Reviewerly/exHarmony huggingface dataset:
Describing and Localizing Multiple Changes with Transformers

Change captioning tasks aim to detect changes in image pairs observed before and after a scene change and generate a natural language description of the changes. Existing change captioning studies have mainly focused on a single change. However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-change captioning dataset; (ii) We benchmark existing state-of-the-art methods of single change captioning on multi-change captioning; (iii) We further propose Multi-Change Captioning transformers (MCCFormers) that identify change regions by densely correlating different regions in image pairs and dynamically determines the related change regions with words in sentences. The proposed method obtained the highest scores on four conventional change captioning evaluation metrics for multi-change captioning. Additionally, our proposed method can separate attention maps for each change and performs well with respect to change localization. Moreover, the proposed framework outperformed the previous state-of-the-art methods on an existing change captioning benchmark, CLEVR-Change, by a large margin (+6.1 on BLEU-4 and +9.7 on CIDEr scores), indicating its general ability in change captioning tasks. The code and dataset are available at the project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
RMS-Net: Regression and Masking for Soccer Event Spotting

The recently proposed action spotting task consists in finding the exact timestamp in which an event occurs. This task fits particularly well for soccer videos, where events correspond to salient actions strictly defined by soccer rules (a goal occurs when the ball crosses the goal line). In this paper, we devise a lightweight and modular network for action spotting, which can simultaneously predict the event label and its temporal offset using the same underlying features. We enrich our model with two training strategies: the first one for data balancing and uniform sampling, the second for masking ambiguous frames and keeping the most discriminative visual cues. When tested on the SoccerNet dataset and using standard features, our full proposal exceeds the current state of the art by 3 Average-mAP points. Additionally, it reaches a gain of more than 10 Average-mAP points on the test set when fine-tuned in combination with a strong 2D backbone.
Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud

3D object grounding aims to locate the most relevant target object in a raw point cloud scene based on a freeform language description. Understanding complex and diverse descriptions, and lifting them directly to a point cloud is a new and challenging topic due to the irregular and sparse nature of point clouds. There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object. In this paper, we address all three challenges. Firstly, we propose a language scene graph module to capture the rich structure and long-distance phrase correlations. Secondly, we introduce a multi-level 3D proposal relation graph module to extract the object-object and object-scene co-occurrence relationships, and strengthen the visual features of the initial proposals. Lastly, we develop a description guided 3D visual graph module to encode global contexts of phrases and proposals by a nodes matching strategy. Extensive experiments on challenging benchmark datasets (ScanRefer [3] and Nr3D [42]) show that our algorithm outperforms existing state-of-the-art. Our code is available at https://github.com/PNXD/FFL-3DOG.
WITHDRAWN: Spam email detection using machine learning algorithm

Nowadays the communication between the organizations or any individuals became easier by use of Electronic mail method. The internet users are increasing rapidly day by day and also spams are increasing with the emails. It was an easy task for the spammers to create an email account and making a fake profile. Therefore, detecting of these spam mails that were fraud is of most important. This paper aims to develop a proposed approach of data science for spam email detection (SMD) using machine learning algorithm. A hybrid bagging approach is used in this proposed method for the detection of spam emails which implements the two, Naïve Bayes and J48 (i.e. decision tree) machine learning algorithms. Each of these algorithms is applied as an input with a data set which is partitioned in to different sets using the data science. Emails classification can be accomplished depends on the patterns of repetitive keywords and several additional parameters like Cc (carbon copy) or Bcc (Blind carbon copy), domain, header etc. that are present in their structure. Whenever the machine learning algorithm is applied for the email classification every one of those parameters are considered as a features. This proposed approach of data science for spam mail detection using machine learning algorithm achieved a 88.12% of overall accuracy with the hybrid bagged approach implementation.
Back-propagation of the Mahalanobis istance through a deep triplet learning model for person Re-Identification

The automatization of the Re-Identification of an individual across different video-surveillance cameras poses a significant challenge due to the presence of a vast number of potential candidates with a similar appearance. This task requires the learning of discriminative features from person images and a distance metric to properly compare them and decide whether they belong to the same person or not. Nevertheless, the fact of acquiring images of the same person from different, distant and non-overlapping views produces changes in illumination, perspective, background, resolution and scale between the person’s representations, resulting in appearance variations that hamper his/her re-identification. This article focuses the feature learning on automatically finding discriminative descriptors able to reflect the dissimilarities mainly due to the changes in actual people appearance, independently from the variations introduced by the acquisition point. With that purpose, such variations have been implicitly embedded by the Mahalanobis distance. This article presents a learning algorithm to jointly model features and the Mahalanobis distance through a Deep Neural Re-Identification model. The Mahalanobis distance learning has been implemented as a novel neural layer, forming part of a Triplet Learning model that has been evaluated over PRID2011 dataset, providing satisfactory results.
Active contour model driven by Self Organizing Maps for image segmentation

• A new function for computing local self-organizing clustering center with SOM clustering algorithm. • An adaptive sign function is presented to replace the constant of the area term in the level set method that enables model to change motion direction of the level set. • An improved double-well potential function is proposed to improve the stability of curve evolution. • The proposed model shows better segmentation accuracy and robustness. Supervised active contour models can use information extracted from supervised samples to guide contour evolution. However, their applicability is limited by the accuracy of the probabilistic models they use, especially when processing images with intensity inhomogeneity. In this paper, an unsupervised activity contour model with Self Organizing Maps (SOM) is proposed. The proposed model employs the self-organizing neural network to perform clustering calculation and the clustering center is called local self-organizing clustering center. An adaptive sign function is used to control the direction of curve evolution. To improve the stability of curve evolution, an improved double-well potential function is proposed. The experiment results show that our model can effectively segment images with intensity inhomogeneity.
Malicious URLs Detection Based on a Novel Optimization Algorithm

In this paper, the issue of malicious URL detection is investigated. Firstly a P system is proposed. Then the new P system is introduced to design the optimization algorithm of BP neural network to achieve the malicious URL detection with better performance. In the end some examples are included and corresponding experimental results display the advantage and effectiveness of the optimization algorithm proposed.
DAHP: Deep Attention-Guided Hashing With Pairwise Labels

To address the problem of inadequate feature extraction and binary code discrete optimization faced by deep hashing methods using a relaxation-quantization strategy, a novel deep attention-guided hashing method with pairwise labels (DAHP) is proposed to enhance global feature fusion, better learn the contextual information of image features to effectively enhance the feature representation, and solve the problem of losing feature information in discrete optimization by optimizing the loss function. First, we introduce a new concept called the anchor hash code generation(AHCG) algorithm, we train the ResNet with the position attention and channel attention mechanisms with the anchor points in Hamming space as supervised information, we fit the binary code representing the picture to the vicinity of each anchor point, and finally, we use the optimized loss function to calculate the pairwise loss and the anchor loss, allowing the hash function to generate hash code with strong discriminative power. The experiments were conducted on four benchmark datasets, and the retrieval accuracy of the proposed method outperformed the retrieval accuracies of the state-of-the-art methods.
GANDaLF: GAN for Data-Limited Fingerprinting

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.
Tight Integrated End-to-End Training for Cascaded Speech Translation

A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation between source speech and target text. Such modeling suffers from error propagation between ASR and MT models. Direct speech translation is an alternative method to avoid error propagation; however, its performance is often behind the cascade system. To use an intermediate representation and preserve the end-to-end trainability, previous studies have proposed using two-stage models by passing the hidden vectors of the recognizer into the decoder of the MT model and ignoring the MT encoder. This work explores the feasibility of collapsing the entire cascade components into a single end-to-end trainable model by optimizing all parameters of ASR and MT models jointly without ignoring any learned parameters. It is a tightly integrated method that passes renormalized source word posterior distributions as a soft decision instead of one-hot vectors and enables backpropagation. Therefore, it provides both transcriptions and translations and achieves strong consistency between them. Our experiments on four tasks with different data scenarios show that the model outperforms cascade models up to 1.8% in BLEU and 2.0% in TER and is superior compared to direct models.
A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs

Detecting the objects of interesting from aerial images captured by UAVs is one of the core modules in the UAV-based applications. However, it is very difficult to detection objects from aerial images. The reason is that the scale of objects in the aerial images captured by UAVs varies greatly and needs to meet certain real-time performance in detection. To deal with these challenges, we proposed a lightweight model named DSYolov3. We made the following improvements to the Yolov3 model: 1) multiple scale-aware decision discrimination network to detect objects in different scales, 2) a multi-scale fusion-based channel attention model to exploit the channel-wise information complementation, 3) a sparsity-based channel pruning to compress the model. Extensive experimental evaluation has demonstrated the effectiveness and efficiency of our approach. By the proposed approach, we could not only achieve better performance than most existing detectors but also ensure the models practicable on the UAVs.
Quantum Algorithm for Simulating Hamiltonian Dynamics with an Off-diagonal Series Expansion

We propose an efficient quantum algorithm for simulating the dynamics of general Hamiltonian systems. Our technique is based on a power series expansion of the time-evolution operator in its off-diagonal terms. The expansion decouples the dynamics due to the diagonal component of the Hamiltonian from the dynamics generated by its off-diagonal part, which we encode using the linear combination of unitaries technique. Our method has an optimal dependence on the desired precision and, as we illustrate, generally requires considerably fewer resources than the current state-of-the-art. We provide an analysis of resource costs for several sample models.
Few-Shot Common-Object Reasoning Using Common-Centric Localization Network

In the few-shot common-localization task, given few support images without bounding box annotations at each episode, the goal is to localize the common object in the query image of unseen categories. The few-shot common-localization task involves common object reasoning from the given images, predicting the spatial locations of the object with different shapes, sizes, and orientations. In this work, we propose a common-centric localization (CCL) network for few-shot common-localization. The motivation of our common-centric localization network is to learn the common object features by dynamic feature relation reasoning via a graph convolutional network with conditional feature aggregation. First, we propose a local common object region generation pipeline to reduce background noises due to feature misalignment. Each support image predicts more accurate object spatial locations by replacing the query with the images in the support set. Second, we introduce a graph convolutional network with dynamic feature transformation to enforce the common object reasoning. To enhance the discriminability during feature matching and enable a better generalization in unseen scenarios, we leverage a conditional feature encoding function to alter visual features according to the input query adaptively. Third, we introduce a common-centric relation structure to model the correlation between the common features and the query image feature. The generated common features guide the query image feature towards a more common object-related representation. We evaluate our common-centric localization network on four datasets, i.e., CL-VOC-07, CL-VOC-12, CL-COCO, CL-VID. We obtain significant improvements compared to state-of-the-art. Our quantitative results confirm the effectiveness of our network.
The impact of industrial revolution 4.0 on basic chemistry learning

Industrial Revolution 4.0, also known as industry 4.0, is currently becoming a technological trend in the smart manufacturing process in the world. It is indicated by the use of advanced technologies including Cyber-Physical System, the Internet of Things, cloud-based IT platforms, Autonomous Robots, Cyber Security, Artificial Intelligent, Augmented Reality, and Virtual Reality. Some of these technologies are not only used by the manufacturing industry but are also used in everyday human activities, for example, e-commerce platforms use the big data cloud-based IT platform to display real-time data stock, product prices and even the payment process becomes easier with e-money. Some of them use augmented reality technology as an application to assist customers in determining the choice of products to be purchased. Industry 4.0 also affected many kinds of fields of life, such as health, tourism, communication, and even education. So, the job market today not only requires workers who know but also requires workers whom literate technologies used in Industry 4.0. Therefore, universities as an institution for prospective workers must link and match with job market needed. So, universities must make efforts to equip their graduates to be a part of society that can survive in the new era. This article discusses how the condition of higher education in Indonesia, especially the basic chemistry course. This is an analytical study with the concept analysis design approach. Based on the resulting study of the lesson plan used in seven universities shows that there has been no attempt to introduce or use the technologies of Industry 4.0 in basic chemistry learning.
Blockchain-Based Flexible Double-Chain Architecture and Performance Optimization for Better Sustainability in Agriculture

Blockchain is an emerging decentralized distributed technology that can cross the boundaries and guarantee safe and trustworthy value transfers between participants. Combining the blockchain technology with the Internet of Things (IoT) technology to enhance the transparency and sustainability of agricultural supply chains, has attracted researchers from both academia and industry. This paper reviews the latest applications of the blockchain and IoT technologies in the sustainable agricultural supply chain management and explores the design and implementation of a blockchain-based sustainable solution. By placing the sustainable agricultural supply chain management at its core, a blockchain-based framework is designed. Considering the heterogeneity of the transaction data and the IoT data, the openness of sustainability information and the sensitivity of participants' data, a double-chain structure is proposed including the consensus method, the transaction mechanisms, the sustainability assessment method and the performance optimization strategy. The sustainable management practices of all participants are introduced into the blockchain network, especially those allowing the government to play a more significant role in agriculture supply management. Meanwhile, to meet the scenario requirements, a data reduction method is designed to improve performance and reduce block size. Simulations are performed to evaluate the latency, throughput, costs and efficiency of the proposed structure. This paper can be a useful reference for further research on the application of blockchain and IoT technologies in sustainable agricultural management.
Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering

In this paper, we aim to address issues of (1) joint spatial-temporal modeling and (2) side information injection for deep-learning based in-loop filter. For (1), we design a deep network with both progressive rethinking and collaborative learning mechanisms to improve quality of the reconstructed intra-frames and inter-frames, respectively. For intra coding, a Progressive Rethinking Network (PRN) is designed to simulate the human decision mechanism for effective spatial modeling. Our designed block introduces an additional inter-block connection to bypass a high-dimensional informative feature before the bottleneck module across blocks to review the complete past memorized experiences and rethinks progressively. For inter coding, the current reconstructed frame interacts with reference frames (peak quality frame and the nearest adjacent frame) collaboratively at the feature level. For (2), we extract both intra-frame and inter-frame side information for better context modeling. A coarse-to-fine partition map based on HEVC partition trees is built as the intra-frame side information. Furthermore, the warped features of the reference frames are offered as the inter-frame side information. Our PRN with intra-frame side information provides 9.0% BD-rate reduction on average compared to HEVC baseline under All-intra (AI) configuration. While under Low-Delay B (LDB), Low-Delay P (LDP) and Random Access (RA) configuration, our PRN with inter-frame side information provides 9.0%, 10.6% and 8.0% BD-rate reduction on average respectively. Our project webpage is https://dezhao-wang.github.io/PRN-v2/.
LightLedger: A Novel Blockchain-Based Domain Certificate Authentication and Validation Scheme

Nowadays, existing public key infrastructures (PKIs) certificate authentication suffers from many security failures. Trusted certificate authorities (CAs) can issue a valid certificate for any domain name. Although CA is supposed to be trusted by a client if the certificate issued to the client links to the chain of trust (e.g., root CA or subordinate CA). By compromising any of the latter (e.g., root CAs or subordinate CAs) an attacker can jeopardize the security of the entire system. Moreover, third-party CAs have to be trusted by domain owners. Currently, the trust is not balanced among the entities involved in the certificate authentication and issuance process (i.e., CAs and domain owners). To counter this problem approaches such as Domain authentication name entity (DANE) and Certificate Authority Authorization (CAA) offer additional securities for domain authentication. However, these approaches depend upon DNS/DNSSEC infrastructure which requires complex requirements for deployment as well as the adoption rate has been low. In this paper, we design, implement a robust and scalable domain authentication scheme based on blockchain technology with privacy-preserving features for low-constrained devices (e.g., mobile, browser, and IoT devices). The proposed system records a set of trusted CAs each associated with a specific domain in the blockchain. That is, each CA has to first verify if it is trusted to perform the actual issuance process. We compare our scheme with existing authentication methods and show that it requires less storage capacity and low bandwidth to authenticate certificates than other methods.
I Don’t Need an Expert! Making URL Phishing Features Human Comprehensible

Judging the safety of a URL is something that even security experts struggle to do accurately without additional information. In this work, we aim to make experts' tools accessible to non-experts and assist general users in judging the safety of URLs by providing them with a usable report based on the information professionals use. We designed the report by iterating with 8 focus groups made up of end users, HCI experts, and security experts to ensure that the report was usable as well as accurately interpreted the information. We also conducted an online evaluation with 153 participants to compare different report-length options. We find that the longer comprehensive report allows users to accurately judge URL safety (93% accurate) and that summaries still provide benefit (83% accurate) compared to domain highlighting (65% accurate).