Reviewerly/exHarmony huggingface dataset:
Efficient Pyramidal GAN for Versatile Missing Data Reconstruction in Remote Sensing Images

Missing data reconstruction is a classical yet challenging problem in remote sensing image processing due to the complex atmospheric environment and variability of satellite sensors. Most of the contemporary reconstruction methods either handle only one specific task or require supplementary data, while the single-input for multi-task reconstruction has not been explored yet. In this paper we propose a novel Generative Adversarial Network-based unified framework for missing remote sensing image reconstruction, which is capable of various reconstruction tasks given only single source data as input. Specifically, we first propose a Mask Extraction Network (MEN) to obtain a united soft mask, which represents the intrinsic prior under various scenarios and indicates not only location but context information. The versatility of mask extraction enables the multi-task reconstruction of remote sensing images. Besides, we propose a Unified Inpainting Network (UIN) to repair diverse degraded images. Being specifically tailored for remote sensing images, Dilated pyramidal convolutions (DPC) and an Attention Fusion Mechanism (AFM) are introduced to further improve the feature extraction ability and thus exhaustly leveraging the single-input information. Extensive experiments demonstrate the uncompromising performance of the proposed method against state-of-the-art multi-input methods on diverse missing restoration. Moreover, further exploration shows the potential of the proposed method to utilize joint spatio-spectral-temporal information, which is evaluated to outperform existing competitors on remote sense images.
A Brief Comparison of K-means and Agglomerative Hierarchical Clustering Algorithms on Small Datasets

Abstract In this work, the agglomerative hierarchical clustering and K-means clustering algorithms are implemented on small datasets. Considering that the selection of the similarity measure is a vital factor in data clustering, two measures are used in this study - cosine similarity measure and Euclidean distance - along with two evaluation metrics - entropy and purity - to assess the clustering quality. The datasets used in this work are taken from UCI machine learning depository. The experimental results indicate that k-means clustering outperformed hierarchical clustering in terms of entropy and purity using cosine similarity measure. However, hierarchical clustering outperformed k-means clustering using Euclidean distance. It is noted that performance of clustering algorithm is highly dependent on the similarity measure. Moreover, as the number of clusters gets reasonably increased, the clustering algorithms’ performance gets higher.
FastVOD-Net: A Real-Time and High-Accuracy Video Object Detector

Video object detection is a tough task due to the severe appearance degradation caused by rapid motion, sudden occlusion or rare poses. The great challenge facing video object detection is the simultaneous requirements on both accuracy and speed because the pursuit of one aspect usually causes significant expense to the other. Most existing methods mainly focus on improving detection accuracy with little attention to computationally efficient solutions, and thus they are impractical for many real-world applications. This motivates us to develop a real-time and high-accuracy video object detection method. In this paper, we propose a novel video object detector, called FastVOD-Net, which can yield highly accurate detection results at real-time speed. Specifically, we first develop a temporally-cascaded deformable alignment (TCDA) module to model the object displacements induced by video motion. Then, we introduce another two modules, namely spatially-refined temporal aggregation (SRTA) and attention-guided semantic distillation (AGSD), to improve the appearance feature of the currently processed frame and enhance the semantic representation of non-keyframes, respectively. For keyframe scheduling, we design an adaptive keyframe selection scheduler (AKSS) to adjust the keyframe interval online, making the keyframe usage more rational. On one hand, the characteristics of our FastVOD-Net enable it to sparsely perform expensive feature extraction, which significantly reduces the computational cost and thus guarantees real-time speed. On the other hand, the collaboration of the above tightly-coupled modules and adaptive keyframe scheduler makes FastVOD-Net fully exploit inter-frame temporal dependencies and thus guarantees high accuracy. Experiments on the ImageNet VID dataset show that our FastVOD-Net achieves 79.3% mAP at 29.6 fps or 81.2% mAP at 23.0 fps on an Nvidia RTX 2080 Ti GPU, which is the state-of-the-art performance in real time.
Self-knowledge Distillation: An Efficient Approach for Falling Detection

Self-knowledge distillation, the related idea to knowledge distillation, is a novel approach to avoid training a large teacher network. In this paper, we propose an efficient self-knowledge distillation approach for falling detection. In our approach, the network shares and learns the knowledge distilled via embedded vectors from two different views of a data point. Moreover, we also present the lightweights yet robust network to address this task based on (2+1)D convolution. Our proposed network uses only 0.1M parameters that reduce hundreds of times compared to other deep networks. To demonstrate the effectiveness of our proposed approach, two standard datasets such as the FDD and URFD datasets, have been experimented. The results illustrated state-of-the-art performance and outperformed that compared to independent training. Moreover, with 0.1M parameters, our network demonstrates easy deployment on edge devices, e.g., phones and cameras, in real-time without GPU. The code is available at https://github.com/vdquang1991/self_KD_falling_detection .
Adversarial Training Against Adversarial Attacks for Machine Learning-Based Intrusion Detection Systems

Intrusion detection system plays an important role in defending networks from security breaches. End-to-end machine learning-based intrusion detection systems are being used to achieve high detection accuracy. However, in case of adversarial attacks, that cause misclassification by introducing imperceptible perturbation on input samples, performance of machine learning-based intrusion detection systems is greatly affected. Though such problems have widely been discussed in image processing domain, very few studies have investigated network intrusion detection systems and proposed corresponding defence. In this paper, we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets and then using adversarial samples to train various machine learning algorithms (adversarial training) to test their defence performance. This is achieved by first creating adversarial sample based on Jacobian-based Saliency Map Attack (JSMA) and Fast Gradient Sign Attack (FGSM) using NSLKDD, UNSW-NB15 and CICIDS17 datasets. The study then trains and tests JSMA and FGSM based adversarial examples in seen (where model has been trained on adversarial samples) and unseen (where model is unaware of adversarial packets) attacks. The experiments includes multiple machine learning classifiers to evaluate their performance against adversarial attacks. The performance parameters include Accuracy, F1-Score and Area under the receiver operating characteristic curve (AUC) Score.
Low-Cost Path Planning in 2D Environment using A⁎ Algorithm by Considering Slope of the Obstacle

This paper deals with path generation of a mobile robot for an environment having slopes. In general, most of the path planning algorithms will take care of 2-dimensional or full 3-dimensional path planning whereas in the proposed method we are considering 2-dimensional environments along with slopes and travelable obstacles, which can be viewed as another layer of a 2-dimensional environment over the first one, and vehicle can manoeuvre over the obstacle rather than taking long road. For this purpose, A* algorithm is chosen for the path planning in this work, by considering its accuracy, efficiency and easiness in extending to Dynamic environment, D* algorithm. This can help mobile robot to conserve energy and reduce time taken to reach a goal. Algorithm presented here tries to find a path, in a 2D environment, with obstacle having slope, by travelling over them rather than going around. Ability to travel over an obstacle is basically based on the angle of the inclination, which can be estimated using sensor, like range finder, Lidar and others.
Importance of Textlines in Historical Document Classification

This paper describes a system prepared at Brno University of Technology for ICDAR 2021 Competition on Historical Document Classification, experiments leading to its design, and the main findings. The solved tasks include script and font classification, document origin localization, and dating. We combined patch-level and line-level approaches, where the line-level system utilizes an existing, publicly available page layout analysis engine. In both systems, neural networks provide local predictions which are combined into page-level decisions, and the results of both systems are fused using linear or log-linear interpolation. We propose loss functions suitable for weakly supervised classification problem where multiple possible labels are provided, and we propose loss functions suitable for interval regression in the dating task. The line-level system significantly improves results in script and font classification and in the dating task. The full system achieved 98.48%, 88.84%, and 79.69% accuracy in the font, script, and location classification tasks respectively. In the dating task, our system achieved a mean absolute error of 21.91 years. Our system achieved the best results in all tasks and became the overall winner of the competition.
M3DGAF: Monocular 3D Object Detection With Geometric Appearance Awareness and Feature Fusion

The object detection task in autonomous driving scenario is usually completed by a complex visual sensor system, such as the LiDAR sensor, the stereo sensor, and the monocular sensor. Recent progress in autonomous driving leverages a monocular sensor to achieve a highly efficient 3D object detection task with geometric constraints. These detectors improve with the explicit geometry projection, which can build the bridge between the 2D image plane and the 3D world space. However, they tend to focus on optimizing depth estimation and ignore the equally important 3D properties of orientation and 3D dimension. In this work, we propose a Geometric Appearance Awareness (GAA) module to improve the estimation of orientation. Specifically, a GAA module is proposed to obtain the geometry-guided appearance feature, which can be used to estimate reliable orientation. Furthermore, we design a Sample-aware Feature Fusion (SFF) head in the 3D dimension regression branch. This head dynamically deals with the uniqueness of different samples for learning 3D dimension. We evaluate our method on the KITTI dataset, and achieve significant improvements in the 3D object detection task. Compared with the latest method, our approach obtains a 1.08 improvement for the metric of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {AP}}_{{3}{D}}$ </tex-math></inline-formula> on the hard level and 1.53/3.41/5.79 improvements for the metric of APBEV under the easy/moderate/hard settings, respectively.
Ontological and Fuzzy-Possibility Approach to the Synthesis of the DM Functional Equivalent for Management of Hierarchical Systems

Modern integrated Control and management systems for Business Entity are extremely complex, but require active participation in management decisions by a qualified operator. The choice of a role from the way of describing his knowledge about the choice of managerial decisions is an urgent task.
Blockchain Empowered Federated Learning for Data Sharing Incentive Mechanism

In the machine learning, data sharing between different participants can increase the amount of data, improve the quality of the dataset, and thereby improve the quality of the model. Under the condition of data supervision, federated learning, as a distributed machine learning, aims to protect data while training models through collaboration among all parties to achieve data sharing and improve model quality. However, there are still some issues. For instance, the lack of trust between the participants makes it impossible to establish a secure and reliable sharing mechanism. In addition, how to fairly share the benefits generated by the model, identify honest participants and punish malicious participants is still a challenge. In this paper, we propose a new federated learning scheme based on blockchain architecture for federated learning data sharing. Moreover, an incentive mechanism based on reputation points and Shaply values is proposed to improve the sustainability of the federated learning system, which provides a credible participation mechanism for data sharing based on federated learning and fair incentives. The experimental results and analysis show that the loss of federated learning is more smooth than that of centralized machine learning.
Comparison of preprocessing approaches for text data in digital shop floor management systems

In an increasing number of production companies shop floor management (SFM) is supported by digital systems. The data generated while working with these systems can be used for assistance systems to further enhance the value of digital SFM. Several assistance systems using text data from problem-solving processes have been suggested but had limited quality due to the domain specific language characteristics: short texts with spelling errors and the usage of synonyms. This research aims to quantify the improvement potentials of different preprocessing approaches on the quality of the assistance systems. For that and for comparison in the research community a public, labeled data set is needed. This paper introduces such a data set based on the characteristics identified in three real industry data sets. To overcome the problems in text processing of shop floor data (e.g. domain specific synonyms), several approaches are suggested, tested, and compared to a generic approach for text clustering. The study identifies best practices for the handling of shop floor text data and provides a data set with the goal of simplifying and stimulating research on this topic.
A Novel Digital Forensic Inspection Model for XSS Attack

Today, nearly everybody linked to the Internet has reported that the penetration percentage of Internet users in the globe is growing which prompted the transition of objectives to the individual targets. It is reported that eight of the top ten Websites reported worldwide are critical to injecting methods like as cross-site scripting and SQL injection, which may be exploited by some parties to steal information or for a specific purpose. The Web is everybody's life. Web applications services such as health insurance, shopping, banking, postal services, news, and so on increase exponentially. Most online applications have security issues such as XSS (cross-site scripting) attacks and phishing attacks which attackers use to compromise the Web application's credentials and data for nefarious purposes. The cyberattacks, carried out in an inattentive way in different industries for which a cloud is also not exempt in several vulnerable attacks, constitute an essential threat which has now developed over a number of days in the world of technology. The prevalence of cybercrime and fraud is being increased rapidly, and lawyers need appropriate methodologies for gathering digital proof for verification. Forensic testers employ realistic frameworks and procedures to acquire digital proofs when criminals utilize digital platforms to conduct crime. In order to identify and analyzes breaches, the digital forensics approach involves analyzing network traffic. Different models with benefits and demerits were presented for cybercrime investigations. This paper explores the features of existing models of digital forensic research and analyzes the problems involved in digital forensic research. A digital forensic inspection model is proposed for XSS attack that is compared with other existing models.
Global optimization of hyper-parameters in reservoir computing

&lt;abstract&gt;&lt;p&gt;Reservoir computing has emerged as a powerful and efficient machine learning tool especially in the reconstruction of many complex systems even for chaotic systems only based on the observational data. Though fruitful advances have been extensively studied, how to capture the art of hyper-parameter settings to construct efficient RC is still a long-standing and urgent problem. In contrast to the local manner of many works which aim to optimize one hyper-parameter while keeping others constant, in this work, we propose a global optimization framework using simulated annealing technique to find the optimal architecture of the randomly generated networks for a successful RC. Based on the optimized results, we further study several important properties of some hyper-parameters. Particularly, we find that the globally optimized reservoir network has a largest singular value significantly larger than one, which is contrary to the sufficient condition reported in the literature to guarantee the echo state property. We further reveal the mechanism of this phenomenon with a simplified model and the theory of nonlinear dynamical systems.&lt;/p&gt;&lt;/abstract&gt;
Integration and study of map matching algorithms in healthcare services for cognitive impaired person

The use of different intelligent Internet of Things (IoTs) future innovations for machine learning has become fairly important in most practical applications these days owing to their high performance and low cost for various devices. Wearable cameras, actuators, the IoTs, and automation technologies are now developing the foundations for modern healthcare technologies such as pattern analysis and prognostic modeling. In the near future, smart assistive devices can allow cognitive impaired person, that is, elderly and disabled participants to interact in everyday activities, participate in friendly events, entertain, and socialize themselves while preserving their health and well-being. The involvement of health-related practices, which necessitates the use of communication and collaboration technology, complements those smart environments. Sophisticated networks and IoT systems are often used in these approaches to improve health and well-being. Intellectual disabilities are a category of conditions marked by reduced cognitive and adaptive functioning. They are also known as mental retardation or general intellectual disability. Mild (approximately 80%), moderate (14%), severe (approximately 4%), and extreme (approximately 2%) developmental disorders are the most prominent. The bulk of cases are associated with mild to moderate form of disorder. For individuals with mild to severe conditions in the early stages with intellectual disabilities, SCCA module is a form of orientation facility. In an advanced, serious, or drastic phase of life, the person is unable to use a mobile screen making orientation inevitable. The main characteristics of this type of LBS are that it is a continuous process. In LBS, augmented reality user interfaces were used to guide cognitively challenged individuals. It diminishes the amount of mental activity needed to comprehend the way to approach. A screen may appear to alert the user to wait as the system decides the best travel direction. The moving path must be fed into the speculative computational module to predict user errors and ensure proper travel direction. The trajectory data mining approach was applied and tested with the goal of completing the travel path as per the user preferences. The trajectory of a person with cognitive impairments is denoted as T from their log L. It is a set of GPS points with a fixed distance between them that does not exceed a certain threshold Th. The trajectory is written as T:p1→p2→···→pm, where, with pi∈P⊂L, (m>i≥1), and Th>pi+t·t>0. The term (|T|=m), and t are known as the number of samplings and the interval of the sampling point, respectively. The point structure P=p1, p2· · ·, pm are known as a GPS log, in which, each point pi ∈ P includes pi lng, pi lat, pi.t as longitude, latitude, and timestamp, respectively. Path W [W: e1→e2→···→en] is a connected street segment arrangement that begins at the vertex Vi and ends at the vertex Vj, where, 1≤k≤n, e1 start=Vi, en end=Vj, and ek end=ek+1 start.
Coordinative Optimization Between Multiple Data Center Operators and a System Operator Based on Two-Level Distributed Scheduling Algorithm

Data centers (DCs) have been playing a significant role in demand response (DR) programs in recent years due to their considerable DR capability. DCs are in the charge of the DC operator (DCO), who is responsible for making a reasonable allocation of computing tasks to provide DR resources. To relieve the transmission pressure of power systems, the system operator (SO) encourages DCOs to participate in the DR programs. To maximize the total welfare, a detailed DR scheduling model of DCOs and SO is proposed for the coordinative optimization. Considering the privacy issue of DCOs, a two-level distributed scheduling algorithm based on the alternating direction multiplier method (ADMM) is designed for privacy protection and distributed autonomy. Simulation results show that the proposed coordinative optimization algorithm can effectively realize the maximization of total social welfare with data privacy protection. For a power system with multiple DCOs, reasonable scheduling of DCO’s DR resources can reduce the peak-valley difference of system loads reliably and economically.