Reviewerly/exHarmony huggingface dataset:
Temporal-MPI: Enabling Multi-plane Images for Dynamic Scene Modelling via Temporal Basis Learning

Novel view synthesis of static scenes has achieved remarkable advancements in producing photo-realistic results. However, key challenges remain for immersive rendering of dynamic scenes. One of the seminal image-based rendering method, the multi-plane image (MPI), produces high novel-view synthesis quality for static scenes. But modelling dynamic contents by MPI is not studied. In this paper, we propose a novel Temporal-MPI representation which is able to encode the rich 3D and dynamic variation information throughout the entire video as compact temporal basis and coefficients jointly learned. Time-instance MPI for rendering can be generated efficiently using mini-seconds by linear combinations of temporal basis and coefficients from Temporal-MPI. Thus novel-views at arbitrary time-instance will be able to be rendered via Temporal-MPI in real-time with high visual quality. Our method is trained and evaluated on Nvidia Dynamic Scene Dataset. We show that our proposed Temporal-MPI is much faster and more compact compared with other state-of-the-art dynamic scene modelling methods.
Decision Tree Algorithm in Machine Learning

Machine learning has been a hot topic in artificial intelligence for quite a few good reasons. In the future, the world’s information would be too massive for us to process. Therefore, it will be necessary of machines to help us process data, do calculations, and help us make decisions. Supervised learning and unsupervised learning are the two most widely used learnings. Decision Tree is one of the most important ones in supervised learning. It is commonly used for classifying categorical as well as numerical variables. In our paper, we will first introduce the metrics used by the algorithms in decision trees. We will then discuss their application in the algorithms CART and ID3. We will also be discussing C4.5, an extending algorithm from ID3. In the end of the paper, we will give an introduction of pruning, which is a common measure to deal with over fitting that is seen in decision trees.
Data visualization of film capacitors

A large amount of data has been accumulated in the field of film capacitors, and these data resources need to be utilized to release the hidden value and provide a reference paradigm for subsequent researchers. Data visualization can open up a new path for the digital transformation of the film capacitor industry. Considering these, the characteristics of the collected film capacitor data are summarized. Next, the role of data visualization, fundamental forms and their respective prominent features of data visualization are combed and refined. Then, case studies on the three main data forms of film capacitors: text, digit, and image are conducted to construct new visualization presentations by using the aforementioned fundamental forms considering the specific data characteristics. The research results show that the fundamental form of data visualization and the specific visualization presentation constructed in this paper can help relevant practitioners efficiently and accurately grasp the laws and knowledge in film capacitor data, inspire researchers to discover hidden relationships, and provide a reference for the data utilization of generators, transformers, converters and other energy equipment in the energy internet. Finally, the challenges and the key issues that need to be addressed in future research have been prospected.
A Survey on Citation Recommendation System

Citation recommendation is a technique that assists an academic publisher or scholar in identifying a selection of relevant works that can be cited while writing a paper. In comparison to prior years, it is clear that there is currently a deluge of articles being published every year. Each year, more than 50,000 publications in the field of artificial intelligence are published. As a result, the traditional way of identifying valid citations appears to be a very difficult process, as there appears to be a massive flow of data. Traditional recommendation systems, despite their prevalence, are ineffective and time-consuming. So far, several techniques to tackle this problem have been offered.In this study, we will look at a range of citation recommendation systems that have been developed so far, as well as their future potential.
Cryptocurrency and money laundering: A literature review

According to previous research, cryptocurrency is a driver of money laundering and is associated with several risks (Fletcher, Larkin, &amp; Corbet, 2021; Teichmann &amp; Falker, 2020; Tsuchiya &amp; Hiramoto, 2021). As a result, the purpose of this paper is to concentrate on empirical research in the accounting and finance fields that deal with the impact of cryptocurrencies on the phenomenon of money laundering. To identify relevant literature, we use the following keywords including “cryptocurrency or digital money” and “bitcoin and money laundering”. We identify 28 research papers published between 2011 and 2021. The findings of the studies that were reviewed emphasized the importance of developing a legal framework for digital currencies. Furthermore, it was revealed that all stakeholders play an important role in lowering the risk of money laundering and illicit activities. The findings highlight the critical role that banks, regulators, and all stakeholders play in reducing money laundering risks. These findings may have policy implications for governments aiming to improve cryptocurrency laws and regulations by enforcing financial security standards and laws and monitoring individuals’ and firms’ compliance with them. The review identifies some of the literature’s limitations and suggests future research directions
Code Comments Generation with Data Flow-Guided Transformer

Code comments generation is a multi-disciplinary research, involving many different fields such as software engineering and natural language processing. In general, the automatic generation of code comments takes the source code as the input and its corresponding natural language description as outputs. In previous work, the code is regarded as a sequence and modeled by sequence models such as RNN, ignoring the data flow between variables and long-range dependencies in the code. Therefore, we propose a novel DFG-trans model to automatically generate code comments. Our model adds data flow guidance to the Transformer, which allows the model to focus on the flow of data between variables. This leads to better learning of semantic information in the code and alleviates the problem of long-distance dependencies. Experiments on a Java dataset show that our proposed model effectively improves the quality of code comments. Under various evaluation metrics, the scores of DFG-trans are better than some existing advanced models.
Compressed String Dictionaries via Data-Aware Subtrie Compaction

String dictionaries are a core component of a plethora of applications, so it is not surprising that they have been widely and deeply investigated in the literature since the introduction of tries in the ’60s. We introduce a new approach to trie compression, called COmpressed COllapsed Trie (CoCo-trie ), that hinges upon a data-aware optimisation scheme that selects the best subtries to collapse based on a pool of succinct encoding schemes in order to minimise the overall space occupancy. CoCo-trie supports not only the classic lookup query but also the more sophisticated rank operation, formulated over a sorted set of strings. We corroborate our theoretical achievements with a large set of experiments over datasets originating from a variety of sources, e.g., URLs, DNA sequences, and databases. We show that our CoCo-trie provides improved space-time trade-offs on all those datasets when compared against well-established and highly-engineered trie-based string dictionaries.
Key Structures: Improved Related-Key Boomerang Attack Against the Full AES-256

This paper introduces structure to key, in the related-key attack settings. While the idea of structure has been long used in key-recovery attacks against block ciphers to enjoy the birthday effect, the same had not been applied to key materials due to the fact that key structure results in uncontrolled differences in key and hence affects the validity or probabilities of the differential trails. We apply this simple idea to improve the related-key boomerang attack against AES-256 by Biryukov and Khovratovich in 2009. Surprisingly, it turns out to be effective, i.e., both data and time complexities are reduced by a factor of about $$2^8$$ , to $$2^{92}$$ and $$2^{91}$$ respectively, at the cost of the amount of required keys increased from 4 to $$2^{19}$$ . There exist some tradeoffs between the data/time complexity and the number of keys. To the best of our knowledge, this is the first essential improvement of the attack against the full AES-256 since 2009. It will be interesting to see if the structure technique can be applied to other AES-like block ciphers, and to tweaks rather than keys of tweakable block ciphers so the amount of required keys of the attack will not be affected.
Black shale-hosted nickel-cobalt deposit: Current understanding and outlook to future prospecting

Ni和Co都是重要的战略性矿产资源, 其中黑色页岩容矿型Ni-Co矿床是沉积岩容矿型Ni-Co矿床的重要组成部分, 具有非常重要的工业价值和科学研究意义。本文按照形成时代的先后顺序综述了芬兰Talvivaara组、英国Gwna群、中国华南牛蹄塘组、瑞典Alum Shale组、加拿大Canol组以及中欧Kupferschiefer组等全球范围内几个重要黑色页岩层位赋存的Ni-Co矿床。这些矿床的矿石具有纹层状构造、多金属共生富集、富有机质和硫等共同特征。矿床中多金属的初始来源可能为陆源风化的产物或者海底热液, 而导致溶解于海水中的金属发生沉淀的机制则是极端条件下海洋环境的变化。Ni-Co的共生分异受到同沉积期海水氧化还原环境和后期改造过程的共同控制。全球范围内几个重要的含Ni-Co黑色页岩层位均发育在第一次全球大氧化事件、新元古代大氧化事件和显生宙的地球表生环境突变期, 显示了地球表生环境对该类型矿床的形成具有控制作用。此外, 后期改造过程则有利于金属的进一步富集形成工业矿体。前人针对黑色页岩容矿型Ni-Co矿床的研究极大的推动了人们对这类矿床的认识, 然而, 对这类矿床中Ni-Co共生-分异机制、同沉积过程中多金属的共生关系以及后期改造过程仍需要更深入的探索。以现代分析技术为手段, 开展成矿学、沉积学、地质生物学、地球化学等多学科交叉研究, 将有助于深入理解黑色页岩容矿型Ni-Co矿床的成矿过程以及与地球多圈层协同演化(尤其是表生环境演化)的关系, 从而推动黑色页岩容矿型Ni-Co矿床成矿理论的发展。
Code reviewer recommendation in tencent

Code review is essential for assuring system quality in software engineering. Over decades in practice, code review has evolved to be a lightweight tool-based process focusing on code change: the smallest unit of the development cycle, and we refer to it as Modern Code Review (MCR). MCR involves code contributors committing code changes and code reviewers reviewing the assigned code changes. Such a reviewer assigning process is challenged by efficiently finding appropriate reviewers. Recent studies propose automated code reviewer recommendation (CRR) approaches to resolve such challenges. These approaches are often evaluated on open-source projects and obtain promising performance.
OurRank: A Software Requirements Prioritization Method Based on Qualitative Assessment and Cost-Benefit Prediction

Requirements prioritization is an activity aimed at determining the essential requirements to include in a software release. Although there are several prioritization methods to systematize this task, there are still unresolved challenges. Existing methods do not guarantee that requirements prioritization meets stakeholder expectations and goals. This is because most prioritization methods operate by considering only quantitative information, making it difficult to formally capture stakeholder interests and perspectives that can rather be made explicit in qualitative terms. Likewise, methods including qualitative information only consider elements associated with benefit estimation, that is, positive aspects of the project, but neglect costs or negative aspects. As a result, the prioritization process is driven by a partial view of constraints. Such methods also fail at capturing and combining expert knowledge that decision-makers can bring into the decision-making process. In this research, we propose a novel method for software requirements prioritization, which facilitates incorporating experts’ qualitative assessment at the outset of the prioritization process and considers both benefit and cost constraints. Details of the method are presented, together with a case study describing a real application scenario. Recommendations and guidelines regarding the application of the method are proposed based on the results of the case study.
Matrix Decomposition based Digital Video Forgery Detection

There are widespread growth of high quality and low cost cameras in our lives. Likewise different powerful video editing tools are also available, using this editing tools, people are intentionally or unintentionally edit contents and make to destroy the originality of the video. In this paper an effective method to detect and locate video frame duplication forgery is proposed. The proposed method converts the video forgery detection problem into an anomaly detection. A video patch cube is sampled from the given video and it is arranged as a matrix. Then it is decomposed into a low rank and sparse component using RPCA. The sparse component which represents anomaly is analyzed further using clustering techniques to detect the presence of forgery. Experimental result indicates that, the proposed method outperforms the state of art methods and offers low computational complexity as well as high degree of accuracy.
Non-disclosing Credential On-chaining for Blockchain-Based Decentralized Applications

Many service systems rely on verifiable identity-related information of their users. Manipulation and unwanted exposure of this privacy-relevant information, however, must at the same time be prevented and avoided. Peer-to-peer blockchain-based decentralization with a smart contract-based execution model and verifiable off-chain computations leveraging zero-knowledge proofs promise to provide the basis for next-generation, non-disclosing credential management solutions. In this paper, we propose a novel credential on-chaining system that ensures blockchain-based transparency while preserving pseudonymity. We present a general model compliant to the W3C verifiable credential recommendation and demonstrate how it can be applied to solve existing problems that require computational identity-related attribute verification. Our zkSNARKs-based reference implementation and evaluation show that, compared to related approaches based on, e.g., CL-signatures, our approach provides significant performance advantages and more flexible proof mechanisms, underpinning our vision of increasingly decentralized, transparent, and trustworthy service systems.
Method for Assessing Objective Video Quality for Automatic License Plate Recognition Tasks

In the modern era, there are numerous metrics for overall Quality of Experience (QoE), both those with Full Reference (FR), such as Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity (SSIM), and those with No Reference (NR), such as Video Quality Indicators (VQI), that are successfully used in video processing systems to assess videos whose quality is diminished by various processing scenarios. However, they are not appropriate for video sequences used for jobs that require recognition (Target Recognition Videos, TRV). As a result, a significant research problem remains to accurately assess the performance of the video processing pipeline in both human and Computer Vision (CV) recognition tasks. For recognition tasks, there is a need for objective ways to assess video quality. In this research, we demonstrate that it is feasible to create a novel idea of an objective model to assess video quality for automatic licence plate recognition (ALPR) tasks in response to this demand. A representative set of image sequences is used to train, test, and validate the model. The collection of degradation scenarios is based on a digital camera model and how a scene’s luminous flux eventually transforms into a digital image. The generated degraded images are evaluated for ALPR and VQI using a CV library. The value of the F-measure parameter of 0.777 represents the measured accuracy of a model.
Extending the Kubernetes Platform with Network-Aware Scheduling Capabilities

Kubernetes is today the de-facto standard container orchestration platform for the lifecycle management of microservices-based applications on Cloud environments. However it is not yet ready to be adopted on node clusters distributed in the Cloud-to-Edge continuum. In particular its scheduling strategy is not suitable for the placement of modern latency-sensitive applications on Edge environments, characterized by frequent node failures and network partitions. In this work we propose a network-aware scheduling extension of the default Kubernetes scheduler that takes into account the ever changing infrastructure network conditions and the dynamic communication interactions between microservices, with the aim to optimize the placement of application containers.
Filter-based feature selection: a comparison among binary and continuous Cuckoo optimisation algorithms along with multi-objective optimisation algorithms using gain ratio-based entropy

Filter-based feature selection used the intrinsic statistical characteristic to select high-rank features from a dataset. However, it affects the classification performance due to lack of feature interaction among the selected features. Gain ration (GR)-based entropy which is a modification of the commonly used information gain (IG) is employed as a filter-based evaluation measure on cuckoo optimisation algorithm (COA) along with its binary counterpart (BCOA) together with non-dominated sorting genetic algorithm (NSGA-III), multi-objective evolutionary algorithm (MOEA) based on decomposition and evolutionary algorithm of non-dominated sorting with radial basis (ENORA). The results achieved showed the superiority of the proposed entropy with GR over the existing entropy with IG in most of the datasets. While BCOA perform better than COA in majority of the datasets. The proposed multi-objective algorithms perform better than both BCOA and COA on the majority of the datasets. NSGA-III performed better than all on majority of the datasets.
Self-Supervised Learning Based Anomaly Detection in Synthetic Aperture Radar Imaging

In this paper, we proposed to investigate unsupervised anomaly detection in Synthetic Aperture Radar (SAR) images.Our approach considers anomalies as abnormal patterns that deviate from their surroundings without prior knowledge of their characteristics.This method deals with the crucial problems related to the presence of speckle, the spatial correlation structures in SAR images, and the lack of annotated data to train a detection algorithm.Our proposed method aims to address these issues through a self-supervised learning algorithm.First, we propose to mitigate the SAR speckle through the deep learning SAR2SAR algorithm.We then develop an Adversarial Autoencoder (AAE) to reconstruct anomaly-free SAR images from despeckled data taking into account potential spatial correlation structures.Finally, a change detection processing step is applied between the input and the output to detect anomalies.Experiments are performed to show the advantages of our method compared to the conventional Reed-Xiaoli algorithm, highlighting the importance of an efficient despeckling pre-processing step.
SAFER: Social Capital-Based Friend Recommendation to Defend against Phishing Attacks

The tremendous growth of social media has been accompanied by highly advanced online social network (OSN) technologies. Such advanced technologies have been heavily utilized by perpetrators as convenient tools for deceiving people in online worlds. Social capital has been discussed as a powerful mechanism to leverage interpersonal relationships in social networks in order for an individual to achieve his/her goal. The beauty of social capital is the ability to materialize non-monetary, less costly, and non-economic resources into tools to solve social problems. In this paper, we aim to leverage social capital (SC) to minimize online users' vulnerabilities to online deception. In particular, we propose a Social cApital-based FriEnd Recommendation scheme, called SAFER, that can protect OSN users from phishing attacks. We quantify three dimensions of social capital, namely, structural, cognitive, and relational, based on user features obtained from real datasets and model a user's friending behavior based on their social capital. In addition, to model a user's behavior upon being attacked by a phishing attacker, we developed the so-called SER-SEIR (Susceptible, Exposed, Recovered-Susceptible, Exposed, Infected, and Recovered) model as a variant of the SEIR model. Via extensive simulation experiments based on two real datasets considering bot-based and human-based attackers performing phishing attacks, we demonstrate the performance of four SC-based friend recommendation schemes with three non-SC-based comparable counterparts in terms of the ratio of detecting attackers and the fraction of users in the states of S, E, I, and R. Based on the performance comparison, we analyze the overall trends of their performance in terms of the extent of resistance against phishing attacks by bot or human attackers.
Collaborating Domain-Shared and Target-Specific Feature Clustering for Cross-domain 3D Action Recognition

In this work, we consider the problem of cross-domain 3D action recognition in the open-set setting, which has been rarely explored before. Specifically, there is a source domain and a target domain that contain the skeleton sequences with different styles and categories, and our purpose is to cluster the target data by utilizing the labeled source data and unlabeled target data. For such a challenging task, this paper presents a novel approach dubbed CoDT to collaboratively cluster the domain-shared features and target-specific features. CoDT consists of two parallel branches. One branch aims to learn domain-shared features with supervised learning in the source domain, while the other is to learn target-specific features using contrastive learning in the target domain. To cluster the features, we propose an online clustering algorithm that enables simultaneous promotion of robust pseudo label generation and feature clustering. Furthermore, to leverage the complementarity of domain-shared features and target-specific features, we propose a novel collaborative clustering strategy to enforce pair-wise relationship consistency between the two branches. We conduct extensive experiments on multiple cross-domain 3D action recognition datasets, and the results demonstrate the effectiveness of our method. Code is at https://github.com/canbaoburen/CoDT .