Reviewerly/exHarmony huggingface dataset:
VizAPI: Visualizing Interactions between Java Libraries and Clients

Software projects make use of libraries extensively. Libraries make available intended API surfaces—sets of exposed library interfaces that library developers expect clients to use. However, in practice, clients only use small fractions of intended API surfaces of libraries. We have implemented the VizAPI tool, which shows a visualization that includes both static and dynamic interactions between clients, the libraries they use, and those libraries’ transitive dependencies (all written in Java). We then present some usage scenarios of VizAPI, targetted at library upgrades. One application, by client developers, is to answer a query about upstream code: will their code be affected by breaking changes in library APIs? Or, library developers can use VizAPI to find out about downstream code: which APIs in their source code are commonly used by clients?
CSKE: Commonsense Knowledge Enhanced Text Extension Framework for Text-Based Logical Reasoning

Text-based logical reasoning requires the model to understand the semantics of input text, and then understand the complex logical relationships within the text. Previous works equip pre-trained language models with the logical reasoning ability by training these models on datasets obtained by logical-driven text extension. However, these methods only generate instances based on logical expressions entailed within the input text. And we argue that external commonsense knowledge is still necessary for restoring the complete reasoning chains for generating more reasonable and abundant instances. To address this issue, in this paper, we propose CSKE, a commonsense knowledge enhanced text extension framework. CSKE incorporates abundant commonsense from an external knowledge base to restore the potentially missing logical expressions and encodes more logical relationships to then extend them through logical equivalence laws. Experiments on the benchmark datasets show that our method can improve the performance of logical reasoning, especially on the instances containing complex logical relationships.
EVALUATE THE QUALITY OF PROCESSED IMAGES IN TERMS OF NOISE REDUCTION AND CONTOUR PRESERVATION

Wavelets are an interesting object of study of mathematics and a useful working tool in many applications such as image compression, signal / noise improvement, numerical analysis, in fields of science such as statistics, physics and even geology. For applications such as data compression, signal analysis, noise reduction, statistical estimation or detection, the method of processing wavelet coefficients differs from case to case. Also, the most useful properties of wavelet transformation differ from application to application. In this article we introduce a new filter with local statistics based on wavelet transforms to improve image quality, we introduce a measure for the quantitative evaluation of contour preservation after processing and describe a method for determining the noise dispersion in images.
Who wants to be a Click-Millionaire? On the Influence of Thumbnails and Captions

How to be successful on a video platform with 720K hours of video uploaded daily? This is the key question for every Youtube content creator. Especially, because there is only a thumbnail image and a short caption to attract the users’ attention and convince them to click a video. We introduce the Youtube-Science-Channels dataset containing more than 19k Youtube thumbnails and captions along with meta attributes, such as likes, dislikes and views from 40 channels. A Youtube video’s success is defined as generating lots of views. Given this labeling, a computational model is built in order to predict the success of a video using solely its thumbnail and caption. Experimental results are presented on both, the individual videos as well as aggregated per Youtube channel. Our combined vision and natural language model can accurately predict success. In addition, we analyse the effect of using clickbait in video captions. We show that clickbait is positively correlated with a Youtube channel’s success but that other factors such as high quality animations are important as well.
Improving Group Search Optimization for Automatic Data Clustering Using Merge and Split Operators

The amount of digital data produced daily has increased considerably in the last years. The need for fast and reliable information in real-world applications demands ever more precise algorithms and Data Mining tools, once most of the systems in our daily lives are executed in real-time. Data clustering is one of the most important and primitive activities in Unsupervised Machine Learning, consisting in a fundamental mechanism for exploratory data analysis. Given the complexity of data clustering task, standard clustering methods, such as the partitional algorithms, are easily trapped in local optima solutions, due to their lack of good global searching operators. In this work, three improved Group Search Optimization-based approaches are proposed, based on merge and split heuristics, in the context of Automatic Clustering Analysis: MGSO, SGSO and MSGSO. Group Search Optimization (GSO) is a natural-inspired meta-heuristic, known for its good global search abilities, and mechanisms to escape from local optima points from the problem space. The proposed models attempt to perform both cluster optimization and the determination of the best number of clusters for each dataset, overcoming the limitations of traditional partitional clustering algorithms. The proposed GSO-based models are evaluated through a testing bed composed of nine real-world problems, and compared to six state-of-the-art partitional automatic clustering approaches, include standard GSO. The experimental evaluation has been performed considering five clustering metrics, and both empirical and statistical analysis. The results showed that the proposed MGSO, SGSO and MSGSO algorithms are very promising and reliable while tackling clustering problems.
Convolutional Neural Networks with analytically determined Filters

In this paper, we propose a new training algorithm for Convolutional Neural Networks (CNNs) based on well-known training methods for neural networks with random weights. Our algorithm analytically determines the filters of the convolutional layers by solving a least squares problem using the Moore-Penrose generalized inverse. The resulting algorithm does not suffer from convergence issues and the training time is drastically reduced compared to traditional CNN training using gradient descent. We validate our algorithm with several standard datasets (MNIST, FashionMNIST and CIFAR10) and show that CNNs trained with our method outperform previous approaches with random or unsupervisedly learned filters in terms of test pre-diction accuracy. Moreover, our approach is up to 25 times faster than training CNNs with equivalent architecture using a gradient-descent based algorithm.
Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.
Online Feature Classification and Clustering for Transformer-based Visual Tracker

Compared with the current booming development of siamese tracking network, online optimization methods for tracking models still update parameters or features in pulses, which is non-real-time and on whole image level. In the past year, similarity measurement components derived from Transformer equiped on Siamese networks have obtained excellent performance in visual tracking task. Leveraging its element-wise attention mechanism, we implement a real-time feature update approach on coarse pixel level. We first construct a classification branch for quality control; and to further reduce the feature amount in online update process, we apply an incremental clustering method to minimize the repetitive contribution of similar features. The proposed method is evaluated on multiple datasets including OTB2015, NfS and GOT-10k. It exceeds the baseline methods on all 3 datasets and achieves competitive performance against the state-of-the-art networks.
Detection of Damaged Insulator Based on Improved Cooratt-yolov5s

Insulators are insulating materials used in the construction of electrical transmission systems. They play vital roles in high-voltage transmission lines. The degree of insulators’ damage is related to the stability of the whole power supply line. Therefore, regular inspection of insulators along transmission lines is necessary. The traditional manual inspection is costly and inefficient. There is a great prospect of replacing manual inspection by unmanned aerial vehicles inspection. To address the problems of complex backgrounds and low damage identification in insulator images as we limited arithmetic power of UAV, this paper proposes an improved Cooratt-yolov5s algorithm model to achieve the rapid detection of damaged insulators, which adds Cooratt-attention module to yolov5s backbone network to strengthen the recognition ability of small damage. In the experiment, compared with the original model, Cooratt-yolov5s model has a stable improvement in mAP index and detection speed, which can accomplish the task of real-time and accurate detection of insulator damage, and has a good reference significance for power companies to improve the traditional inspection methods.
DANCe: Dynamic Adaptive Neuron Coverage for Fuzzing Deep Neural Networks

Deep learning (DL) defines a data-driven paradigm that differs from conventional software. It utilizes training data to construct the internal logic of the deep neural networks. With aggressive development in various security-sensitive domains, deep learning raises safety concerns in academia and industrial community. Numerous researches have shown that even the most advanced deep learning systems have vulnerabilities leading to misbehaviors. Despite the urgent security threat, fuzzing test remains a reasonable way to solve the problem. However, The static parameters design in current mainstream neuron coverage disables the fuzzing work diversely on heterogeneous architectures. To address the above problems, we propose the Dynamic Adaptive Neuron Coverage (DANCe) to model the neuron behavior, which can adapt diverse models with implementing training data. The dynamic adaption mechanism enhances the ability of coverage-based fuzzing to generate adversarial examples as test cases. The proposed model is evaluated on two widely-adopted image datasets and four well-designed deep neural networks. The experimental results show that the DANCe exceeds the SOTA coverage criteria by 7% and 33% on generating adversarial examples within 1 hour and 6 hours of time limitation.
Comparison of Accuracy and Time Of Naïve Bayes Algorithm with Support Vector Machine Algorithm in Twitter Sentiment Analysis of Peduli Lindungi Application

The Peduli Protect application is a government program to monitor and cope with the spike in Covid-19 in Indonesia. However, this application also reaps controversy in the community so that it invites various opinions among the community, one of which is on Twitter. Twitter has become the most popular web and smartphone-based media with more than 200 million users and more than 10.6 billion tweets that have been generated. This information can be used to analyze public opinion and opinion on the Peduli Protect application through sentiment analysis using a classification of income into two classes, namely positive and negative. Sentiment analysis is needed to find out the general assessment of an object. Support vector machine algorithm defines a good hyperplane that gives the SVM algorithm a good accuracy level compared to other algorithms. In contrast, the Naive Bayes algorithm is a machine learning classification algorithm with probability reasoning that is not inferior to other algorithms. From the results of this study, the accuracy shows that the Naïve Bayes method is superior by 80% without validation testing using K - Fold Cross-Validation and 85% with K-Fold Cross Validation on the fold-3. While the calculation of Naïve Bayes processing time is also superior by the getting time of 0.009365 seconds, the Support Vector Machine algorithm is tested for K-Fold-Cross Validation getting 86% in the 3rd iteration.
Context Alignment Network for Video Moment Retrieval

Video Moment Retrieval (VMR) is a challenging cross-modal retrieval task that aims to retrieve the most relevant moment from an untrimmed video via a given language query. In this task, cross-modal semantics should be thoroughly comprehended and supervisory signal of limited annotations should be efficiently mined. Toward this end, we develop a Context Alignment Network (CAN) to tackle VMR by modeling and aligning cross-modal contexts. First, we employ fine-grained fusion to preserve rich low-level information and conduct complementary local-global context modeling to translate low-level information into high-level semantics. Second, we propose a novel context alignment learning to utilize additional context alignment supervision during training. The intuitive motivation is that contextual information around the predicted moment boundaries should be similar to that of the ground truth moment boundaries. Therefore, we define the alignment degree of boundary contexts between video moments as a proxy measure of their temporal overlap. By minimizing the context alignment loss, the model is driven to learn a context-level alignment relationship between moment boundaries. We find context alignment learning is effective to improve the retrieval accuracy by exploiting context alignment as additional supervisory signal. Extensive experiments show that CAN attains competitive performance compared with state-of-the-arts on Charades-STA and TACoS datasets, demonstrating the effectiveness of our proposed method.
Adversarial Attack Detection Based on Example Semantics and Model Activation Features

With the widespread application of deep learning technology, its security issue is also gradually paid attention to. To improve the security and reliability of deep learning technology in practical applications, we focus on the vulnerability of deep neural networks against adversarial attacks and address the problems of existing adversarial example detection algorithms that rely on pre-known attack types, and low detection efficiency, and high detection costs. In this paper, we propose an adversarial attack detection method based on sample semantics and model activation features to provide an effective solution sample semantics for the problems of attack dependence and uninterpretable detection results of detection methods. Firstly, normal examples are input into the deep model to obtain the semantic features and model activation features. Secondly, binary classification datasets are constructed separately to train the binary classifier and then divides by the binary classification detector for we construct the adversarial samples in two parts based on relevance features and model activation features to achieve the detection of adversarial samples. Finally, in the experiments, the detection rate against different attacks under different datasets reached higher than 93.00%. At the same time, the detection algorithm in this paper still maintains high performance in the case of the attacker's known detection algorithm, the average detection rate decreases by no more than 5%.
V2F: Real Time Video Segmentation with Apache Flink

V2F is a distributed video processing system for bounded (i.e., stored) and unbounded (i.e., continuous) or real time video streams. Apache Flink applies a series of operators in a pipeline to transform a video stream into shots. These operators are replicated to work in parallel on Flink-managed computing nodes. The V2F deployment of the standard twin-comparison video segmentation method is more than 7 times faster than its non-parallel (i.e., sequential) implementation.
Recurrent Deformable Fusion for Compressed Video Artifact Reduction

The compressed video inevitably appears in compression artifacts, which seriously affect the Quality of Experience. The state-of-the-art methods employ deformable alignment to gather similar information from multiple neighborhood frames to enhance target frame quality. However, they always align multiple frames to the target frame simultaneously, which brings repetitive and useless information because of multiple and imperfect alignments. In this paper, we propose a recurrent deformable fusion method which considers the alignment quality distortion caused by time distance from the target frame. Specifically, a Deformable Alignment (DA) module aligns each pair of the target frame and an adjacent frame following the time line. At the same time, a Recurrent Fusion (RF) module integrates the current aligned feature with the previous fused feature. After that, the fused features are concatenated along the time line. Then, a Multi-Scale Attention Reconstruction (MSAR) module is proposed to gather useful information from the fused features. Compared with the previous multi-frame alignment approach, our method can avoid obtaining a lot of repetitive and useless information. Experiment results confirm that our method achieves state-of-the-art performance on the standard test sequences.
A Deep Learning-based Unified Solution for Character Recognition

Optical Character Recognition(OCR) has become a crucial area of research due to the vast number of digitized documents to lessen the dependency on paper. One can save time and money on data entry by automatically extracting information off paper and putting it where it needs to go. There has been much research on OCR systems for different languages, but a unified system that is agnostic to language does not exist. In this work, we propose a multi-headed resunet++ based solution that can recognize the low resource languages(Bangla, Assamese, etc.) and performs well on resource-rich languages(such as English, Arabic, etc.). The backbone of the solution, i.e., resunet++, is fundamentally designed for medical image segmentation that is very complex. As the low representative languages are mostly of cursive style and complex in nature, this backbone can help share those higher-level features and pass them to the lower level. Our proposed solution is applied to isolated characters of Bangla, Assamese, and English languages. For Bangla, the segmentation is done by our developed method, and the dataset was pre-segmented for the other two languages. Applying the solution, we achieved a satisfactory performance.
Unsupervised Deep Hyperspectral Inpainting Using a New Mixing Model

In this paper, we propose a deep learning-based hyperspectral inpainting (DeepHyIn). The proposed approach is unsupervised since it only utilizes the observed image for training the network. First, we propose a novel model for hyperspectral inpainting in which the degraded hyperspectral image is a linear mixture of endmembers and degraded abundances. The proposed model is subjected to abundance sum to one and nonnegativity constraints. We further assume that the endmembers are known. Then, we propose an optimization problem to estimate the unknown abundance using an image prior. Inspired by deep image prior, we shift the optimization problem to optimize the parameters of a deep network. The proposed method uses a deep convolutional encoder-decoder architecture as a backbone. Finally, we apply the DeepHyIn to the Samson dataset and evaluate the results. DeepHyIn demonstrates considerable quantitative and qualitative improvements compared with the state-of-the-art. DeepHyIn was implemented in Python (3.9) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/DeepHyIn.
JHTD: An Efficient Joint Scheduling Framework Based on Hypergraph for Task Placement and Data Transfer Across Geographically Distributed Data Centers

As the explosive growth of the data volume, data center is playing a critical role to store and process huge amount of data. Traditional single data center can no longer to adapt into incredibly fast-growing data. Recently, some researches have extended the tasks such data processing to geographically distributed data centers. However, since the joint consideration of task placement and data transfer, it is complex and difficult to design a proper scheduling approach with the goal of minimizing makespan under the constraint of task dependencies, processing capability and network, etc. Therefore, our work proposes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JHTD</i> : an efficient joint scheduling framework based on hypergraph for task placement and data transfer across geographically distributed data centers. Generally, there are two crucial stages in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JHTD</i> . Initially, due to the outstanding of hypergraphs in modeling complex problems, we have leveraged a hypergraph-based model to establish the relationship between tasks, data files, and data centers. Thereafter, a hypergraph-based partition method has been developed for task placement within the first stage. In the second stage, a task reallocation scheme has been devised in terms of each task-to-data dependency. Meanwhile, a data dependency aware transferring scheme has been designed to minimize the makespan. Last, the real-world model China-VO project has been used to conduct a variety of simulation experiments. The results have demonstrated that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JHTD</i> effectively optimizes the problems of task placement and data transfer across geographically distributed data centers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JHTD</i> has been compared with three other state-of-the-art algorithms. The results have demonstrated that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JHTD</i> can reduce the makespan by up to 20.6%. Also, various impacts (data transfer volume and load balancing) have been taken into account to show and discuss the effectiveness of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JHTD</i> .
The Extended Kernel Adaptive Autoregressive-Moving-Average Algorithm

In this paper, we proposed the Extended KAARMA algorithm, which substitutes gradient descent (SGD) by the Extended Kalman (EKF) update equations. Comparing with the stochastic gradient descent method, the EKF method provides a higher rate of convergence. By creating more centers in the memory, it can explore the error space in an efficient manner. Besides, the Extended Kalman method can adjust the learning rate automatically. The decreasing learning rate solves the gradient explosion problem, where a gradient clipping technique is needed in the SGD method instead.