Reviewerly/exHarmony huggingface dataset:
Parallel random block-coordinate forward–backward algorithm: a unified convergence analysis

We study the block-coordinate forward–backward algorithm in which the blocks are updated in a random and possibly parallel manner, according to arbitrary probabilities. The algorithm allows different stepsizes along the block-coordinates to fully exploit the smoothness properties of the objective function. In the convex case and in an infinite dimensional setting, we establish almost sure weak convergence of the iterates and the asymptotic rate o(1/n) for the mean of the function values. We derive linear rates under strong convexity and error bound conditions. Our analysis is based on an abstract convergence principle for stochastic descent algorithms which allows to extend and simplify existing results.
Sustaining Library Makerspaces: Perspectives on Participation, Expertise, and Embeddedness

As the novelty of makerspaces in libraries slowly fades, this study examines how participation, expertise, and embeddedness in the library organization and surrounding community are sustained in library makerspaces. Based on interviews with makerspace practitioners in 13 Danish libraries, practices of maintaining, scaling, replicating, and evolving library makerspaces are analyzed. The findings propose a variety of practices and tensions concerning sharing ownership with user communities; scaling and prolonging users’ participation; building expertise through documentation, repetition, and sharing; collaborating with local community partners; and embedding makerspace practices into existing library practices, resources, and values. The results inform a discussion of participation, expertise, and embeddedness, which are distilled into three guiding principles that can help libraries reflect and address the sustainability of their makerspace over time.
Improving Graph Neural Networks with Structural Adaptive Receptive Fields

The abundant information in graphs helps us to learn more expressive node representations. Different nodes in the neighborhood have different importance to the central node. Thus, average weight aggregation in most Graph Neural Networks would fail to model such difference. GAT-based models introduce the attention mechanism to solve this problem, but they ignore the rich structural information and may suffer from the problem of over-smoothing. In this paper, we propose Graph Neural Networks with STructural Adaptive Receptive fields (STAR-GNN), which adaptively construct a receptive field for each node with structural information and further achieve better aggregation of information. Firstly, we model local structural distribution based on anonymous random walks, followed by using the structural information to construct receptive fields guided with mutual information. Then, as the generated receptive fields are irregular, we design a sub-graph aggregator to boost node representations and theoretically prove that it has the ability to capture the complex structures in receptive fields. Experimental results demonstrate the power of STAR-GNN in learning structural receptive fields adaptively and encoding more informative structural characteristics in real-world networks.
Towards Realistic and ReproducibleWeb Crawl Measurements

Accurate web measurement is critical for understanding and improving security and privacy online. Such measurements implicitly assume that automated crawls generalize to typical web user experience. But anecdotal evidence suggests the web behaves differently when seen via well-known measurement endpoints or measurement automation frameworks, for various reasons. Our work improves the state of web privacy and security by investigating how key measurements differ when using naive crawling tool defaults vs. careful attempts to match “real” users across the Tranco top 25k web domains. We find web privacy and security measurements significantly affected by vantage point and browser configuration. We conclude that unless researchers ensure their web measurement tools match real world user experience, the research community is likely missing important signals systematically. For example, we find browser configuration alone causing shifts in 19% of known ad and tracking domains encountered and altering the loading frequency of up to 10% of distinct JavaScript code units executed. We find network vantage point having similar, though less dramatic, effects on the same web metrics. To ensure reproducibility, we carefully document our methodology and publish both our code and collected data.
Training Images Generation for CNN Based Automatic Modulation Classification

Convolutional neural network (CNN) models have recently demonstrated impressive classification and recognition performance on image and video processing scope. In this paper, we investigate the application of CNN to identifying modulation classes for digitally modulated signals. First, the received baseband data samples of modulated signal are gathered up and transformed to generate the constellation-like training images for convolutional networks. Among the resulting training images, the proposed convolutional gray image is preferred for network training and inference because of the lower computational burden. Second, we propose to use a multiple-scale convolutional neural network (MSCNN) as the classifier. The skip-connection technique is deployed for mitigating the negative effect of vanishing gradients and overfitting during the network training process. Numerical simulations have been carried out to validate the effectiveness of the proposed scheme, the results show that the proposed scheme outperforms the traditional algorithms in terms of classification accuracy.
Big Data Analysis of COVID-19 Mitigation Policy in Indonesia: Democratic, Elitist, and Artificial Intelligence

Abstract In response to mitigate COVID-19 local transmission, the Indonesian government has launched several regulative policies. This paper aims to analyze whether the decision-making process of those regulative policies is closed to a democratic or elitist approach and whether the government has adopted Artificial Intelligence (AI) in these processes. We focus on large-scale social distancing (PSBB), homecoming restriction in the Eid Mubarak (Mudik), and new-normal life policy case studies. The research has shown that elite roles are quite dominant with little consideration for public consultation and little attention to the use of AI to consider public preferences. The data were collected from two well-known national online media, which are nasional.kompas.com and tempo.co , while the big data mining and analysis have been applied through N-Vivo 12 software. We argue that although the elitist paradigm can be recognized as an efficient approach in the pandemic and emergency, the decision-makers can accommodate public preferences if they can get insights from online media news with the help of AI technology.
COVID-19 Sentimental Analysis Using Machine Learning Techniques

With the rise in patients affected by the coronavirus, the World Health Organization declared it a pandemic. Globally, people are forced to stay at home to maintain social distancing. People are sharing their feelings through social media platform like Twitter. Twitter data helps to understand grief and pain due to coronavirus. In this paper, a Twitter dataset has been used for sentiment analysis of people's opinions related to coronavirus (COVID-19) that is a vital issue these days all over the world, and various countries are affected by this pandemic. So analyze the people sentiment regarding this pandemic using machine learning techniques and sentiment analysis model such as Naive Bayes, Support Vector Machine, Logistic Regression, and Random Forest Classifier has been proposed to analyze sentiment more effectively. Further, a comparison of these models has been done to prove extremely effective and accurate based on the analysis of feelings and opinions regarding coronavirus (COVID-19).
Overview of Randomness Test on Cryptographic Algorithms

Abstract Randomness is an important research topic in the field of information security, especially in cryptography. Randomness test techniques are used to examine the quality of random numbers so that they meet the requirements of the application. The randomness of cryptographic algorithms is one of the key concerns in the algorithm design. People have put forward different standards and test requirements for the randomness of cryptographic algorithms, as well as developed corresponding randomness test kits. This paper analyzes the randomness test technologies of cryptographic algorithms and the general randomness test methods, and compares them on this basis. At the end, the actual application scenarios to apply these randomness test methods are discussed.
Trust but Verify: Cryptographic Data Privacy for Mobility Management

The era of big data has brought with it a richer understanding of user behavior through massive datasets, which can help organizations optimize the quality of their services. In the context of transportation research, mobility data can provide municipal authorities (MAs) with insights on how to operate, regulate, or improve the transportation network. Mobility data, however, may contain sensitive information about end users and trade secrets of mobility providers (MPs). Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we propose an interactive protocol between an MA and an MP, in which MA obtains insights from mobility data without MP having to reveal its trade secrets or sensitive data of its users. This is accomplished in two steps: 1) a commitment step and 2) a computation step. In the first step, Merkle commitments and aggregated traffic measurements are used to generate a cryptographic commitment. In the second step, MP extracts insights from the data and sends them to MA. Using the commitment and zero-knowledge proofs, MA can certify that the information received from MP is accurate, without needing to directly inspect the mobility data. We also present a differentially private version of the protocol that is suitable for the large query regime. The protocol is verifiable for both MA and MP in the sense that dishonesty from one party can be detected by the other. The protocol can be readily extended to the more general setting with multiple MPs via secure multiparty computation.
A Universal Transcoding and Transmission Method for Livecast with Networked Multi-Agent Reinforcement Learning

Intensive video transcoding and data transmission are the most crucial tasks for large-scale Crowd-sourced Livecast Services (CLS). However, there exists no versatile model for joint optimization of computing resources (e.g., CPU) and transmission resources (e.g., bandwidth) in CLS systems, making maintaining the balance between saving resources and improving user viewing experience very challenging. In this paper, we first propose a novel universal model, called Augmented Graph Model (AGM), which converts the above joint optimization into a multi-hop routing problem. This model provides a new perspective for the analysis of resource allocation in CLS, as well as opens new avenues for problem-solving. Further, we design a decentralized Networked Multi-Agent Reinforcement Learning (MARL) approach and propose an actor-critic algorithm, allowing network nodes (agents) to distributively solve the multi-hop routing problem using AGM in a fully cooperative manner. By leveraging the computing resource of massive nodes efficiently, this approach has good scalability and can be employed in large-scale CLS. To the best of our knowledge, this work is the first attempt to apply networked MARL on CLS. Finally, we use the centralized (single-agent) RL algorithm as a benchmark to evaluate the numerical performance of our solution in a large-scale simulation. Additionally, experimental results based on a prototype system show that our solution is superior in saving resources and service performance to two alternative state-of-the-art solutions.
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning

Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, surveillance, robotics, and others. In the model-based Bayesian setting, there are conjugate priors that enable us to express the multi-object posterior in closed form, which could theoretically provide Bayes-optimal estimates. However, the posterior involves a super-exponential growth of the number of hypotheses over time, forcing state-of-the-art methods to resort to approximations for remaining tractable, which can impact their performance in complex scenarios. Model-free methods based on deep-learning provide an attractive alternative, as they can, in principle, learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available. In this paper, we propose a high-performing deep-learning method for MTT based on the Transformer architecture and compare it to two state-of-the-art Bayesian filters, in a setting where we assume the correct model is provided. Although this gives an edge to the model-based filters, it also allows us to generate unlimited training data. We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime. The code for all our implementations is made available at https://github.com/JulianoLagana/MT3.
Robust and auditable distributed data storage with scalability in edge computing

With the widespread use of Internet-of-Things (IoT) devices, edge computing is becoming a popular technology for processing and storing data distributed at the edge of the networks. However, the new paradigm also faces a major security challenge: how to ensure the reliability and integrity of data in distributed edge storage servers? In this paper, we propose a robust and auditable distributed data storage (Radds) scheme to support a secure and dependable edge storage in edge computing. Firstly, based on homomorphic verifiable authenticator and regenerating code technique, the proposed scheme allows to check the integrity of data in distributed edge storage servers and guarantees data repairability in case of data corruption. Moreover, the server with corrupted data also can be deduced from integrity proofs, and a proxy is introduced for data reparation to release edge nodes from online burden and computation costs. Secondly, the proposed scheme can protect the privacy of original data from the third party auditor by blinding the encoding coefficients with a keyed pseudorandom function. Thirdly, the proposed scheme supports flexible scalability, i.e., dynamic joining and exiting of the edge nodes. Moreover, even if some data are not collected temporarily, they still can be supplemented to the encoded data file by an efficient way and the integrity checking and data reparation can be performed normally. Finally, security analysis and performance evaluation demonstrate that the proposed scheme is secure and highly efficient.
Comparison among different CNN Architectures for Signature Forgery Detection using Siamese Neural Network

Signature is the most common way to indicate knowledge and acceptance of a document. As many documents and contracts are now starting to use paperless electronic formats, the term "signature" has been substantially broadened. Whichever form it takes, the key importance of the signature is identity authentication for managing security. Signatures being one of the most widely used methods for the same, play a crucial role in financial, legal, and social aspects of one's life. Thus, Signature forgery, that is falsely copying another individual's signature is an issue of utmost concern. The chances of two or more signatures made by the same individual being identical are minimal, thus making signature forgery detection an arduous task. Our paper aims to apply the state-of-the-art methodology, Siamese Neural Networks, on the chosen data set, draw meaningful insights and perform a comparative analysis between some variants of these neural networks to identify and authenticate handwritten signatures.
Short-term anchor linking and long-term self-guided attention for video object detection

We present a new network architecture able to take advantage of spatio-temporal information available in videos to boost object detection precision. First, box features are associated and aggregated by linking proposals that come from the same anchor box in the nearby frames. Then, we design a new attention module that aggregates short-term enhanced box features to exploit long-term spatio-temporal information. This module takes advantage of geometrical features in the long-term for the first time in the video object detection domain. Finally, a spatio-temporal double head is fed with both spatial information from the reference frame and the aggregated information that takes into account the short- and long-term temporal context. We have tested our proposal in five video object detection datasets with very different characteristics, in order to prove its robustness in a wide number of scenarios. Non-parametric statistical tests show that our approach outperforms the state-of-the-art. Our code is available at https://github.com/daniel-cores/SLTnet.
Answer Complex Questions: Path Ranker Is All You Need

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates. Existing studies take the greedy strategy in the sense that they only use samples for ranking at the current hop, and ignore the global information across the whole documents. In this paper, we propose a purely rank-based framework Thinking Path Re-Ranker (TPRR), which is comprised of Thinking Path Ranker (TPR) for generating document sequences called "a path" and External Path Reranker (EPR) for selecting the best path from candidate paths generated by TPR. Specifically, TPR leverages the scores of a dense model and conditional probabilities to score the full paths. Moreover, to further enhance the performance of the dense ranker in the iterative training, we propose a "thinking" negatives selection method that the top-K candidates treated as negatives in the current hop are adjusted dynamically through supervised signals. After achieving multiple supporting paths through TPR, the EPR component which integrates several fine-grained training tasks for QA is used to select the best path for answer extraction. We have tested our proposed solution on the multi-hop dataset "HotpotQA" with a full wiki set ting, and the results show that TPRR significantly outperforms the existing state-of-the-art models. Moreover, our method has won the first place in the HotpotQA official leaderboard since Feb 1, 2021 under the Fullwiki setting. Code is available at https://gitee.com/mindspore/mindspore/ tree/master/model_zoo/research/nlp/tprr.
Research on Cosine Similarity and Pearson Correlation Based Recommendation Models

Abstract Recommendation models are based on data mining and machine learning techniques to suggest product and services to users. Advancement in the machine learning field results in the growth of more efficient recommendation models. Recommendation systems can contribute a lot to increase the business of internet based companies. Techniques for making recommendations generally fall into two categories namely collaborative filtering based models and content based recommendation models. The first techniques explores similarity based approaches like cosine similarity, Pearson correlation similarity etc. The second approaches uses predictions based on explicit and implicit ratings given by users. Recommendation systems try to suggest products to the users based on their previous purchases or general nature. A successful recommendation system is capable of improving the product sales to a large extend. This paper reviews about various recommendation models and performs a comparison of these models.
Hate Speech and Offensive Language Detection: A New Feature Set with Filter-Embedded Combining Feature Selection

Social media has changed the world and play an important role in people lives. Social media platforms like Twitter, Facebook and YouTube create a new dimension of communication by providing channels to express and exchange ideas freely. Although the evolution brings numerous benefits, the dynamic environment and the allowable of anonymous posts could expose the uglier side of humanity. Irresponsible people would abuse the freedom of speech by aggressively express opinion or idea that incites hatred. This study performs hate speech and offensive language detection. The problem of this task is there is no clear boundary between hate speech and offensive language. In this study, a selected new features set is proposed for detecting hate speech and offensive language. Using Twitter dataset, the experiments are performed by considering the combination of word n-gram and enhanced syntactic n-gram. To reduce the feature set, filter-embedded combining feature selection is used. The experimental results indicate that the combination of word n-gram and enhanced syntactic n-gram with feature selection to classify the data into three classes: hate speech, offensive language or neither could give good performance. The result reaches 91% for accuracy and the averages of precision, recall and F1.
Path Planning in Uncertain Environment With Moving Obstacles Using Warm Start Cross Entropy

This article studies the robot’s path planning problem with observation uncertainties in an environment containing moving obstacles. The framework of partially observable Markov decision process is utilized to deal with the planning problem, in which the environment is characterized as a grid map. To design an efficient and solvable optimization problem, we design safer moving rules in the grid map and formulate the new cost functions, including the collision costs incurred by the static and dynamic obstacles. Furthermore, we propose a cross entropy algorithm with a new initialization scheme called the warm start cross entropy to solve the optimization problem with improved searching speed. The simulation and comparison studies validate the advantages of the proposed method in safety and computational speed. In addition, the hardware experiments on a robot platform verify the applicability and feasibility of the proposed path planning method.