Reviewerly/exHarmony huggingface dataset:
Beta Poisoning Attacks Against Machine Learning Models: Extensions, Limitations and Defenses

The rise of machine learning (ML) has made ML models lucrative targets for adversarial attacks. One of these attacks is Beta Poisoning, which is a recently proposed training-time attack based on heuristic poisoning of the training dataset. While Beta Poisoning was shown to be effective against linear ML models, it was originally developed with a fixed Gaussian Kernel Density Estimator (KDE) for likelihood estimation, and its effectiveness against more advanced, non-linear ML models has not been explored. In this paper, we advance the state of the art in Beta Poisoning attacks by making three novel contributions. First, we extend the attack so that it can be executed with arbitrary KDEs and norm functions. We integrate Gaussian, Laplacian, Epanechnikov and Logistic KDEs with three norm functions, and show that the choice of KDE can significantly impact attack effectiveness, especially when attacking linear models. Second, we empirically show that Beta Poisoning attacks are ineffective against non-linear ML models (such as neural networks and multi-layer perceptrons), even with our extensions. Results imply that the effectiveness of the attack decreases as model non-linearity and complexity increase. Finally, our third contribution is the development of a discriminator-based defense against Beta Poisoning attacks. Results show that our defense strategy achieves 99% and 93% accuracy in identifying poisoning samples on MNIST and CIFAR-10 datasets, respectively.
Impact of Big Data Analytics, Knowledge Management, and Capabilities on the Firms' Ability and Performance

Recently, big data analysis (BDA) capabilities and knowledge management are the essential elements for organizational performance, and this aspect needs researchers' and regulators' attention. Hence, the current article investigates the impact of BDA knowledge management, BDA technological, and management capabilities on the firm performance of the oil industry in the UAE. The study also analyzes the mediating role of firms' agility among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in the UAE. The researchers have gathered the primary data from the BDA of the oil industry using survey questionnaires. The article has employed the smart-PLS to check the data reliability and association among variables. The results indicated that BDA knowledge management, BDA technological, and management capabilities have a positive association with the firm performance of the oil industry in the UAE. The findings also exposed that the firms' agility significantly mediates among BDA knowledge management, BDA technological and management capabilities, and firm performance in the oil industry in UAE. This article guides the regulators in making regulations regarding the improvement of firm performance using BDA knowledge management and BDA technological and management capabilities.
Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks

Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great resilience to a poisoned model injected by an attacker. In this paper, we provide our theoretical analysis of the WAFL’s resilience against model poisoning attacks, by formulating the force balance between the poisoned model and the legitimate model. According to our experiments, we confirmed that the nodes directly encountered the attacker has been somehow compromised to the poisoned model but other nodes have shown great resilience. More importantly, after the attacker has left the network, all the nodes have finally found stronger model parameters combined with the poisoned model. Most of the attack-experienced cases achieved higher accuracy than the no-attack-experienced cases.
EI Integrated PR for Online Communication in Customer Loyalty: The Mediating Role of Artificial Intelligence

Emotional Intelligence, when integrated into Public Relations, adds potential value to retail sector organizations. However, with the rise of ICT, these factors are further enriched by Artificial Intelligence as the organizations are more goal-oriented, adopting certain customer-centric approaches. This study also focused on the role of the Emotionally Intelligent Public Relation system in the United Arab Emirates benefiting from Artificial Intelligence integration. The primary focus remained on Artificial Intelligence, upgrading these Public Relations practices and improving customer loyalty. The researchers used a self-proposed conceptual model assessed using Structural Equation Modelling (SEM). Results revealed that Public Relations practices are significantly affecting Social Skills (p>. 020) and Empathy (.000) as primary components of Emotional Intelligence (EI). Besides, the effect of Social Skills on Empathy also remained significant (p> 045). On the other hand, Social Skills (p>. 000) have a significant effect on Customer Loyalty, which is significantly mediated by Artificial Intelligence (p>. 070). Thus, it is concluded that when Public Relations integrated with Emotional Intelligence are linked with Artificial Intelligence, positive, constructive outcomes are ensured. Particularly, in the retail sector, organizations integrating Artificial Intelligence provide the service as best matching the customers, adding more value to the services and product quality of the certain retailer, indicating the same patterns of consumer experiences leading to loyalty among them.
Maintaining Review Credibility Using NLP, Reputation, and Blockchain

This paper presents a novel approach to review credibility in a marketplace, which leverages trust in reviews and reputation of the parties who provide them. We propose an architecture for a reputation-based review evaluation system, which is built on top of the blockchain system, in order to ensure correct and trustworthy assessments. In our proposal, trustworthiness of reviews is evaluated using NLP—specifically, sentimental analysis—and the reviewers’ reputations are adjusted according to this evaluation. These reputations are stored on the blockchain and used as an asset for the consensus process when mapped to stake. We introduce a new type of transaction, a review transaction, which stores the review data and evaluation results. In testing, our simulation results showed that the NLP component incurred a reasonable delay this new type of review transactions. Additionally, we measured the time required to add a standard payment transaction in Algorand and that for our review transaction and observed comparable results. Also, we observed that the NLP component ensures an accurate credible evaluation (compared to the ground truth) of the product review texts. With this new model, we have moved towards showing how NLP can used for self-regulating trust management in a decentralized marketplace ecosystem.
A Harris Hawks Optimizer Based Hybrid Algorithm for Optimal Design of Analog Integrated Circuits

With the latest advancements in semiconductor industry and growing complexity of modern integrated circuits, the rise in the need for design automation tools became inevitable. Unlike digital circuit design, the analog circuit automation, owing to its complexity, still isn’t as mature. In this paper, a hybrid algorithm is proposed that employs traits of Harris Hawks Optimizer (HHO) and Genetic Algorithm (GA) along with archiving technique to design and optimize FinFet folded-cascode operational amplifier. The idea is to facilitate the explorational capabilities of HHO by incorporating basic GA operations. The effectiveness of the proposed approach is first demonstrated on a set of benchmark functions, where it is compared with other techniques. Afterwards the algorithm is used to design a folded-cascode operational amplifier, where the design parameters are refined all at once as a single objective function. Results show clear advantage of the proposed method over the conventional approach.
The classification decision tree fused with neural network realizes the intelligent transformation of data

The data preprocessing stage is a crucial step in the realization of data analysis and the establishment of algorithm models. However, the current data preprocessing including data cleaning, data transformation and other processing methods are not perfect, so a lot of time and human capital are often spent in data cleaning. In order to adapt to the development of artificial intelligence, it is necessary to get rid of traditional processing technologies and tools, and propose methods that can process data through inherent models such as artificial intelligence. This paper takes the transaction amount data in the bidding data as an example to solve the problem of non-uniform units and forms of the transaction amount data, and realize the standardized and unified intelligent processing process of the data. This paper proposes a classification decision tree model (Decision Tree fused with Neural Network, DTFNN) fused with neural network. By training a BP neural network with recognition function for specific characters, it is integrated into the classification decision tree, so as to improve the decision tree. The feature level of classification prevents the occurrence of over-fitting and under-fitting, and realizes the purpose of automatic data classification; then the classified data is transferred to the ICS (Input-Compute-Select) which includes three stages of input, calculation and selection. In the calculation model, a weight matrix of real-time conversion rate is constructed, so as to realize the intelligent conversion of data and simplify the process of data processing.
Optimization-based Association Rule Mining Using Weighted Transactions: A Case Study

An efficient optimization-based method that extracts association rules from a real-world dataset is proposed. The underlying dataset is characterized by considerable size and sparsity, while transactions are weighted according to the corresponding economic revenue. The established particle swarm optimization metaheuristic is used to discover relevant association rules. Further insight regarding the unique characteristics of the products is gained through k-mode clustering and standard ABC analysis. Empirical analysis is used to identify promising parameter settings for the proposed method. The obtained results reveal that products involved in joint rules share common features in almost all cases.
A drawing retrieval model of mechanical parts based on YOLOv5 and a modified VGG network

How to retrieve similar drawings correctly from the existing drawing database is of great significance for companies. In this paper, we propose a drawing retrieval model for mechanical parts based on YOLOv5 and a modified VGG network (FEN-VGG). In this paper, the function of YOLOv5 is to detect stereograms and views in drawings and to classify the targets within the drawings into view, part stereogram, title & material block, and revision block. Then, we use a feature extraction network with pre-trained VGG network, FEN-VGG, to extract the features of stereograms and views respectively. And get the vectors composed of stereograms features and views features. After that, the similarity is calculated by the inner product with the vectors in the stereogram database and the view database respectively. Finally, the top 5 drawings with the sum score are output. The experiments show that the Top-1 accuracy of our model reaches 90% and the Top-5 accuracy reaches 95%. Top-1 accuracy and Top-5 accuracy improved by 36% and 31% compared to the previous respectively.
Shalower and lighter Net:prune and replace

With the development of deep neural net-work, Model compression has also become a hot research area. A lot of theories of model compression focus on reducing the amount of computation, while the actual reasoning speed varies from device to device. In some studies, it is found that the reduction of model depth largely determines the upper limit of delay reduction, but the drop of accuracy caused by the reduction of depth may be difficult to recover. For this reason, we combine layer pruning and layer replacement to reduce the depth of the model, further reduce the amount of computation, and reduce the drop of accuracy. Our work achieves a Flops_reduction of 74.34% on the CIFAR100 dataset while a Top1_Acc boost of 0.14%.
Combining Monocular Camera and 2D Lidar for Target Tracking Using Deep Convolution Neural Network based Detection and Tracking Algorithm

Moving target detection and tracking is one of the key technologies for research of intelligent mobile robot and image recognition. The commonly used sensors include cameras, lidar and infrared sensor. Although lidar is able to locate target precisely thanks to its range and angular information in clear environment, it is difficult to detect target in cluttered environment. On the other hand, with the development of detection models based on deep learning, the target detection can be well solved using images from monocular camera, however, only using monocular camera is hard to determine the depth information of the target object, it can not accurately obtain the position of the target object. Therefore, in order to take advantage of pros of both monocular camera and 2d lidar, this paper proposes a target tracking algorithm for a mobile robot by combining a monocular camera and 2D lidar. The CSI camera is used to detect and track the target by deep convolution neural network models YOLOv5 and Deep SORT, respectively, while the 2D lidar is used to determine the angle and distance of the target corresponding to the one detected and tracked using images from camera. Extensive experiments have been conducted on a mobile robot, showing the effectiveness of the proposed target tracking algorithm.
Identification of selection criteria for blockchain platforms

Blockchain technology is widely used to develop software systems in different industries such as finance, healthcare, supply chain management, data management, Internet of Things (IoT). To adopt blockchain, some critical challenges such as performance, interoperability, availability, security attacks, sustainability, and latency need to be taken into consideration. If the blockchain platform selected is not appropriate, the functionality and quality of the blockchain-based systems will be affected. Additionally, numerous blockchain platforms with unique properties and specifications are available in the market, and the task of selecting the right platform that best meets the project needs is getting complicated. It is crucial to define significant criteria that can help to shortlist the blockchain platforms for further comparison and selection using multicriteria decision-making methods. This research proposes a set of selection criteria that cover both features and quality attributes of the blockchain platforms. A survey was conducted to get the opinions of blockchain practitioners on the suitability of the selection criteria for the evaluation and selection of blockchain platforms.
Old Age Homes Of Pakistan And Effectibility Of Smart Home Technology - Quantitative Study

Pakistan is under develop country, and as far as smart home technology (SHT) is concerned it’s the new way that is under progress. As far as Senior citizens of Pakistan are concerened, most of them are still unaware of smart home technology and its effectiveness. In this paper we have performed survey and gathered the collective opinion of 200 old senior citizens from 10 different old age homes of Pakistan, including 105 male and 95 female age 55 and above by "One on One" survey. We came up with different opinions of senior citizens of old age homes about smart home technology. We used Z-Test and R studio for the analysis of collected data.
Marker-enhanced Hand Tracking with Deep Learning

To meet the accuracy requirements of hand motion capture in virtual simulation environments for large-scale complex product evaluation, we present a finger-level hand tracking method by arranging the markers on the hand key joints and using multiple RGB cameras as the motion capture devices. Our method can initialize marker labels whenever needed by using CNN to predict hand joint positions as prior knowledge and labelling each marker based on the prior knowledge. Since the regular training datasets mainly focus on training neural network for capturing bare hand motion, we propose an image restoration algorithm allowing to continue using existing datasets to train CNN models and make sure the high accuracy of hand joint prediction in application scenarios. Verification experiments show that the root mean square error of our method is 3.9mm, which can meet the accuracy requirement in large complex product virtual simulation. Meanwhile, our method has good robustness for hand self-occlusion.
Chinese Named Entity Recognition Based on Improved K-BERT

Named entity recognition of Chinese text using pre-trained models is the mainstream approach at present, and the proposed K-BERT model overcomes the problem that BERT models do not possess background knowledge. We use the Chinese pre-trained model BERT-wwm and adversarial training based on full-word masking technology on the basis of K-BERT to improve the disadvantages of poor robustness of traditional K-BERT neural network and the existence of WordPiece sequence mask of traditional BERT. The experimental results on three open source datasets, MEDICAL_NER, MSRA_NER and FINANCIAL_NER, show that the evaluation index F1-score is improved after adding the adversarial training model. And the comparison experiments verify that adding adversarial training can elevate the prediction ability and robustness.
A Layered Algorithm of Style Transfer

Image Style transfer techniques can be applied in many fields, such as artistic creation, film, and television special effects, etc. It is a hot and difficult research topic in image processing. With the rapid development of the mobile Internet, fantasy pictures created through style transfer and shared on social networking sites are very popular and hot. The style transfer algorithm based on CNN is the current mainstream method because of its speed and efficiency. However, the current style transfer methods mainly target the entire image and lack pertinence. In addition, these methods always over-transfer style features and lack the details of the main content of the picture, which affects the aesthetics of the resulting picture. In this paper, we mainly studies image layered style transfer technology and propose a new style transfer algorithm that can highlight the details of the ROI in the image by introducing the color transfer Reinhard algorithm which can enrich the diversity of style transfer methods and provide people with new creative methods.
Multi-lingual Offline Signature Recognition Based on LOMO Feature

Despite the development of electronic signatures, most people still prefer to use handwritten signatures. Signature recognition is used in banks, judiciary, financial transactions, and many other fields. Offline signature recognition is well established for common languages like English but is still in the developmental stage for minority languages like Uyghur. This paper proposed a multilingual hybrid handwritten signature recognition method to address this problem based on local maximum occurrence features. Firstly, offline handwritten signature datasets in Uyghur and Chinese were established. Next, the signature images were pre-processed by grayscale, normalization, bilateral filtering, and binarization. Then, we extracted the Local Maximal Occurrence Representation (LOMO) features and reduce the extracted features' dimensions was used principal component analysis(PCA). Finally, in the stage of classification, we utilized the random forest and K-nearest neighbor algorithm. The experimental results show that the best recognition accuracy of this method was 98.4% for the self-built signature dataset, which has a high application value.
Case Matching Algorithm of Emergency Disposal Based on LS-SVM

With the development of military intelligence technology, the personnel on duty usually need to complete the planning of emergency disposal in a very short time in the face of emergencies, so the demand for intelligent emergency disposal is extremely urgent. In this paper, the LS-SVM model is improved based on the Platt Scaling probability calibration model to solve the problem of unbalanced data distribution and probability calibration expansion of multi-generic output. A Calibration Probabilistic Least Squares Support Vector Machine (CPLS-SVM) classification algorithm was proposed. A case matching method of emergency disposal based on the CPLS-SVM algorithm is designed to improve the matching accuracy in a complex case sample environment. By constructing the LS-SVM based on calibration probability and applying it to the simulation data of the emergency disposal instance. The effectiveness and performance of the algorithm are verified by simulation.
Usability Evaluation of AR Human-machine Interface Based on User Experience

The development and design of wearable AR products have gradually become a hot spot with the development of Augmented Reality (AR) technology, and the usability evaluation of AR human-computer interaction interface has also become an effective way to improve the user experience of products, so it has important research value. The current paper built a relatively complete usability evaluation index system of AR human-computer interface by carding the key node elements in the process of AR human-computer interaction based on three layers of emotional experience: instinct, behavior and psychology. Then the experimental evaluation was conducted by using the method of user experience method, and comparative analysis of subjective evaluation results of three typical AR model was conducted, based on which, the interface usability problems were found during AR usage by considering the ergonomics simultaneously. The usability problems were analyzed and summarized, the improvement suggestions of AR human-machine interface were put forward, and the product design criteria were formed. This method helps in AR simulation assessment in the process of product development and optimization, form product optimization design standards, design is more reasonable to meet the demand of the user experience of AR products.
An Intelligent Vehicle Data Security System based on Blockchain for Smart City

With the development of urbanization, the number of vehicles is gradually increasing, and vehicles are gradually developing in the direction of intelligence. How to ensure that the data of intelligent vehicles is not tampered in the process of transmission to the cloud is the key problem of current research. Therefore, we have established a data security transmission system based on blockchain. First, we collect and filter vehicle data locally, and then use blockchain technology to transmit key data. Through the smart contract, the key data is automatically and accurately transmitted to the surrounding node vehicles, and the vehicles transmit data to each other to form a transaction and spread to the whole network. The node data is verified through the node data consensus protocol of intelligent vehicle data security transmission system, and written into the block to form a blockchain. Finally, the vehicle user can query the transaction record through the vehicle address. The results show that we can safely and accurately transmit and query vehicle data in the blockchain database.