Reviewerly/exHarmony huggingface dataset:
Multi-Task Learning With Coarse Priors for Robust Part-Aware Person Re-Identification

Part-level representations are important for robust person re-identification (ReID), but in practice feature quality suffers due to the body part misalignment problem. In this paper, we present a robust, compact, and easy-to-use method called the Multi-task Part-aware Network (MPN), which is designed to extract semantically aligned part-level features from pedestrian images. MPN solves the body part misalignment problem via multi-task learning (MTL) in the training stage. More specifically, it builds one main task (MT) and one auxiliary task (AT) for each body part on the top of the same backbone model. The ATs are equipped with a coarse prior of the body part locations for training images. ATs then transfer the concept of the body parts to the MTs via optimizing the MT parameters to identify part-relevant channels from the backbone model. Concept transfer is accomplished by means of two novel alignment strategies: namely, parameter space alignment via hard parameter sharing and feature space alignment in a class-wise manner. With the aid of the learned high-quality parameters, MTs can independently extract semantically aligned part-level features from relevant channels in the testing stage. MPN has three key advantages: 1) it does not need to conduct body part detection in the inference stage; 2) its model is very compact and efficient for both training and testing; 3) in the training stage, it requires only coarse priors of body part locations, which are easy to obtain. Systematic experiments on four large-scale ReID databases demonstrate that MPN consistently outperforms state-of-the-art approaches by significant margins.
ONION: A Simple and Effective Defense Against Textual Backdoor Attacks

Backdoor attacks are a kind of emergent training-time threat to deep neural networks (DNNs).They can manipulate the output of DNNs and possess high insidiousness.In the field of natural language processing, some attack methods have been proposed and achieve very high attack success rates on multiple popular models.Nevertheless, there are few studies on defending against textual backdoor attacks.In this paper, we propose a simple and effective textual backdoor defense named ONION, which is based on outlier word detection and, to the best of our knowledge, is the first method that can handle all the textual backdoor attack situations.Experiments demonstrate the effectiveness of our model in defending BiLSTM and BERT against five different backdoor attacks.
COVID-19 Case Investigation and Contact Tracing Efforts from Health Departments — United States, June 25–July 24, 2020

Case investigation and contact tracing are core public health tools used to interrupt transmission of pathogens, including SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19); timeliness is critical to effectiveness (1,2).In May 2020, CDC funded* 64 state, local, and territorial health departments † to support COVID-19 response activities.As part of the monitoring process, case investigation and contact tracing metrics for June 25-July 24, 2020, were submitted to CDC by 62 health departments.Descriptive analyses of case investigation and contact tracing load, timeliness, and yield (i.e., the number of contacts elicited divided by the number of patients prioritized for interview) were performed.A median of 57% of patients were interviewed within 24 hours of report of the case to a health department (interquartile range [IQR] = 27%-82%); a median of 1.15 contacts were identified per patient prioritized for interview § (IQR = 0.62-1.76),and a median of 55% of contacts were notified within 24 hours of identification by a patient (IQR = 32%-79%).With higher caseloads, the percentage of patients interviewed within 24 hours of case report was lower (Spearman coefficient = -0.68),and the number of contacts identified per patient prioritized for interview also decreased (Spearman coefficient = -0.60).The capacity to conduct timely contact tracing varied among health departments, largely driven by investigators' caseloads.Incomplete identification of contacts affects the ability to reduce transmission of SARS-CoV-2.Enhanced staffing capacity and ability and improved community engagement could lead to more timely interviews and identification of more contacts.During July 31-August 14, 2020, baseline data on four metrics for
Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture

The present study deals with human awareness, which is a very important aspect of human–robot interaction. This feature is particularly essential in agricultural environments, owing to the information-rich setup that they provide. The objective of this investigation was to recognize human activities associated with an envisioned synergistic task. In order to attain this goal, a data collection field experiment was designed that derived data from twenty healthy participants using five wearable sensors (embedded with tri-axial accelerometers, gyroscopes, and magnetometers) attached to them. The above task involved several sub-activities, which were carried out by agricultural workers in real field conditions, concerning load lifting and carrying. Subsequently, the obtained signals from on-body sensors were processed for noise-removal purposes and fed into a Long Short-Term Memory neural network, which is widely used in deep learning for feature recognition in time-dependent data sequences. The proposed methodology demonstrated considerable efficacy in predicting the defined sub-activities with an average accuracy of 85.6%. Moreover, the trained model properly classified the defined sub-activities in a range of 74.1–90.4% for precision and 71.0–96.9% for recall. It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model’s performance. These results confirm the applicability of the proposed methodology for human awareness purposes in agricultural environments, while the dataset was made publicly available for future research.
Machine Learning Based Automatic Modulation Recognition for Wireless Communications: A Comprehensive Survey

The rapid development of information and wireless communication technologies together with the large increase in the number of end-users have made the radio spectrum more crowded than ever. Besides, providing a stable and reliable service is challenging, as electromagnetic environments are evolving and becoming more sophisticated. Accordingly, there is an urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency and the quality of service to provide agile management of network resources, so as to better meet the needs of future wireless users. Specifically, Automatic Modulation Recognition (AMR) plays an essential role in most intelligent communication systems especially with the emergence of Software Defined Radio (SDR). AMR is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Thanks to the significant advancements in Deep Learning (DL) applications, new and powerful tools have been provided which can tackle problems in this space. Thus, today, integrating DL models into AMR has gained the attention of many researchers. This work aims to provide a comprehensive state-of-the-art review of the most recent Machine Learning (ML) based AMR methods for Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) systems. Furthermore, the architecture of each model will be identified along with a detailed comparison in terms of specifications and performance. Finally, an outline of the open problems, challenges, and potential research directions is provided along with discussion and conclusion.
What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.
Annotating and Modeling Fine-grained Factuality in Summarization

Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input and that contain factual errors.While a number of annotated datasets and statistical models for assessing factuality have been explored, there is no clear picture of what errors are most important to target or where current techniques are succeeding and failing.We explore both synthetic and human-labeled data sources for training models to identify factual errors in summarization, and study factuality at the word-, dependency-, and sentence-level.Our observations are threefold.First, exhibited factual errors differ significantly across datasets, and commonly-used training sets of simple synthetic errors do not reflect errors made on abstractive datasets like XSUM.Second, human-labeled data with fine-grained annotations provides a more effective training signal than sentence-level annotations or synthetic data.Finally, we show that our best factuality detection model enables training of more factual XSUM summarization models by allowing us to identify non-factual tokens in the training data. 1
Structure-Consistent Weakly Supervised Salient Object Detection with Local Saliency Coherence

Sparse labels have been attracting much attention in recent years. However, the performance gap between weakly supervised and fully supervised salient object detection methods is huge, and most previous weakly supervised works adopt complex training methods with many bells and whistles. In this work, we propose a one-round end-to-end training approach for weakly supervised salient object detection via scribble annotations without pre/post-processing operations or extra supervision data. Since scribble labels fail to offer detailed salient regions, we propose a local coherence loss to propagate the labels to unlabeled regions based on image features and pixel distance, so as to predict integral salient regions with complete object structures. We design a saliency structure consistency loss as self-consistent mechanism to ensure consistent saliency maps are predicted with different scales of the same image as input, which could be viewed as a regularization technique to enhance the model generalization ability. Additionally, we design an aggregation module (AGGM) to better integrate high-level features, low-level features and global context information for the decoder to aggregate various information. Extensive experiments show that our method achieves a new state-of-the-art performance on six benchmarks (e.g. for the ECSSD dataset: Fβ = 0.8995, Eξ = 0.9079 and MAE = 0.0489), with an average gain of 4.60% for F-measure, 2.05% for E-measure and 1.88% for MAE over the previous best performing method on this task. Source code is available at http://github.com/siyueyu/SCWSSOD.
Internet-Augmented Dialogue Generation

The largest store of continually updating knowledge on our planet can be accessed via internet search.In this work we study giving access to this information to conversational agents.Large language models, even though they store an impressive amount of knowledge within their weights, are known to hallucinate facts when generating dialogue (Shuster et al., 2021); moreover, those facts are frozen in time at the point of model training.In contrast, we propose an approach that learns to generate an internet search query based on the context, and then conditions on the search results to finally generate a response, a method that can employ up-to-the-minute relevant information.We train and evaluate such models on a newly collected dataset of human-human conversations whereby one of the speakers is given access to internet search during knowledge-driven discussions in order to ground their responses.We find that search-query based access of the internet in conversation provides superior performance compared to existing approaches that either use no augmentation or FAISS-based retrieval (Lewis et al., 2020b).
Object-Based Image Retrieval Using the U-Net-Based Neural Network

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.
Filtering variational quantum algorithms for combinatorial optimization

Abstract Current gate-based quantum computers have the potential to provide a computational advantage if algorithms use quantum hardware efficiently. To make combinatorial optimization more efficient, we introduce the filtering variational quantum eigensolver which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution. Additionally we explore the use of causal cones to reduce the number of qubits required on a quantum computer. Using random weighted MaxCut problems, we numerically analyze our methods and show that they perform better than the original VQE algorithm and the quantum approximate optimization algorithm. We also demonstrate the experimental feasibility of our algorithms on a Quantinuum trapped-ion quantum processor powered by Honeywell.
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification

Encrypted traffic classification requires discriminative and robust traffic representation captured from content-invisible and imbalanced traffic data for accurate classification, which is challenging but indispensable to achieve network security and network management. The major limitation of existing solutions is that they highly rely on the deep features, which are overly dependent on data size and hard to generalize on unseen data. How to leverage the open-domain unlabeled traffic data to learn representation with strong generalization ability remains a key challenge. In this paper, we propose a new traffic representation model called Encrypted Traffic Bidirectional Encoder Representations from Transformer (ET-BERT), which pre-trains deep contextualized datagram-level representation from large-scale unlabeled data. The pre-trained model can be fine-tuned on a small number of task-specific labeled data and achieves state-of-the-art performance across five encrypted traffic classification tasks, remarkably pushing the F1 of ISCX-VPN-Service to 98.9% (5.2%↑), Cross-Platform (Android) to 92.5% (5.4%↑), CSTNET-TLS 1.3 to 97.4% (10.0%↑). Notably, we provide explanation of the empirically powerful pre-training model by analyzing the randomness of ciphers. It gives us insights in understanding the boundary of classification ability over encrypted traffic. The code is available at: https://github.com/linwhitehat/ET-BERT.
Agent Architecture Considerations for Real-Time Planning in Games

Planning in real-time offers several benefits over the more typical techniques of implementing Non-Player Character (NPC) behavior with scripts or finite state machines. NPCs that plan their actions dynamically are better equipped to handle unexpected situations. The modular nature of the goals and actions that make up the plan facilitates re-use, sharing, and maintenance of behavioral building blocks. These benefits, however, come at the cost of CPU cycles. In order to simultaneously plan for several NPCs in real-time, while continuing to share the processor with the physics, animation, and rendering systems, careful consideration must taken with the supporting architecture. The architecture must support distributed processing and caching of costly calculations. These considerations have impacts that stretch beyond the architecture of the planner, and affect the agent architecture as a whole. This paper describes lessons learned while implementing real-time planning for NPCs for F.E.A.R., a AAA first person shooter shipping for PC in 2005.
Multi-Value-Independent Ciphertext-Policy Attribute Based Encryption with Fast Keyword Search

ABKS has drawn much attention from research and industry in recent years, an ABKS scheme is an encryption scheme that supports keyword search and access control. Attribute-Based Encryption is a public key encryption that enables users to encrypt and decrypt message based on attributes. In a typical implementation, the size of the ciphertext is proportional to the number of attributes associated with it and the decryption time is proportional to the number of attributes used during decryption. Inherit from ABE technology, the computation cost and ciphertext size in most ABKS schemes grow with the complexity of the access policy. On the other hand, we found that the traditional ABKS schemes cannot resist our secret-key-recovery attack. To deal with the above problems, we present new ciphertext policy attribute based encryption with fast keyword search constructions. Our constructions preserve the fine-grained access control inherited from the ABE system while supporting hidden policy and fast keyword search. Our constructions feature multi-value-independent compared with the existing attribute based searchable encryption schemes. The performance analysis demonstrates the efficiency of our constructions. We offer rigorous security proof of our second scheme, which is IND-CKA and IND-CPA secure.
Wave Packet Spreading with Disordered Nonlinear Discrete-Time Quantum Walks

We use a novel unitary map toolbox---discrete-time quantum walks originally designed for quantum computing---to implement ultrafast computer simulations of extremely slow dynamics in a nonlinear and disordered medium. Previous reports on wave packet spreading in Gross-Pitaevskii lattices observed subdiffusion with the second moment ${m}_{2}\ensuremath{\sim}{t}^{1/3}$ (with time in units of a characteristic scale ${t}_{0}$) up to the largest computed times of the order of $1{0}^{8}$. A fundamental and controversially debated question---whether this process can continue ad infinitum, or has to slow down---stands unresolved. Current experimental devices are not capable to even reach $1/{10}^{4}$ of the reported computational horizons. With our toolbox, we outperform previous computational results and observe that the universal subdiffusion persists over an additional four decades reaching ``astronomic'' times $2\ifmmode\times\else\texttimes\fi{}{10}^{12}$. Such a dramatic extension of previous computational horizons suggests that subdiffusion is universal, and that the toolbox can be efficiently used to assess other hard computational many-body problems.
New approach for solar tracking systems based on computer vision, low cost hardware and deep learning

In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.
Learning Personalized End-to-End Goal-Oriented Dialog

Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues. In this paper, we present a personalized end-to-end model in an attempt to leverage personalization in goal-oriented dialogs. We first introduce a PROFILE MODEL which encodes user profiles into distributed embeddings and refers to conversation history from other similar users. Then a PREFERENCE MODEL captures user preferences over knowledge base entities to handle the ambiguity in user requests. The two models are combined into the PERSONALIZED MEMN2N. Experiments show that the proposed model achieves qualitative performance improvements over state-of-the-art methods. As for human evaluation, it also outperforms other approaches in terms of task completion rate and user satisfaction.
Trajectory PHD and CPHD Filters

This paper presents the probability hypothesis density filter (PHD) and the cardinality PHD (CPHD) filter for sets of trajectories, which are referred to as the trajectory PHD (TPHD) and trajectory CPHD (TCPHD) filters. Contrary to the PHD/CPHD filters, the TPHD/TCPHD filters are able to produce trajectory estimates from first principles. The TPHD filter is derived by recursively obtaining the best Poisson multitrajectory density approximation to the posterior density over the alive trajectories by minimising the Kullback-Leibler divergence. The TCPHD is derived in the same way but propagating an independent identically distributed (IID) cluster multitrajectory density approximation. We also propose the Gaussian mixture implementations of the TPHD and TCPHD recursions, the Gaussian mixture TPHD (GMTPHD) and the Gaussian mixture TCPHD (GMTCPHD), and the L-scan computationally efficient implementations, which only update the density of the trajectory states of the last L time steps.