Reviewerly/exHarmony huggingface dataset:
Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions

A data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavities with different geometrical parameters are employed to alter the optical properties of monolayer TMDC, aiming at discovering new and intriguing physics associated with the strong coupling effect. Notably, the traditional rigorous coupled wave analysis (RCWA) approach is utilized to generate a relatively small training dataset for the DEDGO algorithm. Importantly, one remarkable feature of DEDGO is the integration the decision theory of reinforcement learning, which remedies the deficiencies of previous research that focused more on modeling over decision making, increasing the success rate of inverse prediction. Specifically, an iterative optimization strategy, namely, deep greedy optimization, is implemented to improve the performance. In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network, making possible the replacement of the time-consuming conventional scanning optimization method with the DEDGO algorithm. This research thoroughly describes the universality, interpretability, and excellent performance of the DEDGO algorithm in exploring the underlying physics of TMDC-cavity heterojunctions, laying the foundations for the on-demand inverse design of low-dimensional material-based nano-devices.
A novel machine learning pipeline to detect malicious anomalies for the Internet of Things

Anomaly detection is an imperative problem in the field of the Internet of Things (IoT). The anomalies are considered as samples that do not follow a normal pattern and significantly differ from the expected values. There can be numerous reasons an IoT sensor data is anomalous. For example, it can be due to abnormal events, IoT sensor faults, or malicious manipulation of data generated from IoT devices . There has been wide-scale research done on anomaly detection problems in general, i.e., finding the samples in data that differ significantly from the expected values. However, there has been limited work done to figure out the inherent cause of the anomalies in IoT sensor data. Accordingly, once an abnormal data sample has been observed, the challenge of detecting whether the anomaly is due to an abnormal event or IoT sensor data manipulation by an attacker has not been explored in detail. In this paper, rather than finding the typical anomalies, we propose a method to detect malicious anomalies. The given paper puts forward an idea of where anomalies in IoT can be categorized into different types. Consequently, rather than finding an anomalous sample point, our method filters only malicious anomalies in the measured IoT data. Initially, we provide an attack model for the IoT sensor data and show how the model can affect the decision-making abilities of IoT-based applications by introducing malicious anomalies. Further, we design a novel Machine Learning (ML) based method to detect these malicious anomalies. Our ML method is inspired by ensemble machine learning and uses threshold and aggregation methods rather than the traditional methods of output aggregation in ensemble learning . The proposed ML architecture is tested using pollutant, telemetry, and vehicular traffic data obtained from the state of California. Simulation results show that our architecture performs with a decent accuracy for various sizes of malicious anomalies. In particular, by setting the parameters of the anomaly detector , the precision, recall, and F-score values of 93%, 94%, and 93% are obtained; i.e., a well-balance between all three metrics. By varying model parameters either precision or recall value can be increased further at the cost of other showing that the model is tunable to meet the application requirement.
A Composite Medical Image Optimization Scheme Using Honey Encryption and Antlion Algorithms for Secured Diagnostic Systems

Medical imaging technology is one of the most critical applications necessitating data protection, particularly if we need to keep track of any important patient information. This medical imaging system employs encryption and decryption. Using several cryptographic techniques, the security key was established to protect the data. Every network that sends and receives data needs to be secure in some way. In this paper, ALO along with the encryption algorithm honey is used to enhance the security of medical imaging technologies, the proposed study uses a variety of ways to protect important health information. In comparison to the existing one, the proposed honey algorithm attains better results. Further, the antlion optimizer uses random keys throughout the encryption and decryption. In the next step, the keys are remodeled using antlion optimization. After that, the updated key is optimized by analyzing every element and generating paths that trigger the traps and latching functions. The mean square error (MSE) is reduced to 1% and the peak signal-to-noise ratio (PSNR) is increased to 98% by using a hybrid strategy.
Robust privacy‐preserving federated learning framework for IoT devices

Federated Learning (FL) is a framework where multiple parties can train a model jointly without sharing private data. Private information protection is a critical problem in FL. However, the communication overheads of existing solutions are too heavy for IoT devices in resource-constrained environments. Additionally, they cannot ensure robustness when IoT devices become offline. In this paper, Democratic Federated Learning (DemoFL) is proposed, which is a privacy-preserving FL framework that has sufficiently low communication overheads. DemoFL involves a consensus module to ensure the system is robust. It also utilizes a tree structure to reduce the time communication overheads and realizes high robustness without reducing accuracy. The proposed algorithm reduces the communication complexity of aggregation at training by M $M$ times, M $M$ being a controllable parameter. Sufficient experiments have been conducted to evaluate the efficiency of the proposed method. The experimental results also demonstrate the practicality of the proposed framework for IoT devices in unstable environments.
Job Recommendation Based on Extracted Skill Embeddings

With the increasing popularity of online recruiting platforms in modern industry, most employers choose these platforms as a means of connecting with potential candidates for open positions. Developing job recommendation systems can significantly help both employers and job seekers in speeding up this process and finding the best matches. Using skill phrases extracted from unformatted and unstructured CVs and Job Descriptions, we propose two approaches with different similarity metrics, namely Word Mover's Distance and Cosine Similarity. We selected TF-IDF with Cosine Similarity as a baseline and evaluated our methods on the real data from an online recruitment company, Kariyer.net. Our results suggest that the previously unstudied Word Mover's Distance-based approach outperforms Cosine Similarity-based approaches and gives promising results in the job recommendation domain.
Vision Transformers for Medical Images Classifications

Image classification method based on Vision Transformers (VT) is gaining popularity as the standard models in natural language processing (NLP). The VT does not depend on the convolution blocks, rather captures the relative relations between image pixels regardless of their three-dimensional distance. In this paper, three different Deep Learning models were developed to detect the presence of Invasive Ductal Carcinoma, the most common form of breast cancer. These models include convolutional neural network (CNN) was used as a baseline, a residual neural network (RNN) and a Vision Transformer (VT). To test these models, we used a dataset of breast cancer tissue images. These images were used to train and validate our three models, which led to different levels of high accuracy. Experimental results demonstrate that the VT model outperforms the CNN and RNN on different tasks by up to 93% accuracy classification rate, while the other models highest rate was 87%.
Recognition of Similar Habits Using Smartwatches and Supervised Learning

Especially in the healthcare system, there is a lot of potential to use wearables to create an even more efficient way to care for patients and have important data available at all times. Wearables, for example, could allow nurses to spend more time with their patients instead of spending their time collecting data and performing other technical tasks. The aim of this project was to track, label and analyze daily activities that people with dementia might perform, by using smartwatches and supervised learning. The analysis is carried out on the basis of sensor systems and classification algorithms and in this framework the performance and predictive power of each algorithm was analyzed after data collection. In this work, it can be stated for all test subjects and in all activity categories that the Fast Forest algorithm generally performs best.
Computing with Words for Industrial Applications

More and more industrial applications are facing the problem of imprecise information handling. Computing with Words (CW) is a mathematical model for approximate knowledge representation, reasoning, and processing of natural language. The very basic idea of CW is to use words instead of numbers for computing and reasoning using fuzzy sets and logic. However, the implementation of this approach in the project requires certain knowledge. This paper presents our initial efforts towards building of a methodology and library based on the extended version of CW, CWiPy. CWiPy can be effectively used to apply CW techniques easily without any prior knowledge in this field. So, developers can add it to an existing system and use it as a black box (plug and play). In CWiPy, the traditional CW was extended to process a bigger variety of linguistic hedges, enhancing the system expressiveness. CWiPy provides an API that allows handling of fuzzy variables, sets, hedges, and quantifiers. Results show that CWiPY can be easily applied in real-life industrial applications to deal with imprecise information and provide help for experts. Two different usage scenarios of the library are presented as a proof of concept: natural language query processing and database summarization .
Vehicle Classification, Rumble Strips Detection, and Mapping Using Artificial Intelligence

The collection of transportation data is crucial to data-driven decision-making. Using artificial intelligence for such data collection efforts could save substantial time and cost and help to improve transportation safety and efficiency. This research study used YOLOv4 object detection and DeepSORT tracking models to count and classify vehicles into 13 FHWA vehicle classes from video footages from existing roadside cameras. YOLOv4 was also used to detect the presence of rumble strips from roadway images and used to create a rumble strip inventory map. The trained vehicle counting/classification model achieves a counting accuracy of ~97% at daytime and ~91% at nighttime. The rumble strip detection model has ~95% accuracy. This study demonstrates that AI model can be trained to accurately count and classify vehicles under various weather, lighting, and traffic conditions, as well as to collect transportation asset data to improve roadway safety.
The Potential Use of Blockchain Technology for Data Collection and Measurement of Trade in Services

The unchangeable, decentralized structure of blockchain makes it a good candidate for potential use in international trade. Services sectors have become a crucial part of the world economy and international trade. Data collection methods used for trade in services may lead to various outcomes such as trade data not being able to be collected accurately and reported timely. If services statistics are not achieved correctly and timely, there is a risk that governments do not have the sufficient data to make correct policy decisions. This paper identifies the reasons that favor the potential application of blockchain technology for services trade data collection and measurement. It is proposed that a database powered by private blockchain acting as a single source of truth implemented for service provider companies to use while completing a transaction or issuing an invoice might be a practical solution for this problem. Arguably, use of blockchain in this field can lead to an instant access to the transactions made in the services sectors. It can also be suggested that, with the help of such a system, the services transactions can be tracked in categories which is difficult to achieve with current data collection methods and not possible in many cases.
Autonomous Navigation Technology for Low-Speed Small Unmanned Vehicle: An Overview

In special locations (scenes) such as campuses and closed parks, small unmanned vehicles have gained more attention and application. Autonomous navigation is one of the key technologies of low-speed small unmanned vehicles. It has become a research hotspot, but there are still many problems, such as perception sensitivity, navigation, and positioning accuracy, motion planning accuracy, and tracking control accuracy. In order to sort out the research status of the key technologies of autonomous navigation for small unmanned vehicles more clearly, this paper firstly reviews the key technologies of autonomous navigation and presents an analysis and summary. Finally, future research trends of small unmanned vehicles with low speed are given.
Digital Vulnerability

This chapter explores the notion of consumer vulnerability. Consumer vulnerability is a normative standard aiming at protecting individuals who are particularly susceptible in the marketplace for their characteristics. The analysis will review the notion enshrined in current EU unfair commercial laws and will point out its conceptual limitations. It will then revisit the notion in light of the contemporary discussion on consumer vulnerability and present conceptual elaboration for a new idea of digital vulnerability. The chapter will argue that seeing digital vulnerability as a multi-layered concept is more productive. The fundamental layer applies to all consumers, given the inherent risks of harm that potentially arise directly from the functioning of algorithmic marketing. At the same time, the likelihood of consumers suffering actual harm depends on how many additional layers of vulnerability they may suffer from, depending on their personal characteristics, situations, and ability to affirm their privacy.
NetDAO: Toward Trustful and Secure IoT Networks without Central Gateways

The Internet of Things (IoT) suffers from a profound lack of trust between central gateways and sensors, e.g., gateways suspect sensors of flooding malicious packets, and vice versa, sensors suspect gateways of manipulating traffic data. One important reason for the mistrust is the asymmetry of a centralized network organization. A Decentralized Autonomous Organization (DAO) can establish a trustful and symmetric network with the blockchain. However, it is a vacant area for IoT networks to build trust between gateways and sensors within the DAO. In this paper, we firstly propose a trustful and secure IoT Network DAO solution (NetDAO) to mitigate the data manipulation and the malicious flooding packets. In particular, the NetDAO has a security rating algorithm to assign a reputation value for each entity in the network. Based on this, each entity can mitigate the malicious flooding packets using a proof-of-reputation packet-forwarding mechanism. In addition, the NetDAO stores traffic data using the blockchain to mitigate the data manipulation. The experimental results show that the NetDAO effectively mitigates malicious flooding packets and costs 1 s for ∼480 entities to complete the rating algorithm.
Online Support Vector Machine with a Single Pass for Streaming Data

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement.
Stereoscopic cell tracking for evaluating cell motility and mobility validated by deep learning

Extracting dynamic features of a cell plays important role in understanding cell response to internal or external perturbations, which can be both a painful and imprecise task as one makes it manually in ocular way. Instead of using complex methods, we introduce a simple approach that uses disparity maps for segmentation by means of sequential frame couples. In our approach, disparity maps provide three-dimensional clues that can be used for cell segmentation. One of the contributions of this work is to generate pseudo 3D cell database using cell video frame couples. In addition, the optical flow method is performed to understand the cell behaviour and local dynamic movements. A mask regional convolutional neural network (Mask R-CNN) approach that requires manual segmented dataset and long training time is used for comparison. Obtained disparity-based segmentation and optical flow data are blended to easily analyse and evaluate the cell motility and mobility. In order to validate the segmentation results, Jaccard similarity index method is applied. Consequently, we succeed in dynamic segmentation-based tracking for understanding the cell behaviour without video enhancement or preprocessing steps, such as colour adjustment, filtering, thresholding.
Human Activity Recognition for Disease Detection Using Machine Learning Techniques—A Comparative Study

Human activity test is now rapidly adopted for disease detection. Many diseases can be detected using different human activities like walking, running, standing or performing any tasks. Parkinson’s disease (PD), schizophrenia, mood disorder, chronic obstructive pulmonary disorder (COPD), peptic ulcer and asthma are some of those. These diseases can be sometimes incurable if treatment is not done at early stages. But, clinical tests and diagnosis are time-consuming. Machine learning gives the opportunity to predict and detect the diseases in less time. So, it is now becoming and important area of research. Some works have already been done by the researchers in the area of early detection and prediction of the above-mentioned diseases using machine learning (ML) and deep learning (DL) techniques. In this paper, we have carried out a detailed survey of different works in this field. We identified the weaknesses of the existing works. Based on these, we tried to find the future scope of the works to enhance their performance in the field of disease detection using human activity.
Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy

Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribution BPA functions with padding terms are used. After that, nrBPAs are determined by two processes, reassigning the high blur degree BPA and transforming them into the form of negation. In addition, evidence of preliminary fusion is obtained using the entropy weight method based on the improved belief entropy of nrBPAs. The final fusion results are calculated from the preliminary fused evidence through the Dempster's combination rule. In the experimental section, the UCI iris data set and the wine data set are used for validating the arithmetic processes of the proposed method. In the comparative analysis, the effectiveness of the BPA determination using a padded Gaussian function is verified by discussing the classification task with the iris data set. Subsequently, the comparison with other methods using the cross-validation method proves that the proposed method is robust. Notably, the classification accuracy of the iris data set using the proposed method can reach an accuracy of 97.04%, which is higher than many other methods.
Quality Assessment of Microseismic P-Phase Arrival Picks and Its Application of Source Location in Coal Mining

Abstract Correctly identifying abnormal and false P-phase arrival picks (P-pick) in underground coal mining is essential to microseismic source location. Manual judgement and identification are time-consuming with the increasingly growing monitoring data. To eliminate the effects of false P-picks, a novel microseismic source location with weighted P-picks was proposed, and ten waveform parameters were selected to characterize the difference between two types of signals with usable and unusable P-picks. The discriminant analysis experiment has revealed that the prediction rate of unusable P-pick set increases dramatically with the sample size when the sample size is less than 2,000 and the prediction rates of unusable P-pick set are around 88% when the sample size is greater than 2,000, while the prediction rates of usable P-pick set are around 80%, which is little affected by the sample size. Considering the prediction rates of usable and unusable P-pick populations, the discrimination function with a sample size of 3,000 was selected to identify the usable and unusable P-picks. The identification rates of usable and unusable P-pick populations are up to 83.24% and 88.99%, respectively. The application of P-pick discriminant analysis model in source location was discussed. The location case and long-term result show that the P-pick discriminant model and its application in source location perform well.