Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Avian Pox in a Ruffed Grouse (Bonasa umbellus) from Pennsylvania

SUMMARY. A free-ranging, adult male ruffed grouse (Bonasa umbellus) was harvested by a hunter during November 2019 in Forest County, PA. The bird was submitted for necropsy due to a skin mass on its left leg. Upon necropsy, two proliferative skin masses were grossly visible, one on the left leg and one on the cere. An additional mass was present on the oropharyngeal mucosa covering the hard palate. These masses were diagnosed as avian pox based on histopathologic and cytologic findings, including marked epithelial hypertrophy, hyperplasia, vacuolar degeneration with eosinophilic stippling, and intracytoplasmic inclusion bodies. An avipoxvirus was detected using PCR and was identified as fowlpox virus through sequencing of the 4b core gene segment. The avipoxvirus from this case showed genetic similarity to isolates from Eastern wild turkeys (Meleagris gallopavo silvestris).
Crafting Adversarial Example to Bypass Flow-&ML- based Botnet Detector via RL

Machine learning(ML)-based botnet detection methods have become mainstream in corporate practice. However, researchers have found that ML models are vulnerable to adversarial attacks, which can mislead the models by adding subtle perturbations to the sample. Due to the complexity of traffic samples and the special constraints that to keep malicious functions, no substantial research of adversarial ML has been conducted in the botnet detection field, where the evasion attacks caused by carefully crafted adversarial examples may directly make ML-based detectors unavailable and cause significant property damage. In this paper, we propose a reinforcement learning(RL) method for bypassing ML-based botnet detectors. Specifically, we train an RL agent as a functionality-preserving botnet flow modifier through a series of interactions with the detector in a black-box scenario. This enables the attacker to evade detection without modifying the botnet source code or affecting the botnet utility. Experiments on 14 botnet families prove that our method has considerable evasion performance and time performance.
Effect analysis of rotor balancing under conditions of external forces

Rotating machinery usually suffers different kinds of failure with the increase of serving time. Meanwhile, determining the root cause of the rotor failure is still a comprehensive and complex task, where external force induced by failure or interference has straightforward effects on the signal processing. In this paper, on the basis of rotor balancing, the external force effect is considered and experimentally studied. Experimental results show that the external force has significant effects on the vibration response of the rotor system, which may indicate that once a rotor balancing task is delivered, the first step is determined whether the measuring target is suffered unbalancing problem only or different failure so as to find the root causes and solve the vibration problems effectively.
High-Level Machine Learning Framework for Sports Events Ticket Sales Prediction

As all the other live events, sports were particularly affected by the covid-19 pandemic. Nevertheless, with the vaccination campaigns and the expected mass immunization chances are that the social life might soon come back to that was normal before 2020. This paper presents a high-level framework for predicting match ticket sales for a football/soccer club activating in a national championship. Written in R and available as a collection of scripts in a GitHub repository, the framework relies heavily on two R ecosystems of packages for data processing and modeling, tidyverse and tidymodels. For illustration, the framework was applied on a data set provided by a struggling team in Romanian first football league. Predictors relate to expected weather conditions at the start of the game, match day of the week and the starting hour, the phase of the season, and also the team's most recent performances relative to the current match. Despite the dataset limits, results of exploratory data analysis and predictive models are encouraging not only in estimating the match ticket sales, but also in identifying the most important variables associated with ticket sales variability.
Encryption is Futile: Reconstructing 3D-Printed Models Using the Power Side-Channel

Outsourced Additive Manufacturing (AM) exposes sensitive design data to external malicious actors. Even with end-to-end encryption between the design owner and 3D-printer, side-channel attacks can be used to bypass cyber-security measures and obtain the underlying design. In this paper, we develop a method based on the power side-channel that enables accurate design reconstruction in the face of full encryption measures without any prior knowledge of the design. Our evaluation on a Fused Deposition Modeling (FDM) 3D Printer has shown 99 % accuracy in reconstruction, a significant improvement on the state of the art. This approach demonstrates the futility of pure cyber-security measures applied to Additive Manufacturing.
Detection and Classification of Malicious Software based on Regional Matching of Temporal Graphs

In this paper we present an integrated graph-based framework that utilizes relations between groups of System-calls, in order to detect whether an unknown software sample is malicious or benign, and to a further extent to classify it to a known malware family. A novel graph-based approach for the representation of software samples over the depiction of the structural evolution over time, the so-called Temporal Graphs, is discussed, and a method for measuring graph similarity among specific Regions of such graphs is proposed, the so-called Regional Matching. The partitioning of the Temporal Graphs that depicts their structural evolution over time is defined by specific time-slots, while the quantitative characteristics that depict the commonalities appeared over the weights of the vertices are measured by a similarity metric in order to conduct the malware detection and classification procedures. Finally, we evaluate the detection and classification ability of our proposed graph-based framework performing an experimental study over the achieved results utilizing a set of known malicious samples that are indexed into malware families.
Static analysis of pattern-free properties

Rewriting is a widely established formalism with major applications in computer science. It is indeed a staple of many formal verification applications as it is especially well suited to describe program semantics and transformations. In particular, constructor based term rewriting systems are generally used to illustrate the behaviour of functional programs. In the context of formal verification, it is often necessary to characterize the shape of the reducts of such rewrite systems and, in a typed context, the underlying type system provides syntactic guarantees on the form of these terms by exhibiting, among others, the constructor symbols that they can contain. On the other hand, when performing (program) transformations we often want to eliminate some symbols and, more generally, to ensure that some patterns are absent from the result of the transformation. We propose in this paper an approach to statically verify the absence of specified patterns from the reachable terms of constructor based term rewriting systems. The proposed approach consists in annotating the function symbols with a set of profiles outlining pre- and post-conditions that must be verified by the rewrite relation, and using a rewrite based method to statically verify that the rewrite system is indeed consistent with the respective annotations.
Minimal Session Types for the π-calculus

Session types enable the static verification of message-passing programs. A session type specifies a channel’s protocol as sequences of messages. Prior work established a minimality result: every process typable with standard session types can be compiled down to a process typable using minimal session types: session types without the sequencing construct. This result justifies session types in terms of themselves; it holds for a higher-order session π-calculus, where values are abstractions (functions from names to processes). This paper establishes a new minimality result but now for the session π-calculus, the language in which values are names and for which session types have been more widely studied. Remarkably, this new minimality result can be obtained by composing known results. We develop optimizations of our new minimality result, and establish its static and dynamic correctness.
On the Usability (In)Security of In-App Browsing Interfaces in Mobile Apps

Due to the frequent encountering of web URLs in various application scenarios (e.g., chatting and email reading), many mobile apps build their in-app browsing interfaces (IABIs) to provide a seamless user experience. Although this achieves user-friendliness by avoiding the constant switching between the subject app and the system built-in browser apps, we find that IABIs, if not well designed or customized, could result in usability security risks. In this paper, we conduct the first empirical study on the usability (in)security of in-app browsing interfaces in both Android and iOS apps. Specifically, we collect a dataset of 25 high-profile mobile apps from five common application categories that contain IABIs, including Facebook and Gmail, and perform a systematic analysis (not end-user study though) that comprises eight carefully designed security tests and covers the entire course of opening, displaying, and navigating an in-app web page. During this process, we obtain three major security findings: (1) about 30% of the tested apps fail to provide enough URL information for users to make informed decisions on opening an URL; (2) nearly all custom IABIs have various problems in providing sufficient indicators to faithfully display an in-app page to users, whereas ten IABIs that are based on Chrome Custom Tabs and SFSafariViewController are generally secure; and (3) only a few IABIs give warnings to remind users of the risk of inputting passwords during navigating a (potentially phishing) login page. Most developers had acknowledged our findings but their willingness and readiness to fix usability issues are rather low compared to fixing technical vulnerabilities, which is a puzzle in usability security research. Nevertheless, to help mitigate risky IABIs and guide future designs, we propose a set of secure IABI design principles.
A tabular sarsa-based stock market agent

Automated stock trading is now the de-facto way that investors have chosen to obtain high profits in the stock market while keeping risk under control. One of the approaches is to create agents employing Reinforcement Learning (RL) algorithms to learn and decide whether or not to operate in the market in order to achieve maximum profit. Automated financial trading systems can learn how to trade optimally while interacting with the market pretty much like a human investor learns how to trade. In this research, a simple RL agent was implemented using the SARSA algorithm. Next, it was tested against 10 stocks from Brazilian stock market B3 (Bolsa, Brasil, Balcão). Results from experiments showed that the agent was able to provide high profits with less risk when compared to a supervised learning agent that used a LSTM neural network.
Utilization of deep learning to mine insights from earning calls for stock price movement predictions

Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed from an earning call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earning call transcripts combined with companies' historical stock data and sector information to predict company's stock price movements. We propose to model these three features in a deep learning framework jointly, where attention mechanism is applied to the earnings call textual feature and a recurrent neural network (RNN) is used on the sequential stock price data. Our empirical experiments show that the proposed model is superior to the traditional baseline models and earnings call information can boost the stock price prediction performance.
Unsupervised-learning financial reconciliation: a robust, accurate approach inspired by machine translation

Financial reconciliation (cross-checking independent sources of data) is a time-honored and widespread function in finance and audit. Its objectives are to ensure completeness, timeliness, and accuracy of recording of transactions. With the proliferation of data over recent decades, the ways in which reconciliation is approached have evolved. There are now a number of software products that promise to automate the process of detailed transaction matching. However, the 'state of the art' in these products is rules-based reconciliation. Rules-based systems in any domain tend to be brittle in that any change in the data streams causes the rules to have to be debugged and rebuilt, itself often a time-consuming process. And to make matters worse, this might be caused not just by data schema changes, but the contents of what is in the data fields themselves. Either of these can occur if a third party such as a bank changes its internal processes. In a sense, automated reconciliation is where machine translation was almost 70 years ago; IBM's 1954 Georgetown experiment approached Russian-to-English translation using rules, but it took another 4 decades for researchers in data-driven Artificial Intelligence (AI) to realize why machine translation did not initially live up to its promises and develop a truly robust methodology for machine translation based on unsupervised learning. It turns out that financial reconciliation can be cast as a machine translation problem based on unsupervised learning. To our knowledge, we are the first to propose this. Here, we demonstrate via experiments on real-life (albeit small-scale) financial data that this way of approaching the problem demonstrates promise in terms of accuracy, as well as solving the problem of lack of robustness inherent in rules-based approaches.
Dealing with transaction costs in portfolio optimization: online gradient descent with momentum

Outperforming the markets through active investment strategies is one of the main challenges in finance. The random movements of assets and the unpredictability of catalysts make it hard to perform better than the average market, therefore, in such a competitive environment, methods designed to keep low transaction costs have a significant impact on the obtained wealth. This paper focuses on investing techniques to beat market returns through online portfolio optimization while controlling transaction costs. Such a framework differs from classical approaches as it assumes that the market has an adversarial behavior, which requires frequent portfolio rebalancing. This paper analyses critically the known online learning literature dealing with transaction costs and proposes a novel algorithm, namely Online Gradient Descent with Momentum (OGDM), to control (theoretically and empirically) the costs. The existing algorithms designed for this setting are either (i) not providing theoretical guarantees, (ii) providing a bound to the total regret, conditionally on unrealistic assumptions or (iii) computationally not efficient. In this paper, we prove that OGDM has nice theoretical, empirical, and computational performances. We show that it has regret, considering costs, of the order [EQUATION], T being the investment horizon, and has Θ(M) per-step computational complexity, M being the number of assets. Furthermore, we show that this algorithm provides competitive gains when compared empirically with state-of-the-art online learning algorithms on a real-world dataset.
Molecular Basis of Urostyle Development: Genes and Gene Regulation Underlying an Evolutionary Novelty

Evolutionary novelties entail the origin of morphologies that enable new functions. These features can arise through changes to gene function and regulation. One important novelty is the fused rod at the end of the vertebral column in anurans, the urostyle. This feature is composed of a coccyx and an ossifying hypochord, and both structures ossify during metamorphosis. We used Laser Capture Micro-dissection of these identified tissues and subjected them to RNA-seq and ATAC-seq analyses at three developmental stages in tadpoles of Xenopus tropicalis. These experiments reveal that the coccyx and hypochord have two different molecular signatures. ATAC-seq data reveals potential regulatory regions that are observed in proximity to candidate genes identified from RNA-seq. Neuronal (TUBB3) and muscle markers (MYH3) are upregulated in coccygeal tissues, whereas T-box genes (TBXT, TBXT.2), corticosteroid stress hormones (CRCH.1), and matrix metallopeptidases (MMP1, MMP8, MMP13) are upregulated in the hypochord. Even though an ossifying hypochord is only present in anurans, this ossification between the vertebral column and the notochord appears to resemble a congenital vertebral anomaly seen prenatally in humans, caused by an ectopic expression of the TBXT/TBXT.2 gene. This work opens the way to functional studies that help us better elucidate anuran bauplan evolution.
Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become “hard currency” of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of gene interaction networks and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations’ co-occurrence in tumour genomes. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. We demonstrate that the individualized discovery revealed driver events which were individually rare, not detectable by other computational approaches, and related to cancer biology domains poorly covered by previous analyses. Considering the novel driver candidates and their constellations in individual tumor genomes opens a novel avenue for personalized cancer medicine.
Bacterial Normalized Binary Fission Growth Model

Mathematical models have traditionally been used to facilitate the interpretation of bacterial growth curves in order to more accurately understand and identify variations in bacterial proliferation. Here, a binary fission growth model was developed to normalize starting bacterial levels, allowing for the identification of changes in bacterial growth and the separation of a bacterial population as it correlates to size. This normalized binary fission model (NBF) relies on a multi-bin growth mode, where each bin is associated with a size range during a growth cycle. The proposed NBF model allows for a determination of the percentage of treated bacteria eradicated compared to a control sample, either generally across all bacterial binary fission sizes or specific to a size range or bin. Comparisons between the NBF model and experimental observations demonstrates that bacterial growth curves, and the ratio of sample growth to a control, can be used to both determine and normalize initial variations in bacterial size, and quantity, among test samples, as well as identify final nutrient levels and the percentage of bacteria affected by treatment. Significance Statement It is difficult to determine the effectiveness of selective bacterial eradication based upon a bacteria’s characteristics size, related to acoustic resonance. Here we develop a binary fission model to analyze effect on growth curves of size depend eradications. Understanding the effect on growth curves provides a method to extract the eradication percentage and initial bacterial level differences between treated sample and control sample, by using the ratio of treated growth curve to control growth curve.
Addressing the Association Between Action Video Game Playing Experience and Visual Search in Naturalistic Multisensory scenes

Prior studies investigating the effects of routine action video game playing have demonstrated improvements in a variety of cognitive processes, including improvements in attentional tasks. However, there is little evidence indicating that the cognitive benefits of playing action video games generalize from simplified unisensory stimuli to naturalistic multisensory scenes – a fundamental characteristic of natural, everyday life environments. In particular, it is unknown whether video game experience has an impact on cross-modal congruence effects when searching through such multisensory scenes. The present pre-registered compared the performance of action video game players (AVGPs) with non-video game players (NVGPs) on a visual search task for objects embedded in video clips of realistic scenes. We run two identical online experiments with gender-balanced samples, for a total N=130. Overall, the data replicated previous findings reporting search benefits when visual targets were accompanied by semantically congruent auditory events, compared to neutral or incongruent ones. However, according to the results, AVGPs did not consistently outperform NVGPs in the search task overall, nor they exploit multisensory cues more efficiently than NVGPs. Exploratory analyses with self-reported gender as a variable revealed a potential difference in response strategy between experienced male and female AVGPs when dealing with cross-modal cues. These findings suggest that the generalization of the advantage of AVG experience to realistic, cross-modal situations should be done with caution, and considering gender-related aspects.