Reviewerly/exHarmony huggingface dataset:
Semi-supervised Medical Image Segmentation with Confidence Calibration

The lack of high-quality expert labeled data is a common shortfall for medical image segmentation, which promotes semi-supervised learning scheme to an active research topic. The pseudo-labeling technique has been demonstrated to be a powerful module in semi-supervised segmentation framework for leveraging unlabeled data. However, simple generated pseudo labels are inevitably noisy and limited by the introduced confirmation biases, for the reason that the prediction errors of these pseudo labels would enhance the misleading to the segmentation network. In this paper, we propose to estimate the prediction confidence during the training process and further utilize the confidence to calibrate the pseudo label with the purpose to mitigate the confirmation bias problem. To emphasize, the pixel-wise confidence of the prediction results are learned through an adversarial network, while untrustworthy areas could be determined based on the prediction confidence. Rectified pseudo labels on untrustworthy areas are modified and further be utilized for medical image segmentation. The effectiveness on segmentation performance and noisy pseudo label calibration are proved by comparing several supervised or semi-supervised methods on BraTS2015 dataset and other two 3D medical image datasets.
Enhancing Separate Encoding with Multi-layer Feature Alignment for Image-Text Matching

There is a surge of interest in cross-modal representation learning, concerning mainly images and texts. Image-Text Matching task is one major challenge in cross-modal tasks. Traditional methods use multi-paths to encode features across modalities separately and project them into a shared latent space. Recently, the development of pre-trained models inspires people to learn cross-modal features jointly and boost performances through large-scale data. However, traditional methods are less effective when both modalities use pre-trained uni-modal encoders. Methods that encode features jointly would face an unacceptable calculation cost during inference, thus less valuable for real-time applications. In this paper, we first explore the pros and cons of these methods, then we propose an enhanced separate encoding framework, using an extra encoding process to project multi-layer features of pre-trained encoders into a similar latent space. Experiments show that our framework outperforms current methods that do not use large-scale image-text pairs in both Flickr30K and MS-COCO datasets while maintaining minimal cost during inference.
Image Stabilization by Orientation of the Unmanned Aerial Vehicle

Various robotic systems designed to help a person in various fields of his activity have received great development at the present time. However, many tasks facing the complexes being developed remain unsolved or are solved with insufficient accuracy. Such problems, requiring an increase in the accuracy of the solution, include the problem of tracking objects on a video sequence. This work is devoted to the algorithm for stabilizing the video image obtained from the camera of an unmanned aerial vehicle (UAV) used as part of image preprocessing before executing the object tracking algorithm. To perform stabilization, data from onboard sensors of the UAV position in the airspace are used. The algorithm described in the work was implemented and tested.
Literature Survey On Video Surveillance Crime Activity Recognition

Presently, the video surveillance system is an important virtue for identifying crimes. The past works related to crime detection using video surveillance are discussed here. The goal of this investigation is to provide a literature review about crime activity recognition using different techniques. The main demerits of video surveillance are facial utterance recognition, and the method consumes more time for detecting the crime. An alert system provided in video surveillance improves crime prediction and also reduces crime activity. This paper presents an overview of present and past reviews for developing future research. The published journals from 2000-2020 were analyzed to know about the video surveillance and crime detection methods in different sectors. A review of the analyzed researchers and their techniques is available in this paper. This survey is useful to improve the crime detection techniques using video surveillance. Moreover, it is a useful tool to gather information.
Database Management System Design for Automatic Weather Information with Twitter Data Collection

Improvement of data management about weather in Indonesia is very supportive of the need for weather information. Currently, the weather information that is disseminated to the public is only based on sensor location points that are spread across Indonesia. However, supporting data generated from these sensors is only limited to certain areas. We hence develop a database system that can help collect weather information automatically via Twitter. This facilitates the Indonesian people in knowing weather information based on the completeness of data from sensor data and Twitter. The steps from the database that are built will be very helpful in collecting data about the weather throughout Indonesia. The basic step that we use in our database are conceptual, logical and physical design. The information obtained in this study is very important for efficient and effective data collection to achieve real-time automation of weather information.
Security Metric for Networks with Intrusion Detection Systems having Time Latency using Attack Graphs

Probabilistic security metrics estimate the vulnerability of a network in terms of the likelihood of an attacker reaching the goal states (of a network) by exploiting the attack graph paths. The probability computation depends upon several assumptions regarding the possible attack scenarios. In this paper, we extend the existing security metric to model networks with intrusion detection systems and their associated uncertainties and time latencies. We consider learning capabilities of attackers as well as detection systems. Estimation of risk is obtained by using the attack paths that are undetectable owing to the latency of the detection system. Thus, we define the overall vulnerability (of a network) as a function of the time window available to an attacker for repeated exploring (via learning) and exploitation of a network, before the attack is mitigated by the detection system. Finally, we consider the realistic scenario where an attacker explores and abandons various partial paths in the attack graph before the actual exploitation. A dynamic programming formulation of the vulnerability computation methodology is proposed for this scenario. The nature of these metrics are explained using a case study showing the vulnerability spectrum from the case of zero detection latency to a no detection scenario.
Amazon enacts new coronavirus measures as employees protest safety conditions

Some workers at  Amazon warehouse facilities across the U.S. have tested positive for COVID-19 as of Thursday morning, and some are worried that Amazon hasn't enforced enough safety rules to make sure other employees don't get infected.
SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks

Self-supervised learning (SSL) methods have resulted in broad improvements to neural network performance by leveraging large, untapped collections of unlabeled data to learn generalized underlying structure. In this work, we harness unsupervised data augmentation (UDA), an SSL technique, to mitigate backdoor or Trojan attacks on deep neural networks. We show that UDA is more effective at removing trojans than current state-of-the-art methods for both feature space and point triggers, over a range of model architectures, trojans, and data quantities provided for trojan removal. These results demonstrate that UDA is both an effective and practical approach to mitigating the effects of backdoors on neural networks.
An Enhanced Support Vector Machine for Face Recognition in Fisher Subspace

With the advances in technology, facial recognition has become a very popular technology to be used majorly as a security technique. Face recognition using support vector machine is being used since years but it does not work well with imbalanced data and computational time which is more. In this work, an enhanced support vector machine (ESVM) is utilized for multi-classifying the face images. Fisher space method is utilized for feature extraction as it is more efficient for dataset consisting of multiple classes with class separability as a vital attribute while compressing dimensionality. It concentrates on type of features from face image data that provide a better demarcation for separation of face images, and then, ESVM-based multi-classification is utilized for classification purpose. The advantage of ESVM-based multiclass classification for face images includes flexibility, enhanced computational time. ESVM-based multiclass classification based on One-vs-One (OVO) and One-vs-All (OVA) which is utilized for performing experiment on two standard databases such as Yale and ORL. A number of experiments are also performed varying sub-dimensions in fisher space with different kernels of proposed ESVM on different training sets of both databases. A remarkable recognition rate of 100 and 92.5% was achieved on Yale and ORL database, respectively.
Generating Adversarial Point Clouds on Multi-modal Fusion Based 3D Object Detection Model

In autonomous vehicles (AVs), a critical stage of perception system is to leverage multi-modal fusion (MMF) detectors which fuse data from LiDAR (Light Detection and Ranging) and camera sensors to perform 3D object detection. While single-modal (LiDAR-based and camera-based) models are found to be vulnerable to adversarial attacks, there are limited studies on the adversarial robustness of MMF models. Recent work has proposed a general spoofing attack on LiDAR-based perception, based on the defect of ignored occlusion patterns in point clouds. In this paper, we are inspired to attack LiDAR channel alone to fool the MMF model into detecting a fake near-front object with high confidence score. We perform the first study to analyze the roubustness of a popular MMF model against the above attack and discover it is invalid due to the correction of camera. We propose a black-box attack method to generate adversarial point clouds with few points and prove the defect still exists in MMF architecture. We evaluate the attack effectiveness of different combinations of points and distances and generate universal adversarial examples at the best distance of 4m, which achieve attack success rates of more than 95% and average confidence scores over 0.9 on the KITTI validation set when the points exceed 30. Furthermore, we verify the generality of our attack and the transferability of generated universal adversarial point clouds across models.
Internet of Vehicles Energy Transaction Security Based on Alliance Blockchain

Based on the research prevalence of "Industrial Internet of Things (IIoT)" and blockchain in recent years, this paper puts forward the integrated technology of these two technologies to achieve the complementary advantages of them and establish a safe and reliable application of IIoT involved with multiple industries , which is identified with a highly promising market prospect. Meanwhile, thanks to the rise of new energy, the "energy Internet" has been created, which results in increasingly active energy transactions. The integration of energy flow, information flow, and value flow is also in an urgent need of solution. Therefore, the term "energy blockchain" was invented according to the combination of energy Internet and blockchain. This paper mainly studies the energy blockchain in the IIoT background. A decentralized energy transaction program based on payment with credit-value loan will be proposed, aiming at the problem of large transaction confirmation delay in the blockchain system, which fails to support frequent and fast transactions. In this case, it supports industrial energy nodes to conduct speedy, safe and frequent energy transactions through credit payment.
A New Method of Named Entity Recognition on Biology Domain

It is the phenotypic data of microbial taxonomy that is of great significance. It can be used not only for the identification of microbial taxonomy but also for the study of the interaction between human and the environment. There are bilstm-CRF, Bert-CRF and other models are commonly used for named entity recognition (NER) recently. However, these methods do not consider the sequence information of neurons and do not analyze the hierarchical structure of sentences. In the field of biological phenotypes, there are longer sentences and complex words, the named entity recognition with neural network performs not very well. Based on this, this paper proposed a new NER method which is named BERT-ON-LSTM to identify and extract microbial phenotypic entities from the literature. This paper improved the Bidirectional Encoder Representations from Transformers (Bert) which enabled the model to learn the hierarchical structure information and be more sensitive to words that require long-term memory. And then, conditional random field (CRF) is used to decode since it can be calculated the joint probability distribution of the whole tag sequence. The experimental results on three data sets prove that our model is effective.
IMPERFECTION OF THE DISTANCE LEARNING SYSTEM AT HIGHER LEARNING INSTITUTIONS OF UKRAINE

This article deals with the imperfection of the distance learning system at higher learning institutions; attention is focused on detailed coverage of each shortcoming and the positive advantages of distance education are revealed; the need for oral communication and accreditation of academic disciplines is emphasized; research methods, in particular inductive and deductive are covered, due to these methods the theoretical understanding of the distance learning system was carried out. The most important shortcomings are highlighted: restrictions on direct communication between students and teachers, lack of direct access of students to the teacher, constant self-motivation of students, distance learning isolation, limitation of available special courses to master specialization, the need for constant reliable access to new learning technologies, mandatory accreditation of distance learning educational programs, restrictions on employment, unforeseen additional costs for distance education, restrictions on improving the skills of oral communication of students. The aim of research is to focus on the implementation of the distant learning system at Higher Learning Institutions, underling the distant system of education with its drawbacks. This problem is studied by I.G. Vlasenko. The idea of the imperfection of the distance learning system at higher learning institutions was discovered by V.Yu. Bykov, O.O. Grytsenchuk, Yu.O.Zhuk. The methodological basis of the study is the initial provisions of philosophy on the dialectical relationship, the interdependence of phenomena and processes of socio-pedagogical activities, the role of the individual in the development of its material and spiritual foundations.
Intrinsic decomposition with deep supervision from a single image

Separating an RGB image into its albedo and shading maps abstracts the idea behind intrinsic image decomposition. It is a prolonged and obscure computer vision problem for which we propose an efficient and efficacious method. This work is an attempt to decompose a single image into its albedo and shading components by using an encoder-decoder architecture based on Deep Convolution Neural Network (DCNN). Our model uses pre-trained DenseNet as the encoder for the input image and two decoders for the two output images viz. albedo and shading. Synthetic images from the MPI-Sintel dataset is used to train our model through a supervised approach. We show and analyze the results of our model on both synthetic and real images, for both DenseNet-121 and DenseNet-169 as encoders. Our approach aims to reduce the number of parameters used while training and has produced better results when compared with previous approaches on the MPI-Sintel dataset.
Public Distributed Ledger Technologies: Architecture and Consensus

Distributed Ledger Technology (DLT) is a disruptive technology as it provides decentralization, transparency and immutability. Various DLT platforms have emerged during the last few years. These platforms can be classified based on i) Permission access-public, consortium or private, ii) Consensus algorithms and iii) Arrangement of the transactions. The underlying architecture and consensus algorithm have a significant impact on the performance of a DLT. Bitcoin and Ethereum are public DLTs with the Proof of Work consensus algorithm. IOTA and Hashgraph are Directed Acyclic Graph-based DLTs with different consensus algorithms and arrangement of transactions. This paper describes and compares public DLTs Bitcoin, Ethereum, IOTA and Hashgraph. The detailed study of architecture and consensus will help developers to leverage an appropriate DLT platform for the specific application.
Cybersecurity Risks and Mitigation Techniques During COVID-19

The global spread of the novel coronavirus (COVID-19) has prompted the workforce, business, healthcare service and education along with individual to migrate from traditional work method to remote environment. The shifting from traditional to remote work environment has created many challenges towards security and privacy risks. In this paper, we have surveyed the potential user data security breach and privacy situation, cyber-threat that can affect business, health organization, and solution to protect data security and privacy.
Are Sequential Patterns Shareable? Ensuring Individuals’ Privacy

Individuals' actions like smartphone usage, internet shopping, bank card transaction, watched movies can all be represented in form of sequences. Accordingly, these sequences have meaningful frequent temporal patterns that scientist and companies study to understand different phenomena and business processes. Therefore, we tend to believe that patterns are de-identified from individuals' identity and safe to share for studies. Nevertheless, we show, through unicity tests, that the combination of different patterns could act as a quasi-identifier causing a privacy breach, revealing private patterns. To solve this problem, we propose to use \(\epsilon \)-differential privacy over the extracted patterns to add uncertainty to the association between the individuals and their true patterns. Our results show that its possible to reduce significantly the privacy risk conserving data utility.
Survey on Medical Data Storage Systems

Nowadays digital data storage and digital communication are widely used in the healthcare sector. Since data in the digital form significantly easier to store, retrieve, manipulate, analyses, and manage. Also, digital data eliminate the threat of data loss considerably. These advantages pushing many hospitals to store their data digitally. But, as the patients reveal their private and important information to the doctor, it is very crucial to maintain the privacy, security, and reliability of the healthcare data. In this process of handling the data securely, several technologies are being used like cloud storage, data warehousing, blockchain, etc. The main aim of this survey is to study the different models and technologies in the healthcare sector and analyses them on different parameters like security, privacy, performance, etc. This study will help the new developing healthcare systems to choose appropriate technology and approach to build a more efficient, robust, secure, and reliable system.
Exploring the Relations of Local Semantic Features with GNNs for Few-Shot Classification

In few-shot learning, methods based on graph neural network (GNN) are proposed for full exploitation of relations between samples. However, most GNN-based methods only explore a very rough relation among instances, ignoring that the relation between two samples is more complicated than a similarity value can convey. In this paper, we propose a model named GNN-based Relations Extraction for local features (GNN-RE in short) to explore the complex relations between instances. Specifically, we represent each image with multiple local semantic features, with each representing one type of semantic information. By exploring the relations among local semantic features in different images, we can learn more complex relations between images. To evaluate the effectiveness of the proposed GNN-RE, we conduct extensive experiments on two benchmark datasets. Our method achieves better or competitive performance comparing with previous work.
$$Dcube_{NN}$$: Tool for Dynamic Design Discovery from Multi-threaded Applications Using Neural Sequence Models

Program comprehension is a major overhead during software maintenance. Approaches have been proposed to mine programs for better understanding, but not much work has been done to reverse engineer design from multi-threaded applications. To address this challenge we propose an approach \(Dcube_{NN}\), for automated inference of various design choices like thread work breakdown, thread-data interaction, thread scheduling, and states from the runtime non-deterministic traces of multi-threaded programs. We use dynamic instrumentation framework to track various runtime events and encode different functions as well as thread-specific behavior. Finally, we formulate the problem in a machine learning framework and build various classifiers like artificial and recurrent neural networks to infer the design from the observed runtime behavior and static structure. \(Dcube_{NN}\) tool has been integrated with Eclipse CDT IDE for efficient use. To validate \(Dcube_{NN}\), we collect 480 programs from Github, CodeProject, and Stack Overflow and 3 benchmark suites—CDAC Pthreads, Open POSIX Test-Suites, and PARSEC 3.0. We achieve an accuracy of around 93.61% for all the design choices.
Domain-Abstraction-Based Approach for Learning Multidomain Planning

Although deep learning techniques have been successfully implemented to solve domain-specific unmanned aerial vehicle planning problems, it is still a challenging task to develop a learning method to solve multidomain planning problems. Because the multidomain problems often involve learning more parameters, a dilated dataset diminishes learning speed due to its size and high dimensionality. The following two observations help tackle the issue: the state space of planning problems can be decomposed into representations of the domain state and system state, and the dimensionality problem often arises due to the huge size of the domain rather than system state. Inspired by such observations, this work presents a learning framework consisting of two networks: 1) a domain abstraction network in the form of a variational autoencoder that reduces the dimension of the domain space into a compact form, and 2) a planning network that generates a planning solution for a given domain setting. The effectiveness of the proposed learning framework is validated in case studies.