Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Phenology and Colony Longevity Patterns in a Predatory Social Wasp

Abstract. Social wasps play critical ecological roles in an ecosystem, providing a diversity of services and some disservices. Yellowjacket wasps (Vespula spp.) in particular are well known for shaping arthropod communities via predation and competition for resources. In part due to their sociality and large colony sizes, Vespula can have a profound ecological impact on local communities. Such effects can be magnified when colonies exhibit a perennial life history, in which a colony will overwinter, persist for more than one year, and become orders of magnitude larger in size compared to typical annual colonies. Despite growing interest in the factors that influence colony success, we currently lack the ability to predict when yellowjackets may have a high-abundance or outbreak year. This highlights the need for a critical understanding of the phenological patterns of foraging activity, colony distribution, and senescence. Here, we quantify the seasonal activity and foraging rates of 123 colonies of the western yellowjacket, V. pensylvanica, in its native range over 4 consecutive years. Average colony longevity was about 1 month longer than previously reported for this species, and colonies with later peaks in activity and higher average traffic rates persisted longer into the winter. Longer-lived colonies tended to cluster together within a year, but not between years. We found 3 perennial colonies (2.4% of all colonies), and they exhibited tenfold higher peak traffic rates compared to annual colonies. By identifying temporal and spatial patterns in survivorship and colony longevity, we gain insight into the factors associated with prolonged survival time and increased likelihood of overwintering in yellowjacket wasps.
Impact of BNT162b2 primary vaccination and homologous booster on anti-SARS-CoV-2 IgA antibodies in baseline seronegative healthcare workers

Abstract Objectives We investigated here the response of anti-SARS-CoV-2 IgA antibodies to BNT162b2 primary vaccination followed by administration of a homologous booster dose in baseline seronegative healthcare workers. Methods The study population included 69 healthy recipients of primary BNT162b2 vaccination (two doses) followed by administration of a single homologous booster after 8 months. Blood samples were collected throughout the study, starting before the first vaccine dose, up to 1 month after the booster. The serum levels of anti-SARS-CoV-2 IgA were assayed with Euroimmun Anti-SARS-CoV-2 spike S1 ELISA IgA. Results A first peak of serum anti-SARS-CoV-2 IgA was seen 1 month after the second BNT162b2 dose, after which values gradually declined, with stabilization after 6 months. The BNT162b2 booster (third dose) elicited a second peak, comparable to that observed 1 month after the second dose (p=0.100). Highly significant correlation was found between pre- and post-booster anti-SARS-CoV-2 IgA serum values (r=0.41; p<0.001), whilst no significant correlation was observed with age (r=0.10; p=0.416) or sex (r=0.04; p=0.729). The rate of SARS-CoV-2 IgA seropositive recipients increased from 0% before vaccination to 80 and 97% after the first and second vaccine dose, but then declined becoming 74% at 3 months and 54% at 6 months, respectively, after which stabilization was reached. The BNT162b2 booster dose restored the seropositivity rate to 99%. Conclusions These results support the suggestion that vaccine boosters may be advisable after 3 months from primary vaccination to restore IgA to protective levels, especially in those at higher risk of SARS-CoV-2 infection and complications.
HLA DRB1 alleles, IFN-γ and TGF-β Gene Variants in childhood ALL patients

Abstract Objectives Graft-versus-host disease (GvHD) is a complex clinical syndrome with organ dysfunction as a consequence of a severe immunological reaction mediated by mainly T cells after hematopoietic stem cell transplantation. Our aim is to evaluate the association of HLA-DRB1 alleles, IFN-γ and TGF-β gene variations, with childhood ALL (c-ALL) patients and with GvHD after transplantation. Methods This study included 30 high-risk c-ALL patients and 100 controls. HLA-DRB1 alleles were studied by the NGS method, and TGF-β and IFN-γ variations were studied by the PCR-RFLP method. Results The rates of HLA-DRB1*15 alleles and IFN-gamma CC genotype were significantly higher in c-ALL patients (p=0.004, p=0.036 respectively). Association of the HLA-DRB1*15 alleles with the TGF-β TC genotype was found with a higher rate in the patient group (p=0.031). Association of the DRB1*04 allele with the IFN-γ CC genotype was found with a higher rate in the patient group (p=0.028). Acute GvHD developed in eight of 19 patients who underwent transplantation. IFN-γ CT was found to have a protective role in occurrence of aGvHD (p=0.044). Association of the DRB1*15 allele with IFN-γ TT was found with a higher rate in a GvHD (p=0.050). Conclusions It is thought that polymorphism of HLA-DR15 and IFN-γ CC may contribute to the development of c-ALL, while IFN-γ CT might be protective for aGvHD.
MAB-Malware: A Reinforcement Learning Framework for Blackbox Generation of Adversarial Malware

Modern commercial antivirus systems increasingly rely on machine learning (ML) to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus (AV) products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points: 1) limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions, 2) reusing the successful payload in modeling; and 3) minimizing the changes on AE samples to correctly assign the rewards in RL learning (which also helps identify the root cause of evasions). As a result, our framework has much higher evasion rates than other off-the-shelf frameworks. Results show it has over 74%--97% evasion rate for two state-of-the-art ML detectors and over 32%--48% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than that between ML-based classifiers and commercial AVs.
On the Significance of Category Prediction for Code-Comment Synchronization

Software comments sometimes are not promptly updated in sync when the associated code is changed. The inconsistency between code and comments may mislead the developers and result in future bugs. Thus, studies concerning code-comment synchronization have become highly important, which aims to automatically synchronize comments with code changes. Existing code-comment synchronization approaches mainly contain two types, i.e., (1) deep learning-based (e.g., CUP), and (2) heuristic-based (e.g., HebCUP). The former constructs a neural machine translation-structured semantic model, which has a more generalized capability on synchronizing comments with software evolution and growth. However, the latter designs a series of rules for performing token-level replacements on old comments, which can generate the completely correct comments for the samples fully covered by their fine-designed heuristic rules. In this article, we propose a composite approach named CBS (i.e., Classifying Before Synchronizing) to further improve the code-comment synchronization performance, which combines the advantages of CUP and HebCUP with the assistance of inferred categories of Code-Comment Inconsistent (CCI) samples. Specifically, we firstly define two categories (i.e., heuristic-prone and non-heuristic-prone) for CCI samples and propose five features to assist category prediction. The samples whose comments can be correctly synchronized by HebCUP are heuristic-prone, while others are non-heuristic-prone. Then, CBS employs our proposed Multi-Subsets Ensemble Learning (MSEL) classification algorithm to alleviate the class imbalance problem and construct the category prediction model. Next, CBS uses the trained MSEL to predict the category of the new sample. If the predicted category is heuristic-prone, CBS employs HebCUP to conduct the code-comment synchronization for the sample, otherwise, CBS allocates CUP to handle it. Our extensive experiments demonstrate that CBS statistically significantly outperforms CUP and HebCUP, and obtains an average improvement of 23.47%, 22.84%, 3.04%, 3.04%, 1.64%, and 19.39% in terms of Accuracy, Recall@5, Average Edit Distance (AED), Relative Edit Distance (RED), BLEU-4, and Effective Synchronized Sample (ESS) ratio, respectively, which highlights that category prediction for CCI samples can boost the code-comment synchronization performance.
Towards practical approximate lineage

Traditionally, provenance and lineage mainly referred to query results. We take a more holistic approach. We consider a system in which tuples (records) that are produced by a query may affect other tuple insertions into the DB, as part of a normal workflow. Therefore, we consider both direct lineage (dependence of a query result on database tuples directly used in solving the query) and distant lineage (dependence on older tuples that caused the existence of the tuples directly used in solving the query). We aim to formulate practical methods for supporting direct and distant lineage. We use a novel genetics-inspired approach for approximating lineage tracking, which is based on word embedding to endow an explicitly inserted tuple with a small set of vectors that "encode" its content, and on an algebra on such sets of vectors that derives a set of vectors which encodes the lineage of a query-inserted tuple. During the execution of a query, we construct the lineage vectors of the final (and intermediate) result tuples in a similar fashion to that of semiring-based exact provenance calculations. We extend the + and operations to generate sets of lineage vectors, while retaining the ability to propagate information and preserve the compact representation. Therefore, our solution does not suffer from space complexity blow-up over time, and it "naturally ranks" explanations to the existence of a tuple. We introduce several fundamental improvements and extensions to the basic method of [19]. The aim is to make the basic scheme practical by taking advantage of additional knowledge regarding timing, query structure, and relative importance. The improvements include: tuple creation timestamps and a temporal sequence of search structures, column emphasis and query dependency DAG. We integrate our lineage computations with these improvements into the PostgreSQL system via an extension (ProvSQL) and perform extensive experiments. The experiments exhibit useful results in terms of accuracy against (the gold-standard) exact, semiring-based, tuple justifications, especially for the column-based (CV) method which exhibits high precision and high per-level recall. We focus on target tuples with multiple generations of tuples in their lineage, namely having a distant lineage, and analyze them in terms of generational lineage accuracy.
EVOLIoT: A Self-Supervised Contrastive Learning Framework for Detecting and Characterizing Evolving IoT Malware Variants

Recent years have witnessed the emergence of new and more sophisticated malware targeting the Internet of Things. Moreover, the public release of the source code of popular malware families such as Mirai has spawned diverse variants, making it harder to disambiguate their ownership, lineage, and correct label. Such a rapidly evolving landscape makes it also harder to deploy and generalize effective learning models against retired, updated, and/or new threat campaigns. In this paper, we present EVOLIoT, a novel approach aiming at combating "concept drift" and the limitations of inter-family IoT malware classification by detecting drifting IoT malware families and understanding their diverse evolutionary trajectories. We introduce a robust and effective contrastive method that learns and compares semantically meaningful representations of IoT malware binaries and codes without the need for expensive target labels. We find that the evolution of IoT binaries can be used as an augmentation strategy to learn effective representations to contrast (dis)similar variant pairs. We discuss the impact and findings of our analysis and present several evaluation studies to highlight the tangled relationships of IoT malware, as well as the efficiency of our contrastively learned feature vectors in preserving semantics and reducing out-of-vocabulary size in cross-architecture IoT malware binaries.
Feedback-Directed Metamorphic Testing

Over the past decade, metamorphic testing has gained rapidly increasing attention from both academia and industry, particularly thanks to its high efficacy on revealing real-life software faults in a wide variety of application domains. On the basis of a set of metamorphic relations among multiple software inputs and their expected outputs, metamorphic testing not only provides a test case generation strategy by constructing new (or follow-up) test cases from some original (or source) test cases, but also a test result verification mechanism through checking the relationship between the outputs of source and follow-up test cases. Many efforts have been made to further improve the cost-effectiveness of metamorphic testing from different perspectives. Some studies attempted to identify “good” metamorphic relations, while other studies were focused on applying effective test case generation strategies especially for source test cases. In this article, we propose improving the cost-effectiveness of metamorphic testing by leveraging the feedback information obtained in the test execution process. Consequently, we develop a new approach, namely feedback-directed metamorphic testing, which makes use of test execution information to dynamically adjust the selection of metamorphic relations and selection of source test cases. We conduct an empirical study to evaluate the proposed approach based on four laboratory programs, one GNU program, and one industry program. The empirical results show that feedback-directed metamorphic testing can use fewer test cases and take less time than the traditional metamorphic testing for detecting the same number of faults. It is clearly demonstrated that the use of feedback information about test execution does help enhance the cost-effectiveness of metamorphic testing. Our work provides a new perspective to improve the efficacy and applicability of metamorphic testing as well as many other software testing techniques.
Speedster: An Efficient Multi-party State Channel via Enclaves

State channel network is the most popular layer-2 solution to the issues of scalability, high transaction fees, and low transaction throughput of public Blockchain networks. However, the existing works have limitations that curb the wide adoption of the technology, such as the expensive creation and closure of channels, strict synchronization between the main chain and off-chain channels, frozen deposits, and inability to execute multi-party smart contracts. In this work, we present Speedster, an account-based state-channel system that aims to address the above issues. To this end, Speedster leverages the latest development of secure hardware to create dispute-free certified channels that can be operated efficiently off the Blockchain. Speedster is peer-to-peer decentralized and provides better privacy protection than prior channel projects. It supports fast native multi-party contract execution, which is previously unavailable in TEE-enabled channel networks. Compared to the Lightning Network, Speedster improves the throughput by about 10,000X and generates 97%$ less on-chain data with a comparable network scale.
No Spring Chicken: Quantifying the Lifespan of Exploits in IoT Malware Using Static and Dynamic Analysis

The Internet of things (IoT) is composed by a wide variety of software and hardware components that inherently contain vulnerabilities. Previous research has shown that it takes only a few minutes from the moment an IoT device is connected to the Internet to the first infection attempts. Still, we know little about the evolution of exploit vectors: Which vulnerabilities are being targeted in the wild, how has the functionality changed over time, and for how long are vulnerabilities being targeted? Understanding these questions can help in the secure development, and deployment of IoT networks. We present the first longitudinal study of IoT malware exploits by analyzing 17,720 samples collected from three different sources from 2015 to 2020. Leveraging static and dynamic analysis, we extract exploits from these binaries to then analyze them along the following four dimensions: (1) evolution of infection vectors over the years, (2) exploit lifespan, vulnerability age, and the time-to-exploit of vulnerabilities, (3) functionality of exploits, and (4) targeted IoT devices and manufacturers. Our descriptive analysis uncovers several patterns: IoT malware keeps evolving, shifting from simply leveraging brute force attacks to including dozens of device-specific exploits. Once exploits are developed, they are rarely abandoned. The most recent binaries still target (very) old vulnerabilities. In some cases, new exploits are developed for a vulnerability that has been known for years. We find that the mean time-to-exploit after vulnerability disclosure is around 29 months, much longer than for malware targeting other environments.
Artificial Intelligence Meets Kinesthetic Intelligence: Mouse-based User Authentication based on Hybrid Human-Machine Learning

Current mainstream biometric user authentication approaches are based on passive measurements of the subject's characteristics, and usually come with less-than-satisfactory accuracy. This paper takes a unique approach to biometric authentication. Specifically, instead of training a machine learning algorithm to recognize a legitimate user, the paper proposes a hybrid type of training, in which the legitimate user is also trained to use a customized instance of the machine. The user thus achieves a level of artificially-induced expertise to interact with the machine, which makes the user easier to recognize. We implement this concept in a mouse-based user authentication system, in which we produce customized machine instances by introducing an angle offset to the standard mouse. Human subjects then rely on their kinesthetic intelligence to achieve motor learning and visual-motor adaptation to the modified mouse. We design a 7-week IRB-approved experiment, collect data from 18 human subjects over this period, and evaluate the proposed approach with two existing state-of-the-art mouse-based authentication schemes. We find that, in both schemes, our approach significantly outperforms the baseline in which a regular unaltered mouse is used. Somewhat surprisingly, results also show that our approach improves the authentication performance even when both legitimate and non-legitimate users are trained to exactly the same instance of customized machine (i.e., the same mouse angle offset). In addition, we also observe that users can generally maintain their learned expertise even after one week of washout, which further demonstrates the practicality of the approach. Finally, we present a practical strategy to manage the enrollment of users in such a proposed system.
Understanding Disparate Effects of Membership Inference Attacks and their Countermeasures

Machine learning algorithms, when applied to sensitive data, can pose severe threats to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose whether specific private data samples are present in the training data to an attacker. However, most existing studies on MIA focus on aggregated privacy leakage for an entire population, while leaving privacy leakage across different demographic subgroups (e.g., females and males) in the population largely unexplored. This raises two important issues: (1) privacy unfairness (i.e., if some subgroups are more vulnerable to MIAs than the others); and (2) defense unfairness (i.e., if the defense mechanisms provide more protection to some particular subgroups than the others). In this paper, we investigate both privacy unfairness and defense fairness.We formalize a new notation of privacy-leakage disparity (PLD), which quantifies the disparate privacy leakage of machine learning models to MIA across different subgroups. In terms of privacy unfairness, our empirical analysis of PLD on real-world datasets shows that privacy unfairness exists. The minority subgroups (i.e., the less represented subgroups) tend to have higher privacy leakage. We analyze how subgroup size and subgroup data distribution impact PLD through the lens of model memorization. In terms of defense unfairness, our empirical evaluation shows the existence of unfairness of three state-of-the-art defenses, namely differential privacy, L2-regularizer, and Dropout, against MIA. However, defense unfairness mitigates privacy unfairness as the minority subgroups receive stronger protection than the others. We analyze how the three defense mechanisms affect subgroup data distribution disparately and thus leads to defense unfairness.
Machine Learning–based Modeling and Prediction of the Intrinsic Relationship between Human Emotion and Music

Human emotion is one of the most complex psychophysiological phenomena and has been reported to be affected significantly by music listening. It is supposed that there is an intrinsic relationship between human emotion and music, which can be modeled and predicted quantitatively in a supervised manner. Here, a heuristic clustering analysis is carried out on large-scale free music archive to derive a genre-diverse music library, to which the emotional response of participants is measured using a standard protocol, consequently resulting in a systematic emotion-to-music profile. Eight machine learning methods are employed to statistically correlate the basic sound features of music audio tracks in the library with the measured emotional response of tested people to the music tracks in a training set and to blindly predict the emotional response from sound features in a test set. This study found that nonlinear methods are more robust and predictable but considerably more time-consuming than linear approaches. The neural networks have strong internal fittability but are associated with a significant overfitting issue. The support vector machine and Gaussian process exhibit both high internal stability and satisfactory external predictability in all used methods; they are considered as promising tools to model, predict, and explain the intrinsic relationship between human emotion and music. The psychological basis and perceptional implication underlying the built machine learning models are also discussed to find out the key music factors that affect human emotion.