Reviewerly/exHarmony huggingface dataset:
Knowledge Distillation Inspired Fine-Tuning Of Tucker Decomposed CNNS and Adversarial Robustness Analysis

The recent works in tensor decomposition of convolutional neural networks have paid little attention to finetuning the decomposed models more effectively. We propose to improve the accuracy as well as the adversarial robustness of decomposed networks over existing noniterative methods by distilling knowledge from the computationally intensive undecomposed (teacher) model to the decomposed (student) model. Through a series of experiments, we demonstrate the effectiveness of knowledge distillation with different loss functions and compare it to the existing fine-tuning strategy of minimizing crossentropy loss with ground truth labels. Finally, we conclude that the student networks obtained by the proposed approach are superior not only in terms of accuracy but also adversarial robustness, which is often compromised in the existing methods.
Event Extraction via Extracting Triggers and Arguments Simultaneously and Matching

In recent years, researchers have proposed various methods to achieve better results on the event extraction task. Among them, the pipelined-based methods first perform event trigger prediction and then identify arguments in separate stages. Therefore, the errors from the trigger identification propagate the argument identification. The joint-based methods generally consider the argument role task as a multi-class classification problem, which undoubtedly reduces efficiency. To address these two shortcomings, in this paper, we propose an effective neural model for event extraction. Specifically, we use a Named Entity Recognition method to label arguments while extracting event triggers. As a result, the labels contain arguments and their role information. If the arguments are assigned to event triggers, we assume that there are inter-dependencies between them. Inspired by their inter-dependencies, we then assign arguments to event triggers by matching. This matching operation greatly improves efficiency as its essence is the binary classification. The experimental results on Chinese Emergency Corpus show that our model is more effective and competitive than baselines.
Recognizing human actions with multiple Fourier transforms

In this paper we present an approach for action recognition that uses various applications of Fourier transform. The main idea is to classify video sequences based on action representations obtained using shape descriptors. For shape representation we use the Two-Dimensional Fourier Descriptor, Generic Fourier Descriptor and UNL-Fourier Descriptor. For each sequence of binary silhouettes we derive a set of shape descriptors and match the descriptor of the first frame with the rest of descriptors to obtain a vector of similarities (correlation coefficients or C1 correlations) or dissimilarities (Euclidean distances). Then normalized vectors are transformed into action representations using discrete Fourier transform, power spectral density estimate or a combination of both. Classification is performed using leave-one-out cross-validation and various matching measures. Additionally, we incorporate a coarse classification step that distinguish between actions performed in place and actions with changing location of a silhouette. Extensive experiments are carried out to investigate all possible combinations of selected processing steps, and experimental results are promising.
TRec: Sequential Recommender Based On Latent Item Trend Information

Recommendation system plays an important role in online web applications. Sequential recommender further models user short-term preference through exploiting information from latest user-item interaction history. Most of the sequential recommendation methods neglect the importance of ever-changing item popularity. We propose the model from the intuition that items with most user interactions may be popular in the past but could go out of fashion in recent days. To this end, this paper proposes a novel sequential recommendation approach dubbed TRec, TRec learns item trend information from implicit user interaction history and incorporates item trend information into next item recommendation tasks. Then a self-attention mechanism is used to learn better node representation. Our model is trained via pairwise rank-based optimization. We conduct extensive experiments with seven baseline methods on four benchmark datasets, The empirical result shows our approach outperforms other state-of-the-art methods while maintains a superiorly low runtime cost. Our study demonstrates the importance of item trend information in recommendation system designs, and our method also possesses great efficiency which enables it to be practical in real-world scenarios.
Single Image Super-Resolution with Hierarchical Receptive Field

As a pixel-level prediction task, it's crucial for single image super-resolution (SISR) to capture contextual information over the multi-scale regions in low-resolution (LR) space, which is used to predict the image details in high-resolution (HR) space. So researchers proposed multiple methods to enhance the size of receptive field and take the contextual information of images into account. But most of them tend to increase the depth of networks or the size of kernels simply, which is inefficient and consumes a large amount of computational resources and memory. In this paper, we combine dilated convolutions with standard convolutions and propose hierarchical receptive field network (HRFN) to enlarge receptive field without additional computational complexity. Specially, in each hierarchical receptive field block (HRFB), we apply standard convolutions with different kernel sizes and dilated convolutions with different dilation factors to adaptively obtain multi-scale features. Meanwhile, to ease the training process and make the model focus on the prediction of image details (high-frequency features), the residual learning is adopted locally and globally to explicitly learn and predict the difference between HR images and LR images. Finally, experimental results on five extensively used datasets show that our model outperforms those state-of-the-art methods for both quantitative and qualitative comparisons.
Handling Faults in Service Oriented Computing: A Comprehensive Study

Recently, service-oriented computing paradigms have become a trending development direction, in which software systems are built using a set of loosely coupled services distributed over multiple locations through a service-oriented architecture. Such systems encounter different challenges, as integration, performance, reliability, availability, etc., which made all associated testing activities to be another major challenge to avoid their faults and system failures. Services are considered the substantial element in service-oriented computing. Thus, the quality of services and the service dependability in a web service composition have become essential to manage faults within these software systems. Many studies addressed web service faults from diverse perspectives. In this paper, a comprehensive study is conducted to investigate the different perspectives to manipulate web service faults, including fault tolerance, fault injection, fault prediction and fault localization. An extensive comparison is provided, highlighting the main research gaps, challenges and limitations of each perspective for web services. An analytical discussion is then followed to suggest future research directions that can be adopted to face such obstacles by improving fault handling capabilities for an efficient testing in service-oriented computing systems.
A Study on Influencing Factors of the Application Level of Computer in Chinese for International Students

In this study, we try to find influencing factors on the computer application level of international students who study Chinese in Beijing Language and Culture University. Several independent variables are taken into consideration including the mastery of computer terminology, production and demonstration of PPT slides, speed of Chinese text typing, accuracy of Chinese text typing and Chinese proficiency. Multiple regression analysis and hierarchical regression analysis are adopted to evaluate factors that impact the computer application level. 27 international students studying in the Basic of Computer Application in Beijing Language and Culture University are taken as the research objects. Of all the mentioned factors, the proficiency of computerized terminology has a positive and predictive effect on the computer application level in Chinese.
A Study on Guidance Learning in Graduation Project Design of Design Department Students

This study mainly guidance students' design learning and indeed masters the implementation of graduation project design through the structured teaching process. The method mainly adopts action research and conducts thematic design teaching experiments for ten collage senior. It conducts research in three stages. The first is the early introduction stage, which mainly guidance students to develop learning interest at the first time; the second is the learning development stage, which uses field demonstrations, interactive learning, and cultivation to improve learning willingness; the third stage is for students The spontaneous learning phase provides students with more thematic design dominance to improve students' spontaneous learning effectiveness. The results of the study found that under the topic of design-inducing instruction, eight subjects were able to effectively improve their design expertise and obtain three factors: "Problem-guidance interest", "Self-skill development", and "Spontaneous learning effectiveness". Positive learning results. In addition, it was also found that the two subjects were substandard due to frequent absent discussions and lack of guidance teaching from learning-by-doing. The reason is that the two subjects exhibited influential characteristics such as "Lack of problem temptations", "Cognitive behavioral deviation", and "Passive learning attitude", which deserves the attention of researchers. Therefore, this research result constructs good and complete thematic design teaching principles and improves the current thematic design teaching mode. For teachers, they can effectively grasp the progress of the thematic design, and for students, they can clearly understand the content of the thematic design. The appearance of operation. Finally, the results of this study will present students' learning results through public display inside and outside the school, and provide reference for future research related to academic education.
An Overview of Quantum Algorithms: From Quantum Supremacy to Shor Factorization

Recently, a team of scientists from Google claims to have carried a computation on their noisy, intermediate-scale quantum (NISQ) computer which no regular computer can achieve. A feat that is sometimes referred as quantum supremacy. In the first part of this work, we explain their approach, their randomised circuit construction and the consequences of their work. Having achieved this milestone, the natural question becomes: what else can we do with a quantum computer? We answer this question in the second part of this work and give an overview of the most widely used quantum primitives. We will see how one can implement them using quantum circuits and how these primitive circuits are composed to create fast algorithms capable of solving commercially relevant problems like simulation of complicated quantum systems for chemistry and material science, Monte Carlo simulation, solving large systems of linear equations, or breaking widely used cryptography like RSA.
Multi-modal Perceptual Adversarial Learning for Longitudinal Prediction of Infant MR Images

Longitudinal magnetic resonance imaging (MRI) is essential in neuroimaging studies of early brain development. However, incomplete data is an inevitable problem in longitudinal studies because of participant attrition and scan failure. Data imputation is a possible way to address such missing data. Here, we propose a novel 3D multi-modal perceptual adversarial network (MPGAN) to predict a missing MR image from an existing longitudinal image of the same subject. To the best of our knowledge, this is the first application of deep generative methods for longitudinal image prediction of structural MRI in the first year of life, where brain volume and image intensities are changing dramatically. In order to produce sharper and more realistic images, we incorporate the perceptual loss into the adversarial training process. To leverage complementary information contained in the multi-modality data, MPGAN predicts T1w and T2w images jointly in the prediction process. We evaluated MPGAN versus six alternative approaches based on visual as well as quantitative assessment. The results indicate that our MPGAN predicts missing MR images in an accurate and visually realistic fashion, and shows better performance than the alternative methods.
Learning to Improve Efficiency for Adaptation Paths

The ability of case-based reasoning systems to deal with new problems depends on the effectiveness of their case adaptation. One approach to increasing flexibility for novel problems is to perform adaptations by using adaptation paths—chains of adaptations—to address differences beyond those addressable by applying single adaptation rules. A recent approach to adaptation path generation, ROAD, proposes building adaptation paths using heuristic search guided by similarity, with a “reset” mechanism for recovering when similarity fails to predict adaptability. The ROAD approach is beneficial when similarity and adaptability are well aligned, but can make poor choices when similarity and adaptability diverge, increasing adaptation cost. This paper presents methods for increasing adaptation efficiency by maintenance exploiting information from adaptation path generation. The methods improve the similarity measure to better reflect adaptability and condense the adaptation rule set. Experimental evaluation supports the benefits for improving adaptation efficiency while preserving accuracy.
Ensemble Methods for Solar Power Forecasting

We consider the task of predicting the solar power generated by a photovoltaic system, one-step ahead, from previous half-hourly photovoltaic power data. We propose a range of strategies for constructing static and dynamic heterogeneous ensembles and conduct an extensive evaluation using data for two years from two Australian solar power plants. We analyse the performance of the proposed static and dynamic ensembles and compare them with classical ensemble methods (bagging, boosting and random forest) and a baseline, showing an improved performance. The best result was achieved by the dynamic ensembles DEPast and DEPast+Future, in conjunction with the Peer Check algorithm for selecting base learners for inclusion in the dynamic ensembles.
Image Clustering Using a Growing Neural Gas with Forbidden Regions

Clustering is one of the most common applications of unsupervised learning, being present in many statistical data analysis processes performed by scientists and engineers. Because of their special features, some categories of Artificial Neural Networks have demonstrated to be specially efficient when it comes to clustering. The Growing Neural Gas (GNG) is a good example of these networks, not only because its capability for revealing the clusters underlying in a certain distribution with an optimized number of neurons, but to faithfully describe the topological relations among the different clusters of a dataset. However, because of their intrinsic nature, there will be some data distributions with regions where no data can be found. Aiming to perform a clustering process on these datasets, this paper presents the design of a Growing Neural Gas-inspired model that keeps its neuron prototypes out of a set of regions previously specified, namely Forbidden Region Growing Neural Gas (FRGNG). Experimental results illustrate how this model can represent an alternative, in terms of accuracy, to one of the most recent region avoiding clustering algorithms such as the Forbidden Region Self-Organizing Map (FRSOFM).
Bayesian Stress Testing of Models in a Classification Hierarchy

Building a machine learning solution in real-life applications often involves the decomposition of the problem into multiple models of various complexity. This has advantages in terms of overall performance, better interpretability of the outcomes, and easier model maintenance. In this work we propose a Bayesian framework to model the interaction amongst models in such a hierarchy. We show that the framework can facilitate stress testing of the overall solution, giving more confidence in its expected performance prior to active deployment. Finally, we test the proposed framework on a toy problem and financial fraud detection dataset to demonstrate how it can be applied for any machine learning based solution, regardless of the underlying modelling required.
RegionSparse: Leveraging Sparse Coding and Object Localization to Counter Adversarial Attacks

Although deep neural networks have demonstrated exceptional performance in substantial computer vision tasks, they can be easily confused by carefully generated adversarial examples. Via a novel technique we call activation visualization, the particular characteristics of adversarial examples are analyzed in this paper. Observing that the dominant features of adversarial examples are distributed over a high-dimensional space, we propose a defense framework named RegionSparse that projects the images into a low-dimensional space to remove the influence of the adversarial perturbations on the performance of deep neural networks. In RegionSparse, after training a robust global dictionary, the region where pixels are highly related to classification is firstly located by an object localization mechanism, then the sparse coding is performed on the located object region, together with a perturbation suppression for the remaining region. Extensive experiments on ImageNet dataset for gray-box, black-box, and transferred attacks are performed and the results show that RegionSparse can eliminate up to 90% attacks delivered by strong attacks including Momentum Iterative Fast Gradient Sign Method and Carlini-Wagner's L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> attack.
Detecting Bad Answers in Survey Data Through Unsupervised Machine Learning

Surveys are one of the most common ways of collecting data on individuals. Such data are of great value for economic and social research. However, the quality of the decisions and research results based on survey data depends on the ability to detect and filter out bad answers. The most common source of bad data are the respondents, who might provide imprecise or fabricated answers due to several reasons. In this paper we present a method to sanitize survey data that relies on combining the classification outcomes of three unsupervised machine learning algorithms (DBSCAN, PCA and IForest) aimed at detecting bad answers. Empirical results on real data show that our approach is able to improve the detection of both completely and partially bad answers with respect to the results provided by each algorithm independently.
Reinforcement Learning-Based Layer-Wise Quantization For Lightweight Deep Neural Networks

Network quantization has been widely studied to compress the deep neural network in mobile devices. Conventional methods quantize the network parameters of all layers with the same fixed precision, regardless of the number of parameters in each layer. However, quantizing the weights of the layer with many parameters is more effective in reducing the model size. Accordingly, in this paper, we propose a novel mixed-precision quantization method based on reinforcement learning. Specifically, we utilize the number of parameters at each layer as a prior for our framework. By using the accuracy and the bit-width as a reward, the proposed framework determines the optimal quantization policy for each layer. By applying this policy sequentially, we achieve weighted-average 2.97 bits for the VGG-16 model on the CIFAR-10 dataset with no degradation of the accuracy, compared with its full-precision baseline. We also show that our framework can provide an optimal quantization policy for the VGG-Net and the ResNet to minimize the storage while preserving the accuracy.
ParVis

In this paper, we present ParVis, an interactive visual system for the animated visualization of logged parser trace executions. The system allows a parser implementer to create a visualizer for generated parsers by simply defining a JavaScript module that maps each logged parser instruction into a set of events driving the visual system interface. The result is a set of interacting graphical/text windows that allows users to explore logged parser executions and helps them to have a complete understanding of how the parser behaves during its execution on a given input.
Software Defect Categorization based on Maintenance Effort and Change Impact using Multinomial Naïve Bayes Algorithm

Cost-to-fix software defects increase exponentially as the software moves forward in its life cycle. The fixing of defects found during the maintenance phase is very costly. Software Defect Categorization (SDC) models categorize the software defects into different levels (high, moderate and low) based on various attributes. Such models can aid the software maintenance team to prioritize the defects and plan the resources effectively in fixing them. This study develops SDC models based on three software bug attributes-i) maintenance effort needed to fix a defect ii) change impact on the software for fixing a defect and iii) the combined approach of both- maintenance effort and change impact. In this study, we extracted the important features from the defect reports using text mining and the classification models are developed for each of the attributes using Multinomial Naïve Bayes (NBM) algorithm. The capability of the models is calculated using Area Under the ROC (Receiver Operating Characteristic) curve (AOC). The results of the study show that i) SDC models based on maintenance effort and change impact are capable to categorize the software defects ii) the performance of the SDC models based on the combined approach is better than SDC models based on maintenance effort and change impact.
Deterministic and Efficient Interactive Coding from Hard-to-Decode Tree Codes

The field of Interactive Coding studies how an interactive protocol can be made resilient to channel errors. Even though this field has received abundant attention since Schulman's seminal paper (FOCS 92), constructing interactive coding schemes that are both deterministic and efficient, and at the same time resilient to adversarial errors (with constant information and error rates), remains an elusive open problem. An appealing approach towards resolving this problem is to construct an efficiently encodable and decodable combinatorial object called a tree code (Schulman, STOC 93). After a lot of effort in this direction, the current state of the art has deterministic constructions of tree codes that are efficiently encodable but require an alphabet of size logarithmic (instead of constant) in the depth of the tree code (Cohen, Haeupler, and Schulman, STOC 18). We emphasize that we still lack (even heuristic) candidate constructions that are efficiently decodable. In this work, we show that tree codes that are efficiently encodable, but not efficiently decodable, also imply deterministic and efficient interactive coding schemes that are resilient to adversarial errors. Our result immediately implies a deterministic and efficient interactive coding scheme with a logarithmic alphabet (i.e., 1/ log log rate). We show this result using a novel implementation of hashing through deterministic tree codes that is powerful enough to yield interactive coding schemes.