Reviewerly/exHarmony huggingface dataset:
Automatic Face Mask Detection System in Public Transportation in Smart Cities Using IoT and Deep Learning

The World Health Organization (WHO) has stated that the spread of the coronavirus (COVID-19) is on a global scale and that wearing a face mask at work is the only effective way to avoid becoming infected with the virus. The pandemic made governments worldwide stay under lock-downs to prevent virus transmissions. Reports show that wearing face masks would reduce the risk of transmission. With the rise in population in cities, there is a greater need for efficient city management in today’s world for reducing the impact of COVID-19 disease. For smart cities to prosper, significant improvements to occur in public transportation, roads, businesses, houses, city streets, and other facets of city life will have to be developed. The current public bus transportation system, such as it is, should be expanded with artificial intelligence. The autonomous mask detection and alert system are needed to find whether the person is wearing a face mask or not. This article presents a novel IoT-based face mask detection system in public transportation, especially buses. This system would collect real-time data via facial recognition. The main objective of the paper is to detect the presence of face masks in real-time video stream by utilizing deep learning, machine learning, and image processing techniques. To achieve this objective, a hybrid deep and machine learning model was designed and implemented. The model was evaluated using a new dataset in addition to public datasets. The results showed that the transformation of Convolution Neural Network (CNN) classifier has better performance over the Deep Neural Network (DNN) classifier; it has almost complete face-identification capabilities with respect to people’s presence in the case where they are wearing masks, with an error rate of only 1.1%. Overall, compared with the standard models, AlexNet, Mobinet, and You Only Look Once (YOLO), the proposed model showed a better performance. Moreover, the experiments showed that the proposed model can detect faces and masks accurately with low inference time and memory, thus meeting the IoT limited resources.
A blockchain and IoT-based lightweight framework for enabling information transparency in supply chain finance

Supply Chain Finance (SCF) refers to the financial service in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream enterprises. SCF adopts a self-testing and closed-loop credit model to control funds and risks. The key factor in a successful SCF service is the deployment of SCF business-oriented information systems that allow businesses to form partnerships efficiently and expedite cash flows throughout the supply chain. Blockchain Technology (BCT), featuring decentralization, tamper-proofing, traceability, which is usually paired with the Internet of Things (IoT) in real-world contexts, has been widely adopted in the field of finance and is perfectly positioned to facilitate innovative collaborations among participants in supply chain networks. In this paper, we propose a BCT and IoT-based information management framework (named BC4Regu), which works as the regulatory to improve the information transparency in the business process of SCF. With BC4Regu, the operation cost of the whole supply chain can be significantly reduced through the coordination and integration of capital flow, information flow, logistics and trade flow in the supply chain. The contributions in this paper include: (1) proposing a novel information management framework which leverages Blockchain and IoT to solve the problem of information asymmetry in the trade of SCF; (2) proposing the technical design of BC4Regu, including the Blockchain infrastructure, distributed ledger-based integrated data flow service, and reshaped SCF process; and (3) applying BC4Regu to a group of scenarios and conducting theoretical analysis by introducing the principal-agent model to validate the BC4Regu.
An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis

Aspect level sentiment analysis is a fine-grained task in sentiment analysis. It extracts aspects and their corresponding sentiment polarity from opinionated text. The first subtask of identifying the opinionated aspects is called aspect extraction, which is the focus of the work. Social media platforms are an enormous resource of unlabeled data. However, data annotation for fine-grained tasks is quite expensive and laborious. Hence unsupervised models would be highly appreciated. The proposed model is an unsupervised approach for aspect term extraction, a guided Latent Dirichlet Allocation (LDA) model that uses minimal aspect seed words from each aspect category to guide the model in identifying the hidden topics of interest to the user. The guided LDA model is enhanced by guiding inputs using regular expressions based on linguistic rules. The model is further enhanced by multiple pruning strategies, including a BERT based semantic filter, which incorporates semantics to strengthen situations where co-occurrence statistics might fail to serve as a differentiator. The thresholds for these semantic filters have been estimated using Particle Swarm Optimization strategy. The proposed model is expected to overcome the disadvantage of basic LDA models that fail to differentiate the overlapping topics that represent each aspect category. The work has been evaluated on the restaurant domain of SemEval 2014, 2015 and 2016 datasets and has reported an F-measure of 0.81, 0.74 and 0.75 respectively, which is competitive in comparison to the state of art unsupervised baselines and appreciable even with respect to the supervised baselines.
CubeNet: X-shape connection for camouflaged object detection

Camouflaged object detection (COD) aims to detect out-of-attention regions in an image. Current binary segmentation solutions fail to tackle COD easily, since COD is more challenging due to object often accompany with weak boundaries, low contrast, or similar patterns to the background. That is, we need a more efficient scheme to address this problem. In this work, we propose a new COD framework called CubeNet by introducing X connection to the standard encoder-decoder architecture. Specifically, CubeNet consists of two square fusion decoder (SFD) and a sub edge decoder (SED). The special designed SFD takes full advantage of low-level and high-level features extracted from encoder-decoder blocks, providing more powerful representations at each stage. To explicitly modeling the weak boundaries of the objects, we introduced a SED between the two SFD. With such kind of holistic designs, these three decoder modules resolve the challenging ambiguity of camouflaged object detection. CubeNet significantly advance the cutting-edge model on three challenging COD datasets (i.e., COD10K, CAMO, and CHAMELEON), and achieves the real-time (50fps) inference.
Aggregated graph convolutional networks for aspect-based sentiment classification

Aspect-based sentiment analysis is a classic fine-grained approach that aims to distinguish sentiment polarities towards a particular aspect target. The majority of research on this topic has been devoted to constructing syntax-based graph convolutional networks (GCNs) for context feature vectors. These approaches perform poorly in terms of node representation and capturing long-distance dependency. In this paper, we focus on the ability of graph convolution and propose an aggregated graph convolutional network (AGCN) to enhance the representation ability of target nodes. To exploit the node feature information, we introduce two aggregator functions to iteratively update the representation of each node from its local neighborhood. To extract more associated node information, we also apply the subdependency of nodes to aggregate the node features, and then employ the attention mechanism to capture the sentiment dependencies between different node feature information. The proposed AGCN is evaluated on large Chinese and English datasets to prove the effect of our model in aspect-based sentiment analysis. The experimental results show that our model is valid compared with other GCN-based methods.
Bridging Video-text Retrieval with Multiple Choice Questions

Pretraining a model to learn transferable video-text representation for retrieval has attracted a lot of attention in recent years. Previous dominant works mainly adopt two separate encoders for efficient retrieval, but ignore local associations between videos and texts. Another line of research uses a joint encoder to interact video with texts, but results in low efficiency since each text-video pair needs to be fed into the model. In this work, we enable fine-grained video-text interactions while maintaining high efficiency for retrieval via a novel pretext task, dubbed as Multiple Choice Questions (MCQ), where a parametric module BridgeFormer is trained to answer the “questions” constructed by the text features via resorting to the video features. Specifically, we exploit the rich semantics of text (i.e., nouns and verbs) to build questions, with which the video encoder can be trained to capture more regional content and temporal dynamics. In the form of questions and answers, the semantic associations between local video-text features can be properly established. BridgeFormer is able to be removed for downstream retrieval, rendering an efficient and flexible model with only two encoders. Our method outperforms state-of-the-art methods on the popular text-to-video retrieval task in five datasets with different experimental setups (i.e., zero-shot andfine-tune), including HowTo100M (one million videos). We further conduct zero-shot action recognition, which can be cast as video-to-text retrieval, and our approach also significantly surpasses its counterparts. As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e.g., action recognition with linear evaluation.
Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping

Machine learning algorithms have been widely applied in mineral prospectivity mapping (MPM). In this study, we implemented ensemble learning of extreme gradient boosting (XGBoost) and random forest (RF) models to create MPM for magmatic hydrothermal tin polymetallic deposits in Xianghualing District, southern Hunan Province, China. Machine-learning models often require careful adjustment of the learning parameters and model hyperparameters for optimal global performance. However, parameter tuning often entails tedious calculations and sufficient expert experience, which is a time-consuming and labor-intensive process. To obtain the global optimal performance of the XGBoost and RF models, a Bayesian optimization algorithm (BOA) was employed with the aid of 5-fold cross validation to search for the most appropriate hyperparameters of the XGBoost and RF models. After the Bayesian optimization, the AUC values of both models were significantly improved, indicating that the BOA is a powerful optimization tool. The optimization results provide a reference for the empirical hyperparameter setting of ensemble learning models. Through a comparative study, the XGBoost model was shown to be superior to the RF model in terms of accuracy, precision, recall, F1 score, and kappa coefficient. In addition, the receiver operating characteristic curves and prediction–area curves showed that the XGBoost model outperformed the RF model, indicating that the XGBoost model had better prediction ability and stability in the case area. In this study, the XGBoost model shows great potential for MPM, offering a significant improvement over the BOA method.
A Long-Text Classification Method of Chinese News Based on BERT and CNN

Text Classification is an important research area in natural language processing (NLP) that has received a considerable amount of scholarly attention in recent years. However, real Chinese online news is characterized by long text, a large amount of information and complex structure, which also reduces the accuracy of Chinese long text classification as a result. To improve the accuracy of long text classification of Chinese news, we propose a BERT-based local feature convolutional network (LFCN) model including four novel modules. First, to address the limitation of Bidirectional Encoder Representations from Transformers (BERT) on the length of the max input sequence, we propose a named Dynamic LEAD-n (DLn) method to extract short texts within the long text based on the traditional LEAD digest algorithm. In Text-Text Encoder (TTE) module, we use BERT pretrained language model to complete the sentence-level feature vector representation of a news text and to capture global features by using the attention mechanism to identify correlated words in text. After that, we propose a CNN-based local feature convolution (LFC) module to capture local features in text, such as key phrases. Finally, the feature vectors generated by the different operations over several different periods are fused and used to predict the category of a news text. Experimental results show that the new method further improves the accuracy of long text classification of Chinese news.
Task-Oriented Multi-User Semantic Communications

While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We propose a Transformer based framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively. In this case, DeepSC-IR is trained to optimize the distance in embedding space between images and DeepSC-MT is trained to minimize the semantic errors by recovering the semantic meaning of sentences. For the multimodal multi-user system, we develop a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting text-image information at the transmitters and fusing it at the receiver. In particular, a novel layer-wise Transformer is designed to help fuse multimodal data by adding connection between each of the encoder and decoder layers. Numerical results show that the proposed models are superior to traditional communications in terms of the robustness to channels, computational complexity, transmission delay, and the task-execution performance at various task-specific metrics.
Evolution of Industry and Blockchain Era: Monitoring Price Hike and Corruption Using BIoT for Smart Government and Industry 4.0

The price gouging or price hike is a worldwide issue, and it is related to inflation. Because of rising prices, people in various countries cannot afford nutritious food or proper treatment. Sometimes shops, restaurants, and transportation service providers charge more than the prescribed product price from buyers. In addition, unauthorized VAT or Tax is taken on products that the government exempts. Another reason for price hikes is bribery, and it occurs in transporting and delivering goods. This article introduces a blockchain-based Internet of Things model to monitor product price hikes and corruption from the Industry 4.0 and blockchain 5.0 point of view. Industries produce and package different products. Wholesalers and retailers purchase products from industrial companies. The primary goal of this article is to propose a blockchain mechanism for monitoring price hikes and corruption where the government can monitor buying and selling between buyers and industrial companies. Here, we have established blockchain-integrated remote database model where blockchain relates to a relational database management system that uses remote database access protocol and Cloud server. This article presents the brief evolution of blockchain and industry generations. Finally, this article provides a next generation blockchain model. An intelligent government connected with Industry 4.0 monitors price hikes and corruption.
Neural Architecture Search With a Lightweight Transformer for Text-to-Image Synthesis

Despite the cross-modal text-to-imagesynthesis task has achieved great success, most of the latest works in this field are based on the network architectures proposed by predecessors, such as StackGAN, AttnGAN, etc. Since the quality for text-to-image synthesis is more and more demanding, these old and tandem architectures with simple convolution operations are no longer suitable. Therefore, a novel text-to-image synthesis network combining with the latest technologies is in urgent need of exploration. To tackle with this challenge, we creatively propose a unique architecture for text-to-image synthesis, dubbed T2IGAN, which is automatically searched by neural architecture search (NAS). In addition, considering the amazing capabilities of the popular transformer in natural language processing and computer vision, a lightweight transformer is applied in our search space to efficiently integrate the text features and image features. Ultimately, the effectiveness of our searched T2IGAN is remarkable by experimentally evaluating it on the typical text-to-image synthesis datasets. Specifically, we achieve an excellent result of IS 5.12 and FID 10.48 on CUB-200 Birds, IS 4.89 and FID 13.55 on Oxford-102 Flowers, IS 31.93 and FID 26.45 on COCO. By contrast with the state-of-the-art works, ours gets better performance on CUB-200 Birds and Oxford-102 Flowers.
Preserving privacy while revealing thumbnail for content-based encrypted image retrieval in the cloud

Owing to the rapid development of cloud services and personal privacy requirements, content-based encrypted image retrieval in the cloud has been increasing. Outsourced images are encrypted into noiselike ones to protect privacy, however, the obtained unrecognized appearance limits their availability. Besides, users have to decrypt all search results to browse, while some of them may not be needed, which undoubtedly wastes bandwidth and computing resources. To cope with this problem, a compromise strategy is proposed that considers the tradeoff between privacy and usability of cipher images. Wherein, a thumbnail preserving encryption (TPE) based on genetic algorithm is proposed. The pixels in the sub-blocks of the plain image are scrambled and diffused at the bit-level through crossover and mutation operators of the genetic algorithm. Moreover, two new operators of Mutation Compensation and Mutation Failure are defined and incorporated into the traditional genetic algorithm to achieve an ideal TPE, that cipher image has the same thumbnail as the original image. Additionally, a color histogram-based retrieval algorithm is introduced to retrieve cipher images using the color information preserved by thumbnails; and to improve retrieval accuracy by using the Bhattacharyya distance. A series of simulations verify the security and effectiveness of our scheme.
Evaluating Competitive Game Balance with Restricted Play

Game balancing is the fine-tuning phase in which a functioning game is adjusted to be deep, fair, and interesting. Balancing is difficult and time-consuming, as designers must repeatedly tweak parameters, and run lengthy playtests to evaluate the effects of these changes. If designers could receive immediate feedback on their designs, they could explore a vast space of variations, and select only the most promising games for playtesting. Such automated design feedback has been difficult to achieve, as there is no mathematical formulation of game balance that unifies many of its forms. We argue for a formulation in which carefully restricted agents are played against standard agents. We develop this restricted-play balance framework, and evaluate its utility by building a tool capable of calculating measures of balance for a large family of games. By applying this tool to an educational card game, we demonstrate how the framework and tool allow designers to rapidly evaluate and iterate on the balance of their games.
A robust single and multiple moving object detection, tracking and classification

Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.
Recognizing Distractions for Assistive Driving by Tracking Body Parts

Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.