Reviewerly/exHarmony huggingface dataset:
Comparative Code Structure Analysis using Deep Learning for Performance Prediction

Performance analysis has always been an afterthought during the application development process, focusing on application correctness first. The learning curve of the existing static and dynamic analysis tools are steep, which requires understanding low-level details to interpret the findings for actionable optimizations. Additionally, application performance is a function of a number of unknowns stemming from the application-, runtime-, and interactions between the OS and underlying hardware, making it difficult to model using any deep learning technique, especially without a large labeled dataset. In this paper, we address both of these problems by presenting a large corpus of a labeled dataset for the community and take a comparative analysis approach to mitigate all unknowns except their source code differences between different correct implementations of the same problem. We put the power of deep learning to the test for automatically extracting information from the hierarchical structure of abstract syntax trees to represent source code. This paper aims to assess the feasibility of using purely static information (e.g., abstract syntax tree or AST) of applications to predict performance change based on the change in code structure. This research will enable performance-aware application development since every version of the application will continue to contribute to the corpora, which will enhance the performance of the model. We evaluate several deep learning-based representation learning techniques for source code. Our results show that tree-based Long Short-Term Memory (LSTM) models can leverage source code's hierarchical structure to discover latent representations. Specifically, LSTM-based predictive models built using a single problem and a combination of multiple problems can correctly predict if a source code will perform better or worse up to 84% and 73% of the time, respectively.
Deep Item Response Theory as a Novel Test Theory Based on Deep Learning

Item Response Theory (IRT) evaluates, on the same scale, examinees who take different tests. It requires the linkage of examinees’ ability scores as estimated from different tests. However, the IRT linkage techniques assume independently random sampling of examinees’ abilities from a standard normal distribution. Because of this assumption, the linkage not only requires much labor to design, but it also has no guarantee of optimality. To resolve that shortcoming, this study proposes a novel IRT based on deep learning, Deep-IRT, which requires no assumption of randomly sampled examinees’ abilities from a distribution. Experiment results demonstrate that Deep-IRT estimates examinees’ abilities more accurately than the traditional IRT does. Moreover, Deep-IRT can express actual examinees’ ability distributions flexibly, not merely following the standard normal distribution assumed for traditional IRT. Furthermore, the results show that Deep-IRT more accurately predicts examinee responses to unknown items from the examinee’s own past response histories than IRT does.
A Novel Intermittent Jumping Coupled Map Lattice Based on Multiple Chaotic Maps

Coupled Map Lattice (CML) usually serves as a pseudo-random number generator for encrypting digital images. Based on our analysis, the existing CML-based systems still suffer from problems like limited parameter space and local chaotic behavior. In this paper, we propose a novel intermittent jumping CML system based on multiple chaotic maps. The intermittent jumping mechanism seeks to incorporate the multi-chaos, and to dynamically switch coupling states and coupling relations, varying with spatiotemporal indices. Extensive numerical simulations and comparative studies demonstrate that, compared with the existing CML-based systems, the proposed system has a larger parameter space, better chaotic behavior, and comparable computational complexity. These results highlight the potential of our proposal for deployment into an image cryptosystem.
A compact multi-pattern encoding descriptor for texture classification

Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above problems, this paper proposes a compact multi-pattern encoding descriptor for visual texture classification. First, we develop local extremum patterns and local center pattern to represent the neighborhood intensity changes. Then, we design a compact encoding scheme to encode local maximum, minimum and center patterns into a three-bit binary code, named MMC pattern. Finally, a compact multi-pattern encoding descriptor is proposed by combining the traditional local sign pattern and MMC pattern. Experimental results on five representative texture databases demonstrate that our method achieves the state-of-the-art texture classification performance.
PIN: A Novel Parallel Interactive Network for Spoken Language Understanding

Spoken Language Understanding (SLU) is an essential part of the spoken dialogue system, which typically consists of intent detection (ID) and slot filling (SF) tasks. Recently, recurrent neural networks (RNNs) based methods achieved the state-of-the-art for SLU. It is noted that, in the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied. In addition, few studies attempt to capture the local context information to enhance the performance of SF. Motivated by these findings, in this paper, Parallel Interactive Network (PIN) is proposed to model the mutual guidance between ID and SF. Specifically, given an utterance, a Gaussian self-attentive encoder is introduced to generate the context-aware feature embedding of the utterance which is able to capture local context information. Taking the feature embedding of the utterance, Slot2Intent module and Intent2Slot module are developed to capture the bidirectional information flow for ID and SF tasks. Finally, a cooperation mechanism is constructed to fuse the information obtained from Slot2Intent and Intent2Slot modules to further reduce the prediction bias. The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach, which achieves a competitive result with state-of-the-art models. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.
Towards improving classification power for one-shot object detection

Object detection based on deep learning typically relies on a large number of training data, which may be very labor-consuming to prepare. In this paper, we attempt to tackle the problem by addressing the One-Shot Object Detection (OSOD) task. Given a novel image denoted as the query image whose category label is not included in the training data, OSOD aims to detect objects of the same class in a complex scene denoted as the target image. The performance of recent OSOD methods is much weaker than general object detection. We find that one of the reasons behind this limited performance is that more false positives (i.e., false detections) are generated. Therefore, we argue that it is important to reduce the number of false positives generated in OSOD task to improve performance. To this end, we present a Focus On Classification One-Shot Object Detection (FOC OSOD) network. Specifically, we design the network from two perspectives: (1) how to obtain the effective similarity feature between the query image and target image; (2) how to classify the similarity feature effectively. To solve the above two challenges, firstly, we propose a Classification Feature Deformation-and-Attention (CFDA) module to obtain the high-quality query feature and target feature, so we can further generate effective similarity feature between them. Secondly, we present a Split Iterative Head (SIH) to improve the ability to classify the similarity feature. Extensive experiments on two public datasets (i.e., PASCAL VOC and COCO) demonstrate that the proposed framework achieves superior performance which outperforms other state-of-the-art methods with a considerable margin.
Exploiting personalized calibration and metrics for fairness recommendation

Recommendation systems are used to suggest items that users can be interested in. These systems are based on the user preference historic to create a recommendation list with items that have the higher similarity with the user's interests, in order to achieve the best possible user's satisfaction, which is usually measured as recommendation precision. However, the search for the best precision can cause some side effects such as overspecialization, few diversity and miscalibration of genres, classes and niches. Calibration provides fairer recommendations, which respect the genre proportionality on the user's preferences, avoiding overspecialization. This article aims to explore ways to balance the trade-off weight between precision and calibration based on divergence measures, as well as to propose metrics to evaluate the calibration in the suggested list. The proposed system works in a post-processing step and does not depend on a specific recommender algorithm or workflow. For this purpose, we evaluate six recommender algorithms applied in the movie domain, analyzing variations of three fairness measures, two personalized trade-off weights and eleven constant weights. To understand the results we use the precision, the reciprocal rank and two proposed metrics. The results indicate that the trade-off formulation of personalized weights obtains better results when used to compare the recommendation lists using matrix factorization-based approaches on Movielens dataset. In addition, the calibration also impacts the precision and fairness of all considered algorithms used in evaluation.
A Multi-Layer Parallel Hardware Architecture for Homomorphic Computation in Machine Learning

Homomorphic Encryption (HE) allows untrusted parties to process encrypted data without revealing its content. People could encrypt the data locally and send it to the cloud to conduct neural network training or inferencing, which achieves data privacy in AI. However, the combined AI and HE computation could be extremely slow. To deal with it, we propose a multi-level parallel hardware accelerator for homomorphic computations in machine learning. The vectorized Number Theoretic Transform (NTT) unit is designed to form the low-level parallelism, and we apply a Residue Number System (RNS) to form the mid-level parallelism in one polynomial. Finally, a fully pipelined and parallel accelerator for two ciphertext operands is proposed to form the high-level parallelism. To address the core computation (matrix-vector multiplication) in neural networks, our work is designed to support Multiply-Accumulate (MAC) operations natively between ciphertexts. We have analyzed our design on FPGA ZCU102, and experimental results show that it outperforms previous works and achieves over an order of magnitude acceleration than software implementations.
Robust moving object detection based on fusing Atanassov's Intuitionistic 3D Fuzzy Histon Roughness Index and texture features

Background modeling is a crucial step in various computer vision applications such as video surveillance, object tracking, and moving object detection. Classifying image pixels as foreground or background is yet a challenging task particularly in complicated situations such as illumination variations, rippling water, camera jitter, and the presence of fast and slow moving objects. Therefore, for a better detection of the moving objects, the multi-modal nature of a scene in those intricate situations should be modeled by multiple models for each image pixel. To this end, in this article, we improve our previous work by fusing color features and texture features using Choquet fuzzy integral. Thereby, our proposed spatial color features that are described by Atanassov's Intuitionistic 3D Fuzzy Histon Roughness Index are fused by the texture features extracted using a covariance matrix. As handling multi-modal background updating is an arduous task, we also propose a new model updating for tackling various challenges such as model initializing with moving objects, existence of fast and slow moving objects in a scene, and existence of the moving objects that stop for a while. We intensively evaluate our proposed approach on diverse benchmark datasets. Experimental results demonstrate the robustness and supremacy of our proposed approach compared to its previous version and the state-of-the-art algorithms in the field.
Online trajectory recovery from offline handwritten Japanese kanji characters of multiple strokes

We propose a deep neural network-based method to recover dynamic online trajectories from offline handwritten Japanese kanji character images. It is a challenging task since Japanese kanji characters consist of multiple strokes. Our proposed model has three main components: Convolutional Neural Network-based encoder, Long Short-Term Memory Network-based decoder with an attention layer, and Gaussian Mixture Model (GMM). The encoder focuses on feature extraction while the decoder refers to the extracted features and generates time-sequences of GMM parameters. The attention layer is the key component for trajectory recovery. The GMM provides robustness to style variations so that the proposed model does not overfit to training samples. In the experiments, the proposed method is evaluated by both visual verification and handwritten character recognition. This is the first attempt to use online recovered trajectories to help improve offline handwriting recognition performance. Although the visual verification reveals some problems, the recognition experiments demonstrate the effect of trajectory recovery in improving offline handwritten character recognition accuracy when online recognition of the recovered trajectories are combined.
Research on singular value detection method of concrete dam deformation monitoring

Stable and reliable deformation monitoring data of concrete dam is the information basis and powerful guarantee for comprehensively analyzing the evolution law of structural morphology and evaluating the safety of long-term service. Given the characteristics of concrete dam deformation monitoring data such as small amplitude variation between neighbors, complex distribution characteristics, prominent local singular features, and the defect of traditional singular value detection methods, such as the prior hypothesis of the normal distribution, low local singular values sensitivity, and poor detection performance, the singular value detection method of concrete dam deformation based on improved local outlier factor (LOF) is established in this paper. Based on LOF that is sensitive to the local singular characteristics, differential expansion theory is applied to improve the adaptability and sensitivity to deformation monitoring data of concrete dam. A scientific and reasonable judgment standard for singular values is built with the help of the principle of small probability meanwhile. The application of engineering examples can draw that the concrete dam deformation singular value detection method based on improved LOF has high detection accuracy and stability, the recall rate of the test result is higher than 90%, the weighted evaluation indicator is higher than 80%, and the detection performance is significantly better than traditional singular value detection methods repeatedly. The application results of engineering examples show that the concrete dam deformation singular value detection method based on improved local outlier factor is highly sensitive to the singular values of concrete dam deformation, can effectively mine the local singular features of data, and has better singular value detection performance and detection efficiency.
Multiagent Reinforcement Learning for Hyperparameter Optimization of Convolutional Neural Networks

Nowadays, deep convolutional neural networks (DCNNs) play a significant role in many application domains, such as computer vision, medical imaging, and image processing. Nonetheless, designing a DCNN, able to defeat the state of the art, is a manual, challenging, and time-consuming task, due to the extremely large design space, as a consequence of a large number of layers and their corresponding hyperparameters. In this work, we address the challenge of performing hyperparameter optimization of DCNNs through a novel multiagent reinforcement learning (MARL)-based approach, eliminating the human effort. In particular, we adapt <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning and define learning agents per layer to split the design space into independent smaller design subspaces such that each agent fine tunes the hyperparameters of the assigned layer concerning a global reward. Moreover, we provide a novel formation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -tables along with a new update rule that facilitates agents’ communication. Our MARL-based approach is data driven and able to consider an arbitrary set of design objectives and constraints. We apply our MARL-based solution to different well-known DCNNs, including GoogLeNet, VGG, and U-Net, and various datasets for image classification and semantic segmentation. Our results have shown that compared to the original CNNs, the MARL-based approach can reduce the model size, training time, and inference time by up to, respectively, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$83\times $ </tex-math></inline-formula> , 52%, and 54% without any degradation in accuracy. Moreover, our approach is very competitive to state-of-the-art neural architecture search methods in terms of the designed CNN accuracy and its number of parameters while significantly reducing the optimization cost.
Personalizing 3D virtual fashion stores: an action research approach to modularity development

Purpose The authors conducted an action research study with the aim of understanding current commercial offerings in modular designs in virtual environments and to explore modularity development based on consumer input for the purpose of personalizing three-dimensional (3D) virtual fashion stores. Design/methodology/approach Through five phases of diagnosing, action planning, action taking, evaluating and specifying learning, the authors attempted to diagnose the current commercial offerings of modular designs in virtual spaces and to identify the right type and the number of modules and modular options for personalizing 3D virtual stores based on consumers' actual designs and focus group input. The authors then further conceptualized modules to serve as an example for developing modularity in 3D virtual reality (VR) stores. Findings In the diagnosing phase, the authors investigated the modularity structure of cocreating a retail store in two popular virtual worlds: Second Life and The Sims 4. In the evaluation phase, the authors identified modules and modular options for personalizing 3D virtual stores based on a content analysis of consumers' post-design focus group discussions. In the last phase (specifying learning), the authors conceptualized a total of nine modules and 38 modular options for personalizing 3D virtual stores, including style, price point, product category, color, presence of avatar, virtual product try-on, music, product recommendation and product customization. Originality/value The significance of this study lies in the pioneering methodological work of identifying, creating and visualizing 3D VR modular store options based on consumer input and in improving the authors’ understanding of current commercial offerings. This study also enriches design theories on cocreation systems. The authors’ suggested modules for personalizing 3D virtual stores could inspire future evidence-based designs to be readily used by VR retailers as well extend the application of mass customization theory from the realm of product development to retail environments.
Cross-scene foreground segmentation with supervised and unsupervised model communication

In this paper1, we investigate cross-scene video foreground segmentation via supervised and unsupervised model communication. Traditional unsupervised background subtraction methods often face the challenging problem of updating the statistical background model online. In contrast, supervised foreground segmentation methods, such as those that are based on deep learning, rely on large amounts of training data, thereby limiting their cross-scene performance. Our method leverages segmented masks from a cross-scene trained deep model (spatio-temporal attention model (STAM), pyramid scene parsing network (PSPNet), or DeepLabV3+) to seed online updates for the statistical background model (CPB), thereby refining the foreground segmentation. More flexible than methods that require scene-specific training and more data-efficient than unsupervised models, our method outperforms state-of-the-art approaches on CDNet2014, WallFlower, and LIMU according to our experimental results. The proposed framework can be integrated into a video surveillance system in a plug-and-play form to realize cross-scene foreground segmentation.
Security Education, Training, and Awareness Programs: Literature Review

Security education, training, and awareness (SETA) is one of the most common and prominent strategies for organizational security governance. However, only a small portion of practitioners claimed that their SETA programs were “very effective”. A possible reason for this is the lack of a systematic understanding of the nature of SETA programs, the paths through which SETA impacts employees’ security-related beliefs or behavioral intentions, and the conditions that might influence such a relationship. This study argues that a comprehensive literature review regarding SETA is vital for holistically investigating the findings of previous SETA research and unveiling the characteristics and factors that influence the effectiveness of SETA. A total of 80 articles, published between 1998 and 2020, were included to conduct an in-depth systematic review on SETA.