Reviewerly/exHarmony huggingface dataset:
Channel-wise Distillation for Semantic Segmentation.

Knowledge distillation (KD) has been proven to be a simple and effective tool for training compact models. Almost all KD variants for semantic segmentation align the student and teacher networks' feature maps in the spatial domain, typically by minimizing point-wise and/or pair-wise discrepancy. Observing that in semantic segmentation, some layers' feature activations of each channel tend to encode saliency of scene categories (analogue to class activation mapping), we propose to align features channel-wise between the student and teacher networks. To this end, we first transform the feature map of each channel into a distribution using softmax normalization, and then minimize the Kullback-Leibler (KL) divergence of the corresponding channels of the two networks. By doing so, our method focuses on mimicking the soft distributions of channels between networks. In particular, the KL divergence enables learning to pay more attention to the most salient regions of the channel-wise maps, presumably corresponding to the most useful signals for semantic segmentation. Experiments demonstrate that our channel-wise distillation outperforms almost all existing spatial distillation methods for semantic segmentation considerably, and requires less computational cost during training. We consistently achieve superior performance on three benchmarks with various network structures. Code is available at: this https URL
A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant

Power plants are required to supply the electric demand efficiently, and appropriate failure analysis is necessary for ensuring their reliability. This paper proposes a framework to extend the failure analysis: indeed, the outcomes traditionally carried out through techniques such as the Failure Mode and Effects Analysis (FMEA) are elaborated through data-driven methods. In detail, the Association Rule Mining (ARM) is applied in order to define the relationships among failure modes and related characteristics that are likely to occur concurrently. The Social Network Analysis (SNA) is then used to represent and analyze these relationships. The main novelty of this work is represented by support in the maintenance management process based not only on the traditional failure analysis but also on a data-driven approach. Moreover, the visual representation of the results provides valuable support in terms of comprehension of the context to implement appropriate actions. The proposed approach is applied to the case study of a hydroelectric power plant, using real-life data.
Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification

Person re-identification aims to identify whether pairs of images belong to the same person or not. This problem is challenging due to large differences in camera views, lighting and background. One of the mainstream in learning CNN features is to design loss functions which reinforce both the class separation and intra-class compactness. In this paper, we propose a novel Orthogonal Center Learning method with Subspace Masking for person re-identification. We make the following contributions: 1) we develop a center learning module to learn the class centers by simultaneously reducing the intra-class differences and inter-class correlations by orthogonalization; 2) we introduce a subspace masking mechanism to enhance the generalization of the learned class centers; and 3) we propose to integrate the average pooling and max pooling in a regularizing manner that fully exploits their powers. Extensive experiments show that our proposed method consistently outperforms the state-of-the-art methods on large-scale ReID datasets including Market-1501, DukeMTMC-ReID, CUHK03 and MSMT17.
Class-Incremental Learning With Deep Generative Feature Replay for DNA Methylation-Based Cancer Classification

Developing lifelong learning algorithms are mandatory for computational systems biology.Recently, many studies have shown how to extract biologically relevant information from high-dimensional data to understand the complexity of cancer by taking the benefit of deep learning (DL).Unfortunately, new cancer growing up into the hundred types that make systems difficult to classify them efficiently.In contrast, the current state-of-the-art continual learning (CL) methods are not designed for the dynamic characteristics of high-dimensional data.And data security and privacy are some of the main issues in the biomedical field.This paper addresses three practical challenges for class-incremental learning (Class-IL) such as data privacy, high-dimensionality, and incremental learning problems.To solve this, we propose a novel continual learning approach, called Deep Generative Feature Replay (DGFR), for cancer classification tasks.DGFR consists of an incremental feature selection (IFS) and a scholar network (SN).IFS is used for selecting the most significant CpG sites from high-dimensional data.We investigate different dimensions to find an optimal number of selected CpG sites.SN employs a deep generative model for generating pseudo data without accessing past samples and a neural network classifier for predicting cancer types.We use a variational autoencoder (VAE), which has been successfully applied to this research field in previous works.All networks are sequentially trained on multiple tasks in the Class-IL setting.We evaluated the proposed method on the publicly available DNA methylation data.The experimental results show that the proposed DGFR achieves a significantly superior quality of cancer classification tasks with various state-of-the-art methods in terms of accuracy.
Managing latency in edge–cloud environment

Modern Cyber–physical Systems (CPS) include applications like smart traffic, smart agriculture, smart power grid, etc. Commonly, these systems are distributed and composed of end-user applications and microservices that typically run in the cloud. The connection with the physical world, which is inherent to CPS, brings the need to operate and respond in real-time. As the cloud becomes part of the computation loop, the real-time requirements have to be also reflected by the cloud. In this paper, we present an approach that provides soft real-time guarantees on the response time of services running in cloud and edge–cloud (i.e., cloud geographically close to the end-user), where these services are developed in high-level programming languages. In particular, we elaborate a method that allows us to predict the upper bound of the response time of a service when sharing the same computer with other services. Importantly, as our approach focuses on minimizing the impact on the developer of such services, it does not require any special programming model nor limits usage of common libraries, etc.
Research on Multidomain Authentication of IoT Based on Cross-Chain Technology

Blockchain is an innovated and revolutionized technology, which has attracted wide attention from academia and industry. At present, blockchain has been widely used in certificate management and credential delivery in network access authentication. In a large-scale multidomain Internet of Things (IoT) environment, one of the important issues is cross-domain key sharing and secure data exchange between different IoT. In this paper, aiming at the multidomain authentication requirements of the IoT, this paper introduces the blockchain cross-chain technology into the cross-domain authentication process of the IoT and proposes an effective cross-domain authentication scheme of the IoT based on the improved PBFT algorithm. First, an architecture of blockchain-based cross-domain authentication is proposed. Then, the block data structure is designed in order to enhance the function of access authentication. Third, the authentication process is realized by intelligent contract. The authentication information is encrypted and distributed by a key sharing method to ensure the security of authentication data. Simulation results show that the proposed scheme has significant advantages in security and availability.
Towards Isogeny-Based Password-Authenticated Key Establishment

Abstract Password authenticated key establishment (PAKE) is a cryptographic primitive that allows two parties who share a low-entropy secret (a password) to securely establish cryptographic keys in the absence of public key infrastructure. We propose the first quantum-resistant password-authenticated key exchange scheme based on supersingular elliptic curve isogenies. The scheme is built upon supersingular isogeny Diffie-Hellman [15], and uses the password to generate permutations which obscure the auxiliary points. We include elements of a security proof, and discuss roadblocks to obtaining a proof in the BPR model [1]. We also include some performance results.
Vehicle Pedestrian Detection Method Based on Spatial Pyramid Pooling and Attention Mechanism

Object detection for vehicles and pedestrians is extremely difficult to achieve in autopilot applications for the Internet of vehicles, and it is a task that requires the ability to locate and identify smaller targets even in complex environments. This paper proposes a single-stage object detection network (YOLOv3-promote) for the detection of vehicles and pedestrians in complex environments in cities, which improves on the traditional You Only Look Once version 3 (YOLOv3). First, spatial pyramid pooling is used to fuse local and global features in an image to better enrich the expression ability of the feature map and to more effectively detect targets with large size differences in the image; second, an attention mechanism is added to the feature map to weight each channel, thereby enhancing key features and removing redundant features, which allows for strengthening the ability of the feature network to discriminate between target objects and backgrounds; lastly, the anchor box derived from the K-means clustering algorithm is fitted to the final prediction box to complete the positioning and identification of target vehicles and pedestrians. The experimental results show that the proposed method achieved 91.4 mAP (mean average precision), 83.2 F1 score, and 43.7 frames per second (FPS) on the KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset, and the detection performance was superior to the conventional YOLOv3 algorithm in terms of both accuracy and speed.
Bi-directional CognitiveThinking Network for Machine Reading Comprehension

We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.
Breeding Gender-aware Direct Speech Translation Systems

In automatic speech translation (ST), traditional cascade approaches involving separate transcription and translation steps are giving ground to increasingly competitive and more robust direct solutions.In particular, by translating speech audio data without intermediate transcription, direct ST models are able to leverage and preserve essential information present in the input (e.g.speaker's vocal characteristics) that is otherwise lost in the cascade framework.Although such ability proved to be useful for gender translation, direct ST is nonetheless affected by gender bias just like its cascade counterpart, as well as machine translation and numerous other natural language processing applications.Moreover, direct ST systems that exclusively rely on vocal biometric features as a gender cue can be unsuitable and potentially harmful for certain users.Going beyond speech signals, in this paper we compare different approaches to inform direct ST models about the speaker's gender and test their ability to handle gender translation from English into Italian and French.To this aim, we manually annotated large datasets with speakers' gender information and used them for experiments reflecting different possible real-world scenarios.Our results show that gender-aware direct ST solutions can significantly outperform strong -but gender-unaware -direct ST models.In particular, the translation of gender-marked words can increase up to 30 points in accuracy while preserving overall translation quality.
Perspective Camera Model With Refraction Correction for Optical Velocimetry Measurements in Complex Geometries

Camera calibration is among the most challenging aspects of the investigation of fluid flows around complex transparent geometries, due to the optical distortions caused by the refraction of the lines-of-sight at the solid/fluid interfaces.This work presents a camera model which exploits the pinhole-camera approximation and represents the refraction of the lines-of-sight directly via Snell's law.The model is based on the computation of the optical ray distortion in the 3D scene and dewarping of the object points to be projected.The present procedure is shown to offer a faster convergence rate and greater robustness than other similar methods available in the literature.Issues inherent to estimation of the refractive extrinsic and intrinsic parameters are discussed and feasible calibration approaches are proposed.The effects of image noise, volume size of the control point grid and number of cameras on the calibration procedure are analyzed.Finally, an application of the camera model to the 3D optical velocimetry measurements of thermal convection inside a polymethylmethacrylate (PMMA) cylinder immersed in water is presented.A specific calibration procedure is designed for such a challenging experiment where the cylinder interior is not physically accessible and its effectiveness is demonstrated by providing velocity field reconstructions.
Sentiment Analysis and Topic Detection of Mobile Banking Application Review

Understanding user needs and application quality are difficult things in developing an application. Sentiment analysis and topic modeling based on application review can be used to understand the user needs and application quality. This research aimed to determine customer sentiment towards mobile banking applications and what aspects need to be improved or maintained from the application. The data come from application user reviews on Google Play Store, which amounted to 6194 data. Labeling is done manually, generates two main classes namely positive and negative classes. The sentiment analysis process is done using Naive Bayes models. While the topic modeling process is carried out using the LDA algorithm. The results of the experiment were Naive Bayes method has a good level of accuracy, recall, and precision. The highest accuracy, recall, and precision are at the value of k=5, which is 86.762% accuracy, 93.474% for recall, and 92.482% for precision. Based on the LDA algorithm, the most frequent topics in negative classes are related to OTP code delivery constraints, application login problems, and network connection. On the other hand, the most frequent topics in positives classes included ease, simplicity, and helpfulness.
Indonesian Tweets Hate Speech Target Classification using Machine Learning

In recent years, hate speech found in social media is increasing. The increase in the number of hate speech is caused by the increasing number of social media active users around the world. A lot of hate speech is aimed at governments or certain individuals. Hate speech is very harmful because it may affect the target negatively, whether the target is individuals or groups. Identification of targets in hate speech is crucial as it can be used to prevent the impact of hate speech such as exclusion, discrimination, and violence directed to the target in the hate speech. In this paper, we present our study in hate speech target classification in Indonesian Twitter. We studied hate speech target classification on Indonesian Twitter by comparing the classification performance based on the algorithms and feature representations used. Word n-grams were used as the feature representation combine with Bag-of-Words and Term Frequency - Inverse Document Frequency (TF-IDF). The classification was performed using Naive Bayes, Support Vector Machine (SVM), and Random Forest Decision Tree (RFDT). The best result achieved F1-score of 0.84772 when using TF-IDF with word unigram features combine with SVM classifier.
Efficient Feature Mapping in Classifying Proportional Data

In image classification, traditional kernels or feature mapping functions of Support Vector Machine(SVM) use discriminative features without considering the true nature of the data.Our work in this paper is motivated by the need to consider intrinsic distribution of L1 normalized histograms and develop a flexible feature mapping technique by combining histogram based features and distribution based density features.The proposed mapping technique contains prior knowledge about the the data which provides a flexible representation and thus increases the discriminative power of the classifier.Such flexibility is achieved due to the explanatory capabilities of Dirichlet, generalized Dirichlet and Beta-Liouville distributions to model proportional data.In addition to that, we present a general framework to estimate the parameters of these distributions by taking maximum likelihood (MLE) approach.Experimental results show that the proposed technique increases the effectiveness of SVM kernels for different computer vision tasks such as natural scene recognition, satellite image classification and human action recognition in videos.
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.
IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection

Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech.It is a frequently used pattern of communication on various platforms such as social media sites, online gaming, product reviews, etc. Sentiment analysis of the monolingual text is a well-studied task.Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style.We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sentiment classes positive, negative, or neutral.The proposed approach shows an improvement in the system performance as compared to the Bi-LSTM based neural classifier.The results present an opportunity to understand various other nuances of code-mixing in the textual data, such as humor-detection, intent classification, etc.
Cross-Channel Graph Convolutional Networks for Skeleton-Based Action Recognition

In recent years, skeleton-based action recognition, graph convolutional networks, have achieved remarkable performance.In these existing works, the features of all nodes in the neighbor set are aggregated into the updated features of the root node, while these features are located in the same feature channel determined by the same 1 × 1 convolution filter.This may not be optimal for capturing the features of spatial dimensions among adjacent vertices effectively.Besides, the effect of feature channels that are independent of the current action on the performance of the model is rarely investigated in existing methods.In this paper, we propose cross-channel graph convolutional networks for skeleton-based action recognition.The features fusion mechanism in our network is cross-channel, i.e, the updated feature of the root node is derived from different feature channels.Because different feature channels come from different 1 × 1 convolution filters, the cross-channel fusion mechanism significantly improves the ability of the model to capture local features among adjacent vertices.Moreover, by introducing a channel attention mechanism to our model, we suppress the influence of feature channels unrelated to action recognition on model performance, which improves the robustness of the model against the feature channels independent of the current action.Extensive experiments on the two large-scale datasets, NTU-RGB+D and Kinetics-Skeleton, demonstrate that the performance of our model exceeds the current mainstream methods.
Improved computer-generated moiré profilometry with flat image calibration

An improved computer-generated moiré profilometry (CGMP) with flat image calibration is proposed. In CGMP, the purification of the AC component plays a decisive role. While a composite grating modulated with both the sinusoidal grating and its background light substitutes for the sinusoidal grating itself, the sinusoidal deformed pattern and flat image can be demodulated from the captured pattern. It is found that the sinusoidal deformed pattern and flat image may deviate, which is caused by ambient light. So flat image calibration is conducted to obtain a purer AC component that can effectively suppress the influence of ambient light and ensure the measurement accuracy, even if spectrum aliasing exists. Experimental results show the feasibility and validity of the proposed method.