Reviewerly/exHarmony huggingface dataset:
A novel test case prioritization method based on problems of numerical software code statement defect prediction

Test case prioritization (TCP) has been considerably utilized to arrange the implementation order of test cases, which contributes to improve the efficiency and resource allocation of software regression testing. Traditional coverage-based TCP techniques, such as statement-level, method/function-level and class-level, only leverages program code coverage to prioritize test cases without considering the probable distribution of defects. However, software defect data tends to be imbalanced following Pareto principle. Instinctively, the more vulnerable the code covered by the test case is, the higher the priority it is. Besides, statement-level coverage is a more fine-grained method than function-level coverage or class-level coverage, which can more accurately formulate test strategies. Therefore, we present a test case prioritization approach based on statement software defect prediction to tame the limitations of current coverage-based techniques in this paper. Statement metrics in the source code are extracted and data pre-processing is implemented to train the defect predictor. And then the defect detection rate of test cases is calculated by combining the prioritization strategy and prediction results. Finally, the prioritization performance is evaluated in terms of average percentage faults detected in four open source datasets. We comprehensively compare the performance of the proposed method under different prioritization strategies and predictors. The experimental results show it is a promising technique to improve the prevailing coverage-based TCP methods by incorporating statement-level defect-proneness. Moreover, it is also concluded that the performance of the additional strategy is better than that of max and total, and the choice of the defect predictor affects the efficiency of the strategy.
Remotivating inflectional classes

In Titsch, a Walser German dialect spoken in the Aosta Valley, a remodeling of the old strong/weak verb classes found in the rest of the Germanic languages has taken place, whereby verbs belonging to the strong class turn out to display both strong and weak past participles. This outcome results from the reuse or exaptation of the original morphological differentiation based on a purely lexically-governed distribution which has been remotivated by associating the morphological features with specific constructional schemas resulting from several processes of grammaticalization.
Text Summarization Generation Based on Semantic Similarity

In recent years, with the continuous development of deep learning and natural language processing, sequence-to-sequence based model has become the mainstream of abstractive summarization generation, and achieved good results. However, there are some problems of low semantic similarity in the abstracts generated by traditional models. In this work, our goal is to improve semantic relevance between source texts and simplified texts for abstract generation, we propose a text Abstract generation model based on semantic similarity. We use encoder to represent the source document and decoder to generate the summary, and then calculate the semantic similarity between the source document and the generated summary. Experiments show that the generated abstract is more similar to the original text, and more in line with the standards of human writing Abstract.
Groupwise registration with global-local graph shrinkage in atlas construction

Graph-based groupwise registration methods are widely used in atlas construction. Given a group of images, a graph is built whose nodes represent the images, and whose edges represent a geodesic path between two nodes. The distribution of images on an image manifold is explored through edge traversal in a graph. The final atlas is a mean image at the population center of the distribution on the manifold. The procedure of warping all images to the mean image turns to dynamic graph shrinkage in which nodes become closer to each other. Most conventional groupwise registration frameworks construct and shrink a graph without considering the local distribution of images on the dataset manifold and the local structure variations between image pairs. Neglecting the local information fundamentally decrease the accuracy and efficiency when population atlases are built for organs with large inter-subject anatomical variabilities. To overcome the problem, this paper proposes a global-local graph shrinkage approach that can generate accurate atlas. A connected graph is constructed automatically based on global similarities across the images to explore the global distribution. A local image distribution obtained by image clustering is used to simplify the edges of the constructed graph. Subsequently, local image similarities refine the deformation estimated through global image similarity for each image warping along the graph edges. Through the image warping, the overall simplified graph shrinks gradually to yield the atlas with respecting both global and local features. The proposed method is evaluated on 61 synthetic and 20 clinical liver datasets, and the results are compared with those of six state-of-the-art groupwise registration methods. The experimental results show that the proposed method outperforms non-global-local method approaches in terms of accuracy.
Dynamic Clustering with Discrete Time Event Prediction

Discrete time event sequences from web users are commonly encountered in a variety of real-world domains, from digital marketing to recommender systems. Most of the literature in the area focuses on supervised prediction tasks of the user's next action or return time. However, little attention has been paid to the challenging task of modeling user evolution and behavior in a quantitative fashion. For example, progress of the users' knowledge in an online tutorial, user exploration, or engagement. We propose methods to perform a time-varying clustering along with event predictions in a unified domain agnostic framework. Our framework can help track the evolution of the users as they interact with the platform. We evaluate our methods on three real-world datasets and show that our method performs at par with the supervised baseline on the prediction tasks while providing meaningful clusters.
A Rapid Scalable Embedding and Mapping Model for Recommendation

The recommendation systems based on deep neural networks show high accuracy and precision in advertising prediction and rating prediction tasks, but the performance declines when they encounter sparsity problems. Furthermore, the low scalability framework makes them expand new users or items inefficiently. We propose a Rapid Scalable Embedding and Mapping model for Recommendation (RSEMR) to solve the problems. RSEMR uses the bi-directional mapping structure to build the transformation between the embedding vector and the unified vector space, which can rapidly expand new users or items into the existing domains and achieve a better initial performance of prediction. Two effective mapping structures, including vector elements-based single connection and autoencoder-based fully connection, can be used in RSEMR to capture linear or non-linear mapping functions, respectively. Besides, RSEMR does not require any additional information other than a very small amount of user behavior data, and it deals with the relationships between subjects based on the spatial distance in the unified vector space, which is more adaptable to the scarce attribute labels and sparse interaction behaviors. We select several open datasets for experiments and the results have demonstrated RSEMR's superiority over the mainstream neural network-based recommendation models in sparsity situation, in terms of accuracy, efficiency, and scalability.
Rank-Metric Codes, Generalized Binomial Moments and their Zeta Functions

In this paper we introduce a new class of extremal codes, namely the i-BMD codes. We show that for this family several of the invariants are determined by the parameters of the underlying code. We refine and extend the notion of an i-MRD code and show that the i-BMD codes form a proper subclass of the i-MRD codes. Using the class of i-BMD codes we then obtain a relation between the generalized rank weight enumerator and its corresponding generalized zeta function. We also establish a MacWilliams identity for generalized rank weight distributions.
Context-based learning for autonomous vehicles

This research seeks to prove that Deep Recurrent Q-Network (DRQN) approaches are great options for the control of autonomous vehicles. DRQN algorithms are widely used in video game competitions, but not many studies are available for their use in autonomous vehicles. In this paper, we present a context-based learning approach using DRQN for driverless vehicles. Our experiments demonstrate the effectiveness of using the DRQN algorithm over others.
Class-dependent Pruning of Deep Neural Networks

Today’s deep neural networks require substantial computation resources for their training, storage and inference, which limits their effective use on resource-constrained devices. Many recent research activities explore different options for compressing and optimizing deep models. On the one hand, in many real-world applications we face the data imbalance challenge, i.e., when the number of labeled instances of one class considerably outweighs the number of labeled instances of the other class. On the other hand, applications may pose a class imbalance problem, i.e., higher number of false positives produced when training a model and optimizing its performance may be tolerable, yet the number of false negatives must stay low. The problem originates from the fact that some classes are more important for the application than others, e.g., detection problems in medical and surveillance domains. Motivated by the success of the lottery ticket hypothesis, in this paper we propose an iterative deep model compression technique, which keeps the number of false negatives of the compressed model close to the one of the original model at the price of increasing the number of false positives if necessary. Our experimental evaluation using two benchmark data sets shows that the resulting compressed sub-networks 1) achieve up to 35% lower number of false negatives than the compressed model without class optimization, 2) provide an overall higher AUC-ROC measure compared to conventional Lottery Ticket algorithm and three recent popular pruning methods, and 3) use up to 99% fewer parameters compared to the original network. The code is publicly available <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://github.com/rahimentezari/Sensys-ml2020
Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve

In this paper, we propose a new metric to address the long-standing problem of center bias in saliency evaluation. We first show that distribution-based metrics cannot measure saliency performance across datasets due to ambiguity in the choice of standard deviation, especially for Convolutional Neural Networks. Therefore, our proposed metric is AUC-based because ROC curves are relatively robust to the standard deviation problem. However, this requires sufficient unique values in the saliency prediction to compute AUC scores. Secondly, we propose a global smoothing function for the problem of few value degrees in predicted saliency output. Compared with random noise, our smoothing function can create unique values without losing the existing relative saliency relationship. Finally, we show our proposed AUC-based metric can generate a more directional negative set for evaluation, denoted as Farthest-Neighbor AUC (FN-AUC). Our experiments show FN-AUC can measure spatial biases, central and peripheral, more effectively than S-AUC without penalizing the fixation locations.
Deep Image Quality Assessment Driven Single Image Deblurring

Motion deblurring is a challenging task in computer vision, which aims to recover the sharp image from the blurry one. Recently, deep learning based methods have made a significant improvement in the metric of PSNR due to the optimazation of the Mean Squared Error (MSE) loss function between deblurring results and sharp images. However, PSNR prefers smooth images and fails to evaluate the sharpness of deblurred images. To solve this problem, we firstly propose the deep learning based multi-scale non-reference quality assessment network (Deep DEBLUR-IQA) for assessing deblurred results. Moreover, we propose an efficient deblurring network, which is over 50 times faster than SOTA multi-scale networks. Finally, the combination of our Deep DEBLUR-IQA network and novel single image deblurring network can significantly increase subjective quality while maintaining satisfactory PSNR. Experiments show that the proposed method outperforms state-of-the-art methods, both qualitatively and quantitatively.
Research on the Application of Big Data and Artificial Intelligence Technology in Computer Network Technology

In order to realize the application of big data and artificial intelligence technology in computer network technology, this paper puts forward a novel application method of computer network technology based on big data technology and artificial intelligence technology. This method is based on big data technology and artificial intelligence technology, and can give full play to the data analysis ability of big data technology and the intelligent thinking of artificial intelligence technology. The research results show that this method can fundamentally realize the application of big data and artificial intelligence technology in computer network technology, and bring great convenience to human production and life.
A Tree-Structured Decoder for Image-to-Markup Generation

Recent encoder-decoder approaches typically employ string decoders to convert images into serialized strings for image-to-markup. However,for tree-structured representational markup, stringrepresentations can hardly cope with the structural complexity. In this work, we first showvia a set of toy problems that string decodersstruggle to decode tree structures, especially asstructural complexity increases, we then proposea tree-structured decoder that specifically aimsat generating a tree-structured markup. Our decoders works sequentially, where at each step achild node and its parent node are simultaneouslygenerated to form a sub-tree. This sub-tree is consequently used to construct the final tree structurein a recurrent manner. Key to the success of ourtree decoder is twofold, (i) it strictly respects theparent-child relationship of trees, and (ii) it explicitly outputs trees as oppose to a linear string.Evaluated on both math formula recognition andchemical formula recognition, the proposed treedecoder is shown to greatly outperform strongstring decoder baselines.
The enhancement of security measures in advanced encryption standard using double precision floating point multiplication model

Abstract The multiplication strategy is a trending technology in chip programming model; also, it is used in different applications for different purpose. This research aims to develop the floating point double precision Furer Toom cook fast integer multiplication model in the Advanced Encryption Standard process round to enhance the security. In addition, the cryptography algorithms have been broken due to some harmful attack. So the main focus of this present research is to enhance the security measures in communication channel. Moreover, the proposed model is implemented in Xilinx and simulated in cadence 90 nm technology. By comparing the proposed work with recent existing models, the proposed research has reduced the power consumption as 5331.73 nW, thus 89% of power is reduced by the proposed Furer Toom cook multiplication model. The reduction of power enhanced the performance of multiplication algorithm so, while it is utilized in any application, it could not cause any loss of process. In addition, the developed strategy is process within 563 MHz frequency and utilized 11 250 cells. Thus, the proposed model provides better security for all wireless appliances.
Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge

In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model. However, due to user data privacy concerns as well as storage and communication bandwidth limitations, this data cannot be moved from the device to the data centre for further improvement of the model and subsequent deployment. As such there is a need for increased edge intelligence, where the deployed models can be fine-tuned on the edge, leading to improved accuracy and/or reducing the model's workload as well as its memory and power footprint. In the case of Convolutional Neural Networks (CNNs), both the weights of the network as well as its topology can be tuned to adapt to the data that it processes. This paper provides a first step towards enabling CNN finetuning on an edge device based on structured pruning. It explores the performance gains and costs of doing so and presents an extensible open-source framework that allows the deployment of such approaches on a wide range of network architectures and devices. The results show that on average, data-aware pruning with retraining can provide 10.2pp increased accuracy over a wide range of subsets, networks and pruning levels with a maximum improvement of 42.0pp over pruning and retraining in a manner agnostic to the data being processed by the network.
An Unsupervised Learning Approach to Discontinuity-Preserving Image Registration

Most traditional image registration algorithms aimed at aligning a pair of images impose well-established regularizers to guarantee smoothness of unknown deformation fields. Since these methods assume global smoothness within the image domain, they pose issues for scenarios where local discontinuities are expected, such as the sliding motion between the lungs and the chest wall during the respiratory cycle. Furthermore, an objective function must be optimized for each given pair of images, thus registering multiple sets of images become very time-consuming and scale poorly to higher resolution image volumes. Using recent advances in deep learning, we propose an unsupervised learning-based image registration model. The model is trained over a loss function with a custom regularizer that preserves local discontinuities, while simultaneously respecting the smoothness assumption in homogeneous regions of image volumes. Qualitative and quantitative validations on 3D pairs of lung CT datasets will be presented.
Unawareness and Theorizing in Modern Geology: Two Examples Based on Citation Analysis

Progress in science is significantly influenced by the treatment of information generated by the international research community. A relevant problem is the unawareness of scientists regarding more widely published works and ideas. This problem is illustrated with two examples from geological studies. In the first case, the citation analysis implies that many geologists still use outdated reconstructions regarding eustasy for the Mesozoic–Cenozoic, and important updates are missed. This erroneous practice leads to the accumulation of questionable regional interpretations. In the second case, it is found that studies in which the end-Pleistocene extraterrestrial impact hypothesis was first proposed are cited more prolifically than contrary studies using arguments against this hypothesis.A kind of ‘abandonment’ of this still debatable but potentially important hypothesis also is found. The root cause behind such a patterns of unawareness by the research community is explained by insufficient attention being paid by today’s geologists to critical literature reviewing, the rare use of bibliometric approaches, and, more generally, limited theorizing (especially in comparison to social sciences). A shift to full-scale theoretical geology is proposed, which would also help to minimize any negative consequences brought on by unawareness of a more global information base.
Attentive feature integration network for detecting salient objects in images

Benefiting from the development of convolutional neural networks, salient object detection has yielded a qualitative leap in performance. In recent years, most of deep learning based methods utilize multi-level features and obtain inferred saliency map in a coarse-to-fine manner. However, how to learn and represent powerful features is still a challenge. In this paper, we propose a novel FCN-like approach named attentive feature integration network (AFINet) for pixel-wise salient object detection, which results saliency maps with explicit boundary and uniform highlighted regions. Specifically, it adopts feature enhancement module (FEM) to extract rich and enhanced features from backbone net. A feature discrimination module (FDM) is designed to utilize the predicted saliency map generated by deeper layer to help shallower layer learn useful and discriminative attentive features. Moreover, we introduce the saliency information from deeper layer to the shallower one in saliency prediction module (SPM), which helps shallow side outputs accurately locate salient regions. In addition, we design a saliency fusion module (SFM) to integrate different side outputs for utilizing multi-level features. Finally, a fully connected CRF scheme can be optimally incorporated for obtaining saliency results with a higher accuracy. Both qualitative and quantitative comparisons and evaluations conducted on five publicly benchmark datasets demonstrate that our proposed approach compares favorably against 17 state-of-the-art approaches.
Phronetic leadership style evaluation with a fuzzy logic application

Phronetic Leadership Style Evaluation with a
Proxy Task Learning For Cross-Domain Person Re-Identification

Person re-identification (ReID) has achieved rapid improvement recently. However, exploiting the model in a new scene is always faced with huge performance drop. The cause lies in distribution discrepancy between domains, including both low-level (e.g. image quality) and high-level (e.g. pedestrian attribute) variance. To alleviate the problem of domain shift, we propose a novel framework Proxy Task Learning (PTL), which performs body perception tasks on target-domain images while training source-domain ReID, in a multi-task manner. The backbone is shared between tasks and domains, hence both low- and high-level distributions are deeply aligned. We experimentally verify two proxy tasks, i.e. human parsing and attribute recognition, that prominently enhance generalization of the model. When integrating our method into an existing cross-domain pipeline, we achieve state-of-the-art performance on large-scale benchmarks.