Reviewerly/exHarmony huggingface dataset:
Holistic Runtime Performance and Security-aware Monitoring in Public Cloud Environment

The emergence of cloud computing allows users to execute their applications in a ubiquitous manner. Public cloud offers various ready-to-use services e.g. AWS EC2, Amazon RDS on a pay-per-use basis. Alongside these advantages, the cloud also brings a number of issues, for example offloading data for storage and computation may lead to privacy and security concerns. Also, it is not easy to guarantee the performance of the underlying system. With the increasing performance and security concerns, it is necessary to continuously monitor and evaluate the system and its performance. This can help us to quickly detect anomalies that can hinder system performance and/or make the system untrusted. In this paper, we present PERSECMON: performance and security-aware monitoring framework for continuous run-time monitoring in the public cloud environment. PERSECMON provides not only the system performance metrics but also the security measurements which can be used to analyse the system state at run-time. It uses the BCC/eBPF (BPF Compiler Collection/ Extended Berkeley Packet Filters) framework to instrument the system. PERSECMON is integrated with the open-source user interface framework, Kibana which provides a clear visualisation of the obtained metrics. To show the efficacy of our proposed work, we have developed a benchmarking case study using Bonnie++, Fibonacci Sequence and Netperf executed on Ubuntu Server 21.04. The results show that PERSECMON successfully captures relevant metrics that can be utilised in real-time to analyse the system performance. These metrics can further be accessed to detect the system state including memory leaks, queuing delay and remote access time which may lead to security or reliability events.
Fed-UserPro: A user profile construction method based on federated learning

Abstract User profiles constructed using vast network behaviour data are widely used in various fields. However, data island and central server capacity problems limit the implementation of centralised big data training. This paper proposes a user profile construction method, Fed-UserPro, based on federated learning, which uses non-independent and identically distributed unstructured user text to jointly construct user profiles. Latent Dirichlet allocation model and softmax multi-classification regression method are introduced into the federated learning structure to train data. The results show that the accuracy of the Fed-UserPro method is 8.69%–19.71% higher than that of single-party machine learning methods.
Well Begun is Half Done: An Empirical Study of Exploitability &amp; Impact of Base-Image Vulnerabilities

Container technology, (e.g., Docker) is being widely adopted for deploying software infrastructures or applications in the form of container images. Security vulnerabilities in the container images are a primary concern for developing containerized software. Exploitation of the vulnerabilities could result in disastrous impact, such as loss of confidentiality, integrity, and availability of containerized software. Understanding the exploitability and impact characteristics of vulnerabilities can help in securing the configuration of containerized software. However, there is a lack of research aimed at empirically identifying and understanding the exploitability and impact of vulnerabilities in container images. We carried out an empirical study to investigate the exploitability and impact of security vulnerabilities in base-images and their prevalence in open-source containerized software. We considered base-images since container images are built from base-images that provide all the core functionalities to build and operate containerized software. Besides, security vulnerabilities in a base-image can propagate to derived container images, which can host different applications. That means a single exploitable vulnerability in base-images can result in security attacks in several containerized software. Our analysis of a set of 1, 983 unique base-image security vulnerabilities revealed 13 novel findings. These findings are expected to help developers to understand the potential security problems related to base-images and encourage them to investigate base-images from security perspective before developing their applications. For researchers, this study highlights the need of developing tools for mitigating the exploitability of vulnerable base-images.
Exploring Relevant Artifacts of Release Notes: The Practitioners' Perspective

A software release note is one of the essential documents in the software development life cycle. The software release contains a set of information, e.g., bug fixes and security fixes. Release notes are used in different phases, e.g., requirement engineering, software testing and release management. Different types of practitioners (e.g., project managers and clients) get benefited from the release notes to understand the overview of the latest release. As a result, several studies have been done about release notes production and usage in practice. However, two significant problems (e.g., duplication and inconsistency in release notes contents) exist in producing well-written &#x0026; well-structured release notes and organizing appropriate information regarding different targeted users&#x0027; needs. For that reason, practitioners face difficulties in writing and reading the release notes using existing tools. To mitigate these problems, we execute two different studies in our paper. First, we execute an exploratory study by analyzing 3,347 release notes of 21 GitHub repositories to understand the documented contents of the release notes. As a result, we find relevant key artifacts, e.g., issues (29&#x0025;), pull-requests (32&#x0025;), commits (19&#x0025;), and common vulnerabilities and exposures (CVE) issues (6&#x0025;) in the release note contents. Second, we conduct a survey study with 32 professionals to understand the key information that is included in release notes regarding users&#x0027; roles. For example, project managers are more interested in learning about new features than less critical bug fixes. Our study can guide future research directions to help practitioners produce the release notes with relevant content and improve the documentation quality.
Recommending Code Reviewers for Proprietary Software Projects: A Large Scale Study

Code review is an important activity in software development, which offers benefits such as improving code quality, reducing defects and distributing knowledge. Tencent, as a giant company, hosts a great number of proprietary software projects that are only open to specific internal developers. Since these proprietary projects receive up to 100,000 of newly submitted code changes per month, it is extremely needed to automatically recommend code reviewers. To this end, we first conduct an empirical study on a large scale of proprietary projects from Tencent, to understand their characteristics and how code reviewer recommendation approaches work on them. Based on the derived findings and implications, we propose a new approach named Camp that recommends reviewers by considering their collaboration and expertise in multiple projects, to fit the context of proprietary software development. The evaluation results show that Camp can achieve higher scores on proprietary projects across most metrics than other state-of-the-art approaches, i.e., Revfinder, CHREV, Tie and Comment Network and produce acceptable performance scores for more projects. In addition, we discuss the possible directions of code reviewer recommendation.
Exploring API Deprecation Evolution in JavaScript

Building an application using third-party libraries is a common practice in software development. As any other system, software libraries and their APIs evolve. To support version migration and ensure backward compatibility, a recommended practice during development is to deprecate API. Different from other popular programming languages such as Java and C#, JavaScript has no native support to deprecate API elements. However, several strategies are commonly adopted to communicate that an API should be avoided, such as the project documentation, JSDoc annotation, code comment, console message, and deprecation utility. Indeed, there have been many studies on deprecation strategies and evolution mostly on Java, C#, and Python. However, to the best of our knowledge, there are no detailed studies aiming at analyzing how API deprecation changes over time in the JavaScript ecosystem. This paper provides an empirical study on how API deprecation evolves in JavaScript by analyzing 1,918 releases of 50 popular packages. Results show that close to 60&#x0025; have rising trends in the number of deprecated APIs, while only 9.4&#x0025; indicate a downward trend. Also, most deprecation occurrences are both added and removed on minor releases instead of removed on major releases, as recommended by best practices.
Phishing Kits Source Code Similarity Distribution: A Case Study

Attackers (&#x201C;phishers&#x201D;) typically deploy source code in some host website to impersonate a brand or in general a situation in which a user is expected to provide some personal information of interest to phishers (e.g. credentials, credit card number). Phishing kits are ready-to-deploy sets of files that can be simply copied on a web server and used almost as they are. In this paper, we consider the static similarity analysis of the source code of 20871 phishing kits totalling over 182 million lines of PHP, Javascript and HTML code, that have been collected during phishing attacks and recovered by forensics teams. Reported experimental results show that as much as 90&#x0025; of the analyzed kits share 90&#x0025; or more of their source code with at least another kit. Differences are small, less than about 1000 programming words &#x2013; identifiers, constants, strings and so on &#x2013; in 40&#x0025; of cases. A plausible lineage of phishing kits is presented by connecting together kits with the highest similarity. Obtained results show a very different reconstructed lineage for phishing kits when compared to a publicly available application such as Wordpress. Observed kits similarity distribution is consistent with the assumed hypothesis that kit propagation is often based on identical or near-identical copies at low cost changes. The proposed approach may help classifying new incoming phishing kits as &#x201C;near-copy&#x201D; or &#x201C;intellectual leaps&#x201D; from known and already encountered kits. This could facilitate the identification and classification of new kits as derived from older known kits.
Human Activity Recognition Using Ambient Sensor Data

Variety and volume of data make human activity recognition especially interesting field for machine learning. It has thus seen incredible growth in past several years taking part in big data questions as well. In a broad sense question of HAR - human activity recognition is a very complex one, often times dealing with large amounts of data not belonging to a predefined class. However, this paper deals with supervised learning classifications task, focusing on several activity classes known as Activities of Daily Living - ADL. Generalized models for common activities and issues are looked into, and issues that appear due to the huge volume of data that is recognized as "other" when the models are applied to the real life data sets. Support vector machine method (SVM), naïve Bayes classifiers, KNN, Random Tree and Bagged Trees (Ensemble) algorithms are applied, and venturing into artificial neural network algorithms Multilayer Perceptron algorithm is applied as well.
Context-Patch Representation Learning With Adaptive Neighbor Embedding for Robust Face Image Super-Resolution

Representation learning steered robust face image super-resolution (FSR) methods have attracted extensive attention in the past few decades. Most previous methods were devoted to exploiting the local position patches in the training set for FSR. However, they usually overlooked the sufficient usage of the contextual information around the testing patches, which are useful for stable representation learning. In this article, we attempt to utilize the context-patch around the testing patch and propose a method named context-patch representation learning with adaptive neighbor embedding (CRL-ANE) for FSR. On one hand, we simultaneously use the testing position patch and its adjacent ones for stable representation weight learning. This contextual information can compensate for recovering missing details in the target patch. On the other hand, for each input patch set, due to its inherent facial structural properties, we design an adaptive neighbor embedding strategy to elaborately and adaptively choose primary candidates for more accurate reconstruction. These two improvements enable the proposed method to achieve better SR performance than some of the other methods. Qualitative and quantitative experiments on some benchmarks have validated the superiority of the proposed method over some state-of-the-art methods.
Simple Approach to Realizing Verifiable Secret Sharing for Secure Cloud System

In (k, n) threshold secret sharing, a secret is converted into n shares such that any threshold k or more shares allow for the reconstruction of this secret; however, the total storage cost increases.By contrast, asymmetric secret sharing reduces the total shares to be stored.When implementing secret sharing in the cloud, if malicious players submit forged shares during the reconstruction process, the reconstructed value will differ from the original secret.Therefore, a method that quickly verifies the integrity of the restored secret should be developed.Many research papers investigate cheater detection/identification for (k, n) threshold secret sharing.However, most of them require additional information, such as an authenticator.Harn et al. proposed a method for cheat detection using only the shares for (k, n) threshold secret sharing.In this study, we improved and extended the method proposed by Harn et al. to realize the detection and identification of shares forgery (cheating) in asymmetric secret sharing suitable for a cloud system.The proposed method uses the shares generated during asymmetric secret sharing to reconstruct and verify the secret.We also included an attack that assumes a cloud system and shows that most methods cannot work against it.Finally, we discussed the requirements for a secret sharing scheme suitable for the cloud and showed that the proposed method is ideal for use in a cloud environment.
Federated Imitation Learning for UAV Swarm Coordination in Urban Traffic Monitoring

The popularization of unmanned aerial vehicles (UAVs) has boosted various civil applications such as traffic monitoring, in which the effective coordination of the UAV swarm plays a significant role in expanding the monitoring range and enhancing the execution efficiency. However, due to the isolated local environments as well as the heterogeneous execution capabilities, it is challenging to achieve highly consistent actions. In this article, we incorporate the federated learning framework with the imitation learning technique to coordinate the UAVs' maneuvers by interactively imitating the leader UAV's operations. During the interagent global model download phase, we utilize the generative adversarial imitation learning (GAIL) model to accurately follow the leader UAV's operations by removing the biased estimates of imitation parameters. While in the intraagent local model training phase, we utilize the self-imitation learning (SIL) model to correct delicate imitation errors by virtue of the follower UAVs' own historical valuable experiences. In order to achieve more efficient distributed parameter interactions, we regularize the federated gradient updates and eventually yield coordinated swarm policies. We evaluate the proposed algorithm in the UAV-based traffic monitoring scenario. Evaluation results demonstrate the superiorities on training and execution efficiencies.
Legal Judgement Prediction of Sentence Commutation with Multi-document Information

Legal judgment prediction is a task that automatically predicts the outcome of court judgments. The sentence prediction contained in it aims to predict the range of the sentence based on past textual information, providing reference comments for relevant departments. Since previous related researches mainly focused on criminal cases, they did not pay attention to penalty change activities, for example, commutation cases. The main difficulty is that the amount of direct information in commutation cases is less than that in criminal cases. Based on the actual process of commutation, this paper proposes a method of commutation prediction with multi-document information, by expanding the information in the commutation documents. We collect criminal documents related to commutation cases and integrate them into a knowledge base. Then, based on hierarchical attention networks for document classification, we design a multi-document commutation model with dynamic context vectors. Indirect information, such as criminal documents, can generate document vectors that are more relevant to specific commutation prediction tasks based on direct information, such as commutation documents. The experimental results prove that this method can combine the vector features of multiple documents to better predict the sentence of commutation.
Delay-Resistant Geo-Distributed Analytics

Big data analytics platforms have played a critical role in the unprecedented success of data-driven applications. However, real-time and streaming data applications, and recent legislation, e.g., GDPR in Europe, have posed constraints on exchanging and analyzing data, especially personal data, across geographic regions. To address such constraints data has to be processed and analyzed in-situ and aggregated results have to be exchanged among the different sites for further processing. This introduces additional network delays due to the geographic distribution of the sites and potentially affecting the performance of analytics platforms that are designed to operate in datacenters with low network delays. In this paper, we show that the three most popular big data analytics systems (Apache Storm, Apache Spark, and Apache Flink) fail to tolerate round-trip times more than 30 milliseconds even when the input data rate is low. The execution time of distributed big data analytics tasks degrades substantially after this threshold, and some of the systems are more sensitive than others. A closer examination and understanding of the design of these systems show that there is no winner in all wide-area settings. However, we show that it is possible to improve the performance of all these popular big data analytics systems significantly amid even transcontinental delays (where inter-node delay is more than 30 milliseconds) and achieve performance comparable to this within a datacenter for the same load.
Regression-Selective Feature-Adaptive Tracker for Visual Object Tracking

As a challenging visual task, visual object tracking has recently been composed of the classification and regression subtasks. The anchor-free regression network gets rid of the dependence on the anchors, but the redundant range makes it usually regress some samples involving non-target information. Evenly dividing a target by the regular receptive field often causes ambiguous target localization. To address these issues, we propose a regression-selective feature-adaptive tracker ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RSFA</i> ), where the regression-selective subnetwork can not only free the regression task from anchors, but can also select more effective regression samples using the refined criterion. The proposed feature-adaptive strategy concentrates the classification subnetwork on target feature extraction via adaptively modifying the receptive field, and the attached centrality branch offers a correction for target localization by exploiting the spatial information. Additionally, the designed online update mechanism realizes the tracker's online optimization, improving robustness against target deformation. Extensive experiments are conducted on challenging benchmarks, including GOT10 K, OTB2015, UAV123, NFS, VOT2018, VOT2019 and VOT2020-ST. Our tracker achieves satisfactory tracking results, and the evaluations of its tracking performance rank first or second in comparison with the state-of-the-art tracking algorithms.
MCPA: A Fast Single Image Haze Removal Method Based on the Minimum Channel and Patchless Approach

The image haze removal algorithm is challenging regarding computational processing speed and the hazy removal effect.Instead of using the local patch approach, which assumes the scene transmission to be locally constant and uses various filters to smooth the transmission map, this paper proposes a fast single image haze removal method based on a minimum channel and patchless approach.A new simple approach to estimate the atmospheric light and the scene transmission is proposed based on the minimum channel of images.The histogram of the minimum channel of the image is used to extract the atmospheric light pixels and exclude the non-hazy bright pixels in the image.The histogram equalization and image multiplication are applied to achieve better visual quality.In order to verify the performance of the proposed method, 100 images are collected from datasets I-HAZE, O-HAZE, and websites.Experimental results show that our proposed method outperforms up-to-date state-of-the-art haze removal algorithms using quantitative evaluations.From subjective comparisons, the proposed method outperforms most current haze removal algorithms in color restoration.Also, time assessment results show that our proposed method is the fastest among the up-to-date state-of-the-art haze removal methods and is about 15 times faster than the secondfastest method.The main contribution of the proposed method is significantly reducing computation time because it uses a patchless approach that does not need any filter and complicated algorithms.In addition to significantly reducing the computational processing speed, our proposed method can achieve better visual quality.
Extrinsic Camera Calibration From a Moving Person

We propose a novel camera calibration method for a room-scale multi-view imaging system. Our key idea is to leverage our articulated body movements as a calibration target. We show that a freely moving person provides trajectories of a set of oriented points ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., neck joint with spine direction) from which we can estimate the locations and poses of all cameras observing them. The method only requires the cameras to be synced and that 2D human poses are estimated in each view sequence. By elevating these 2D poses to 3D which directly provides a set of oriented 3D joints, we compute the extrinsic parameters of all cameras with a linear algorithm. We also show that this enables self-supervision of the 3D joint estimator for refinement, and the iteration of the two leads to accurate camera extrinsics and 3D pose estimates up to scale. Extensive experiments using synthetic and real data demonstrate the effectiveness and flexibility of the method. The method will serve as a useful tool to expand the utility of multi-view vision systems as it eliminates the need for cumbersome on-site calibration procedures.