Reviewerly/exHarmony huggingface dataset:
Integrating Threshold Opening With Threshold Issuance of Anonymous Credentials Over Blockchains for a Multi-Certifier Communication Model

Authentication while maintaining anonymity when availing a service over the internet is a significant privacy challenge. Anonymous credentials (AC) address this by providing the user with a credential issued by a trusted entity that convinces the service provider ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SP</i> ) that the user is authenticated but reveals no other information. The existing AC schemes assume a single trusted authority (certifier) that validates all the user attributes. In practice, however, a user may require different attributes to be attested by different certifiers. This means that the user has to get multiple credentials, increasing the burden on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SP</i> who has to verify each one of them. Moreover, complete anonymity can be misused. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized threshold revocable anonymous credential (DTRAC)</i> scheme over blockchains that supports – a) attestation of attributes by multiple certifiers, and b) anonymity revocation through a set of distributed openers, by integrating threshold opening to the state-of-the-art threshold anonymous credential issuance scheme, Coconut [34]. DTRAC generates a single credential on attributes that are attested by multiple certifiers, freeing the SP from the hassle of verifying multiple credentials. We analyze the security of DTRAC formally in the universal composability (UC) framework. We also implement a prototype on Ethereum using smart contracts and give a detailed analysis of its performance.We compare the verification time for credentials with attributes attested by multiple certifiers in both DTRAC and Coconut and see that in terms of execution time and gas consumption, DTRAC performs significantly better than Coconut. It also scales better, with the performance gain of DTRAC over Coconut increasing linearly with the number of certifiers.
The use of educational multimedia technologies in the study of a foreign language

The use of pedagogical multimedia technologies in teaching a foreign language makes it possible to increase the degree of assimilation of educational material. Multimedia technologies, having visibility, are able to revive the course of the educational process, give it dynamics and increase the motivation of students. Multimedia educational technologies create favorable conditions for the development of creative abilities and aesthetic taste in students, and also make the learning process more exciting. The use of multimedia technologies in the educational process allows students to develop abstract thinking, as well as to apply personality-oriented teaching methods in practice. Multimedia educational technologies are able to form students' skills of self-assessment, abstract thinking, reflection and independent planning of their activities. With the effective use of multimedia in the educational process, students' critical thinking is activated and educational activity is activated.
Deep Open-Set Segmentation in Visual Learning

Collecting samples that exhaust all possible classes for real-world tasks is usually hard or even impossible due to many different factors. In a realistic/feasible scenario, methods should be aware that the training data is incomplete and not all knowledge is available. In this scenario, in test time, developed methods should be able to identify the unknown samples while correctly executing the proposed task to the known classes. Open-Set Recognition and Semantic Segmentation models emerge to handle this sort of scenario for visual recognition and dense labeling tasks, respectively. In this work, we propose a novel taxonomy aiming to organize the literature and provide an understanding of the theoretical trends that guided the existing approaches which may influence future methods. Moreover, we also provide the first systematic review of open-set semantic segmentation methods.
DCT-CenterMask: A Real-Time Instance Segmentation Network for Kidney Ultrasound Images

Ultrasound is the preferred imaging method to detect chronic kidney disease. It has the characteristics of real-time dynamic imaging. The existing real-time instance segmentation model can meet the real-time requirements of 30fps of segmentation speed, but there is still a certain gap in segmentation accuracy compared with the instance segmentation model. In this paper, a real-time instance segmentation model based on CenterMask is proposed. Using the characteristics that discrete cosine transform (DCT) can reduce the energy loss in the process of down sampling the ground truth mask, a new mask representation is trained on the premise of maintaining the real-time segmentation speed. We also integrate deformable convolution and a new attention mechanism into the network to improve the overall receptive field range of the network and the ability to learn detailed features. For further research, we made a COCO format kidney instance segmentation dataset. Through experiments, our model has improved the mask AP accuracy by about 6% compared with the original model.
Evaluating the Robustness of Depth Image Super-Resolution Models

Depth image super-resolution (DISR) is one of the hot topics in computer vision. Although great progress has been made in this research topic, the robustness of DISR models is not sufficiently investigated, which is of great importance in the real applications. Accordingly, in this paper, we make an initial attempt to investigate the robustness of DISR models. Specifically, we test their generalization ability when the input depth image suffers from visual quality degradation. To facilitate this study, we construct a large-scale depth image dataset in which the reference depth images are perturbed to generate the degraded depth images automatically. Then, we test six top-performing DISR models on the constructed dataset and then compare their strengths and weaknesses. By conducting comprehensive experiments, we find that depth image super-resolution models perform poorly on Gaussian noise, and that the higher the level, the lower the quality of the predicted depth map. Furthermore, some DISR models only outperform at lower magnifications (such as 2x and 4x).
Framework to Benchmark CNNs (FaBCNN) for Processing Real-Time HD Video Streams on FPGAs

The deployment of Convolutional Neural Networks (CNNs) on resource-constrained edge devices for inference is challenging due to its computation, memory, energy, and bandwidth requirements. To address these issues, FPGAs are commonly used to implement CNNs because of their high flexibility and low power consumption. This paper proposes a methodology that provides a technique to benchmark CNNs using HDMI input and output in real-time with 720p high definition (HD) resolution. This methodology can be utilized in a classroom set up to teach CNN and computer vision fundamentals. To illustrate the effectiveness of the proposed methodology, several object detection and image classification CNNs were deployed on the Xilinx ZCU104 FPGA board. Video is provided to the FPGA in real-time from an HDMI input source. The output of a given CNN is converted to an HDMI stream and displayed on a separate monitor at 720p HD resolution. The experimental results show that this methodology can perform object detection and image classification on real-time video at speeds of around 10 FPS and 30 FPS, respectively.
A Comparison of Text Selection Algorithms for Sequence-to-Sequence Neural TTS

Previous research demonstrated that text selection algorithms applied in the context of concatenative and parametric text-to-speech (TTS) systems were able to increase synthesis quality (i.e., intelligibility and naturalness). In this work, we investigate the effects of such algorithms on the quality of a sequence-to-sequence neural TTS system when they are used to create the training set. We compare how the mel spectrograms generated by Tacotron are affected by training sets with a total duration of six hours created using three selection approaches: random selection, greedy selection, and a modified greedy selection that attempts to produce a uniform symbol distribution. The evaluation was done objectively with the mel-cepstral distance, showing that the random approach did not give favorable results at all compared to the two greedy approaches, which in turn achieved close results to training on about three times as much data. Thereby, the second greedy approach achieved significantly better results than the first one, making it the recommended approach for the task of reading script creation. We expect these findings to help build shorter scripts and thus reduce recording costs, especially for low-resource languages for which TTS systems should be deployed.
A Spatial-Temporal Similarity-Based Cooperative Surveillance Framework by Edge

Most of the current video cooperative surveillance strategies upload all video clips the camera takes, which will cause great data redundancy and bandwidth waste. In this paper, we combine temporal similarity with spatial similarity and introduce the concept of spatiotemporal similarity. In particular, we design a framework to calculate spatial-temporal similarity to reduce the complexity of the collection and the transmission of source data. Besides, we model the problem of minimum spatiotemporal similarity with the bandwidth limitation into a knapsack problem and propose a dynamic programming-based algorithm to determine the selection of video uploading. The results show the framework can make a 10% data redundancy reduction and bandwidth saving.
A Simple but Effective Non-local Means Method For Salt and Pepper Noise Removal

In order to expand the application field of the non-local means algorithm and improve the performance of removing salt and pepper noise, this paper proposes a simple but effective non-local means method for removing salt and pepper noise. Firstly, this method uses a sliding window to identify noise points and uses local means or median filtering to initially remove noise. Secondly, the proposed non-local means filtering algorithm with adaptive parameter is used to perform secondary denoising. In this paper, the smoothing parameter is designed as a piecewise function based on the intensity level of salt and pepper noise. The experimental results on the public dataset show that the new filter balances the relationship between the denoising effect and the time-consuming, and achieves state-of-the-art (SOTA) effect. Moreover, the new filter can effectively restore the pixels of the contaminated image and preserve the texture details of the image. Through comparative experiments, the superiority of the new method is proved.
Structural String Decoder for Handwritten Mathematical Expression Recognition

Recently, recognition of handwritten mathematical expression has been greatly improved by employing sequence modeling methods such as encoder-decoder based methods. Existing encoder-decoder models use string decoders or tree decoders to generate markup of mathematical expression recognition. String decoders directly generate LaTeX strings and tree decoders decode expressions into tree structures. The generalization of string decoders is poor on mathematical expressions with complex hierarchical structures, but its language model is better. Tree decoders can deal with the complex hierarchical structures, but its language model is weakened. In order to take advantage of the above two decoders, we propose a novel structural string decoder (SSD) which not only has good generalization but also can make good use of language model. We demonstrate how the proposed SSD outperforms state-of-the-art string decoders and tree decoders through a set of experiments on CROHME database, which is currently the largest benchmark for online handwritten mathematical expression recognition.
Collaborative Contrastive Learning-Based Generative Model for Image Inpainting

The critical challenge of image inpainting is to infer reasonable semantics and textures for a corrupted image.Typical methods for image inpainting are built upon some prior knowledge to synthesize the complete image.One potential limitation is that those methods often remain undesired blurriness or semantic mistakes in the synthesized image while handling images with large corrupted areas.In this paper, we propose a Collaborative Contrastive Learning-based Generative Model (C2LGM), which learns the content consistency in the same image to ensure that the inferred content of corrupted areas is reasonable compared to the known content by pixel-level reconstruction and high-level semantic reasoning.C2LGM leverages the encoder-decoder based framework to directly learn the mapping from the corrupted image to the intact image and perform the pixel-level reconstruction.To perform semantic reasoning, our C2LGM introduces a Collaborative Contrastive Learning (C2L) mechanism that learns highlevel semantic consistency between inferred and known content.Specifically, C2L mechanism introduces the high-frequency edge maps to participate in the process of typical contrastive learning and enables the deep model to ensure the semantic reasonableness between high-frequency structures and pixel-level content by pushing the representations of inferred content and known content close and keeping unrelated semantic content away in the latent feature space.Moreover, C2LGM also directly absorbs the prior knowledge of structural information from the proposed structural spatial attention module, and leverages the texture distribution sampling to improve the quality of synthesized content.As a result, our C2LGM achieves a 0.42 dB improvement over competing methods in terms of the PSNR metric while coping with a 40∼ 50% corruption ratio in the Places2 dataset.Extensive experiments on three benchmark datasets, including Paris Street View, CelebA-HQ, and Places2, demonstrate the advantages of our proposed C2LGM over other state-of-the-art methods for image inpainting both qualitatively and quantitatively.
A speaker verification method using frame-level self-attention

A new method of applying the self-attention mechanism for text-independent speaker verification is presented in this paper. There are differences between the speech frames, which may affect the performance of the speaker verification system. Generally, to capture the differences, the speaker verification system needs to perform a weighted average for the frame-level output in the pooling stage of extracting the speaker embedding, and the weights are learned through the self-attention mechanism. In this work, the self-attention mechanism is introduced into the frame-level model in the system to extract frame-level embeddings, which provides a new way of capturing the differences of speech frames for speaker verification. A self-attention layer is added between the frame-level layers to directly obtain different frame-level features. These features are then combined to form the more discriminative speaker embeddings so that the system achieves better performance. Results of experiments conducted on the Voxceleb1 dataset show that the proposed system outperforms the baselines. Moreover, the improvement is consistent with different speech duration.
Image Subset Union for Compressed Image Sets in Cloud Servers

Image deletion and image insertion, the current commonly used two kinds of image set management, refer to removing compressed images from and adding new photos to a compressed image set, respectively. However, they do not deal with well the problem of the combination of images selected from multiple compressed image sets. To address this issue, in this paper we first propose an image subset union algorithm for compressed image sets. Image subset union aims to integrate image subsets derived from multiple compressed image sets into a new compressed image set. First, we put forward a way to identify the root vertex candidate of the new compressed image set. Second, we classify all the images of image subsets into three categories: images needed to be re-encoded, images needed to be only decoded, and images unneeded to be re-encoded or decoded. Third, we also employ minimum spanning tree production, a proposed vertex layer candidate assignment method, depth- and subtree-constrained minimum spanning tree generation, as well as image re-encoding to build the new compressed image set. Experimental results show that our proposed algorithm can effectively accomplish image subset union with low complexity.
Spatial-Aware GAN for Instance-Guided Cross-Spectral Face Hallucination

An efficient strategy to solve the Heterogeneous Face Recognition (HFR) is to translate the probes to the same spectrum domain of the galleries using generative models. However, without or with only globally-pooled appearance representation from a reference, the low-quality generated images restrict the recognition accuracy. The intuition of our paper is the spatially-distributed appearance contains details beneficial to higher-quality image synthesis. Particularly, we propose a semantic spatial adaptive alignment module to solve the inevitable misalignment between the content from the near-infrared (NIR) image and the appearance from the visible (VIS) reference. In this way, arbitrary VIS reference can provide appearance with sufficient details to assist the NIR-to-VIS translation. Based on this, we propose an unsupervised spatial-aware instance-guided cross-spectral facial hallucination network (SICFH) for visual-pleasing and identity-preserved VIS image translation. Qualitative and quantitative experiments on three challenging NIR-VIS datasets demonstrate the synthesized VIS images address the HFR problem effectively and achieve state-of-the-art recognition accuracy.
Light-weight Video Encoding Complexity Prediction using Spatio Temporal Features

The increasing demand for high-quality and low-cost video streaming services calls for the prediction of video encoding complexity. The prior prediction of video encoding complexity including encoding time and bitrate predictions are used to allocate resources and set optimized parameters for video encoding effectively. In this paper, a light-weight video encoding complexity prediction (VECP) scheme that predicts the encoding bitrate and the encoding time of video with high accuracy is proposed. Firstly, low-complexity Discrete Cosine Transform (DCT)-energy-based features, namely spatial complexity, temporal complexity, and brightness of videos are extracted, which can efficiently represent the encoding complexity of videos. The latent vectors are also extracted from a Convolutional Neural Network (CNN) with MobileNet as the backend to obtain additional features from representative frames of each video to assist the prediction process. The extreme gradient boosting (XGBoost) regression algorithm is deployed to predict video encoding complexity using the extracted features. The experimental results demonstrate that VECP predicts the encoding bitrate with an error percentage of up to 3.47% and encoding time with an error percentage of up to 2.89%, but with a significantly low overall latency of 3.5 milliseconds per frame which makes it suitable for both Video on Demand (VoD) and live streaming applications.
Proactivity in Intelligent Personal Assistants: A Simulation-Based Approach

Intelligent personal assistants (IPAs) are playing an increasingly significant role in our everyday lives. However, their reactive behaviour limits their assistance to a user’s explicit requests. Integrating proactivity in these assistants would allow them to perform tasks on behalf of users without an explicit request and, in turn, improve the user experience. To explore this goal, this work presents MAIS (Multi Agent Interactions Simulator) which is capable of generating simulations of proactive agents. MAIS allows us to study the behaviour of agents, their interactions with each other in a multi-agent environment, and the importance of different agent features to accomplish a particular task. This paper presents the results from my experiments for a use case based on meeting scheduling in an organisational setting and the potential opportunities and challenges related to the adoption of IPAs. Human-in-the-loop control is introduced as a key component and the challenges arising with varying levels of autonomy in agents are also studied.
Decoding Feature Maps with Multi-scale Convolutional Kernels

Short-term extreme precipitation can easily cause natural disasters, so accurate and timely warnings and forecasts are needed for the extent and intensity of precipitation. Conv-LSTM extend the FC-LSTM to contain convolutional structures not only in the input-to-state transitions, but also in state-to-state transitions, being able to solve precipitation nowcasting problem with an end-to-end model which is trainable. When processing images, combine images from different scale will add more spatial information into the results, which is helpful in handling images related to weather radar echo data. In this paper, we consider precipitation nowcasting as a spatiotemporal sequence forecasting problem, including the input data and the prediction target. We use ConvLSTM as the encoder from which we will get the temporal information and use muti-scale convolutional layers as the decoder to get the final results.
A Hybrid Trajectory Planning Strategy for Intelligent Vehicles with Collision Avoidance

This paper presents a hybrid trajectory planning strategy with interpolation curve optimization method under the constraints of collision avoidance and vehicle dynamics. With an objective function consisting of a weighted sum of comfort and efficiency, the cubic polynomial curve is firstly optimized under the initial state and the terminal state constraints to generate a reference trajectory. Then, in order to generate collision-free trajectory, lateral and longitudinal turning factors are introduced to cluster the reference trajectory. According to the dimension relationship between vehicle and road, the maximum lane change distance and the maximum turning curvature, the number of trajectories in the cluster is constrained. Furthermore, an algorithm is designed to detect the collision of the trajectories. The priority of the trajectories is defined to determine the optimal trajectory. Finally, a lateral vehicle dynamic model is established. To judge the feasibility, under the condition of dynamic constraints, the reference trajectory is tracked with optimal control signal and judged by the tracking effect. In this way, the collision risk is eliminated, and the trajectory performance is improved. Simulation results show that the trajectory planning strategy is effective in different scenarios.
Planning Searches Using an Orienteering-based Vehicle Routing Approach

We develop a new efficient heuristic for search-based route planning as a vehicle routing problem. Given the nondeterministic polynomial time hardness or NP-hard nature of vehicle routing problems, robust formulations are often relegated to solutions by way of heuristics or numerical approximations. While the solutions are not optimal solutions for the original problem, they are often accepted as they are close enough. However, if we want to expand these approaches beyond the standard well-established vehicle routing problems (such as the traveling salesman problem and the orienteering problem), new solutions are sought. We present a greedy heuristic that uses simple rules that expands an initial path to find solutions to vehicle routing problems of the orienteering type, particularly modified to account for search planning. The resulting algorithm provides an efficient computation of an optimized search route that can take into account both vehicle and environmental conditions.