Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM)

Twitter sentiment analysis is an automated process of analyzing the text data which determining the opinion or feeling of public tweets from the various fields. For example, in marketing field, political field huge number of tweets is posting with hash tags every moment via internet from one user to another user. This sentiment analysis is a challenging task for the researchers mainly to correct interpretation of context in which certain tweet words are difficult to evaluate what truly is negative and positive statement from the huge corpus of tweet data. This problem violates the integrity of the system and the user reliability can be significantly reduced. In this paper, we identify the each tweet word and we are assigning a meaning into it. The feature work is combined with tweet words, word2vec, stop words and integrated into the deep learning techniques of Convolution neural network model and Long short Term Memory, these algorithms can identify the pattern of stop word counts with its own strategy. Those two models are well trained and applied for IMDB dataset which contains 50,000 movie reviews. With huge amount of twitter data is processed for predicting the sentimental tweets for classification. With the proposed methodology, the samples are experimentally collected from the real-time environment can be discriminated well and the efficacy of the system is improved. The result of Deep Learning algorithms aims to rate the review tweets and also able to identify movie review with testing accuracy as 87.74% and 88.02%.
Black-box Adversarial Attacks in Autonomous Vehicle Technology

Despite the high quality performance of the deep neural network in real-world applications, they are susceptible to minor perturbations of adversarial attacks. This is mostly undetectable to human vision. The impact of such attacks has become extremely detrimental in autonomous vehicles with real-time "safety" concerns. The black-box adversarial attacks cause drastic misclassification in critical scene elements such as road signs and traffic lights leading the autonomous vehicle to crash into other vehicles or pedestrians. In this paper, we propose a novel query-based attack method called Modified Simple black-box attack (M-SimBA) to overcome the use of a white-box source in transfer based attack method. Also, the issue of late convergence in a Simple black-box attack (SimBA) is addressed by minimizing the loss of the most confused class which is the incorrect class predicted by the model with the highest probability, instead of trying to maximize the loss of the correct class. We evaluate the performance of the proposed approach to the German Traffic Sign Recognition Benchmark (GTSRB) dataset. We show that the proposed model outperforms the existing models like Transfer-based projected gradient descent (T-PGD), SimBA in terms of convergence time, flattening the distribution of confused class probability, and producing adversarial samples with least confidence on the true class.
Feature Selection by Firefly Algorithm with Improved Initialization Strategy

In this paper, the application of the swarm intelligence algorithm for feature selection challenges in the field of machine learning is presented. Feature selection, which is part of the dimension reduction process, helps in selecting those features from the data set that have the most significant impact on the performance and accuracy of the machine learning model. Since the feature selection searches for an optimal (sub-optimal) set of features in a large search area and bearing in mind that swarm intelligence algorithms have proven to be good optimizers for tackling these kinds of problems, they can be efficiently employed as a wrapper method for feature selection. This manuscript proposes the improved implementation of a widely used firefly algorithm, adapted for tackling this important and current problem. Observed drawbacks of the original firefly algorithm are overcome by introducing a quasi-reflection-based learning procedure in the initialization phase. The proposed method was validated against 21 datasets, while the k-nearest neighbors were used as a classification model. The enhanced approach is compared with other state-of-the-art algorithms validated on the same data sets and it has been shown that the proposed implementation overcomes others in terms of features’ number, as well as in the terms of classification accuracy.
Leveraging human factors in cybersecurity: an integrated methodological approach

Computer and Information Security (CIS) is usually approached adopting a technology-centric viewpoint, where the human components of sociotechnical systems are generally considered as their weakest part, with little consideration for the end users' cognitive characteristics, needs and motivations. This paper presents a holistic/Human Factors (HF) approach, where the individual, organisational and technological factors are investigated in pilot healthcare organisations to show how HF vulnerabilities may impact on cybersecurity risks. An overview of current challenges in relation to cybersecurity is first provided, followed by the presentation of an integrated top-down and bottom-up methodology using qualitative and quantitative research methods to assess the level of maturity of the pilot organisations with respect to their capability to face and tackle cyber threats and attacks. This approach adopts a user-centred perspective, involving both the organisations' management and employees, The results show that a better cyber-security culture does not always correspond with more rule compliant behaviour. In addition, conflicts among cybersecurity rules and procedures may trigger human vulnerabilities. In conclusion, the integration of traditional technical solutions with guidelines to enhance CIS systems by leveraging HF in cybersecurity may lead to the adoption of non-technical countermeasures (such as user awareness) for a comprehensive and holistic way to manage cyber security in organisations.
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

In this paper, we present a new dataset with the target of advancing the scene parsing task from images to videos. Our dataset aims to perform Video Scene Parsing in the Wild (VSPW), which covers a wide range of real-world scenarios and categories. To be specific, our VSPW is featured from the following aspects: 1) Well-trimmed long-temporal clips. Each video contains a complete shot, lasting around 5 seconds on average. 2) Dense annotation. The pixel-level annotations are provided at a high frame rate of 15 f/s. 3) High resolution. Over 96% of the captured videos are with high spatial resolutions from 720P to 4K. We totally annotate 3,536 videos, including 251,633 frames from 124 categories. To the best of our knowledge, our VSPW is the first attempt to tackle the challenging video scene parsing task in the wild by considering diverse scenarios. Based on VSPW, we design a generic Temporal Context Blending (TCB) network, which can effectively harness long-range contextual information from the past frames to help segment the current one. Extensive experiments show that our TCB network improves both the segmentation performance and temporal stability comparing with image-/video-based state-of-the-art methods. We hope that the scale, diversity, long-temporal, and high frame rate of our VSPW can significantly advance the research of video scene parsing and beyond. The dataset is available at https://www.vspwdataset.com/.
Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion

Cancellable biometrics is the art of generating distorted or encrypted templates of original biometric templates. The evolution of cancellable biometrics is attributed to the advanced hacking technologies that can capture the original stored biometrics from databases. One of the solutions for this problem is to store cancellable biometric templates in the database rather than the original ones. This paper presents a cancellable face recognition scheme that is based on face image encryption with Fractional-Order (FO) Lorenz chaotic system. The basic idea is to generate user-specific random keys to be XORed with the red, green, and blue components of color face images. These keys are generated from the fractional-order Lorenz chaotic system. In addition, some post-processing is implemented on the encrypted color components of the face images with rotation and transposition of matrices. Finally, a wavelet fusion process is applied on these encrypted and processed face image components. The reason behind the utilization of wavelet fusion is to generate a single cancellable template for each color face image. Furthermore, wavelet fusion after post-processing of encrypted color components leads to a better degree of diffusion in the encrypted face templates. Actually, the encryption with the proposed algorithm is not full encryption, but it is appropriate for cancellable biometric applications. Moreover, the proposed scheme is secure due to the power of fractional-order Lorenz chaotic system that is very sensitive to initial conditions selected by the user. In addition, the post-processing incorporated with wavelet fusion is a non-invertible process. The validation of the proposed scheme is performed with experiments on FERET, LFW, and ORL databases. Evaluation metrics including Equal Error Rate (EER), Area under the Receiver Operating Characteristic (AROC) curve are utilized in the proposed scheme. Numerical values reveal EER levels close to zero and AROC values of 100% at low and mild noise levels.
Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2, and we selected Gaofen-5 (GF-5) satellite hyperspectral image of the study area to explore an applicable and accurate denoising method that can effectively improve the prediction accuracy of soil organic matter (SOM) content. First, fractional-order derivative (FOD) processing is performed on the original reflectance (OR) to evaluate the optimal FOD. Second, singular value decomposition (SVD), Fourier transform (FT) and discrete wavelet transform (DWT) are used to denoise the OR and optimal FOD reflectance. Third, the spectral indexes of the reflectance under different denoising methods are extracted by optimal band combination algorithm, and the input variables of different denoising methods are selected by the recursive feature elimination (RFE) algorithm. Finally, the SOM content is predicted by a random forest prediction model. The results reveal that 0.6-order reflectance describes more useful details in satellite hyperspectral data. Five spectral indexes extracted from the reflectance under different denoising methods have a strong correlation with the SOM content, which is helpful for realizing high-accuracy SOM predictions. All three denoising methods can reduce the noise in hyperspectral data, and the accuracies of the different denoising methods are ranked DWT &gt; FT &gt; SVD, where 0.6-order-DWT has the highest accuracy (R2 = 0.84, RMSE = 3.36 g kg−1, and RPIQ = 1.71). This paper is relatively novel, in that GF-5 satellite hyperspectral data based on different denoising methods are used to predict SOM, and the results provide a highly robust and novel method for mapping the spatial distribution of SOM content at the regional scale.
ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection

In this paper, we introduce the task of multi-view RGB-based 3D object detection as an end-to-end optimization problem. To address this problem, we propose ImVoxel-Net, a novel fully convolutional method of 3D object detection based on posed monocular or multi-view RGB images. The number of monocular images in each multi-view input can variate during training and inference; actually, this number might be unique for each multi-view input. ImVoxelNet successfully handles both indoor and outdoor scenes, which makes it general-purpose. Specifically, it achieves state-of-the-art results in car detection on KITTI (monocular) and nuScenes (multi-view) benchmarks among all methods that accept RGB images. Moreover, it surpasses existing RGB-based 3D object detection methods on the SUN RGB-D dataset. On ScanNet, ImVoxelNet sets a new benchmark for multi-view 3D object detection. The source code and the trained models are available at https://github.com/saic-vul/imvoxelnet.
Blockchain-based decentralized reputation system in E-commerce environment

With the rapid development of E-commerce, more and more people are used to shopping online, in which the reputation scores of sellers play an important role in helping consumers purchase satisfactory products. However, in the existing E-commerce environments, the reputation scores of users (including sellers and buyers) are centrally computed and stored on a centralized cloud server, which might make errors or even engage in fraud and forgery. To address this issue, we propose a blockchain-based decentralized reputation system (BC-DRS) in the E-commerce environment, i.e., online shopping. In this system, the product information including product descriptions and comments is stored in the interplanetary file system (IPFS) and the corresponding address is returned, and the returned address and the reputation scores of users are stored on the blockchain. The reputation evaluation is implemented by designing and deploying a smart contract on the blockchain. Different from the traditional centralized reputation systems (CRSs), the proposed BC-DRS can protect the product information and users reputation scores from intentional and unintentional modifications, since it is very hard to change any data stored in the blockchain and IPFS. Also, as the reputation scores of users are computed and updated by all the ratings of their transactions weighted by the practical transaction factors, it is effective to resist the common attacks, i.e., unfair rating and collusion. In addition, this system also contains a monetary incentive mechanism for the evaluation, which is helpful to form a virtuous circle in online shopping. The proposed BC-DRS is simulated on the popular blockchain platform, i.e., Ethereum with Solidity language. The experimental results and analysis demonstrate that the proposed BC-DRS has desirable usability and reliability.
Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: Effects of H-index centrality, similarity in items and friends

In recent years, the use of trust-based recommendation systems to predict the scores of items not rated by users has attracted many researchers' interest. Accordingly, they create a trusted network of users, move in the trust graph, and search for the desired rank among the users by creating a Trust Walker and Random walk algorithm. Meanwhile, we face some challenges such as calculating the level of trust between users, the movement of Trust Walker using Random walk (random route selection), not discovering the desired rank, and as a result, the algorithm failure. In the present study, in order to solve the mentioned challenges, a trust-based recommender system is presented that predicts the ranks of items that the target user has not rated. In the first stage, a trusted network is developed based on the three criteria. In the next step, we define a Trust Walker to calculate the level of trust between users, and we apply the Biased Random Walk (BRW) algorithm to move it; the proposed method recommends it to the target user in the case of finding the desired rank of the item, and if that item does not exist in the defined trust network, it uses association rules to recognize items that are dependent on the item being searched and recommends them to the target user. The evaluation of this research has been performed on three datasets, and the obtained results indicate higher efficiency and more accuracy of the proposed method.
Resource Allocation in IoT Edge Computing via Concurrent Federated Reinforcement Learning

Resource allocation is a fundamental research issue in IoT edge computing, and reinforcement learning is fast becoming a common solution. The majority of the current techniques involve decision makers who determine how and where resources should be distributed. In a standard cloud system, this decision maker is a central server. In an edge system, the decision maker is an edge host. Both approaches have drawbacks. Edge hosts do not always have access to enough global information to create the most optimal resource allocation strategy. Central servers do but at the cost of privacy. A solution is needed that can do both. This article, therefore, presents a novel resource allocation method called concurrent federated reinforcement learning. The scheme inherits the privacy protection of federated learning, the complex problem solving power of reinforcement learning and adds concurrency in the form of joint decision making so the resource allocation strategies work to the benefit of the global system. The experiments demonstrate that the approach provides the state-of-the-art performance in system-wide utility, speed of task completion, and resource utilization.
Detecting Hallucinated Content in Conditional Neural Sequence Generation

Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input.These variety of fluent but wrong outputs are particularly problematic, as it will not be possible for users to tell they are being presented incorrect content.To detect these errors, we propose a task to predict whether each token in the output sequence is hallucinated (not contained in the input) and collect new manually annotated evaluation sets for this task.We also introduce a method for learning to detect hallucinations using pretrained language models fine tuned on synthetic data that includes automatically inserted hallucinations.Experiments on machine translation (MT) and abstractive summarization demonstrate that our proposed approach consistently outperforms strong baselines on all benchmark datasets.We further demonstrate how to use the token-level hallucination labels to define a fine-grained loss over the target sequence in low-resource MT and achieve significant improvements over strong baseline methods.We also apply our method to word-level quality estimation for MT and show its effectiveness in both supervised and unsupervised settings 1 .
Shallow Convolutional Neural Networks for Human Activity Recognition Using Wearable Sensors

Due to rapid development of sensor technology, human activity recognition (HAR) using wearable inertial sensors has recently become a new research hotspot. Deep learning, especially convolutional neural network (CNN) that can automatically learn intricate activity features have gained a lot of attention in ubiquitous HAR task. Most existing CNNs process sensor input by extracting channel-wise features, and the information from each channel can be separately propagated in a hierarchical way from lower layers to higher layers. As a result, they typically overlook information exchange among channels within the same layer. In this article, we first propose a shallow CNN that considers cross-channel communication in HAR scenario, where all channels in the same layer have a comprehensive interaction to capture more discriminative features of sensor input. One channel can communicate with all other channels by graph neural network to remove redundant information accumulated among channels, which is more beneficial for deploying lightweight deep models. Extensive experiments are conducted on multiple benchmark HAR datasets, namely UCI-HAR, OPPORTUNITY, PAMAP2 and UniMib-SHAR, which indicates that the proposed method enables shallower CNNs to aggregate more useful information, and surpasses baseline deep networks and other competitive methods. The inference speed is evaluated via deploying the HAR systems on an embedded system.
Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking

In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches between tracks and detections, and is updated based on the matching. Many approaches model each target in isolation and lack the ability to use all the targets in the scene to jointly update the memory. This can be problematic when there are similarly looking objects in the scene. In this paper, we solve the problem of simultaneously considering all tracks during memory updating, with only a small spatial overhead, via a novel multi-track pooling module. We additionally propose a training strategy adapted to multi-track pooling which generates hard tracking episodes online. We show that the combination of these innovations results in a strong discriminative appearance model under the bilinear LSTM tracking framework, enabling the use of greedy data association to achieve online tracking performance. Our experiments demonstrate real-time, state-of-the-art online tracking performance on public multi-object tracking (MOT) datasets. The code and trained models are available at https://github.com/chkim403/blstm-mtp.
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink

Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario. In this work, we show by simply using a laser beam that DNNs are easily fooled. To this end, we propose a novel attack method called Adversarial Laser Beam (AdvLB), which enables manipulation of laser beam’s physical parameters to perform adversarial attack. Experiments demonstrate the effectiveness of our proposed approach in both digital- and physical-settings. We further empirically analyze the evaluation results and reveal that the proposed laser beam attack may lead to some interesting prediction errors of the state-of-the-art DNNs. We envisage that the proposed AdvLB method enriches the current family of adversarial attacks and builds the foundation for future robustness studies for light.
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this paper, we introduce a probabilistic autoregressive motion model to score tracklet proposals by directly measuring their likelihood. This is achieved by training our model to learn the underlying distribution of natural tracklets. As such, our model allows us not only to assign new detections to existing tracklets, but also to inpaint a tracklet when an object has been lost for a long time, e.g., due to occlusion, by sampling tracklets so as to fill the gap caused by misdetections. Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences; it outperforms the state of the art in most standard MOT metrics on multiple MOT benchmark datasets, including MOT16, MOT17, and MOT20.