Reviewerly/exHarmony huggingface dataset:
Real-time Detection of Aircraft Objects in Remote Sensing Images Based on Improved YOLOv4

In recent years, the application of object detection in the military field has become more and more extensive, and the detection of aircraft objects in remote sensing images can provide data support for accurate object strikes. In this paper, we propose an real-time aircraft object detection method in remote sensing images based on YOLOv4 object detection algorithm. We improved the YOLOv4 object detection algorithm by replacing the traditional convolution in Res_unit with a depthwise separable convolution, replacing the Mish activation function in the backbone with the ELU activation function, and adding an SE module in each CSP_unit. The obtained algorithm was named Aircraft-YOLOv4 finally. The mAP and fps of Aircraft-YOLOv4 when detecting aircraft objects in remote sensing images can reach 86.92% and 29.62, respectively, realizing real-time detection, which is 2.82% and 7.01 higher than YOLOv4. And Aircraft-YOLOv4 has improved performance in all aspects when model is tested on UCAS-AOD, a dataset similar to the RSOD-Dateset used for training. The experimental results show that Aircraft-YOLOv4 has good generalization and is more suitable for aircraft object detection tasks in remote sensing images in the military field than YOLOv4.
A Two-Stage Data Association Approach for 3D Multi-Object Tracking

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.
Transfer Learning Based Breast cancer Classification using One-Hot Encoding Technique

Early diagnosis of breast cancer can be curable with precise techniques and improve patients prognosis with cancer. Most people failed to detect cancer early, leading to an increased death rate. In recent years, several studies have developed in various imaging modalities to predict breast cancer. The medical practitioner sometimes diagnosis diseases mistakenly due to misinterpretation. The automated assistance for practitioners using different computer-aided diagnosis (CAD) will give an accurate prediction for critical diseases. This paper presented a CAD system to perform automatic breast cancer diagnosis by employing the one-hot encoding technique. The system has experimented with BreakHis dataset partitioned into training and testing sets. The system performance can be measured with accuracy, sensitivity, specificity, precision, and recall. The system acquired 98.62% accuracy using the one-hot encoding technique, which is better than state-of-art methods. The proposed system outperformed the existing system by utilizing various performance metrics.
Pano-SfMLearner: Self-Supervised Multi-Task Learning of Depth and Semantics in Panoramic Videos

With the advent of virtual reality and augment reality applications, omnidirectional imaging and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> cameras become increasingly popular in many scenarios such as entertainment and autonomous systems. In this paper, we propose a self-supervised framework for multi-task learning on depth, camera motion and semantics from panoramic videos. Specifically, our method is based on differentiable warping of adjacent views to the target. Two improvements are provided. First, we introduce a view synthesis module based on equirectangular projection to enable direct optimization on panoramic images. Second, we introduce a self-supervised segmentation branch to involve the constraint of semantic consistency for further improvement. Extensive experiments on two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video and two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> image datasets demonstrate that our method outperforms the state-of-the-art and achieves favorable cross-modality performance.
Learning to share by masking the non-shared for multi-domain sentiment classification

Multi-domain sentiment classification deals with the scenario where labeled data exists for multiple domains but is insufficient for training effective sentiment classifiers that work across domains. Thus, fully exploiting sentiment knowledge shared across domains is crucial for real-world applications. While many existing works try to extract domain-invariant features in high-dimensional space, such models fail to explicitly distinguish between shared and private features at the text level, which to some extent lacks interpretability. Based on the assumption that removing domain-related tokens from texts would help improve their domain invariance, we instead first transform original sentences to be domain-agnostic. To this end, we propose the BERTMasker model which explicitly masks domain-related words from texts, learns domain-invariant sentiment features from these domain-agnostic texts and uses those masked words to form domain-aware sentence representations. Empirical experiments on the benchmark multiple domain sentiment classification datasets demonstrate the effectiveness of our proposed model, which improves the accuracy on multi-domain and cross-domain settings by 1.91% and 3.31% respectively. Further analysis on masking proves that removing those domain-related and sentiment irrelevant tokens decreases texts' domain separability, resulting in the performance degradation of a BERT-based domain classifier by over 12%.
AC-AC: Dynamic revocable access control for acute care teams to access medical records

Acute care demands the collaboration of multiple healthcare professionals and various organisations. During an emergency, the availability of Electronic Medical Records (EMR) allows acute care teams to access a patient's data promptly, which facilitates the decision-making process. Cloud solutions offer an environment to store and share patients' EMR. However, security and privacy issues arise, which affect the availability of the patients' EMR. Inspired by a hybrid encryption scheme combining Dynamic index-based Symmetric Searchable Encryption (DSSE) and Attribute-Based Encryption (ABE), we proposed the data Access Control for Acute Care teams (AC-AC). AC-AC is a dynamic revocable access control protocol that enables break-glass access for an authorised member of an acute care team that is treating the patient. The proposed protocol allows a team to grant and revoke access for other teams to the patient's EMR dynamically according to the treatment's demands. We present a formal security analysis proving that AC-AC protocol is resilient to multiple attacks. Finally, we analysed the overhead in time complexity for the protocol execution and experimented with each algorithm. The experimental expected execution time for the AC-AC algorithms was below 170 ms, therefore feasible for an acute care timeline.
Three trajectories for narrative visualisation

Abstract Narrative Visualisation (NarVis) is the pairing of data visualisation with narrative techniques. Due to its interdisciplinary applications and scholarship, NarVis presentations often feature vastly different interpretations of “narrative” and “visualisation”, which is echoed in NarVis authoring tools. To map the morphology of how the narratives of NarVis manifest, we identify three different trajectories for the field. These trajectories are identified through an analysis of selected NarVis presentations and tools, with an emphasis on identifying how traditional narrative techniques are adopted, transposed or indeed challenged by NarVis examples. We then populate our categories with additional examples and tools, providing a foundational point of reference for NarVis scholars, authors and tool developers.
Remote 3D face reconstruction by means of autonomous unmanned aerial vehicles

The 3D face model of an individual includes a rich amount of information useful in many application scenarios. Currently, the 3D reconstruction from images is obtained through multiple acquisitions at different distances and angles in a controlled environment. “In the wild’ conditions, or inadequate acquisition systems, can make 3D reconstruction a strongly error-prone problem. This paper illustrates a system that creates 3D face models from images taken by unmanned aerial vehicles in a completely automatic way. The proposed method is adaptive, dynamic, and contactless. No human intervention is required for image acquisition. Experimental results are described—in particular, the comparison between the ideal 3D reconstructions, obtained in controlled and cooperative conditions, and the reconstructions obtained by the drone during flight at different resolutions. The results show good overlap of the models and a low co-registration error. Given the free mobility of the UAVs, the system is suitable, among other applications, for biometrics and open-air access control over large areas.
Image to Bengali Caption Generation Using Deep CNN and Bidirectional Gated Recurrent Unit

There is not more research on the linguistic characteristics of the Bengali language. Bengali is spoken by about 193 million people globally, and it is one of the top ten spoken languages worldwide. In this paper, a CNN and Bidirectional GRU architecture is proposed for producing a natural language caption from an image in the Bengali language. Bangladeshi people may use this study to grasp one another better and crack language barriers and increase their cultural understanding. This study would immensely help several blind people in their daily lives. The encoder-decoder approach was used in this paper for captioning. We used a pre-trained Deep CNN named InceptionV3 image encoder to interpret, identify, and annotate the dataset’s images and used a Bidirectional GRU architecture as the decoder to produce captions. In order to deliver the finest and subtle Bengali captions from our model, argmax search and beam search are included. We proposed a new dataset named BNATURE that contains five captions per image with the amount of eight thousand pictures; among them, six thousand were used for training, one thousand for validation, and one thousand for testing. We obtained BLEU1-42.6,BLEU2-27.95,BLEU3-23.66,BLEU4-16.41 and Meteor-28.7 respectively
In-fat pointer: hardware-assisted tagged-pointer spatial memory safety defense with subobject granularity protection

Programming languages like C and C++ are not memory-safe because they provide programmers with low-level pointer manipulation primitives. The incorrect use of these primitives can result in bugs and security vulnerabilities: for example, spatial memory safety errors can be caused by dereferencing pointers outside the legitimate address range belonging to the corresponding object. While a range of schemes to provide protection against these vulnerabilities have been proposed, they all suffer from the lack of one or more of low performance overhead, compatibility with legacy code, or comprehensive protection for all objects and subobjects.
Health Chatbot: Design, Implementation, Acceptance and Usage Motivation

This paper presents the health chatbot application created on the Chatfuel platform. This application allows people to interact with the health chatbot in the same way as they do with other people. The health chatbot identifies their symptoms through a series of queries and guides them to decide whether or not to go to doctor. Such application can be of great benefit to people who are not sure whether their symptoms are transients or require a response to a doctor for detailed tests. It also offers advice to users on minor illnesses, and in that way, encourages people to take appropriate measures to stay healthy, which is a great example of promoting a healthy life. For the purpose of this research, an end-user survey was created and conducted with aim to collect the users' opinion regarding the acceptance and usage motivation of health chatbot. The results showed good acceptance and usage motivation of health chatbot.
Social Network Spam Detection Based on ALBERT and Combination of Bi-LSTM with Self-Attention

Social networks are full of spams and spammers. Although social network platforms have established a variety of strategies to prevent the spread of spam, strict information review mechanism has given birth to smarter spammers who disguise spam as text sent by ordinary users. In response to this, this paper proposes a spam detection method powered by the self-attention Bi-LSTM neural network model combined with ALBERT, a lightweight word vector model of BERT. We take advantage of ALBERT to transform social network text into word vectors and then input them to the Bi-LSTM layer. After feature extraction and combined with the information focus of the self-attention layer, the final feature vector is obtained. Finally, SoftMax classifier performs classification to obtain the result. We verify the excellence of the model with accuracy, precision, F1-score, etc. The results show that the model has better performance than others.
Simultaneous Video Stabilization and Rolling Shutter Removal

Due to the delay in the row-wise exposure and the lack of stable support when a photographer holds a CMOS camera, video jitter and rolling shutter distortion are closely coupled degradations in the captured videos. However, previous methods have rarely considered both phenomena and usually treat them separately, with stabilization approaches that are unable to handle the rolling shutter effect and rolling shutter removal algorithms that are incapable of addressing motion shake. To tackle this problem, we propose a novel method that simultaneously stabilizes and rectifies a rolling shutter shaky video. The key issue is to estimate both inter-frame motion and intra-frame motion. Specifically, for each pair of adjacent frames, we first estimate a set of spatially variant inter-frame motions using a neighbor-motion-aware local motion model, where the classical mesh-based model is improved by introducing a new constraint to enhance the neighbor motion consistency. Then, different from other 2D rolling shutter removal methods that assume the pixels in the same row have a single intra-frame motion, we build a novel mesh-based intra-frame motion calculation model to cope with the depth variation in a mesh row and obtain more faithful estimation results. Finally, temporal and spatial motion constraints and an adaptive weight assignment strategy are considered together to generate the optimal warping transformations for different motion situations. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with other state-of-the-art methods.
Musical robot swarms, timing, and equilibria

This paper studies swarms of autonomous musical robots and its contributions are twofold. First, I introduce Dr. Squiggles, a simple rhythmic musical robot, which serves as a general platform for studying human-robot and robot-robot musical interaction. Secondly, I use three Dr. Squiggles robots to study what happens when musical robots listen to, learn from, and respond to one another while improvising music together. This paper has a supplementary video at https://www.youtube.com/watch?v=yN711HXPfuY which shows the three robots playing some of the equilibrium rhythms.
Single Neuronal Dynamical System in Self-Feedbacked Hopfield Networks and Its Application in Image Encryption

Image encryption is a confidential strategy to keep the information in digital images from being leaked. Due to excellent chaotic dynamic behavior, self-feedbacked Hopfield networks have been used to design image ciphers. However, Self-feedbacked Hopfield networks have complex structures, large computational amount and fixed parameters; these properties limit the application of them. In this paper, a single neuronal dynamical system in self-feedbacked Hopfield network is unveiled. The discrete form of single neuronal dynamical system is derived from a self-feedbacked Hopfield network. Chaotic performance evaluation indicates that the system has good complexity, high sensitivity, and a large chaotic parameter range. The system is also incorporated into a framework to improve its chaotic performance. The result shows the system is well adapted to this type of framework, which means that there is a lot of room for improvement in the system. To investigate its applications in image encryption, an image encryption scheme is then designed. Simulation results and security analysis indicate that the proposed scheme is highly resistant to various attacks and competitive with some exiting schemes.
Targeted Attack and Defense for Deep Hashing

Deep hashing methods have been intensively studied and successfully applied in massive fast image retrieval. However, inherited from the deficiency of deep neural networks, deep hashing models can be easily fooled by adversarial examples, which brings a serious security risk to hashing based retrieval. In this paper, we propose a novel targeted attack method and the first defense scheme for deep hashing based retrieval. Specifically, a simple yet effective PrototypeNet is designed to generate category-level semantic embedding (dubbed prototype code) regarded as the semantic representative of the target label, which preserves the semantic similarity with relevant labels and dissimilarity with irrelevant labels. Subsequently, we conduct the targeted attack by minimizing the Hamming distance between the hash code of the adversarial sample and the prototype code. Moreover, we provide an adversarial training algorithm to improve the adversarial robustness of deep hashing networks. Extensive experiments demonstrate our method can produce high-quality adversarial samples with the benefit of superior targeted attack performance over state-of-the-arts. Importantly, our adversarial defense framework can significantly boost the robustness of hashing networks against adversarial attacks on deep hashing based retrieval. The code is available at https://github.com/xunguangwang/Targeted-Attack-and-Defense-for-Deep-Hashing.
Review on Consensus Protocols for Blockchain

Blockchain has been an important keyword in many real-time applications such as sharing a secure medical data, online payment, supply chain, identification mechanisms etc. Different types like Permission less and Permissioned blockchain serve as their requirements on various applications such as Permissioned blockchain is beneficial for resource constraint devices in terms of storage, energy, resources, and efficiency. Permission-less blockchainis efficient for those devices that have a lot of resources and energy. The consensus is a term used in all types of blockchain, which place themselves in the important place of bringing all institutions together in one agreement. The consensus protocol plays an important role in preventing attacks and also keeping the stability of nodes in blockchain systems alive. In blockchain systems, consensus protocols are an important contribution to maintaining security and scalability. They maintain the order of fixed nodes and also activate nodes for correct information. In this paper, the blockchain consensus protocols based on different types of blockchains are analyzed. Finally, we will evaluate the protocols through comparison tables and some factors such as persistence, robustness, efficiency, rigidity, stability, and optimality, etc., known as PREStO.
Siamese target estimation network with AIoU loss for real-time visual tracking

The fully convolutional siamese network based trackers achieve great progress recently. Most of these methods focus on improving the capability of siamese network to represent the target. In this paper, we propose our model which focuses on estimating the state of the target with our proposed novel IoU (intersection over union) loss function which is named AIoU. Our model consists of a siamese subnetwork for feature extraction and a target estimation subnetwork for state representation. The target estimation subnetwork contains a classification head for classifying background and foreground and a regression head for estimating target. In order to regress better bounding boxes, we further study the loss function utilized in the regression head and propose a powerful IoU loss function. Our tracker achieves competitive performance on OTB2015, VOT2018, and VOT2019 benchmarks with a speed of 180 FPS, which proves the effectiveness of our method.
VFH+D: An Improvement on the VFH+ Algorithm for Dynamic Obstacle Avoidance and Local Planning

This paper highlights some limitations of the VFH+ algorithm on the domain of local obstacle avoidance. An enhanced algorithm dubbed VFH+D is proposed, which considers a different way of calculating the obstacle vector magnitude and a decay algorithm for dynamic obstacle avoidance. Experiments were conducted to compare both algorithms on two different mecanum wheeled robots, VFH+D achieved higher average speeds and lower distance traveled to reach the goal.
Integration of Pre-Trained Networks with Continuous Token Interface for End-to-End Spoken Language Understanding

Most End-to-End (E2E) Spoken Language Understanding (SLU) networks leverage the pre-trained Automatic Speech Recognition (ASR) networks but still lack the capability to understand the semantics of utterances, crucial for the SLU task. To solve this, recently proposed studies use pre-trained Natural Language Understanding (NLU) networks. However, it is not trivial to fully utilize both pre-trained networks; many solutions were proposed, such as Knowledge Distillation (KD), cross-modal shared embedding and network integration with Interface. We propose a simple and robust integration method for the E2E SLU network with a novel Interface, Continuous Token Interface (CTI). CTI is a junctional representation of the ASR and NLU networks when both networks are pre-trained with the same vocabulary. Thus, we can train our SLU network in an E2E manner without additional modules, such as Gumbel-Softmax. We evaluate our model using SLURP, a challenging SLU dataset and achieve state-of-the-art scores on intent classification and slot filling tasks. We also verify that the NLU network, pre-trained with Masked Language Model (MLM), can utilize a noisy textual representation of CTI. Moreover, we train our model with extra data, SLURP-Synth, and get better results.