Reviewerly/exHarmony huggingface dataset:
BoxShrink: From Bounding Boxes to Segmentation Masks

One of the core challenges facing the medical image computing community is fast and efficient data sample labeling. Obtaining fine-grained labels for segmentation is particularly demanding since it is expensive, time-consuming, and requires sophisticated tools. On the contrary, applying bounding boxes is fast and takes significantly less time than fine-grained labeling, but does not produce detailed results. In response, we propose a novel framework for weakly-supervised tasks with the rapid and robust transformation of bounding boxes into segmentation masks without training any machine learning model, coined BoxShrink. The proposed framework comes in two variants – rapid-BoxShrink for fast label transformations, and robust-BoxShrink for more precise label transformations. An average of four percent improvement in IoU is found across several models when being trained using BoxShrink in a weakly-supervised setting, compared to using only bounding box annotations as inputs on a colonoscopy image data set. We open-sourced the code for the proposed framework and published it online.
A Practical Style Guide and Templates Repository for Writing Effective Use Cases

Software developers are most effective when handed with a set of consistent and self-contained use cases. This will reduce the lengthy discussion and expensive rework later during the design, implementation, and testing phase. Moreover, well-written use cases can be reused in future projects. However, writing high-quality use cases is a difficult and time-consuming task. Inspired by the success of various coding style guides, we developed a use case writing style guide and a small set of use case templates to slightly constrain the way a use case is written. This style guide will help reduce excessive personal idiosyncrasy in use case writing and give a uniform appearance to the use case written by different requirements engineers so that it is much easier to understand a large set of use cases. Instead of overly restricting the use case writing process, the purpose of this style guide is to find a balance between readability and creativity. The style guide and templates were applied to an actual software project. The results of this case study show qualitative improvements compared with an unconstrained use case writing approach.
Quantum-Resistant Password-Based Threshold Single-Sign-On Authentication with Updatable Server Private Key

Passwords are the most prevalent authentication mechanism, and proliferate on nearly every new web service. As users are overloaded with the tasks of managing dozens even hundreds of passwords, accordingly password-based single-sign-on (SSO) schemes have been proposed. In password-based SSO schemes, the authentication server needs to maintain a sensitive password file, which is an attractive target for compromise and poses a single point of failure. Hence, the notion of password-based threshold authentication (PTA) system has been proposed. However, a static PTA system is threatened by perpetual leakage (e.g., the adversary perpetually compromises servers). In addition, most of the existing PTA schemes are built on the intractability of conventional hard problems, and become insecure in the quantum era. In this work, we first propose a threshold oblivious pseudorandom function (TOPRF) to harden the password so that PTA schemes can resist offline password guessing attacks. Then, we employ the threshold homomorphic aggregate signature (THAS) over lattices to construct the first quantum-resistant password-based threshold single-sign-on authentication scheme with the updatable server private key. Our scheme resolves various issues arising from user corruption and server compromise, and it is formally proved secure against quantum adversaries. Comparison results show that our scheme is superior to its counterparts.
Deterministic Wallets for Adaptor Signatures

Adaptor signatures are a new cryptographic primitive that binds the authentication of a message to the revelation of a secret value. In recent years, this primitive has gained increasing popularity both in academia and practice due to its versatile use-cases in different Blockchain applications such as atomic swaps and payment channels. The security of these applications, however, crucially relies on users storing and maintaining the secret values used by adaptor signatures in a secure way. For standard digital signature schemes, cryptographic wallets have been introduced to guarantee secure storage of keys and execution of the signing procedure. However, no prior work has considered cryptographic wallets for adaptor signatures. In this work, we introduce the notion of adaptor wallets. Adaptor wallets allow parties to securely use and maintain adaptor signatures in the Blockchain setting. Our adaptor wallets are both deterministic and operate in the hot/cold paradigm, which was first formalized by Das et al. (CCS 2019) for standard signature schemes. We introduce a new cryptographic primitive called adaptor signatures with rerandomizable keys, and use it to generically construct adaptor wallets. We further show how to instantiate adaptor signatures with rerandomizable keys from the ECDSA signature scheme and discuss that they can likely be built for Schnorr and Katz-Wang schemes as well. Finally, we discuss the limitations of the existing ECDSA- and Schnorr-based adaptor signatures w.r.t. deterministic wallets in the hot/cold setting and prove that it is impossible to overcome these drawbacks given the current state-of-the-art design of adaptor signatures.
DualDory: Logarithmic-Verifier Linkable Ring Signatures Through Preprocessing

A linkable ring signature allows a user to sign anonymously on behalf of a group while ensuring that multiple signatures from the same user are detected. Applications such as privacy-preserving e-voting and e-cash can leverage linkable ring signatures to significantly improve privacy and anonymity guarantees. To scale to systems involving large numbers of users, short signatures with fast verification are a must. Concretely efficient ring signatures currently rely on a trusted authority maintaining a master secret, or follow an accumulator-based approach that requires a trusted setup. In this work, we construct the first linkable ring signature with both logarithmic signature size and verification that does not require any trusted mechanism. Our scheme, which relies on discrete-log type assumptions and bilinear maps, improves upon a recent concise ring signature called DualRing by integrating improved preprocessing arguments to reduce the verification time from linear to logarithmic in the size of the ring. Our ring signature allows signatures to be linked based on what message is signed, ranging from linking signatures on any message to only signatures on the same message. We provide benchmarks for our scheme and prove its security under standard assumptions. The proposed linkable ring signature is particularly relevant to use cases that require privacy-preserving enforcement of threshold policies in a fully decentralized context, and e-voting.
Cross-Stream Interactions: Segmentation of Lung Adenocarcinoma Growth Patterns

Lung adenocarcinoma has histologically distinct growth patterns that have been associated with patient prognosis. Precision segmentation of growth patterns in routine histology samples is challenging due to the complexity of patterns and high intra-class variability. In this paper, we present a novel model with a multi-stream architecture, Cross-Stream Interactions (CroSIn), which fully considers crucial interactions across scales to gather abundant information. The first-order attention introduces contextual information at an early stage to guide low-level feature encoding. The second-order attention then focuses on learning high-level feature relations among scales to extract discriminative features. Experimental results show interactions at both low- and high-level feature learning stages are crucial in performance improvement. The proposed method outperforms state-of-the-art networks, achieving an average Dice of $$60.34\%$$ at patch level, and an average accuracy of $$65.31\%$$ at sample level, which is also verified in an independent cohort.
Discovery of Periodic Rare Correlated Patterns from Static Database

Finding the associations among the itemsets and discovering the unknown or unexpected behavior are the major tasks of rare pattern mining. The support measure has the main contribution during the discovery of low support patterns. As the association of low support patterns may generate a bundle of spurious patterns, other measures are used to find the correlation between the itemsets. A generalization of frequent pattern mining called periodic frequent pattern mining (PFPM) is emerged as a promising field, focusing on the occurrence behavior of frequent patterns. On the contrary, the shape of occurrence in the case of rare pattern mining is not much studied. In this paper, a single scan algorithm called $$ PRCPMiner$$ is proposed to study the shape of occurrence of rare patterns. The proposed algorithm discovers periodic rare correlated patterns using different thresholds with respect to support, bond, and periodicity measures. The research shows the influence of these thresholds on the runtime performance for various datasets.
Variational Bayesian implicit unscented Kalman filter for celestial navigation using time delay measurement

Sunlight arrival time delay is an innovative measurement for celestial navigation, which provides the distance information between the spacecraft and the nearby celestial body. For deep space detectors, the time delay measurement noise is time-varying, and its statistics cannot be accurately determined. The too-large deviation between the measured noise covariance matrix (R) and the noise statistics seriously reduces navigation accuracy. A variational Bayesian implicit unscented Kalman filter (VBIUKF) method is proposed and applied to celestial navigation using time delay measurement in this paper. VBIUKF adaptively estimates R using the innovation sequence and the predicted measurement covariance matrix to make R closest to the measurement noise statistics. Simulation results indicate that VBIUKF can obtain high accuracy navigation results under time-varying measurement noise.
On the Nature of Duplicate Pull Requests: An Empirical Study Using Association Rules

In open source communities, developers submit pull requests, either to add features, fix bugs or make any modifications to software artifacts, which can be accepted or rejected after review by a core team member of the project. Considering the large-scale growth of collaborative software development in recent years, several challenges have arisen that have become barriers for developers, such as the occurrence of duplicate pull requests. Some works applied techniques to detect duplicate pull requests and performed a study with mixed approach to analyze the extent to which duplicate pull requests affect development in open source communities. However, these studies do not address descriptively, the relationships that exist between the occurrence of duplicate pull requests and other characteristics, such as lifetime and number of commits performed, that are present in these contributions. In this paper, the data mining technique called association rules is used to perform a set of studies, from 49.762 pull requests over 6 OSS projects hosted on GitHub, revealing new knowledge about the nature of duplicate pull requests. The results indicate that some structural characteristics, the developer inexperience and the requester profile individually or jointly, are present in different intensities in duplicate pull requests. Identifying these aspects can support software communities in understanding the nature of duplicate pull requests.
A Survey of Requirements Engineering and Software Testing Practices in Agile Teams

In agile methods, the priority of fast delivery of operational software has directly reached the execution of the activities foreseen in the Requirements Engineering (RE) and Software Testing (ST) stages. Thus, understanding how these activities have been performed in agile teams and how they can contribute in a coordinated way to the quality of the software developed, appears as a need for both academia and industry. For this, we conducted an unsupervised exploratory research (Survey) with seventy-two professionals who work in the software industry and who have experience with RE and ST activities. In a complementary way, we compared the findings of the Survey with the results already found in the literature and with agile practices. Such findings highlight that: (i) the activities of Agile RE and Agile ST still need to be matured in terms of their implementation in a coordinated way in the projects; (ii) there are some similarities and differences between what has been investigated in academia and the real practices and challenges of the industry; (iii) some agile practices, specific to RE and ST have been used, as well as other agile practices more related to processes, approaches, and software development. Such practices act in a complementary manner.
Evaluating a Conceptual Framework for Supporting Technical Debt Management in Testing Activities - A Feasibility Study

Context: Test-related technical debt (TRTD) refers to debt items that affect software testing activities, compromising their quality. Having information on indicators of their presence, causes, effects, and preventive practices can support the management of TRTD items. Although several works have investigated TD indicators, causes, effects, and prevention, there is a need to investigate how to use this information for supporting the management of debt items in the context of testing activities. Aims: This work (i) presents an updated version of a conceptual framework that organizes a set of indicators, causes, effects, and preventive practices of TRTD and (ii) evaluates it with respect to its ease of use, usefulness, and possible future use. Method: We analyzed 46 answers given to a global family of industrial surveys to assemble a conceptual framework for TRTD management and evaluate it by applying the technology acceptance model (TAM) in a feasibility study with 95 participants. Results: The conceptual framework can be useful for supporting the management of TRTD items, as pointed out by 89% of the participants. Most of the participants also indicated that they would gain productivity, performance, agility, and effectiveness by using the conceptual framework. Conclusion: Results indicate that the data embedded into the conceptual framework and the conceptual framework itself are promising to support the management of TRTD items.
Hiding in the forest: Privacy-preserving process performance indicators

Event logs recorded during the execution of business processes provide a valuable starting point for operational monitoring, analysis, and improvement. Specifically, measures that quantify any deviation between the recorded operations and organizational goals enable the identification of operational issues. The data to compute such process-specific measures, commonly referred to as process performance indicators (PPIs), may contain personal data of individuals, though, which implies an inevitable risk of privacy intrusion that must be addressed. In this article, we target the privacy-aware computation of process performance indicators. To this end, we adopt tree-based definitions of PPIs according to the well-established PPINOT meta-model. For such a PPI, we design data release mechanisms for the functions in a PPI tree. Using a probabilistic formulation of the expected result of a privatized PPI, we further show how to determine the combination of release mechanisms that inflicts the least loss in utility. Moreover, given a set of PPIs, we provide an algorithmic framework to manage an inherent trade-off: Privatization may strive for maximal utility of each single PPI or for maximal reuse of privatized functions among all PPIs to use a privacy budget most effectively. Results from experiments with synthetic as well as real-world data indicate the general feasibility of privacy-aware PPIs and shed light on the trade-offs once a set of them is considered.
Rethinking referring relationships from a perspective of mask-level relational reasoning

Referring relationship aims at localizing subject and object entities in an image, according to a triple text <subject, predicate, object>. Previous methods use iterative attention to shift between image regions for modeling predicate. However, predicate sometimes is implicit and difficult to be represented in the image domain. Convolution modeling method to express predicate is simple and inappropriate. Besides, relational reasoning information in the text itself is not fully utilized. To this end, we rethink referring relationship from a mask-level relational reasoning perspective to improve model interpretability. For text-to-image reasoning, we design Mask Generate and Mask Transfer modules, so as to fully integrate the text priors into the reasoning and prediction of masks. For image-to-text reasoning, we propose an unsupervised triple reconstruction method to guide text-to-image reasoning and improve multimodal generalization. By bi-directional reasoning between image and text, the proposed method MRR fully conforms to the multimodal relational reasoning process. Experiments show that MRR achieves state-of-the-art performance on two datasets of referring relationships, VRD and Visual Genome.
Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Sentiment analysis (AS) is one of the basic research directions in natural language processing (NLP), it is widely adopted for news, product review, and politics.Aspect-based sentiment analysis (ABSA) aims at identifying the sentiment polarity of a given target context, previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy.Hence this research work develops a deep-semantic and contextual knowledge networks (DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects.At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced.Further, a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions.Novelty of DSCNet model lies in introducing the deep contextual.DSCNet is evaluated on three datasets i.e., Restaurant, Laptop, and Twitter dataset considering different deep learning (DL) metrics like precision, recall, accuracy, and Macro-F1 score.Also, comparative analysis is carried out with different baseline methods in terms of accuracy and Macro-F1 score.DSCNet achieves 92.59% of accuracy on restaurant dataset, 86.99% of accuracy on laptop dataset and 78.76% of accuracy on Twitter dataset.
Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

The extraction of features from unstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease.Furthermore, an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems.In this paper, an improved Term Weighting technique combined with Parts-Of-Speech (POS) Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease.Term Frequency-Inverse Document Frequency (TF-IDF) is the most often used term weighting scheme (TWS).However, TF-IDF has several developments to improve its drawbacks, in particular, it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data.In this research, we proposed a modification term weighting scheme: RTF-C-IEF and compare the proposed model with four extraction methods: TF, TF-IDF, TF-IHF, and TF-IEF.The experiment was conducted on two new datasets for COVID-19 patients.The first dataset was collected from government hospitals in Iraq with 3053 clinical records, and the second dataset with 1446 clinical reports, was collected from several different websites.Based on the experimental results using several popular classifiers applied to the datasets of Covid-19, we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments.Further, the modified RTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments.Thus, the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.
Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result, an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of this method is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm's performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existing methods New Genetic Algorithm (NGA), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), Gravitational Search Algorithm (GSA), and Hybrid Heuristic and Genetic (HHG) by 7.9%, 2.1%, 8.8%, 7.7%, 3.4% respectively according to makespan.
Using BlazePose on Spatial Temporal Graph Convolutional Networks for Action Recognition

The ever-growing available visual data (i.e., uploaded videos and pictures by internet users) has attracted the research community's attention in the computer vision field.Therefore, finding efficient solutions to extract knowledge from these sources is imperative.Recently, the BlazePose system has been released for skeleton extraction from images oriented to mobile devices.With this skeleton graph representation in place, a Spatial-Temporal Graph Convolutional Network can be implemented to predict the action.We hypothesize that just by changing the skeleton input data for a different set of joints that offers more information about the action of interest, it is possible to increase the performance of the Spatial-Temporal Graph Convolutional Network for HAR tasks.Hence, in this study, we present the first implementation of the BlazePose skeleton topology upon this architecture for action recognition.Moreover, we propose the Enhanced-BlazePose topology that can achieve better results than its predecessor.Additionally, we propose different skeleton detection thresholds that can improve the accuracy performance even further.We reached a top-1 accuracy performance of 40.1% on the Kinetics dataset.For the NTU-RGB+D dataset, we achieved 87.59% and 92.1% accuracy for Cross-Subject and Cross-View evaluation criteria, respectively.