OpenRaiser/Intern-Atlas huggingface dataset:
SyncPCN/PSyncPCN: Payment Channel Networks without Blockchain Synchrony
Venue: arXiv cs.CR 2022
Year: 2022

Payment channel networks (PCNs) enhance the scalability of blockchains by allowing parties to conduct transactions off-chain, i.e, without broadcasting every transaction to all blockchain participants. To conduct transactions, a sender and a receiver can either establish a direct payment channel with a funding blockchain transaction or leverage existing channels in a multi-hop payment. The security of PCNs usually relies on the synchrony of the underlying blockchain, i.e., evidence of misbehavior needs to be published on the blockchain within a time limit. Alternative payment channel proposals that do not require blockchain synchrony rely on quorum certificates and use a committee to register the transactions of a channel. However, these proposals do not support multi-hop payments, a limitation we aim to overcome. In this paper, we demonstrate that it is in fact impossible to design a multi-hop payment protocol with both network asynchrony and faulty channels, i.e., channels that may not correctly follow the protocol. We then detail two committee-based multi-hop payment protocols that respectively assume synchronous communications and possibly faulty channels, or asynchronous communication and correct channels. The first protocol relies on possibly faulty committees instead of the blockchain to resolve channel disputes, and enforces privacy properties within a synchronous network. The second one relies on committees that contain at most f faulty members out of 3f+1 and successively delegate to each other the role of eventually completing a multi-hop payment. We show that both protocols satisfy the security requirements of a multi-hop payment and compare their communication complexity and latency.
A Parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System
Venue: arXiv cs.DC 2022
Year: 2022

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction; temperature; etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).
Constructions of linear codes with two or three weights from vectorial dual-bent functions
Venue: arXiv cs.IT 2022
Year: 2022

Linear codes with a few weights are an important class of codes in coding theory and have attracted a lot of attention. In this paper, we present several constructions of $q$-ary linear codes with two or three weights from vectorial dual-bent functions, where $q$ is a power of an odd prime $p$. The weight distributions of the constructed $q$-ary linear codes are completely determined. We illustrate that some known constructions in the literature can be obtained by our constructions. In some special cases, our constructed linear codes can meet the Griesmer bound. Furthermore, based on the constructed $q$-ary linear codes, we obtain secret sharing schemes with interesting access structures.
Learnable Privacy-Preserving Anonymization for Pedestrian Images
Venue: arXiv cs.CV 2022
Year: 2022

This paper studies a novel privacy-preserving anonymization problem for pedestrian images, which preserves personal identity information (PII) for authorized models and prevents PII from being recognized by third parties. Conventional anonymization methods unavoidably cause semantic information loss, leading to limited data utility. Besides, existing learned anonymization techniques, while retaining various identity-irrelevant utilities, will change the pedestrian identity, and thus are unsuitable for training robust re-identification models. To explore the privacy-utility trade-off for pedestrian images, we propose a joint learning reversible anonymization framework, which can reversibly generate full-body anonymous images with little performance drop on person re-identification tasks. The core idea is that we adopt desensitized images generated by conventional methods as the initial privacy-preserving supervision and jointly train an anonymization encoder with a recovery decoder and an identity-invariant model. We further propose a progressive training strategy to improve the performance, which iteratively upgrades the initial anonymization supervision. Experiments further demonstrate the effectiveness of our anonymized pedestrian images for privacy protection, which boosts the re-identification performance while preserving privacy. Code is available at \url{https://github.com/whuzjw/privacy-reid}.
Discovering adoption barriers of Clinical Decision Support Systems in primary health care sector
Venue: arXiv cs.CY 2022
Year: 2022

Adopting a good health information system (HIS) is essential for providing high-quality healthcare. With rapid advances in technology in the healthcare industry in recent years, healthcare providers seek effective options to deal with numerous diseases and a growing number of patients, adopting advanced HIS such as for clinical decision support. While the clinical decision support systems (CDSS) can help medical personnel make better decisions, they may bring negative results due to a lack of understanding of the elements that influence GP's adoption of CDSS. This paper focuses on discovering obstacles that may contribute to the problems surrounding CDSS adoption. Thirty general practitioners were interviewed from different primary health centers in Saudi Arabia in order to determine the challenges and obstacles in the sector. While the outcome confirms that there are obstacles that affect the aspects, such as time risk, quality of the system used, slow Internet speed, user interface, lack of training, high costs, patient satisfaction, multiple systems used, technical support, computer skills, lack of flexibility, system update, professional skills and knowledge, computer efficiency and quality and accuracy of data.
Approach to Alleviate Wealth Compounding in Proof-of-Stake Cryptocurrencies
Venue: arXiv cs.IT 2022
Year: 2022

Due to its minimal energy requirement the PoS consensus protocol has become an attractive alternative to PoW in modern cryptocurrencies. In this protocol the chance of being selected as a block proposer in each round is proportional to the current stake of any node. Thus, nodes with higher stakes will achieve more block rewards, resulting in the so-called rich-getting-richer problem. In this paper, we introduce a new block reward mechanism called the FRD (Fair Reward Distribution) mechanism, in which for each block produced, in addition to a major reward given to the block proposer, a small reward is given to all other nodes. We prove that this reward mechanism makes the PoS protocol fairer in terms of concentration of wealth by developing on the Bagchi-Pal urn model.
Snapshot Metrics Are Not Enough: Analyzing Software Repositories with Longitudinal Metrics
Venue: arXiv cs.SE 2022
Year: 2022

Software metrics capture information about software development processes and products. These metrics support decision-making, e.g., in team management or dependency selection. However, existing metrics tools measure only a snapshot of a software project. Little attention has been given to enabling engineers to reason about metric trends over time -- longitudinal metrics that give insight about process, not just product. In this work, we present PRiME (PRocess MEtrics), a tool for computing and visualizing process metrics. The currently-supported metrics include productivity, issue density, issue spoilage, and bus factor. We illustrate the value of longitudinal data and conclude with a research agenda. The tool's demo video can be watched at https://youtu.be/YigEHy3_JCo. The source code can be found at https://github.com/SoftwareSystemsLaboratory/prime.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Venue: arXiv cs.LG 2022
Year: 2022

Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical applications such as autonomous vehicles and healthcare. The detection of a shift from the training distribution of individual datapoints has gained attention. A number of techniques have been proposed for such out-of-distribution (OOD) detection. But in many applications, the inputs to a machine learning model form a temporal sequence. Existing techniques for OOD detection in time-series data either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data.Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher's method leads to the proposed detector CODiT with guarantees on false detection in time-series data. We illustrate the efficacy of CODiT by achieving state-of-the-art results on computer vision datasets in autonomous driving. We also show that CODiT can be used for OOD detection in non-vision datasets by performing experiments on the physiological GAIT sensory dataset. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD.
Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis
Venue: arXiv cs.LG 2022
Year: 2022

Behavior prediction based on historical behavioral data have practical real-world significance. It has been applied in recommendation, predicting academic performance, etc. With the refinement of user data description, the development of new functions, and the fusion of multiple data sources, heterogeneous behavioral data which contain multiple types of behaviors become more and more common. In this paper, we aim to incorporate heterogeneous user behaviors and social influences for behavior predictions. To this end, this paper proposes a variant of Long-Short Term Memory (LSTM) which can consider context information while modeling a behavior sequence, a projection mechanism which can model multi-faceted relationships among different types of behaviors, and a multi-faceted attention mechanism which can dynamically find out informative periods from different facets. Many kinds of behavioral data belong to spatio-temporal data. An unsupervised way to construct a social behavior graph based on spatio-temporal data and to model social influences is proposed. Moreover, a residual learning-based decoder is designed to automatically construct multiple high-order cross features based on social behavior representation and other types of behavior representations. Qualitative and quantitative experiments on real-world datasets have demonstrated the effectiveness of this model.
Data-driven Models to Anticipate Critical Voltage Events in Power Systems
Venue: arXiv cs.AI 2022
Year: 2022

This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. By treating the prediction as a categorical classification task, the workflow is characterized by a low computational and data burden. A proof-of-concept case study on a real portion of the Italian 150 kV sub-transmission network, which hosts a significant amount of wind power generation, demonstrates the general validity of the proposal and offers insight into the strengths and weaknesses of several widely utilized prediction models for this application.
Optimizing Resource Allocation and VNF Embedding in RAN Slicing
Venue: arXiv cs.NI 2022
Year: 2022

5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate's resources into a set of self-contained programmable RAN slices. Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on substrate nodes. In this work, we focus on the following fundamental tasks: i) establishing the theoretical foundation for constructing a VNF mapping plan for RAN slice recovery optimization and ii) developing algorithms needed to map/embed VNFs efficiently. In particular, we propose four efficient algorithms, including Resource-based Algorithm (RBA), Connectivity-based Algorithm (CBA), Group-based Algorithm (GBA), and Group-Connectivity-based Algorithm (GCBA) to solve the resource allocation and VNF mapping problem. Extensive experiments are also conducted to validate the robustness of RAN slicing via the proposed algorithms.
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling
Venue: arXiv cs.LG 2022
Year: 2022

A particular challenge for disease progression modeling is the heterogeneity of a disease and its manifestations in the patients. Existing approaches often assume the presence of a single disease progression characteristics which is unlikely for neurodegenerative disorders such as Parkinson's disease. In this paper, we propose a hierarchical time-series model that can discover multiple disease progression dynamics. The proposed model is an extension of an input-output hidden Markov model that takes into account the clinical assessments of patients' health status and prescribed medications. We illustrate the benefits of our model using a synthetically generated dataset and a real-world longitudinal dataset for Parkinson's disease.
One-off Negative Sequential Pattern Mining
Venue: arXiv cs.DB 2022
Year: 2022

Negative sequential pattern mining (SPM) is an important SPM research topic. Unlike positive SPM, negative SPM can discover events that should have occurred but have not occurred, and it can be used for financial risk management and fraud detection. However, existing methods generally ignore the repetitions of the pattern and do not consider gap constraints, which can lead to mining results containing a large number of patterns that users are not interested in. To solve this problem, this paper discovers frequent one-off negative sequential patterns (ONPs). This problem has the following two characteristics. First, the support is calculated under the one-off condition, which means that any character in the sequence can only be used once at most. Second, the gap constraint can be given by the user. To efficiently mine patterns, this paper proposes the ONP-Miner algorithm, which employs depth-first and backtracking strategies to calculate the support. Therefore, ONP-Miner can effectively avoid creating redundant nodes and parent-child relationships. Moreover, to effectively reduce the number of candidate patterns, ONP-Miner uses pattern join and pruning strategies to generate and further prune the candidate patterns, respectively. Experimental results show that ONP-Miner not only improves the mining efficiency, but also has better mining performance than the state-of-the-art algorithms. More importantly, ONP mining can find more interesting patterns in traffic volume data to predict future traffic.
Domain-invariant Feature Exploration for Domain Generalization
Venue: arXiv cs.LG 2022
Year: 2022

Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (DG) enables a model to generalize to an unseen test distribution, i.e., to learn domain-invariant representations. In this paper, we argue that domain-invariant features should be originating from both internal and mutual sides. Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i.e., the property within a domain, which is agnostic to other domains. Mutual invariance means that the features can be learned with multiple domains (cross-domain) and the features contain common information, i.e., the transferable features w.r.t. other domains. We then propose DIFEX for Domain-Invariant Feature EXploration. DIFEX employs a knowledge distillation framework to capture the high-level Fourier phase as the internally-invariant features and learn cross-domain correlation alignment as the mutually-invariant features. We further design an exploration loss to increase the feature diversity for better generalization. Extensive experiments on both time-series and visual benchmarks demonstrate that the proposed DIFEX achieves state-of-the-art performance.
Contrastive Learning for Interactive Recommendation in Fashion
Venue: arXiv cs.IR 2022
Year: 2022

Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths of recommender systems to accurately capture user tastes with search systems' ability to process user queries. We propose a novel remedy to this problem by automatically recommending personalized fashion items based on a user-provided text request. Our proposed model, WhisperLite, uses contrastive learning to capture user intent from natural language text and improves the recommendation quality of fashion products. WhisperLite combines the strength of CLIP embeddings with additional neural network layers for personalization, and is trained using a composite loss function based on binary cross entropy and contrastive loss. The model demonstrates a significant improvement in offline recommendation retrieval metrics when tested on a real-world dataset collected from an online retail fashion store, as well as widely used open-source datasets in different e-commerce domains, such as restaurants, movies and TV shows, clothing and shoe reviews. We additionally conduct a user study that captures user judgements on the relevance of the model's recommended items, confirming the relevancy of WhisperLite's recommendations in an online setting.
What makes you change your mind? An empirical investigation in online group decision-making conversations
Venue: arXiv cs.CL 2022
Year: 2022

People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods for detecting what makes someone change their mind. To this end, we leverage a recently introduced dataset containing group discussions of people collaborating to solve a task. To find out what makes someone change their mind, we incorporate various techniques such as neural text classification and language-agnostic change point detection. Evaluation of these methods shows that while the task is not trivial, the best way to approach it is using a language-aware model with learning-to-rank training. Finally, we examine the cues that the models develop as indicative of the cause of a change of mind.
Decision-oriented two-parameter Fisher information sensitivity using symplectic decomposition
Venue: arXiv cs.IT 2022
Year: 2022

The eigenvalues and eigenvectors of the Fisher information matrix (FIM) can reveal the most and least sensitive directions of a system and it has wide application across science and engineering. We present a symplectic variant of the eigenvalue decomposition for the FIM and extract the sensitivity information with respect to two-parameter conjugate pairs. The symplectic approach decomposes the FIM onto an even-dimensional symplectic basis. This symplectic structure can reveal additional sensitivity information between two-parameter pairs, otherwise concealed in the orthogonal basis from the standard eigenvalue decomposition. The proposed sensitivity approach can be applied to naturally paired two-parameter distribution parameters, or decision-oriented pairing via re-grouping or re-parameterization of the FIM. It can be utilised in tandem with the standard eigenvalue decomposition and offer additional insight into the sensitivity analysis at negligible extra cost.
ASA: A Simulation Environment for Evaluating Military Operational Scenarios
Venue: arXiv cs.DC 2022
Year: 2022

The Aerospace Simulation Environment (Ambiente de Simulação Aeroespacial -- ASA in Portuguese) is a custom-made object-oriented simulation framework developed mainly in C++ that enables the modeling and simulation of military operational scenarios to support the development of tactics and procedures in the aerospace context for the Brazilian Air Force. This work describes the ASA framework, bringing its distributed architecture for managing multiple simulation machines, a data analysis platform for post-processing simulation data, the capability of loading models at simulation runtime, and a batch mode execution platform to perform multiple independent executions simultaneously. In addition, we present a list of recent works using the ASA framework as a simulation tool in the air combat context.
Efficient Resource Management in Cloud Environment
Venue: arXiv cs.DC 2022
Year: 2022

In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload. Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are offered by cloud computing to provide compute, network, and storage capabilities to the cloud users utilizing the pay-per-usage approach. Resource allocation is a prior solution to address various demanding situations like the under/overload handling, resource wastage, load balancing, Quality-of-Services (QoS) violations, VM migration and many more. The primary aim of Virtual Machine Placement (VMP) is mapping of Virtual Machines (VMs) to physical machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption to a larger extent. The inefficient VMP leads to wastage of resources, excessive energy consumption and also increase overall operational cost of the data center. On this context, this article provides an extensive survey of resource management schemes in cloud environment. A conceptual scheme for resource management, grouping of current machine learning based resource allocation strategies, and fundamental problems of ineffective distribution of physical resources are analyzed. Thereafter, a complete survey of existing techniques in machine learning based mechanisms in the field of cloud resource management are explained. Ultimately, the paper explores and concludes distinct approaching challenges and future research guidelines associated to resource management in cloud environment.