Reviewerly/exHarmony huggingface dataset:
Context-Sensitive Fencing

This paper describes context-sensitive fencing (CSF), a microcode-level defense against multiple variants of Spectre. CSF leverages the ability to dynamically alter the decoding of the instruction stream, to seamlessly inject new micro-ops, including fences, only when dynamic conditions indicate they are needed. This enables the processor to protect against the attack, but with minimal impact on the efficacy of key performance features such as speculative execution. This research also examines several alternative fence implementations, and introduces three new types of fences which allow most dynamic reorderings of loads and stores, but in a way that prevents speculative accesses from changing visible cache state. These optimizations reduce the performance overhead of the defense mechanism, compared to state-of-the-art software-based fencing mechanisms by a factor of six.
Software Defect Prediction via Attention-Based Recurrent Neural Network

In order to improve software reliability, software defect prediction is applied to the process of software maintenance to identify potential bugs. Traditional methods of software defect prediction mainly focus on designing static code metrics, which are input into machine learning classifiers to predict defect probabilities of the code. However, the characteristics of these artificial metrics do not contain the syntactic structures and semantic information of programs. Such information is more significant than manual metrics and can provide a more accurate predictive model. In this paper, we propose a framework called defect prediction via attention-based recurrent neural network (DP-ARNN). More specifically, DP-ARNN first parses abstract syntax trees (ASTs) of programs and extracts them as vectors. Then it encodes vectors which are used as inputs of DP-ARNN by dictionary mapping and word embedding. After that, it can automatically learn syntactic and semantic features. Furthermore, it employs the attention mechanism to further generate significant features for accurate defect prediction. To validate our method, we choose seven open-source Java projects in Apache, using F1-measure and area under the curve (AUC) as evaluation criteria. The experimental results show that, in average, DP-ARNN improves the F1-measure by 14% and AUC by 7% compared with the state-of-the-art methods, respectively.
Improvement of self-referenced continuous-variable quantum key distribution with quantum photon catalysis

Quantum photon-catalysis operations can be utilized for improving the performance of continuous-variable quantum key distribution (CVQKD) systems. Motivated by characteristics of quantum photon-catalysis operations that can be implemented by the existing technologies, we consider the performance improvement of self-referenced (SR) CVQKD involving zero-photon catalysis operation. We find that the zero-photon catalysis can be regarded as a noiseless attenuation, and the numerical simulations show that the zero-photon catalysis (ZPC)-based SR-CVQKD scheme outperforms the original SR-CVQKD scheme. In addition, to highlight the advantage of applying zero-photon catalysis operation into SR-CVQKD systems, we make a comparison about the performances between the ZPC-based SR-CVQKD scheme and the previous single-photon subtraction (SPS)-based SR-CVQKD scheme. Numerical simulations show that the ZPC-based SR-CVQKD is superior to the single-photon subtraction case with respect to the transmission distance and the tolerable excess noise. Especially, the ZPC-based SR-CVQKD allows the lower quantum detection efficiency and the higher electronic noise to achieve the same performance. These results show that the proposed protocol is expected to provide theoretical reference for the practical application of SR-CVQKD in metropolitan areas.
A Behavioral Approach to Visual Navigation with Graph Localization Networks

Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps.Our goal is to enable a robot to navigate from one location to another, relying only on its visual observations and the topological map of the environment.To this end, we propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks.Using the Gibson simulator and the Stanford 2D-3D-S dataset, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen indoor environments.
Revisiting Self-Training for Neural Sequence Generation

Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model’s prediction. Self-training has mostly been well-studied to classification problems. However, in complex sequence generation tasks such as machine translation, it is still not clear how self-training woks due to the compositionality of the target space. In this work, we first show that it is not only possible but recommended to apply self-training in sequence generation. Through careful examination of the performance gains, we find that the noise added on the hidden states (e.g. dropout) is critical to the success of self-training, as this acts like a regularizer which forces the model to yield similar predictions for similar inputs from unlabeled data. To further encourage this mechanism, we propose to inject noise to the input space, resulting in a “noisy” version of self-training. Empirical study on standard benchmarks across machine translation and text summarization tasks under different resource settings shows that noisy self-training is able to effectively utilize unlabeled data and improve the baseline performance by large margin.
Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes

In recent years, the supervised learning strategy for real noisy image denoising has been emerging and has achieved promising results. In contrast, realistic noise removal for raw noisy videos is rarely studied due to the lack of noisy-clean pairs for dynamic scenes. Clean video frames for dynamic scenes cannot be captured with a long-exposure shutter or averaging multi-shots as was done for static images. In this paper, we solve this problem by creating motions for controllable objects, such as toys, and capturing each static moment for multiple times to generate clean video frames. In this way, we construct a dataset with 55 groups of noisy-clean videos with ISO values ranging from 1600 to 25600. To our knowledge, this is the first dynamic video dataset with noisy-clean pairs. Correspondingly, we propose a raw video denoising network (RViDeNet) by exploring the temporal, spatial, and channel correlations of video frames. Since the raw video has Bayer patterns, we pack it into four sub-sequences, i.e RGBG sequences, which are denoised by the proposed RViDeNet separately and finally fused into a clean video. In addition, our network not only outputs a raw denoising result, but also the sRGB result by going through an image signal processing (ISP) module, which enables users to generate the sRGB result with their favourite ISPs. Experimental results demonstrate that our method outperforms state-of-the-art video and raw image denoising algorithms on both indoor and outdoor videos.
NILE : Natural Language Inference with Faithful Natural Language Explanations

The recent growth in the popularity and success of deep learning models on NLP classification tasks has accompanied the need for generating some form of natural language explanation of the predicted labels.Such generated natural language (NL) explanations are expected to be faithful, i.e., they should correlate well with the model's internal decision making.In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions?We propose Naturallanguage Inference over Label-specific Explanations (NILE), a novel NLI method which utilizes auto-generated label-specific NL explanations to produce labels along with its faithful explanation.We demonstrate NILE's effectiveness over previously reported methods through automated and human evaluation of the produced labels and explanations.Our evaluation of NILE also supports the claim that accurate systems capable of providing testable explanations of their decisions can be designed.We discuss the faithfulness of NILE's explanations in terms of sensitivity of the decisions to the corresponding explanations.We argue that explicit evaluation of faithfulness, in addition to label and explanation accuracy, is an important step in evaluating model's explanations.Further, we demonstrate that task-specific probes are necessary to establish such sensitivity.
Blockwise Self-Attention for Long Document Understanding

We present BlockBERT, a lightweight and efficient BERT model for better modeling longdistance dependencies.Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short-or long-range contextual information.We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths.BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model.During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.
Catalyzing next-generation Artificial Intelligence through NeuroAI

Abstract Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities – inherited from over 500 million years of evolution – that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.
Bit-Tactical

Weight and activation sparsity can be leveraged in hardware to boost the performance and energy efficiency of Deep Neural Networks during inference. Fully capitalizing on sparsity requires re-scheduling and mapping the execution stream to deliver non-zero weight/activation pairs to multiplier units for maximal utilization and reuse. However, permitting arbitrary value re-scheduling in memory space and in time places a considerable burden on hardware to perform dynamic at-runtime routing and matching of values, and incurs significant energy inefficiencies. Bit-Tactical (TCL) is a neural network accelerator where the responsibility for exploiting weight sparsity is shared between a novel static scheduling middleware, and a co-designed hardware front-end with a lightweight sparse shuffling network comprising two (2- to 8-input) multiplexers per activation input. We empirically motivate two back-end designs chosen to target bit-sparsity in activations, rather than value-sparsity, with two benefits: a) we avoid handling the dynamically sparse whole-value activation stream, and b) we uncover more ineffectual work. TCL outperforms other state-of-the-art accelerators that target sparsity for weights and activations, the dynamic precision requirements of activations, or their bit-level sparsity for a variety of neural networks.
Laconic deep learning inference acceleration

We present a method for transparently identifying ineffectual computations during inference with Deep Learning models. Specifically, by decomposing multiplications down to the bit level, the amount of work needed by multiplications during inference can be potentially reduced by at least 40× across a wide selection of neural networks (8b and 16b). This method produces numerically identical results and does not affect overall accuracy. We present Laconic, a hardware accelerator that implements this approach to boost energy efficiency for inference with Deep Learning Networks. Laconic judiciously gives up some of the work reduction potential to yield a low-cost, simple, and energy efficient design that outperforms other state-of-the-art accelerators: an optimized DaDianNao-like design [13], Eyeriss [15], SCNN [71], Pragmatic [3], and BitFusion [83]. We study 16b, 8b, and 1b/2b fixed-point quantized models.
Adam Deep Learning With SOM for Human Sentiment Classification

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing.
A Survey on True Random Number Generators Based on Chaos

With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.
Optimal Feature Transport for Cross-View Image Geo-Localization

This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep metric learning which aims to learn global feature representations of the scene seen by the two different views. Despite promising results are obtained by such deep metric learning methods, they, however, fail to exploit a crucial cue relevant for localization, namely, the spatial layout of local features. Moreover, little attention is paid to the obvious domain gap (between aerial view and ground view) in the context of cross-view localization. This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. Specifically, we implement the CVFT as network layers, which transports features from one domain to the other, leading to more meaningful feature similarity comparison. Our model is differentiable and can be learned end-to-end. Experiments on large-scale datasets have demonstrated that our method has remarkably boosted the state-of-the-art cross-view localization performance, e.g., on the CVUSA dataset, with significant improvements for top-1 recall from 40.79% to 61.43%, and for top-10 from 76.36% to 90.49%. We expect the key insight of the paper (i.e., explicitly handling domain difference via domain transport) will prove to be useful for other similar problems in computer vision as well.
Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer

Objective: The death rate of breast tumour is falling as there is progress in its research area. However, it is the most common disease among women. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumour. Methods: Implementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs two machine learning (ML) algorithms for the categorization of breast tumour; Decision Tree and K-Nearest Neighbour (KNN) algorithm is used for the breast tumour classification. Result: This classification includes the two levels of disease as benign or malignant. These two machine learning algorithms are verified using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after feature selection using Principal Component Analysis (PCA). The comparison of these two ML algorithms is done using the standard performance metrics. Conclusion: The comparative analysis results indicate that the KNN classifier outperforms the result of the decision-tree classifier in the breast cancer classification.
Opinion mining and emotion recognition applied to learning environments

This paper presents a comparison among several sentiment analysis classifiers using three different techniques – machine learning, deep learning, and an evolutionary approach called EvoMSA – for the classification of educational opinions in an Intelligent Learning Environment called ILE-Java. To make this comparison, we develop two corpora of expressions into the programming languages domain, which reflect the emotional state of students regarding teachers, exams, homework, and academic projects, among others. A corpus called sentiTEXT has polarity (positive and negative) labels, while a corpus called eduSERE has positive and negative learning-centered emotions (engaged, excited, bored, and frustrated) labels. From the experiments carried out with the three techniques, we conclude that the evolutionary algorithm (EvoMSA) generated the best results with an accuracy of 93% for the corpus sentiTEXT, and 84% for the corpus eduSERE.
Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

Cytology is the first pathological examination performed in the diagnosis of lung cancer. In our previous study, we introduced a deep convolutional neural network (DCNN) to automatically classify cytological images as images with benign or malignant features and achieved an accuracy of 81.0%. To further improve the DCNN's performance, it is necessary to train the network using more images. However, it is difficult to acquire cell images which contain a various cytological features with the use of many manual operations with a microscope. Therefore, in this study, we aim to improve the classification accuracy of a DCNN with the use of actual and synthesized cytological images with a generative adversarial network (GAN). Based on the proposed method, patch images were obtained from a microscopy image. Accordingly, these generated many additional similar images using a GAN. In this study, we introduce progressive growing of GANs (PGGAN), which enables the generation of high-resolution images. The use of these images allowed us to pretrain a DCNN. The DCNN was then fine-tuned using actual patch images. To confirm the effectiveness of the proposed method, we first evaluated the quality of the images which were generated by PGGAN and by a conventional deep convolutional GAN. We then evaluated the classification performance of benign and malignant cells, and confirmed that the generated images had characteristics similar to those of the actual images. Accordingly, we determined that the overall classification accuracy of lung cells was 85.3% which was improved by approximately 4.3% compared to a previously conducted study without pretraining using GAN-generated images. Based on these results, we confirmed that our proposed method will be effective for the classification of cytological images in cases at which only limited data are acquired.
Application of personal-oriented digital technology in preventing transmission of COVID-19, China

We reported several personal-oriented and mobile phone-based information technologies which were recently developed and widely used during the outbreak of COVID-19 in China. These technologies help reduce the transmission of COVID-19 and maintain normal social order.
A Moving Shadow Elimination Method Based on Fusion of Multi-Feature

Moving shadow elimination plays an important role in the field of moving object detection. However, the accuracy of shadow elimination is an open question, due to illumination and complex texture. Furthermore, the problem of misclassification of moving object caused by shadow has also become increasingly serious. To address this problem, this paper presents a moving shadow elimination algorithm based on the fusion of multi-feature pattern, which can enhance the accuracy of moving object detection system. In this approach, a dual-channel HSV color space feature and a uniform extended scale invariant local ternary pattern (UESILTP) texture feature are synthesized to elimination shadow. It greatly overcomes the misjudgment of dark object by color feature and the false detection caused by inconspicuous texture characteristics of moving object. Meantime, a method of region growth is adopted to fill the existing cavities in the color space. Finally, qualitative and quantitative comparisons with state-of-the-art methods show that the algorithm is effective.
Instance Guided Proposal Network for Person Search

Person detection networks have been widely used in person search. These detectors discriminate persons from the background and generate proposals of all the persons from a gallery of scene images for each query. However, such a large number of proposals have a negative influence on the following identity matching process because many distractors are involved. In this paper, we propose a new detection network for person search, named Instance Guided Proposal Network (IGPN), which can learn the similarity between query persons and proposals. Thus, we can decrease proposals according to the similarity scores. To incorporate information of the query into the detection network, we introduce the Siamese region proposal network to Faster-RCNN and we propose improved cross-correlation layers to alleviate the imbalance of parameters distribution. Furthermore, we design a local relation block and a global relation branch to leverage the proposal-proposal relations and query-scene relations, respectively. Extensive experiments show that our method improves the person search performance through decreasing proposals and achieves competitive performance on two large person search benchmark datasets, CUHK-SYSU and PRW.