Reviewerly/exHarmony huggingface dataset:
A study on classifying Stack Overflow questions based on difficulty by utilizing contextual features

Technical question-answering sites like Stack Overflow are gaining enormous attention from practitioners of specialized fields looking to exchange their programming knowledge. They ask questions on different topics with varying degrees of complexity and difficulty. All practitioners do not have the same level of expertise on those topics to respond to such questions. However, the current approach used by Stack Overflow mostly filters questions based on topics alone and does not take difficulty into account. For this reason, a large percentage of questions fail to attract the attention of appropriate users, resulting in questions having no answer or a significant delay in response time. To address these limitations, we incorporate three models, TF-IDF, LDA, and Doc2Vec, to extract semantic and context-dependent features that can measure the difficulty of questions. Each of these models is paired with different classifiers along with other features to classify the questions based on difficulty. Extensive experiments on three different datasets exhibit the effectiveness of our models, and Doc2Vec outperforms the other models. We also identified that the contextual features are correlated with question difficulty, and one subset of features outperforms others. The proposed approach can be beneficial for building an automatic tagger based on question difficulty.
Recursive Nonlinear Filtering via Gaussian Approximation With Minimized Kullback–Leibler Divergence

In order to solve various problems in a Bayesian framework efficiently, it is critical to approximate a posterior distribution. This work provides a Gaussian approximation of a general distribution via Kullback–Leibler (KL) divergence minimization by deterministic sampling. Two algorithms, feasible direction method and linearized alternating direction method of multipliers, each having its strengths, are proposed for the Gaussian approximation. Theoretical results of complexity, convergence, convergence rate, and guidelines for parameter selection of the proposed algorithms are also provided. Based on the Gaussian approximation, two recursive filters are developed for nonlinear dynamic systems. Examples are given to demonstrate the effectiveness and efficiency of the proposed Gaussian approximation and the related filters.
Point cloud optimization of multi-view images in digital image correlation system

Post-processing of the initial point cloud is one of the important ways to improve the measurement accuracy of digital image correlation (DIC). In this study, a multi-level point cloud optimization scheme is proposed for processing point clouds of multi-view images in DIC systems. Dense point clouds obtained after interpolation of primary point clouds from multi-view images contain noise. First, the local Random Sample Consensus (RANSAC) algorithm is proposed to retain local features while removing coarse matching points. Defects are filled in by the adaptive cavity repair technique based on information around the cavities and restores the original surface shape to the greatest extent. Finally, a multi-layer surface filtering is executed to smooth the mesh. Experiments verify the comprehensive performance of the multi-level optimization scheme, providing excellent results in point cloud fusion, cavity repair, and mesh smoothing, regardless of static 3D reconstruction or dynamic deformation measurement.
Placenta segmentation in magnetic resonance imaging: Addressing position and shape of uncertainty and blurred placenta boundary

Automatic segmentation of the placenta in MRI facilitates quantitative measurements and further analysis. Automatic segmentation of the placenta in magnetic resonance imaging (MRI) is challenging due to the fact that (1) the position of the placenta varies greatly in different views of MRI. In addition, the shape of the placenta changes layer by layer in different MRI slices. The position and shape of the placenta are highly uncertain. (2) PAS results in a blurred placental boundary. To address the above issues, a refinement fusion based on the U-Net (RFU-Net) is proposed for the segmentation of the placenta with variable position and shape in MR images, and the boundary de-precision explores the distribution of blood vessels around the placental boundary. In RFU-Net, ResNet34 is used as a feature extractor, and a fusion multiscale feature (FMF) is proposed to extract multiscale features and their contextual information to solve the problem of variable placental shape. A refinement segmentation module (RSM) is designed to provide information about the position of the placenta to solve the problem of variable placental position, which effectively improves the segmentation of the placenta. The mean intersection over union (MIoU), hausdorff distance (HD), dice coefficient (Dice) and accuracy (Acc) are 0.8620, 4.3142, 0.9314 and 0.9987, respectively. Extensive experiments show that RFU-Net effectively improves the segmentation accuracy of the placenta. Boundary de-precision resolves the blurring of placental boundaries, effectively highlighting areas of the placental boundary that may contain blood vessels to aid in the physician's diagnosis.
Otel Yorumlarının Sınıflandırılmasında Makine Öğrenmesi Algoritmalarının Karşılaştırılması: Tripavdisor Yorumlarının Duygu Analizi

Sentiment analysis can help extract meaningful information from these data piles from various websites and social media and measure consumers' reactions by classifying consumers' emotions as positive, negative or neutral. The success of sentiment analysis varies according to feature selection, vector space selection and machine learning method. For this reason, determining the most successful method in sentiment analysis is still controversial and important. A limited number of studies have been conducted comparing the success of various machine learning methods in sentiment analysis of hotel reviews in English. Considering this gap, the purpose of this research is to determine the most successful machine learning algorithm for sentiment analysis of hotel reviews. For this purpose, 708 reviews for 5-star hotels in Istanbul were collected manually. Obtained data were classified as positive and negative using logistic regression, k-nearest neighbor, naive Bayes and support vector machine methods. Analysis results show that the logistic regression method was the most successful classification algorithm, with an accuracy rate of 0.92. It is followed by support vector machine (0.90), naive Bayes method (0.77) and k-nearest neighbor algorithms (0.66).
Multivariate time series anomaly detection by fusion of deep convolution residual autoencoding reconstruction model and ConvLstm forecasting model

Multivariate time-series anomaly detection is very essential to ensure the normal operation of physical equipment. Great achievements have been made in this field in recent years. However, two critical challenges limit the generalization ability of the model. Graph attention networks without prior knowledge are unable to extract the distribution features of dependencies among variables in a fine granularity. This reduces the accuracy of the dependence matrix to represent the correlations between variables. Beyond this challenge, the reconstruction model loses the fine-grained seasonal feature information in the spatial dimension during the reconstruction analysis process of the sample. In order to solve the above challenges, we propose a multivariate time-series anomaly detection model consisting of characterization network and forecasting network. In the characterization network, we first remove non-existent dependencies between variables by using prior knowledge. This approach can accurately capture the fine-grained dependency distribution features between variables and improve the accuracy of the dependence matrix representation of the correlations between variables. We then construct a deep convolutional residual autoencoder to reduce the loss of seasonal feature information in the spatial dimension. In forecasting network, we construct a temporal attention-based ConvLstm forecasting network to make fine-grained anomaly decisions on the output of the characterization network in the time dimension. We perform systematic experiments on six open-source datasets, in social network security scenario,the malicous acount behavior can be detected. Experimental results show that the proposed method outperforms the baseline anomaly detection methods with excellent detection performance and robustness. Specifically, our method achieves an average F1 score improvement of 0.23 over the baseline method at its highest level.
Spurious heritability of ability tilts: A comment on

Ability tilts refer to within-individual differences between two abilities, e.g. math ability – verbal ability. Coyle et al. (2023) found ability tilts to be genetically heritable and concluded that ability tilts are genuine and, presumably, genetically coded individual characteristics. Moreover, Coyle et al. found a large portion of variance in ability tilts to be attributable to non-shared environmental factors (i.e. environmentability), which they interpreted to indicate that ability tilts are potentially generated by niche-picking. However, through simulations we show that heritability and environmentability of X-Y tilts are spurious consequences of heritability and environmentability of the constituent variables X and Y. Furthermore, we reanalyzed data used by Coyle et al. and show that the logic of their arguments would lead to the conclusions, for example, that the human genome codes for a difference between head circumference and verbal ability and that some individuals have picked a niche that includes a long nose at the expense of spatial ability. We do not find these conclusions tenable and propose, instead, that heritability and environmentability of tilts are spurious consequences of heritability and environmentability of the constituent variables.
On estimators of the mean of infinite dimensional data in finite populations

The Horvitz-Thompson (HT), the Rao-Hartley-Cochran (RHC) and the generalized regression (GREG) estimators of the finite population mean are considered, when the observations are from an infinite dimensional space. We compare these estimators based on their asymptotic distributions under some commonly used sampling designs and some superpopulations satisfying linear regression models. We show that the GREG estimator is asymptotically at least as efficient as any of the other two estimators under different sampling designs considered in this paper. Further, we show that the use of some well known sampling designs utilizing auxiliary information may have an adverse effect on the performance of the GREG estimator, when the degree of heteroscedasticity present in linear regression models is not very large. On the other hand, the use of those sampling designs improves the performance of this estimator, when the degree of heteroscedasticity present in linear regression models is large. We develop methods for determining the degree of heteroscedasticity, which in turn determines the choice of appropriate sampling design to be used with the GREG estimator. We also investigate the consistency of the covariance operators of the above estimators. We carry out some numerical studies using real and synthetic data, and our theoretical results are supported by the results obtained from those numerical studies.
Small object detection in unmanned aerial vehicle images using multi-scale hybrid attention

Small object detection in unmanned aerial vehicle images is always challenging due to the low resolution and the limited amount of information that they contain. Many feature enhancement effects have been introduced to improve the detection of small objects, but the extracted effective information is still insufficient, and redundant information interference is an issue. In this paper, we propose a new multi-scale hybrid attention based detector (MHA-YOLOv5), which integrates the similarity relationships between objects into you only look once version 5 (YOLOv5) for small object detection. Specifically, a novel multi-scale hybrid attention (MHA) structure is proposed to enhance the feature representation of small objects. This structure contains three modules: multi-scale attention (MsA), foreground enhancement module (FEM) and depthwise separable channel attention (DSCA). The MsA module is designed to build connections between large objects with abundant details and small objects with insufficient features on multiple scale features and capture the similarity relationships between objects. To reduce the interference of redundant information, the FEM is used to focus on the foreground features of multiple scale features, and the DSCA module is utilized to effectively extract multidimensional channel information. Sufficient experiments on the challenging VisDrone2019-DET, UAVDT and CARPK datasets demonstrate the effectiveness and superiority of the proposed approach. Specifically, compared with the performance of YOLOv5, MHA-YOLOv5 demonstrates a 2.82% mean average precision (mAP) improvement on the VisDrone2019-DET dataset, a 2.25% mAP improvement on the UAVDT dataset, and a 3.07% mAP improvement on the CARPK dataset.
Municipality2HTTPS: A study on HTTPS protocol's usage in Italian municipalities’ websites

The usage of HTTPS protocol is essential for secure communication with websites, especially when it comes to the websites of local municipalities. This protocol ensures the confidentiality, integrity, and authenticity of online data transmissions, protecting users from unauthorised access and data breaches. By implementing HTTPS, municipalities can provide a secure and reliable online experience for their citizens and build trust in their digital services. This paper presents the Municipality2HTTPS research project, which aim is to provide a comprehensive analysis of the current state of HTTPS implementation in approximately 8,000 Italian municipalities’ websites. The study was conducted using the purpose-built software platform, named MunicipalityEvaluator, and the results were aggregated both geographically and demographically based on a numerical score derived from a specific scoring system: this system was obtained by expanding the requirements set forth by Italian regulations on the topic. The obtained results identified several areas for improvement in HTTPS implementation and regulatory compliance, including the need for more awareness-raising activities and support for municipalities with limited technical expertise. The results also revealed discrepancies between regions and demographic groups in terms of HTTPS implementation and regulatory compliance.
Detecting security vulnerabilities with vulnerability nets

Detecting security vulnerabilities is a crucial part in secure software development. Many static analysis tools have proved to be effective in finding vulnerabilities, but generally there are some complex and subtle vulnerabilities that can escape from detection. Manual audits are a complementary approach to using tools. Unfortunately, most manual analyses are tedious and error prone. To benefit from both the tools and manual audits, some approaches incorporate the auditor's expertise into a static analysis tool during vulnerability discovery. Following this strategy, this paper presents a representation of source code called a vulnerability net, which is a special Petri net that integrates with data dependence graphs and control flow graphs. The combined representation can facilitate the detection of taint-style vulnerabilities such as buffer overflows and injection vulnerabilities. We test the proposed approach on Securibench Micro and demonstrate that it has the capability to identify a variety of vulnerabilities while keeping the rates of false negatives and positives low.
Mental image reconstruction from human brain activity: Neural decoding of mental imagery via deep neural network-based Bayesian estimation

Visual images observed by humans can be reconstructed from their brain activity. However, the visualization (externalization) of mental imagery is challenging. Only a few studies have reported successful visualization of mental imagery, and their visualizable images have been limited to specific domains such as human faces or alphabetical letters. Therefore, visualizing mental imagery for arbitrary natural images stands as a significant milestone. In this study, we achieved this by enhancing a previous method. Specifically, we demonstrated that the visual image reconstruction method proposed in the seminal study by Shen et al. (2019) heavily relied on low-level visual information decoded from the brain and could not efficiently utilize the semantic information that would be recruited during mental imagery. To address this limitation, we extended the previous method to a Bayesian estimation framework and introduced the assistance of semantic information into it. Our proposed framework successfully reconstructed both seen images (i.e., those observed by the human eye) and imagined images from brain activity. Quantitative evaluation showed that our framework could identify seen and imagined images highly accurately compared to the chance accuracy (seen: 90.7%, imagery: 75.6%, chance accuracy: 50.0%). In contrast, the previous method could only identify seen images (seen: 64.3%, imagery: 50.4%). These results suggest that our framework would provide a unique tool for directly investigating the subjective contents of the brain such as illusions, hallucinations, and dreams.
Hierarchical features extraction and data reorganization for code search

According to a natural language query, code search aims to retrieve relevant code snippets from a codebase. Recent works mainly rely on transformer-based pretraining models to measure the matching degree of queries and codes. Compared with works that rely on earlier deep learning methods, such as LSTM and Attention, they can significantly improve the performance of code search tasks. However, the different layers of the transformer-based models have different features that are intuitive and efficient for understanding the semantics of codes and queries but are rarely considered. Moreover, existing methods do not consider further increasing the amount of training data during training to improve the model’s performance. Toward this end, we propose a novel method called HFEDR, which utilizes the hierarchical features of transformer-based models and reorganizes original training data during a training phase. Specifically, we first extract high-level and low-level features of queries and codes from the higher and lower layers of GraphCodeBERT, respectively, achieving multi-view and comprehensive semantic representation. After that, we organize the original training data into hierarchical-uncorrelated feature pairs and then reorganize them into hierarchical-correlated feature pairs, achieving training the model with more data. Finally, we update the model’s parameters using a contrastive training method. We conduct extensive experiments on CodeSearchNet, demonstrating the effectiveness and rationality of our proposed approach.
The b-symbol weight hierarchy of the Kasami codes

The symbol-pair read channel was first proposed by Cassuto and Blaum. Later, Yaakobi et al. generalized it to the b-symbol read channel. It is motivated by the limitations of the reading process in high density data storage systems. One main task in b-symbol coding theory is to determine the b-symbol weight hierarchy of codes. In this paper, we study the b-symbol weight hierarchy of the Kasami codes, which are well known for their applications to construct sequences with optimal correlation magnitudes. The complete symbol-pair weight distribution of the Kasami codes is determined.
Self-calibrated defocused speckle imaging for remote surface motion measurements

Defocused Speckle Imaging (DSI) is a non-contact optical method that can measure multiaxial object motions at microscopic sensitivity using a simple and mechanically robust optical setup. The object surface is illuminated by a laser beam, and the scattered interference speckle pattern is tracked with a defocused camera. DSI measurement sensitivity increases with distance, and the sensitivity can be tuned by simple camera defocus adjustment. These characteristics make DSI an attractive choice for tracking remote objects in field conditions. However, the use of DSI for practical measurements is limited because the speckle signals due to linear and rotational surface motions mix together, and because the instrument calibration requires accurate object range and surface orientation parameters. To address these issues, the work described here combines two recently presented concepts: 1) a defocused camera pair, and 2) a diffraction-based self-calibration principle. The proposed approach was demonstrated by a series of self-calibrated multiaxial motion measurements performed at an extended (30.7-m) distance. The studied motions included in-plane displacements and out-of-plane rotations (tilts), whereas out-of-plane displacements and in-plane rotations were excluded. The experiments showed DSI's capability for high tilt sensitivity down to 0.0006°, standalone remote angle measurements, and suitability for retroreflective surfaces. The diffraction-based self-calibration approach could monitor sampling distances at a 6.4 % accuracy and the relative surface angles of 2.5–7.4° at a 0.2° accuracy. The defocused camera pair tracked microscopic in-plane displacements (400 µm) and very fine out-of-plane tilts (0.003°) at a high accuracy, with a maximum uncertainty of 6.0 %. The proposed DSI approach is particularly suited for monitoring large objects and for operating in hazardous environments. The findings pave a way for interesting new applications, like high-range remote angle measurements, high-sensitivity straightness measurements, and 3D-positioning utilizing retroreflective markers that already exist in the built infrastructure.
Calculating the distance between languages with deep learning

Artificial intelligence (AI) has been implemented in various fields, including speech recognition. In this paper, a computational method is proposed for calculating the similarity between different languages or language varieties, with their similarity represented in terms of distance. In this process, we extracted mel spectrogram features from speech signals to provide the feature vectors and derived pairs of signal tokens based on vectors. Then, we trained a Siamese time-delay neural network to calculate the distance between two signal tokens. If the token pairs are from the same language group, the distance obtained using this Siamese network model is zero. In this preliminary experiment, three types of regional Mandarin Chinese (BJ, FJ, GD) were used as the dataset. The results gave the F1-score of 0.794, 0.623, and 0.715 for the classification task with respect to BJ, FJ, and GD dataset. In addition, 10 Taiwan Mandarin (TM) native speakers participated in identification and a pair-wise discrimination experiment to allow comparison with the Siamese network model. The 10 TM natives tended to misidentify GD-accented Mandarin as FJ-accented Mandarin resulting in a much greater distance between the two in the FJ-GD discrimination task compared to the Siamese network model. Overall, the results show that the performance of our model is better than the comparative experiment in completing the identification task, with the distance between the Siamese network model and the experiment having a mean absolute error (MAE) of 0.35. The familiarity might be the reason why the 10 TM natives displayed a bias towards BJ-accented Mandarin Chinese. To sum up, we provide a computational method to calculate the distance between two languages or language varieties, which can help linguists pre-classify sound files.