainly extract local features at a low level and then model long-range dependencies at a high level, this sequential pipeline may be sub-optimal to construct complete and effective representation. Except for high-level features, long-range contextual relation is of importance in low-level features as well since it can help separate different characters based on the intervals between characters and thus enhance the character features. To address this issue, we develop a dual relation module to extract complementary features in a parallel manner for scene text recognition, which consists of a local visual branch and a long-range contextual branch. The local visual branch employs a topological-aware operation to model intra-character characteristic and extract discriminative features of different characters. Meanwhile, the long-range contextual branch utilizes a simple but effective strategy to incorporate inter-character relations into feature maps. Our dual relation module is a plug-and-play block which can be easily incorporated into modern deep architectures. Experimental results demonstrate that our methods achieved top performance on several standard benchmarks. Code and models will become publicly available in the future.
Application of Magic Squares in Cryptography

In this paper, the magic square is used as a substitution cipher text in cryptography. The method for encryption and decryption of information is proposed by constructing magic squares with the help of Narayana’s folding method and Knight’s move method. The validity of proposed model has been analyzed by encryption and decryption of alphabets, numeric digits and symbols. Also, both the methods used for the construction of magic square are represented in a different way as compared to the traditional method.
Enhancing the Social Learning Ability of Spider Monkey Optimization Algorithm

In the arena of swarm intelligence algorithms, spider monkey works as a very powerful algorithm. In this article, an efficient modification of SMO is presented. In the proposed method the social learning of a spider monkey is enhanced with using the local leader of the neighboring group. The proposed algorithm is titled the social learner spider monkey optimization algorithm (SLSMO). This modified variant exploits the search space efficiently as well as the convergence speed of the algorithm is also enhanced respected the optimal solution. For validating the authenticity of this proposed SLSMO, it is collated with three benchmark sets i.e. 23 global optimization problems, 3 engineering design problems, and 16 constraint optimization problems. The attained outcomes are also collated with the significant approaches available in the literature. The obtained outcomes prove the authenticity of the propounded approach.
Implementation of Blockchain in IoT

The concept of the Internet of Things is arising and developing quickly. IoT addresses another innovation that empowers both virtual and actual items to be associated and interact with one another and gives rise to new digitized services that enhance our satisfaction. The IoT framework gives a few benefits; notwithstanding, the current incorporated design presents various issues including, security, protection, straightforwardness, and data integrity. The quick advancement and usage of IoT innovations have raised security concerns and made a sensation of vulnerability among IoT adopters. These difficulties impede the method of things to come improvements of IoT implementations. Shifting the IoT toward distributed ledger technology might be the right decision to determine such problems. Among the normal and well-known kinds of such innovation is the Blockchain. Coordinating the IoT with Blockchain innovation may induce incalculable advantages. The motivation behind this paper is to analyze the latest research patterns identified with security worries of the IoT idea and give an itemized comprehension of the point. Subsequently, this paper gives a thorough conversation of incorporating the IoT framework with Blockchain innovation. The study is elaborated into four segments. Segment 1 introduces the description of IoT. The next segment will present the fundamentals and working of Blockchain. Segment 3 portrays a few instances of utilization for Blockchain to give security and protection at IoT. In segment 4, various platforms for Blockchain are presented. Segment 5 presents the last contemplations.
A COVID-19 Auxiliary Diagnosis Based on Federated Learning and Blockchain

Due to the high transmission rate and high pathogenicity of the novel coronavirus (COVID-19), there is an urgent need for the diagnosis and treatment of outbreaks around the world. In order to diagnose quickly and accurately, an auxiliary diagnosis method is proposed for COVID-19 based on federated learning and blockchain, which can quickly and effectively enable collaborative model training among multiple medical institutions. It is beneficial to address data sharing difficulties and issues of privacy and security. This research mainly includes the following sectors: in order to address insufficient medical data and the data silos, this paper applies federated learning to COVID-19's medical diagnosis to achieve the transformation and refinement of big data values. With regard to third-party dependence, blockchain technology is introduced to protect sensitive information and safeguard the data rights of medical institutions. To ensure the model's validity and applicability, this paper simulates realistic situations based on a real COVID-19 dataset and analyses problems such as model iteration delays. Experimental results demonstrate that this method achieves a multiparty participation in training and a better data protection and would help medical personnel diagnose coronavirus disease more effectively.
An Efficient Object and Railway Track Recognition in Thermal Images Using Deep Learning

To avoid rail accidents, an efficient railway safety system is essential. The collision between the trains and obstacles on the railway track is one of the main reasons for accidents that result in terms reduced safety and higher financial burdens. Researchers are ceaselessly working to enhance railway safety for curtailing the accident rates. In this paper, a novel technique is discussed for recognizing the objects (obstacles) on the railway track in front of moving train. This methodology presents identification of the railway track along with deep network-based technique for recognition of obstacles on the railway track. The deep network gives the model understanding of real-world objects and enables obstacle recognition. The thermal image data is used for the training and validation of deep network. In this work, Faster R-CNN is utilized to effectively recognize obstacles on the railway tracks. This work can be an incredible assistance for railway to curtail mishaps and monetary burdens. The results demonstrate that the proposed work assists to boost railway safety with good accuracy.