Miracle-dz/UniFAR-FTUs huggingface dataset:
UOEP: User-Oriented Exploration Policy for Enhancing Long-Term User Experiences in Recommender Systems

Reinforcement learning (RL) has gained traction for enhancing user long-term experiences in recommender systems by effectively exploring users' interests. However, modern recommender systems exhibit distinct user behavioral patterns among tens of millions of items, which increases the difficulty of exploration. For example, user behaviors with different activity levels require varying intensity of exploration, while previous studies often overlook this aspect and apply a uniform exploration strategy to all users, which ultimately hurts user experiences in the long run. To address these challenges, we propose User-Oriented Exploration Policy (UOEP), a novel approach facilitating fine-grained exploration among user groups. We first construct a distributional critic which allows policy optimization under varying quantile levels of cumulative reward feedbacks from users, representing user groups with varying activity levels. Guided by this critic, we devise a population of distinct actors aimed at effective and fine-grained exploration within its respective user group. To simultaneously enhance diversity and stability during the exploration process, we further introduce a population-level diversity regularization term and a supervision module. Experimental results on public recommendation datasets demonstrate that our approach outperforms all other baselines in terms of long-term performance, validating its user-oriented exploration effectiveness. Meanwhile, further analyses reveal our approach's benefits of improved performance for low-activity users as well as increased fairness among users.
Hamiltonian formulation of the maximal valence model for liquid polyamorphism

Liquid-liquid phase transitions have been found experimentally or proposed to exist based on computer simulations in many compounds such as water, hydrogen, sulfur, phosphorus, carbon, silica, and silicon. Recently introduced maximal valence model implemented via discrete molecular dynamics (DMD) provides simple generic mechanism of the liquid-liquid phase transitions for all these diverse cases. Here we will show that certain variants of the maximal valence model have a well defined Hamiltonian i.e a unique algorithm by which the potential energy of the system of particles can be computed solely from the coordinates of the particles and is thus equivalent to a complex multi-body potential. We will present several examples of the model which can be used to reproduce liquid-liquid phase transition in systems with maximal valence $z=1$ (hydrogen), $z=2$ (sulfur) and $z=4$ (water), where $z$ is the maximal valence, i.e. the maximal number of bonds an atom is allowed to have. The maximal valence model can be modified to forbid not only too large valences but also too low valences. In the case of sulfur, we forbid also the formation of monomers, thus restricting the valence $v$ of an atom to be within an interval $1=v_{min}\leq v\leq v_{max}\equiv z=2$.
Online Anomaly Detection over Live Social Video Streaming

Social video anomaly is an observation in video streams that does not conform to a common pattern of dataset's behaviour. Social video anomaly detection plays a critical role in applications from e-commerce to e-learning. Traditionally, anomaly detection techniques are applied to find anomalies in video broadcasting. However, they neglect the live social video streams which contain interactive talk, speech, or lecture with audience. In this paper, we propose a generic framework for effectively online detecting Anomalies Over social Video LIve Streaming (AOVLIS). Specifically, we propose a novel deep neural network model called Coupling Long Short-Term Memory (CLSTM) that adaptively captures the history behaviours of the presenters and audience, and their mutual interactions to predict their behaviour at next time point over streams. Then we well integrate the CLSTM with a decoder layer, and propose a new reconstruction error-based scoring function $RE_{IA}$ to calculate the anomaly score of each video segment for anomaly detection. After that, we propose a novel model update scheme that incrementally maintains CLSTM and decoder. Moreover, we design a novel upper bound and ADaptive Optimisation Strategy (ADOS) for improving the efficiency of our solution. Extensive experiments are conducted to prove the superiority of AOVLIS.
Sample Relationship from Learning Dynamics Matters for Generalisation

Although much research has been done on proposing new models or loss functions to improve the generalisation of artificial neural networks (ANNs), less attention has been directed to the impact of the training data on generalisation. In this work, we start from approximating the interaction between samples, i.e. how learning one sample would modify the model's prediction on other samples. Through analysing the terms involved in weight updates in supervised learning, we find that labels influence the interaction between samples. Therefore, we propose the labelled pseudo Neural Tangent Kernel (lpNTK) which takes label information into consideration when measuring the interactions between samples. We first prove that lpNTK asymptotically converges to the empirical neural tangent kernel in terms of the Frobenius norm under certain assumptions. Secondly, we illustrate how lpNTK helps to understand learning phenomena identified in previous work, specifically the learning difficulty of samples and forgetting events during learning. Moreover, we also show that using lpNTK to identify and remove poisoning training samples does not hurt the generalisation performance of ANNs.
Inductive Models for Artificial Intelligence Systems are Insufficient without Good Explanations

This paper discusses the limitations of machine learning (ML), particularly deep artificial neural networks (ANNs), which are effective at approximating complex functions but often lack transparency and explanatory power. It highlights the `problem of induction' : the philosophical issue that past observations may not necessarily predict future events, a challenge that ML models face when encountering new, unseen data. The paper argues for the importance of not just making predictions but also providing good explanations, a feature that current models often fail to deliver. It suggests that for AI to progress, we must seek models that offer insights and explanations, not just predictions.
Supporting Student Decisions on Learning Recommendations: An LLM-Based Chatbot with Knowledge Graph Contextualization for Conversational Explainability and Mentoring

Student commitment towards a learning recommendation is not separable from their understanding of the reasons it was recommended to them; and their ability to modify it based on that understanding. Among explainability approaches, chatbots offer the potential to engage the student in a conversation, similar to a discussion with a peer or a mentor. The capabilities of chatbots, however, are still not sufficient to replace a human mentor, despite the advancements of generative AI (GenAI) and large language models (LLM). Therefore, we propose an approach to utilize chatbots as mediators of the conversation and sources of limited and controlled generation of explanations, to harvest the potential of LLMs while reducing their potential risks at the same time. The proposed LLM-based chatbot supports students in understanding learning-paths recommendations. We use a knowledge graph (KG) as a human-curated source of information, to regulate the LLM's output through defining its prompt's context. A group chat approach is developed to connect students with human mentors, either on demand or in cases that exceed the chatbot's pre-defined tasks. We evaluate the chatbot with a user study, to provide a proof-of-concept and highlight the potential requirements and limitations of utilizing chatbots in conversational explainability.
Cross-Domain AI for Early Attack Detection and Defense Against Malicious Flows in O-RAN

Only the chairs can edit In the fight against cyber attacks, Network Softwarization (NS) is a flexible and adaptable shield, using advanced software to spot malicious activity in regular network traffic. However, the availability of comprehensive datasets for mobile networks, which are fundamental for the development of Machine Learning (ML) solutions for attack detection near their source, is still limited. Cross-Domain Artificial Intelligence (AI) can be the key to address this, although its application in Open Radio Access Network (O-RAN) is still at its infancy. To address these challenges, we deployed an end-to-end O-RAN network, that was used to collect data from the RAN and the transport network. These datasets allow us to combine the knowledge from an in-network ML traffic classifier for attack detection to bolster the training of an ML-based traffic classifier specifically tailored for the RAN. Our results demonstrate the potential of the proposed approach, achieving an accuracy rate of 93%. This approach not only bridges critical gaps in mobile network security but also showcases the potential of cross-domain AI in enhancing the efficacy of network security measures.
Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision

There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series. First, the time series signal is transformed to the frequency domain and then compressed into task-agnostic temporal embeddings by a contractive autoencoder, which preserves cyclic temporal patterns observed in time series. The pixel-wise embeddings are converted to image-like channels that can be used for task-based, multimodal modeling of downstream geospatial tasks using deep semantic segmentation. Experiments show that temporal embeddings are semantically meaningful representations of time series data and are effective across different tasks such as classifying residential area and commercial areas. Temporal embeddings transform sequential, spatiotemporal motion trajectory data into semantically meaningful image-like tensor representations that can be combined (multimodal fusion) with other data modalities that are or can be transformed into image-like tensor representations (for e.g., RBG imagery, graph embeddings of road networks, passively collected imagery like SAR, etc.) to facilitate multimodal learning in geospatial computer vision. Multimodal computer vision is critical for training machine learning models for geospatial feature detection to keep a geospatial mapping service up-to-date in real-time and can significantly improve user experience and above all, user safety.
Detection of Distributed Denial of Service Attacks Carried Out by Botnets in Software-Defined Networks

Recent years witnessed a surge in network traffic due to the emergence of new online services, causing periodic saturation and complexity problems. Additionally, the growing number of IoT devices further compounds the problem. Software Defined Network (SDN) is a new architecture which offers innovative advantages that help to reduce saturation problems. Despite its benefits, SDNs not only can be affected by traditional attacks but also introduce new security challenges. In this context, Distributed Denial of Service (DDoS) is one of the most important attacks that can damage an SDN network's normal operation. Furthermore, if these attacks are executed using botnets, they can use thousands of compromised devices to disrupt critical online services. This paper proposes a framework for detecting DDoS attacks generated by a group of botnets in an SDN network. The framework is implemented using open-source tools such as Mininet and OpenDaylight and tested in a centralized network topology using BYOB and SNORT. The results demonstrate real-time attack identification by implementing an intrusion detection mechanism in the victim client. Our proposed solution offers quick and effective detection of DDoS attacks in SDN networks. The framework can successfully differentiate the type of attack with high accuracy in a short time
Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center

Abstract Diabetic foot ulcers can have vital consequences, such as amputation for patients. The primary purpose of this study is to predict the amputation risk of diabetic foot patients using machine‐learning classification algorithms. In this research, 407 patients treated with the diagnosis of diabetic foot between January 2009–September 2019 in Istanbul University Faculty of Medicine in the Department of Undersea and Hyperbaric Medicine were retrospectively evaluated. Principal Component Analysis (PCA) was used to identify the key features associated with the amputation risk in diabetic foot patients within the dataset. Thus, various prediction/classification models were created to predict the “overall” risk of diabetic foot patients. Predictive machine‐learning models were created using various algorithms. Additionally to optimize the hyperparameters of the Random Forest Algorithm (RF), experimental use of Bayesian Optimization (BO) has been employed. The sub‐dimension data set comprising categorical and numerical values was subjected to a feature selection procedure. Among all the algorithms tested under the defined experimental conditions, the BO‐optimized “RF” based on the hybrid approach (PCA‐RF‐BO) and “Logistic Regression” algorithms demonstrated superior performance with 85% and 90% test accuracies, respectively. In conclusion, our findings would serve as an essential benchmark, offering valuable guidance in reducing such hazards.
Geometric Deep Learning sub-network extraction for Maximum Clique Enumeration

The paper presents an algorithm to approach the problem of Maximum Clique Enumeration, a well known NP-hard problem that have several real world applications. The proposed solution, called LGP-MCE, exploits Geometric Deep Learning, a Machine Learning technique on graphs, to filter out nodes that do not belong to maximum cliques and then applies an exact algorithm to the pruned network. To assess the LGP-MCE, we conducted multiple experiments using a substantial dataset of real-world networks, varying in size, density, and other characteristics. We show that LGP-MCE is able to drastically reduce the running time, while retaining all the maximum cliques.
Neurocognitive Profiles of 22q11.2 and 16p11.2 Deletions and Duplications

Rare recurrent copy number variants (CNVs) at chromosomal loci 22q11.2 and 16p11.2 are among the most common rare genetic disorders associated with significant risk for neuropsychiatric disorders across the lifespan. Microdeletions and duplications in these loci are associated with neurocognitive deficits, yet there are few studies comparing these groups using the same measures. We address this gap in a prospective international collaboration applying the same computerized neurocognitive assessment. The Penn Computerized Neurocognitive Battery (CNB) was administered in a multi-site study on rare genomic disorders: 22q11.2 deletion (n = 397); 22q11.2 duplication (n = 77); 16p11.2 deletion (n = 94); and 16p11.2 duplication (n = 26). Domains examined include executive functions, episodic memory, complex cognition, social cognition, and sensori-motor speed. Accuracy and speed for each neurocognitive domain were included as dependent measures in a mixed-model repeated measures analysis, with locus (22q11.2, 16p11.2) and copy number (deletion/duplication) as grouping factors and neurocognitive domain as a repeated measures factor, with age and sex as covariates. We also examined correlation with IQ and site effects. We found that 22q11.2 deletions were associated with greater deficits in overall performance accuracy than 22q11.2 duplications, while 16p11.2 duplications were associated with greater deficits than 16p11.2 deletions. Duplications at both loci were associated with reduced speed. Performance profiles differed among the groups with particularly poor performance of 16p11.2 duplication on non-verbal reasoning and social cognition. Average accuracy on the CNB was moderately correlated with Full Scale IQ. No site effects were observed. Deletions and duplications of 22q11.2 and 16p11.2 have varied effects on neurocognition indicating locus specificity, with performance profiles differing among the groups. These profile differences can help inform mechanistic substrates to heterogeneity in presentation and outcome. Future studies could aim to link performance profiles to clinical features and brain function.
The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism

Breast tumours are embedded in a collagen I-rich extracellular matrix (ECM) network, where nutrients are scarce due to limited blood flow and elevated tumour growth. Metabolic adaptation is required for cancer cells to endure these conditions. Here, we demonstrated that the presence of ECM supported the growth of invasive breast cancer cells, but not non-transformed mammary epithelial cells, under amino acid starvation, through a mechanism that required macropinocytosis-dependent ECM uptake. Importantly, we showed that this behaviour was acquired during carcinoma progression. ECM internalisation, followed by lysosomal degradation, contributed to the up-regulation of the intracellular levels of several amino acids, most notably tyrosine and phenylalanine. This resulted in elevated tyrosine catabolism on ECM under starvation, leading to increased fumarate levels, potentially feeding into the tricarboxylic acid (TCA) cycle. Interestingly, this pathway was required for ECM-dependent cell growth and invasive cell migration under amino acid starvation, as the knockdown of p-hydroxyphenylpyruvate hydroxylase-like protein (HPDL), the third enzyme of the pathway, opposed cell growth and motility on ECM in both 2D and 3D systems, without affecting cell proliferation on plastic. Finally, high HPDL expression correlated with poor prognosis in breast cancer patients. Collectively, our results highlight that the ECM in the tumour microenvironment (TME) represents an alternative source of nutrients to support cancer cell growth by regulating phenylalanine and tyrosine metabolism.
Gift-giving intentions in pan-entertainment live streaming: Based on social exchange theory

Pan-entertainment live streaming combines video with two-way communication and real-time viewer participation, allowing viewers to send virtual gifts to their favorite streamers. Drawing on social exchange theory, this study investigates the factors that influence viewers’ gift-giving intentions in live streaming from the perspectives of both viewers and streamers. It also explores the moderating role of streamers’ deceptive self-presentation. The theoretical framework is tested using AMOS and PROCESS Macro based on survey responses collected from 331 TikTok users in China. The results suggest that streamers’ attractiveness, expertise, parasocial interaction, and the viewers’ deceptive self-presentation significantly affect viewers’ gift-giving intention, and that streamers’ deceptive self-representation moderates the relationship between attractiveness, expertise, parasocial inter-action and the viewer’s consumer intention. These findings contribute to social exchange theory by highlighting the importance of streamers’ deceptive self-presentation in moderating the effects of attractiveness, expertise, parasocial interaction, and the viewer’s deceptive self-presentation on the viewer’s gift-giving intention.
Use of tannase‐producing bacteria isolated from the rumen to improve the nutritional value of pomegranate peel for fattening lambs

Abstract Background The use of plants and by‐products, which are containing a high amount of secondary and anti‐nutritional compounds such as tannins, in animal feed is limited. The methods that can reduce these compounds make facilitate their use in animal feed. Objectives The aim of this study was to reduce the adverse effects of pomegranate peel (PP) tannin for fattening lambs using the tannase‐producing bacteria. Methods Twenty‐one Arabi male lambs (averagely 35 ± 3.8 kg weight and 8 ± 1.0 months age) were used in a completely randomized design with three treatments and seven replications in the present experiment. The experimental treatments included 1 – control diet (CNT, no PP), 2 – diet containing untreated PP (raw PP, UTPP) and 3 – diet containing PP treated with tannase‐producing bacteria (bacteria treating PP, BTPP). Results Using UTPP decreased nutrient intake compared to the control and treatment with tannase‐producing bacteria again significantly increased nutrient intake compared to the UTPP (p < 0.05). The digestibilities of organic matter, neutral detergent fibre and acid detergent fibre in the control treatment were significantly higher than UTPP and BTPP and in the BTPP were significantly higher than the UTPP (p < 0.05). The use of UTPP in the diet significantly decreased the pH, ammonia nitrogen concentration and the total protozoa population of the rumen compared to the control (p < 0.05), and treatment with bacteria increased them again. The lowest total protozoa population was observed in UTPP treatments (p < 0.05). The highest concentration of blood glucose was observed in UTPP; however, the highest concentrations of blood urea nitrogen, cholesterol, triglyceride, high‐density lipoprotein (non‐significant) and low‐density lipoprotein were in the control treatment. The effect of experimental treatments on the dry matter consumption of the whole period was significant; however, there was no significant effect on average daily gain, feed conversion ratio, feed efficiency and longissimus muscle colorimetric systems. Conclusions Therefore, considering the positive effects of treatment PP with tannin‐degrading bacteria relative to raw PP, using these bacteria is a proper way to reduce tannin, thus improving the nutritional value of PP for ruminants.
Hypothesis testing and sample size considerations for the test-negative design

The test-negative design (TND) is an observational study design to evaluate vaccine effectiveness (VE) that enrolls individuals receiving diagnostic testing for a target disease as part of routine care. VE is estimated as one minus the adjusted odds ratio of testing positive versus negative comparing vaccinated and unvaccinated patients. Although the TND is related to case-control studies, it is distinct in that the ratio of test-positive cases to test-negative controls is not typically pre-specified. For both types of studies, sparse cells are common when vaccines are highly effective. We consider the implications of these features on power for the TND. We use simulation studies to explore three hypothesis-testing procedures and associated sample size calculations for case-control and TND studies. These tests, all based on a simple logistic regression model, are a standard Wald test, a continuity-corrected Wald test, and a score test. The Wald test performs poorly in both case-control and TND when VE is high because the number of vaccinated test-positive cases can be low or zero. Continuity corrections help to stabilize the variance but induce bias. We observe superior performance with the score test as the variance is pooled under the null hypothesis of no group differences. We recommend using a score-based approach to design and analyze both case-control and TND. We propose a modification to the TND score sample size to account for additional variability in the ratio of controls over cases. This work expands our understanding of the data mechanisms of the TND.