Reviewerly/exHarmony huggingface dataset:
TFE: Energy-efficient Transferred Filter-based Engine to Compress and Accelerate Convolutional Neural Networks

Although convolutional neural network (CNN) models have greatly enhanced the development of many fields, the untenable number of parameters and computations in these models yield significant performance and energy challenges in hardware implementations. Transferred filter-based methods, as very promising techniques that have not yet been explored in the architecture domain, can substantially compress CNN models. However, their straightforward hardware implementation inherently incurs massive redundant computations, causing significant energy and time consumption. In this work, a highly efficient transferred filter-based engine (TFE) is developed to alleviate this deficiency, with CNN models compressed and accelerated. First, the filters of CNN models are flexibly transferred according to specific tasks to reduce the model size. Then, two hardware-friendly mechanisms are proposed in the TFE to remove duplicate computations caused by transferred filters, which can further accelerate transferred CNN models. The first mechanism exploits the shared weights hidden in each row of transferred filters and reuses the corresponding same partial sums, reducing at least 25% of repetitive computations in each row. The second mechanism can intelligently schedule and access the memory system to reuse the repetitive partial sums among different rows of the transferred filters with at least 25% of computations eliminated. Furthermore, an efficient hardware architecture is proposed in the TFE to fully reap the benefits of the two proposed mechanisms such that different types of networks are flexibly supported. To achieve high energy efficiency, the sub-array-based filter mapping method (SAFM) is proposed, where the process element (PE) subarray is used as the elementary computational unit to support various filters. Therein, input data can be efficiently broadcast in each PE sub-array and the load can be stripped from each PE and intensively alleviated, which can dramatically reduce the area and power consumption. Excluding MobileNet-like networks that adopt depth-wise convolution, most mainstream networks can be compressed and accelerated by the proposed TFE. Two state-of-the-art transferred filter-based methods, i.e., doubly CNN and symmetry CNN are implemented by exploiting the TFE. Compared with Eyeriss, average speedup improvements of 2.93× and 3.17× are achieved in the convolutional layers of various modern CNNs. The overall energy efficiency can be improved by 12.66× and 13.31× on average. Compared with other state-of-the-art related works, the TFE can maximally achieve a parameter reduction of 4.0×, a speedup of 2.72× and an energy efficiency improvement of 10.74× on VGGNet.
Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function

This article contributes to the current statistical theory of deep neural networks (DNNs). It was shown that DNNs are able to circumvent the so-called curse of dimensionality in case that suitable restrictions on the structure of the regression function hold. In most of those results the tuning parameter is the sparsity of the network, which describes the number of non-zero weights in the network. This constraint seemed to be the key factor for the good rate of convergence results. Recently, the assumption was disproved. In particular, it was shown that simple fully connected DNNs can achieve the same rate of convergence. Those fully connected DNNs are based on the unbounded ReLU activation function. In this article we extend the results to smooth activation functions, i.e., to the sigmoid activation function. It is shown that estimators based on fully connected DNNs with sigmoid activation function also achieve the minimax rates of convergence (up to lnn-factors). In our result the number of hidden layers is fixed, the number of neurons per layer tends to infinity for sample size tending to infinity and a bound for the weights in the network is given.
Towards More Reliable Deep Learning-Based Link Adaptation for WiFi 6

The problem of selecting the modulation and coding scheme (MCS) that maximizes the system throughput, known as link adaptation, has been investigated extensively, especially for IEEE 802.11 (WiFi) standards. Recently, deep learning has widely been adopted as an efficient solution to this problem. However, in failure cases, predicting a higher-rate MCS can result in a failed transmission. In this case, a retransmission is required, which largely degrades the system throughput. To address this issue, we model the adaptive modulation and coding (AMC) problem as a multi-label multi-class classification problem. The proposed modeling allows more control over what the model predicts in failure cases. We also design a simple, yet powerful, loss function to reduce the number of retransmissions due to higher-rate MCS classification errors. Since wireless channels change significantly due to the surrounding environment, a huge dataset has been generated to cover all possible propagation conditions. However, to reduce training complexity, we train the CNN model using part of the dataset. The effect of different subdataset selection criteria on the classification accuracy is studied. The proposed model adapts the IEEE 802.11ax communications standard in outdoor scenarios. The simulation results show the proposed loss function reduces up to 50% of retransmissions compared to traditional loss functions.
Wind driven dragonfly algorithm for global optimization

Summary Dragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness. However, the DA algorithm itself also has insufficient solution accuracy and slow convergence speed. The Wind Driven Optimization algorithm (WDO) has the characteristics of fast convergence speed and strong global search capability. So as to improve the optimization performance of the DA algorithm and avoid premature convergence, the speed of the WDO is introduced into the later calculation of the algorithm iteration, which speeds up the convergence speed of the global optimal solution. This paper proposes a dragonfly algorithm based on wind driven (WDDA), that is to reduce the blindness of the dragonfly algorithm search, improve the solution accuracy and convergence speed, to improve the overall optimization performance of the algorithm. The 23 benchmark test functions and one engineering example for optimization and comparison experiments. The experimental results show that WDDA algorithm has better performance in function optimization.
An Improved Early Detection Method of Autism Spectrum Anarchy using Euclidean Method

Autistic Spectrum Disorder (ASD) is one of the mental disorders that affect the acquisition of linguistic, communication, cognitive, and social skills and abilities. Regardless of being determined to have ASD, a few people display extraordinary educational, non-scholastic, and aesthetic capacities, in such cases representing a moving assignment for researchers to give answers. In the most recent couple of years, ASD has been examined by social and computational insight researchers using trend-s etting innovations, for example, AI to improve analytic planning, exactness, and quality. AI procedures, for example, uphold vector machines, choice trees, strategic relapses, and others, these types of strategies have been applied to various and particular datasets identified with a chemical imbalance to develop prescient models. These models guarantee to improve the capacity of clinicians to give strong determinations and visualizations of ASD. Nonetheless, examines concerning the utilization of AI in ASD determination and treatment experiences theoretical, execution, and information issues, for example, how symptomatic codes are utilized, the sort of highlight choice utilized, the assessment estimates picked, and class irregular characteristics in information among others. A more genuine case in ongoing examinations is their advancement of another strategy for ASD analyses dependent on AI. This undertaking of our own fundamentally examinations these ongoing analytical investigations on chemical imbalance, articulating the issues in these examinations as well as suggesting ways forward that improve AI use in ASD concerning conceptualization, execution, and information. Future investigations concerning AI in mental imbalance research are incredibly profited by such a proposition.
Intelligent and Agile Control of Edge Resources for Latency-Sensitive IoT Services

This paper presents an intelligent and agile resource control scheme for a latency-sensitive virtual network function (VNF) of Internet of things directory service (IoT-DS) deployed in a virtualized edge cloud whose computational and networking resources can be adjusted dynamically.The objective of the proposed scheme is to adjust resources dynamically such that the IoT-DS function can resolve IoT queries and provide IoT records within a bounded delay for latency-sensitive services such as automated driving, despite fluctuations in workloads.The proposed scheme leverages multiple regression models for resource demand prediction and dynamic adjustment.These models are trained offline before their deployment with a large training dataset collected from the system operating with simulated workloads.After the deployment, they are updated regularly by online retraining for more accurate performances.We aim to optimize resource allocation to satisfy both the target performance in terms of service latency and resource utilization.The results obtained from an experimental system implementation of the IoT-DS function in Docker containers show that the dynamic adjustment of CPU resources by the proposed scheme with supervised offline training reduces the CPU resource demand by 21.9% and the number of lookup latency requirement violations by 58.2% in comparison with a threshold rule-based conventional algorithm.Moreover, the proposed scheme can offer an agile control of CPU resources within a 1 s interval, which is five times faster than those reported in previous studies.The addition of unsupervised online retraining further reduces CPU resource requirements by 52% and lookup latency requirement violation cases by 62.5% compared with when no adjustments are performed.
Relative Analysis of ML Algorithm QDA, LR and SVM for Credit Card Fraud Detection Dataset

The task is to detect fraudulent online purchases such that consumers of credit card companies are not compensated for purchases they have also not purchased. Several machine learning is techniques were used to identify the fraud detection and accuracy of fraud detections has varied based on the data and model design, In this paper, having been worked on evaluating and pre-processing data sources and perhaps even the development of several feature extraction algorithms such as Quadratic Discriminant Analysis (QDA), Logistic Regression (LR) and Support Vector Machine (SVM).
Asymptotic properties for Markovian dynamics in quantum theory and general probabilistic theories

We address asymptotic decoupling in the context of Markovian quantum dynamics. Asymptotic decoupling is an asymptotic property on a bipartite quantum system, and asserts that any correlation between two quantum systems is broken after a sufficiently long time passes. In this paper, we show that asymptotic decoupling is equivalent to local mixing which asserts the convergence to a unique stationary state on at least one quantum system. If dynamics is asymptotically decoupling, any correlation between two quantum systems is broken exponentially. Also, we give a criterion of mixing that is a system of linear equations. All results in this paper are proved in the framework of general probabilistic theories, but we also summarize them in quantum theory.
Semantic Segmentation for Road Surface Detection in Snowy Environment

Inclement weather conditions such as snow can severely hamper visibility and make it more challenging to recognize the obstacles on the road. This situation is very challenging for drivers to locate the drivable regions on the road. For autonomous driving vehicles, inclement weather conditions such as snow are a severe challenge due to poor visibility, lack of features on the ground, and slippery surfaces on roads. This research attempts to classify the road and obstacle regions on the snowy roads for the application of autonomous driving. The dataset using in this research are based on RGB images from such conditions. It includes different image resolutions that were collected by multiple camera systems such as USB camera, dashcam, and a smartphone camera. A deep learning network employing the structures are proposed in this research. The proposed structure uses the atrous convolution network and pyramid supervision for extracting features from snow road images. The proposed method can reach 86.0% mean ratio of the intersection with a little difference in the processing time. The boundary characterization of the proposed method has higher efficiency than other deep learning networks and also gained the highest classification performance when compared with existing techniques.
Representation learning of writing style

In this paper, we introduce a new method of representation learning that aims to embed documents in a stylometric space.Previous studies in the field of authorship analysis focused on feature engineering techniques in order to represent document styles and to enhance model performance in specific tasks.Instead, we directly embed documents in a stylometric space by relying on a reference set of authors and the intra-author consistency property which is one of two components in our definition of writing style.The main intuition of this paper is that we can define a general stylometric space from a set of reference authors such that, in this space, the coordinates of different documents will be close when the documents are by the same author, and spread away when they are by different authors, even for documents by authors who are not in the set of reference authors.The method we propose allows for the clustering of documents based on stylistic clues reflecting the authorship of documents.For the empirical validation of the method, we train a deep neural network model to predict authors of a large reference dataset consisting of news and blog articles.Albeit the learning process is supervised, it does not require a dedicated labeling of the data but it relies only on the metadata of the articles which are available in huge amounts.We evaluate the model on multiple datasets, on both the authorship clustering and the authorship attribution tasks.
Cross-Project Aging-Related Bug Prediction Based on Joint Distribution Adaptation and Improved Subclass Discriminant Analysis

Software aging, which is caused by Aging-Related Bugs (ARBs), refers to the phenomenon of performance degradation and eventual crash in long running systems. In order to discover and remove ARBs, ARB prediction is proposed. However, due to the low presence and reproducing difficulty of ARBs, it is usually difficult to collect sufficient ARB data within a project. Therefore, cross-project ARB prediction is proposed as a solution to build the target project's ARB predictor by using the labeled data from the source project. A key point for cross-project ARB prediction is to reduce distribution difference between source and target project. However, existing approaches mainly focus on the marginal distribution difference while somehow overlook the conditional distribution difference, and they mainly use random oversampling to alleviate the class imbalance which may lead to overfitting. To address these problems, we propose a new crossproject ARB prediction approach based on Joint Distribution Adaptation (JDA) and Improved Subclass Discriminant Analysis (ISDA), called JDA-ISDA. The key idea of JDA-ISDA is first to use JDA to reduce the marginal distribution and conditional distribution difference jointly and then apply ISDA to alleviate the severe class imbalance problem. A set of experiments are carried out on two large open-source projects with six different machine learning (ML) classifiers. The experimental results demonstrate that compared with the state-of-the-art Transfer Learning based Aging-related bug Prediction (TLAP) and Supervised Representation Learning Approach (SRLA), JDA-ISDA is much more robust to different ML classifiers than TLAP, and the average improvement in terms of the balance value can be achieved up to 31.8%, and JDA-ISDA also outperforms TLAP and SRLA on average when logistic regression is chosen as the classifier for best performance prediction.
Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.
Quantum percolation in quasicrystals using continuous-time quantum walk

We study the percolation of a quantum particle on quasicrystal lattices and compare it with the square lattice. For our study, we have considered quasicrystal lattices modelled on the pentagonally symmetric Penrose tiling and the octagonally symmetric Ammann-Beenker tiling. The dynamics of the quantum particle is modelled using continuous-time quantum walk (CTQW) formalism. We present a comparison of the behaviour of the CTQW on the two aperiodic quasicrystal lattices and the square lattice when all the vertices are connected and when disorder is introduced in the form of disconnections between the vertices. Unlike on a square lattice, we see a significant fraction of quantum state localised around the origin in quasicrystal lattice. With increase in disorder, the percolation probability of a particle on a quasicrystal lattice decreases significantly faster when compared to the square lattice. This study sheds light on the minimum fraction of disconnections allowed to see percolation of quantum particle on these quasicrystal lattices.
Blockchain-Based Participant Selection for Federated Learning

Federated Learning (FL) advocates training a global model using a large-scale of distributed devices such that the collaborative model training can be benefited from the rich local datasets, while preserving the privacy of local training dataset in each participant. This is because only the training results, i.e., the updated model parameters and model weights are needed to report to the FL server for aggregation in each round of FL-training. However, during the model transmission of the original FL protocol, there is no security guarantee towards the training results. Thus, every step during model uploading phase can be attacked by malicious attackers. To address such threat, we propose a new federated learning architecture by taking the advantages of blockchain into account. The proposed architecture includes two-phase design. The first phase is a numerical evaluation, which can prevent the malicious devices from being selected. For the second phase, we devise a participant-selection algorithm that enables the FL server to select the appropriate group of devices for each round of FL-training. We believe that our study can shed new light on the joint research of blockchain and federated learning.
Information extraction framework to build legislation network

This paper concerns an Information Extraction process for building a dynamic Legislation Network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply Information Extraction methodologies by identifying distinct expressions in legal text and extract quality network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques for producing reliable network data-sets with more than 98 percent precision and recall. The values, applications, and the complexity of the created dynamic Legislation Network are also discussed and challenged.
Cross-Organ, Cross-Modality Transfer Learning: Feasibility Study for Segmentation and Classification

We conducted two analyses by comparing the transferability of a traditionally transfer-learned CNN (TL) to that of a CNN fine-tuned with an unrelated set of medical images (mammograms in this study) first and then fine-tuned a second time using TL, which we call the cross-organ, cross-modality transfer learned (XTL) network, on 1) multiple sclerosis (MS) segmentation of brain magnetic resonance (MR) images and 2) tumor malignancy classification of multi-parametric prostate MR images.We used 2133 screening mammograms and two public challenge datasets (longitudinal MS lesion segmentation and ProstateX) as intermediate and target datasets for XTL, respectively.We used two CNN architectures as basis networks for each analysis and fine-tuned it to match the target image types (volumetric) and tasks (segmentation and classification).We evaluated the XTL networks against the traditional TL networks using Dice coefficient and AUC as figure of merits for each analysis, respectively.For the segmentation test, XTL networks outperformed TL networks in terms of Dice coefficient (Dice coefficients of 0.72 vs [0.70 -0.71] with p-value < 0.0001 in differences).For the classification test, XTL networks (AUCs = 0.77 -0.80) outperformed TL networks (AUC = 0.73 -0.75).The difference in the AUCs (AUC diff = 0.045 -0.047) was statistically significant (p-value < 0.03).We showed XTL using mammograms improves the network performance compared to traditional TL, despite the difference in image characteristics (x-ray vs. MRI and 2D vs. 3D) and imaging tasks (classification vs. segmentation for one of the tasks).
Does code review really remove coding convention violations?

Many software developers perceive technical debt as the biggest problems in their projects. They also perceive code reviews as the most important process to increase code quality. As inconsistent coding style is one source of technical debt, it is no surprise that coding convention violations can lead to patch rejection during code review. However, as most research has focused on developer's perception, it is not clear whether code reviews actually prevent the introduction of coding convention violations and the corresponding technical debt.Therefore, we investigated how coding convention violations are introduced, addressed, and removed during code review by developers. To do this, we analysed 16,442 code review requests from four projects of the Eclipse community for the introduction of convention violations. Our result shows that convention violations accumulate as code size increases despite changes being reviewed. We also manually investigated 1,268 code review requests in which convention violations disappear and observed that only a minority of them have been removed because a convention violation has been flagged in a review comment. The investigation results also highlight that one can speed up the code review process by adopting tools for code convention violation detection.