Miracle-dz/UniFAR-FTUs huggingface dataset:
TrustCF: A Hybrid Collaborative Filtering Recommendation Model with Trust Information

Recommender systems have been recognized as an effective way to deal with the information overload problem, which can recommend accurate and positive items to users from a large volume of choices. Due to its capability and simplicity, collaborative filtering (CF) is one of the most popular techniques for recommender systems. However, CF suffers from three issues which are user cold-start, item cold-start and data sparsity problems. These issues severely degrade the performance of CF. To address these issues, a hybrid collaborative filtering recommendation model, termed TrustCF, is proposed in this paper, based on user-item ratings and trust relations among users. TrustCF integrates ratings from trusted friends and similar users, ratings of similar items, item reputation and user history ratings. In particular, we modify the similarity calculation formula, considering the effect of the number of co-ratings. Trust relations among users are used to make predictions. In this way, TrustCF can alleviate the data sparsity problem and improve the recommendation performance. Experimental results on two real-world datasets verify the effectiveness of the proposed TrustCF model and show TrustCF has better recommendation accuracy than other five counterparts.
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks

Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks

A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations. PMvGE is a probabilistic model for predicting new associations via graph embedding of the nodes of data vectors with links of their associations.Multi-view data vectors with many-to-many associations are transformed by neural networks to feature vectors in a shared space, and the probability of new association between two data vectors is modeled by the inner product of their feature vectors. While existing multi-view feature learning techniques can treat only either of many-to-many association or non-linear transformation, PMvGE can treat both simultaneously. By combining Mercer's theorem and the universal approximation theorem, we prove that PMvGE learns a wide class of similarity measures across views. PMvGE generalizes various existing multi-view methods such as Multiset Canonical Correlation Analysis (MCCA) and Cross-view Graph Embedding (CvGE). Our likelihood-based estimator enables efficient computation of non-linear transformations of data vectors in large-scale datasets by minibatch SGD. Numerical experiments illustrate that PMvGE outperforms existing multi-view methods.
Delay-Aware Upload Balancing Cross APs Based on User Relays

Recent years have witnessed the rapid growth of user-generated content, produced by end users and uploaded via edge networks, in which 802.11 wireless LANs (Wi-Fi) serve a dominant fraction of such traffic. However, the user experience for content uploading via Wi-Fi networks, especially in today's metropolises, remains unsatisfactory. In this paper, we address this problem using a delay-aware wireless Access Point (AP) upload balancing solution: content items can be carried by users from one overloaded AP to another AP for uploading before their deadlines, to balance the load between Wi-Fi APs and achieve a high overall upload capacity. Our contributions are as follows. First, we conduct measurement studies to validate that the AP upload balancing framework is feasible in practice. Second, we formulate the delay-aware AP upload balancing problem as a Cache Utility Maximization (CUM) model and propose an efficient algorithm to guarantee a high success upload rate and average AP load utilization. Third, trace-driven experiments are conducted to evaluate our solution. The results show that our solution can achieve significant performance improvements.
Computable approximations for average Markov decision processes in continuous time

Abstract In this paper we study the numerical approximation of the optimal long-run average cost of a continuous-time Markov decision process, with Borel state and action spaces, and with bounded transition and reward rates. Our approach uses a suitable discretization of the state and action spaces to approximate the original control model. The approximation error for the optimal average reward is then bounded by a linear combination of coefficients related to the discretization of the state and action spaces, namely, the Wasserstein distance between an underlying probability measure μ and a measure with finite support, and the Hausdorff distance between the original and the discretized actions sets. When approximating μ with its empirical probability measure we obtain convergence in probability at an exponential rate. An application to a queueing system is presented.
Multihop Multicast Header Compression in MANETs

A common technique for improving throughput and increasing spectrum efficiency in wireless networks is the use of header compression. Removing static, implied or redundant protocol fields by synchronizing state information between a sender and receiver reduces per-packet overhead for long lived communication flows. Previous header compression work has focused primarily on single-hop unicast traffic. In this paper we describe a multi-hop multicast header compression (MMHC) scheme applicable to mobile ad-hoc networks. MMHC provides an end-to-end solution where multicast forwarders and receivers are synchronized simultaneously and not hop-by-hop as in previous schemes. Simulation results show that the reductions in per-transmission overhead achieved provide significant gains in both throughput and latency, especially when the average payload size of packets aggregated into the communication flow is small.
Intra-Thalamic and Thalamocortical Connectivity: Potential Implication for Deep Learning

Contrary to the traditional view that the thalamus acts as a passive relay station of sensory information to the cortex, a number of experimental studies have demonstrated the effects of peri-geniculate and cortico-thalamic projections on the transmission of visual input. In the present study, we implemented a mechanistic model to facilitate the understanding of peri-geniculate and cortico-thalamic effects on the transfer function of geniculate cells and their firing patterns. As a result, the model successfully captures some fundamental properties of early-stage visual processing in mammalian brain. We conclude, therefore, that the thalamus is not a passive relay center and the intra-thalamic circuitry is of great importance to biological vision. In summary, intra-thalamic and thalamocortical circuitries have implications in early-stage visual processing, and could constitute a valid tool for refining information relay and compression in artificial neural networks (ANN), leading to deep learning models of higher performance.
Fine-grained consistency for geo-replicated systems

To deliver fast responses to users worldwide, major Internet providers rely on geo-replication to serve requests at data centers close to users. This deployment leads to a fundamental tension between improving system performance and reducing costly cross-site coordination for maintaining service properties such as state convergence and invariant preservation. Previous proposals for managing this trade-off resorted to coarse-grained operations labeling or coordination strategies that were oblivious to the frequency of operations. In this paper, we present a novel fine-grained consistency definition, Partial Order-Restrictions consistency (or short, PoR consistency), generalizing the trade-off between performance and the amount of coordination paid to restrict the ordering of certain operations. To offer efficient PoR consistent replication, we implement Olisipo, a coordination service assigning different coordination policies to various restrictions by taking into account the relative frequency of the confined operations. Our experimental results show that PoR consistency significantly outperforms a state-of-the-art solution (RedBlue consistency) on a 3-data center RUBiS benchmark.
Neural Sparse Topical Coding

Topic models with sparsity enhancement have been proven to be effective at learning discriminative and coherent latent topics of short texts, which is critical to many scientific and engineering applications. However, the extensions of these models require carefully tailored graphical models and re-deduced inference algorithms, limiting their variations and applications. We propose a novel sparsity-enhanced topic model, Neural Sparse Topical Coding (NSTC) base on a sparsity-enhanced topic model called Sparse Topical Coding (STC). It focuses on replacing the complex inference process with the back propagation, which makes the model easy to explore extensions. Moreover, the external semantic information of words in word embeddings is incorporated to improve the representation of short texts. To illustrate the flexibility offered by the neural network based framework, we present three extensions base on NSTC without re-deduced inference algorithms. Experiments on Web Snippet and 20Newsgroups datasets demonstrate that our models outperform existing methods.
On the Existence of Infinite Localizable Nodes in Stochastic Networks

The ability to localize a majority of nodes is important in many scenarios concerning large-scale stochastic networks. Classical percolation, i.e., the existence of infinite connected components in a stochastic network, has been analyzed in previous studies. However, a single connection is not enough for localization, which requires at least two connections to anchors. In this paper, we generalize classical percolation to localization problems. We find that for distributed localization, phase transition appears around a critical network density, i.e., an infinite localizable subnetwork exists with probability zero in the sub- critical case and with probability one in the super-critical case. Further, simulation methods are developed to estimate upper and lower bounds of critical densities, and precise estimates of densities in angle-of-arrival (AOA) and time-of- arrival (TOA) localization are performed using proposed methods.
Does Nasal Surgery Affect Right Ventricular Myocardial Functions at the Tissue Level in Patients with Nasal Septum Deviation?

Objective: One of the most common causes of upper airway obstruction in adults is nasal septum deviation (NSD). The chronic hypoxia caused by this obstruction gradually leads to increased pulmonary vascular resistance, pulmonary hypertension (PHT), and right ventricular (RV) failure. The purpose of this study was to determine changes in RV myocardial functions at the tissue level before, and after surgery in patients with NSD. Subjects and Methods: Fifty-eight patients with symptoms of nasal obstruction and snoring were included in this observational study. Preoperative and postoperative third-month peripheral arterial oxygen saturation (SpO2), and RV systolic and diastolic functions measured by pulmonary artery systolic pressure (PASP), tissue Doppler parameters, and speckle tracking echocardiography (STE) were studied in these patients. Results: We observed a very significant decrease in PASP in the postoperative period (32.54 ± 5.24 mmHg vs. 24.22 ± 4.55 mmHg, p = 0.001). Postoperative SpO2 values, measured at room temperature also increased significantly (93.5 ± 0.82% vs. 95.6 ± 0.79%, p = 0.001). There was a significant improvement after surgery in RV systolic functions, represented by global longitudinal strain (GLS) (21.12 ± 2.07 vs. 22.49 ± 1.89, p = 0.013) and systolic global longitudinal strain rate (GLSRs) (1.30 ± 0.12 vs. 1.38 ± 0.13, p = 0.015). No significant differences in terms of RV diastolic function parameters were detected, including the RV early diastolic global longitudinal strain rate (GLSRe) (1.56 ± 0.21 vs. 1.55 ± 0.26, p = 0.86) and RV late diastolic global longitudinal strain rate (GLSRa) (0.88 ± 0.19; 0.89 ± 0.18, p = 0.76). Conclusion: This study was performed with an advanced technique capable of tissue level examination. The findings demonstrated significant improvement in both chronic hypoxia and RV systolic myocardial functions, measured at the tissue level after nasal surgery.
RAFI: Risk-Aware Failure Identification to Improve the RAS in Erasure-coded Data Centers

Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. Compared to the recovery phase of the data repair, which is widely studied and well optimized, the failure identification phase of the data repair is less investigated. Moreover, in a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS. To solve this problem, we propose RAFI, a novel riskaware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high risk stripe (a stripe with many failed chunks), a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low risk stripe (one with only a few failed chunks), a longer identification time is adopted, thus reducing the repair network traffic. Therefore, the RAS can be improved simultaneously. We use both simulations and prototyping implementation to evaluate RAFI. Results collected from extensive simulations demonstrate the effectiveness and efficiency of RAFI on improving the RAS. We implement a prototype on HDFS to verify the correctness and evaluate the computational cost of RAFI.
Association of p16 expression with prognosis varies across ovarian carcinoma histotypes: an Ovarian Tumor Tissue Analysis consortium study

We aimed to validate the prognostic association of p16 expression in ovarian high‐grade serous carcinomas (HGSC) and to explore it in other ovarian carcinoma histotypes. p16 protein expression was assessed by clinical‐grade immunohistochemistry in 6525 ovarian carcinomas including 4334 HGSC using tissue microarrays from 24 studies participating in the Ovarian Tumor Tissue Analysis consortium. p16 expression patterns were interpreted as abnormal (either overexpression referred to as block expression or absence) or normal (heterogeneous). CDKN2A (which encodes p16) mRNA expression was also analyzed in a subset (n = 2280) mostly representing HGSC (n = 2010). Association of p16 expression with overall survival (OS) was determined within histotypes as was CDKN2A expression for HGSC only. p16 block expression was most frequent in HGSC (56%) but neither protein nor mRNA expression was associated with OS. However, relative to heterogeneous expression, block expression was associated with shorter OS in endometriosis‐associated carcinomas, clear cell [hazard ratio (HR): 2.02, 95% confidence (CI) 1.47–2.77, p < 0.001] and endometrioid (HR: 1.88, 95% CI 1.30–2.75, p = 0.004), while absence was associated with shorter OS in low‐grade serous carcinomas (HR: 2.95, 95% CI 1.61–5.38, p = 0.001). Absence was most frequent in mucinous carcinoma (50%), and was not associated with OS in this histotype. The prognostic value of p16 expression is histotype‐specific and pattern dependent. We provide definitive evidence against an association of p16 expression with survival in ovarian HGSC as previously suggested. Block expression of p16 in clear cell and endometrioid carcinoma should be further validated as a prognostic marker, and absence in low‐grade serous carcinoma justifies CDK4 inhibition.
Tailored Sequence to Sequence Models to Different Conversation Scenarios

Sequence to sequence (Seq2Seq) models have been widely used for response generation in the area of conversation. However, the requirements for different conversation scenarios are distinct. For example, customer service requires the generated responses to be specific and accurate, while chatbot prefers diverse responses so as to attract different users. The current Seq2Seq model fails to meet these diverse requirements, by using a general average likelihood as the optimization criteria. As a result, it usually generates safe and commonplace responses, such as ‘I don’t know’. In this paper, we propose two tailored optimization criteria for Seq2Seq to different conversation scenarios, i.e., the maximum generated likelihood for specific-requirement scenario, and the conditional value-at-risk for diverse-requirement scenario. Experimental results on the Ubuntu dialogue corpus (Ubuntu service scenario) and Chinese Weibo dataset (social chatbot scenario) show that our proposed models not only satisfies diverse requirements for different scenarios, but also yields better performances against traditional Seq2Seq models in terms of both metric-based and human evaluations.
Design, Development and Implementation of a Network Intrusion Detection Tool for Air Traffic Management Systems

An Air Traffic Management (ATM) relies on a set of critical systems composed of control centers, sensors, communication means and radio navigation systems. These critical systems may be subject to different attacks and thus compromise their security. Indeed as there is a desire to open this system more and more outward and there is a gap between this world and the interconnected world, threats are increasing. The ATM system have particular characteristics as the fact that it is a very distributed system with a lot of real-time applications using proprietary and/or legacy protocols. Thus, the need to have an efficient Intrusion Detection System (IDS) is primordial in terms of reliability (a false negative rate as low as possible) and relevance (a lowest possible false-positive rate). The development of an IDS combining misuse detection (i.e., defining attack scenarios and finding traces of these scenarios in the traffic.) and anomaly detection (i.e., the construction of a reference model of the behavior of the supervised entity to which we will be able to compare the observed behavior) based on wavelet theory is a promising approach as they are already shown for this type of systems. The detection capability for such complex system could be enhanced using the specific characteristics of its exchanges, use them to enrich its normal signature and reduce the probability of false positive and false negatives. This paper describes the context and the state of the art of the current research direction of the authors with the aim to present the challenges and the future works that the student aims to perform in the next years.
Discrete Functional Analysis Tools for Some Evolution Equations

Abstract We present some discrete functional analysis tools for the proof of convergence of numerical schemes, mainly for equations including diffusion terms such as the Stefan problem or the Navier–Stokes equations in the incompressible and compressible cases. Some of the results covered here have been proved in previous works, coauthored with several coworkers.
Dietary l-arginine Supplementation Alleviates the Intestinal Injury and Modulates the Gut Microbiota in Broiler Chickens Challenged by Clostridium perfringens

Our previous reports suggested that Dietary l-arginine supplementation attenuated gut injury of broiler chickens infected with Clostridium perfringens by enhancing intestinal immune responses, absorption and barrier function, but its effect on the gut microbiome of broiler chickens remains unclear. This experiment aimed at evaluating the effects of Dietary l-arginine supplementation on the gut bacterial community composition and function of broiler chickens challenged with C. perfringens. In total, 105 1-day-old male Arbor Acres broiler chickens were assigned to three groups: Control (CTL), C. perfringens-challenged (CP), and C. perfringens-challenged and fed diet supplemented with 0.3% l-arginine (ARGCP) groups. The challenge led to macroscopic and histomorphological gut lesions, decreased villus height and increased the number of Observed species, Shannon, Chao1 and ACE indices of ileal microbiota, whereas l-arginine addition reversed these changes. Moreover, the three treatments harbored distinct microbial communities (ANOSIM, P < 0.05). At the genus level, 24 taxa (e.g., Nitrosomonas spp., Coxiella spp., Ruegeria spp., and Thauera spp.) were significantly more abundant in CP group than in CTL group (P < 0.05), whereas the levels of 23 genera of them were significantly decreased by l-arginine supplementation (P < 0.05). The abundances of only 3 genera were different between CTL and ARGCP groups (P < 0.05). At the species level, the challenge promoted the relative abundance of Nitrospira sp. enrichment culture clone M1-9, Bradyrhizobium elkanii, Nitrospira bacterium SG8-3, and Pseudomonas veronii, which was reversed by l-arginine supplementation (P < 0.05). Furthermore, the challenge decreased the levels of Lactobacillus gasseri (P < 0.05). Predictive functional profiling of microbial communities by PICRUSt showed that compared with CP group, ARGCP group had enriched pathways relating to membrane transport, replication and repair, translation and nucleotide metabolism and suppressed functions corresponding to amino acid and lipid metabolisms (P < 0.05). The relative abundances of KEGG pathways in l-arginine-fed broilers were almost equal to those of the controls. In conclusion, l-arginine alleviated the gut injury and normalized the ileal microbiota of C. perfringens-challenged chickens to resemble that of unchallenged controls in terms of microbial composition and functionality.
A Distributed Efficient Algorithm for Self-Protection of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely leveraged for military and surveillance applications. Each node in a WSN plays a critical role and it can be targeted for the attackers of the network. Thus, it is required to preserve a certain level of protection for each node while exploiting the nodes for the global goals of the network. The self-protection problem focuses on scenarios that sensor nodes should protect themselves instead of protecting a set of event or objects in order to resist against any types of attacks to the nodes. Choosing a set of proper nodes to maintain self-protection requirements is an NP-Complete problem. In this paper, we devise a distributed learning automaton-based algorithm to select a subset of nodes in the network so that each node is under protection of at least one active node. The pooled simulation results validate the effectiveness of our algorithm in selecting nodes and prove that it acts better than state-of- the-art competing algorithms in term of efficiency by using a small number of nodes to provision the self-protection requirements.