Miracle-dz/UniFAR-FTUs huggingface dataset:
Rationally designed modular STAT-activating scaffolds enforce cell-intrinsic transcriptional programs augmenting the anti-tumor potency of CAR T cells

Chimeric antigen receptor (CAR)-expressing T cells can mediate anti-tumor responses in a variety of preclinical models and clinical settings, however, strategies to enhance anti-tumor potency is the subject of intense investigation. Signals emanating from gamma-c cytokine receptors modulate the transcriptional state of activated T cells impacting proliferation, survival, differentiation, and effector functioning through the STAT family of transcription factors. Design of ligand-independent cell-intrinsic cytokine STAT activation scaffolds is a conceptually attractive strategy to provide CAR T cells with a surrogate for exogenous cytokine support. Here, we designed a series of ligand-autonomous STAT inducer (LASI) scaffolds comprised of an extracellular identification tag, a homodimerizing transmembrane domain, and a membrane proximal IL7R Box1 domain followed by STAT5 and/or STAT3 docking sequences derived from IL7R and IL21R, respectively. We constructed LASI scaffolds having STAT5 (LASI-5), STAT3 (LASI-3), and combined STAT5 and STAT3 (LASI-5+3) docking domains and then interrogated their impact in primary human CD8+ anti-CD19 (4-1BB:zeta) CAR T cells. While LASI-5 expression had limited effects on CAR T cells, LASI-3 transcriptional programming was found to be indispensable to achieving anti-tumor functional enhancement associated with limited terminal differentiation, heightened T cell proliferation in response to antigen, and dampened expression of exhaustion-associated genes. Moreover, CAR T cells supplemented with LASI-3 or 5+3 displayed superior potency against human leukemia tumors in NSG mice. LASI-5+3 mediated the highest magnitude of CAR T cell engraftment in vivo that evolved into a fatal lymphoproliferative syndrome. However, the same efficacy enhancement was achieved with LASI-3 without the lymphoproliferative complication. Our findings provide a rationale for utilization of constitutively expressed LASI-3 to enhance the anti-tumor potency of CAR T cells, the need to regulate the activity of LASI-5+3, and a generalizable scaffold design for studying additional combinations of STAT family transcription factors.
CryoVirusDB: A Labeled Cryo-EM Image Dataset for AI-Driven Virus Particle Picking

With the advancements in instrumentation, image processing algorithms, and computational capabilities, single-particle electron cryo-microscopy (cryo-EM) has achieved nearly atomic resolution in determining the 3D structures of viruses. The virus structures play a crucial role in studying their biological function and advancing the development of antiviral vaccines and treatments. Despite the effectiveness of artificial intelligence (AI) in general image processing, its development for identifying and extracting virus particles from cryo-EM micrographs (images) has been hindered by the lack of manually labelled high-quality datasets. To fill the gap, we introduce CryoVirusDB, a labeled dataset containing the coordinates of expert-picked virus particles in cryo-EM micrographs. CryoVirusDB comprises 9,941 micrographs of 9 different viruses along with the coordinates of 339,398 labeled virus particles. It can be used to train and test AI and machine learning (e.g., deep learning) methods to accurately identify virus particles in cryo-EM micrographs for building atomic 3D structural models for viruses.
Malware Detection with Artificial Intelligence: A Systematic Literature Review

In this survey, we review the key developments in the field of malware detection using AI and analyze core challenges. We systematically survey state-of-the-art methods across five critical aspects of building an accurate and robust AI powered malware detection model: malware sophistication, analysis techniques, malware repositories, feature selection and machine learning vs deep learning. The effectiveness of an AI model is dependent on the quality of the features it is trained with. In turn, the quality and authenticity of these features is dependent on the quality of the dataset and the suitability of the analysis tool. Static analysis is fast but is limited by the widespread use of obfuscation. Dynamic analysis is not impacted by obfuscation but is defeated by ubiquitous anti-analysis techniques and requires more computational power. Sophisticated and evasive malware is challenging to extract authentic discriminatory features from and combined with poor quality datasets this can lead to a situation where a model achieves high accuracy with only one specific dataset.
The influence of individual authenticity experience on tourists’ behavioral intentions: the chain mediating role of place dependence and place identity

ABSTRACT This paper introduces the concept of individual authenticity from psychology into tourism research, aiming to develop and test the intricate mechanisms of individual authenticity, place attachment, and the behavioral intentions of tourists. Several relevant hypotheses are formulated and empirically tested using a structural equation model (SEM), with data from China. Results demonstrate that individual authenticity experience is an important antecedent of tourists’ behavioral intentions, where place dependence and place identity play a partial chain mediating role. The research provides fresh insights into the role of subject-related authenticity in tourism, which also provides anchor points for enhancing destination marketing.
Ibn-Ginni: An Improved Morphological Analyzer for Arabic

Arabic is a morphologically rich language, which means that the Arabic language has a complicated system of word formation and structure. The affixes in the Arabic language (i.e., prefixes and suffixes) can be added to root words to generate different meanings and grammatical functions. These affixes can indicate aspects such as tense, gender, number, case, person, and more. In addition, the meaning and function of words can be modified in Arabic using an internal structure known as morphological patterns. Computational morphological analyzers of Arabic are vital to developing Arabic language processing toolkits. In this paper, we introduce a new morphological analyzer (Ibn-Ginni) that inherits the speed and quality of the Buckwalter Arabic Morphological Analyzer (BAMA). The BAMA has poor coverage of the classical Arabic language. Hence, the coverage of classical Arabic is improved by using the Alkhalil analyzer. Although it is slow, it was used to generate a huge number of solutions for 3 million unique Arabic words collected from different resources. These wordform-based solutions were converted to stem-based solutions, refined manually, and added to the database of BAMA, resulting in substantial improvements in the quality of the analysis. Hence, Ibn-Ginni is a hybrid system between BAMA and Alkhalil analyzers and may be considered an efficient large-scale analyzer. The Ibn-Ginni analyzer analyzed 0.6 million more words than the BAMA analyzer. Therefore, our analyzer significantly improves the coverage of the Arabic language. Besides, the Ibn-Ginni analyzer is high-speed at providing solutions; the average time to analyze a word is 0.3 ms. Using a corpus designed for benchmarking Arabic morphological analyzers, our analyzer was able to find all solutions for 72.72% of the words. Moreover, the analyzer did not provide all possible morphological solutions for 24.24% of the words. The analyzer and its morphological database are publicly available on GitHub.
Leveraging Dual Gloss Encoders in Chinese Biomedical Entity Linking

Entity linking is the task of assigning a unique identity to named entities mentioned in a text, a sort of word sense disambiguation that focuses on automatically determining a pre-defined sense for a target entity to be disambiguated. This study proposes the DGE (Dual Gloss Encoders) model for Chinese entity linking in the biomedical domain. We separately model a dual encoder architecture, comprising a context-aware gloss encoder and a lexical gloss encoder, for contextualized embedding representations. Dual gloss encoders are then jointly optimized to assign the nearest gloss with the highest score for target entity disambiguation. The experimental datasets consist of a total of 10,218 sentences that were manually annotated with glosses defined in the BabelNet 5.0 across 40 distinct biomedical entities. Experimental results show that the DGE model achieved an F1-score of 97.81, outperforming other existing methods. A series of model analyses indicate that the proposed approach is effective for Chinese biomedical entity linking.
Cross-Level Network Security Element Fusion Extraction Method Based on Deep Learning

Element extraction of Network Security Situation is the foundation for Network Security Situation awareness. Traditional methods for network security element extraction struggle with efficiently processing large-scale data and establishing effective cross-level element associations, leading to a higher false alarm rate in situation awareness. This paper proposes a cross-level network security element fusion extraction method based on deep learning. Firstly, through a data preprocessing method based on element correlation, we reduce data dimensionality, addressing the challenge of large-scale nonlinear data processing. Secondly, we employ principal component analysis to construct a method for cross-level network security element fusion extraction. Moreover, by integrating convolutional neural networks and long short-term memory networks, we establish a cross-level network security element fusion classification model, achieving precise perception of the Network Security Situation. Experimental results indicate that the proposed cross-level network security element fusion extraction method effectively reduces dataset dimensions, extracts 32-dimensional crucial network security fusion elements, and achieves a detection accuracy rate of up to 99.99% for network attack behaviors, significantly enhancing the capability of network security situation awareness.
Constructing Dynamic Honeypot Using Machine Learning

Honeypot is an active security defense technology that uses false information to lure attackers into attacking and record their behavior. Traditional honeypots are usually static, and inherent features and services can accelerate attackers' recognition of honeypots, causing them to lose value. We designs a dynamic honeypot based on machine learning, which can adapt to dynamic and constantly changing network environments while improving the authenticity of the honeypot. It automatically generates configuration files, simulates the characteristics and behavior of devices in the network. The method proposed is to achieve active monitoring and defense of network attacks by actively scanning Nmap and obtaining network device information through P0f, and combining feature clustering methods to classify devices and generate honeypot configuration files, active monitoring and defense of network attacks can be achieved. The results shows that this methods can effectively enhance the attack capture ability and camouflage ability of honeypots.
E-commerce Platform Quality, User satisfaction and Continuous Use Intention under Data-driven marketing

Under the data-driven marketing, e-commerce platforms have achieved rapid development using big data technology. Taking the users of e-commerce platform as the research object, the paper based on SEM technology to verify the relationship between the content quality, function quality, user satisfaction, platform privacy protection and continuous use intention. The results show that content quality and function quality play a partial mediating role through user satisfaction. Platform privacy protection positively moderates the relationship between content quality, function quality and user satisfaction. The research conclusion provides some theoretical foundation for e-commerce platforms to deeply understand users and enhance the sustainable operation ability of the platforms, which is conducive to the high-quality development of the e-commerce industry.
Conducting Sound, Equity-Enabling Computing Education Research

Problem. To investigate and identify promising practices in equitable K-12 and tertiary computer science (CS) education, the capacity for education researchers to conduct this research must be rapidly built globally. Simultaneously, concerns have arisen over the last few years about the quality of research that is being conducted and the lack of research that supports teaching all students computing. Research Question. Our research question for our study was: In what ways can existing research standards and practices inform methodologically sound, equity-enabling computing education research? Methodology. We conducted a concept analysis using existing research and various standards (e.g. European Educational Research Association, Australian Education Research Organisation, American Psychological Association). We then synthesised key features in the context of equity-focused K-12 computing education research. Findings. We present a set of guidelines for general research design that takes into account best practices across the standards that are infused with equity-enabling research practices. Implications. Our guidelines will directly impact future equitable computing education research by providing guidance on conducting high-quality research such that the findings can be aggregated and impact future policy with evidence-based results. Because we have crafted these guidelines to be broadly applicable across a variety of settings, we believe that they will be useful to researchers operating in a variety of contexts.
Research on Network Intrusion Detection Techniques Based on Feature Selection Model and Recurrent Neural Network

The number of network attacks has also increased rapidly. Therefore, it is necessary to conduct in-depth research on network intrusion detection technologies. Compared with traditional intrusion detection systems, AI-based intrusion detection systems can better detect network traffic, with lower false positive and false negative rates. This paper first expanded the CIC-IDS-2017 dataset by adding two new attack forms. Secondly, the SMOTE algorithm was used to expand the minority samples in the dataset. Through the sequence forward selection algorithm based on decision trees, the features in the dataset were selected, improving the algorithm's efficiency without significantly affecting its accuracy. Finally, a multi-task network intrusion detection model was constructed by integrating the prediction results of the recurrent neural network model and the one-class support vector machine model to determine the final network traffic type. The system has been quite successful in achieving all of its initial goals at this stage of research. The system achieved a detection accuracy of over 90% for network traffic can predict unknown types of attack traffic. The real-time detection function allows the system to be applied to practical network traffic detection on a daily basis.
An Adaptive Sampling Augmented Lagrangian Method for Stochastic Optimization with Deterministic Constraints

The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective function and deterministic constraints. Our main contribution is combining the augmented Lagrangian framework with adaptive sampling, resulting in an efficient optimization methodology validated with practical examples. To achieve the presented efficiency, we consider inexact solutions for the augmented Lagrangian subproblems, and through an adaptive sampling mechanism, we control the variance in the gradient estimates. Furthermore, we analyze the theoretical performance of the proposed scheme by showing equivalence to a gradient descent algorithm on a Moreau envelope function, and we prove sublinear convergence for convex objectives and linear convergence for strongly convex objectives with affine equality constraints. The worst-case sample complexity of the resulting algorithm, for an arbitrary choice of penalty parameter in the augmented Lagrangian function, is $O(\epsilon^{-3-\delta})$, where $\epsilon>0$ is the expected error of the solution and $\delta>0$ is a user-defined parameter. If the penalty parameter is chosen to be $O(\epsilon^{-1})$, we demonstrate that the result can be improved to $O(\epsilon^{-2})$, which is competitive with the other methods employed in the literature. Moreover, if the objective function is strongly convex with affine equality constraints, we obtain $O(\epsilon^{-1}\log(1/\epsilon))$ complexity. Finally, we empirically verify the performance of our adaptive sampling augmented Lagrangian framework in machine learning optimization and engineering design problems, including topology optimization of a heat sink with environmental uncertainty.
THE INFLUENCE OF VARIOUS SAMPLE STORAGE CONDITIONS AND SAMPLE BACTERIAL CONTAMINATION ON CONCENTRATIONS OF ROUTINE BIOCHEMICAL PARAMETERS

Background: The pre-analytical (PA) phase is the most vulnerable phase of the laboratory testing procedure, with critical procedures-collection, handling, sample transport, and time and temperature of sample storage. This study aimed to examine the stability of basic biochemical parameters depending on the samples’ storage conditions and the number of freeze-thaw cycles (FTCs). In parallel, the presence of sample bacterial contamination during routine laboratory work was examined
Evaluation of sustainable utilization of regional water resources based on GRNN neural network model: case of Wenshan Prefecture of Yunnan Province

AHP is used to establish an index system and evaluation standard of water resources sustainable utilization for water-abundant area,and 50 evaluation indexes are put forward from 5 aspects of water resources,water resources development and utilization efficiency,ecological environment,rational allocation of water resources and water management,GRNN neural network evaluation model for sustainable use of water resources is established by the principle of GRNN neural network theory and methods,and the model is used to comprehensively evaluate the water resources sustainable utilization of Wenshan Prefecture in different planning years.The results show that: in different planning years,the sustainable utilization of water resources of Wenshan Prefecture is evaluated as grade 2 to 3,namely,between sustainable utilization and basically sustainable utilization,which reflects the current situation and the mid-long term sustainable utilization of water resources and is consistent with the actual development of the region;GRNN network evaluation model has the characteristics of fast convergence,high prediction accuracy,less parameters adjusting,uneasy to fall into local minimums,and can predict and evaluate more quickly to meet the objective evaluation requirements of regional water resources sustainable utilization.
Uncertainty analysis of hardware in loop setup for testing building technologies: A case study

Hardware in the loop simulation (HIL) offers an intuitive real-time procedure for the validation and evaluation of building technology. The effect of uncertainty due to the components present in the test bench of the HIL is taken into account since it might affect the performance of the product that is measured. In the case of measuring the seasonal performance/annual performance, one should extrapolate the performance based on short-term testing which makes it important to consider the uncertainties. In this case, the probability distributions are used to model the uncertainties. The uncertainty propagation is based on the Monte Carlo Simulation. The model of the entire HIL test setup is developed in Modelica as well as propagation of uncertainties, which is not common. In the future, the modeled HIL setup compared with the real experimental setup for discrepancies(simulated efficiency vs real-time efficiency) and calibrated so. The aim is to confirm thanks to Monte Carlo Simulations that the test bench parameters don’t affect the efficiency of the product being tested. In the future, if one cannot specify input values for the parameter distribution, the shape of the probability distribution, or the uncertainty due to dependencies between the inputs then a second-order probability can be used.
Hidden in Plain Sight: Locating the Men and Women of the 1954 Boston Special Youth Program

The growing prevalence of longitudinal research in the social sciences, coupled with technological advances, provide new opportunities for researchers to more readily find participants from earlier studies. In addition, these advances necessitate the development of new strategies and methodologies for locating and building rapport with respondents. Drawing on previous methods of subject identification and location, this paper examines techniques for locating and interviewing former gang members from the 1954-1957 Special Youth Program in Roxbury, MA. In contrast to most longitudinal studies, (1) more than 50 years have passed since the original study with no contact with respondents in the interim, (2) we build on a study not intended to include long-term follow-ups, and (3) much of the location and identification of participants occurred using digital resources. Implications for harnessing digital technologies and methods of developing relationships with study participants otherwise wary of engaging researchers are discussed.
StarFinder: Identifying Constellations from a Subset of Stars

This project aims to develop an efficient and parallelized Constellation Finder program in Haskell, capitalizing on the language’s inherent strengths in concurrent and parallel programming. This system will take a collection of 2D-coordinate dots as input and deduce their corresponding constellations by employing rotation and pattern recognition algorithms, utilizing a predefined database of constellation coordinates for comparison.
Dynamic crosstalk-aware lightpath provisioning in spectrally-spatially flexible optical networks

We focus on the problem of crosstalk-aware lightpath provisioning in a dynamic, spectrally and spatially flexible optical network, in which spectral super-channels are carried over multi-core fibers with distance-adaptive transmission. The problem involves establishing a lightpath for a connection such that the inter-core crosstalk (XT) between the new lightpath and any exiting lightpaths does not cause the quality of transmission of any of these light-paths to be below an acceptable level. To this end, we analyze and compare two broad classes of schemes that aim to ensure acceptable XT levels in the network. The first is based on static/worst-case XT, whereas the second uses dynamic/ precise XT estimation. These methods are implemented in a dynamic XT-aware routing, spatial mode, and spectrum allocation algorithm that solves the lightpath-provisioning problem efficiently with the goal of minimizing bandwidth blocking. Extensive simulation experiments performed in realistic network scenarios indicate that the utilized XT estimation methodology has a significant impact on network performance, with the precise XT approach favored over the worst-case XT strategy.