Reviewerly/exHarmony huggingface dataset:
Towards Real-time Object Detection on Edge Devices for Vehicle and Pedestrian Interaction Scenarios

Object detection in complex road environments has always been an important and challenging task in assisted driving systems for autonomous driving. However, the low accuracy or the inability to achieve real-time performance limits the application of current object detectors in autonomous driving. In view of the difficulty in real-time and accurate detection of vehicles and pedestrians in a road environment, a lightweight detection network (MRS-YOLOv3) applied on edge devices is proposed based on the structure of YOLOv3. Combining the multi-receptive field spatial pyramid pooling block and the bidirectional feature pyramid path aggregation structure, the output feature map of the backbone is interacted in the spatial domain and the scale domain. By introducing not-adjacent scale feature interaction module before multi-scale feature aggregation, cross-scale features can be efficiently interacted. In addition, we also use DIoU Loss and Focal Loss as the loss function to make the model achieve better performance. Finally, we deployed the proposed model to the edge device Jetson TX2 for actual evaluation. The results show that MRS-YOLOv3 can perform real-time and efficient detection in vehicle and pedestrian interaction scenarios, achieving a better trade-off between detection accuracy and speed.
Privacy Protection Revolution: Zero-knowledge Proof

Online privacy protection is highly associated with verification since it is a part of communication between users and servers. Because privacy has a probability of leaking out during the transmissions. With a new method without knowing and verifying the privacy, the verification method will have a revolution. The traditional verification pattern can be rewritten. This paper introduces the history of verification or secret protection and specifically discusses the theory of zero-knowledge proof. After pointing out that the disadvantage of zero-knowledge is time-consuming, this paper expends the theory with zk-SNARK which stands for “zero-knowledge succinct non-interactive argument of knowledge”. The part of zk-SNARK gives 2 implementations by linear algebra and polynomial. This paper lists 3 applications of zk-SNARK, which are z-cash, filecoin and loopring 3.0 in detail. In the end, this paper presents the advantage of zero-knowledge proof with the comparison of other currencies and discusses the risk of using zero-knowledge proof as contracts.
A Self-Supervised Hyperspectral Image Restoration Method Based on Convolutional Sparse Coding and Superpixel Segmentation

Denoising is a common preprocessing step before hyperspectral image (HSI) analysis and interpretation. Since noise-free training samples are not required, self-supervised deep learning is becoming a new research trend in image restoration. In this paper, we propose a self-supervised denoising method based on noise2noise learning scheme that assumes two noisy signals in a pair being randomly generated from the same clean signal. To obtain noisy-noisy training pairs that meet the requirement of noise2noise learning, the target noisy HSI is divided into several homogeneous image patches by superpixel segmentation. Pixels located in the same superpixel have strong spectral similarity and spatial neighborhood, so they are used to construct noisy-noisy training pairs and the spectral-spatial correlations in an HSI can be embedded in our denoiser via training samples. Moreover, the convolutional sparse coding is introduced and unfolded as the backbone network for spectral signature representation and restoration. Experimental results show that our method is competitive compared with the state-of-the-art methods.
Blockchain abnormal transaction detection method based on weighted sampling neighborhood nodes

In view of the increasingly serious problem of money laundering crime in the blockchain industry, the existing solutions in this scenario can not be applied to reality, or there is a high false positive rate and false negative rate, a method based on weighted sampling neighborhood nodes is designed to find and analyze the implied interrelationship in the data between blockchain transaction features, and learn more effective aggregate input features in the local neighborhood of nodes through model training. Good results have been achieved on the public data set, which verifies the effectiveness of the model in the field of blockchain abnormal transaction detection. Moreover, it provides an idea of modelling transaction entity data by using graph neural network structure for the field of financial data transactions such as anti-money laundering monitoring.
Neural Recognition of Dashed Curves with Gestalt Law of Continuity

Dashed curve is a frequently used curve form and is widely used in various drawing and illustration applications. While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of continuity in Gestalt psychology, it is extremely difficult for computers to model the Gestalt law of continuity and recognize the dashed curves since high-level semantic understanding is needed for this task. The various appear-ances and styles of the dashed curves posed on a potentially noisy background further complicate the task. In this paper, we propose an innovative Transformer-based framework to recognize dashed curves based on both high-level features and low-level clues. The framework manages to learn the computational analogy of the Gestalt Law in various do-mains to locate and extract instances of dashed curves in both raster and vector representations. Qualitative and quantitative evaluations demonstrate the efficiency and ro-bustness of our framework over all existing solutions.
Creative Visualisation Opportunities Workshops: A Case Study in Population Health

Population Health Management (PHM) relies on the analysis of data from several sources to account for the complex interaction of factors that contribute to the health and well-being of a population, while considering biases and inequalities across sub-populations. Visualisation is emerging as an essential tool for insight generation from data shared and linked across services including healthcare, education, housing, policing, etc. However, visualisation design is challenged by poor data connectivity and quality, high dimensionality and complexity of real-world routinely collected data, in addition to the heterogeneity of users’ backgrounds and tasks. The Creative Visualisation Opportunities (CVO) framework provides a structured approach for working with diverse communities of visualisation stakeholders and defines a set of participatory activities for the effective elicitation of requirements and visualisation design alternatives. We conducted three workshops, applying variations of the CVO framework, with over one hundred participants from the PHM domain, including clinicians, researchers, government and private sector representatives, and local communities. In this paper, we present the results of preliminary analysis of these activities and report on the perceived impact of visualisation in this domain from a stakeholders’ perspective. We report real-world successes and limitations of applying the framework in different formats (through online and in-person workshops), and reflect on lessons learned for task analysis and visualisation design in the PHM domain.
DS-AT: An efficient watermarking technique based on Arnold transform in DCT and SVD domain

The researchers have a difficult task in preserving the trade-off between robustness and imperceptibility. To address this problem, this paper presents a discrete cosine transform (DCT)-singular value decomposition (SVD)-based watermarking technique. This technique initially partitioned the host image into 8 × 8 DC blocks followed by DCT. The encrypted watermark logo by the Arnold transform is concealed into the SVD blocks of host image. The performance of the presented technique is measured against Lena and Barbara images under ten watermarking attacks in terms of PSNR and NC. The simulation results validate that the proposed techniques performs well than the compared watermarking schemes.
A Secure Word Vector Training Scheme Based on Inner-Product Functional Encryption

Word vector training is usually deployed in machine learning to provide a model architecture and optimization, for example, to learn word embeddings from a large amount of datasets. Training word vector in machine learning needs a lot of datasets to train and output a model, however, some of which might contain private and sensitive information, and the training phase will lead to the exposure of the trained model and user datasets. In order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. In this article, we investigate the problem of training high-quality word vectors on encrypted datasets by using privacy-preserving learning algorithms. Firstly, we use a pseudo-random function to generate a statistical token for each word to help build the vocabulary of the word vector. Then we employ functional inner-product encryption to calculate the activation function to obtain the inner product, securely. Finally, we use BGN cryptosystem to encrypt and hide the sensitive datasets, and complete the homomorphic operation over the ciphertexts to perform the training procedure. Compared with the existing solutions, it indicates that our scheme can provide a higher efficiency and less communication overhead.
Self-Supervised Image Representation Learning with Geometric Set Consistency

We propose a method for self-supervised image representation learning under the guidance of 3D geometric consistency. Our intuition is that 3D geometric consistency priors such as smooth regions and surface discontinuities may imply consistent semantics or object boundaries, and can act as strong cues to guide the learning of 2D image representations without semantic labels. Specifically, we introduce 3D geometric consistency into a contrastive learning framework to enforce the feature consistency within image views. We propose to use geometric consistency sets as constraints and adapt the InfoNCE loss accordingly. We show that our learned image representations are general. By fine-tuning our pre-trained representations for various 2D image-based downstream tasks, including semantic segmentation, object detection, and instance segmentation on real-world indoor scene datasets, we achieve superior performance compared with state-of-the-art methods.
Image-Based Storytelling Using Deep Learning

In order to describe a journey, a story could be automatically generated from a group of digital photographs. Most of the existing methods focus on descriptions of specific content of a single image, such as image captioning, which lack of correlation between the images and the spatiotemporal relationships. To this end, in this paper, our goal is to propose a novel storytelling architecture based on computer vision. It makes use of visual object detection from digital images. Combining the changes in spatiotemporal domain and filling in the predetermined template, we automatically generate a text-based travel diary. In this project, compared with conventional image captioning, our aims are to effectively connect correlation between digital images and background information. The contributions of this paper are: (1) Innovative use of preset templates to generate travel diaries from photographs, associating content and context of the images as an event, (3) augmenting the images to expand the dataset, (4) shortening training time of deep learning models.
Multi-Reception and Multi-Gradient Discriminator for Image Inpainting

Many deep learning methods for image inpainting rely on the encoder-decoder architecture to estimate missing contents. When guidance information from uncorrupted regions could not be adequately represented or utilized, the encoder may have difficulty handling the rich surrounding or background pixels, and the decoder could not recover visually sophisticated or realistic contents. This paper proposes an effective multi-scale optimization network to alleviate these issues and generate coherent results with fine details. It encodes multi-receptive fields feature maps adaptively and puts multi-scale outputs into a discriminator to guide training. Specifically, we propose a Multi-Receptive feature maps & masks Selective Fusion (MRSF) operator that can adaptively extract features at different receptive fields to handle sophisticated destroyed images. Then a Multi-Gradients Discriminator (MGD) module uses intermediate features of the discriminator to guide the generator to produce results with natural textures and semantically real contents. Experiments on several benchmark datasets demonstrate that our proposed method can synthesize more realistic and coherent image contents.
A Comparative Study of Short Text Classification Methods for Bug Report Type Identification

This document is a model and instructions for LATEX. Previous related studies often used the ‘summary’ of bug reports because this part contains less noise. However, bug report summaries are often short, leading to short text classification issues which may have been overlooked. This study compares short text classification methods by categorizing bug reports into two classes as real-bug and non-bug based on three major factors namely bug report features, term weighting schemes and machine learning algorithms. Four bug report features (i.e. unigram, unigram + bigram, unigram + CamelCase, and all features), three term weighting schemes (i.e. tf, tf-idf and tf-igm) and three machine learning algorithms (i.e. random forest, support vector machine, and k-means clustering) are compared using bug reports relating to the Mozilla Firefox open source. Finally, unigram + CamelCase features along with tf-igm and support vector machine provide the most optimal bug report classification performance.
Automated Key Recovery Attacks on Round-Reduced Orthros

Orthros is a low-latency keyed pseudo-random function designed by Banik et al. in FSE 2022. It adopts the parallel structure composed of two keyed permutations. Both branches take the same 128-bit input and their outputs are XORed to generate the final 128-bit output. Benefiting from this special structure, it’s security is hard to evaluate, especially for key recovery attacks. In its specification, the most effective distinguisher proposed is a 7-round integral one. However, it can only lead to key recovery attacks worse than exhaustive attack. Besides, there is no key recovery attack presented in the design document. Therefore, we are motivated to see whether a valid key recovery attack exists and how powerful it can be. In this paper, we aim to proceed differential and differential-linear key recovery attacks on Orthros. To deal with the special structure, we introduce two automated key recovery attack frameworks that work for such two-branch ciphers. With the help of them, we finally got a 7-round differential-linear key recovery attack and a 6-round differential one. Both attacks are the first key recovery attacks on this cipher. However, they are so far from threatening its full-round security.
RGB-X Classification for Electronics Sorting

Effectively disassembling and recovering materials from waste electrical and electronic equipment (WEEE) is a critical step in moving global supply chains from carbon-intensive, mined materials to recycled and renewable ones. Conventional recycling processes rely on shredding and sorting waste streams, but for WEEE, which is comprised of numerous dissimilar materials, we explore targeted disassembly of numerous objects for improved material recovery. Many WEEE objects share many key features and therefore can look quite similar, but their material composition and internal component layout can vary, and thus it is critical to have an accurate classifier for subsequent disassembly steps for accurate material separation and recovery. This work introduces RGB-X, a multi-modal image classification approach, that utilizes key features from external RGB images with those generated from X-ray images to accurately classify electronic objects. More specifically, this work develops Iterative Class Activation Mapping (iCAM), a novel network architecture that explicitly focuses on the finer-details in the multi-modal feature maps that are needed for accurate electronic object classification. In order to train a classifier, electronic objects lack large and well annotated X-ray datasets due to expense and need of expert guidance. To overcome this issue, we present a novel way of creating a synthetic dataset using domain randomization applied to the X-ray domain. The combined RGB-X approach gives us an accuracy of 98.6% on 10 generations of modern smartphones, which is greater than their individual accuracies of 89.1% (RGB) and 97.9% (X-ray) independently. We provide experimental results <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Experimental work done at Biorobotics Lab, Robotics Institute, Carnegie Mellon University to corroborate our results.
Blood Cells Classification Using Deep Learning Technique

There are three major types of blood cells, red blood cells (erythrocytes), white blood cells (leukocytes), and platelets (thrombocytes). Together, these three kinds of blood cells add up to a total of 45% of the blood tissue by volume, with the remaining 55% of the volume composed of plasma, the liquid component of blood. These three types play an important role in the human body by increasing immunity by fighting against infectious diseases. The classification and count of blood cells play an important role in the detection of a disease in an individual. It can also assist with the identification of diseases like infections, anemia, leukemia, cancer, etc. This classification will assist the hematologist to distinguish between the types of white blood cells, red blood cells, and platelets present in the human body and find the root cause of diseases. Currently, there is a large amount of research going on in this field. Considering a huge potential in the significance of the classification of different blood cells, a deep learning technique called Convolution Neural Networks will be used which can classify the images of human blood cells into their subtypes namely Neutrophils, Eosinophils, Basophils, Lymphocytes, Monocytes, Immature Granulocytes (Promyelocytes, Myelocytes, and Metamyelocytes), Red blood cells or Erythroblasts and Platelets or Thrombocytes. In this paper, the discussion have been done on a dataset that was acquired in the Core Laboratory at the Hospital Clinic of Barcelona using Convolution Neural Networks.
Robust and Generalized DeepFake Detection

Images that are manipulated are prevalent and are on the spike because of the advancement in deep convolutional neural networks (CNNs) techniques. There have been several concerns regarding the advent spread of false information. There exists a need for a reliable and robust method to detect such fake images. In this paper, analysis was done using the architecture SlowFast in detecting manipulated videos. This paper focuses on detecting DeepFake videos under three distinct scenarios, which are (i) all manipulation detection, (ii) single manipulation detection, and then (iii) cross manipulation detection used to test the veracity of the videos. The manipulation methods and designing algorithms to categorize such unknown manipulation techniques were used.
Video Instance Segmentation by Instance Flow Assembly

Instance segmentation is a challenging task aiming at classifying and segmenting all object instances of specific classes. While two-stage box-based methods achieve top performances in the image domain, they cannot easily extend their superiority into the video domain. This is because they usually deal with features or images cropped from the detected bounding boxes without alignment, failing to capture pixel-level temporal consistency. We embrace the observation that bottom-up methods dealing with box-free features could offer accurate spacial correlations across frames, which can be fully utilized for object and pixel level tracking. We first propose our bottom-up framework equipped with a temporal context fusion module to better encode inter-frame correlations. Intra-frame cues for semantic segmentation and object localization are simultaneously extracted and reconstructed by corresponding decoders after a shared backbone. For efficient and robust tracking among instances, we introduce an instance-level correspondence across adjacent frames, which is represented by a center-to-center flow, termed as instance flow, to assemble messy dense temporal correspondences. Experiments demonstrate that the proposed method outperforms the state-of-the-art online methods (taking image-level input) on the challenging Youtube-VIS dataset (Yang et al., 2019).
Short-Term Load Forecasting by FOS-ELM with Re-Learning Method

This article presents a method for short-term load forecasting by Fully Online Sequential Extreme Learning Machine (FOS-ELM) model. The model can learn incrementally without the need for initial training samples. The model is suitable for use in locations where historical load data is not available to train the model. This article also presents the improvement of the forecasting accuracy of the FOS-ELM model by letting the FOS-ELM model learn from each newly received sample more than once, called “re-learning”. A comparative experiment between the FOS-ELM model without re-learning and the FOS-ELM model with re-learning found that the re-learning method can reduce the error in load forecasting. This is because the model can quickly adapt to the trends of new data. But after the fifth round of re-learning, the forecasting error value could no longer decrease. This is because the model has an over-fit with the latest learned data. In other words, re-learning allows the model to have lower forecasting errors, but the number of re-learning times must be chosen appropriately.
Multi-Task Learning Framework for Detecting Hashtag Hijack Attack in Mobile Social Networks

As a refinement of a microblog, hashtag supports the services of search and recommendation for mobile social networks. However, it's vulnerable to hashtag hijack attack. To solve this problem, previous works either only direct at some specific hijack or extract features to match the microblog's content to the hashtag. However, these solutions cannot distinguish between non-malicious use of hashtags by normal users and real hashtag hijack by attackers. By analyzing the behavioral intentions of users, we propose a multi-task learning framework to address this issue, which includes three relevant learning tasks. First, we introduce the task of text matching by combining a pre-trained language model with LSTM, to improve the accuracy and generalization of our model. Second, we design the task of informative text classification to classify informative and non-informative microblogs, which excludes the effect of the non-malicious use of hashtags. Third, we propose the task of hashtag hijack identification with the cooperation of the other two tasks. Hence, our hard-parameter sharing multi-task learning framework is constructed by utilizing the internal connections among three tasks. Finally, we design a dynamic weighting method to optimize multi-task loss function for our multi-task learning framework. Extensive experiments have demonstrated that our model can significantly improve the detection accuracy of hashtag hijack attack.