Reviewerly/exHarmony huggingface dataset:
Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras

The 3D visual perception for vehicles with the surround-view fisheye camera system is a critical and challenging task for low-cost urban autonomous driving. While existing monocular 3D object detection methods perform not well enough on the fisheye images for mass production, partly due to the lack of 3D datasets of such images. In this paper, we manage to overcome and avoid the difficulty of acquiring the large scale of accurate 3D labeled truth data, by breaking down the 3D object detection task into some sub-tasks, such as vehicle’s contact point detection, type classification, re-identification and unit assembling, etc. Particularly, we propose the concept of Multidimensional Vector to include the utilizable information generated in different dimensions and stages, instead of the descriptive approach for the bird’s eye view (BEV) or a cube of eight points. The experiments of real fisheye images demonstrate that our solution achieves state-of-the-art accuracy while being real-time in practice.
Mask-Embedded Discriminator with Region-based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis

Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available. Learning precise class semantics is crucial for class-conditional image synthesis with limited supervision. Toward this end, we propose a semi-supervised Generative Adversarial Network with a Mask-Embedded Discriminator, which is referred to as MED-GAN. By incorporating a mask embedding module, the discriminator features are associated with spatial information, such that the focus of the discriminator can be limited in the specified regions when distinguishing between real and synthesized images. A generator is enforced to synthesize the instances holding more precise class semantics in order to deceive the enhanced discriminator. Also benefiting from mask embedding, region-based semantic regularization is imposed on the discriminator feature space, and the degree of separation between real and fake classes and among object categories can thus be increased. This eventually improves class-conditional distribution matching between real and synthesized data. In the experiments, the superior performance of MED-GAN demonstrates the effectiveness of mask embedding and associated regularizers in facilitating SSGL.
HeteroKV: A Scalable Line-rate Key-Value Store on Heterogeneous CPU-FPGA Platforms

In-memory key-value store (KVS) has become crucial for many large-scale Internet services providers to build highperformance data centers. While most of the state-of-the-art KVS systems are optimized for read-intensive applications, a wide range of applications have been proven to be insert-intensive or scan-intensive, which scale poorly with the current implementations. With the availability of FPGA-based smart NICs in data centers, hardware-aided and hardware-based KVS systems are gaining their popularity. In this paper, we present HeteroKV, a scalable line-rate KVS on heterogeneous CPU-FPGA platforms, aiming to provide high throughput in read-, insert- and scan-intensive scenarios. To achieve this, HeteroKV leverages a heterogeneous data structure consisting of a b+ tree, whose leaf nodes are cache-aware partitioned hash tables. Experiments demonstrate HeteroKV's high performance in all scenarios. Specifically, a single node HeteroKV is able to achieve 430M, 315M and 15M key-value operations per second in read-, insert- and scan-intensive scenarios respectively, which are more than 1.5x, 1.4x and 5x higher than state-of-the-art implementations.
UnImplicit Shared Task Report: Detecting Clarification Requirements in Instructional Text

This paper describes the data, task setup, and results of the shared task at the First Workshop on Understanding Implicit and Underspecified Language (UnImplicit).The task requires computational models to predict whether a sentence contains aspects of meaning that are contextually unspecified and thus require clarification.Two teams participated and the best scoring system achieved an accuracy of 68%.
Robust Visual Tracking via Multitask Sparse Correlation Filters Learning

In this article, a novel multitask sparse correlation filters (MTSCF) model, which introduces multitask sparse learning into the CFs framework, is proposed for visual tracking. Specifically, the proposed MTSCF method exploits multitask learning to take the interdependencies among different visual features (e.g., histogram of oriented gradient (HOG), color names, and CNN features) into account to simultaneously learn the CFs and make the learned filters enhance and complement each other to boost the tracking performance. Moreover, it also performs feature selection to dynamically select discriminative spatial features from the target region to distinguish the target object from the background. A l2,1 regularization term is considered to realize multitask sparse learning. In order to solve the objective model, alternating direction method of multipliers is utilized for learning the CFs. By considering multitask sparse learning, the proposed MTSCF model can fully utilize the strength of different visual features and select effective spatial features to better model the appearance of the target object. Extensive experiment results on multiple tracking benchmarks demonstrate that our MTSCF tracker achieves competitive tracking performance in comparison with several state-of-the-art trackers.
Object detection and recognition system based on computer vision analysis

Abstract Artificial intelligence based on deep learning enables the machine to have the ability of understanding and cognition, but the application of artificial intelligence technology in supermarket shopping scene is limited. In the post-epidemic era, the contactless self-checkout of unmanned supermarket is more in line with the development needs of modern society. We build Pytorch environment, first to collect pictures of a large number of commodities and labeling information, and training model is obtained by YOLO neural network algorithm, finally through a call to model to realize the recognition of goods. Neural network algorithm is used to improve the recognition rate of goods step by step and achieve the detection and recognition of objects. We have tested our model on the real supermarket commodity data set and the public data set ImageNet, and the results show that our model can achieve a certain practical effect.
Video-Based Facial Recognition Develop for Accurately Identify People Wearing Surgical Masks

During COVID-19 epidemic, governments around the world now recommend or require their citizens to cover the lower half of their faces in public places. The researchers investigated whether surgical face masks affected the performance of the Video-Based facial recognition system. The purposes of this study are 1) to study Video-Based Facial Recognition algorithm for accurately identify people wearing masks. 2) to design the architecture of the Video-Based Facial Recognition develop for accurately identify people wearing masks.3) to develop a Video-Based Facial Recognition develop for accurately identify people wearing masks. The researcher found that face masks have a large effect on human face matching performance. But our findings demonstrate that face masks hardly impair the ability of Video-Based Facial recognition systems, significantly.
A Deep Learning Approach to Recognize Faces After Plastic Surgery

Facial recognition is a challenging research area since the past decade due to exponentially increasing intervention of humans to achieve anything and everything using technology. Facial recognition is a task; that is, trivially accomplished by human beings, even under fluctuating light and when faces are transformed by age or hindered by accessories or facial hair. Nevertheless, it has remained a perplexing computer vision challenge for decades. Due to upsurge in acceptance and accomplishment of deep learning models in computer vision, features extracted by convolutional neural networks (CNNs) can be employed for facial recognition. However, all the techniques involving deep learning require huge amounts of data, thereby increasing the computational requirements of any algorithm. In this paper, a method has been introduced with minimal computational requirements and minimal time complexity for identifying and recognizing faces which are surgically altered. Recognition is done by introducing a simple CNN for facial trait extraction which is followed by performing the classification task. Plastic surgery facial dataset is used for training, testing and validating the proposed deep network. This indeed is very beneficial for ensuring safety and security of an individual’s identity as it would assist to recognize offenders, impersonators or anyone who conceals their individuality.
Analysis of the Cybersecurity Weaknesses of DLT Ecosystem

The article examines the cybersecurity status of DLT technology in the holistic context of dynamic management of cyber protection. The advent of the DLT technologies arrived together with the continuous expansion of the surface for cyber-attacks against information systems and communication networks. The analysis is targeted to unknown dangers against the DLT ecosystem.
Exponential Smoothing Model for Photovoltaic Power Forecasting

The aim of the paper is to develop a method for short-term deterministic and probabilistic photovoltaic (PV) power forecasting, and to present the results for an actual applicative case study. The proposed method is based on the exponential smoothing models, and two variants of this approach are analyzed within both the deterministic and the probabilistic frameworks. In particular, the probabilistic forecasting method is developed through an ensemble-based procedure that includes point forecasts obtained through several smoothing exponents. The case study discussed in this paper is based on experimental data obtained from an existing plant, and the corresponding results are compared with a benchmark based on the persistence model. It is found that the proposed exponential smoothing method significantly improves the result of the short-term PV power forecasting, both in the deterministic and probabilistic frameworks.
Evolution of ICTs-empowered-identification: A general re-ranking method for person re-identification

Re-ranking is becoming a critical part of retrieved based Person re-identification algorithms. Existing re-ranking methods always require lots of queries and memory to go through the k-nearest neighbors. To solve this problem, we introduce a Feature Relation Map (FRM) to mine the latent relation between the k-neighbors through convolution neural network, and propose a metric learning based Similarity Evaluation (SE) model to obtain the re-ranking distance from the FRM. The dilated convolution is then introduced by concatenating the dilated convolution kernels and normal convolution kernels along the channel dimension in the improvement of the SE model (named SE-d model), to allow the SE model to efficiently compare more samples pairs for obtaining the final re-ranking distance. Further, we embedding out FRM-SE model to the existing re-ranking methods to prove the effectiveness of our re-ranking model. The experiments on Market1501, CUHK03, DukeMTMC and MSMT17 benchmarks illustrates the superiority of proposed method to the state-of-the-art re-ranking methods. Although the SE model make a great improve performance, the SE-d model gains an increase of 0.31%, 0.63% in top-1 respectably compared with the SE model in the DukeMTMC and MSMT17 datasets. Meanwhile, the SE-d model speed up the model convergence and shorten the training time. Furthermore, in the transfer learning setting, the model trained on either Market1501, CUHK03 or DukeMTMC can achieve a comparable accuracy improvement on the MSMT17 dataset, which validates the generalization of our SE model.
Obfuscating Finite Automata

We construct a virtual black box and perfect circuit-hiding obfuscator for evasive deterministic finite automata using a matrix encoding scheme with a limited zero-testing algorithm. We construct the matrix encoding scheme by extending an existing matrix fully homomorphic encryption scheme. Using obfuscated deterministic finite automata we can for example evaluate secret regular expressions or disjunctive normal forms on public inputs. In particular, the possibility of evaluating regular expressions solves the open problem of obfuscated substring matching.
Integer Wavelet Transform based Secured Image Steganography using LSB and Coefficient Value Differencing

A steganography technique based on the Integer Wavelet Transform (IWT) is proposed. High-frequency coefficients are exploited more to embed the confidential message since they are less sensitive to the human eye. Based on this notion, the 2-LSB method is employed in the wavelet coefficients of the approximation subband which contains most of the signal energy and the 4-LSB method is applied in the wavelet coefficients of the diagonal subband. The Coefficient Value Differencing (CVD) method has been introduced in the horizontal and vertical subbands. To obtain the highest embedding capacity with an acceptable distortion, a threshold value has been applied in the CVD method. The proposed technique achieves an average embedding capacity of 2.28 bpp for all the stego images while maintaining a lower distortion. It shows strength against the standard tool, StirMark Benchmark 4.0, and withstands the steganalysis attacks.
Proof-of-principle experimental demonstration of quantum gate verification

To employ a quantum device, the performance of the quantum gates in the device needs to be evaluated first. Since the dimensionality of a quantum gate grows exponentially with the number of qubits, evaluating the performance of a quantum gate is a challenging task. Recently, a scheme called quantum gate verification (QGV) has been proposed, which can verifies quantum gates with near-optimal efficiency. In this paper, we implement a proof-of-principle optical experiment to demonstrate this QGV scheme. We show that for a single-qubit quantum gate, only $\ensuremath{\sim}300$ samples are needed to confirm the fidelity of the quantum gate to be, at least, 97% with a 99% confidence level using the QGV method, whereas, at least, $\ensuremath{\sim}3000$ samples are needed to achieve the same result using the standard quantum process tomography method. The QGV method validated by this paper has the potential to be widely used for the evaluation of quantum devices in various quantum information applications.
Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning

Google Scholar has been a widely used platform for academic performance evaluation and citation analysis. The issue about the mis-configuration of author profiles may seriously damage the reliability of the data, and thus affect the accuracy of analysis. Therefore, it is important to detect the mis-configured author profiles. Dealing with this issue is challenging because the scale of the dataset is large and manual annotation is time-consuming and relatively subjective. In this paper, we first collect a dataset of Google Scholar’s author profiles in the field of computer science and compare the mis-configured author profiles with the reliable ones. Then, we propose an integrated model that utilizes machine learning and node embedding to automatically detect mis-configured author profiles. Additionally, we conduct two application case studies based on the data of Google Scholar, i.e., outstanding scholar searching and university ranking, to demonstrate how the improved dataset after filtering out the mis-configured author profiles will change the results. The two case studies validate the importance and meaningfulness of the detection of mis-configured author profiles.
Monitoring Video Resolution of Adaptive Encrypted Video Traffic Based on HTTP/2 Features

With the rapid growth of mobile video traffic, a wide range of third-party entities need to better understand the status of video quality for QoE monitoring and network management. Video resolution, as a direct reflection of video quality, is a key influence factor of QoE. However, the existing approaches cannot accurately identify the resolution of video traffic under HTTP/2 due to the multiplexing mechanism of HTTP/2. To address this issue, we proposed a method, dubbed as H2CI, to consider the size of the mixed data calculated based on HTTP/2 features as the fingerprint for identification. H2CI can accurately restore the original length of chunks from encrypted video streaming and perform the fine-grained resolution identification. The experimental results show the promise of our method, yielding more than 99% accuracy for fine-grained video resolution identification. Our method can be effectively applied to infer the adaption behavior of video streaming and monitor the QoE of mobile video services under HTTP/2.
Virtual Hand Experience

Similar to robotic approaches, virtual hand illusion (VHI) experiments enable deeper insights into human-robot body experience. This chapter discusses the influence of different haptic feedback modalities as well as interrelations to autonomy and controllability. The first study compares wearable force feedback, vibrotactile feedback, and no haptic feedback during pick-and-place tasks in a virtual environment. Both kinds of haptic feedback significantly improve the embodiment of the virtual hand and the human-in-the-loop paradigms can guide wearable haptic designs. A second line of research analyzes the physical interaction of humans with intelligent robotic tools in an immersive virtual reality. Results suggest embodiment as a valid metric for the user experience of shared control quality and, furthermore, agency as an objective measure of task-appropriate and intuitive assistance. This underlines the potential of technically augmented psychological paradigms, to investigate human-in-the-loop control of physical human-robot interaction.
Transformation of the Institute of Education in the Context of Digitalization

Modern information technologies, interactive forms of communication and communication have been actively introduced into the field of education over the past few years, making it possible to transfer the process of reproduction of knowledge to a new level of quality. The transition from computerization to informatization leads to the creation of a developed information and educational environment, which has a number of advantages in the development of new knowledge and certain contradictions. One of the consequences of the depth processes of digitalization of education is the strengthening of social stratification and social inequality. On the one hand, information technologies provide access to knowledge, and on the other, they contribute to the preampiness and degradation of society. Unequal access to knowledge with apparent accessibility exacerbates the elitization of education. An exception (or restriction of interaction) from the educational process of the teacher's personality leads to the loss of social skills, the ability to interact, critical thinking, a decrease in creative potential through the development of template thinking.
Learning Contextual Causality between Daily Events from Time-consecutive Images

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://github.com/HKUST-KnowComp/Vis_Causal.
Towards Demystifying Adversarial Robustness of Binarized Neural Networks

Quantized neural networks are proposed for reduced computation and memory costs. When quantized neural networks are designed for edge or terminal devices, they may be more vulnerable to adversarial perturbations. We focus on the extreme cases, i.e. binarized neural networks (BNNs) where weights and activations are binarized, and investigate their adversarial robustness. Six different binarized neural networks are considered with their full-precision counterpart as the baseline. We conduct the first empirical study on adversarial robustness over various BNNs in terms of their naive adversarial robustness, the effectiveness of adversarial training on BNNs, and explore attack transferability among BNNs. Our analysis provides quantitative study on how BNNs perform in terms of model accuracy and adversarial robustness.
English Audio Language Retrieval Based on Adaptive Speech-Adjusting Algorithm

For the purpose of language retrieval for English listening, this paper designs and implements a cross-language information retrieval system for English listening. Different implementation methods of cross-language information retrieval, query, and translation are analyzed. The system adopts cross-language information retrieval technology based on bilingual dictionaries. According to the cross-language retrieval system of the existing bilingual dictionaries and monolingual dictionaries, based on the design and implementation of the fuzzy search dictionary lookup mechanism, the existing dictionary lookup mechanism is constructed and analyzed. Aiming at the problem of translation ambiguity in information retrieval systems based on bilingual dictionaries, a disambiguities elimination algorithm based on cooccurrence technology is proposed. In continuous speech, the speed of different speakers in different contexts is very different. Deviation from normal speech speed often leads to recognition errors, which makes recognition performance decline. Considering that the influence of speech speed on the length of speech units increases or decreases synchronously, and there is a strong correlation between the lengths of adjacent speech units, an adaptive speech speed algorithm is proposed based on the framework of implicit Markov model based on the information of the length of speech units. Experiments on number string and large vocabulary continuous speech recognition show that the algorithm is effective.
Approximate Stochastic Reachability for High Dimensional Systems

We present a method to compute the stochastic reachability safety probabilities for high-dimensional stochastic dynamical systems. Our approach takes advantage of a nonparametric learning technique known as conditional distribution embeddings to model the stochastic kernel using a data-driven approach. By embedding the dynamics and uncertainty within a reproducing kernel Hilbert space, it becomes possible to compute the safety probabilities for stochastic reachability problems as simple matrix operations and inner products. We employ a convergent approximation technique, random Fourier features, in order to alleviate the increased computational requirements for high-dimensional systems. This technique avoids the curse of dimensionality, and enables the computation of safety probabilities for high-dimensional systems without prior knowledge of the structure of the dynamics or uncertainty. We validate this approach on a double integrator system, and demonstrate its capabilities on a million-dimensional, nonlinear, non-Gaussian, repeated planar quadrotor system.