Reviewerly/exHarmony huggingface dataset:
An Improved Chicken Swarm Optimization Algorithm and its Application in Robot Path Planning

Chicken swarm optimization (CSO) algorithm is one of very effective intelligence optimization algorithms, which has good performance in solving global optimization problems (GOPs). However, the CSO algorithm performs relatively poorly in complex GOPs for some weaknesses, which results the iteration easily fall into a local minimum. An improved chicken swarm optimization algorithm (ICSO) is proposed and applied in robot path planning. Firstly, an improved search strategy with Levy flight characteristics is introduced in the hen's location update formula, which helps to increase the perturbation of the proposed algorithm and the diversity of the population. Secondly, a nonlinear weight reduction strategy is added in the chicken's position update formula, which may enhance the chicken's self-learning ability. Finally, multiple sets of unconstrained functions are used and a robot simulation experimental environment is established to test the ICSO algorithm. The numerical results show that, comparing to particle swarm optimization (PSO) and basic chicken swarm optimization (CSO), the ICSO algorithm has better convergence accuracy and stability for unconstrained optimization, and has stronger search capability in the robot path planning.
P<sup>3</sup>: Privacy-Preserving Scheme Against Poisoning Attacks in Mobile-Edge Computing

Mobile-edge computing (MEC) has emerged to enable users to offload their location data into the MEC server, and at the same time, the MEC server executes the location-aware data processing to compute the statistical results about these collected locations. However, malicious users may deliberately generate poisoning locations and send these poisoning locations to the MEC server, aiming to poison the statistical results learned by the MEC server and even the other users' location privacy. Existing work concerning privacy preservation in MEC has not studied such poisoning attacks in MEC. Another line of somehow related work focused on poisoning attacks in a different scenario-adversarial machine learning. However, MEC exhibits different features with the machine learning settings, and thus, the privacy preservation against poisoning attacks in MEC faces significantly new challenges. To address the problem, we propose the privacy-preserving scheme, i.e., privacy-preserving scheme against poisoning (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), that utilizes the feature learning model to infer the social relationships among users from their location data and then constructs the inferred social graph. Thereafter, it searches the optimal map between the inferred social graph and the social graph from social networks to identify the poisoning locations. Experiments on two real-world data sets, two baseline works, and two kinds of poisoning attacks have demonstrated the privacy preservation against the poisoning attacks in MEC P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> provides.
Exploiting geographical-temporal awareness attention for next point-of-interest recommendation

With the prosperity of the location-based social networks, next point-of-interest (POI) recommendation has become an increasingly significant requirement since it can benefit both users and business. Obtaining insight into user mobility for the next POI recommendations is a vital yet challenging task. Existing approaches to understanding user mobility mainly gloss over the check-in sequence, making it fail to explicitly capture the subtle POI–POI interactions across the entire user check-in history and distinguish relevant check-ins from the irrelevant. In this paper, we proposed a novel recommendation approach, namely geographical-temporal awareness hierarchical attention network (GT-HAN) to resolve those issues. We first establish a geographical-temporal attention network to simultaneously uncover the overall sequence dependence and the subtle POI–POI relationships. Then, a context-specific co-attention network was designed to learn to change user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different check-ins. Finally, we make a POI recommendation using a conditional probability distribution function. Experimental results on real world datasets (obtained from Foursquare and Gowalla) show that the GT-HAN model significantly outperforms current state-of-the-art approaches, and demonstrating the benefits produced by new technologies incorporated into GT-HAN.
Enhancing Video Streaming in Vehicular Networks via Resource Slicing

Vehicle-to-everything (V2X) communication is a key enabler that connects vehicles to neighboring vehicles, infrastructure and pedestrians. In the past few years, multimedia services have seen an enormous growth and it is expected to increase as more devices will utilize infotainment services in the future i.e. vehicular devices. Therefore, it is important to focus on user centric measures i.e. quality-of-experience (QoE) such as video quality (resolution) and fluctuations therein. In this paper, a novel joint video quality selection and resource allocation technique is proposed for increasing the QoE of vehicular devices. The proposed approach exploits the queuing dynamics and channel states of vehicular devices, to maximize the QoE while ensuring seamless video playback at the end users with high probability. The network wide QoE maximization problem is decoupled into two subparts. First, a network slicing based clustering algorithm is applied to partition the vehicles into multiple logical networks. Secondly, vehicle scheduling and quality selection is formulated as a stochastic optimization problem which is solved using the Lyapunov drift plus penalty method. Numerical results show that the proposed algorithm ensures high video quality experience compared to the baseline. Simulation results also show that the proposed technique achieves low latency and high-reliability communication.
Chua's diode and strange attractor: a three‐layer hardware–software co‐design for medical image confidentiality

Medical image security relies upon the arrival of improved techniques for enhancing the confusion as well as diffusion processes in the pixel level manipulations. Non-linear circuits constructed with hardware components can be employed for hardware–software co-design in medical image encryption. The proposed approach has adopted Chua diode circuit for the generation of 256 × 256 random synthetic image with the entropy of 7.9972. This synthetic image has been utilised to scramble the DICOM image for the second level of diffusion as the cellular automata provides the first level of scrambling and diffusion. The average correlation coefficients of encrypted pixels are 0.00204, −0.00298 and −0.00054 in three directions. The encrypted pixels pass the NIST SP 800-22 test suite and offer resistance to statistical, differential and chosen plain text attacks.
RETRACTED: FPGA implementation of low power and high speed image edge detection algorithm

Image processing is a vital task in data processing system for applications in medical fields, remote sensing, microscopic imaging etc., Algorithms for processing image exist except for real time system style, hardware implementation is most popular principally. This paper presents a design for Sobel filter based edge detection on Field Programmable Gate Array (FPGA) board. Hardware implementation of the Sobel edge detection algorithm is chosen because it presents an honest scope for similarity over software package. On the opposite hand, Sobel edge detection will work with less deterioration in high level of noise. Edges are primarily the noticeable variation of intensities in a picture. Edges facilitate to spot the placement of an object and also the boundary of a selected entity within the image. It conjointly helps in feature extraction and pattern recognition. Hence, edge detection is of nice importance in pc vision. The planned design for edge detection exploitation Sobel algorithm is designed using structural Verilog lipoprotein synthesized exploitation Cadence Genus and enforced using Cadence Innovus. The practicality of the planning is verified exploitation normal pictures by FPGA implementation. The proposed architecture reduce the power, delay and space complexity compare to three existing architectures.
Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning

Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC.
A Novel Counterfeit Feature Extraction Technique for Exposing Face-Swap Images Based on Deep Learning and Error Level Analysis

The quality and efficiency of generating face-swap images have been markedly strengthened by deep learning. For instance, the face-swap manipulations by DeepFake are so real that it is tricky to distinguish authenticity through automatic or manual detection. To augment the efficiency of distinguishing face-swap images generated by DeepFake from real facial ones, a novel counterfeit feature extraction technique was developed based on deep learning and error level analysis (ELA). It is related to entropy and information theory such as cross-entropy loss function in the final softmax layer. The DeepFake algorithm is only able to generate limited resolutions. Therefore, this algorithm results in two different image compression ratios between the fake face area as the foreground and the original area as the background, which would leave distinctive counterfeit traces. Through the ELA method, we can detect whether there are different image compression ratios. Convolution neural network (CNN), one of the representative technologies of deep learning, can extract the counterfeit feature and detect whether images are fake. Experiments show that the training efficiency of the CNN model can be significantly improved by the ELA method. In addition, the proposed technique can accurately extract the counterfeit feature, and therefore achieves outperformance in simplicity and efficiency compared with direct detection methods. Specifically, without loss of accuracy, the amount of computation can be significantly reduced (where the required floating-point computing power is reduced by more than 90%).
Efficient continual learning in neural networks with embedding regularization

Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures. Previous approaches to the problem have considered either the progressive increase in the size of the networks, or have tried to regularize the network behavior to equalize it with respect to previously observed tasks. In the latter case, it is essential to understand what type of information best represents this past behavior. Common techniques include regularizing the past outputs, gradients, or individual weights. In this work, we propose a new, relatively simple and efficient method to perform continual learning by regularizing instead the network internal embeddings. To make the approach scalable, we also propose a dynamic sampling strategy to reduce the memory footprint of the required external storage. We show that our method performs favorably with respect to state-of-the-art approaches in the literature, while requiring significantly less space in memory and computational time. In addition, inspired inspired by to recent works, we evaluate the impact of selecting a more flexible model for the activation functions inside the network, evaluating the impact of catastrophic forgetting on the activation functions themselves.
Identification of cyberbullying on multi‐modal social media posts using genetic algorithm

Abstract Cyberbullying is one of the detrimental effects, social media is facing nowadays. With the increasing use of photo sharing and text comments, the severity of cyberbullying has increased many folds. Automated tools to detect these events have become necessary to make this platform healthy and secure. Sometimes innocent‐looking images and text also convey bullying messages when posted together. So, the separate systems for processing text and images may not work properly to identify all cases of cyberbullying. In this research, we have tried to extract combined features of text and images to identify different cases of cyberbullying. We used a pre‐trained VGG‐16 network and convolutional neural network to extract the features from images and text, respectively. These features are further optimized using genetic algorithm to increase the efficiency of the whole system. Our proposed model is validated with a dataset containing text and image to achieve an F1‐score 78% which shows an improvement of 9% over earlier reported results on the same dataset.
Multiclass covert speech classification using extreme learning machine

The objective of the proposed research is to classify electroencephalography (EEG) data of covert speech words. Six subjects were asked to perform covert speech tasks i.e mental repetition of four different words i.e 'left', 'right', 'up' and 'down'. Fifty trials for each word recorded for every subject. Kernel-based Extreme Learning Machine (kernel ELM) was used for multiclass and binary classification of EEG signals of covert speech words. We achieved a maximum multiclass and binary classification accuracy of (49.77%) and (85.57%) respectively. The kernel ELM achieves significantly higher accuracy compared to some of the most commonly used classification algorithms in Brain–Computer Interfaces (BCIs). Our findings suggested that covert speech EEG signals could be successfully classified using kernel ELM. This research involving the classification of covert speech words potentially leading to real-time silent speech BCI research.
Actor Conditioned Attention Maps for Video Action Detection

While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context. The surrounding context provides essential information for understanding actions. To this end, we propose to replace region of interest(RoI) pooling with an attention module, which ranks each spatio-temporal region's relevance to a detected actor instead of cropping. We refer to these as Actor-Conditioned Attention Maps (ACAM), which amplify/dampen the features extracted from the entire scene. The resulting actor-conditioned features focus the model on regions that are relevant to the conditioned actor. For actor localization, we leverage pre-trained object detectors, which transfer better. The proposed model is efficient and our action detection pipeline achieves near real-time performance. Experimental results on AVA 2.1 and JHMDB demonstrate the effectiveness of attention maps, with improvements of 7 mAP on AVA and 4 mAP on JHMDB.
Towards Good Practice for CNN-Based Monocular Depth Estimation

Monocular depth estimation has gained increasing attention in recent years, and various techniques have been proposed to tackle this problem. In this work, we aim to provide a comprehensive study on the techniques widely used in monocular depth estimation, and examine their individual influence on the performance. More specifically, we provide a study on: 1) network architectures, including different combinations of encoders/decoders. 2) supervision losses, including fully supervised losses and self-supervised losses and 3) other practices such as input resolution. The experiments are conducted on two commonly used public datasets, KITTI and NYU Depth v2. We also provide an analysis on the errors produced by different models, to reveal the limitations of current methods. Furthermore, by a careful redesign, we present a model for depth estimation, which achieves competitive performance on KITTI and state-of-the-art performance on NYU Depth v2. Our code is publicly available at https://github.com/zenithfang/supervised_dispnet.
BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos

Monitoring of protected areas to curb illegal activities like poaching and animal trafficking is a monumental task. To augment existing manual patrolling efforts, unmanned aerial surveillance using visible and thermal infrared (TIR) cameras is increasingly being adopted. Automated data acquisition has become easier with advances in unmanned aerial vehicles (UAVs) and sensors like TIR cameras, which allow surveillance at night when poaching typically occurs. However, it is still a challenge to accurately and quickly process large amounts of the resulting TIR data. In this paper, we present the first large dataset collected using a TIR camera mounted on a fixed-wing UAV in multiple African protected areas. This dataset includes TIR videos of humans and animals with several challenging scenarios like scale variations, background clutter due to thermal reflections, large camera rotations, and motion blur. Additionally, we provide another dataset with videos synthetically generated with the publicly available Microsoft AirSim simulation platform using a 3D model of an African savanna and a TIR camera model. Through our benchmarking experiments on state-of-the-art detectors, we demonstrate that leveraging the synthetic data in a domain adaptive setting can significantly improve detection performance. We also evaluate various recent approaches for single and multi-object tracking. With the increasing popularity of aerial imagery for monitoring and surveillance purposes, we anticipate this unique dataset to be used to develop and evaluate techniques for object detection, tracking, and domain adaptation for aerial, TIR videos.
An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.
Recurrent Generative Adversarial Network for Face Completion

Most recently-proposed face completion algorithms use high-level features extracted from convolutional neural networks (CNNs) to recover semantic texture content. Although the completed face is natural-looking, the synthesized content still lacks lots of high-frequency details, since the high-level features cannot supply sufficient spatial information for details recovery. To tackle this limitation, in this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> ecurrent <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> enerative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> dversarial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</u> etwork (RGAN) for face completion. Unlike previous algorithms, RGAN can take full advantage of multi-level features, and further provide advanced representations from multiple perspectives, which can well restore spatial information and details in face completion. Specifically, our RGAN model is composed of a CompletionNet and a DisctiminationNet, where the CompletionNet consists of two deep CNNs and a recurrent neural network (RNN). The first deep CNN is presented to learn the internal regulations of a masked image and represent it with multi-level features. The RNN model then exploits the relationships among the multi-level features and transfers these features in another domain, which can be used to complete the face image. Benefiting from bidirectional short links, another CNN is used to fuse multi-level features transferred from RNN and reconstruct the face image in different scales. Meanwhile, two context discrimination networks in the DisctiminationNet are adopted to ensure the completed image consistency globally and locally. Experimental results on benchmark datasets demonstrate qualitatively and quantitatively that our model performs better than the state-of-the-art face completion models, and simultaneously generates realistic image content and high-frequency details. The code will be released available soon.
AI-Enabled Blockchain: An Outlier-Aware Consensus Protocol for Blockchain-Based IoT Networks

A new framework for a secure and robust consensus in blockchain-based IoT networks is proposed using machine learning. Hyperledger fabric, which is a blockchain platform developed as part of the Hyperledger project, though looks very apt for IoT applications, has comparatively low tolerance for malicious activities in an untrustworthy environment. To that end, we propose AI-enabled blockchain (AIBC) with a 2-step consensus protocol that uses an outlier detection algorithm for consensus in an IoT network implemented on hyperledger fabric platform. The outlier-aware consensus protocol exploits a supervised machine learning algorithm which detects anomaly activities via a learned detector in the first step. Then, the transactions go through the inherent Practical Byzantine Fault Tolerance (PBFT) consensus protocol in the hyperledger fabric for ledger update. We measure and report the performance of our framework with respect to the various delay components. Results reveal that our implemented AIBC network (2-step consensus protocol) improves hyperledger fabric performance in terms of fault tolerance by marginally compromising the delay performance.