Reviewerly/exHarmony huggingface dataset:
Toward Robust Pedestrian Detection With Data Augmentation

In this article, the problem of creating a safe pedestrian detection model that can operate in the real world is tackled.While recent advances have led to significantly improved detection accuracy on various benchmarks, existing deep learning models are vulnerable to invisible to the human eye changes in the input image which raises concerns about its safety.A popular and simple technique for improving robustness is using data augmentation.In this work, the robustness of existing data augmentation techniques is evaluated to propose a new simple augmentation scheme where during training, an image is combined with a patch of a stylized version of that image.Evaluation of pedestrian detection models robustness and uncertainty calibration under naturally occurring corruption and in realistic cross-dataset evaluation setting is conducted to show that our proposed solution improves upon previous work.In this paper, the importance of testing the robustness of recognition models is emphasized and it shows a simple way to improve it, which is a step towards creating robust pedestrian and object detection models.
A Composable Security Treatment of the Lightning Network

The high latency and low throughput of blockchain protocols constitute one of the fundamental barriers for their wider adoption. Overlay protocols, notably the lightning network, have been touted as the most viable direction for rectifying this in practice. In this work we present for the first time a full formalisation and security analysis of the lightning network in the (global) universal composition setting that leverages a global ledger functionality, for which realisability by the Bitcoin blockchain protocol has been demonstrated in previous work [Badertscher et al., Crypto’17]. As a result, our treatment delineates exactly how the security guarantees of the protocol depend on the properties of the underlying ledger and the frequent availability of the protocol participants. Moreover, we provide a complete and modular description of the core of the lightning protocol that highlights precisely its dependency to underlying basic cryptographic primitives such as igital signatures, pseudorandom functions, identity-based signatures and a less common two-party primitive, which we term a combined digital signature, that were originally hidden within the lightning protocol’s implementation.
App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.
An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning

The way in which people learn and institutions teach is changing due to the ever-increasing impact of technology. People access the Internet anywhere, anytime and request online training. This has brought about the creation of numerous online learning platforms which offer comprehensive and effective educational solutions which are 100% online. These platforms benefit from intelligent tutoring systems that help and guide students through the learning process, emulating the behavior of a human tutor. However, these systems give the student little freedom to experiment with the knowledge of the subject, that is, they do not allow him/her to propose and carry out tasks on his/her own initiative. They are very restricted systems in term of what the student can do, as the tasks are defined in advance. An intelligent tutoring system is proposed in this paper to encourage students to learn through experimentation, proposing tasks on their own initiative, which involves putting into use all the skills, abilities tools and knowledge needed to successfully solve them. This system has been designed developed and applied for learning predictive parsing techniques and has been used by Computer Science students during four academic courses to evaluate its suitability for improving the student’s learning process.
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In 2019, we organized the first iteration of a shared task dedicated to the underlying units used in discourse parsing across formalisms: the DISRPT Shared Task on Elementary Discourse Unit Segmentation and Connective Detection. In this paper we review the data included in the task, which cover 2.6 million manually annotated tokens from 15 datasets in 10 languages, survey and compare submitted systems and report on system performance on each task for both annotated and plain-tokenized versions of the data.
Physical education image analysis based on virtual crowd simulation and FPGA

Physical Culture and Sports research are coordinated with the game (rational, educators, game director, and game theory from a specific or a general point) medicine sociology. They revolve around representatives, value theory, and modern games near parts. This journal is dedicated to humanists, social researchers, students, and practitioners, social analysts, research institutions, and professionals. To practice city models, company directors need to incorporate a large number of personnel for the legs, automobiles, and other powerful elements. Analysts use the same design of registration, i.e. more than 100 million long-distance race. To reproduce and become a member, they continue to be referring many inferences using the fluffy ground on the Graphical Process Unit (GPU). This is an element of PC Vision that is often used to extract the perspective of one of the computing where the histogram is used. Using this calculation requires a large amount of isolated bright spots in the image calculation. One approach, Adaptive speed from the games, for example to support calculations, a Field-Programmable GateArray (FPGA) is equal to the front door. First, an overview of the position and inclination calculation apparatus histogram is given. To implement the full classification, it only uses the equations of decision boundaries. This is to simplify the FPGA implementation through simple expressions, rather than the whole classification. At this point, will see how field-programmable front position has been used in the last decade, i.e. the slope of the histogram. The remainder of the beam is information including resolution, a large number of the task of the receptacle and the direction, progress, and standardized way of calculation. The third cluster is a first program which is a common computing device program, including the position angle dependence modemed histogram and highlights its execution apparatus implemented. The goal uses different pixel per clock cycle analysis and the asset uses the so-called fast measurement purposes. Finally, the speed and accuracy are calculated and present a summary indicator of the value of using the planned use of assets, and the ability to locate the image to the overall performance.
Entropic uncertainty lower bound for a two-qubit system coupled to a spin chain with Dzyaloshinskii–Moriya interaction

The uncertainty principle is one of the key concepts in quantum theory. This principle states that it is not possible to measure two incompatible observables simultaneously and accurately. In quantum information theory, the uncertainty principle is formulated using the concept of entropy. In this work we consider the entropic uncertainty relation in the presence of quantum memory. We study the dynamics of entropic uncertainty bound for a two-qubit quantum system coupled to a spin chain with Dzyaloshinskii–Moriya interaction and we investigate the effect of environmental parameters on the entropic uncertainty bound. Notably, our results reveal that there exist some environmental parameters which can be changed to suppress the entropic uncertainty bound.
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

With the ever-increasing growth of online recruitment data, job-resume matching has become an important task to automatically match jobs with suitable resumes. This task is typically casted as a supervised text matching problem. Supervised learning is powerful when the labeled data is sufficient. However, on online recruitment platforms, job-resume interaction data is sparse and noisy, which affects the performance of job-resume match algorithms.
Motion Prediction and Pre-Rendering at the Edge to Enable Ultra-Low Latency Mobile 6DoF Experiences

As virtual reality (VR) applications become popular, the desire to enable high-quality, lightweight, and mobile VR can potentially be achieved by performing the VR rendering and encoding computations at the edge and streaming the rendered video to the VR glasses. However, if the rendering has to be performed after the edge gets to know of the user’s new head and body position, the ultra-low latency requirements of VR will not be met by the roundtrip delay. In this article, we introduce edge intelligence, wherein the edge can predict, pre-render and cache the VR video in advance, to be streamed to the user VR glasses as soon as needed. The edge-based predictive pre-rendering approach can address the challenging six Degrees of Freedom (6DoF) VR content. Compared to 360-degree videos and 3DoF (head motion only) VR, 6DoF VR supports both head and body motion, thus not only viewing direction but also viewing position can change. Hence, our proposed VR edge intelligence comprises of predicting both the head and body motions of a user accurately using past head and body motion traces. In this article, we develop a multi-task long short-term memory (LSTM) model for body motion prediction and a multi-layer perceptron (MLP) model for head motion prediction. We implement the deep learning-based motion prediction models and validate their accuracy and effectiveness using a dataset of over 840,000 samples for head and body motion.
AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network

Abstract Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.
Enhancing Monotonic Multihead Attention for Streaming ASR

We investigate a monotonic multihead attention (MMA) by extending hard monotonic attention to Transformer-based automatic speech recognition (ASR) for online streaming applications. For streaming inference, all monotonic attention (MA) heads should learn proper alignments because the next token is not generated until all heads detect the corresponding token boundaries. However, we found not all MA heads learn alignments with a naive implementation. To encourage every head to learn alignments properly, we propose HeadDrop regularization by masking out a part of heads stochastically during training. Furthermore, we propose to prune redundant heads to improve consensus among heads for boundary detection and prevent delayed token generation caused by such heads. Chunkwise attention on each MA head is extended to the multihead counterpart. Finally, we propose head-synchronous beam search decoding to guarantee stable streaming inference.
Person Re-Identification With Reinforced Attribute Attention Selection

Person re-identification (Re-ID) aims to match pedestrian images across various scenes in video surveillance. There are a few works using attribute information to boost Re-ID performance. Specifically, those methods leverage attribute information to boost Re-ID performance by introducing auxiliary tasks like verifying the image level attribute information of two pedestrian images or recognizing identity level attributes. Identity level attribute annotations cost less manpower and are well-fitted for person re-identification task compared with image-level attribute annotations. However, the identity attribute information may be very noisy due to incorrect attribute annotation or lack of discriminativeness to distinguish different persons, which is probably unhelpful for the Re-ID task. In this paper, we propose a novel Attribute Attentional Block (AAB), which can be integrated into any backbone network or framework. Our AAB adopts reinforcement learning to drop noisy attributes based on our designed reward and then utilizes aggregated attribute attention of the remaining attributes to facilitate the Re-ID task. Experimental results demonstrate that our proposed method achieves state-of-the-art results on three benchmark datasets.
A Closer Look at Accuracy vs. Robustness

Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning. We take a closer look at this phenomenon and first show that real image datasets are actually separated. With this property in mind, we then prove that robustness and accuracy should both be achievable for benchmark datasets through locally Lipschitz functions, and hence, there should be no inherent tradeoff between robustness and accuracy. Through extensive experiments with robustness methods, we argue that the gap between theory and practice arises from two limitations of current methods: either they fail to impose local Lipschitzness or they are insufficiently generalized. We explore combining dropout with robust training methods and obtain better generalization. We conclude that achieving robustness and accuracy in practice may require using methods that impose local Lipschitzness and augmenting them with deep learning generalization techniques. Code available at this https URL
Advancements of Federated Learning Towards Privacy Preservation: From Federated Learning to Split Learning

In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the latest innovations such as industry 4.0 and smart vehicles. FL provides privacy-by-design. It trains a machine learning model collaboratively over several distributed clients (ranging from two to millions) such as mobile phones, without sharing their raw data with any other participant. In practical scenarios, all clients do not have sufficient computing resources (e.g., Internet of Things), the machine learning model has millions of parameters, and its privacy between the server and the clients while training/testing is a prime concern (e.g., rival parties). In this regard, FL is not sufficient, so split learning (SL) is introduced. SL is reliable in these scenarios as it splits a model into multiple portions, distributes them among clients and server, and trains/tests their respective model portions to accomplish the full model training/testing. In SL, the participants do not share both data and their model portions to any other parties, and usually, a smaller network portion is assigned to the clients where data resides. Recently, a hybrid of FL and SL, called splitfed learning, is introduced to elevate the benefits of both FL (faster training/testing time) and SL (model split and training). Following the developments from FL to SL, and considering the importance of SL, this chapter is designed to provide extensive coverage in SL and its variants. The coverage includes fundamentals, existing findings, integration with privacy measures such as differential privacy, open problems, and code implementation.
Decision making of optimal investment in information security for complementary enterprises based on game theory

With the development of information technology and the deepening of enterprise informatization, there are new challenges of enterprise information security investment decisions because of cooperative relationships among multi-enterprises. In this paper, the Gordon-Loeb model is extended to the multi-enterprise game environment, and combined with the probability of hacker invasion, which can stimulate enterprises to increase investment in information security and reduce costs, the game model of information security investment among complementary enterprises is constructed. Through this model, the impact of factors on optimal investment can be analyzed. It is found that the information security level of enterprises in cooperation situation is higher than that in the non-cooperation situation. Our research shows that the optimal investment will increase with the increase of the probability of one spread in cooperative situations, which is contrary to the changing trend of enterprises in non-cooperation situations, and there is a minimum expected cost threshold. According to the results, enterprise compensation mechanism and information sharing mechanism are designed to ensure the optimal level of social information security, it provides a new solution to deal with the information security investment decision of complementary enterprises under the characteristics of multi-enterprise and non-cooperative.
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control

Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single global policy that can generalize to control a wide variety of agent morphologies -- ones in which even dimensionality of state and action spaces changes. We propose to express this global policy as a collection of identical modular neural networks, dubbed as Shared Modular Policies (SMP), that correspond to each of the agent's actuators. Every module is only responsible for controlling its corresponding actuator and receives information from only its local sensors. In addition, messages are passed between modules, propagating information between distant modules. We show that a single modular policy can successfully generate locomotion behaviors for several planar agents with different skeletal structures such as monopod hoppers, quadrupeds, bipeds, and generalize to variants not seen during training -- a process that would normally require training and manual hyperparameter tuning for each morphology. We observe that a wide variety of drastically diverse locomotion styles across morphologies as well as centralized coordination emerges via message passing between decentralized modules purely from the reinforcement learning objective. Videos and code at this https URL
Latent Video Transformer

The video generation task can be formulated as a prediction of future video frames given some past frames. Recent generative models for videos face the problem of high computational requirements. Some models require up to 512 Tensor Processing Units for parallel training. In this work, we address this problem via modeling the dynamics in a latent space. After the transformation of frames into the latent space, our model predicts latent representation for the next frames in an autoregressive manner. We demonstrate the performance of our approach on BAIR Robot Pushing and Kinetics-600 datasets. The approach tends to reduce requirements to 8 Graphical Processing Units for training the models while maintaining comparable generation quality.
Deep industrial transfer learning at runtime for image recognition

Abstract The utilization of deep learning in the field of industrial automation is hindered by two factors: The amount and diversity of training data needed as well as the need to continuously retrain as the use case changes over time. Both problems can be addressed by industrial deep transfer learning allowing for the performant, continuous and potentially distributed training on small, dispersed datasets. As a specific example, a dual memory algorithm for computer vision problems is developed and evaluated. It shows the potential for state-of-the-art performance while being trained only on fractions of the complete ImageNet dataset at multiple locations at once.