Reviewerly/exHarmony huggingface dataset:
A Deep-Learned Embedding Technique for Categorical Features Encoding

Many machine learning algorithms and almost all deep learning architectures are incapable of processing plain texts in their raw form.This means that their input to the algorithms must be numerical in order to solve classification or regression problems.Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques.Categorical features are common and often of high cardinality.One-hot encoding in such circumstances leads to very high dimensional vector representations, raising memory and computability concerns for machine learning models.This paper proposes a deep-learned embedding technique for categorical features encoding on categorical datasets.Our technique is a distributed representation for categorical features where each category is mapped to a distinct vector, and the properties of the vector are learned while training a neural network.First, we create a data vocabulary that includes only categorical data, and then we use word tokenization to make each categorical data a single word.After that, feature learning is introduced to map all of the categorical data from the vocabulary to word vectors.Three different datasets provided by the University of California Irvine (UCI) are used for training.The experimental results show that the proposed deeplearned embedding technique for categorical data provides a higher F1 score of 89% than 71% of onehot encoding, in the case of the Long short-term memory (LSTM) model.Moreover, the deep-learned embedding technique uses less memory and generates fewer features than one-hot encoding.
Private blockchain-envisioned drones-assisted authentication scheme in IoT-enabled agricultural environment

In an Intelligent Precision Agriculture (IPA), several Internet of Things (IoT) smart devices and drones can be deployed to monitor an agricultural environment. The drones can be further utilized to collect the data from smart devices and send to the Ground Station Server (GSS). However, insecure communication among the smart devices, drones and the GSS make the IoT agriculture environment vulnerable to various potential attacks. For this goal, a new authentication and key management scheme for IoT-enabled IPA, called AKMS-AgriIoT, has been put forward with the private blockchain-based solution. The blocks formed with the encrypted transactions and their respective signatures by the GSS are mined by the cloud servers to verify and add the blocks in the private blockchain center. A detailed security analysis and comparative study reveal that the proposed AKMS-AgriIoT supports better security, and provides more functionality features, less communication costs and comparable computation costs as compared to other relevant schemes. In addition, the blockchain-based implementation on the proposed AKMS-AgriIoT has been also carried out.
An Intelligent Cognitive-Inspired Computing with Big Data Analytics Framework for Sentiment Analysis and Classification

Advancements in recent networking and information technology have always been a natural phenomenon. The exponential amount of data generated by the people in their day-to-day lives results in the rise of Big Data Analytics (BDA). Cognitive computing is an Artificial Intelligence (AI) based system that can reduce the issues faced during BDA. On the other hand, Sentiment Analysis (SA) is employed to understand such linguistic based tweets, feature extraction, compute subjectivity and sentimental texts placed in these tweets. The application of SA on big data finds it useful for businesses to take commercial benefits insight from text-oriented content. In this view, this paper presents new cognitive computing with the big data analysis tool for SA. The proposed model involves various process such as pre-processing, feature extraction, feature selection and classification. For handling big data, Hadoop Map Reduce tool is used. The proposed model initially undergoes pre-processing to remove the unwanted words. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a Binary Brain Storm Optimization (BBSO) algorithm is being used for the Feature Selection (FS) process and thereby achieving improved classification performance. Moreover, Fuzzy Cognitive Maps (FCMs) are used as a classifier to classify the incidence of positive or negative sentiments. A comprehensive experimental results analysis ensures the better performance of the presented BBSO-FCM model on the benchmark dataset. The obtained experimental values highlights the improved classification performance of the proposed BBSO-FCM model in terms of different measures.
Virtual Reality in Destination Marketing: Telepresence, Social Presence, and Tourists’ Visit Intentions

Destination marketing organizations (DMOs) have widely adopted virtual reality (VR) commercials to highlight attractions for potential tourists and encourage visitation. This study extends the stimulus-organism-response (SOR) model by incorporating telepresence and social presence as stimuli and elucidating these factors’ possible interplay in jointly shaping users’ cognition, affection, and (re)visit intentions. A 2 × 2 between-subjects experiment was conducted with a sample of 254 participants. Results showed that VR commercials with higher telepresence elicited stronger (re)visit intention, and this effect was mediated by cognition (i.e., education) and affection (i.e., entertainment, and esthetics). Such causal effects appeared stronger in contexts with lower levels of social presence. Findings offer valuable insight into the design of destination VR commercials.
Improved single shot multibox detector target detection method based on deep feature fusion

Summary The feature layers of different layers in the single shot multibox detector (SSD) are independently used as the input of the classification network, so it is easy to detect the same object. This article proposes an improved SSD model based on deep feature fusion. In the SSD algorithm, the deep feature fusion between the target detection layer and its adjacent feature layer is used, including convolution kernels and pooling kernels of different sizes, down‐sampling of low‐level features and up‐sampling of deconvolution of high‐level features. The network is improved by combining the target frame recommendation strategy in the SSD algorithm and the frame regression algorithm. The experimental results show that the improved SSD algorithm improves the detection accuracy and detection rate of the target, and the effect is more obvious for the relatively small‐scale target.
Deep learning for bone marrow cell detection and classification on whole-slide images

Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, there are many challenges to perform automated BM NDC analysis on whole-slide images (WSIs), including large dimensions of data to process, complicated cell types with subtle differences. To the authors best knowledge, this is the first study on fully automatic BM NDC using WSIs with 40x objective magnification, which can replace traditional manual counting relying on light microscopy via oil-immersion 100x objective lens with a total 1000x magnification. In this study, we develop an efficient and fully automatic hierarchical deep learning framework for BM NDC WSI analysis in seconds. The proposed hierarchical framework consists of (1) a deep learning model for rapid localization of BM particles and cellular trails generating regions of interest (ROI) for further analysis, (2) a patch-based deep learning model for cell identification of 16 cell types, including megakaryocytes, mitotic cells, and four stages of erythroblasts which have not been demonstrated in previous studies before, and (3) a fast stitching model for integrating patch-based results and producing final outputs. In evaluation, the proposed method is firstly tested on a dataset with a total of 12,426 annotated cells using cross validation, achieving high recall and accuracy of 0.905 ± 0.078 and 0.989 ± 0.006, respectively, and taking only 44 seconds to perform BM NDC analysis for a WSI. To further examine the generalizability of our model, we conduct an evaluation on the second independent dataset with a total of 3005 cells, and the results show that the proposed method also obtains high recall and accuracy of 0.842 and 0.988, respectively. In comparison with the existing small-image-based benchmark methods, the proposed method demonstrates superior performance in recall, accuracy and computational time.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection

RGB-D salient object detection (SOD) recently has attracted increasing research interest by benefiting conventional RGB SOD with extra depth information. However, existing RGB-D SOD models often fail to perform well in terms of both efficiency and accuracy, which hinders their potential applications on mobile devices and real-world problems. An underlying challenge is that the model accuracy usually degrades when the model is simplified to have few parameters. To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy. DQFM resorts to the alignment of low-level RGB and depth features, as well as holistic attention of the depth stream to explicitly control and enhance cross-modal fusion. We embed DQFM to obtain an efficient light-weight model called DFM-Net, where we also design a tailored depth backbone and a two-stage decoder for further efficiency consideration. Extensive experimental results demonstrate that our DFM-Net achieves state-of-the-art accuracy when comparing to existing non-efficient models, and meanwhile runs at 140ms on CPU (2.2x faster than the prior fastest efficient model) with only ~8.5Mb model size (14.9% of the prior lightest). Our code will be available at https://github.com/zwbx/DFM-Net.
FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection

Accurate detection of obstacles in 3D is an essential task for autonomous driving and intelligent transportation. In this work, we propose a general multimodal fusion framework FusionPainting to fuse the 2D RGB image and 3D point clouds at a semantic level for boosting the 3D object detection task. Especially, the FusionPainting framework consists of three main modules: a multi-modal semantic segmentation module, an adaptive attention-based semantic fusion module, and a 3D object detector. First, semantic information is obtained for 2D image and 3D Lidar point clouds based on 2D and 3D segmentation approaches. Then the segmentation results from different sensors are adaptively fused based on the proposed attention-based semantic fusion module. Finally, the point clouds painted with the fused semantic label are sent to the 3D detector for obtaining the 3D objection results. The effectiveness of the proposed framework has been verified on the large-scale nuScenes detection benchmark by comparing with three different baselines. The experimental results show that the fusion strategy can significantly improve the detection performance compared to the methods using only point clouds, and the methods using point clouds only painted with 2D segmentation information. Furthermore, the proposed approach outperforms other state-of-the-art methods on the nuScenes testing benchmark. Code will be available at https://github.com/Shaoqing26/FusionPainting/.
Interactive voice assistants – Does brand credibility assuage privacy risks?

Artificial intelligence-enabled conversational user interfaces, commonly known as voice assistants, are increasingly being used by individuals in their day-to-day lives to fulfill diverse needs (e.g. utility, hedonic, and social). However, the homeostatic (steadily present) attraction of the voice assistants is offset by the privacy risk concerns these devices present to users. Despite the growing literature on the adoption and usage of voice assistants, little has been said on whether brand credibility plays a mitigating role in assuaging the perception of privacy risk. This study combines both empirical and qualitative methods to shed light on the relationship between individuals’ perception of the overall value of voice assistants and their behavioral intention towards continued usage of voice assistants. A key study finding is that brand credibility significantly moderates the relationship between VA features and the overall perceived value of VAs – higher brand credibility reduces users’ perception of privacy risks.
C2FDA: Coarse-to-Fine Domain Adaptation for Traffic Object Detection

Object detection in traffic scenes has attracted considerable attention from both academia and industry recently. Modern detectors achieve excellent performance under a simple constrained environment while performing poorly under the actual complex and open traffic environment. Therefore, the capability of adapting to new and unseen domains is a key factor for the large-scale application and proliferation of detectors in autonomous driving. To this end, this paper proposes a novel category-induced coarse-to-fine domain adaptation approach (C2FDA) for cross-domain object detection, which consists of three pivotal components: (1) Attention-induced coarse-grained alignment module (ACGA), which strengthens the distribution alignment across disparate domains within the foreground features in category-agnostic way by the minimax optimization between the domain classifier and the backbone feature extractor; (2) Attention-induced feature selection module, which assists the model to emphasize the crucial foreground features and enables the ACGA to focus on the relevant and discriminative foreground features, without being affected by the distribution of inconsequential background features; (3) Category-induced fine-grained alignment module (CFGA), which reduces the domain shift in category-aware way by minimizing the distance of centroids with the same category from different domains and maximizing that of centroids with disparate categories. We evaluate the performance of our approach in various source/target domain pairs and comprehensive results demonstrate that C2FDA significantly outperforms the state-of-the-art on multiple domain adaptation scenarios, i.e., the synthetic-to-real adaptation, the weather adaptation, and the cross camera adaptation.
Lightweight cryptography in IoT networks: A survey

With the advent of advanced technology, the IoT has made possible the connection of numerous devices that can collect vast volumes of data. Hence, the demands of IoT security is paramount. Cryptography is being used to secure the authentication, confidentiality, data integrity and access control of networks. However, due to the many constraints of IoT devices, traditional cryptographic protocols are no longer suited to all IoT environments, such as the smart city. As a result, researchers have been proposing various lightweight cryptographic algorithms and protocols to secure data on IoT networks. This paper discusses state-of-the-art lightweight cryptographic protocols for IoT networks and presents a comparative analysis of popular contemporary ciphers. In doing so, it has classified the most current algorithms into two parts: symmetric and asymmetric lightweight cryptography. Additionally, we evaluate several recently developed block cipher and stream cipher algorithms in terms of their security. In the final section of this paper, we address the changes that need to be made and suggest future research topics.
SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning

The canonical approach to video captioning dictates a caption generation model to learn from offline-extracted dense video features. These feature extractors usually operate on video frames sampled at a fixed frame rate and are often trained on image/video understanding tasks, without adaption to video captioning data. In this work, we present SwinBERT, an end-to-end transformer-based model for video captioning, which takes video frame patches directly as inputs, and outputs a natural language description. Instead of leveraging multiple 2D/3D feature extractors, our method adopts a video transformer to encode spatial-temporal representations that can adapt to variable lengths of video input without dedicated design for different frame rates. Based on this model architecture, we show that video captioning can benefit significantly from more densely sampled video frames as opposed to previous successes with sparsely sampled video frames for video-and-language understanding tasks (e.g., video question answering). Moreover, to avoid the inherent redundancy in consecutive video frames, we propose adaptively learning a sparse attention mask and optimizing it for task-specific performance improvement through better long-range video sequence modeling. Through extensive experiments on 5 video captioning datasets, we show that Swinbert achieves across-the-board performance improvements over previous methods, often by a large margin. The learned sparse attention masks in addition push the limit to new state of the arts, and can be transferred between different video lengths and between different datasets. Code is available at https://github.com/microsoft/SwinBERT.
A new n-dimensional conservative chaos based on Generalized Hamiltonian System and its’ applications in image encryption

In view of the problem that dissipative chaos has attractors and is easy to be attacked by reconstruction, which leads to the security defects of encryption algorithm based on dissipative chaos, we design a new general form of n-dimensional conservative chaos according to the generalized Hamiltonian system. Taking four-dimensional (4D) as an example, numerical verification and performance analysis show that the conservative chaos has excellent chaotic characteristics such as wide ergodicity, no attractors, no chaotic degradation, and it can resist reconstruction and other attacks. Based on this 4D conservative chaos, we propose a new image encryption algorithm, which includes the plaintext related dynamic scrambling method and the dynamic diffusion mechanism of quadrilateral rule (MQR). Moreover, the initial values of the system are controlled by the external key stream and the internal key stream, so that the generation of ciphertext information are closely related with that of plaintext information, part of the ciphertext information, pseudo-random sequence and the key stream, which can increase the ability of the algorithm to resist plaintext and other attacks. Experimental simulation and performance analysis show that the encryption algorithm has better security and real time communication.
A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification

Breast cancer diagnosis is one of the many areas that has taken advantage of artificial intelligence to achieve better performance, despite the fact that the availability of a large medical image dataset remains a challenge. Transfer learning (TL) is a phenomenon that enables deep learning algorithms to overcome the issue of shortage of training data in constructing an efficient model by transferring knowledge from a given source task to a target task. However, in most cases, ImageNet (natural images) pre-trained models that do not include medical images, are utilized for transfer learning to medical images. Considering the utilization of microscopic cancer cell line images that can be acquired in large amount, we argue that learning from both natural and medical datasets improves performance in ultrasound breast cancer image classification. The proposed multistage transfer learning (MSTL) algorithm was implemented using three pre-trained models: EfficientNetB2, InceptionV3, and ResNet50 with three optimizers: Adam, Adagrad, and stochastic gradient de-scent (SGD). Dataset sizes of 20,400 cancer cell images, 200 ultrasound images from Mendeley and 400 ultrasound images from the MT-Small-Dataset were used. ResNet50-Adagrad-based MSTL achieved a test accuracy of 99 ± 0.612% on the Mendeley dataset and 98.7 ± 1.1% on the MT-Small-Dataset, averaging over 5-fold cross validation. A p-value of 0.01191 was achieved when comparing MSTL against ImageNet based TL for the Mendeley dataset. The result is a significant improvement in the performance of artificial intelligence methods for ultrasound breast cancer classification compared to state-of-the-art methods and could remarkably improve the early diagnosis of breast cancer in young women.
A fuzzy logic controller for a three links robotic manipulator

The achievement of non-linear models has been quite a complex challenge. Order to simplify assumptions are also used to achieve simpler forms. The purpose of this paper is to provide an outline and discuss about the advantages and disadvantages of the fuzzy controls in the robotics manipulator and presents an approach to design a controller for multivariable nonlinear system using Fuzzy Logic Controller "FLC" approach as controller. The Robot Manipulator performance may not be satisfied when the conventional controller is used. The performance of a system is regulated by input of Fuzzy which represented by the error and error change. The suggested controller has been simulating the behaviour of a three serial rotational links. The fuzzy controller uses a variable input-output information to control robot's joints velocity and position.
Deep Learning Model for the Automatic Classification of White Blood Cells

Blood cell count is highly useful in identifying the occurrence of a particular disease or ailment. To successfully measure the blood cell count, sophisticated equipment that makes use of invasive methods to acquire the blood cell slides or images is utilized. These blood cell images are subjected to various data analyzing techniques that count and classify the different types of blood cells. Nowadays, deep learning-based methods are in practice to analyze the data. These methods are less time-consuming and require less sophisticated equipment. This paper implements a deep learning (D.L) model that uses the DenseNet121 model to classify the different types of white blood cells (WBC). The DenseNet121 model is optimized with the preprocessing techniques of normalization and data augmentation. This model yielded an accuracy of 98.84%, a precision of 99.33%, a sensitivity of 98.85%, and a specificity of 99.61%. The proposed model is simulated with four batch sizes (BS) along with the Adam optimizer and 10 epochs. It is concluded from the results that the DenseNet121 model has outperformed with batch size 8 as compared to other batch sizes. The dataset has been taken from the Kaggle having 12,444 images with the images of 3120 eosinophils, 3103 lymphocytes, 3098 monocytes, and 3123 neutrophils. With such results, these models could be utilized for developing clinically useful solutions that are able to detect WBC in blood cell images.