Reviewerly/exHarmony huggingface dataset:
A simplified verifiable blind quantum computing protocol with quantum input verification

Blind quantum computing (BQC) enables a client with limited quantum capability to delegate her quantum computation to a remote quantum server and still keeps the client's input, output, and the algorithm private. One important property of BQC is verifiability, which means the client can verify the correctness of the computation or unknown quantum inputs. Recently, Morimae proposed a verifiable blind quantum computing protocol where the client can verify both the correctness of computing and the quantum input. However, the client needs much quantum memory and takes a lot of qubits to get the true result. We reduce the size of quantum memory and the number of qubits used in Morimae's protocol. Besides, we extend the resource state in Morimae's protocol from graph state to hypergraph state. For certain hypergraph states as resources states, such as Union Jack State, the client only needs Pauli measurements in the computation stage but he needs non-Clifford basis measurements if graph states are considered as resources states.
Why Dataset Properties Bound the Scalability of Parallel Machine Learning Training Algorithms

As the training dataset size and the model size of machine learning increase rapidly, more computing resources are consumed to speedup the training process. However, the scalability and performance reproducibility of parallel machine learning training, which mainly uses stochastic optimization algorithms, are limited. In this paper, we demonstrate that the sample difference in the dataset plays a prominent role in the scalability of parallel machine learning algorithms. We propose to use statistical properties of dataset to measure sample differences. These properties include the variance of sample features, sample sparsity, sample diversity, and similarity in sampling sequences. We choose four types of parallel training algorithms as our research objects: (1) the asynchronous parallel SGD algorithm (Hogwild! algorithm), (2) the parallel model average SGD algorithm (minibatch SGD algorithm), (3) the decentralization optimization algorithm, and (4) the dual coordinate optimization (DADM algorithm). Our results show that the statistical properties of training datasets determine the scalability upper bound of these parallel training algorithms.
Leveraging edges and optical flow on faces for deepfake detection

Deepfakes can be used maliciously to sway public opinion, defame an individual, or commit fraud. Hence, it is vital for journalists and social media platforms, as well as the general public, to be able to detect deepfakes. Existing deepfake detection methods, while highly accurate on datasets they have been trained on, falter in open-world scenarios due to different deepfake generations algorithms, video formats, and compression levels. In this paper, we seek to address this by building on the XceptionNet-based deepfake detection technique that utilizes convolutional latent representations with recurrent structures. In particular, we explore how to leverage a combination of visual frames, edge maps, and dense optical flow maps together as inputs to this architecture. We evaluate these techniques using the FaceForensics++ and DFDC-mini datasets. We also perform extensive studies to evaluate the robustness of our network against adversarial post-processing as well as the generalization capabilities to out-of-domain datasets and manipulation strategies. Our methods, which we call XceptionNet*, achieve 100% accuracy on the popular Face-Forensics-s+ dataset and set new benchmark standards on the difficult DFDC-mini dataset. The XceptionNet* models are shown to exhibit superior performance on cross-domain testing and demonstrate surprising resilience to adversarial manipulations.
Reducing the Annotation Effort for Video Object Segmentation Datasets

For further progress in video object segmentation (VOS), larger, more diverse, and more challenging datasets will be necessary. However, densely labeling every frame with pixel masks does not scale to large datasets. We use a deep convolutional network to automatically create pseudo-labels on a pixel level from much cheaper bounding box annotations and investigate how far such pseudo-labels can carry us for training state-of-the-art VOS approaches. A very encouraging result of our study is that adding a manually annotated mask in only a single video frame for each object is sufficient to generate pseudo-labels which can be used to train a VOS method to reach almost the same performance level as when training with fully segmented videos. We use this workflow to create pixel pseudo-labels for the training set of the challenging tracking dataset TAO, and we manually annotate a subset of the validation set. Together, we obtain the new TAO-VOS benchmark, which we make publicly available at www.vision.rwth-aachen.de/page/taovos. While the performance of state-of-the-art methods on existing datasets starts to saturate, TAO-VOS remains very challenging for current algorithms and reveals their shortcomings.
Weakly-Supervised Saliency Detection via Salient Object Subitizing

Salient object detection aims at detecting the most visually distinct objects and producing the corresponding masks. As the cost of pixel-level annotations is high, image tags are usually used as weak supervisions. However, an image tag can only be used to annotate one class of objects. In this paper, we introduce saliency subitizing as the weak supervision since it is class-agnostic. This allows the supervision to be aligned with the property of saliency detection, where the salient objects of an image could be from more than one class. To this end, we propose a model with two modules, Saliency Subitizing Module (SSM) and Saliency Updating Module (SUM). While SSM learns to generate the initial saliency masks using the subitizing information, without the need for any unsupervised methods or some random seeds, SUM helps iteratively refine the generated saliency masks. We conduct extensive experiments on five benchmark datasets. The experimental results show that our method outperforms other weakly-supervised methods and even performs comparable to some fully-supervised methods.
C4AV: Learning Cross-Modal Representations from Transformers

In this paper, we focus on the object referral problem in the autonomous driving setting. We propose a novel framework to learn cross-modal representations from transformers. In order to extract the linguistic feature, we feed the input command to the transformer encoder. Meanwhile, we use a resnet as the backbone for the image feature learning. The image features are flattened and used as the query inputs to the transformer decoder. The image feature and the linguistic feature are aggregated in the transformer decoder. A region-of-interest (RoI) alignment is applied to the feature map output from the transformer decoder to crop the RoI features for region proposals. Finally, a multi-layer classifier is used for object referral from the features of proposal regions.
An Efficient Sound and Data Steganography Based Secure Authentication System

The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented cyber-attacks. Further, the pin-based security system is an inadequate mechanism for handling such a scenario. The reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private data. This research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound, thereby disregarding the pins' manual verification. Further, the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication approaches. Firstly, a random signal is encrypted, and then it is transformed into a wave file, after which it gets transmitted in a short burst via the device's speakers. Subsequently, the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for pairing. Besides, this model requires two devices/gadgets with speakers and a microphone, and no extra hardware such as a camera, for reading the QR code is required. The first module is tested with real-time data and generates high scores for the widely accepted accuracy metrics, including precision, Recall, F1 score, entropy, and mutual information (MI). Additionally, this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other files. This steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a file. Several encryption algorithms and their combinations are taken for this system to compare the resultant file size. Both these systems engender high accuracies and provide secure connectivity, leading to a sustainable communication ecosystem.
Facial Expression Recognition with CNN-LSTM

Facial expression is one of the most important means to express the human emotions in social communication. Automatic facial expression recognition has become a "favorite" topic in the research area. In this paper, we propose an approach of using CNN-LSTM to learn facial expressions. By combination of the superior features of our own convolutional neural network and long short-term memory models, our proposed model gains the better results than the others. The experiments are conducted on JAFFE database and the accuracy and confusion matrix score are used to evaluate our model.
A consensus-based decentralized training algorithm for deep neural networks with communication compression

Facing the challenge of distributed computing on processing large-scale data, this paper proposes a consensus-based decentralized training method with communication compression. First, the decentralized training method is designed based on the decentralized topology to reduce the communication burden on the busiest agent and avoid any agent revealing its locally stored data. The convergence of the decentralized training algorithm is then analyzed, which demonstrates that the decentralized trained model can reach the minimal empirical risk on the whole dataset, without the sharing of data samples. Furthermore, model compression combined with the error-compensated method is considered to reduce communication costs during the decentralized training process. At last, the simulation study shows that the proposed decentralized training with error-compensated communication compression is applicable for both IID and non-IID datasets, and exhibits much better performance than the local training method. Besides, the proposed algorithm with an appropriate compression rate shows comparable performance with decentralized training and centralized training, while saving a lot of communication costs.
DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.
Learning of low-level feature keypoints for accurate and robust detection

Joint learning of feature descriptor and detector has offered promising 3D reconstruction results; however, they often lack the low-level feature awareness, which causes low accuracy in matched keypoint locations. The others employed fixed operations to select the keypoints, but the selected keypoints may not correspond to the descriptor matching. To address these problems, we propose the supervised learning of keypoint detection with low-level features. Our detector is a single CNN layer extended from the descriptor backbone, which can be jointly learned with the descriptor for maximizing the descriptor matching. This results in a state-of-the-art 3D reconstruction, especially on improving reprojection error, and the highest accuracy in keypoint detection and matching on benchmark datasets. We also present a dedicated study on evaluation metrics to measure the accuracy of keypoint detection and matching.
Aerial-BiSeNet: A real-time semantic segmentation network for high resolution aerial imagery

The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes. However, accurate and fast semantic segmentation of high-resolution aerial images for remote sensing applications is still facing three challenges: the requirements for limited processing resources and low-latency operations based on aerial platforms, the balance between high accuracy and real-time efficiency for model performance, and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images. To address these issues, a lightweight and dual-path deep convolutional architecture, namely Aerial Bilateral Segmentation Network (Aerial-BiSeNet), is proposed to perform real-time segmentation on high-resolution aerial images with favorable accuracy. Specifically, inspired by the receptive field concept in human visual systems, Receptive Field Module (RFM) is proposed to encode rich multi-scale contextual information. Based on channel attention mechanism, two novel modules, called Feature Attention Module (FAM) and Channel Attention based Feature Fusion Module (CAFFM) respectively, are proposed to refine and combine features effectively to boost the model performance. Aerial-BiSeNet is evaluated on the Potsdam and Vaihingen datasets, where leading performance is reported compared with other state-of-the-art models, in terms of both accuracy and efficiency.
VisDrone-MOT2020: The Vision Meets Drone Multiple Object Tracking Challenge Results

The Vision Meets Drone (VisDrone2020) Multiple Object Tracking (MOT) is the third annual UAV MOT tracking evaluation activity organized by the VisDrone team, in conjunction with European Conference on Computer Vision (ECCV 2020). The VisDrone-MOT2020 consists of 79 challenging video sequences, including 56 videos ( $$\sim $$ 24K frames) for training, 7 videos ( $$\sim $$ 3K frames) for validation and 17 videos ( $$\sim $$ 6K frames) for evaluation. All frames in these sequences are manually annotated with high-quality bounding boxes. Results of 12 participating MOT algorithms are presented and analyzed in detail. The challenging results, video sequences as well as the evaluation toolkit are made available at http://aiskyeye.com/ . By holding VisDrone-MOT2020 challenge, we hope to facilitate future research and applications of MOT algorithms on drone videos.
Automated Focal Loss for Image based Object Detection

Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter. Manually tuning this hyperparameter for each training task is highly time-consuming. With automated focal loss we introduce a new loss function which substitutes this hyperparameter by a parameter that is automatically adapted during the training progress and controls the amount of focusing on hard training examples. We show on the COCO benchmark that this leads to an up to 30% faster training convergence. We further introduced a focal regression loss which on the more challenging task of 3D vehicle detection outperforms other loss functions by up to 1.8 AOS and can be used as a value range independent metric for regression.
Semi-Supervised Low-Rank Semantics Grouping for Zero-Shot Learning

Zero-shot learning has received great interest in visual recognition community. It aims to classify new unobserved classes based on the model learned from observed classes. Most zero-shot learning methods require pre-provided semantic attributes as the mid-level information to discover the intrinsic relationship between observed and unobserved categories. However, it is impractical to annotate the enriched label information of the observed objects in real-world applications, which would extremely hurt the performance of zero-shot learning with limited labeled seen data. To overcome this obstacle, we develop a Low-rank Semantics Grouping (LSG) method for zero-shot learning in a semi-supervised fashion, which attempts to jointly uncover the intrinsic relationship across visual and semantic information and recover the missing label information from seen classes. Specifically, the visual-semantic encoder is utilized as projection model, low-rank semantic grouping scheme is explored to capture the intrinsic attributes correlations and a Laplacian graph is constructed from the visual features to guide the label propagation from labeled instances to unlabeled ones. Experiments have been conducted on several standard zero-shot learning benchmarks, which demonstrate the efficiency of the proposed method by comparing with state-of-the-art methods. Our model is robust to different levels of missing label settings. Also visualized results prove that the LSG can distinguish the test unseen classes more discriminative.
Imagining 5G: Public sense-making through advertising in China and the US

This study initiates a line of research on how the fifth generation of wireless infrastructure (“5G”) is being imagined through media portrayals—in this case through advertising. At the time of this writing, 5G is not yet widely available, however the media is saturated with narratives about how it will revolutionize everyday life. Drawing from the social imaginaries and media infrastructures traditions, this textual analysis examines the social shaping of 5G through advertisements from leading telecoms in leading markets, including China and the United States. Findings reveal an overarching trend with ads from both societies imagining 5G in futuristic and utopian ways, suggesting new possibilities for people, objects, and places to be connected through smart homes, vehicles, factories, and cities—not just through smart phones. The findings also reveal distinctions in how 5G is envisioned at the societal level. For example, ads from China imagine 5G as a source of national pride that will elevate its global standing, while the US telecoms have a more inward focus on domestic competition. The discussion offers interpretations of these and other findings, along with directions for future research.
CLASTER: Clustering with Reinforcement Learning for Zero-Shot Action Recognition

Zero-Shot action recognition is the task of recognizing action classes without visual examples. The problem can be seen as learning a representation on seen classes which generalizes well to instances of unseen classes, without losing discriminability between classes. Neural networks are able to model highly complex boundaries between visual classes, which explains their success as supervised models. However, in Zero-Shot learning, these highly specialized class boundaries may overfit to the seen classes and not transfer well from seen to unseen classes. We propose a novel cluster-based representation, which regularizes the learning process, yielding a representation that generalizes well to instances from unseen classes. We optimize the clustering using reinforcement learning, which we observe is critical. We call the proposed method CLASTER and observe that it consistently outperforms the state-of-the-art in all standard Zero-Shot video datasets, including UCF101, HMDB51 and Olympic Sports; both in the standard Zero-Shot evaluation and the generalized Zero-Shot learning. We see improvements of up to 11.9% over SOTA. Project Page: https://sites.google.com/view/claster-zsl/home
A Secure and Distributed Framework for sharing COVID-19 patient Reports using Consortium Blockchain and IPFS

Today healthcare industries are maintaining COVID-19 patients' information electronically which includes patients' diagnostic reports, patients' private information, and doctor prescriptions.However, the COVID-19, patient sensitive information is currently stored in centralized or third-party storage model.One of the key challenge of centralized storage model is the preserving privacy of patient information and transparency in the system.The privacy risk include illegitimate access to sensitive information of patient such as identification details access and misutilization of patient information and their clinical records.To overcome this challenge, we proposed a distributed on-chain and off-chain storage model using consortium blockchain and interplanetary file systems (IPFS).The proposed framework though maintaining patient privacy makes it easier for legitimate entities like healthcare providers (e.g., physicians and clinical staffs) to access clinical data of COVID-19 patients'.
UDA‐Net: Densely attention network for underwater image enhancement

Underwater imaging usually suffers from negative impacts due to the absorption and scattering effects in water. Underwater images thus have unfavourable visual quality to support the work in such environment. This paper addresses the problem of image improvement for single underwater image. The core idea lies in a new enhancement model based on deep learning architecture, in which a feature-level attention model is developed. This model is a multi-scale grid convolutional neural network that can facilitate fusing different types of information during representation learning. According to this information combination, a synergistic pooling mechanism is proposed to extract the channel-wise attention maps to derive the locally weighted features. Therefore, this model can adaptively focus on the feature regions corresponding to degraded patches in one underwater image and improve these patches consistently. Comprehensive experiments are conducted on benchmark and natural underwater images, and it can be demonstrated that this model is effective.