Reviewerly/exHarmony huggingface dataset:
Automatic Detection Algorithm of Mine Detection Based on Improved YOLOv5 in Complex Underwater Environment for AUV

In this paper, an autonomous real-time mine detection algorithm based on improved YOLOv5 in a complex underwater environment is proposed. The target recognition system based on the optical image is used to guide autonomous underwater vehicles (AUV) to achieve high precision mine exploration and assist the autonomous mine hunting weapon system to finish the mine-hunting task. In view of all kinds of various interference information of the underwater optical images, a set of image preprocessing systems has been added to the algorithm based on YOLOv5 which has the advantages of both detection accuracy and detection speed. The original data are processed by data enhancement, image enhancement, sample equalization, negative sample addition, and Poisson fusion, which can reduce the problem of poor detection results caused by the input image quality to some extent. At the same time, the number of layers of YOLOv5 is reduced, which makes the detection algorithm meet the requirements of the AUV embedded system without affecting the detection accuracy, and improves the real-time detection of the mobile terminals. The experimental results show that the proposed method could effectively improve the detection accuracy and real-time performance of the underwater mine detection system.
Comparison of three image‐analysis‐based visual texture calculation methods: energy, entropy, and texture change index

Abstract BACKGROUND Three image analysis methods to measure visual texture were applied to an image with much texture (scaled carp), and one with little texture (mirror carp). For each method, the effect of image rotation at 0°, 10°, 20°, 30°, 45°, 60°, 75° and 90° was evaluated. RESULTS Rotation did not change energy ( E ) and entropy ( H ) calculations using image histograms. Using co‐occurrence matrices with different step size d (1–19 in increases of 2) and step angle θ (0°, 45°, 90° and 135°) showed that, as d increased, E decreased and H increased, and the number of legitimate pixel pairs decreased linearly. Averaging E and H at different θ values rendered the results rotation invariant. Theoretically, the ‘texture primitives’ method is not rotation independent. However, the variation in texture change index (TCI) with image rotation was negligible. Also, the increase in TCI between the less textured image and the more textured image was 5.3–11. In comparison, the E values from histograms for the images above were 0.0069–0.0081. For co‐occurrence matrix‐based calculations, at d = 1 and for all θ , E range was from 220 to 389 for scaled carp and from 232 to 412 for mirror carp. CONCLUSION The more than doubling of TCI for these images implies that it is a more precise method than energy and entropy to discern between visual texture levels. © 2022 Society of Chemical Industry.
Latent Class Model for the External Factors Affecting the Mathematics Performance

Among the different factors that affect the students’ academic performance in mathematics, three known external factors – learning environment, peers, and parents are used in this study. The aim is to use these factors in determining latent class models that affect the students’ academic performance in mathematics. A self-made checklist was conducted to the Grade 7 students of both campuses of De La Salle Santiago Zobel School. Optimal fitting model was used to determine the conditional and class probabilities thru AIC and BIC value for the preferred class model number. ANOVA was further applied to investigate the mean difference. The latent classes formed were dual factors, peers & parents, multi-factor, and peers which shown significant difference in the students’ mathematics performance between the latent classes formed. Possible remediation programs should be given to address the issue on these factors that mostly affect the students’ academic performance in mathematics.
User demographic analysis of music streaming platforms

Data analytics and mining is the gold rush of the 21st century. Corporations use data mining to gain a edge over their rivals and explore new avenues. Different type of ventures have different use cases to their data. Some use their collected data to find new customers or invent new marketing strategies. Companies employ unique data mining solutions to suit their information and market research needs. The music industry is a competitive space and directly deals with their customers. Since the entrance of streaming services into the already competitive music industry the industry had to accept a modern approach towards making decisions to better satisfy their customer’s needs. Data mining approaches have been increasingly used to determine general trends, personalized recommendations and much more. Thus this paper explores techniques like Naive Bayes, J48, SMO and Random trees to classify data based on user attributes of which are age, gender and nationality of users have been considered.
EmiR: Evolutionary minimization for R

Classical minimization methods, like the steepest descent or quasi-Newton techniques, have been proved to struggle in dealing with optimization problems with a high-dimensional search space or subject to complex nonlinear constraints. In the last decade, the interest on metaheuristic nature-inspired algorithms has been growing steadily, due to their flexibility and effectiveness. In this paper we present EmiR, a package for R which implements several metaheuristic algorithms for optimization problems. Unlike other available tools, EmiR can be used not only for unconstrained problems, but also for problems subjected to inequality constraints and for integer or mixed-integer problems. Main features of EmiR, its usage and the comparison with other available tools are presented.
Reprint of: Sensing beyond itself: Multi-functional use of ubiquitous signals towards wearable applications

Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to realizing the sensing capability of multiple sensors via the signal from one sensor, which expanded the functionalities and sensing domains of traditional sensors. For the first time, we defined such sensing approach as “cross-sensing” and provided a comprehensive review on the cross-sensing towards wearable applications (i.e., human-machine interface, health services, and security). Specifically, this paper summarized the applied signal processing and machine learning algorithms, and discussed how cross-sensing would affect the development and innovation trends of wearable electronics.
DDBTC approach with binary particle swarm optimization for greedy-DCNN based CBIR system

Recently, the researchers have been focused on making image retrieval as effective as possible since it has significant importance in many fields. A semantic distance between the features and the human concepts derived from images has become a major problem that reduces retrieval precision. Several algorithms have been created between the images to minimize this semantic gap. However, no successful algorithm for extracting images from the massive database has been proposed. As a result, retrieving data from a huge database array has become more difficult till now. So, in order to address these issues, a dot diffused block truncation coding (DDBTC) with meta-heuristic optimization named binary particle swarm optimization (BPSO) is introduced in this paper. Moreover, the novel optimization based DDBTC method to solve the proposed CBIR optimization task. The optimized DDBTC is utilized to develop the image feature descriptor such as Colour Histogram Features (CHF), Colour Co-occurrence Features (CCF), Bit Histogram Feature (BHF), and Bit Pattern Feature (BPF). Besides, to proficiently solve the dictionary learning issues in the compressed domain Greedy-DCNN based Dictionary Learning algorithm is introduced. The Euclidean distance metric measures the similarity among two images. The proposed scheme is implemented, and performance measures such as precision, accuracy, and recall are utilized to evaluate performance. The performance is compared to various feature vectors, and the introduced scheme's run time/retrieval time is compared to existing BTC methods. It shows the introduced scheme achieved better retrieval time than the ADBTC scheme. The accuracy achieved by the proposed approach for three different datasets are found to be 98% (corel 1 k dataset), 99% (corel 10 k dataset), and 96% (Caltech dataset).
Accessing the availability of inverse scope in German in the covered box paradigm

This paper presents the results of a novel experimental approach to relative quantifier scope in German that elicits data in an indirect manner. Applying the covered-box method (Huang et al. 2013) to scope phenomena, we show that inverse scope is available to some extent in the free constituent order language German, thereby validating earlier findings on other syntactic configurations in German (Radó &amp;amp; Bott 2018) and empirical claims on other free constituent order languages (Japanese, Russian, Hindi), as well as recent corpus findings in Webelhuth (2020). Moreover, the results of the indirect covered-box experiment replicate findings from an earlier direct-query experiment with comparable target items, in which participants were asked directly about the availability of surface scope and inverse scope readings. The configuration of interest consisted of canonical transitive clauses with deaccented existential subject and universal object QPs, in which the restriction of the universal QP was controlled for by the context.
Migrating Face Swap to Mobile Devices: A Lightweight Framework and a Supervised Training Solution

Existing face swap methods rely heavily on large-scale networks for adequate capacity to generate visually plausible results, which inhibits its applications on resource-constraint platforms. In this work, we propose MobileFSGAN, a novel lightweight GAN for face swap that can run on mobile devices with much fewer parameters while achieving competitive performance. A lightweight encoder-decoder structure is designed especially for image synthesis tasks, which is only 10.2MB and can run on mobile devices at a real-time speed. To tackle the unstability of training such a small network, we construct the FSTriplets dataset utilizing facial attribute editing techniques. FSTriplets provides source-target-result training triplets, yielding pixel-level labels thus for the first time making the training process supervised. We also designed multi-scale gradient losses for efficient back-propagation, resulting in faster and better convergence. Experimental results show that our model reaches comparable performance towards state-of-the-art methods, while significantly reducing the number of network parameters. Codes and the dataset have been released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://githuh.com/HoiM/MobileFSGAN.
Stable Gaze Tracking with Filtering Based on Internet of Things

Gaze tracking is basic research in the era of the Internet of Things. This study attempts to improve the performance of gaze tracking in an active infrared source gaze-tracking system. Owing to unavoidable noise interference, the estimated points of regard (PORs) tend to fluctuate within a certain range. To reduce the fluctuation range and obtain more stable results, we introduced a Kalman filter (KF) to filter the gaze parameters. Considering that the effect of filtering is relevant to the motion state of the gaze, we design the measurement noise that varies with the speed of the gaze. In addition, we used a correlation filter-based tracking method to quickly locate the pupil, instead of the detection method. Experiments indicated that the variance of the estimation error decreased by 73.83%, the size of the extracted pupil image decreased by 93.75%, and the extraction speed increased by 1.84 times. We also comprehensively discussed the advantages and disadvantages of the proposed method, which provides a reference for related research. It must be pointed out that the proposed algorithm can also be adopted in any eye camera-based gaze tracker.
Some Bayesian biclustering methods: Modeling and inference

Abstract Standard one‐way clustering methods form homogeneous groups in a set of objects. Biclustering (or, two‐way clustering) methods simultaneously cluster rows and columns of a rectangular data array in such a way that responses are homogeneous for all row‐cluster by column‐cluster cells. We propose a Bayes methodology for biclustering and corresponding MCMC algorithms. Our method not only identifies homogeneous biclusters, but also provides posterior probabilities that particular instances or features are clustered together. We further extend our proposal to address the biclustering problem under the commonly occurring situation of incomplete datasets. In addition to identifying homogeneous sets of rows and sets of columns, as in the complete data scenario, our approach also generates plausible predictions for missing/unobserved entries in the rectangular data array. Performances of our methodology are illustrated through simulation studies and applications to real datasets.
Paint / Writing Application through WebCam using MediaPipe and OpenCV

Writing, a cohesive form of communication that helps us convey our thoughts as efficiently as possible. But in today&#x0027;s modern world, typing is also considered as a way to record information along with writing. Characters or phrases are written withinside the unfastened area with a marker or finger. With the improvement of clever wearable gadgets, the virtual international can now be controlled with the aid of using human gestures. These wearable gadgets can understand and recognize our actions. Recognizing and interpreting a non-stop sequential gesture to circulate from the given set of input statistics is gesture recognition. Gestures are non-verbal information used to enhance laptop language understanding. Human gestures are perceived with the aid of using imaginative and prescient, and laptop imaginative and prescient is used to research diverse gestures. The project takes advantage of this gap and makes a speciality of growing a motion-to-textual content converter that can potentially serve as a program for clever wearable gadgets for writing in the thin air. The device will use the laptop imaginatively and presciently to hint the finger&#x0027;s path, and in that way, it is easy to write from above. The generated textual content also can be used for different purposes, the same as any different content would serve. It will be a whole new means of communication for the deaf as well. It is a powerful communication method that reduces mobile phone and computer utilization with the aid of disposing of what you want to write.
A Novel Mixed Integer Programming Formulation for Data Perturbation

Data leakage will lead to privacy exposure and cause the loss of data assets and brand reputation. Data perturbation is an efficient approach to overcome the aforementioned challenge, which can protect the privacy by introducing noise into the original data or transforming the raw data. Existing literature perform data perturbation with random perturbation, clustering, generalization, suppression, anatomization, permutation, etc. The results offered by these methods are feasible solutions, whereas the optimality guarantee is not provided. In this work, we propose a novel mixed integer programming (MIP) formulation for the data perturbation problem that aims at minimizing the change of the mean and the variance. The proposed MIP model is able to yield optimal solution with respect to multiple constraints. Since the objective of the proposed formulation incorporates the distribution relevant information, it is of high flexibility and can control the range of the noise. Results reveal that our model outperforms the existing approaches in most of the tested datasets.
UAV Fuel Allocation Method Based on Centroid Balance

Unmanned Aerial Vehicle (UAV) flight control is a concerned issue. In order to maintain the flight stability, the UAV fuel allocation method based on centroid balance is proposed in this paper. Firstly, the position calculation approach of the center of mass of UAV is given under different pitch angles. Secondly, according to the actual conditions, a model that minimizes the offset of the center of mass is presented. To solve this problem, an approximate local optimal method is proposed. Finally, the method is applied to the situations of level flight and non-level flight, and the results show that the method is effective.
Log filtering method based on user behaviors

Abstract In the big data environment, various websites on the Internet have generated more and more user behaviors. Designing a universal log filtering method based on user behaviors is the current research trend. However, the current log filtering technology has disadvantages such as low filtering accuracy and low efficiency. In this paper, we propose a log filtering method based on user behaviors. First, divide user behaviors into multiple sub-behaviors and assign corresponding weights. Obtain and store log information of user behaviors through distributed log collection tools, and the log information of corresponding sub-behaviors below the weight threshold is filtered. Then, the log information of the retained sub-behaviors is processed in parallel through the utility function. The utility function establishes the mapping relationship between user interest degree and sub-behavior indicators. The corresponding log information of the sub-behaviors below the user interest degree threshold is deleted, and the log information of the user’s preferred sub-behaviors is retained, forming an optimized data source for recommendation results, and stored in the data cluster. This method can perform secondary filtering of the massive log information, respond to users’ current requirements and interesting information promptly, improving processing efficiency.
The Prediction Method of KPIs by Using LS-TSVR

Closely monitoring service performance and making predictions of Key Performance Indicators (KPIs) are critical for Internet-based services. However, fast yet accurate prediction of these seasonal KPIs with various patterns and data quality has been a great challenge. This paper tackles this challenge through a novel approach based on auto-regressive Least Square Twin Support Vector Regression (LS-TSVR). As an improved version of SVR, LS-TSVR can handle big data without any external optimization, and meanwhile, the prediction accuracy is better than that of SVR. For seasonal KPI data in a production dataset, our methods satisfy or approximate a mean average error (MAE) of around 0.013, which is significantly lower than the baseline method.
Writer verification of partially damaged handwritten Arabic documents based on individual character shapes

Author verification of handwritten text is required in several application domains and has drawn a lot of attention within the research community due to its importance. Though, several approaches have been proposed for the text-independent writer verification of handwritten text, none of these have addressed the problem domain where author verification is sought based on partially-damaged handwritten documents (e.g., during forensic analysis). In this paper, we propose an approach for offline text-independent writer verification of handwritten Arabic text based on individual character shapes (within the Arabic alphabet). The proposed approach enables writer verification for partially damaged documents where certain handwritten characters can still be extracted from the damaged document. We also provide a mechanism to identify which Arabic characters are more effective during the writer verification process. We have collected a new dataset, Arabic Handwritten Alphabet, Words and Paragraphs Per User (AHAWP), for this purpose in a classroom setting with 82 different users. The dataset consists of 53,199 user-written isolated Arabic characters, 8,144 Arabic words, 10,780 characters extracted from these words. Convolutional neural network (CNN) based models are developed for verification of writers based on individual characters with an accuracy of 94% for isolated character shapes and 90% for extracted character shapes. Our proposed approach provided up to 95% writer verification accuracy for partially damaged documents.
Generating diverse chinese poetry from images via unsupervised method

Automatic poetry generation represents a typical exhibition of artificial intelligence creativity, and the cross-modal generation methods reveal a promising direction for improvement. Although previous methods have made some progress, they still suffer from the following challenges: (1) lack of annotated multimodal Chinese poetry datasets; (2) insufficient diversity of generated poetry; (3) inadequate semantic consistency between images and poems. In this paper, we propose a novel Unsupervised Image to Poetry Model (UI2P) with a newly designed generative adversarial network to address the above issues. Specifically, the unsupervised learning framework eliminates the dependence on annotated multimodal poetry datasets. We present a contrastive learning approach to optimize the diversity of generated poems. Furthermore, a consistency strategy is developed, including constructing a modern-classical concept dictionary to ensure semantic coherence between poems and images. Extensive experiments are conducted on the CCPC dataset, and the results with both automatic and manual evaluations demonstrate the superiority of our model compared with several state-of-the-art baselines.
Preface to the special issue on the 12th Workshop on Programming Language Approaches to Concurrency and Communication-Centric Software (PLACES) 2020

This volume contains the JLAMP special issue on the twelfth edition of the workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software (PLACES 2020). The articles cover topics ranging from the semantics of communicating systems, to static and dynamic analysis techniques for session-based communications, to information flow analysis in heterogeneous organisations.
WhyGen: Explaining ML-powered Code Generation by Referring to Training Examples

Deep learning has demonstrated great abilities in various code generation tasks. However, despite the great convenience for some developers, many are concerned that the code generators may recite or closely mimic copyrighted training data without user awareness, leading to legal and ethical concerns. To ease this problem, we introduce a tool, named WhyGen, to explain the generated code by referring to training examples. Specifically, we first introduce a data structure, named inference fingerprint, to represent the decision process of the model when generating a prediction. The fingerprints of all training examples are collected offline and saved to a database. When the model is used at runtime for code generation, the most relevant training examples can be retrieved by querying the fingerprint database. Our experiments have shown that WhyGen is able to precisely notify the users about possible recitations and highly similar imitations with a top-10 accuracy of 81.21%. The demo video can be found at https://youtu.be/EtoQP6850To.