Reviewerly/exHarmony huggingface dataset:
Dynamic Small Target Detection and Tracking Based on Hierarchical Network and Adaptive Input Image Stream

Small target detection and dynamic target continuous tracking are difficulties in the field of target detection and tracking respectively. The experimental results show that the smaller the target size is, the lower the detection accuracy is, the more dynamic the target is, and the lower the tracking accuracy is. This paper presents a high dynamic small target detection and tracking system which is composed of a yolov4 network, KCF tracker, and Kalman filter prediction algorithm. In this system, the detection network is divided into global and local levels, in which the global network is used for initial detection and re-detection after tracking failure, while the local detection network and tracker are used for real-time tracking of small targets, a more robust system is constructed. Compared with the original target detection network, this method weakens the influence of external factors such as the change of target shape and the fast-moving speed on the detection accuracy. The recognition rate of the small target is more than 96%, and the detection accuracy achieves 78%.
GAN-Assisted YUV Pixel Art Generation

Procedural Content Generation (PCG) in games has grown in popularity in recent years, with Generative Adversarial Networks (GANs) providing a promising option for applying PCG for game artistic asset generation. In this paper, we introduce a model that uses GANs and the YUV colour encoding system for automatic colouring of game assets. In this model, conditional GANs in Pix2Pix architecture are chosen as the main structure and the YUV colour encoding system is used for data preprocessing and result visualisation. We experimented with parameter settings (number of epochs, activation functions, optimisers) to optimise output. Our experimental results show that the proposed model can generate evenly coloured outputs for both small and larger datasets.
Generating Adversarial Examples of Source Code Classification Models via Q-Learning-Based Markov Decision Process

Adversarial robustness becomes an essential concern in Deep Learning (DL)-based source code processing, as DL models are vulnerable to the deception by attackers. To address a new challenge posed by the discrete and structural nature of source code to generate adversarial examples for DL models, and the insufficient focus of existing methods on code structural features, we propose a Q-Learning-based Markov decision process (QMDP) performing semantically equivalent transformations on the source code structure. Two key issues are mainly addressed: (i) how to perform attacks on source code structural information and (ii) what transformations to perform when and where in the source code. We demonstrate that effectively tackling these two issues is crucial for generating adversarial examples for source code. By evaluating C/C++ programs working on the source code classification task, we verified that QMDP can effectively generate adversarial examples and improve the robustness of DL models over 44%.
Movie Recommendation Algorithm Based on Ensemble Learning

With the rapid development of personalized services, major websites have launched a recommendation module in recent years.This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users.This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm.However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which will cause the recommended content of the website to deviate from the need of users, and affect the experience of using.Aiming at this problem, this paper combines the above algorithms and proposes three ensemble recommendation algorithms, which are the ensemble recommendation of KNN + text, the recommendation of user KNN + movie KNN, and the recommendation of user KNN + singular value decomposition.Compared with the traditional collaborative filtering algorithm based on matrix factorization, the method we proposed can realize the recommendation of specific movies input by specific users and make more personalized recommendations and can deal with the problem of cold start and sparse matrix processing issues to a certain extent.
Analysis of an Ethereum Private Blockchain Network Hosted by Virtual Machines Against Internal DoS Attacks

The blockchain technology is increasingly adopted by distinct areas of knowledge and institutions filling administrative gaps related to data integrity, and audit procedures. In parallel, the adoption of resource virtualization is a reality to efficiently manage data centers. Consequently, the institutions have integrated blockchain networks into their own systems, hosting blockchain nodes atop virtual machines (VMs). In this sense, we present a resistance analysis of an Ethereum-based network hosted by VMs. The Denial of Service (DoS) attacks are used to identify the impact on the standard VM flavor. Specifically, we developed an environment for carrying out experiments using Ethereum configured with distinct consensus mechanisms: RAFT, Istanbul Byzantine Fault Tolerance (iBFT), and Proof-of-Authority (PoA). Each scenario is in-depth analyzed facing the DoS attack. Our results show that a private blockchain can suffer DoS when configured only to satisfy the default configuration defined by the application.
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding

Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio. While dialogue history has been exploited to improve conventional text-based natural language understanding systems, current E2E SLU approaches have not yet incorporated such critical contextual signals in multi-turn and task-oriented dialogues. In this work, we propose a contextual E2E SLU model ar-chitecture that uses a multi-head attention mechanism over encoded previous utterances and dialogue acts (actions taken by the voice assistant) of a multi-turn dialogue. We detail alternative methods to integrate these contexts into the state-of-the-art recurrent and transformer-based models. When applied to a large de-identified dataset of utterances collected by a voice assistant, our method reduces average word and seman-tic error rates by 10.8% and 12.6%, respectively. We also present results on a publicly available dataset and show that our method significantly improves performance over a non-contextual baseline.
Embedding Secret Data in Digital Media Using Texture Synthesis

A steganography is an art of hiding confidential data into digital media such as image, audio, video etc. Texture synthesis uses the concept of the patch which represents an image block of source texture where its size is user specified. A texture synthesis process resamples a smaller texture image and provides a new image with arbitrary size and shape. Instead of using an existing cover image to hide messages, the algorithm conceals the source texture image and embeds secret messages using the process of texture synthesis. This allows extracting the hidden messages and source texture from a stego synthetic texture. This offers the advantages like, First, it provides the embedding capacity that is proportional to the size of the stego texture image. Second, the reversible capability inherited from this includes functionality, which allows recovery of the source texture. And third, there will be no image distortion since the size of the new texture image is user specified.
Scalable robust graph embedding with Spark

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. While several techniques to scale graph embedding using compute clusters have been proposed, they require continuous communication between the compute nodes and cannot handle node failure. We therefore propose a framework for scalable and robust graph embedding based on the MapReduce model, which can distribute any existing embedding technique. Our method splits a graph into subgraphs to learn their embeddings in isolation and subsequently reconciles the embedding spaces derived for the subgraphs. We realize this idea through a novel distributed graph decomposition algorithm. In addition, we show how to implement our framework in Spark to enable efficient learning of effective embeddings. Experimental results illustrate that our approach scales well, while largely maintaining the embedding quality.
Dadu-SV: Accelerate Stereo Vision Processing on NPU

Binocular vision and neural networks (CNNs) are widely seen in modern intelligent vision processing systems, such as robotics, autonomous vehicles, and AR gadgets. However, both the classic semiglobal matching (SGM) and deep CNNs entail substantial computing resource to reach the performance goal. Traditional embedded CPU/graphic processor unit (GPU) cannot simultaneously meet the processing speed and energy requirement, while the specialized circuits dedicated to SGM and CNN processing, respectively, will take considerable hardware and development costs. However, as the popularity of deep learning, neural processing units (NPUs) become prevalent in many embedded and edge devices, which possess high throughput computing power to deal with the matrix operations involved by neural networks. In this work, we attempt to take advantage of the neural processing architectures integrated in SoC chips to accelerate the SGM process, so that the hardware resources will be better utilized instead of investing more resources to create specialized SGM components. Thereby, this letter first deploys SGM on NPU by converting the incompatible operations into the neural-computing flow, and a configurable neural processing element is proposed to flexibly support various vector operation sequences. Then, a hybrid dataflow scheduler and the corresponding hardware modification are introduced to accelerate the cost processing, improving hardware utilization and on-chip memory footprint and access. Our solution runs at 45 fps for an image size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$640\times 480$ </tex-math></inline-formula> , with 128 disparity levels. The speed-energy efficiency is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$52\times $ </tex-math></inline-formula> better than the GPU (Jetson TX1) solution with negligible additional hardware overhead and accuracy loss.
Exploring Exposure Bias in Recommender Systems from Causality Perspective

Exposure bias widely exists in recommender systems, particularly in the case of with implicit feedbacks. It seriously influences user's satisfaction of recommendations. There are a number of methods for mitigating the exposure bias from different perspectives. In this paper, we survey the publications that focus on addressing the exposure bias issue in RS with the help of causal inference ideas. We propose a simple taxonomy consisting of bias discovery, evaluation estimator, recommendation modeling, ranking algorithm for the debiasing methods in our study. Based on the taxonomy, we discuss how those methods are beneficial to recommender systems to mitigate the exposure bias using causal graph and propensity score. Finally, we conduct the challenges and point out the future research directions.
A Survey on Dropout Methods and Experimental Verification in Recommendation

Overfitting is a common problem in machine learning, which means the model too closely fits the training data while performing poorly in the test data. Among various methods of coping with overfitting, dropout is one of the representative ways. From randomly dropping neurons to dropping neural structures, dropout has achieved great success in improving model performances. Although various dropout methods have been designed and widely applied in past years, their effectiveness, application scenarios, and contributions have not been comprehensively summarized and empirically compared by far. It is the right time to make a comprehensive survey. In this paper, we systematically review previous dropout methods and classify them into three major categories according to the stage where dropout operation is performed. Specifically, more than seventy dropout methods published in top AI conferences or journals (e.g., TKDE, KDD, TheWebConf, SIGIR) are involved. The designed taxonomy is easy to understand and capable of including new dropout methods. Then, we further discuss their application scenarios, connections, and contributions. To verify the effectiveness of distinct dropout methods, extensive experiments are conducted on recommendation scenarios with abundant heterogeneous information. Finally, we propose some open problems and potential research directions about dropout that worth to be further explored.
LogoMotive: Detecting Logos on Websites to Identify Online Scams - A TLD Case Study

Logos give a website a familiar feel and promote trust. Scammers take advantage of that by using well-known organizations’ logos on malicious websites. Unsuspecting Internet users see these logos and think they are looking at a government website or legitimate webshop, when it is a phishing site, a counterfeit webshop, or a site set up to spread misinformation. We present the largest logo detection study on websites to date. We analyze 6.2M domain names from the Netherlands ’ country-code top-level domain .nl, in two case studies to detect logo misuse for two organizations: the Dutch national government and Thuiswinkel Waarborg, an organization that issues certified webshop trust marks. We show how we can detect phishing, spear phishing, dormant phishing attacks, and brand misuse. To that end, we developed LogoMotive, an application that crawls domain names, generates screenshots, and detects logos using supervised machine learning. LogoMotive is operational in the .nl registry, and it is generalizable to detect any other logo in any DNS zone to help identify abuse.
Blockchain Technology Applications in Businesses and Organizations

Blockchain technology has the ability to disrupt industries and transform business models since all intermediaries and stakeholders can now interact with little friction and at a fraction of the current transaction costs. Using blockchain technology, firms can undergo new applications and processes by pursuing transparency and control, low bureaucracy, trustless relationships, high standards of responsibility, and sustainability. As a result, business and organizations can successfully implement blockchain to grant transparency to consumers and end-users; remove challenges linked to pollution, frauds, human rights, abuse, and other inefficiencies; as well as guaranteed traceability of goods and services by univocally identifying the provenance inputs' quantity and quality along with their treatment and origin. Blockchain Technology Applications in Businesses and Organizations reveals the true advantages that blockchain entails for firms by creating transparent and digital transactions, resolves conflicts and exceptions, and provides incentive-based mechanisms and smart contracts. This book seeks to create a clear understanding of blockchain's applications such that business leaders can see and evaluate its real advantages. Blockchain is then analyzed not from the typical perspective of financial tools using cryptocurrencies and bitcoins but from the perspective of the business advantages for business and organizations. Specifically, the book highlights the advantages of blockchain across different segments and industries by analyzing specific aspects like procurement, manufacturing, contracts, inventory, logistics, operations, sustainability, technology, and innovation. It is an essential reference source for managers, executives, IT specialists, students, operations managers, supply chain managers, project managers, technology managers, academicians, and researchers.
Temporal Knowledge Graph Entity Alignment via Representation Learning

Entity alignment aims to construct a complete knowledge graph (KG) by matching the same entities in multi-source KGs. Existing methods mainly focused on the static KG, which assumes that the relationship between entities is permanent. However, almost every KG will evolve over time in practical applications, resulting in the need for entity alignment between such temporal knowledge graphs (TKGs). In this paper, we propose a novel entity alignment framework suitable for TKGs, namely Tem-EA. To incorporate temporal information, we use recurrent neural networks to learn temporal sequence representations. Furthermore, we use graph convolutional network (GCN) and translation-based embedding model to fully learn structural information representation and attribute information representation. Based on these two representations, the entity similarity is calculated separately and combined using linear weighting. To improve the accuracy of entity alignment, we also propose a concept of nearest neighbor matching, which matches the most similar entity pair according to distance matrix. Experiments show that our proposed model has a significant improvement compared to previous methods.
Solar Power Output Forecasting Using Artificial Neural Network

The solar power generated by photovoltaic modules depends on many parameters namely the solar radiation and the cell temperature as these variables affect the current and voltage provided by the modules. In addition, cable loses, conversion losses and cloud coverage can also affect the power output. In this work, we propose to build a deep learning model that will implicitly take all these parameters into account and provide us with a prediction of the output power generated by PV power plants installed. The Artificial Neural Network used takes as an input the solar radiation, ambient temperature and modules’ temperature, and as target the solar power. The ANN model was trained in a first experiment to give an hourly prediction. The second one provides an entire day forecast. The obtained results are very promising and the predicted output power profile is in good agreement with the measured one.
A Deep Transfer Learning Design Rule Checker With Synthetic Training

Deep transfer learning is applied to the task of design rule checking (DRC). A parameterized synthetic data set generator is used to train the model. —Ulf Schlichtmann, Technical University of Munich
Unsupervised approach for online outlier detection in industrial process data

In this paper, we present a novel unsupervised approach for online outlier detection in multivariate streaming process data, which we developed in collaboration with our industrial company partner from the field of plastics industry. The main idea of our approach is to robustly capture the contemporaneous structure of the data and to use this to predict the future states, on the basis of which outliers are then detected. The prediction model is incrementally retrained on data from recent past, such that domain-specific outliers are identified, while meeting the requirements for evaluation speed. The main benefits of our approach are its applicability to heterogeneous data streams, real-time outlier detection, no need for historically labeled data, and its good performance for datasets with a high percentage of possibly consecutive outliers. In terms of prescriptive analytics, the algorithm can be used for real-time quality and process control. Furthermore, its graphical and numerical outputs can help to indicate by outliers most affected variables which can be related to a specific physical problem on the corresponding part of production machine.
Quaternion-based dynamic mode decomposition for background modeling in color videos

Scene Background Initialization (SBI) is one of the challenging problems in computer vision. Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. However, this method needs to convert the color image into a grayscale image for processing, which leads to the neglect of the coupling information between the three channels of the color image. In this study, we propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis, so as to ultimately preserve the inherent color structure of the color image and the color video. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues. The results on the publicly available benchmark datasets prove that our Q-DMD outperforms the exact DMD method, and experiment results also demonstrate that the performance of our approach is comparable to that of the state-of-the-art ones.
Ballistic Object Trajectory and Impact Point Estimation in the Reentry Phase From a Moving Passive Sensor

This article considers the problem of estimating the trajectory of a ballistic target in the reentry phase using 2-D measurements (azimuth and elevation angles) from a moving passive sensor. Previous works investigated the estimation problem of an object in the thrusting and initial ballistic phase from a single fixed passive sensor. This article shows that the 3-D trajectory in the reentry phase can be obtained by estimating the target’s state at the end of the observation interval. The 7-d motion parameter vector (velocity azimuth angle, velocity elevation angle, drag coefficient, target speed, and 3-D position) is estimated by the maximum likelihood (ML) estimator with numerical search. Then we can predict the future position at an arbitrary time and the impact point of the target. The observability of the system for a sensor on a fast aircraft moving with constant velocity or maneuvering is verified via the invertibility of the Fisher information matrix. This is a major extension of the applicability of the recent observability proof for a stationary passive sensor observing a target in a gravitational field. The Cramer–Rao lower bound for the estimated parameters is evaluated and it shows that the estimates are statistically efficient. The angle estimation performance for the ML estimator is also compared with that of the polynomial fitting method. Simulation results illustrate the effectiveness of the proposed method.
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation

In many image-classification applications, understanding the reasons of model’s prediction can be as critical as the prediction’s accuracy itself. Various feature-based local explainers have been designed to provide explanations on the decision of complex classifiers. Nevertheless, there is no consensus on evaluating the quality of different explanations. In response to this lack of comprehensive evaluation, we introduce the c-Eval metric and its corresponding framework to quantify the feature-based local explanation’s quality. Given a classifier’s prediction and the corresponding explanation on that prediction, c-Eval is the minimum-distortion perturbation that successfully alters the prediction while keeping the explanation’s features unchanged. To show that c-Eval captures the importance of input’s features, we establish a connection between c-Eval and the features returned by explainers in affine and nearly-affine classifiers. We then introduce the c-Eval plot, which not only displays a strong connection between c-Eval and explainers’ quality, but also helps automatically determine explainer’s parameters.