Reviewerly/exHarmony huggingface dataset:
YOLBO: You Only Look Back Once–A Low Latency Object Tracker Based on YOLO and Optical Flow

One common computer vision task is to track an object as it moves from frame to frame within a video sequence.There are a myriad of applications for such capability and the underlying technologies to achieve this tracking are very well understood.More recently, deep convolutional neural networks have been employed to not only track, but also to classify objects as they are tracked from frame to frame.These models can be used in a tracking paradigm known as tracking by detection and can achieve very high tracking accuracy.The major drawback to these deep neural networks is the large amount of mathematical operations that must be performed for each inference which negatively impacts the number of tracked frames per second.For edge applications residing on size, weight, and power limited platforms, such as unmanned aerial vehicles, high frame rate and low latency real time tracking can be an elusive target.To overcome the limited power and computational resources of an edge compute device, various optimizations have been performed to trade off tracking speed, accuracy, power, and latency.Previous works on motion based interpolation with neural networks either do not take into account the latency accrued from camera image capture to tracking result or they compensate for this latency but are bottlenecked by the motion interpolation operation instead.The algorithm presented in this work gains the performance speedup used in previous motion based neural network inference papers and also performs a novel look back operation that is less cumbersome than other competing motion interpolation methods.
A Proposal to Systematize Introducing DevOps into the Software Development Process

The software development industry has been evolving with new development standards and service delivery models. Agile methodologies have reached their completion with DevOps, thereby increasing the quality of the software and creating greater speed in delivery. However, a gap regarding the formalization of its adoption and implementation doubts became relevant. My hypothesis is that, by systematizing the introduction of DevOps into the software development process and defining the function of the members of the DevOps team members, may well make it quicker to implement this process, thus reducing conflicts between the teams. As part of the investigation of this hypothesis, the result of the research will be applied in practical development environments i.e. in a Technology Agency of the State of the Brazilian Government and also at the Brazilian Company Neurotech in order to evaluate its effectiveness from metrics appropriate for DevOps environments.
Graphon and Graph Neural Network Stability

Graph neural networks (GNNs) are learning architectures that rely on knowledge of the graph structure to generate meaningful representations of large-scale network data. GNN stability is thus important as in real-world scenarios there are typically uncertainties associated with the graph. We analyze GNN stability using kernel objects called graphons. Graphons are both limits of convergent graph sequences and generating models for deterministic and stochastic graphs. Building upon the theory of graphon signal processing, we define graphon neural networks and analyze their stability to graphon perturbations. We then extend this analysis by interpreting the graphon neural network as a generating model for GNNs on deterministic and stochastic graphs instantiated from the original and perturbed graphons. We observe that GNNs are stable to graphon perturbations with a stability bound that decreases asymptotically with the size of the graph. This asymptotic behavior is further demonstrated in an experiment of movie recommendation.
Learning Error-Driven Curriculum for Crowd Counting

Density regression has been widely employed in crowd counting. However, the frequency imbalance of pixel values in the density map is still an obstacle to improve the performance. In this paper, we propose a novel learning strategy for learning error-driven curriculum, which uses an additional network to supervise the training of the main network. A tutoring network called TutorNet is proposed to repetitively indicate the critical errors of the main network. TutorNet generates pixel-level weights to formulate the curriculum for the main network during training, so that the main network will assign a higher weight to those hard examples than easy examples. Furthermore, we scale the density map by a factor to enlarge the distance among inter-examples, which is well known to improve the performance. Extensive experiments on two challenging benchmark datasets show that our method has achieved state-of-the-art performance.
Meta-Learning for Improving Rare Word Recognition in End-to-End ASR

In this work we take on the challenge of rare word recognition in end-to-end (E2E) automatic speech recognition (ASR) by integrating a meta learning mechanism into an E2E ASR system, enabling few-shot adaptation. We propose a novel method of generating embeddings for speech, changes to four meta learning approaches, enabling them to perform keyword spotting and an approach to using their outcomes in an E2E ASR system. We verify the functionality of each of our three contributions in two experiments exploring their performance for different amounts of classes (N-way) and examples per class (k-shot) in a few-shot setting. We find that the information encoded in the speech embeddings suffices to allow the modified meta learning approaches to perform continuous signal spotting. Despite the simplicity of the interface between keyword spotting and speech recognition, we are able to consistently improve word error rate by up to 5%.
The Realities of Blockchain-Based New Public Governance

Blockchain technology and New Public Governance (NPG) represent promising concepts for various researchers. As such, both concepts offer the vision of an altered relationship between public administration and its non-public actors by emphasizing a strong position of non-public actors for public service delivery. This research aims to identify the relevance of NPG to leading blockchain implementations in the European public sector. For this purpose, both topics are combined in an explorative analysis. The analysis leverages an adapted analysis framework designed for this research effort to structure the expectations around NPG. Qualitative interviews with multiple key stakeholders of blockchain implementations projects were conducted to understand the actual impact of blockchain on the actor's relationships for public service delivery. This article presents the findings to this question and concludes that the use of blockchain has the changed actor relationships only in parts. Consequently, the author finally draws attention to the importance of blockchain governance and blockchain regulation for further developing the relationships of public administrations and their non-public counterparts.
In Quest of Genocide Understanding: Multiple Faces of Genocide

The paper focuses on genocide and its multidimensional emanations. First, the authors present the definition of genocide and its types as elaborated by Lemkin (Axis rule in occupied Europe: laws of occupation, analysis of government, proposals for redress. Carnegie Endowment for International Peace Division of International Law, Columbia University Press, Washington, DC, New York, 1944): physical, political, social, cultural, economic, biological, religious, and finally moral genocide. Next, ten stages of genocide by Stanton (Genocide Watch, 2016) are scrutinized with some emphasis placed on the verbal issues enabling polarization and dehumanization. The authors point out that modern means of communication, ubiquitous nowadays, make it possible to dehumanize and discriminate against groups of people on an unprecedented scale. Therefore, hate speech and infodemic spread on the Internet, especially social media may be a skillfully used tool of lynch and genocide incitement.
Multiphish: Multi-Modal Features Fusion Networks for Phishing Detection

Phishing is an increasingly serious cybercrime. Phishers create phishing websites by mimicking legitimate websites to confuse users and steal their personal information. The proliferation of phishing websites and more advanced camouflage techniques are problems faced by most existing methods. In this paper, we propose a features fusion networks (MultiPhish) which is the first study on fusing multi-modal features with neural networks for the phishing detection task. In this end-to-end network, the domain and favicon of the website are represented via deep neural networks, and the representation of the website identity is obtained through multi-modal features fusion. In addition, the variation autoencoder (VAE) is introduced to optimize the representation. In the phishing detection module, we incorporate URL features to improve situations where phishing websites cannot be detected only by estimating whether the website identity is disguised. Based on the latest collected dataset, we have carried out extensive experiments and proved that our model is superior to the relevant methods. In addition, MultiPhish is a completely language-independent strategy, so it can perform phishing detection regardless of the text language.
Convolutional Dropout and Wordpiece Augmentation for End-to-End Speech Recognition

Regularization and data augmentation are crucial to training end-to-end automatic speech recognition systems. Dropout is a popular regularization technique, which operates on each neuron independently by multiplying it with a Bernoulli random variable. We propose a generalization of dropout, called "convolutional dropout", where each neuron’s activation is replaced with a randomly-weighted linear combination of neuron values in its neighborhood. We believe that this formulation combines the regularizing effect of dropout with the smoothing effects of the convolution operation. In addition to convolutional dropout, this paper also proposes using random word-piece segmentations as a data augmentation scheme during training, inspired by results in neural machine translation. We adopt both these methods during the training of transformer-transducer speech recognition models, and show consistent WER improvements on Librispeech as well as across different languages.
A Dataset of Vulnerable Code Changes of the Chromium OS Project

This paper presents a an empirically built and validated dataset of code reviews from the Chromium OS project that either identified or missed security vulnerabilities. The dataset includes total 890 vulnerable code changes categorized based on the CWE specification and is publicly available at: https://zenodo.org/record/4539891.
Dense Feature Pyramid Grids Network for Single Image Deraining

Rainy images degrade the visional performance that may bring down the accuracy of various applications. In this paper, we propose a novel densely connected network with Dense Feature Pyramid Grids Modules, called DFPGN, to solve the rain removal task. Specifically, in the proposed DFPG, there are five operations from different layers with various pathways and scales as the input of the current layer so that each layer can fuse various features from shallower and deeper ones to improve the deraining ability of the network. Extensive experiments on real and synthetic rainy images are conducted to demonstrate the proposed method achieves superior rain removal performance over state-of-the-art approaches.
Fast Hierarchy Preserving Graph Embedding via Subspace Constraints

Hierarchy preserving network embedding is a method that project nodes into feature space by preserving the hierarchy property of networks. Recently, researches on network representation have considerably profited from taking hierarchy into consideration. Among these works, SpaceNE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> [1] stands out by preserving hierarchy with the help of subspace constraints on the hierarchy subspace system. However, like all other hierarchy preserving network embedding methods, SpaceNE is time-consuming and cannot generalize to new nodes. In this paper, we propose an inductive method, FastHGE, to learn node representations more efficiently and generalize to new nodes more easily. Empirically, the experiment of node classification demonstrates that the convergence speed of FastHGE is increased by 30 times in the case of the same accuracy with SpaceNE.
5D Light Field Synthesis from a Monocular Video

Commercially available light field cameras have difficulty in capturing 5D (4D + time) light field videos. They can only capture still light field images or are excessively expensive for normal users to capture the light field video. To tackle this problem, we propose a deep learning-based method for synthesizing a light field video from a monocular video. We propose a new synthetic light field video dataset that renders photorealistic scenes using Unreal Engine because no light field video dataset is available. The proposed deep learning framework synthesizes the light field video with a full set (9 × 9) of sub-aperture images from a normal monocular video. The proposed network consists of three sub-networks, namely, feature extraction, 5D light field video synthesis, and temporal consistency refinement. Experimental results show that our model can successfully synthesize the light field video for synthetic and real scenes and outperforms the previous frame-by-frame method quantitatively and qualitatively.
Particle Swarm Optimization in Global Path Planning for Swarm of Robots

Planning of the optimal path of an autonomous swarm of mobile robots is quite challenging since they may need to meet multiple targets while avoiding obstacles. This chapter addresses the problem using a method of global navigation based on particle swarm optimization technique. Since it is a meta-heuristic search technique, the path can be found following any optimization criteria such as shortest distance or minimum time. The chapter explores different traditional path planning approaches in contrast with the evolutionary algorithms. The PSO algorithm is tested in different scenarios. Several modifications were implemented in the algorithm for optimization improvements and faster convergence, leading to better results. Geometrical illustrations were used to explain the changes in position of the particles with respect to the environment and obstacles. Consequently, the experiments are conducted on a simulated robot, and the visualizations demonstrated the feasibility of the technique to solve global path planning problem.
Crowd Density Estimation by Using Attention Based Capsule Network and Multi-Column CNN

We propose a strategy that focuses on estimating the number of people in a crowd, one of the aims of crowd analysis, using static images or video images. While manual feature extraction was not performed with pixel and regression-based methods in the first studies on crowd analysis, recent studies use Convolutional Neural Networks (CNN) based models. However, it is still difficult to extract spatial information such as position, orientation, posture, and angular value for crowd estimation from a density map. This study uses capsule networks and routing by agreement algorithm as an attention module. Our proposed approach consists of both CNN and capsule network-based attention modules in a two-column deep neural network architecture. We evaluate our proposed approach compared with other state-of-the-art methods using three well-known datasets: UCF-QNRF, UCF_CC_50, UCSD, ShangaiTech Part A, and WorldExpo'10.
Improving the adaptability of the optical performance monitor by transfer learning

Owing to their mighty fitting ability, the supervised learning-based deep-learning (DL) models have been widely used in the area of optical performance monitoring (OPM) to improve the optical monitors’ performance. However, the supervised learning-based DL models used in OPM are based on two important premises. The first premise is enough training data with labels; the second premise is the same distribution of the training and test data. Nevertheless, it is hard to meet the two premises in the real-world environment where the optical performance monitors are deployed, since the data are unlabeled and the optical network environment is dynamic (such as component aging caused by slow parameter variation), causing the degradation of the monitoring performance. This is because the supervised learning-based DL models lack the adaptability of the dynamic environment. For the purpose of improving the optical performance monitors’ adaptability, we propose a transfer-learning-based convolutional neural network model to maintain the monitoring performance in the dynamic optical network environment. The transfer-learning method can transfer the learned knowledge from the labeled data under an invariant optical network environment to the unlabeled data under a dynamic optical network environment. During the training phase, the maximum mean discrepancy (MMD) is applied to match the features extracted from the source and the target domains. When the trained model is deployed in the OPM monitor, the robustness of the system to the dynamic environment would be enhanced. Four signals (60/100 Gbps 16/64 QAM) under different working environments are used to verify the adaptability of the method. The influence of the MMD’s weight rates, batch size, and weight parameters confirmed the effectiveness of our method.
A Comprehensive Review of Machine Learning in Multi-objective Optimization

In the real world, it is challenging to calculate a trade-off alternative with traditional classical methods for complex non-linear systems, which always involve multiple conflicting objectives. Such complicated systems urgently desire advanced methods to conquer the multi-objective optimization problems (MOPs). As a promising AI method, the development and application of Machine Learning (ML) attract increasingly more attention from researchers. The natures of ML methods, such as parallel computation possibility, no need for any priori assumptions, etc., ensure the effectiveness and efficiency for solving MOPs. However, as we know, there is no literature related to the comprehensive review of ML in multi-objective optimization domain until now. This literature review aims to provide researchers a global view of mainstream ML methods for MOO in a general domain and a reference for applying ML methods to solve a specific type of MOPs. In this paper, the general ML mainstream methods are summarized, based on which the literature relating to ML on MOPs are retrieved in comprehensive domains. The relevant literature is categorized according to the emphasis of object types, purposes and methods, and the categorization results are finally analyzed and discussed.
Shadowsocks Traffic Identification Based on Convolutional Neural Network

Network traffic has been massively produced since the development of the Internet. While the encryption of traffic has ensured the security and reliability of information, it also brings great challenge to the traffic identification and monitoring. The present study proposes a method of shadowsocks traffic identification based on the one-dimensional Convolutional Neural Network. This method simplifies the feature extraction of traffic identification and the recognition accuracy is over 98%. Because we can not find the published shadowsocks traffic dataset, we gathered four encryption kinds of shadowsocks traffic to study on the influence of different encryption on shadowsocks traffic. Moreover, we include VPN traffic and do contrast experiment based on four deep-learning models to verify the efficiency of one-dimensional convolutional neural network.
Data cleaning issues in class imbalanced datasets: instance selection and missing values imputation for one-class classifiers

Purpose Class imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one representative solution for class imbalanced datasets. Since one-class classifiers are trained using only normal data to create a decision boundary for later anomaly detection, the quality of the training set, i.e. the majority class, is one key factor that affects the performance of one-class classifiers. Design/methodology/approach In this paper, we focus on two data cleaning or preprocessing methods to address class imbalanced datasets. The first method examines whether performing instance selection to remove some noisy data from the majority class can improve the performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter is used to handle incomplete datasets that contain missing values. Findings The experimental results are based on 44 class imbalanced datasets; three instance selection algorithms, including IB3, DROP3 and the GA, the CART decision tree for missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance selection algorithm is carefully chosen, performing this step could improve the quality of the training data, which makes one-class classifiers outperform the baselines without instance selection. Moreover, when class imbalanced datasets contain some missing values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain similar data quality as datasets without missing values. Originality/value The novelty of this paper is to investigate the effect of performing instance selection on the performance of one-class classifiers, which has never been done before. Moreover, this study is the first attempt to consider the scenario of missing values that exist in the training set for training one-class classifiers. In this case, performing missing value imputation and instance selection with different orders are compared.
Demystifying deepfakes using deep learning

Manipulation of images, videos and audios using face edit apps and web services have long been in use, since decades but recent advances in deep learning has led to rising AI generated fake images and videos with swapped faces, lip synced audios and puppet masters, popularly known as Deepfakes. Generated primarily using one of the following two approaches namely, Autoencoders and Generator Adversarial Networks which rests on trained deep neural networks, deepfakes offer unprecedented challenges. The degree of realism achieved by deep learning powered deepfakes increases with increasing amounts of data i.e, fake images and videos readily available on the internet at disposal to train GANs. Deepfake algorithms create media leaving a bare margin of difference between the authentic or original source and the forged or deepfaked targets. Thus, new mechanisms and techniques to detect and filter out such deepfakes is the need of the hour.This paper exploits two powerful deep learning based CNN architectures namely, Inception-Resnet-v2 and XceptionNet for detecting the deepfakes. The proposed approach not only outshines the existing approaches in terms of efficiency and accuracy but also offers the best in terms of the given space and time complexity.