Miracle-dz/UniFAR-FTUs huggingface dataset:
BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable Data with Item- and Attribute-level Signals

Most sequential recommendation systems (SRSs) predict the next item as the target for users given its preceding items as input, assuming the target is definitely related to its input. However, users may unintentionally click items that are inconsistent with their preference due to external factors, causing unreliable instances whose target mismatches the input. We, for the first time, verify SRSs can be misguided by such unreliable instances and design a generic SRS framework By Eliminating unReliable Data (BERD+), which can be flexibly plugged into existing SRSs. Specifically, BRED+ is guided with observations on the training process of instances: Unreliable instances generally have high training loss; high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential patterns; and item attributes help rectify instance loss and uncertainty, but may also introduce disturbance. Accordingly, BERD+ models both the loss and uncertainty of each instance via a Gaussian distribution, whereby a heterogeneous uncertainty-aware graph convolution network is designed to learn accurate embeddings for different entities while reducing the disturbance caused by uncertain attribute values. Thereafter, an explicit preference extractor rectifies instance loss and uncertainty and reduces the disturbance caused by less-focused attribute types. Finally, instances with high loss and low uncertainty are eliminated as unreliable data. Extensive experiments verify the efficacy of BERD+.
Program Characterization for Software Exploitation Detection

Software exploitation is an ever-growing problem. Signature-based exploitation detection techniques have not been effective as malicious actors continuously develop circumvention techniques. Current ML-based (signature-less) exploitation detection research is limited in quantity and use cases. Key to the success of any ML model is the characteristics used to depict program behaviour (i.e., features). Current work on using ML for software exploitation is focused on novelty ML algorithms while neglecting program characterization and under-reporting the approach for data preparation. There are two main competing program characterization techniques, micro-architecture independent (MAI) and micro-architecture dependent (MAD) techniques. This study evaluates MAI program characterization techniques for use with ML-based exploitation detection. A publicly available runtime-based traces of 11 Windows applications under buffer-overflow exploitation is used to replicate the feature engineering work found in research that uses MAI for ML-based exploitation detection. The performance and feature importance are evaluated with two different ensemble ML models (Random Forests and XGBoost). The results demonstrate that, although 0% FPR has been achieved in all datasets, MAI features that are purely fine-grained in nature can achieve a maximum recall value of 100% and an average recall of 40%, respectively. While features that contain a higher coarse-grained to fine-grained features ratio can achieve a maximum recall of 100% with an average value of 62%. The study provides a detailed discussion of the feature importance and reveals that the most important features relate to memory traffic characteristics.
An Analysis of Stack Exchange Questions: Identifying Challenges in Software Design and Development with a Focus on Data Privacy and Data Protection

Data privacy and protection are essential in today’s digital landscape, with software developers’ playing a critical role in addressing these challenges. This paper presents a comprehensive study of the challenges and issues faced by software developers’ in the context of data privacy and protection. Our analysis is based on a dataset of questions posted on popular online platforms, such as Stack Overflow, Information Security Stack Exchange, and Software Engineering Stack Exchange. Our findings reveal a range of challenges, including the design and generation of privacy policies, compliance with legal frameworks, and implementation of privacy-preserving features in software systems. We also observed interest in policy-related questions and confusion between data privacy concepts and programming language access control mechanisms. Based on our findings, we provide recommendations to address these challenges and promote privacy-by-design principles in software development.
Revisiting Condorcet Fusion

The fusion task is to aggregate ranked document lists retrieved for a query. The Condorcet voting criterion served as inspiration for a commonly used fusion method proposed by Montague and Aslam (2002). The method is stochastic as it is based on the QuickSort sorting algorithm. We empirically show that the performance of the method can substantially vary due to this stochastic aspect. We propose approaches that improve the performance robustness of this fusion method with respect to its stochastic nature. The resultant performance is on par with the state-of-the-art.
GGN: a model-free, data-driven deep learning framework for reconstructing gene regulatory networks

Reconstructing gene regulatory networks based on time-series gene expression data is a huge challenge in the field of systems biology. However, the accuracy of traditional methods can be further improved. In practical situations, the gene regulatory network topology and the dynamic rules of genes are not observable. Therefore, reconstructing the underlying network structure and dynamics from observed time-series gene expression data is an important task. In this work, we introduce a new framework, Gumbel Graph Network (GGN), a model-free, data-driven deep learning framework for accomplishing the reconstruction of gene regulatory networks and the reconstruction of gene dynamics. Our model consists of two co-trained parts: a network generator, which generates a discrete network using Gumbel Softmax technique; and a dynamic learner, which uses the generated network and single-step trajectory values to predict the state at the next moment. According to the experimental results on multiple simulated and real datasets, GGN has better performance and stability compared with other state-of-the-art algorithms.
Enabling the forensic study of application-level encrypted data in Android via a Frida-based decryption framework

The forensic study of mobile apps that use application-level encryption requires the decryption of the data they generate. Such a decryption requires the knowledge of the encryption algorithm and key. Determining them requires, however, a quite complex analysis that is time-consuming, error prone, and often beyond the reach of many forensic examiners. In this paper, we tackle this problem by devising a framework able to automate the decryption of these data when third-party encryption libraries or platforms are used. Our framework is based on the use of dynamic instrumentation of app’s binary code by means of hooking, which enables it to export the plaintext of data after they have been decrypted by the app, as well as the corresponding encryption key and parameters. This framework has been conceived to be used only with test devices used for forensic study purposes, and not with devices that need to be forensically analyzed. We describe the architecture of the framework as well as the implementation of its components and of the hooks supporting three prominent and popular encryption libraries, namely SQLCipher, Realm and Jetpack Security. Also, we validate our framework by comparing its decryption results against those published in the literature for Wickr Me, Signal, Threema, and Element.
Mitigate Data Poisoning Attack by Partially Federated Learning

An efficient machine learning model for malware detection requires a large dataset to train. Yet it is not easy to collect such a large dataset without violating or leaving vulnerable to potential violation various aspects of data privacy. Our work proposes a federated learning framework that permits multiple parties to collaborate on learning behavioral graphs for malware detection. Our proposed graph classification framework allows the participating parties to freely decide their preferred classifier model without acknowledging their preferences to the others involved. This mitigates the chance of any data poisoning attacks. In our experiments, our classification model using the partially federated learning achieved the F1-score of 0.97, close to the performance of the centralized data training models. Moreover, the impact of the label flipping attack against our model is less than 0.02.
Application of large language model in intelligent Q&A of digital government

LLM (Large Language Model) is developing rapidly today, and it is very important to use LLM to help existing question answering systems improve. The government question answering system is of great value in improving government administrative efficiency. Existing intelligent questions and answers mostly use the combination of keyword matching and machine learning. But keyword matching is difficult to understand complex and multi-round dialogue questions, resulting in limited quality of reply content. Based on machine learning method, it has further improved the understanding of semantics, but because the understanding of words in the field of government affairs is too difficult and professional; and the semantic recognition of colloquial questions is difficult, the performance is not even as good as the question answering system based on keyword matching. This paper uses the large language model as a tool to help understand user questions, and integrates it into the existing government question answering system. On the premise of effectively utilizing the advantages of the large language model, it avoids its defects. And it is demonstrated by experiments that the above system has a great improvement compared with the previous method.
A Concept-Based Validation Approach to Validate Security Systems for Protection of Interconnected Critical Infrastructures

When it comes to securing critical infrastructures, it is evident to not only provide a toolbox which allows to detect when vulnerabilities are exploited but also to support the operations in performing mitigation procedures. This paper explains how a validation was conducted in the Horizon 2020 project PRAETORIAN to evaluate the operational feasibility of a system which observes and manages security within interconnected critical infrastructures. To this end, a concept-based approach involving presentation of scenarios with the help of narrations and visual elements, hands-on experience as well as discussions and questionnaires was used. Some results are discussed to demonstrate the applicability of this approach.
Multiomics characterization of the of the zoo-housed gorilla gut microbiome reveals bacterial community compositions shifts, fungal cellulose-degrading, and archaeal methanogenic activity

We carried out a comparative analysis between the bacterial microbiota composition of zoo-housed western lowland gorillas and their wild counterparts through 16S rRNA gene amplicon sequencing. In addition, we characterized the carbohydrate-active and methanogenic potential of the zoo-housed gorilla microbiome through shotgun metagenomics and RNA sequencing. The zoo-housed gorilla microbiota showed increased alpha diversity in terms of bacterial species richness and a distinct composition from that of the wild gorilla microbiota, including a loss of abundant fiber-degrading and hydrogenic Chloroflexi. Metagenomic analysis of the CAZyome indicated predominant oligosaccharide-degrading activity, while RNA sequencing revealed diverse cellulase and hemi-cellulase activities in the zoo-housed gorilla gut, contributing to a total of 268 identified carbohydrate-active enzymes. Metatranscriptome analysis revealed a substantial contribution of 38% of the transcripts from anaerobic fungi and archaea to the gorilla microbiome. This activity originates from cellulose-degrading and hydrogenic fungal species belonging to the class Neocallimastigomycetes, as well as from methylotrophic and hydrogenotrophic methanogenic archaea belonging to the classes Thermoplasmata and Methanobacteria, respectively. Our study shows the added value of RNA sequencing in a multiomics approach and highlights the contribution of eukaryotic and archaeal activities to the gut microbiome of gorillas.
Can Prediction Error Explain Predictability Effects on the N1 during Picture-Word Verification?

Do early effects of predictability in visual word recognition reflect prediction error? Electrophysiological research investigating word processing has demonstrated predictability effects in the N1, or first negative component of the event-related potential (ERP). However, findings regarding the magnitude of effects and potential interactions of predictability with lexical variables have been inconsistent. Moreover, past studies have typically used categorical designs with relatively small samples and relied on by-participant analyses. Nevertheless, reports have generally shown that predicted words elicit less negative-going (i.e., lower amplitude) N1s, a pattern consistent with a simple predictive coding account. In our preregistered study, we tested this account via the interaction between prediction magnitude and certainty. A picture-word verification paradigm was implemented in which pictures were followed by tightly matched picture-congruent or picture-incongruent written nouns. The predictability of target (picture-congruent) nouns was manipulated continuously based on norms of association between a picture and its name. ERPs from 68 participants revealed a pattern of effects opposite to that expected under a simple predictive coding framework.
Using deep long-read RNAseq in Alzheimer’s disease brain to assess medical relevance of RNA isoform diversity

Due to alternative splicing, human protein-coding genes average over eight RNA isoforms, resulting in nearly four distinct protein coding sequences per gene. Long-read RNAseq (IsoSeq) enables more accurate quantification of isoforms, shedding light on their specific roles. To assess the medical relevance of measuring RNA isoform expression, we sequenced 12 aged human frontal cortices (6 Alzheimer’s disease cases and 6 controls; 50% female) using one Oxford Nanopore PromethION flow cell per sample. Our study uncovered 53 new high-confidence RNA isoforms in medically relevant genes, including several where the new isoform was one of the most highly expressed for that gene. Specific examples include WDR4 (61%; microcephaly), MYL3 (44%; hypertrophic cardiomyopathy), and MTHFS (25%; major depression, schizophrenia, bipolar disorder). Other notable genes with new high-confidence isoforms include CPLX2 (10%; schizophrenia, epilepsy) and MAOB (9%; targeted for Parkinson’s disease treatment). We identified 1,917 medically relevant genes expressing multiple isoforms in human frontal cortex, where 1,018 had multiple isoforms with different protein coding sequences, demonstrating the need to better understand how individual isoforms from a single gene body are involved in human health and disease, if at all. Exactly 98 of the 1,917 genes are implicated in brain-related diseases, including Alzheimer’s disease genes such as APP (Aβ precursor protein; five), MAPT (tau protein; four), and BIN1 (eight). As proof of concept, we also found 99 differentially expressed RNA isoforms between Alzheimer’s cases and controls, despite the genes themselves not exhibiting differential expression. Our findings highlight the significant knowledge gaps in RNA isoform diversity and their medical relevance. Deep long-read RNA sequencing will be necessary going forward to fully comprehend the medical relevance of individual isoforms for a “single” gene.
Directional uncertainty in chase and escape dynamics

Intercepting and avoiding collisions with moving targets are crucial skills for survival. However, little is known about how these behaviors are implemented when the trajectory of the moving target introduces variability and ambiguity into the perceptual-motor system. We developed a simple visuomotor task in which participants used a joystick to interact with a computer-controlled dot that moved along two-dimensional trajectories. This virtual system allowed us to define the role of the moving object (predator or prey) and adjust its speed and directional uncertainty (i.e., magnitude and frequency of random directional changes) during chase and escape trials. These factors had a significant impact on participants’ performance in both chasing and escaping trials. We developed a simple geometrical model of potential chaser/escaper interactions to distinguish pursuit from interception chasing trajectories. We found that participants initially pursued the target but switched to a late interception strategy. The amount of late interception strategy followed an inverted U-shaped curve with the highest values at intermediate speeds. We tested the applicability of our task and methods in children who showed a robust developmental improvement in task performance and late interception strategy. Our task constitutes a flexible system in a virtual space for studying chasing and escaping behavior in adults and children. Our analytical methods allow detecting subtle changes in interception strategies, a valuable tool for studying the maturation of predictive and prospective systems, with a high potential to contribute to cognitive and developmental research.
IRE1 regulated autophagy and chaperone levels cooperatively modulate acquired thermotolerance in Arabidopsis thaliana

Rapidly changing global climatic conditions emphasize the urgent need for the development of heat stress-resilient plants. Priming, a stress-training program that aids in imparting tolerance to subsequent stress(es) emerged as a promising approach to improve stress tolerance. However, molecular mechanisms underlying priming-induced plant stress responses still remain largely elusive. In the current study, the impact of heat stress on Arabidopsis thaliana seedlings pre-exposed to elevated temperature (primed) was found to be minimal, compared to naïve (unprimed) seedlings, which failed to survive. Our findings suggest that thermopriming-mediated heat stress tolerance operates through the activation of IRE1-bZIP60 cascade triggering unfolded protein response (UPR). Further, IRE1 activation triggers autophagy response through attenuation of autophagy-negative regulators such as BGLU21, ROSY1 and PR-14 via RIDD-mediated mRNA degradation. Alongside UPR and autophagy, priming temporally induces HSP levels, rendering plants tolerant to acute heat stress. Contrarily, unprimed seedlings fail to mount such temporally coordinated regulation, thus fail to survive. Moreover, loss of bZIP60 reduces the impact of priming-induced response, suggesting its role in the maintenance of stress memory. Taken together, the current study suggests that priming-mediated regulation of protein homeostasis through the temporal regulation of autophagy in concert with chaperone synthesis promotes heat stress tolerance.
Differential conformational dynamics in two type-A RNA-binding domains drive the double-stranded RNA recognition and binding

TAR RNA binding protein (TRBP) has emerged as a key player in the RNA interference (RNAi) pathway, wherein it binds to different pre-miRNAs and siRNAs, each varying in sequence and/or structure. We hypothesize that TRBP displays dynamic adaptability to accommodate heterogeneity in target RNA structures. Thus, it is crucial to ascertain the role of intrinsic and RNA-induced protein dynamics in RNA recognition and binding. We have previously elucidated the role of intrinsic and RNA-induced conformational exchange in the double-stranded RNA-binding domain 1 (dsRBD1) of TRBP in shape-dependent RNA recognition. The current study delves into the intrinsic and RNA-induced conformational dynamics of the TRBP-dsRBD2 and then compares it with the dsRBD1 study carried out previously. Remarkably, the two domains exhibit differential binding affinity to a 12 bp dsRNA owing to the presence of critical residues and structural plasticity. Further, we report that dsRBD2 depicts constrained conformational plasticity when compared to dsRBD1. Although, in the presence of RNA, dsRBD2 undergoes induced conformational exchange within the designated RNA-binding regions and other residues, the amplitude of the motions remains modest when compared to those observed in dsRBD1. We propose a dynamics-driven model of the two tandem domains of TRBP, substantiating their contributions to the versatility of dsRNA recognition and binding. Significance Statement Exploring the intricacies of RNA-protein interactions by delving into dynamics-based measurements not only adds valuable insights into the mechanics of RNA-protein interactions but also underscores the significance of conformational dynamics in dictating the functional outcome in such tightly regulated biological processes. In this study, we measure intrinsic and RNA-induced conformational dynamics in the second dsRBD, i.e., TRBP-dsRBD2, and compare the same with that carried out in the first dsRBD (TRBP-dsRBD1) of TRBP protein, a key player of the RNAi pathway. The study unveils the differential conformational space accessible to the two domains of TRBP, even though they both adopt a canonical dsRBD fold, thereby affecting how they interact with target RNAs.
Figures for : Analyses of cell wall synthesis in Clostridioides difficile reveal a diversification in cell division mechanisms in endospore-forming bacteria

1Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA. 2Program in Molecular Microbiology, Tufts University Graduate School of Biomedical Sciences, Boston, MA, USA. 3Institut Pasteur, Université de Paris, Unit Evolutionary Biology of the Microbial Cell, Paris, France. 4Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, F-75015 Paris, France.
Neural crest cells give rise to non-myogenic mesenchymal tissue in the adult murid ear pinna

Despite being a major target of reconstructive surgery, development of the external ear pinna remains poorly studied. As a craniofacial organ highly accessible to manipulation and highly conserved among mammals, the ear pinna represents a valuable model for the study of appendage development and wound healing in the craniofacial complex. Here we provide a cellular characterization of late gestational and postnatal ear pinna development in Mus musculus and Acomys cahirinus and demonstrate that ear pinna development is largely conserved between these species. Using Wnt1-cre;ROSAmT/mG mice we find that connective tissue fibroblasts, elastic cartilage, dermal papilla cells, dermal sheath cells, vasculature, and adipocytes in the adult pinna are derived from cranial crest. In contrast, we find that skeletal muscle and hair follicles are not derived from neural crest cells. Cellular analysis using the naturally occurring short ear mouse mutant shows that elastic cartilage does not develop properly in distal pinna due to impaired chondroprogenitor proliferation. Interestingly, while chondroprogenitors develop in a mostly continuous sheet, the boundaries of cartilage loss in the short ear mutant strongly correlate with locations of vasculature-conveying foramen. Concomitant with loss of elastic cartilage we report increased numbers of adipocytes, but this seems to be a state acquired in adulthood rather than a developmental abnormality. In addition, chondrogenesis remains impaired in the adult mid-distal ear pinna of these mutants. Together these data establish a developmental basis for the study of the ear pinna with intriguing insights into the development of elastic cartilage.