Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Anonymous Data Sharing With Accountability for Internet of Things

Blockchain has been a promising infrastructure for enabling secure data sharing for the Internet of Things (IoT). With the widespread of IoT applications, security issues, such as data privacy, anonymity, and accountability become critical concerns for the users, which are essential principles for secure communication in those applications. However, the existing blockchain-based data-sharing schemes mainly consider data privacy. Only a few works can support anonymity with strong, trusted assumptions. Thus, there is a research gap on the anonymity of blockchain-based data sharing for IoT, which does not rely on any trusted party. In this article, we propose a blockchain-based anonymous data-sharing scheme (BA-DS) by adopting a novel public key encryption derived from a ring signature. In BA-DS, we remove the trusted party and ensure anonymity by using an unconditional linkable ring signature and Signature of Knowledge (SoK). During the revocation, we apply blockchain infrastructure to record the valid revocation list and generate a tag for data stored on the cloud, providing solid accountability. The formal security analysis shows that BA-DS is selective indistinguishable secure in the random oracle model. Additionally, we also prove that BA-DS holds anonymity, data privacy, accountability, and authenticity. The extensive experiments indicate that our proposed BA-DS achieves reasonable efficiency in terms of computational complexity, communication overhead, and consumption on the blockchain.
VadTR: Video Anomaly Detection with Transformer

Video scenes often have cluttered backgrounds and numerous stationary and moving objects. Detecting the occurrence of anomalous events in such scenarios is very challenging. Traditional approaches in the field employ hand-designed features to identify anomalies. This method is not competent in learning the temporal-spatial knowledge of the video. In this paper, we propose a new model for Video Anomaly Detection (VadTR) with Transformer, which utilizes Transformer to automatically learn video representations to extract features from both spatial-temporal dimensions. Concurrently, we use the reconstruction loss method to achieve video anomaly detection. The comparisons on public datasets show that our method has leading performance.
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection

Average precision (AP) loss has recently shown promising performance on the dense object detection task. However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP) loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss. The first of these is a novel Adaptive Pairwise Error (APE) loss that focusing on ranking pairs in both positive and negative samples. Moreover, we select more accurate ranking pairs by exploiting the normalized ranking scores and localization scores with a clustering algorithm. Experiments conducted on the MSCOCO dataset support our analysis and demonstrate the superiority of our proposed method compared with current classification and ranking loss. The code is available at https://github.com/Xudangliatiger/APE-Loss.
Vector Quantized Image-to-Image Translation

Current image-to-image translation methods formulate the task with conditional generation models, leading to learning only the recolorization or regional changes as being constrained by the rich structural information provided by the conditional contexts. In this work, we propose introducing the vector quantization technique into the image-to-image translation framework. The vector quantized content representation can facilitate not only the translation, but also the unconditional distribution shared among different domains. Meanwhile, along with the disentangled style representation, the proposed method further enables the capability of image extension with flexibility in both intra- and inter-domains. Qualitative and quantitative experiments demonstrate that our framework achieves comparable performance to the state-of-the-art image-to-image translation and image extension methods. Compared to methods for individual tasks, the proposed method, as a unified framework, unleashes applications combining image-to-image translation, unconditional generation, and image extension altogether. For example, it provides style variability for image generation and extension, and equips image-to-image translation with further extension capabilities.
Integration of Content-Based Recommendation System with 360° video player for Virtual Tourism

Nations that rely on tourists visiting their countries as a key source of income have suffered significantly because of the Covid-19 outbreak. As a result, we've chosen to develop a 360° Video Tourism mobile application that will allow users to view the sights of the destination they desire to visit and feel as if they've been transported there. This Android application makes use of 360° video playback technology, which allows users to watch videos in all directions. When the user starts the 360° video, he or she can swipe and move their finger around the screen of their Android device, and the video will appear to shift its orientation and show scenes accordingly. The application uses Firebase as its database to store data, right from the user's personal information to the videos that can be viewed. This application was written in the JAVA programming language and connected with a content-based recommendation system. This algorithm, which is based on Cosine Similarity, suggests similar areas to the user's selected location. If a user chooses “Taj Mahal,” for example, the application will suggest monuments that are similar to the Taj Mahal. This application serves as a solution, not only for users for exploring unexplored areas from the comfort of their homes but also a chance for the tourism industry to advertise and promote new tourism destinations, leading to increased visitor numbers and higher revenue.
Self-Admitted Technical Debt in the Embedded Systems Industry: An Exploratory Case Study

Technical debt denotes shortcuts taken during software development, mostly for the sake of expedience. When such shortcuts are admitted explicitly by developers (e.g., writing a TODO/Fixme comment), they are termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Admitted Technical Debt</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SATD</i> . There has been a fair amount of work studying SATD management in Open Source projects, but SATD in industry is relatively unexplored. At the same time, there is no work focusing on developers’ perspectives towards SATD and its management. To address this, we conducted an exploratory case study in cooperation with an industrial partner to study how they think of SATD and how they manage it. Specifically, we collected data by identifying and characterizing SATD in different sources (issues, source code comments, and commits) and carried out a series of interviews with 12 software practitioners. The results show: 1) the core characteristics of SATD in industrial projects; 2) developers’ attitudes towards identified SATD and statistics; 3) triggers for practitioners to introduce and repay SATD; 4) relations between SATD in different sources; 5) practices used to manage SATD; 6) challenges and tooling ideas for SATD management.
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies

Existing Binary Neural Networks (BNNs) mainly operate on local convolutions with binarization function. However, such simple bit operations lack the ability of modeling contextual dependencies, which is critical for learning discriminative deep representations in vision models. In this work, we tackle this issue by presenting new designs of binary neural modules, which enables BNNs to learn effective contextual dependencies. First, we propose a binary multi-layer perceptron (MLP) block as an alternative to binary convolution blocks to directly model contextual dependencies. Both short-range and long-range feature dependencies are modeled by binary MLPs, where the former provides local inductive bias and the latter breaks limited receptive field in binary convolutions. Second, to improve the robustness of binary models with contextual dependencies, we compute the contextual dynamic embeddings to determine the binarization thresholds in general binary convolutional blocks. Armed with our binary MLP blocks and improved binary convolution, we build the BNNs with explicit Contextual Dependency modeling, termed as BCDNet. On the standard ImageNet-1K classification benchmark, the BCDNet achieves 72.3% Top-1 accuracy and outperforms leading binary methods by a large margin. In particular, the proposed BCDNet exceeds the state-of-the-art ReActNet-A by 2.9% Top-1 accuracy with similar operations. Our code is available at https://github.com/Sense-GVT/BCDNet .
Automatic Question Generation from Indonesian Texts Using Text-to-Text Transformers

Answering questions is one method to increase or measure understanding. However, creating relevant and answerable questions from the given context is not easy. Automatic Question Generation (AQG) is a part of Natural Language Processing (NLP) which can generate questions automatically from text input. Many studies related to AQG have been carried out but are still very limited in Indonesian texts, especially those that use the latest Transformer variations. This study proposes an AQG system that utilizes the latest power Transformer, the multilingual Text-to-Text Transfer Transformer (mT5). We fine-tune the mT5 model to extract answers from context and generate questions based on those answers. We use the Indonesian dataset extracted from the TyDiQA dataset and evaluate this model against the TyDiQA validation set using BLEU (BiLingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics. This model achieved BLEU-1, BLEU-2, BLEU-3, BLEU-4, and ROUGE-L scores of 36.54, 28.24, 22.61, 18.44, and 39.57, respectively. Our model performs well and generates questions in understandable Indonesian with good word choice and grammar based on manual validation.
Methodological support system for scientific libraries: conceptual approach

The purpose of the article is to set forth the fundamental conceptual regulations of the scientific and methodological support system for scientific libraries. The problems of scientific libraries that can be solved in the frames of scientific and methodological work are considered. The purpose and tasks facing scientific and methodological support and the requirements that it should meet are justified based on a system approach. The forms of activity and organizational structure that meet these requirements are proposed.The expediency of using spatial approach in presenting the results of scientific and methodological activities implemented through a specialized portal is substantiated.Though the leading scientific libraries, including the central research libraries of the Russian Academy of Sciences in the structure of the Ministry of Science and Higher Education, are decentralized and each library has its own system of methodological support for the libraries of scientific institutions, there are all necessary and sufficient prerequisites for the formation of a multifactorial Concept of the system of scientific and methodological support for scientific libraries in the Russian Federation.
Fast, Accurate and Memory-Efficient Partial Permutation Synchronization

Previous partial permutation synchronization (PPS) algorithms, which are commonly used for multi-object matching, often involve computation-intensive and memory-demanding matrix operations. These operations become intractable for large scale structure-from-motion datasets. For pure permutation synchronization, the recent Cycle-Edge Message Passing (CEMP) framework suggests a memory-efficient and fast solution. Here we overcome the restriction of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for estimating the corruption levels of the observed partial permutations. It allows us to subsequently implement a nonconvex weighted projected power method without the need of spectral initialization. The resulting new PPS algorithm, MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse matrix operations, and thus enjoys lower time and space complexities in comparison to previous PPS algorithms. We prove that under adversarial corruption, though without additive noise and with certain assumptions, CEMP-Partial is able to exactly classify corrupted and clean partial permutations. We demonstrate the state-of-the-art accuracy, speed and memory efficiency of our method on both synthetic and real datasets.
AutoPRTitle: A Tool for Automatic Pull Request Title Generation

With the rise of the pull request mechanism in software development, the quality of pull requests has gained more attention. Prior works focus on improving the quality of pull request descriptions and several approaches have been proposed to automatically generate pull request descriptions. As an essential component of a pull request, pull request titles have not received a similar level of attention. To further facilitate automation in software development and to help developers draft high-quality pull request titles, we introduce AutoPRTitle. AutoPRTitle is specifically designed to generate pull request titles automatically. AutoPRTitle can generate a precise and succinct pull request title based on the pull request description, commit messages, and the associated issue titles. AutoPRTitle is built upon a state-of-the-art text summarization model, BART, which has been pre-trained on large-scale English corpora. We further fine-tuned BART in a pull request dataset containing high-quality pull request titles. We implemented AutoPRTitle as a stand-alone web application. We conducted two sets of evaluations: one concerning the model accuracy and the other concerning the tool usability. For model accuracy, BART outperforms the best baseline by 24.6%, 40.5%, and 23.3%, respectively. For tool usability, the evaluators consider our tool as easy-to-use and useful when creating a pull request title of good quality.
Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

This paper focuses on the weakly-supervised audio-visual video parsing task, which aims to recognize all events belonging to each modality and localize their temporal boundaries. This task is challenging because only overall labels indicating the video events are provided for training. However, an event might be labeled but not appear in one of the modalities, which results in a modality-specific noisy label problem. In this work, we propose a training strategy to identify and remove modality-specific noisy labels dynamically. It is motivated by two key observations: 1) networks tend to learn clean samples first; and 2) a labeled event would appear in at least one modality. Specifically, we sort the losses of all instances within a mini-batch individually in each modality, and then select noisy samples according to the relationships between intra-modal and inter-modal losses. Besides, we also propose a simple but valid noise ratio estimation method by calculating the proportion of instances whose confidence is below a preset threshold. Our method makes large improvements over the previous state of the arts (e.g., from 60.0% to 63.8% in segment-level visual metric), which demonstrates the effectiveness of our approach. Code and trained models are publicly available at https://github.com/MCG-NJU/JoMoLD .
Deep Learning Networks for Eating and Drinking Recognition based on Smartwatch Sensors

Smartwatches are becoming more popular for recognizing and monitoring human actions in everyday life. These wearable devices are equipped with various IMU sensors for ubiquitous data processing and recording of human physical activity data. Sensor-based human activity recognition (HAR) has risen to the top of the list of the most active research topic due to its widely real-life applications in various practical domains, such as healthcare monitoring, sports and exercise tracking, and misbehavior prevention. Many machine learning and deep learning approaches have been recently proposed to solve the problem of human activity recognition, focusing on activities of daily living. However, an exciting and challenging HAR topic deals with more complex human activities such as eating-related activities. This paper proposes a sensor-based HAR framework using data from eating-related activities recorded by a smartwatch sensor. In this framework, five d eep learning networks (CNN, LSTM, BiLSTM, Stacked LSTM, CNN-LSTM, and LSTM-CNN) are evaluated for their recognition of eating-related activities. To ensure the model’s dependability, data from eating-related activities on the standard publicly available dataset WISDM-HARB are utilized to evaluate the proposed framework using state-of-the-art metrics: accuracy and confusion matrices. Experiment findings demonstrate that the S tacked LSTM model outperforms other deep learning models, achieving an accuracy of 97.37%.
Synthetic Training Data Generator for Hand Gesture Recognition Based on FMCW RADAR

Hand gesture recognition has attracted a lot of attention recently. However, gesture recognition based on machine learning (ML) requires a huge training data set in order to achieve high recognition accuracy. Creating this training data set requires a significant effort. In order to solve this problem, a synthetic frequency modulated continuous waves (FMCW) RADAR data generator for hand gestures is proposed. This generator can produce a large amount of data that can be used for training the ML model, without collecting real data involving multiple people. For evaluation, 3600 synthetic samples for six hand gestures are generated with rich variations in hand size, speed and position. Those synthetic data are utilized to train the ML models for hand gesture recognition. Further, the models are tested by real data set acquired by an AWR 1642 RADAR. The results indicate that a convolutional neural network with 19 layers (VGG19) pre-trained model in conjunction with the XGBoost classifier can achieve an average accuracy of 87.53% on the test set. If only 2% of the real data is used for training, the XGBoost classifier alone achieves an average accuracy of 56.31%. But if the synthetic data set and 2% of the real data set are combined, the average recognition accuracy of the XGBoost classifier on the test data set is increased to 94.63%.
PoS Blockchain-Based Forward-Secure Public Key Encryption with Immutable Keys and Post-Compromise Security Guarantees

We present a forward-secure public-key encryption (PKE) scheme without key update, i.e. both public and private keys are immutable. In contrast, prior forward-secure PKE schemes achieve forward security by constantly updating the secret keys. Our scheme is based on witness encryption by Garg et al. (STOC 2013) and a proof-of-stake blockchain with the distinguishable forking property introduced by Goyal et al. (TCC 2017), and ensures a ciphertext cannot be decrypted more than once, thereby rendering a compromised secret key useless with respect to decryption of past ciphertext the legitimate user has already decrypted. In this work, we formalize the notion of blockchain-based forward-secure PKE, show the feasibility of constructing a forward-secure PKE scheme without key update, and discuss interesting properties of our scheme such as post-compromise security.
XVCollab: An Immersive Analytics Tool for Asymmetric Collaboration across the Virtuality Spectrum

Research has shown that when a group of people collaborate in decision-making scenarios, they can be more effective than when they work alone. Studies also show that in a data analytics context, using immersive technologies could make users perform better in data understanding, pattern recognition, and finding connections. In this work, we are leveraging previous knowledge in Collaborative Immersive Analytics (CIA) and Cross-virtuality Analytics (XVA) to develop an asymmetric system that enables two groups from different places on the Virtuality-Reality spectrum to simultaneously work on analyzing data. We divide users into two groups: the nonimmersive desktop group and the immersive AR group. These two groups can both author and modify visualizations in their virtuality and share it with the other group when they see fit. For this, we designed a seamless interface for both groups to transform a visualization from non-immersive 2D to immersive AR and vice-versa. We also provide multiple awareness cues in the system that keep either group aware of the other and their actions. We designed these features to boost user performance and ease of use in a collaborative setting and incentivize them to rely on the other group for visualization tasks that are difficult to perform on their end of the virtuality spectrum. Our limited pilot study shows that users find the system engaging, easy to use, and helpful in their data-understanding journey within a collaborative context. Going forward, we plan to conduct more rigorous studies to verify our claims and explore other research questions on this topic.
An Efficient Blockchain Assisted Reputation Aware Decentralized Federated Learning Framework

Because of the widespread presence and ease of access to the Internet, edge devices are the perfect candidates for providing quality training on a variety of applications. However, their participation is restrained due to potential leakage of sensitive and private data. Federated learning targets to address these issues by bringing the model to the device and keeping the data in place. Still, it suffers from inherent security issues such as malicious participation and unfair contribution. The central server may become a bottleneck as well as induce biased aggregation and incentives. This article proposes a blockchain assisted federated learning framework, which fosters honest participation with reduced overheads, facilitating fair contribution-based weighted incentivization. A new consensus mechanism named PoIS (Proof of Interpretation and Selection) is proposed based on honest clients’ contributions. PoIS uses model interpretation techniques for evaluating and calculating individual contributions. The aggregation of feature attributions in PoIS, is able to detect the adversaries, and the label-wise aggregation of attributions across the participants helps to define the prominent contributors. Further, we devise a credit function based on the contribution, relevance as well as the past performance for calculating incentives. Extensive experiments have been carried out for the proposed architecture with different settings, models, and datasets, to verify our claim. It successfully restricts the attack to less than 5%, and selects the prominent (top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> ) contributors. Theoretical analysis provides the guarantee for byzantine-robust aggregation, in a malicious setting.