Reviewerly/exHarmony huggingface dataset:
Sppn-Rn101: Spatial Pyramid Pooling Network with Resnet101-Based Foreign Object Debris Detection in Airports

Over the past few years, aviation security has turned into a vital domain as foreign object debris (FOD) on the airport paved path possesses an enormous possible threat to airplanes at the time of takeoff and landing. Hence, FOD’s precise identification remains significant for assuring airplane flight security. The material features of FOD remain the very critical criteria for comprehending the destruction rate endured by an airplane. Nevertheless, the most frequent identification systems miss an efficient methodology for automated material identification. This study proffers a new FOD technique centered on transfer learning and also a mainstream deep convolutional neural network. For object detection (OD), this embraces the spatial pyramid pooling network with ResNet101 (SPPN-RN101), which assists in concatenating the local features upon disparate scales within a similar convolution layer with fewer position errors while identifying little objects. Additionally, Softmax with Adam Optimizer in CNN enhances the training speed with greater identification accuracy. This study presents FOD’s image dataset called FOD in Airports (FODA). In addition to the bounding boxes’ principal annotations for OD, FODA gives labeled environmental scenarios. Consequently, every annotation instance has been additionally classified into three light-level classes (bright, dim, and dark) and two weather classes (dry and wet). The proffered SPPN-ResNet101 paradigm is correlated to the former methodologies, and the simulation outcomes exhibit that the proffered study executes an AP medium of 0.55 for the COCO metric, 0.97 AP for the pascal metric, and 0.83 MAP of pascal metric.
A Survey of Bit-Flip Attacks on Deep Neural Network and Corresponding Defense Methods

As the machine learning-related technology has made great progress in recent years, deep neural networks are widely used in many scenarios, including security-critical ones, which may incura great loss when DNN is compromised. Starting from introducing several commonly used bit-flip methods, this paper concentrates on bit-flips attacks aiming DNN and the corresponding defense methods. We analyze the threat models, methods design, and effect of attack and defense methods in detail, drawing some helpful conclusions about improving the robustness and resilience of DNN. In addition, we point out several drawbacks to existing works, which can hopefully be researched in the future.
Improved Chimpanzee Search Algorithm with Multi-Strategy Fusion and Its Application

An improved chimpanzee optimization algorithm incorporating multiple strategies (IMSChoA) is proposed to address the problems of initialized population boundary aggregation distribution, slow convergence speed, low precision, and proneness to fall into local optimality of the chimpanzee search algorithm. Firstly, the improved sine chaotic mapping is used to initialize the population to solve the population boundary aggregation distribution problem. Secondly, a linear weighting factor and an adaptive acceleration factor are added to join the particle swarm idea and cooperate with the improved nonlinear convergence factor to balance the global search ability of the algorithm, accelerate the convergence of the algorithm, and improve the convergence accuracy. Finally, the sparrow elite mutation and Bernoulli chaos mapping strategy improved by adaptive change water wave factor are added to improve the ability of individuals to jump out of the local optimum. Through the comparative analysis of benchmark functions seeking optimization and the comparison of Wilcoxon rank sum statistical test seeking results, it can be seen that the IMSChoA optimization algorithm has stronger robustness and applicability. Further, the IMSChoA optimization algorithm is applied to two engineering examples to verify the superiority of the IMSChoA optimization algorithm in dealing with mechanical structure optimization design problems.
Automatic Detection and Measurement of Renal Cysts in Ultrasound Images: A Deep Learning Approach

Ultrasonography is widely used for diagnosis of diseases in internal organs because it is nonradioactive, noninvasive, real-time, and inexpensive. In ultrasonography, a set of measurement markers is placed at two points to measure organs and tumors, then the position and size of the target finding are measured on this basis. Among the measurement targets of abdominal ultrasonography, renal cysts occur in 20-50% of the population regardless of age. Therefore, the frequency of measurement of renal cysts in ultrasound images is high, and the effect of automating measurement would be high as well. The aim of this study was to develop a deep learning model that can automatically detect renal cysts in ultrasound images and predict the appropriate position of a pair of salient anatomical landmarks to measure their size. The deep learning model adopted fine-tuned YOLOv5 for detection of renal cysts and fine-tuned UNet++ for prediction of saliency maps, representing the position of salient landmarks. Ultrasound images were input to YOLOv5, and images cropped inside the bounding box and detected from the input image by YOLOv5 were input to UNet++. For comparison with human performance, three sonographers manually placed salient landmarks on 100 unseen items of the test data. These salient landmark positions annotated by a board-certified radiologist were used as the ground truth. We then evaluated and compared the accuracy of the sonographers and the deep learning model. Their performances were evaluated using precision-recall metrics and the measurement error. The evaluation results show that the precision and recall of our deep learning model for detection of renal cysts are comparable to standard radiologists; the positions of the salient landmarks were predicted with an accuracy close to that of the radiologists, and in a shorter time.
Management of IoT Devices Data Security Using Blockchain and Proxy Re-encryption Algorithm

One of the most beneficial uses of the Internet of Things in cloud is data sharing. As tempting since this technology is, data security is still one of the issues it faces, as inappropriate data usage may lead to a range of issues. We offer a proxy re-encryption strategy to securely exchange data in cloud contexts in this paper. Data owners can utilize identity-based encryption to send encrypted data to the cloud, and legitimate users can access the data via proxy re-encryption. Because IoT devices have limited resources, an edge device functions as a proxy server to do complex calculations. We also make effective use of information-centric networking capabilities to supply cached data in the proxy, resulting in better service quality and more network capacity. Our system also uses block chain, a revolutionary technology that allows for decentralized data sharing. It improves centralized system efficiency and enables fine-grained data access control. The security analysis and assessment of our system show that it has the potential to offer privacy protection, authenticity, and dependability.
Voronoi Centerline-Based Seamline Network Generation Method

Seamline network generation is a crucial step in mosaicking multiple orthoimages. It determines the topological and mosaic contribution area for each orthoimage. Previous methods, such as Voronoi-based and AVOD (area Voronoi)-based, may generate mosaic holes in low-overlap and irregular orthoimage cases. This paper proposes a Voronoi centerline-based seamline network generation method to address this problem. The first step is to detect the edge vector of the valid orthoimage region; the second step is to construct a Voronoi triangle network using the edge vector points and extract the centerline of the network; the third step is to segment each orthoimage by the generated centerlines to construct the image effective mosaic polygon (EMP). The final segmented EMP is the mosaic contribution region. All EMPs are interconnected to form a seamline network. The main contribution of the proposed method is that it solves the mosaic holes in the Voronoi-based method when processing with low overlap, and it solves the limitation of the AVOD-based method polygon shape requirement, which can generate a complete mosaic in any overlap and any shape of the orthoimage. Five sets of experiments were conducted, and the results show that the proposed method surpasses the well-known state-of-the-art method and commercial software in terms of adaptability and effectiveness.
Tsetlin Machine for Sentiment Analysis and Spam Review Detection in Chinese

In Natural Language Processing (NLP), deep-learning neural networks have superior performance but pose transparency and explainability barriers, due to their black box nature, and, thus, there is lack of trustworthiness. On the other hand, classical machine learning techniques are intuitive and easy to understand but often cannot perform satisfactorily. Fortunately, many research studies have recently indicated that the newly introduced model, Tsetlin Machine (TM), has reliable performance and, at the same time, enjoys human-level interpretability by nature, which is a promising approach to trade off effectiveness and interpretability. However, nearly all of the related works so far have concentrated on the English language, while research on other languages is relatively scarce. So, we propose a novel method, based on the TM model, in which the learning process is transparent and easily-understandable for Chinese NLP tasks. Our model can learn semantic information in the Chinese language by clauses. For evaluation, we conducted experiments in two domains, namely sentiment analysis and spam review detection. The experimental results showed thatm for both domains, our method could provide higher accuracy and a higher F1 score than complex, but non-transparent, deep-learning models, such as BERT and ERINE.
A joint triple extraction method by entity role attribute recognition

In recent years, joint triple extraction methods have received extensive attention because they have significantly promoted the progress of information extraction and many related downstream tasks in the field of natural language processing. However, due to the inherent complexity of language such as relation overlap, joint extraction model still faces great challenges. Most of the existing models to solve the overlapping problem adopt the strategy of constructing complex semantic shared encoding features with all types of relations, which makes the model suffer from redundancy and poor inference interpretability in the prediction process. Therefore, we propose a new model for entity role attribute recognition based on triple holistic fusion features, which can extract triples (including overlapping triples) under a limited number of relationships, and its prediction process is simple and easy explain. We adopt the strategy of low-level feature separation and high-level concept fusion. First, we use the low-level token features to perform entity and relationship prediction in parallel, then use the residual connection with attention calculation to perform feature fusion on the candidate triples in the entity-relation matrix, and finally determine the existence of triple by identifying the entity role attributes. Experimental results show that the proposed model is very effective and achieves state-of-the-art performance on the public datasets.
Neural Machine Translation for Sinhala-English Code-Mixed Text

Multilingual societies use a mix of two or more languages when communicating. It has become a famous way of communication in social media in South Asian communities. Sinhala-English Code-Mixed Texts (SCMT) are known as the most popular text representation used in Sri Lanka in the informal context such as social media chats, comments, small talks etc. The challenges in utilizing the SCMT sentences are addressed in this paper. The main focus of this study is translating code-mixed sentences written in Sinhala-English to the standard Sinhala language. Since Sinhala is a low-resource language, we were able to collect only a limited number of SCMT-Sinhala parallel sentences. Creating the parallel corpus of SCMT-Sinhala was a time-consuming and costly task. The proposed architecture of Neural Machine Translation(NMT) to translate SCMT text to Sinhala, is built with a combination of normalization pipeline, Long Short Term Memory(LSTM) units, Sequence to Sequence(Seq2Seq) and Teachers Forcing mechanism. The proposed model is evaluated against the current state-of-the-art models using the same experimental setup, which proves the Teacher Forcing Algorithm combined with Seq2Seq and Normalization improves the quality of the translation. The predicted outputs from the model are compared using the BLEU (Bilingual Evaluation Understudy) metric and our proposed model achieved a better BLEU score of 33.89 in the evaluation.
The Effect of Economic, Financial and Political Stabilities on the Banking Sector: Cases of Six Balkan Countries

Today, banks, which act as financial intermediaries, ensure the growth of the economy. Banks, being a channel in monetary policies and monetary issues, are effective institutions in ensuring sustainable macroeconomic stability. In addition, banks are one of the institutions that are exposed to the most financial risk by nature. This article explores the long-term effects of economic, financial and political stabilities on the banking sector. This research covers the 21-year period between 1996–2017. The aim of this study is to examine the effects of economic, financial and political stabilities, which are the components of country risk, on the banking sector in selected Balkan countries and using the panel data analysis technique, which is one of the econometric analysis methods. Panel cross-sectional dependence, unit root tests, cointegration and causality tests were applied according to country risk indicators for the six selected Balkan countries. The selected Balkan countries have paid attention to their development in the country’s economies and their involvement in global trade and to adopt the modern banking system. In addition, six Balkan countries have been selected as examples in the study due to their close relations with Turkey in the socio-cultural and economic fields. The results of the panel tests indicated the of unit root tests as well as the existence of cointegration. The p values obtained as a result of cointegration analysis are less than 0.005, indicating the existence of cointegration. It has been concluded that there is a cointegration relationship in all three hypotheses, which have the effects of economic, financial and political stabilities on the banking sector. According to the Granger causality test results, there is a bidirectional causality relationship between the banking variable and the economic stabilities variable. Having long-term and causal relationships between selected Balkan economies requires financial and economic stability to be supported simultaneously. In short, it has been determined that economic, financial and political stabilities have a relationship with sustainable banking in the long term.
Phase I Analysis of Nonlinear Profiles Using Anomaly Detection Techniques

In various industries, the process or product quality is evaluated by a functional relationship between a dependent variable y and one or a few input variables x, expressed as y=fx. This relationship is called a profile in the literature. Recently, profile monitoring has received a lot of research attention. In this study, we formulated profile monitoring as an anomaly-detection problem and proposed an outlier-detection procedure for phase I nonlinear profile analysis. The developed procedure consists of three key processes. First, we obtained smoothed nonlinear profiles using the spline smoothing method. Second, we proposed a method for estimating the proportion of outliers in the dataset. A distance-based decision function was developed to identify potential outliers and provide a rough estimate of the contamination rate. Finally, PCA was used as a dimensionality reduction method. An outlier-detection algorithm was then employed to identify outlying profiles based on the estimated contamination rate. The algorithms considered in this study included Local Outlier Factor (LOF), Elliptic Envelope (EE), and Isolation Forest (IF). The proposed procedure was evaluated using a nonlinear profile that has been studied by various researchers. We compared various competing methods based on commonly used metrics such as type I error, type II error, and F2 score. Based on the evaluation metrics, our experimental results indicate that the performance of the proposed method is better than other existing methods. When considering the smallest and hardest-to-detect variation, the LOF algorithm, with the contamination rate determined by the method proposed in this study, achieved type I errors, type II errors, and F2 scores of 0.049, 0.001, and 0.951, respectively, while the performance metrics of the current best method were 0.081, 0.015, and 0.899, respectively.
Passive Continuous Variable Quantum Key Distribution through the Oceanic Turbulence

Continuous variable quantum key distribution (CVQKD) can be potentially implemented through seawater channels, whereas the involved oceanic turbulence has a negative effect on the maximal transmission distance of quantum communication systems. Here, we demonstrate the effects of the oceanic turbulence on the performance of the CVQKD system and suggest an implementation feasibility of the passive CVQKD through the oceanic turbulence-based channel. We achieve the channel transmittance characterized by the transmission distance and depth of the seawater. Moreover, a non-Gaussian approach is used for performance improvement while counteracting the effects of excess noises on the oceanic channel. Numerical simulations show that the photon operation (PO) unit can bring reductions of excess noise when taking into account the oceanic turbulence, and hence results in performance improvement in terms of transmission distance and depth as well. The passive CVQKD explores the intrinsic field fluctuations of a thermal source without using an active scheme and hence has a promising application in chip integration for portable quantum communications.
Exploring the characteristics of pre-service EFL teachers’ practicum experiences: a complexity theory-based case study in China

Abstract With increasing attention being paid to teacher education, various theoretical perspectives have been adopted to study on how pre-service teachers learn to teach. This study employed complexity theory as conceptual framework and examined two pre-service EFL teachers’ practicum experiences by qualitative case study and identified three core characteristics of their learning-to-teach: complexity, dynamism and self-organization. It is found that pre-service EFL teacher learning, as a complex system, interacted between and among a network of socio-contextual factors and thus influenced and being influenced during the learning process in a non-linear and complex way. This study made empirical and theoretical contributions to the research field of teacher education and provided implications for university-school collaboration-based teacher education practice.
Using Airborne Geophysical and Geochemical Methods to Map Structures and Their Related Gold Mineralization

Egypt’s mineral resources are an effective means of raising the country’s income. Consequently, searching for deep subsurface ore deposits is essential. In this study, we map the subsurface structure and the occurrence of related gold-ore mineralization deposits in ophiolitic-assemblage rocks around the Wadi El-Saqia area in the Central Eastern Desert of Egypt. Our approach combines airborne geophysical data (aeromagnetic mapping) and geochemical analyses. We enhanced the aeromagnetic data and interpreted them using edge-detection methods, such as the first-order vertical derivative (FVD), the analytic signal (AS), the total horizontal derivative (THD), the tilt derivative (TD), the tilt angle (TA), the theta map, both grid and porphyry analyses from the Centre for Exploration Targeting (CET), and Euler deconvolution (ED) techniques. Utilizing these methods, we located the main structural lineaments/contacts that control the distribution of hydrothermal alteration zones. In addition, our geochemical analyses use the mineral chemistry of pyroxene and plagioclase to describe their tectonic and magmatic evolution. The airborne geophysical results revealed that NW–SE, NE–SW, N–S, and E–W structural orientations are prevalent in the studied area, with depths ranging from less than 50 m to about 600 m. To validate the findings, we carried out geochemical sampling, which indicates that pyrite, galena, pyrrhotite, and electrum contain good percentages of gold (ranging from 0.01–0.09 wt%, 0.03–0.1 wt%, 0.12–0.14 wt%, and 53.55–55.01 wt%, respectively). In this study, we were thus able to find preferred locations for gold mineralization, which highlights the value of combining aeromagnetic and geochemical data for mineral exploration.
Quantum counterfactuality with identical particles

Abstract Quantum self-interference enables the counterfactual transmission of information, whereby the transmitted bits involve no particles traveling through the channel. In this work, we show how counterfactuality can be realized even when the self-interference is replaced by interference between identical particles. Interestingly, the facet of indistinguishability called forth here is associated with first-order coherence, and is different from the usual notion of indistinguishability associated with the (anti-)commutation relations of mode operators. From an experimental perspective, the simplest implementation of the proposed idea can be realized by slight modifications to existing protocols for differential-phase-shift quantum key distribution or interaction-free measurement.
A Novel Approach for Estimating Ovarian Cancer Tissue Heterogeneity through the Application of Image Processing Techniques and Artificial Intelligence

Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity.Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10-3 mm2/s).The average recorded accuracy of the proposed automated classification system was equal to 0.86.The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.