Reviewerly/exHarmony huggingface dataset:
Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments

This paper presents TrustSign, a novel, trusted automatic malware signature generation method based on high-level deep features transferred from a VGG-19 neural network model pretrained on the ImageNet dataset. While traditional automatic malware signature generation techniques rely on static or dynamic analysis of the malware’s executable, our method overcomes the limitations associated with these techniques by producing signatures based on the presence of the malicious process in the volatile memory. By leveraging the cloud’s virtualization technology, TrustSign analyzes the malicious process in a trusted manner, since the malware is unaware and cannot interfere with the inspection procedure. Additionally, by removing the dependency on the malware’s executable, our method is fully capable of signing fileless malware as well. TrustSign’s signature generation process does not require feature engineering or any additional model training, and it is done in a completely unsupervised manner, eliminating the need for a human expert. Because of this, our method has the advantage of dramatically reducing signature generation and distribution time. In fact, in this paper we rethink the typical use of deep convolutional neural networks and use the VGG-19 model as a topological feature extractor for a vastly different task from the one it was trained for. The results of our experimental evaluation demonstrate TrustSign’s ability to generate signatures impervious to the process state over time. By using the signatures generated by TrustSign as input for various supervised classifiers, we achieved up to 99.5% classification accuracy.
BioBERT Based Named Entity Recognition in Electronic Medical Record

The identification and mining of entities in Electronic Medical Record (EMR) plays an important role in medical diagnosis. This paper used the BioBERT model based on Google BERT model for automatic annotation of clinical problems, treatments and tests in EMR. This method firstly pre-trained BioBERT model on the corpus of medical related fields to convert the text into a numerical vector. Next, the BiLSTM-CRF model was used to train the processed vectors and finally complete the entity tagging. The I2B2 2010 challenge dataset was used in the experiment. The experimental results show that the method can obviously improve performance of named entity recognition (NER) for EMR. The F1 score of the experiment is 87.10%, which meet the needs of clinical system applications and can promote the study of clinical decision in the future.
Online signature verification using single-template matching with time-series averaging and gradient boosting

In keeping with recent developments in artificial intelligence in the era of big data, there is a demand for online signature verification systems that operate at high speeds, provide a high level of security, and allow high tolerances while achieving sufficient performance. In response to these needs, the present study proposes a novel, single-template strategy using a mean template set and weighted multiple dynamic time warping (DTW) distances for a function-based approach to online signature verification. Specifically, to obtain an effective mean template for each feature while reflecting intra-user variability between all the reference samples, we adopt a novel time-series averaging method based on Euclidean barycenter-based DTW barycenter averaging. Then, by using the mean template set, we calculate multiple DTW distances from multivariate time series based on dependent and independent warping. Finally, to boost the discriminative power, we apply a weighting scheme using a gradient boosting model to efficiently combine the multiple DTW distances. Experimental results using the common SVC2004 Task1/Task2 and MCYT-100 signature datasets confirm that the proposed method is effective for online signature verification.
Multi Object Tracking with UAVs using Deep SORT and YOLOv3 RetinaNet Detection Framework

Over the years, object tracking and detection has emerged as one of the most important aspects of UAV applications such as surveillance, reconnaissance, etc. In our paper, we present a tracking-by-detection approach for real-time Multiple Object Tracking (MOT) of footage from a drone-mounted camera. Tracking-by-detection is the leading paradigm considering its computational effectiveness and improved detection algorithms. Our algorithm builds on the baseline Deep SORT algorithm implemented for MOT benchmarks. However, to circumvent the challenges posed by videos captured from a significant height we use a combination of YOLOv3 and RetinaNet for generating detections in each frame. The results of our experiment on the VisDrone 2018 dataset exhibit competitive performance in comparison to the existing trackers.
Enhancement of e-commerce security through asymmetric key algorithm

Electronic commerce offers reduced transaction costs and much convenient mode of business to all over global consumers. This paper explains asymmetric methods which uses electronic commerce transactions and other assisted algorithms of cryptography that are important in set up of electronic commerce working. This study explains the essential security problems in electronic commerce. To avoid the issues of security certain secure conditions must be followed which offers sufficient security to transaction data for every entity in transaction of electronic commerce. In this study a multi-layer encryption algorithm namely RSA encryption algorithm and Fernet cipher encryption algorithm is proposed based on security. Multi-layer encryption algorithm is used to construct a sophisticated and complex approach of encryption. This algorithm integrates the strength of several techniques of encryption at the same time. This study reveals how much safe consumer and payment data order will be managed effectively by security-based approaches. Encryption technique discussed in this study is the major technique to make the transaction over internet secure. A better technology of encryption can decrease the fraudlent activities easily and effectively. This study proposes a multi-layer encryption algorithm and implemented it in order to enable delivery of messages through network in a secure way. This study will be helpful to control the decryption and encryption with the use of private and public key of receiver and sender.
Survey: Using Augmented Reality to Improve Learning Motivation in Cultural Heritage Studies

Cultural Heritage (CH) refers to the representation of historical places and traditional customs of a specific city or country. Its principal aim is to transmit to future generations how their ancestors lived, and what their customs and buildings were like, etc. Nowadays, there are different technology systems and research investigations that are focused on CH education that use augmented reality (AR), virtual reality (VR), and mixed reality (MR). The aim of this document is to specifically identify if the use of AR improves students’ motivation to learn about topics related to CH. To this end, studies from different databases and specific journals, along with those concerning technology systems, were evaluated, and comparisons were made between them. Additionally, the aspects that should be considered in future research to improve student motivation and technology systems were identified.
Fair decision making using privacy-protected data

Data collected about individuals is regularly used to make decisions that impact those same individuals. We consider settings where sensitive personal data is used to decide who will receive resources or benefits. While it is well known that there is a trade-off between protecting privacy and the accuracy of decisions, we initiate a first-of-its-kind study into the impact of formally private mechanisms (based on differential privacy) on fair and equitable decision-making. We empirically investigate novel tradeoffs on two real-world decisions made using U.S. Census data (allocation of federal funds and assignment of voting rights benefits) as well as a classic apportionment problem.
A 2020 perspective on “The limits of trust-free systems: A literature review on blockchain technology and trust in the sharing economy”

Blockchain technology has reached a trough of disillusionment, especially with regard to sharing economy platforms. Following up on our reasoning in the limits of trust-free systems, we provide propositions as to why. In particular, we suggest three common misconceptions on the applicability of blockchain technology to the sharing economy as well as the broader platform economy. First, blockchain technology does not inherently solve all trust issues when it comes to real-world interactions between human beings: the trust-free fallacy. Second, the role and importance of platforms as the broker and curator must not be neglected, but often is: the disintermediation fallacy. Third, unlike tech enthusiasts, main street consumers do not put much weight on the underlying technology: the consumer will fallacy.
Characterizing and Understanding GCNs on GPU

Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of GCNs are accelerated with GPUs. Therefore, characterizing and understanding the execution pattern of GCNs on GPU is important for both software and hardware optimization. Unfortunately, to the best of our knowledge, there is no detailed characterization effort of GCN workloads on GPU. In this letter, we characterize GCN workloads at inference stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and exploration, we propose several useful guidelines for both software optimization and hardware optimization for the efficient execution of GCNs on GPU.
Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles

We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios. We propose to lift the 2D images to 3D representations using learned neural networks and leverage existing networks working directly on 3D data to perform 3D object detection and localization. We show that, with carefully designed training mechanism and automatically selected minimally noisy data, such a method is not only feasible, but gives higher results than many methods working on actual 3D inputs acquired from physical sensors. On the challenging KITTI benchmark, we show that our 2D to 3D lifted method outperforms many recent competitive 3D networks while significantly outperforming previous state-of-the-art for 3D detection from monocular images. We also show that a late fusion of the output of the network trained on generated 3D images, with that trained on real 3D images, improves performance. We find the results very interesting and argue that such a method could serve as a highly reliable backup in case of malfunction of expensive 3D sensors, if not potentially making them redundant, at least in the case of low human injury risk autonomous navigation scenarios like warehouse automation.
CI-GNN: Building a Category-Instance Graph for Zero-Shot Video Classification

With the ever-growing video categories, Zero-Shot Learning (ZSL) in video classification has drawn considerable attention in recent years. To transfer the learned knowledge from seen categories to unseen categories, most existing methods resort to an implicit model that learns a projection between visual features and semantic category-representations. However, such methods ignore the explicit relationships among video instances and categories, which impede the direct information propagation in a Category-Instance graph (CI-graph) consisting of both instances and categories. In fact, exploring the structure of the CI-graph can capture the invariances of the ZSL task with good generality for unseen instances. Inspired by these observations, we propose an end-to-end framework to directly and collectively model the relationships between category-instance, category-category, and instance-instance in the CI-graph. Specifically, to construct node features of this graph, we adopt object semantics as a bridge to generate unified representations for both videos and categories. Motivated by the favorable performance of Graph Neural Networks (GNNs), we design a Category-Instance GNN (CI-GNN) to adaptively model the structure of the CI-graph and propagate information among categories and videos. With the task-driven message passing process, the learned model is able to transfer label information from categories towards unseen videos. Extensive experiments on four video datasets demonstrate the favorable performance of the proposed framework.
KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning

Kuzushiji, a cursive writing style, had been used in Japan for over a thousand years starting from the 8th century. Over 3 millions books on a diverse array of topics, such as literature, science, mathematics and even cooking are preserved. However, following a change to the Japanese writing system in 1900, Kuzushiji has not been included in regular school curricula. Therefore, most Japanese natives nowadays cannot read books written or printed just 150 years ago. Museums and libraries have invested a great deal of effort into creating digital copies of these historical documents as a safeguard against fires, earthquakes and tsunamis. The result has been datasets with hundreds of millions of photographs of historical documents which can only be read by a small number of specially trained experts. Thus there has been a great deal of interest in using Machine Learning to automatically recognize these historical texts and transcribe them into modern Japanese characters. Nevertheless, several challenges in Kuzushiji recognition have made the performance of existing systems extremely poor. To tackle these challenges, we propose KuroNet, a new end-to-end model which jointly recognizes an entire page of text by using a residual U-Net architecture which predicts the location and identity of all characters given a page of text (without any pre-processing). This allows the model to handle long range context, large vocabularies, and non-standardized character layouts. We demonstrate that our system is able to successfully recognize a large fraction of pre-modern Japanese documents, but also explore areas where our system is limited and suggest directions for future work.
A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market

Bitcoin is a kind of Cryptocurrency and now is one of type of investment on the stock market. Stock markets are influenced by many risks of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes sudden fall without knowing influence behind it on the stock market. Because it's fluctuations, there's a need and automation tool to predict bitcoin on the stock market. This research study learns how to create model prediction bitcoin stock market prediction using LSTM, LSTM (Long Short Term Memory) is another type of module provided for RNN later developed and popularized by many researchers, like RNN, the LSTM also consists of modules with recurrent consistency. The Method that we apply on this research, also technique and tools to predict Bitcoin on stock market yahoo finance can predict the result above $ 12600 USD for next days after prediction, in the last section we make conclusions and discuss future works.
Learning to Translate for Cross-Source Remote Sensing Image Retrieval

Content-based remote sensing image retrieval (CBRSIR) is one of the important techniques for the mining and analysis of big remote sensing data. Recently, unified-source CBRSIR (US-CBRSIR) has been extensively studied and explored, but there has been little attention on cross-source CBRSIR (CS-CBRSIR). Although there is motivation for exploration of CS-CBRSIR for the continually increasing multisource remote sensing data, CS-CBRSIR suffers data drift due to multisource data. In this article, we explore to explicitly address the problem by mapping the source domain to target domain and propose an image translation-based framework for CS-CBRSIR. On the one hand, a novel cycle-identity-generative adversarial network (CI-GAN) is proposed based on the cycle-GAN. In addition to the generator and discriminator, a pretrained classifier, identity module, is designed to further boost the discriminative ability of translated images and facilitate the implementation of feature extraction and similarity measure. On the other hand, to alleviate the impact of style difference between the generated and real images, translated image augmentations and label smoothing regularization (LSR) are adopted to enhance training and contribute toward generation of a robust feature extractor. Extensive experiments on a public data set and a detailed ablation study confirm the effectiveness of our approach.
A survey on multi-factor authentication for online banking in the wild

In recent years, the usage of online banking services has considerably increased. To protect the sensitive resources managed by these services against attackers, banks have started adopting Multi-Factor Authentication (MFA). To date, a variety of MFA solutions have been implemented by banks, leveraging different designs and features and providing a non-homogeneous level of security and user experience. Public and private authorities have defined laws and guidelines to guide the design of more secure and usable MFA solutions, but their influence on existing MFA implementations remains unclear. In this work, we present a latitudinal study on the adoption of MFA and the design choices made by banks operating in different countries. In particular, we evaluate the MFA solutions currently adopted in the banking sector in terms of (i) compliance with laws and best practices, (ii) robustness against attacks and (iii) complexity. We also investigate possible correlations between these criteria. Based on this study, we identify a number of lessons learned and open challenges.
Data Augmentation for Discrimination Prevention and Bias Disambiguation

Machine learning models are prone to biased decisions due to biases in the datasets they are trained on. In this paper, we introduce a novel data augmentation technique to create a fairer dataset for model training that could also lend itself to understanding the type of bias existing in the dataset i.e. if bias arises from a lack of representation for a particular group (sampling bias) or if it arises because of human bias reflected in the labels (prejudice based bias). Given a dataset involving a protected attribute with a privileged and unprivileged group, we create an "ideal world'' dataset: for every data sample, we create a new sample having the same features (except the protected attribute(s)) and label as the original sample but with the opposite protected attribute value. The synthetic data points are sorted in order of their proximity to the original training distribution and added successively to the real dataset to create intermediate datasets. We theoretically show that two different notions of fairness: statistical parity difference (independence) and average odds difference (separation) always change in the same direction using such an augmentation. We also show submodularity of the proposed fairness-aware augmentation approach that enables an efficient greedy algorithm. We empirically study the effect of training models on the intermediate datasets and show that this technique reduces the two bias measures while keeping the accuracy nearly constant for three datasets. We then discuss the implications of this study on the disambiguation of sample bias and prejudice based bias and discuss how pre-processing techniques should be evaluated in general. The proposed method can be used by policy makers who want to use unbiased datasets to train machine learning models for their applications to add a subset of synthetic points to an extent that they are comfortable with to mitigate unwanted bias.
Geochemical Discrimination of Monazite Source Rock Based on Machine Learning Techniques and Multinomial Logistic Regression Analysis

Detrital monazite geochronology has been used in provenance studies. However, there are complexities in the interpretation of age spectra due to their wide occurrence in both igneous and metamorphic rocks. We use the multinomial logistic regression (MLR) and cross-validation (CV) techniques to establish a geochemical discrimination of monazite source rocks. The elemental abundance-based geochemical discrimination was tested by selecting 16 elements from granitic and metamorphic rocks. The MLR technique revealed that light rare earth elements (REEs), Eu, and some heavy REEs are important discriminators that reflect elemental fractionation during magmatism and/or metamorphism. The best model yielded a discrimination rate of ~97%, and the CV method validated this approach. We applied the discrimination model to detrital monazites from African rivers. The detrital monazites were mostly classified as granitic and of garnet-bearing metamorphic origins; however, their proportion of metamorphic origin was smaller than the proportion that was obtained by using the elemental-ratio-based discrimination proposed by Itano et al. in Chemical Geology (2018). Considering the occurrence of metamorphic rocks in the hinterlands and the different age spectra between monazite and zircon in the same rivers, a ratio-based discrimination would be more reliable. Nevertheless, our study demonstrates the advantages of machine-learning-based approaches for the quantitative discrimination of monazite.
Cluster Pruning: An Efficient Filter Pruning Method for Edge AI Vision Applications

Even though the Convolutional Neural Networks (CNN) has shown superior results in the field of computer vision, it is still a challenging task to implement computer vision algorithms in real-time at the edge, especially using a low-cost IoT device due to high memory consumption and computation complexities in a CNN. Network compression methodologies such as weight pruning, filter pruning, and quantization are used to overcome the above mentioned problem. Even though filter pruning methodology has shown better performances compared to other techniques, irregularity of the number of filters pruned across different layers of a CNN might not comply with majority of the neural computing hardware architectures. In this paper, a novel greedy approach called cluster pruning has been proposed, which provides a structured way of removing filters in a CNN by considering the importance of filters and the underlying hardware architecture. The proposed methodology is compared with the conventional filter pruning algorithm on Pascal-VOC open dataset, and Head-Counting dataset, which is our own dataset developed to detect and count people entering a room. We benchmark our proposed method on three hardware architectures, namely CPU, GPU, and Intel Movidius Neural Computer Stick (NCS) using the popular SSD-MobileNet and SSD-SqueezeNet neural network architectures used for edge-AI vision applications. Results demonstrate that our method outperforms the conventional filter pruning methodology, using both datasets on above mentioned hardware architectures. Furthermore, a low cost IoT hardware setup consisting of an Intel Movidius-NCS is proposed to deploy an edge-AI application using our proposed pruning methodology.