Reviewerly/exHarmony huggingface dataset:
A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification

As a crucial task in surveillance and security, person re-identification (re-ID) aims to identify the targeted pedestrians across multiple images captured by non-overlapping cameras. However, existing person re-ID solutions have two main challenges: the lack of pedestrian identification labels in the captured images, and domain shift issue between different domains. A generative adversarial networks (GAN)-based self-training framework with progressive augmentation (SPA) is proposed to obtain the robust features of the unlabeled data from the target domain, according to the preknowledge of the labeled data from the source domain. Specifically, the proposed framework consists of two stages: the style transfer stage (STrans), and self-training stage (STrain). First, the targeted data is complemented by a camera style transfer algorithm in the STrans stage, in which CycleGAN and Siamese Network are integrated to preserve the unsupervised self-similarity (the similarity of the same image between before and after transformation) and domain dissimilarity (the dissimilarity between a transferred source image and the targeted image). Second, clustering and classification are alternately applied to enhance the model performance progressively in the STrain stage, in which both global and local features of the target-domain images are obtained. Compared with the state-of-the-art methods, the proposed method achieves the competitive accuracy on two existing datasets.
ECascade-RCNN: Enhanced Cascade RCNN for Multi-scale Object Detection in UAV Images

Due to the change of flight altitude and attitude of UAV, the object scale in UAV images exists difference which leads to a great challenge for object detection and has drawn wide attention. In this paper, an improved object detection network named ECascade-RCNN is proposed to deal with the multi-scale problem in object detection task for UAV images. We present an innovative Trident-FPN backbone to extract features and design a new attention mechanism to enhance the performance of the detector. Moreover, k-means algorithm is adapted to generate anchors so that the detection model can get better regression accuracy. We evaluate the proposed ECascade-R-CNN on Visdrone dataset through several ablation experiments and the results show that the ECascade-RCNN given in the paper is effective. The ECascade-RCNN is also used in the Visdrone2020 challenge and ranked 8th on the object detection track.
Inspirational Adversarial Image Generation

The task of image generation started receiving some attention from artists and designers, providing inspiration for new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control over the output. We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image. Specifically, we allow the generation given an inspirational image of the user’s choosing by performing several optimization steps to recover optimal parameters from the model’s latent space. We tested several exploration methods from classical gradient descents to gradient-free optimizers. Many gradient-free optimizers just need comparisons (better/worse than another image), so they can even be used without numerical criterion nor inspirational image, only with human preferences. Thus, by iterating on one’s preferences we can make robust facial composite or fashion generation algorithms. Our results on four datasets of faces, fashion images, and textures show that satisfactory images are effectively retrieved in most cases.
Study on center-of-sets type-reduction of interval type-2 fuzzy logic systems with noniterative algorithms

In recent years, interval type-2 fuzzy logic systems (IT2 FLSs) have become a hot topic for the capability of coping with uncertainties. Compared with the centroid type-reduction (TR), investigating the center-of-sets (COS) TR of IT2 FLSs is more favorable for applying IT2 FLSs. Actually, it is still an open question for comparing Karnik-Mendel (KM) types of algorithms and other types of alternative algorithms for COS TR. This paper gives the block of fuzzy reasoning, COS TR, and defuzzification of IT2 FLSs based on Nagar-Bardini (NB), Nie-Tan (NT) and Begian-Melek-Mendel (BMM) noniterative algorithms. Six simulation experiments are used to show the performances of three types of noniterative algorithms. The proposed noniterative algorithms can obtain much higher computational efficiencies compared with the KM algorithms, which give the potential value for designing T2 FLSs.
Comparative Analysis of Supervised Models for Diamond Price Prediction

Diamond is one of the expensive gemstones on the planet that occurs naturally in the form of minerals made of carbons. Precious stones like diamonds are always high in demand due to their monetary rewards. The price of such stones varies according to their features. Given this, we carried out a comparative analysis and implementation of various supervised models in predicting the price of the diamond. In our work, we evaluated eight different supervised models like linear regression, lasso regression, ridge regression, decision tree, random forest, ElasticNet, AdaBoost Regressor, and Gradient-Boosting Regressor and showcases the best suitable model with the more accurate result of all. This paper aims from data preprocessing, finding a correlation between the dataset attributes, training the above-given models, testing their accuracy, analyzing their outcomes, and in turn finding the best of them is the Random Forest Regression Model.
The Burn-to-Claim cross-blockchain asset transfer protocol

The future of multi-blockchain architecture depends on the emergence of new protocols that achieve communication between trustless cross-chain participants.However, interoperability between blockchains remains an open problem.Existing approaches provide integration through solutions using a middleware system, which makes it harder to gain confidence mainly in terms of security and correctness of the process.A crosschain protocol needs to provide a self-verifiable state-proof that embeds trust in the transfer process.We propose a Burn-to-Claim cross-chain protocol to seamlessly exchange assets between networks.Our scheme transfers assets from one blockchain system to another in a way that the asset is burned from the source blockchain and claimed on the destination blockchain.Our mechanism employs combinations of crypto mechanisms such as digital signatures and time lock to operate the protocol in a distributed manner.We provide an analysis which proves that our cross-chain protocol transfers assets correctly and securely.
ExEm: Expert embedding using dominating set theory with deep learning approaches

A collaborative network is a social network that is comprised of experts who cooperate with each other to fulfill a special goal. Analyzing this network yields meaningful information about the expertise of these experts and their subject areas. To perform the analysis, graph embedding techniques have emerged as an effective and promising tool. Graph embedding attempts to represent graph nodes as low-dimensional vectors. In this paper, we propose a graph embedding method, called ExEm, that uses dominating-set theory and deep learning approaches to capture node representations. ExEm finds dominating nodes of the collaborative network and constructs intelligent random walks that comprise of at least two dominating nodes. One dominating node should appear at the beginning of each path sampled to characterize the local neighborhoods. Moreover, the second dominating node reflects the global structure information. To learn the node embeddings, ExEm exploits three embedding methods including Word2vec, fastText and the concatenation of these two. The final result is the low-dimensional vectors of experts, called expert embeddings. The extracted expert embeddings can be applied to many applications. In order to extend these embeddings into the expert recommendation system, we introduce a novel strategy that uses expert vectors to calculate experts' scores and recommend experts. At the end, we conduct extensive experiments to validate the effectiveness of ExEm through assessing its performance over the multi-label classification, link prediction, and recommendation tasks on common datasets and our collected data formed by crawling the vast author Scopus profiles. The experiments show that ExEm outperforms the baselines especially in dense networks.
Artificial and beneficial – Exploiting artificial images for aerial vehicle detection

Object detection in aerial images is an important task in environmental, economic, and infrastructure-related tasks. One of the most prominent applications is the detection of vehicles, for which deep learning approaches are increasingly used. A major challenge in such approaches is the limited amount of data that arises, for example, when more specialized and rarer vehicles such as agricultural machinery or construction vehicles are to be detected. This lack of data contrasts with the enormous data hunger of deep learning methods in general and object recognition in particular. In this article, we address this issue in the context of the detection of road vehicles in aerial images. To overcome the lack of annotated data, we propose a generative approach that generates top-down images by overlaying artificial vehicles created from 2D CAD drawings on artificial or real backgrounds. Our experiments with a modified RetinaNet object detection network show that adding these images to small real-world datasets significantly improves detection performance. In cases of very limited or even no real-world images, we observe an improvement in average precision of up to 0.70 points. We address the remaining performance gap to real-world datasets by analyzing the effect of the image composition of background and objects and give insights into the importance of background.
Drone identification based on CenterNet-TensorRT

In recent years, due to the frequent occurrence of incidents of drone invasion and collisions, there exists a higher rate of safety accidents in crowded places. With the rapid development of drone recognition in the security field, there exist more and more drone recognition methods, most of which are costly and hard to implement. In the context of the rapid development of 5G technology, we propose a method utilizing the existing monitoring network to acquire data and deep learning approaches to detect drone targets to achieve drone recognizing, tracking and positioning. Our approach uses an improved CenterNet model to detect the presence of drones in the video frames. It also takes advantage of TensorRT to accelerate the inference of the trained model and abstract the spatio-temporal features of the drones in each video frame. A proportion integration differentiation (PID) framework is used to adjust the center of the camera and acquire the consequential actual coordinate to achieve positioning. We establish a largescale database, which includes 1097 images of drones in variant environments. Both the COCO2017 dataset and our drone dataset are used to test the original model and the accelerated model of resdcn101 and resdcn18, respectively. Extensive evaluations on both datasets demonstrate that the accelerated models with TensorRT can achieve a higher speed and a maintained detection precision.
Interpretable feature subset selection: A Shapley value based approach

While performing Feature Subset Selection (FSS) to identify important features, a weight is assigned to each feature that is not necessarily meaningful or interpretable w.r.t. final task and in turn leads to non-actionable information. To provide a solution to this problem of interpretable FSS, we introduce a novel notion of classification game with features as players and hinge loss based characteristic function. We use the Shapley value of this game to apportion the total training error to explicitly compute the contribution of each feature (Shapley Value based Error Apportioning, SVEA) to the total training error. We formalize the notion of interpret ability in FSS by identifying 3 final task related conditions. We empirically demonstrate that features with SVEA values less than zero are the dominant ones; this set is unique for a dataset as Shapley value is unique for a game instance. For the datasets that had negative apportioning, we observe a high value of the power of classification, P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SV</sub> . It compares the performance of a set of linear and non-linear classifiers learned on Shapley value-based important features and the full feature set, in most of the cases. We customize a known Monte Carlo based approximation algorithm to avoid expensive Shapley value computations. We demonstrate the sample bias robustness of SVEA scheme by providing interval estimates. We illustrate all the above aspects on both synthetic and real datasets and showed that our scheme out-performs many existing approaches like recursive feature elimination and ReliefF in most of the cases.
Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs

Fault tolerance is a property which needs deeper consideration when dealing with streaming jobs requiring high levels of availability and low-latency processing even in case of failures where Quality-of-Service constraints must be adhered to. Typically, systems achieve fault tolerance and the ability to recover automatically from partial failures by implementing Checkpoint and Rollback Recovery. However, this is an expensive operation which impacts negatively on the overall performance of the system and manually optimizing fault tolerance for specific jobs is a difficult and time consuming task.In this paper we introduce Chiron, an approach for automatically optimizing the frequency with which checkpoints are performed in streaming jobs. For any chosen job, parallel profiling runs are performed, each containing a variant of the configurations, with the resulting metrics used to model the impact of checkpoint-based fault tolerance on performance and availability. Understanding these relationships is key to minimizing performance objectives and meeting strict Quality-of-Service constraints. We implemented Chiron prototypically together with Apache Flink and demonstrate its usefulness experimentally.
Efficient Combination of RSA Cryptography, Lossy, and Lossless Compression Steganography Techniques to Hide Data

One must compress data prior to sending it to the internet since it helps minimize time and the cost. The main aim of the essay is to develop and design a systemic and a secure method for encrypting data, so as it can be used in executing a lossless compacting steganography method. The technique can be used to decrease the amount of every transmitted data aiding fast transmission while using slow internet or take a small space ion a disk. The paper proposes a compacting with stenography method which might be used to hide of data with significant security and also a perfect invisibility while using a combination of RSA (Rivest–Shamir–Adleman) cryptography method and also used steganography proficiency which are Huffman coding, Run-length encoding (RLE), or Discrete Wavelet Transform (DWT) with Least Significant Bits (LSB) steganography technique. The first step is grounded on the RSA skill to both decrypts and encrypt confidential messages. The next step is compression, which is Huffman coding based techniques that will be used to compress the sensitive data through lossless technology, RLE, which is a more natural way to compress data or DWT based that compacts cover image through lossy compression in order reducing the cover image's dimensions. LSB will then be used to implant the encrypted pieces of information in the compacted cover image. This system shows high ranking performance and a systematic system as compared to other states [1-5] of the structure that uses simulation outcomes.
Stacked authorship attribution of digital texts

In computational authorship attribution (AA) – the task of identifying the author of a given text based on a set of possible candidates – existing differences across domains, languages or input settings may require using knowledge from multiple sources, ranging from surface character patterns to deeper semantics and others. Moreover, since increasing the model complexity may easily lead to overfitting, sources of this kind have to be selected judiciously according to each particular input. Based on these observations, this article introduces a novel approach to AA consisting of stacked classifiers built from multiple knowledge sources - words, characters, part-of-speech n-grams, syntactic dependencies, word embeddings and more - that are dynamically included in the AA model according to the relevant input. In doing so, we would like to show that a stacking approach not only outperforms previous work in the field, but also that dynamic model selection outperforms the use of any of the individual components alone. The current model - called DynAA - is evaluated in a number of AA scenarios covering multiple languages, domains and input sizes, and is shown to generally outperform a number of baseline alternatives, including convolutional neural networks, BERT and others.
A Non-linear Function-on-Function Model for Regression with Time Series Data

In the last few decades, building regression models for non-scalar variables, including time series, text, image, and video, has attracted increasing interests of researchers from the data analytic community. In this paper, we focus on a multivariate time series regression problem. Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations. The first two types of models can only handle regularly observed time series. Besides, the conventional multivariate regression models tend to be biased and inefficient, as they are incapable of encoding the temporal dependencies among observations from the same time series. The sequential learning models explicitly use the same set of parameters along time, which has negative impacts on accuracy. The function-on-function linear model in functional data analysis (a branch of statistics) is insufficient to capture complex correlations among the considered time series and suffer from under-fitting easily. In this paper, we propose a general functional mapping that embraces the function-on-function linear model as a special case. We then propose a non-linear function-on-function model using the fully connected neural network to learn the mapping from data, which addresses the aforementioned concerns in the existing approaches. For the proposed model, we describe in detail the corresponding numerical implementation procedures. The effectiveness of the proposed model is demonstrated through the application to two real-world problems.
Unsupervised Learning Based Diagnosis Model for Anomaly Detection of Motor Bearing with Current Data

Lifecycle engineering is a key concept for promoting environmentally sustainable practices among manufacturing firms. A key aspect of life cycle management for pursuing sustainability is condition-based maintenance system that uses data analytics process such as anomaly detection to understand machine condition. Conventional machine learning technique applied to anomaly detection uses classifiers based on supervised learning to detect anomalies of motor bearings using the bearing specifications. However, supervised learning generally requires a large volume of data at the time of abnormal operation, which takes time to acquire. This paper therefore proposes a data analytics process to detect motor bearing failure using data during normal condition. In the data analytics process, we first make a power spectrum from current sensor signals. Then, after grouping a power spectrum into bins, we employ a Gaussian Mixture Model (GMM) to learn the normal condition of a motor bearing. Next, we calculate likelihood with the GMM to check for any difference from the normal condition. Experimental results demonstrate that the proposed method can show increasing anomaly of a bearing condition corresponding to insufficient grease.
Smart University: Key Factors for an Artificial Intelligence Adoption Model

The emergence of new technologies has brought many advantages to organizations and other sectors,Rico-Bautista, Dewar they manage to streamline manual processes and are of vital importance in decision-making.Medina-Cardenas, Yurley Today many entities decide to make use of emerging technologies that facilitate the work and perform it more effectively, however, the adoption rate is not as high as expected and that is due to a widespread fear due to lack of knowledge of information management and intelligent technologies.Coronel-Rojas, Luis Anderson It is necessary to analyze the levels and the relationship with the processes where they are involved, that is, how the IT technologies are used and each factor within the university can enrich the university community.Cuesta-Quintero, Fabian The purpose of this article is to present an artificial intelligence adoption model proposal for universities.Maestre-Gongora, Gina The IT organizational unit of the universities is in charge of adapting all these “Smart” technologies to the university environment in each of its teachings, research, management, or government facets.Guerrero, Cesar D. The complexity is great, we are talking about very diverse environments with very different needs.
Real-Time Outlier Detection Applied to a Doppler Velocity Log Sensor Based on Hybrid Autoencoder and Recurrent Neural Network

This article presents a real-time outlier detection deep-learning (OD-DL)-based method using a hybridized artificial neural network (ANN) approach. We propose an unsupervised ANN scheme that runs in parallel, a denoising autoencoder (DAE) and a recurrent neural network (RNN). The DAE aims to reconstruct relevant/normal input data, whereas it seeks to ignore outliers; the RNN, with a recursive structure, is used to predict time-series data. As measurements arrive, two tasks are performed: 1) the outlier decision, which is based on a reconstruction error and an energy score criteria from the output difference between the DAE and the RNN; and 2) the training procedure for both DAE and RNN. The proposed OD-DL scheme is specifically targeted to address the outlier problem of the data generated by a Doppler velocity log (DVL) sensor installed on board of an autonomous underwater vehicle (AUV) to enhance the AUV navigation system performance. In particular, the DVL data enter into the OD-DL scheme whose output is fed into an AUV navigation system that runs an error-state Kalman filter that integrates the corrected DVL data with the measurements of an inertial measurement unit and a depth meter. The experimental results show that the AUV navigation system with the OD-DL method outperforms in terms of a more accurate estimated position when compared with the case that there is no outlier detection and with the case of a navigation system using a conventional outlier detection method, or other simpler deep-learning methods.