Reviewerly/exHarmony huggingface dataset:
Biased Extrapolation in Latent Space forImbalanced Deep Learning

Addressing class data imbalance to improve generalization on minor classes is critical in medical applications. Traditional approaches including re-weighing and re-sampling have shown the potential of the generalization but ignore statistical characteristics of classes. We study the potential and effectiveness of data extrapolation in latent space of deep learning networks to address data imbalance. We propose biased normal sample selection and latent space sample extrapolation methods for imbalanced deep learning. Two types of biases in the extrapolations are sample bias and extrapolation bias. Experimental evaluation is performed for ulcer classification in endoscopy images and Cardiomegaly detection from CXR. We show that new abnormal samples extrapolated asymmetrically from biased normal samples of low probability improve the separation between normal and abnormal classes.
Multi-Scale Deep Nearest Neighbors

We propose a differentiable loss function for learning an embedding space by minimizing the upper bound of the leave-one-out classification error rate of 1-nearest neighbor classification error in the latent space. To evaluate the resulting space, in addition to the classification performance, we examine the problem of finding subclasses. In many applications, it is desired to detect unknown subclasses that might exist within known classes. For example, discovering subtypes of a known disease may help develop customized treatments. Analogous to the hierarchical clustering, subclasses might exist on different scales. The proposed method provides a mechanism to target subclasses in different scales.
An uneven game of hide and seek: Hiding botnet CnC by encrypting IPs in DNS records

Botnets frequently use DGA and fast-flux techniques to ensure the availability of their command and control (CnC) infrastructure. However, the CnC IP addresses are still exposed in plain-text in publicly available DNS A records, which can be exploited by defenders to disrupt botnet operations. This paper presents the concept of the IP Generation Algorithm (IGA) as a novel method, usable by botmasters, to encrypt the CnC IP address in DNS records to avoid plain-text IP address exposure. This raises the bar for blacklisting malicious IP addresses, and can also be combined with existing techniques to further harden the CnC. For use by defenders, an IGA botnet detection method based on the combination of DNS and NetFlow data is presented and validated using an emulated botnet and an ISP data set.
Detection of Cut Transition in Videos Using Optical Flow and Clustering

In our everyday lives, videos have become an immersive and interactive media of communications. The huge number of video makes it very hard to peruse and get the necessary data. Manual indexing and analyzing the contents of videos is time consuming process. The automatic detection of some events within the video is a clear option of this problem. The collection of frames or images from a single camera is referred to as a shot in a digital video sequence. The abrupt changes of shots from one scene to another are the determination of shot boundary. Shot detection is just one of the basic techniques for digital video analysis. The process of video shot detection is also considered as basic temporal segmentation which is needed as a pre-processing for video summary. The proposed research presents the detection of abrupt transition CUT with varied constrictions based on the Optical Flow Model and clustering techniques. We have used two different corner detectors FAST and ORB to determine Optical Flow from the sequence of Video. The characteristic of the flow determines the CUT transition from the video sequence. We then used the corner points as features and use clustering methods CLARANS and WARD to get the number of clusters, which eventually ascertain the number of CUT transition in the Video sequence. To verify the correctness of the method, we used two different features from corner points, location of the corner points and corner strength. Results obtain ascertain the number of shot transition. Finally we demonstrate our approach showing the conventional ELBOW method as conclusion of the procedures adopted. These algorithms have the higher efficiency to detect cuts in speedy scene changes in the video.
Differentially Private Coordination of Second-Order Multi-Agent Systems Via Dynamic Encoding-Decoding

This paper considers the privacy protection problems for distributed consensus of discrete-time second-order multiagent systems with limited communication channel capacity and partially measurable state. Differential privacy is applied to protect the sensitive information of agents. We proved that two initial state sets of a multi-agent system which satisfies δ-adjacent will export identical information with a certain probability, which means that adversaries will confuse them. In addition, each agent has its real-valued state but can only transmit limited digital signals because of communication constraints. In order to overcome this problem and the condition of partially measurable state, quantized communication method is utilized. Each agent can transmit only integer values to heighbors and estimate the neighbors’ real-valued state by dynamic encoding and decoding strategies. A differentially private consensus algorithm based on dynamic encoding-decoding scheme is proposed for discrete-time second-order multi-agent systems. Finally, we built some simulations to visualize our conclusions.
Data combination method in Remote Sensing tasks in case of conflicting information sources

Nowadays technologies of UAV-based Remote Sensing are used in different areas, such as: ecological monitoring, agriculture tasks, exploring for minerals, oil and gas, forest monitoring and warfare. Drones provide information more rapidly than piloted aerial vehicles and give images of a very high resolution, sufficiently low cost and high precision.Let’s note, that processing of conflicting information is the most important task in remote sensing. Dempster’s rule of data combination is widely used in solution of different remote sensing tasks, because it can processes incomplete and vague information. However, Dempster’s rule has some disadvantage, it can not deal with highly conflicted data. This rule of data combination yields wrong results, when bodies of evidence highly conflict with each other. That’s why it was proposed a data combination method in UAV-based Remote Sensing. This method has several important advantages: simple calculation and high accuracy. In this paper data combination method based on application of Jaccard coefficient and Dempster’s rule of combination is proposed. The described method can deal with conflicting sources of information. This data combination method based on application of evidence theory and Jaccard coefficient takes into consideration the associative relationship of the evidences and can efficiently handle highly conflicting sources of data (spectral bands).The frequency approach to determine basic probability assignment and formula to determine Jaccard coefficient are described in this paper too. Jaccard coefficient is defined as the size of the intersection divided by the size of the union of the sample sets. Jaccard coefficient measures similarity between finite sets. Some numerical examples of calculation of Jaccard coefficient and basic probability assignments are considered in this work too.This data combination method based on application of Jaccard coefficient and Dempster’s rule of combination can be applied in exploring for minerals, different agricultural, practical and ecological tasks.
A Controllable Image Steganography with Chaos and User Key

In recent years, the steganography of images has been greatly developed. In the existing image steganography algorithms, users cannot participate in the encryption process and cannot fully trust the steganography process. Therefore, this paper hopes to increase the importance of user roles in the steganography process so that it can control the hiding process. This paper is based on the classic F5 algorithm to improve, to achieve user participation, so that users can control the embedding position of secret information. At the same time, in response to the traditional methods of cracking the F5 algorithm and steganographic keys, chaos technology is incorporated on the basis of the original algorithm to jointly improve the overall security of the algorithm. This paper can effectively solve the problem of lack of user participation and security guarantee in existing steganography algorithms. This has important theoretical and practical significance for the application of steganography technology.
Coupling light to higher order transverse modes of a near-concentric optical cavity

In the near-concentric regime, the transverse modes of an optical cavity have small mode volumes with a beam waist on the order of the atomic cross section. This shows potential for strong interaction between single atoms and light, which is a key component in distributed quantum computing architectures [1] . Furthermore, with centimetresized near-concentric cavities, the frequency spacing of the transverse modes ranges from tens of MHz to a GHz, matching well with the hyperfine or the Zeeman level splitting of an atom [2] . This near-degeneracy in resonant frequencies close to the critical point allows to explore the physics of atomic nonlinearities with multiple photonic modes strongly coupled to different hyperfine or magnetic energy levels of a quantum emitter.
Point Spread Function Engineering for 3D Imaging of Space Debris Using a Continuous Exact $$\ell _0$$ Penalty (CEL0) Based Algorithm

We consider three-dimensional (3D) localization and imaging of space debris from only one two-dimensional (2D) snapshot image. The technique involves an optical imager that exploits off-center image rotation to encode both the lateral and depth coordinates of point sources, with the latter being encoded in the angle of rotation of the PSF. We formulate 3D localization into a large-scale sparse 3D inverse problem in discretized form. A recently developed penalty called continuous exact \(\ell _0\) (CEL0) is applied in this problem for the Gaussian noise model. Numerical experiments and comparisons illustrate the efficiency of the algorithm.
Brain MRI based on Otsu and region growth for ventricle segmentation

Brain MRI (magnetic resonance imaging) ventricle segmentation is the key basis for brain disease diagnosis and threedimensional reconstruction. Aiming at the characteristics of insignificant gray-scale difference and low resolution between brain MRI brain tissues, a ventricle segmentation method integrated with Otsu and region growth method was proposed. This method first used Otsu to determine the best global segmentation threshold to improve the growth point selection and growth rules of the region growth method, and then used the improved region growth method and prior knowledge to segment the ventricle region, and used mathematical morphology related techniques to make up the image Holes and edges that are not smooth. Experiments show that compared with Otsu threshold segmentation method and traditional region growth method for brain MRI images, the method in this paper has obvious advantages in segmentation effect, segmentation precision and recall rate. The average value of segmentation and intersection is 0.706319 than IOU, which is better than others. The average value of the algorithm is 42.13% and 23.71% higher, and it has good robustness.
Improved object detection algorithms for optical aerial images based on region proposal network

In this paper, we firstly propose that the region proposal network has some defects, such as effect depending on preset parameters, and poor adaptability to different tasks. Secondly, we integrate the idea of adaptive training sample selection into the region proposal network to define samples adaptively and add a centerness branch to constrain low-quality anchors. In the meanwhile, we try to merge the branch structure before classification and regression tasks to further improve the performance of ATSS_RPN. As demonstrated in our experiment, the proposed ATSS_RPN gets a 7.5% higher recall than the origin RPN. We also verify our scheme through two classic optical aerial image datasets. Experiments show that our ATSS_RPN has a considerate performance on the 4 mainstream two-stage detectors(including state of the arts DecteoRS ) than with the origin RPN network. (7% higher mAP in Airbus ship and 1.5% higher mAP in Visdrone2019 when we use Faster RCNN as a baseline).
Intelligent Video Surveillance System Based on Cloud Network

Abstract There are two shortcomings in traditional video surveillance systems: First, the amount of data is so huge that the processing, analysis, and viewing of massive data requires a lot of manpower, material resources and financial resources. Second, it is difficult to store and transmit video data. Mass video data often needs to be stored and passed to the backstage for analysis, and huge data makes data storage and transmission difficult. In view of the above trouble, this paper proposes a set of intelligent video surveillance system based on cloud network. It contains modules such as face detection, quick retrieval, etc., and uses the cloud network as a medium to transmit data and information. It can complete the automatic detection, capture, enhancement, and coding processing of the human face in the video, search and compare with the background database automatically and quickly.
The impact of open science on science and technology activities and policy suggestions for Vietnam

Open Science has been a topic of interest to scholars around the world for nearly two decades. Research results show that open science is a new approach to the scientific research process conducted through collaboration and a new way to spread knowledge widely and quickly based on digital technology platform. This article has analyzed and pointed out the positive impacts and challenges posed by open science on S&T activities, and evaluated the current situation of policies related to open science in Vietnam. On that basis, some suggestions on policies to promote the development of open science in Vietnam are proposed.Code: 21042001
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks in Low-Dimensional Spaces

Black-box attacks aim to generate adversarial noise to fail the victim deep neural network in the black box. The central task in black-box attack method design is to estimate and characterize the victim model in the high-dimensional model space based on feedback results of queries submitted to the victim network. The central performance goal is to minimize the number of queries needed for successful at-tack. Existing attack methods directly search and refine the adversarial noise in an extremely high-dimensional space, requiring hundreds or even thousands queries to the victim network. To address this challenge, we propose to explore a consistency and sensitivity guided ensemble attack (CSEA) method in a low-dimensional space. Specifically, we estimate the victim model in the black box using a learned linear composition of an ensemble of surrogate models with diversified network structures. Using random block masks on the input image, these surrogate models jointly construct and submit randomized and sparsified queries to the victim model. Based on these query results and guided by a consistency constraint, the surrogate models can be trained using a very small number of queries such that their learned composition is able to accurately approximate the victim model in the high-dimensional space. The randomized and sparsified queries also provide important information for us to construct an attack sensitivity map for the input image, with which the adversarial attack can be locally refined to further increase its success rate. Our extensive experimental results demonstrate that our proposed approach significantly reduces the number of queries to the victim network while maintaining very high success rates, outperforming existing black-box attack methods by large margins.
Business Anomaly Detection Method of Power Dispatching Automation System Based on Clustering Under-Sampling in the Boundary Region

Abstract Timely detecting business anomaly in the power dispatching automation system is significant for the steady operation of the power grid. Though the imbalanced binary classification method in machine learning is an effective way to achieve the business anomaly detection of the system, the overlap of boundary samples is an urgent issue affecting the classification effect. An under-sampling method by removing the clustering noises of the majority samples in the boundary region is proposed. Firstly, KNN is used to search adjacent points of the majority class, and the boundary region and the safety region are divided according to the proportion of the majority samples in adjacent points. Secondly, DBSCAN is used to cluster the majority samples in the boundary region, and noise points are removed. Finally, it’s combined with the method based on model dynamic selection driven by data partition hybrid sampling (DPHS-MDS). The purpose of reducing the overlap degree of boundary samples, balancing the dataset and improving the classification effect is achieved. Experimental results show that the proposed method is superior to the relevant mainstream methods under F-measure and G-mean.
Research and Application of "Digital Transformation" in Teaching "History of Vietnam Wars in Modern Times" at FPT Education

In the time of global integration, the Vietnam's education sector in general and Vietnam's history in particular has to be in line with the global trend which is to take advantage of the achievements resulted from the Fourth Industrial Revolution (Industry 4.0) to increase excitement for learners. Therefore, studying and applying the achievements of the "digital transformation revolution" into teaching the subject "History of Vietnam wars in modern times" is an indispensable task. For this reason, the article proposed a number of strategies to apply achievements of the "digital transformation revolution" in this subject in order to increase the learners’ interest and also to follow trend of the Industry 4.0.
An Analytical View of Revocable Attribute Based Encryption Schemes

Cryptographic techniques are a promising solution in the untrusted environment to share data securely. Traditional cryptographic techniques are not a suitable choice for secure data transfer in a cloud-based environment. Attribute based encryption schemes enabled secure data transfer and one-to-many encryptions possible for cloud and IoT applications. CPABE, one of the variants of ABE is studied by various researchers to regulate the access control of sensitive data uploaded on the distrustful cloud. Issues of CPABE schemes particularly for lightweight devices need more attention from researchers. A dynamic environment, where users join and leave frequently needs an efficient CPABE scheme to handle revocation under both single authority and multi-authority schemes. Researchers have either highlighted or proposed solutions to user revocation or attribute revocation and have also proposed CPABE schemes addressing computational cost, particularly for resource-constrained devices. In this paper, the revocation ability of various CPABE scheme are outlined and reviewed. The paper also studied the methodology used to address the user and attribute revocation by the existing schemes and compared the schemes on various resource-constrained devices and cloud storage criteria. Finally, the paper discussed the challenges which need to be further investigated and possible solutions for an efficient revocable CPABE scheme.
Blockchain-based Data Integrity and Privacy Protection Method for Swarm Robotics

Swarm robotics realize collaboration through information interaction among robots. One of the key aspects of stable and reliable operation of swarm robotics is protecting the privacy and content of the interactive data. Blockchain has been traditionally applied to digital currency due to its decentralization and high security features. Since robots can serve as a node in blockchain, and both swarm robotics and blockchain share the distributed architecture, there is great potential to apply blockchain technology to ensure the security of interactive data in swarm robotics. In this paper, a brief review of the current studies on the applying blockchain to swarm robotics is given. In addition, a blockchain data protection method is employed to protect the data integrity and privacy of swarm robotics. Finally, a simulation using the swarm robotics simulator and the Ethereum blockchain test network is conducted. Results demonstrate the effectiveness of the proposed method.
GNS: Forge High Anonymity Graph by Nonlinear Scaling Spectrum

It is crucial to generate random graphs with specific structural properties from real graphs, which could anonymize graphs or generate targeted graph data sets. The state-of-the-art method called spectral graph forge (SGF) was proposed at INFOCOM 2018. This method uses a low-rank approximation of the matrix by throwing away some spectrums, which provides privacy protection after distributing graphs while ensuring data availability to a certain extent. As shown in SGF, it needs to discard at least 20% spectrum to defend against deanonymous attacks. However, the data availability will be significantly decreased after more spectrum discarding. Thus, is there a way to generate a graph that guarantees maximum spectrum and anonymity at the same time? To solve this problem, this paper proposes graph nonlinear scaling (GNS). We firmly prove that GNS can preserve all eigenvectors meanwhile providing high anonymity for the forged graph. Precisely, the GNS scales the eigenvalues of the original spectrum and constructs the forged graph with scaled eigenvalues and original eigenvectors. This approach maximizes the preservation of spectrum information to guarantee data availability. Meanwhile, it provides high robustness towards deanonymous attacks. The experimental results show that when SGF discards only 10% of the spectrum, the forged graph has high data availability. At this time, if the distance vector deanonymity algorithm is used to attack the forged graph, almost 100% of the nodes can be identified, while when achieving the same availability, only about 20% of the nodes in the forged graph obtained from GNS can be identified. Moreover, our method is better than SGF in capturing the real graph’s structure in terms of modularity, the number of partitions, and average clustering.
Multi-feature learning-based extreme learning machine for rolling bearing fault diagnosis

Rolling bearing has been becoming an important part of human life and work. The working environment of rolling bearing is very complex and variable, which makes it difficult for fault diagnosis and monitor of rolling bearing from raw vibration data. Then, in this paper, a novel multi-feature learning-based extreme learning machine is proposed for rolling bearing fault diagnosis (FL-ELM). Extreme learning machine (ELM) is a fast and generalized algorithm proposed for training single-hidden-layer feed-forward networks (SLFNs), which has fast computing speed and small testing error. The novel architecture has two hidden layers and an experience pool sandwiched between two hidden layers. The first hidden layer consists of multi-feature learning methods. The experience pool is used to sort and choose new data, with old data being filtered out. Firstly, the first hidden layer is adopted for feature extraction. Secondly, the experience pool is used to rearrange and select data, which is extracted by first hidden layer. Thirdly, ELM is employed to further learn and classify. The proposed method (FL-ELM) is applied to the rolling bearing fault diagnosis. The results confirm that the proposed method is more effective than traditional methods and standard deep learning methods.