Reviewerly/exHarmony huggingface dataset:
Automated Detection of Oil/Gas Well Sites Detection from Multi-Source High Spatial Resolution Images

With the development of oil/gas production, its adverse impact has drawn much attention. Therefore, automated detecting oil/gas well sites become important. Current research has been focused on detection using sole source RGB images, which was not the common case in remote sensing. In this study, we explored the use of a combination of the Residual Channel Attention Network (RCAN) and the state-of-the-art object detection method, You Only Look Once (YOLO) v4, to detect oil/gas well sites from multi-sensor images. To testify the feasibility of the combination, we selected 18 RapidEye and 15 WorldView images which cover the oil sands area in Alberta, Canada. We applied a pre-trained RCAN to unify the spatial resolution of different images to 2m/pixel. To maximize the feature space, we preserved 5 bands which were available in both images. YOLO v4 was applied on cropped image patches to detect oil/gas well sites. The experiment results showed that using the framework proposed in this study, oil/gas well sites can be localized accurately although the bounding boxes of the sites may not perfectly align with the objects.
Randomly Hiding Secret Data Using I-Blocks and E-Blocks for Image Steganography

Various methods to protect the data over transmission channel like cryptography and steganography are analyzed. Steganography is covered writing. One can hide secret data in image, audio or video called cover. To hide data in covers having different formats, with high embedding payload and still maintaining visual imperceptibility, is a challenging task. Security, robustness and payload of secret data play a major role in deciding the approach for hiding secret data. In proposed approach, the cover image is partitioned into blocks each of 4 × 4 pixels. The blocks are divided into index block called I-blocks and embed blocks called E-blocks. I-blocks are used to hide the index of selected pixels of E-blocks. Embed blocks are the blocks in which to hide the secret information. The approach works fine for different image formats as well as for hiding large text over color cover image with high imperceptions.
Summarization and Simplification of Medical Articles using Natural Language Processing

Health literacy is the ability of a person to read and understand medical text and to use that information to make informed healthcare decisions. Unfortunately, medical articles are difficult to comprehend by common people as they use complex language and domain-specific terms. Improving health literacy is important for empowering communities against emerging threats and the COVID-19 pandemic bears testimony to this statement. One way to improve health literacy is easing access to complex healthcare information by summarising medical texts and simplifying them lexically by translating specific medical terminology to laymen’s terms. In this paper we propose a system that performs extractive summarization on the medical article given as input followed by named entity recognition for identifying medical terms. The meanings of identified medical entities are then found through web scraping and displayed to the user along with the summary. We have experimented with state-of-the-art summarization models and Albert (A lite BERT) has provided the best ROUGE-1 score of 0.3789 and ROUGE-L of 0.2084.
Target Detection Algorithm Based on Feature Optimization and Sample Equalization

In the field of intelligent security, due to the negative effect of complicated influence factors, such as low video quality, different size of the target and occlusion, target detection is hard to be well-applicated in real life. Based on the above problems, this paper proposes a multi-layer feature cascade aggregation pyramid network (MCA-FPN) on the basis of Faster RCNN, which can fully combine the different level of semantic information to generate optimized feature maps and improve the expression ability of different size of features finally. Besides, to remove the negative effect from the imbalance distribution of samples, this paper discusses a new sample balanced weighted loss function SB-Loss to increase convergence speed and make the training process more efficiency. Finally, the method proposed by this paper has been experimented on the Pascal VOC dataset, with a maximum accuracy of 86.0%, which is highly competitive in this research area.
Learning Seq2Seq Model with Dynamic Schema Linking for NL2SQL

NL2SQL (Natural Language to SQL) is a cutting-edge problem in the field of semantic parsing and TableQA. “CCKS2022: Financial NL2SQL Evaluation” raises a challenging scenario for building NL2SQL systems in the financial domain. To deal with the problem of small-scale data and the requirement of adapting to financial scenarios, we propose an NL2SQL approach to automatically converts natural language questions into SQL queries to achieve accurate table question answering. We use cross-validation and schema linking method that fuses table-column-value information to make full use of all the training data. Then we train a Transformer-based Seq2Seq semantic parsing model with T5 as pre-training model to understand common questions in the financial field and parse the database’s tables, attributes, foreign keys and other complex relationships, and finally generate SQL queries. Experiments are conducted to test the effectiveness of our strategy. Our best ensemble model achieves EM score of 0.287 on testing set and ranks 2nd in the competition.
Benchmark for Arbitrary-Oriented SAR Ship Detection

A growing number of researchers have begun to use arbitrary-oriented detectors to detect ships. Because in the remote sensing dataset, ships are shown to be narrow and dense. Most of the deep learning methods used in synthetic aperture radar (SAR) ship detection are the same as or variants of those of optical remote sensing. However, the experimental settings of different papers are different. Therefore, various arbitrary-oriented target detectors can not be compared fairly on the SAR dataset. To solve this problem, we developed an arbitrary-oriented SAR ship detection benchmark, which provides strong baselines and state-of-the-art methods in rotation detection. All benchmark methods are tested on RSSDD datasets, and the code is publicly released at https://github.com/open-mmlab/mmrotate. Meanwhile, this paper further explores the role of ImageNet pretrained weight in SAR target detection and tries to train the SAR pretrained weight through unsupervised learning.
Competency Model of College Students’ Innovation and Entrepreneurship from the Perspective of Deep Learning

In order to avoid problems in the process of innovation and entrepreneurship, a competency model is created to help further development. Therefore, this paper constructs and designs the college students’ innovation and entrepreneurship competency model from the perspective of deep learning. Set the goal of creating competency model under deep learning, build the competency model matrix under deep learning, and SDNN algorithm realizes the construction of competency model. The test results show that the normalized completion ratio of the designed deep learning competency application model test group is relatively high, indicating that the effect of this innovation and entrepreneurship competency model is relatively good, stable and comprehensive, and has practical application significance.
Parallel Spectral Clustering with FEAST Library

Spectral clustering is one of the most relevant unsupervised learning methods capable of classifying data without any a priori information. At the heart of this method is the computation of the dominant eigenvectors of an affinity matrix in order to work on a low-dimensional data space in which the clustering is made. We propose in this paper a study of the integration of the FEAST library to compute these eigenvectors in our parallel spectral clustering method by domain decomposition. We also show that this library allows to add a second level of parallelism in addition to the domain decomposition level.
Context-Aware Network for Person Search

The key to effective person search is aiming to localize the pedestrians and obtain the discriminative embeddings representation for person ReID from numerous surveillance scene images. And the existing one-step anchor-free methods can achieve a trade-off between speed and accuracy, but it can not fully exploit the contextual feature information of search context, resulting in undesirable localization. To alleviate this issue, we propose a Context-Aware Network for Person Search (CANPS) to delve into the high-level contextual information. In CANPS, firstly, context encoder is proposed to bridge the gap between the feature maps, achieved by distributing rich contextual information to prediction head layers. Second, we design the malleable center sampling strategy to reasonably expose sample region and focus on the centroid feature representations. What’s more, we design above components in a trainable bag-of-freebies manner, so that real-time person search can greatly improve the accuracy without increasing extra inference cost. Extensive experiments show that the approach we proposed can outperform current state-of-the-art methods in public CUHK-SYSU datasets.
Using cluster information to improve label propagation

In graph-based semi-supervised learning, using few samples as supervised information is usually not enough for classification tasks, while more labeled samples are often challenging and time-consuming to obtain. In this study, we use the clustering results as prior knowledge to guide the classification process in graph-based learning. At first, we combine density peaks clustering and label propagation algorithm to obtain the cluster information. Subsequently, the cluster information is transformed into a style factor represented by a symmetric nonnegative matrix. At last, the labels of labeled objects are propagated to others using the style factor as the supervised information. We validate our method in real datasets, and the results show that our method has statistically improved the accuracy of classification.
Data-Driven Prediction of Athletes’ Performance Based on Their Social Media Presence

It is well known in the sports industry that the performance of athletes is strongly influenced by physiological and psychological factors. In recent years, many researchers have analysed whether athlete-generated social media content can be used as proxies for such performance factors, with some promising results. In this study, we investigated whether such proxies are useful features for a machine learning model to predict athletes’ performance in subsequent competitions. We extracted millions of tweets that NBA basketball players posted themselves or were tagged in and derived features reflecting players’ mood, social media behaviour, and sleep quality before games. Using these and other social media-unrelated features, we performed statistical tests to examine whether the features significantly improve the accuracy of a random forest model for predicting players’ BPM scores in upcoming games. The results show that, in particular, the number of tweets a player is tagged in prior to a game significantly improves the predictions of the model. Our findings provide insights for practitioners on the effects of social media on athlete performance that can be used prospectively for mental health awareness training and optimisation of pre-game routines.
A Trusted Classification Method of Encrypted Traffic Based on Attention Mechanism

With the continuous expansion and development of the Internet, the network traffic data has grown rapidly. Analyzing network traffic helps network managers plan, optimize, and monitor the network. At present, deep learning technology is widely used in traffic classification. This paper proposes a classification method of encrypted traffic based on the attention mechanism, which uses CNN to learn the classification features of encrypted traffic and then adopts the attention mechanism to adaptively obtain the importance of each feature, which effectively improves the accuracy of the model. Due to the black box characteristics of deep learning, the model lacks interpretability and cannot verify the reliability of the model. Therefore, this paper uses the chi-square test method combined with the SHAP interpretable method to verify the proposed model. The analysis of the experimental results proves that the model proposed in this paper is true and reliable.
E-SPORTS (ELECTRONIC SPORTS) FROM ISLAMIC PERSPECTIVE

E-Sports (Electronic Sports) is a phenomenon that is gaining popularity among the younger generation today. E-sports is an effective platform for developing and training mental and sensorimotor abilities especially in the coordination of human limbs. In the implementation of e-sports activities, there are various negative perceptions in the minds of society despite the fact that there are many advantages and positive effects of e-sports activities. Issues related to e-sports need to be analyzed and evaluated carefully according to various perspectives including the perspective of Islamic law. This study aims to explain the concept of e-sports comprehensively and holistically so that the implementation of e-sports meets its true concept and purpose. The public's negative perception of e-sports needs to be assessed fairly and comprehensively so as not to become an ongoing polemic. Next this study analyzes e-sports from the perspective of Islamic law and compliance with Islamic standards. Qualitative methods were implemented in this study by using data collection and data analysis methods. The method of data collection was done through library research. The data obtained were analyzed based on inductive, deductive and comparative methods to obtain comprehensive study results. The results of the study found that e-sports is a positive activity to society and has many advantages, while the negative elements found in e-sports need to be identified and refined so that it is beneficial to all.
Detection Capability Analysis for AWACS Based on the Doppler Blind Zone Map

In order to carry out omnidirectional, large deep early warning detection of the enemy`s key attacking areas, multi-AWACS cooperation will be studied based on the Doppler blind zone map. The restricted detection area and Doppler blind area are two main factors that affect the detection ability of AWACS aircraft. Considering the important role of the relative radial velocity inflected on the Doppler detection performance of the AWACS, this paper revealed the reasons and changing rules of the relative radial velocity blind area, which are helpful to improve the efficiency of early warning and control aircraft target detection. This paper proposed the definitions of Doppler blind zone and area blind speed probability, and the detection capability of AWACS under two different patrol routes was analyzed. The analysis process can verify the rationality of the results of early warning aircraft route planning, which can significantly guide for early warning aircraft task planning.
Issues and Challenges in Existing Re-engineering Methodologies of Object Oriented Systems

Software products in use today need to be updated frequently to stay alive in the market and meet the increasing needs of their customers. In general, a software system experience constant modification and extension in its life cycle. Most enhancements are performed based on developers’ knowledge and experience using ad-hoc approaches. Re-engineering a software system is an economical way to provide a much-needed boost to alter the current software. In this paper, researchers examine the existing methodologies in terms of their inherent quality attributes like flexibility, simplicity, and effectiveness. This study also intends to address the limitations that software engineering teams are currently facing when they do not use a specific methodology in executing the re-engineering process.
Bridging Between LegalRuleML and TPTP for Automated Normative Reasoning

LegalRuleML is a comprehensive XML-based representation framework for modeling and exchanging normative rules. The TPTP input and output formats, on the other hand, are general-purpose standards for the interaction with automated reasoning systems. In this paper we provide a bridge between the two communities by (i) defining a logic-pluralistic normative reasoning language based on the TPTP format, (ii) providing a translation scheme between relevant fragments of LegalRuleML and this language, and (iii) proposing a flexible architecture for automated normative reasoning based on this translation. We exemplarily instantiate and demonstrate the approach with three different normative logics.
Usage of Convolutional Neural Network for Deepfake Video Detection with Face-Swapping Technique

Deepfakes have started to become a tool that brings a negative impact on society. There are various approaches that have been implemented in Deepfake Video detection, such as human-centered approach via dynamic prototypes, dynamic face augmentation, and usage of Neural Networks. However, some of the approaches that have been proposed only used several features, such as frame-by-frame detection. This paper will demonstrate the usage of Convolutional Neural Network (CNN) in detecting deepfake videos that are made with the face-swapping method. The aim of the study is to assess the feasibility of multiple combination between CNN architectures and their training dataset to detect deepfake videos made with face-swapping method that is taken from the Celeb-DF dataset. The assessment results in EfficientNetB4, combined with FaceForensics++, become the best model according to its detectability and false positive rate, when compared with other CNN architectures, while Xception trained with DFDC has the most minimum false positive rate, but the least detectability. Several improvements can be made to the research, such as the usage of GAN-based dataset for testing, usage of self-trained model with training dataset, usage of Siamese CNN architecture and comparison between Siamese and non-Siamese CNN architecture.
Computationally Efficient Non-uniform Dehazing Network in Wavelet Domain

Data-driven methods for single image dehazing have achieved considerable results in recent years. However, recent methods lead to huge computational and memory requirements, which limits the application of the model in practical scenarios. Observing that the influence of low frequency in wavelet domain on dehazing is much greater than that of high frequency, we propose a non-uniform dehazing network which spends more computational resources on low frequency deblurring and less on high frequency denoising. Our method deblurs the low-frequency components generated by the wavelet transform through a MD (Multi-scale Dehazing) network with self-attention module, and performs denoising operations on high-frequency components through an ED (Enhance-Denoising) network with low computational cost. At the same time color restoration is done by a CAC (Color Adaptive Correction) network seperately. The extensive experiments on synthetic and real datasets show that our method is only slightly inferior to the state-of-the-art methods and outperforms all other methods in dehazing metrics. But our method requires less than half the computation of the best method. So we achieve a good trade-off in performance and computation.
Localization of Concept Drift: Identifying the Drifting Datapoints

The notion of concept drift refers to the phenomenon that the distribution which is underlying the observed data changes over time. As a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift, to find change points in data streams, or to adjust models in the presence of observed drift, the problem of localizing drift, i.e. identifying it in data space, is yet widely unsolved - in particular from a formal perspective. This problem however is of importance, since it enables an inspection of the most prominent characteristics, e.g. features, where drift manifests itself and can therefore be used to make informed decisions, e.g. efficient updates of the training set of online learning algorithms, and perform precise adjustments of the learning model. In this paper we present a general theoretical framework that reduces drift localization to a supervised machine learning problem. We construct a new method for drift localization thereon and demonstrate the usefulness of our theory and the performance of our algorithm by comparing it to other methods from the literature.
Shaming tweets detection on Twitter using Machine learning Algorithms

Twitter has essential and often unpleasant consequences in everyday life. Users have turned major social networking sites into a platform for disseminating much unnecessary and undesired material. Twitter has become one of the best and most popular little blogging services for sharing random thoughts. The majority of the participants who make comments on a specific occurrence are inclined to disgrace the victim. In this paper, to identify the shameful tweets or comments on twitter are done. Specifically, after identifying shameful tweets, it categorized into five categories: ill-treat, social comparison, Bad judgment, blasphemy, and unpleasant jokes, with each shaming tweet falling into one of these categories. After categorization the shaming user automatically blocked after giving the one message to that shamer. To detect these tweets, the system recommends utilizing machine learning classifiers like Random Forest, Naive Bayes, KNN. The classifier analysis aids in determining the accuracy of each for spotting shaming tweets. These classifiers are for better analysis of tweets.
MRF–PSO: MultiRoot Finding Particle Swarm Optimization Algorithm for Nonlinear Functions

Finding approximations to the zeros or roots of nonlinear functions is a task that is often required in various applications in different fields of science and engineering. Although there are many iterative methods that can be used for finding such roots, they typically need good initial approximations to converge, and a number of them also demand the computation of derivatives, which is not always possible and in addition can be computationally intensive, or even request repeated deflations or many algorithm runs with different initial guesses for finding all roots. Finding roots of systems of nonlinear functions is an even more complex problem. This paper presents a novel variant of the particle swarm optimization algorithm for root finding aiming to surpass these drawbacks. The MultiRoot Finding Particle Swarm Optimization (MRF–PSO) algorithm here proposed uses multiple swarms for exploring the space of solutions and simultaneously find approximations for the different single or multiple roots of a given nonlinear function. An architecture for information sharing, a technique for detecting equal roots within a given tolerance, and an intelligent particle positioning strategy are also suggested. The proposed algorithm was tested with a set of nonlinear functions, and the results obtained were compared with others available in the literature. Results revealed that the MRF–PSO algorithm constitutes an effective approach for root finding and can be certainly exploited in real-world problems from science and engineering.