Reviewerly/exHarmony huggingface dataset:
SOLOv2: Dynamic, Faster and Stronger.

In this work, we aim at building a simple, direct, and fast instance segmentation framework with strong performance. We follow the principle of the SOLO method of Wang et al. SOLO: segmenting objects by locations. Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location. Specifically, the mask branch is decoupled into a mask kernel branch and mask feature branch, which are responsible for learning the convolution kernel and the convolved features respectively. Moreover, we propose Matrix NMS (non maximum suppression) to significantly reduce the inference time overhead due to NMS of masks. Our Matrix NMS performs NMS with parallel matrix operations in one shot, and yields better results. We demonstrate a simple direct instance segmentation system, outperforming a few state-of-the-art methods in both speed and accuracy. A light-weight version of SOLOv2 executes at 31.3 FPS and yields 37.1% AP. Moreover, our state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation show the potential to serve as a new strong baseline for many instance-level recognition tasks besides instance segmentation. Code is available at: this https URL
MemLock

Uncontrolled memory consumption is a kind of critical software security weaknesses. It can also become a security-critical vulnerability when attackers can take control of the input to consume a large amount of memory and launch a Denial-of-Service attack. However, detecting such vulnerability is challenging, as the state-of-the-art fuzzing techniques focus on the code coverage but not memory consumption. To this end, we propose a memory usage guided fuzzing technique, named MemLock, to generate the excessive memory consumption inputs and trigger uncontrolled memory consumption bugs. The fuzzing process is guided with memory consumption information so that our approach is general and does not require any domain knowledge. We perform a thorough evaluation for MemLock on 14 widely-used real-world programs. Our experiment results show that MemLock substantially outperforms the state-of-the-art fuzzing techniques, including AFL, AFLfast, PerfFuzz, FairFuzz, Angora and QSYM, in discovering memory consumption bugs. During the experiments, we discovered many previously unknown memory consumption bugs and received 15 new CVEs.
Multiplex Labeling Graph for Near-Online Tracking in Crowded Scenes

In recent years, the demand for intelligent devices related to the Internet of Things (IoT) is rapidly increasing. In the field of computer vision, many algorithms have been preinstalled in IoT devices to achieve higher efficiency, such as face recognition, area detection, target tracking, etc. Tracking is an important but complex task that needs high efficiency solutions in real applications. There is a common assumption that detection can only represent one pedestrian to describe nonoverlapping in physical space. In fact, the pixels of the image do not exactly correspond to the positions in the real world. In order to overcome the limitation of this assumption, we remove this unreasonable assumption and present a novel idea that each detector response can have multiple labels to describe different targets at the same time. Therefore, we propose a graph-based method for near-online tracking in this article. We introduce a detection multiplexing method for tracking in the monocular image and propose a multiplex labeling graph (MLG) model. Each node in MLG has the ability to represent multiple targets. In addition, we improve the shortage of graph-based trackers in using temporal features. We construct long short-term memory networks to model motion and appearance features for MLG optimization. On the public multiobject tracking challenge benchmark, our near-online method gains satisfactory efficiency and achieves state-of-the-art results without additional private detection as well.
Long-Term Tracking With Deep Tracklet Association

Recently, most multiple object tracking (MOT) algorithms adopt the idea of tracking-by-detection. Relevant research shows that the performance of the detector obviously affects the tracker, while the improvement of detector is gradually slowing down in recent years. Therefore, trackers using tracklet (short trajectory) are proposed to generate more complete trajectories. Although there are various tracklet generation algorithms, the fragmentation problem still often occurs in crowded scenes. In this paper, we introduce an iterative clustering method that generates more tracklets while maintaining high confidence. Our method shows robust performance on avoiding internal identity switch. Then we propose a deep association method for tracklet association. In terms of motion and appearance, we construct motion evaluation network (MEN) and appearance evaluation network (AEN) to learn long-term features of tracklets for association. In order to explore more robust features of tracklets, a tracklet-based training mechanism is also introduced. Tracklet groups are used as the input of the networks instead of discrete detections. Experimental results show that our training method enhances the performance of the networks. In addition, our tracking framework generates more complete trajectories while maintaining the unique identity of each target as the same time. On the latest MOT 2017 benchmark, we achieve state-of-the-art results.
Representation Learning for Information Extraction from Form-like Documents

We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.We propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates, and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document.These learned representations are not only useful in solving the extraction task for unseen document templates from two different domains, but are also interpretable, as we show using loss cases.
Deep learning based emotion analysis of microblog texts

Traditional text emotion analysis methods are primarily devoted to studying extended texts, such as news reports and full-length documents. Microblogs are considered short texts that are often characterized by large noises, new words, and abbreviations. Previous emotion classification methods usually fail to extract significant features and achieve poor classification effect when applied to processing of short texts or micro-texts. This study proposes a microblog emotion classification model, namely, CNN_Text_Word2vec, on the basis of convolutional neural network (CNN) to solve the above-mentioned problems. CNN_Text_Word2vec introduces a word2vec neural network model to train distributed word embeddings on every single word. The trained word vectors are used as input features for the model to learn microblog text features through parallel convolution layers with multiple convolution kernels of different sizes. Experiment results show that the overall accuracy rate of CNN_Text_Word2vec is 7.0% higher than that achieved by current mainstream methods, such as SVM, LSTM and RNN. Moreover, this study explores the impact of different semantic units on the accuracy of CNN_Text_Word2vec, specifically in processing of Chinese texts. The experimental results show that comparing to using feature vectors obtained from training words, feature vector obtained from training Chinese characters yields a better performance.
Dynamic Butterfly Optimization Algorithm for Feature Selection

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimization algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimization algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimization process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics.
Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space

When trained effectively, the Variational Autoencoder (VAE) (Kingma and Welling, 2013;Bowman et al., 2016) can be both a powerful generative model and an effective representation learning framework for natural language.In this paper, we propose the first large-scale language VAE model OPTIMUS 1 .A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks.Compared with GPT-2, OPTIMUS enables guided language generation from an abstract level using the latent vectors.Compared with BERT, OPTIMUS can generalize better on low-resource language understanding tasks due to the smooth latent space structure.Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of OPTIMUS.It achieves new state-of-the-art on VAE language modeling benchmarks.Encoderz x
Asymptotic Security Analysis of Discrete-Modulated Continuous-Variable Quantum Key Distribution

Continuous-variable quantum key distribution (CV QKD) protocols with discrete modulation are interesting due to their experimental simplicity and their great potential for massive deployment in the quantum-secured networks, but their security analysis is less advanced than that of Gaussian modulation schemes. In this work, we apply a numerical method to analyze the security of discrete-modulation protocols against collective attacks in the asymptotic limit, paving the way for a full security proof with finite-size effects. While our method is general for discrete-modulation schemes, we focus on two variants of the CV QKD protocol with quaternary modulation. Interestingly, thanks to the tightness of our proof method, we show that this protocol is capable of achieving much higher key rates over significantly longer distances with experimentally feasible parameters compared with previous security proofs of binary and ternary modulation schemes and also yielding key rates comparable to Gaussian modulation schemes. Furthermore, as our security analysis method is versatile, it allows us to evaluate variations of the discrete-modulated protocols, including direct and reverse reconciliation, and postselection strategies. In particular, we demonstrate that postselection of data in combination with reverse reconciliation can improve the key rates.
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization

We introduce WikiLingua, a large-scale, multilingual dataset for the evaluation of crosslingual abstractive summarization systems.We extract article and summary pairs in 18 languages from WikiHow 12 , a high quality, collaborative resource of how-to guides on a diverse set of topics written by human authors.We create gold-standard articlesummary alignments across languages by aligning the images that are used to describe each how-to step in an article.As a set of baselines for further studies, we evaluate the performance of existing cross-lingual abstractive summarization methods on our dataset.We further propose a method for direct crosslingual summarization (i.e., without requiring translation at inference time) by leveraging synthetic data and Neural Machine Translation as a pre-training step.Our method significantly outperforms the baseline approaches, while being more cost efficient during inference.
Machine Learning of Noise-Resilient Quantum Circuits

Noise mitigation and reduction will be crucial for obtaining useful answers from near-term quantum computers. In this work, we present a general framework based on machine learning for reducing the impact of quantum hardware noise on quantum circuits. Our method, called noise-aware circuit learning (NACL), applies to circuits designed to compute a unitary transformation, prepare a set of quantum states, or estimate an observable of a many-qubit state. Given a task and a device model that captures information about the noise and connectivity of qubits in a device, NACL outputs an optimized circuit to accomplish this task in the presence of noise. It does so by minimizing a task-specific cost function over circuit depths and circuit structures. To demonstrate NACL, we construct circuits resilient to a fine-grained noise model derived from gate set tomography on a superconducting-circuit quantum device, for applications including quantum state overlap, quantum Fourier transform, and W-state preparation.
In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval

We present an efficient training approach to text retrieval with dense representations that applies knowledge distillation using the Col-BERT late-interaction ranking model.Specifically, we propose to transfer the knowledge from a bi-encoder teacher to a student by distilling knowledge from ColBERT's expressive MaxSim operator into a simple dot product.The advantage of the bi-encoder teacherstudent setup is that we can efficiently add inbatch negatives during knowledge distillation, enabling richer interactions between teacher and student models.In addition, using Col-BERT as the teacher reduces training cost compared to a full cross-encoder.Experiments on the MS MARCO passage and document ranking tasks and data from the TREC 2019 Deep Learning Track demonstrate that our approach helps models learn robust representations for dense retrieval effectively and efficiently.
VQE method: a short survey and recent developments

Abstract The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation (QPE) because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by the choice of the variational ansatz used to represent the trial wave function. Hence, the construction of an efficient ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatzes for problems that map onto more than several qubits. In this review, we present recent developments in the field of designing efficient ansatzes that fall into two categories—chemistry–inspired and hardware–efficient—that produce quantum circuits that are easier to run on modern hardware. We discuss the shortfalls of ansatzes originally formulated for VQE simulations, how they are addressed in more sophisticated methods, and the potential ways for further improvements.
More is Better: Precise and Detailed Image Captioning Using Online Positive Recall and Missing Concepts Mining

Recently, a great progress in automatic image captioning has been achieved by using semantic concepts detected from the image. However, we argue that existing concepts-to-caption framework, in which the concept detector is trained using the image-caption pairs to minimize the vocabulary discrepancy, suffers from the deficiency of insufficient concepts. The reasons are two-fold: 1) the extreme imbalance between the number of occurrence positive and negative samples of the concept and 2) the incomplete labeling in training captions caused by the biased annotation and usage of synonyms. In this paper, we propose a method, termed online positive recall and missing concepts mining, to overcome those problems. Our method adaptively re-weights the loss of different samples according to their predictions for online positive recall and uses a two-stage optimization strategy for missing concepts mining. In this way, more semantic concepts can be detected and a high accuracy will be expected. On the caption generation stage, we explore an element-wise selection process to automatically choose the most suitable concepts at each time step. Thus, our method can generate more precise and detailed caption to describe the image. We conduct extensive experiments on the MSCOCO image captioning data set and the MSCOCO online test server, which shows that our method achieves superior image captioning performance compared with other competitive methods.
Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device

Ad-hoc network is a temporary self-organizing network that needs no fixed infrastructure. So it has been applied extensively in many areas requesting temporary communication such as military field, emergency disaster relief and road traffic. While, due to the feature of self-organization and wireless communication channels, ad-hoc network is more vulnerable to various attacks compared to the traditional network. In this paper, we proposed a plug and play device to detect Denial of Service (DoS) and privacy attacks. This device mainly includes capture tool and deep learning detection model. Capture tool is used to grab packets in ad-hoc networks, deep learning detection model is used for detecting attacks. An alarm will be triggered if the detected result is attack. In this way, we can avoid the detected attack to spreading out in larger scale. The proposed method can be used as the second line of dense to issue the early-warning signal. In the experiment, first, we use Deep neural network (DNN) detection model to detect DoS attacks; next, we use DNN, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) detection model to detect XSS and SQL attacks. The results show that these detection models can achieve very high Accuracy, Precision, Recall and F1−score. In addition, the time efficiency among the CNN, the LSTM and the DNN is in acceptable range. It proofs that the proposed method can be effectively applied for attack detection. It is important to note that the proposed method can be extended to all other attacks with little modification in ad-hoc networks.
A new microscopic telecentric stereo vision system - Calibration, rectification, and three-dimensional reconstruction

In stereo vision based three-dimensional (3D) measurements, calibration and stereo matching are the most challenging tasks for accurate 3D reconstruction. In this paper, a new microscopic telecentric stereo vision system is proposed to retrieve 3D data of micro-level objects by direct triangulation from two accurately calibrated telecentric cameras. The complex projector calibration procedure commonly seen in traditional structured-light based systems can be avoided. Besides, an improved and practical calibration framework of telecentric cameras is presented. Compared with existing calibration methods which retrieve the distortion parameters without reasonable initial guesses, our proposed approach derives reliable initial guesses for all the camera parameters to avoid the local minima problem based on the estimation algorithms before iterations. To realize precise sub-pixel stereo matching, we propose an effective searching algorithm based on the epipolar rectification of the absolute phase maps obtained from fringe projection profilometry. Experimental results indicate that a measurable volume of 10 mm (L) × 7 mm (W) × 7 mm (H) is achieved with the standard deviation of 1.485 μm.
Exploiting Coarse-to-Fine Task Transfer for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small. Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models. In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task. To resolve both the aspect granularity inconsistency and feature mismatch between domains, we propose a Multi-Granularity Alignment Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same finegrained level with the AT task. To alleviate the feature false alignment, a contrastive feature alignment method is adopted to align aspect-specific feature representations semantically. In addition, a large-scale multi-domain dataset for the AC task is provided. Empirically, extensive experiments demonstrate the effectiveness of the MGAN.
Improved Bootstrapping for Approximate Homomorphic Encryption

Since Cheon et al. introduced a homomorphic encryption scheme for approximate arithmetic (Asiacrypt ’17), it has been recognized as suitable for important real-life usecases of homomorphic encryption, including training of machine learning models over encrypted data. A follow up work by Cheon et al. (Eurocrypt ’18) described an approximate bootstrapping procedure for the scheme. In this work, we improve upon the previous bootstrapping result. We improve the amortized bootstrapping time per plaintext slot by two orders of magnitude, from $$\sim $$ 1 s to $$\sim $$ 0.01 s. To achieve this result, we adopt a smart level-collapsing technique for evaluating DFT-like linear transforms on a ciphertext. Also, we replace the Taylor approximation of the sine function with a more accurate and numerically stable Chebyshev approximation, and design a modified version of the Paterson-Stockmeyer algorithm for fast evaluation of Chebyshev polynomials over encrypted data.