Reviewerly/exHarmony huggingface dataset:
Generation of Environment-Irrelevant Adversarial Digital Camouflage Patterns

Digital camouflage is the most common and effective means to combat military reconnaissance. Traditional digital camouflage generation methods must regenerate camouflage images according to the current environment. When the environment changes, generated camouflage images may be detected by neural network classification models. We present a digital camouflage generation model based on disentangled representation, which can decompose images into a content space and a style space, thereby recombining the current content of the environment image with different digital camouflage styles. When the environment changes, our model can generate digital camouflage images based on the original environment content and the corresponding digital camouflage style, without obtaining the current environment image. To counter the detection of the classification models, we design a category reordering function to mislead the classification result of the classification model. Experiments show that the proposed method can generate digital camouflage images in different seasons and successfully implement an adversarial attack on the classification model.
Application of PBFT Algorithm Based on Trust Model in Internet Public Welfare Organizations

With “the Internet plus” concept put forward, the Internet public welfare organizations emerge as the times require, but the Internet organizations are also facing many problems and challenges. This paper mainly analyzes the problems faced by the Internet public welfare organizations, so as to draw the conclusion that the credibility of the Internet public welfare organizations is decreasing. This paper proposes a combination of fuzzy comprehensive evaluation model and PBFT algorithm, so as to put forward improvement suggestions for the staff and management platform of the Internet public welfare organizations, and promote the better development of the Internet public welfare organizations.
Fine-Grained Image Classification With Global Information and Adaptive Compensation Loss

Fine-grained image classification differs from traditional image classification in that the former needs to divide subclasses under a basic level of categories. Previous works always focus on how to locate discriminative parts of objects, but we find that the global and background information of objects neglected by them is also valuable in some situations. This letter proposes a method to combine the global information and discriminative parts information of objects to do classification, which includes three modules: (1) Activation map based crop-erase module localizes objects while avoiding localization bias due to excessive bias of the network to learn one discriminative part. (2) Part attention module helps learning discriminative part features of objects. (3) Two-level fusion module gives consideration to the global and local information of objects and some potentially effective background information. Meanwhile, we propose an adaptive compensation loss to distinguish easily confused categories. Experiments show that our method achieves state-of-the-art performance on three open benchmarks.
Research on multiple observation stations target tracking based on UKF algorithm

Aiming at the problem of multiple observation stations target tracking, a multiple observation stations target tracking based on the unscented Kalman filter (UKF) algorithm is studied. Firstly, the modeling principle of multiple observation stations target tracking is given. On this basis, the multiple observation stations target tracking based on the UKF algorithm is proposed. Finally, a simulation experiment was carried out through MATLAB. The simulation results show that compared with a single observation station, the UKF target tracking algorithm based on multiple observation stations has higher tracking accuracy.
RGB-D Based Visual Navigation Using Direction Estimation Module

Target-driven visual navigation without mapping works to solve navigation problems that given a target object, mobile robots can navigate to the target object. Recently, visual navigation has been researched and improved largely by learning-based methods. However, their methods lack depth information and spatial perception, using only single RGB images. To overcome these problems, two methods are presented in this paper. Firstly, we encode visual features of objects by dynamic graph convolutional network and extract 3D spatial features for objects by 3D geometry, a high level visual feature for agent to easily understand object relationship. Secondly, as human beings, they solve this problem in two steps, first exploring a new environment to find the target object and second planning a path to arrive. Inspired by the way of humans navigation, we propose direction estimation module (DEM) based on RGB-D images. DEM provides direction estimation of the target object to our learning model by a wheel odometry. Given a target object, first stage, our agent explores an unseen scene to detect the target object. Second stage, when detected the target object, we can estimate current location of the target object by 3D geometry, after that, each step of the agent, DEM will estimate new location of target object, and give direction information of the target object from a first-view image. It can guide our agent to navigate to the target object. Our experiment results outperforms the result of state of the art method in the artificial environment AI2-Thor.
Multi-task infrared pedestrian detection method

Infrared pedestrian detection often suffers from two problems, i.e., 1) the weak features of infrared images result in false alarms; 2) the generalization ability of infrared pedestrian detection methods is not satisfactory since the infrared images are similar due to the limited the acquisition method. To solve these problems, we proposed a multi-task infrared pedestrian detection method. Firstly, the domain adaptation is introduced to align the feature of visible light images and infrared images, by which visible light images are used as additional data to improve scene diversity and generalization ability. Secondly, the U-Net segmentation network is used to predict the pedestrian activity area, and the detected objects in nonpedestrian parts are filtered out to reduce the false alarm. The experiment results show that, Compared with EfficientDet, our method improved the average precision (AP) by 1.4% on the XDU-NIR2020 dataset.
Metaheuristic based MLP-SARIMAX HybridizationOne Hour Ahead Solar Radiation Forecasting

Within the context of clean energy generation, solar radiation forecast is applied for photovoltaic plants to increase maintainability and reliability. Statistical models of time series like ARIMA and machine learning techniques help to improve the results. Hybrid Statistical + ML are found in all sorts of time series forecasting applications. This work presents a new way to automate the SARIMAX modeling, nesting PSO and ACO optimization algorithms, differently from R's AutoARIMA, its searches optimal seasonality parameter and combination of the exogenous variables available. This work presents 2 distinct hybrid models that have MLPs as their main elements, optimizing the architecture with Genetic Algorithm. A methodology was used to obtain the results, which were compared to LSTM, CLSTM, MMFF and NARNN-ARMAX topologies found in recent works. The obtained results for the presented models is promising for use in automatic radiation forecasting systems since it outperformed the compared models on at least two metrics.
A Study on Energy-Aware Virtual Machine Consolidation Policies in Cloud Data Centers Using Cloudsim Toolkit

In cloud computing, many data centers are used to serve the user’s service request and lead to high energy consumption. These vast data centers generate high carbon emissions in the environment and high operational costs for the cloud service provider. The method of reducing energy consumption by the data center is called Virtual Machine Consolidation (VMC). In VMC, one host’s migration to another saves energy by putting some hosts to sleep mode. There are four VMC methods: overload detection of the host, underload detection host, VM selection, and VM placement. VMC improves the utilization of energy consumption in data centers with maintain Quality of Service (QoS). Since VM consolidation is an important area for research, many researchers are working on it. In this article, we implement and execute the different twenty VMC policies using PlanetLab workload traces in the CloudSim toolkit and perform a comparative analysis of simulation results.
A Novel Initial Population Construction Heuristic for the DINOS Subgroup Discovery Algorithm

Evolutionary algorithms for subgroup discovery usually randomly initialize the population, which often causes them to spend part of their time evaluating unpromising solutions. This situation causes the algorithm to take more time to converge to good solutions. In this paper, we present a new initial population construction heuristic for DINOS, a genetic subgroup discovery algorithm that mines non-redundant subgroups with high quality in a short time. The proposed heuristic is based on the generation of a collection of decision trees, allowing to obtain an initial population in which all the rules are valid and with a large coverage of the database. The quality of these rules is also high and they contain a large diversity in the attributes used, allowing to deal with problems having a large number of dimensions. The experiments carried out show that the new method allows mining more high-quality and diverse subgroups in a slightly higher computational time.
Homography-guided stereo matching for wide-baseline image interpolation

Abstract Image interpolation has a wide range of applications such as frame rate-up conversion and free viewpoint TV. Despite significant progresses, it remains an open challenge especially for image pairs with large displacements. In this paper, we first propose a novel optimization algorithm for motion estimation, which combines the advantages of both global optimization and a local parametric transformation model. We perform optimization over dynamic label sets, which are modified after each iteration using the prior of piecewise consistency to avoid local minima. Then we apply it to an image interpolation framework including occlusion handling and intermediate image interpolation. We validate the performance of our algorithm experimentally, and show that our approach achieves state-of-the-art performance.
Software Validation and Daubert Standard Compliance of an Open Digital Forensics Model

With the widespread increase in Cybersecurity incidents, there has been increased attention on the development of digital forensics tools and methodologies.We investigate a suite of Cyber-Forensic tools in the Open Source CAINE Linux Distribution, and conduct experimental software validation testing in support of open source code compliance with the well-established Daubert Standard for forensic evidence collection.We propose how tools such as Guymager, Autopsy, Fred, and PhotoRec can can be applied as part of a four tier forensic architecture, including experimental results which demonstrate the application of these tools.
Adaptive Square Root Cubature Quadrature Kalman Smoother for Nonlinear State Estimation

This paper presents a new variant of Rauch-Tung-Striebel type smoother for nonlinear state estimation where the knowledge of sensor noise covariance remains unavailable. For improved estimation accuracy the proposed algorithm is formulated based on Cubature Quadrature rule to numerically approximate the Gaussian integrals. The algorithm also prefers square root framework for numerical stability. Finally the adaptation algorithm to automatically tune the sensor noise covariance is designed such that satisfactory estimation performance is ensured for the situations with unidentified sensor noise covariance. The proposed smoother is characterized in simulation with the help of a nontrivial nonlinear estimation problem. Superiority of the proposed smoother is demonstrated over other competing smoothing algorithms.
Copy-Move Image Forgery Detection Using Discrete Cosine Transforms

Digital image forgeries have led us to a situation where no digital image obtained using computing devices like PCs, smartphones and laptops are trusted to be authentic. As there are plenty of tools available free and open source, digital image forgery is no more a sophisticated job. Here we have come up with a simple but an effective technique for copy-move forgery detection in digital images as this type of forgeries is hard to be identified visually. The proposed technique uses block-based forgery detection technique. The features are extracted for each overlapping block, and then, it is compared with the features of other blocks to identify if there is forgery existing in the image. Few different images were tried, and difference in the accuracies has been observed. Accuracy varies depending on the copied image.
Natural light field compressive imaging

Compressed imaging of natural scenes utilises the sparsity a priori of most scenes in nature. It can achieve higher-resolution imaging through low-resolution measurements recorded on a smaller focal plane array. This paper proposes an implementation scheme of a compression imaging system for large-scale natural scenes. First, the degree of freedom of the imaging transmission matrix is reduced through separable compressed sensing, thereby reducing the storage space of the measurement matrix and the calibration workload; secondly, for the separable imaging transmission matrix, a separate calibration method is designed to realise the left and Calibration of the separable measurement matrix on the right; finally, high-resolution reconstruction is achieved through the calibrated imaging transmission matrix and the recorded low-resolution measurement. The simulation and experimental results show the effectiveness of the compressed imaging scheme proposed in this paper and successfully achieve the calibration and rapid reconstruction of the natural scene compressed imaging system, providing ideas and references for establishing a new generation of camera design.
Analysis on Approaches and Structures of Image Semantic Segmentation

In recent years, due to the increasing demand for the understanding and recognition of content in images, image semantic segmentation technology has developed rapidly. Image semantic segmentation technology has also seen more and more reforms and innovations Each classical model has its own innovation and characteristics, which contributes to the development of image semantic segmentation.In this paper, four popular semantic segmentation models are reviewed and their characteristics are introduced.The results show that compared with other models, the SETR model based on Transformer has a higher performance level in semantic segmentation results.
Impact of Covid-19 Pandemic on Recruitment Process and its Sentiment Analysis

Covid-19 Pandemic has affected the entire human kind in a devastating way and the effects it has caused to different sectors of life are not yet gauged. In this research paper we are investigating the effects the pandemic has had on the recruitment process in the campuses and on students and employers. The various perspectives of the recruitment process and the changes the students and the trainers had to make in the process are considered here. The various stakeholders of the process like the human resource (HR) managers, placement officers, students undergoing placements and students who are recently placed are considered here to get a 360-degree perspective of the same. A sentiment analysis of the data procured is performed to categorize the opinions into “Positive”, “Negative” and “Neutral” from the different stakeholders. We are using the rule based approach here to find the polarity of the sentences based on their scores. The study has disclosed the apprehensions of all the stakeholders regarding the shift from offline to online mode of recruitment and also training.
Non-linear Storytelling Applied to Data-Driven Web Design

The growing climate problem has been exacerbating the importance of appropriate sustainable solutions, as well as the need for information and the sharing of scientific knowledge with the public, so as to develop a shared ecological conscience. Design is, therefore, a relevant tool in the creation of spaces and visual identities suitable for the sharing and promotion of sustainable messages, products or services. The exercise at hand aims at the exploration of reactive storytelling in the context of education in the field of eco-efficient architecture. Our work is to be applied to the website of Lugar-Zero, a company that focuses on this exact area of study. Therefore, a visual representation of the company’s core values through these same techniques of storytelling is also a relevant aspect to this paper.
Hybrid-Trusted/Untrusted-Relay based Protection Strategy in Quantum Key Distribution Enabled Optical Networks (QKD-ON)

This paper addresses the survivability issue by proposing a hybrid-trusted/untrusted-relay based protection strategy in QKD-ON. Simulation results show it performs well on service blocking rate and protection path construction rate.
BadASLR: Exceptional Cases of ASLR Aiding Exploitation

Address Space Layout Randomization (ASLR) is de-facto standard exploit mitigation in our daily life software. The simplest idea of unpredictably randomizing memory layout significantly raises the bar for memory exploitation due to the additionally required attack primitives such as information leakage. Ironically, although exceptional, there are rare edge cases where ASLR becomes handy for memory exploitation. In this paper, we dig into such theoretical set of cases and name it as BadASLR. To evaluate if BadASLR can be an actual plausible scenario, we look into real-world bug bounty cases, CTF/wargame challenges. Surprisingly, we found multiple vulnerabilities in commercial software where ASLR becomes handy for attacker. With BadASLR cases, we succeeded in exploiting peculiar vulnerabilities, and received total 10,000 USD as bug bounty reward including one CVE assignment.
Energy Aware Scheduling Algorithms for Cloud Environments -A Survey

Energy-aware scheduling algorithms are emerging as important components in economic-conscious heterogeneous computing systems such as IoT-enabled edge, fog, or cloud environments. Most of the IoT applications utilize cloud infrastructure to process information or perform analytics. The design of energy-aware scheduling algorithms for cloud infrastructures is especially challenging given the highly variable state changes of processors and virtual machines, and the changing available compute nodes typically encountered in such infrastructures. In this paper, we have reviewed the merits and demerits of the available energy-efficient scheduling techniques for cloud environments which could be applied for IoT applications. The paper, in addition, discussed a few design challenges for creating an energy-efficient scheduler.
An Effective System for Multi-format Information Extraction

The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots relation extraction subtask and two event extraction subtasks that extract events from both sentence-level and document-level. Here we describe our system for this multi-format information extraction competition task. Specifically, for the relation extraction subtask, we convert it to a traditional triple extraction task and design a voting based method that makes full use of existing models. For the sentence-level event extraction subtask, we convert it to a NER task and use a pointer labeling based method for extraction. Furthermore, considering the annotated trigger information may be helpful for event extraction, we design an auxiliary trigger recognition model and use the multi-task learning mechanism to integrate the trigger features into the event extraction model. For the document-level event extraction subtask, we design an Encoder-Decoder based method and propose a Transformer-alike decoder. Finally, our system ranks No.4 on the test set leader-board of this multi-format information extraction task, and its F1 scores for the subtasks of relation extraction, event extractions of sentence-level and document-level are 79.887%, 85.179%, and 70.828% respectively. The codes of our model are available at https://github.com/neukg/MultiIE.
Efficient and Real-Time Particle Detection via Encoder-Decoder Network

Particle detection aims to locate and count valid particles in pad images accurately. However, existing methods fail to achieve both high detection accuracy and inference efficiency in real applications. In order to keep a good trade-off between inference efficiency and accuracy, we propose a computation-efficient particle detection network (PAD-Net) with an encoder-decoder architecture. For the encoder part, MobileNetV3 is tailored to greatly reduce parameters at a little cost of accuracy drop. And the decoder part is designed based on the light-weight RefineNet, which further boosts particle detection performance. Besides, the proposed network is equipped with the adaptive attention loss (termed AAL), which improves the detection accuracy with a negligible increase in computation cost. Finally, we employ a knowledge distillation strategy to further boost the final detection performance of PAD-Net without increasing its parameters and floating-point operations (FLOPs). Experimental results on the real datasets demonstrate that our methods can achieve high-accuracy and real-time detection performance on valid particles compared with the state-of-the-art methods.
Deep Learning for Modulation Classification in Two-Tier Cognitive Femtocell Networks

A two-tier cognitive femtocell network combines the conventional femtocell idea with the physical and organizational structure of cognitive radio systems. This architecture is especially beneficial in an indoor environment for both femtocell networks and many cognitive operations. In this paper, a novel concept of automatic modulation recognition (AMR) to modulation classification techniques, based on convolutional neural network (CNN) and SVM method, is introduced. The proposed model of these classification techniques has resultet in the properly of online incremental learning and AMR tool useful in two-tier cognitive femtocell networks. Deep learning architecture was obtained here through the stochastic recursive gradient training algorithm. It allows for a modulation recognition system with cognitive radio equipment.