Miracle-dz/UniFAR-FTUs huggingface dataset:
Aggregated-Proofs Based Privacy-Preserving Authentication for V2G Networks in the Smart Grid

Vehicle-to-grid (V2G) as an essential network component of smart grid, provides services by periodically collecting the charging status of a battery vehicle (BV). A BV is normally associated with a default interest group (e.g., power grid operator). When the BV accesses its default charging or communication point, it works in the home mode. The BV may move around and temporarily access other aggregators, and then it works in the visiting mode. In this paper, we first identify that, for an aggregator, BVs have different security challenges when they work in different modes. Then, we propose an aggregated-proofs based privacy-preserving authentication scheme (AP3A) to achieve simultaneous identification and secure identification for different working mode BVs. In AP3A, BVs are differentiated into either home or visiting mode, and multiple BVs can be simultaneously authenticated by an aggregator to conserve communication resources. In addition, the aggregated pseudo-status variation is presented to realize that multiple BVs' power status can be collected as a whole without revealing any individual privacy. We perform comprehensive analysis on the proposed scheme, including attack analysis, security analysis, and performance analysis. It is shown that AP3A can resist major attacks for security protection and privacy preservation, and can be an efficient authentication approach for V2G networks.
Multi-layer GCNs for Travel Text Aspect-level Sentiment Classification

Aspect category based travel text sentiment analysis is to extract the polarity and perspectives of sentimental travel experiences and feelings expressed by tourists on social media or tourism applications, in order to understand their satisfaction with scenic spots, tourism products and services. This paper proposes a tourism text category sentiment classification model based on hierarchical graph convolution to address the inherent relationship between the two subtasks of category detection and sentiment analysis that cannot be clearly modeled in traditional models. In this model, multiple aspect categories in a sentence are first detected, and then text sentiment features are obtained through two different graph convolutional networks. Then, sentiment information for each category is obtained through pooling output. Finally, the final category sentiment is obtained by combining the category detection results. The experimental results show that the proposed method outperforms baseline models such as Add-BERT, H-BERT, etc. in terms of accuracy, recall, and F1 value evaluation indicators.
In Vitro Antimicrobial Susceptibility of Staphylococcus pseudintermedius Isolates of Human and Animal Origin

ABSTRACT MIC results for 115 Staphylococcus intermedius group isolates are presented. Of these, 33% were methicillin resistant, among which 51.4% were susceptible to doxycycline, 29.7% to clindamycin, and 21.6% to trimethoprim-sulfamethoxazole. All of the isolates were susceptible to ceftaroline, daptomycin, linezolid, nitrofurantoin, quinupristin-dalfopristin, rifampin, tigecycline, and vancomycin. Of all the isolates, 82.6%, 67.8%, and 23.5% were susceptible to ciprofloxacin, erythromycin, and penicillin, respectively. No isolates harbored mupA or qacA/B genes, which suggested a lack of resistance to mupirocin or chlorhexidine.
Impact of estrogen receptor expression and other clinicopathologic features on tamoxifen use in ductal carcinoma in situ

Recent data have demonstrated that benefit from adjuvant tamoxifen therapy for patients with ductal carcinoma in situ (DCIS) is limited to estrogen receptor (ER)‐positive lesions. The objective of the current study was to correlate clinicopathologic features of DCIS with ER expression and the impact of this information on tamoxifen counseling.
High Rate of Virological Response to Peginterferon α-2a–Ribavirin Among Non-Cirrhotic Iranian Hemophilia Patients With Chronic Hepatitis C

Background Hepatitis C is a major reason of morbidity and mortality among hemophilia patients. Although combination therapy with peginterferon (peg-INF) and ribavirin is considered as standard treatment for chronic hepatitis C (CHC), but more evidence of the efficacy and safety is needed. Objectives In this study, efficacy and tolerability of combination therapy with peginterferon α-2a–ribavirin was investigated among hemophilia HCV infected patients. Patients and Materials In a quasi-experimental, 45 naive hemophilia patients with chronic HCV received 180 mg of pegylated interferon (Pegasys) by subcutaneous injection weekly plus an oral dose of 800-1200 µg ribavirin daily according to body weight. The treatment continued 48 weeks in patients with genotype one and 24 weeks in those with genotype 3. Sustained virological response (SVR) was considered as efficacy of treatment. Result Forty three patients (95.6%) reached to end of treatment response (ETR); only two (4.4%) patients did not respond and were discontinued from treatment. None of 43 patients relapsed. SVR obtained in 43 of 45 patients (95.6%), in multivariate logistic regression model, third month’s treatment WBC (WBC > 2000) remained the only significant predictor of SVR. Regimen dose reduced in three patients; two of those because of ALT increasing and other one for his retinal bleeding. In repeated measurement analysis, alanine aminotransferase (ALT) and hemoglobin (Hb) decreased significantly during treatment, but reduction of platelet (PLT) was not significant. Conclusons Results show high efficacy and safety of combination therapy of Peg-IFN-α 2a plus ribavirin among hemophiliacs with chronic hepatitis C.
Eai Endorsed Transactions on Collaborative Computing Wayfinding and Navigation for People with Disabilities Using Social Navigation Networks

To achieve safe and independent mobility, people usually depend on published information, prior experience, the knowledge of others, and/or technology to navigate unfamiliar outdoor and indoor environments. Today, due to advances in various technologies, wayfinding and navigation systems and services are commonplace and are accessible on desktop, laptop, and mobile devices. However, despite their popularity and widespread use, current wayfinding and navigation solutions often fail to address the needs of people with disabilities (PWDs). We argue that these shortcomings are primarily due to the ubiquity of the compute-centric approach adopted in these systems and services, where they do not benefit from the experience-centric approach. We propose that following a hybrid approach of combining experience-centric and compute-centric methods will overcome the shortcomings of current wayfinding and navigation solutions for PWDs. permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
A GCN-based Model for Recommendation Using Local Differential Privacy Method

Differential privacy (DP) aims to protect user privacy in the age of ubiquitous data collection through inserting perturbations and achieves great success in the area of recommender systems. However, current recommendation models based on differential privacy treat all users equally, but some users want stronger privacy-preserving while some users pay less attention on privacy and want more accurate recommendations. To overcome the above challenge, this paper proposes a new security recommendation model based on graph convolutional network. It allows the users to decide the level of privacy-preserving by them-selves. The proposed model utilizes a personalized-oriented local differential privacy during collecting user data to protect user data. Then a clustering denoising module rebuilds the connections between users and items by deleting unreliable feedback. In this way, users can adjust their level of privacy-preserving locally and meanwhile, the server can still provide accurate personalized recommendations to those who prefer better recommendation performance. This work conducts comprehensive experiments on several real-world benchmark datasets and the experimental results validate the effectiveness of the proposed model.
Probability Theory and Intrinsically Non-Stochastic Phenomena in AI: How to Reject a Metaphysical Issue.

The paper discusses some arguments that have been raised against the use of probability theory in artificial intelligence. We identify two main elements in these arguments: The first is related to practical issues on the use of probability theory, while the second is clearly of philosophical nature and concerns the contraposition stochastic/deterministic and the status of the stochastic object. We call these two el- ements technical and methodological, respectively. The methodological element has not been addressed by the AI community, and our paper is intended precisely to fill this gap. In the methodological element we spot the misleading assumption that since probability theory supposes that data are observations of a stochastic object, a proba- bilistic model is adequate only for dealing with intrinsically stochastic phenomena. In the paper, on the basis of ideas borrowed from the empiricist and instrumentalist epistemology, we point out that the central hypothesis formulated when adopting a probabilistic model, namely the existence of the stochastic object, is simply a working hypothesis. It follows that a probabilistic model is to be evaluated only on the basis of its predicting power, leaving aside any further metaphysical consideration on the truth of the underlying working hypothesis.
Activation of the RAS Pathway Is Predictive for a Chemosensitive Phenotype of Acute Myelogenous Leukemia Blasts

Purpose: Activation of the RAS pathway plays a major role in cancer cells. In acute myeloid leukemia (AML), mutations of the RAS genes cause an intrinsic activation of this pathway. Until now, clinical studies could not find clear association of RAS mutations with the clinical outcome after AML therapy. This could be due to alternative initiating events for activation of the RAS pathway like constitutive tyrosine kinase activation or mutations in Ras-regulating genes. Experimental Design: In total, 191 AML patients (126 as training population and 65 as test population) were studied for Ras activity with a glutathione S-transferase pull-down assay using Raf binding of activated Ras. Results: AML samples showed a wide range of Ras activity values, which was in contrast to normal bone marrow donors who showed no or very limited Ras activity. Using a Ras binding score based on semiquantitative Western blotting, we defined patients with strong Ras activity and compared Ras activity with RAS mutation. Surprisingly, only a minority of RAS mutated AML samples (22.2%) showed strong Ras activity, whereas 25 patients presented strong Ras activity in the absence of RAS mutations. Clinical outcome did not show differences according to RAS mutations. In contrast, Ras activity predicted for a high response rate (P < 0.05) and proved to be an independent factor for overall survival rate (P < 0.05) in younger AML patients receiving high-dose 1-β-d-arabinofuranosylcytosine as induction therapy. Conclusion: The data highlight the role for alternative pathways of Ras activation without RAS mutations. Intrinsically activated Ras seems to increase sensitivity of the AML blast to high-dose 1-β-d-arabinofuranosylcytosine therapy.
A Scalable Sequential Pattern Mining Algorithm

Sequential pattern mining is a technique used to discoverfrequentsubsequencesaspatternsina sequencedatabase. Many excellent sequential pattern mining approaches such as GSP, SPADE, and PrefixSpan have been proposed. However, the existing approaches still encounter problems when the set of all different items in a sequence database is large. Scalability with respect to not only the varying size of a sequence database, but also the varying size of the set of all different items is crucial for many applications. In this paper we address the subject of mining frequent sequential patterns in the large sequence database with numerous kinds of items. An effective algorithm for the purpose, called SSPM (ScalableSequentialPattern Mining), is proposed. SSPM is characterized by its fast convergence, meaning that the search space will shrink quickly as mining operation proceeds. SSPM does not limit the number of different items in a sequence database, and thus it has better scalability, and can be applicable to a large scale of sequence databases. The experimental and analytical results demonstrated that SSPM is effective and is faster than conventional algorithms in the above stated cases.
Assessing two large area burnt area products across Australian Southern Forests

ABSTRACT Burnt area is a critical parameter for estimating emissions of greenhouse gases associated with biomass burning. Several burnt area products (BAPs) derived from Earth Observation satellites/sensors have been released; these are based on different spatial resolutions and derived using different methodologies so that accuracies can vary amongst them. This study validates a global (MODIS) and a national (AVHRR) BAP across Australian southern forests using two reference datasets: state fire histories (SFHs) from 2000 to 2013 and a forest cover map derived through high resolution air photo interpretation (API). The spatial and temporal agreement between fires in the BAPs and reference SFH were analysed based on 2610 sample points representative of Australian southern forest types (successful detection was evaluated according to fire type: planned burn vs. wildfire, size of fire, and land tenure). Results show that both BAPs were most successful when identifying large wildfires (>5000 ha). Overall accuracy for AVHRR and MODIS was 73.9% and 62.5%, respectively. When compared to the API derived forest cover map as reference dataset, both products achieved higher overall accuracies (94.1% for AVHRR and 87.1% for MODIS); an expected result given that the fires detected in this dataset are known to be observable using Earth observation data. But regardless of reference dataset, the AVHRR BAP which is tailored to Australian conditions achieved better results than the MODIS global BAP. Also, the AVHRR archive in Australia goes back to 1988, which is an important consideration for calculating wildfire history for greenhouse gas accounting.
Musical Instrument Detection Detecting instrumentation in polyphonic musical signals on a frame-by-frame basis

Knowledge of the instrumentation of a musical signal at any given time could be useful for major audio signal processing problems such as sound source separation and automated music transcription. Knowing which instruments are playing is a first step toward more intelligently designed solutions to these very important and largely unsolved challenges. So, in this paper, we attempt the problem of identifying the instrumentation of a musical signal at any given time using several machine learning techniques (logistic regression, K-NN, SVM). We approached the problem as a series of separate binary classifications (as opposed to a multivariate problem) so that we could mix and match the best algorithm for each instrument to create the best overall classifier. 2 Data Data collection and labeling in audio research can be extremely problematic. Hand labeling audio frames (of lengths of 20 ms) is very difficult and time consuming. So, in order to expediate this process, we chose to artificially manufacture our polyphonic music by combining solo recordings of each instrument. We chose to investigate 3 combinations of instruments: We collected 4 samples each from 13 different recordings for each of the five instruments (leading to 52 total signals for each instrument). The samples were each between 1 and 6 seconds in length, and consisted only of the specified instrument playing in solo (no silence). Then, to create one of the combinations above, a random signal for each instrument were all combined randomly. In this way, we created 52 total signals for each instrumental combination. The final 12 signals of these 52 were created from recordings completely independent of those for the first 40, giving our training (the first 40) and test (the final 12) data. We used examples from multiple recordings in order to create a more robust system. An instrument will have many unique features on a given recording. Every individual instrument has a unique character (due to materials. construction process, body shape, age, etc.), and every individual performer creates a different sound with the instrument. There are also many different playing techniques, and then microphone choice and positioning as well as digital effects and equalization will all create significant variations between recordings. So, in order to effectively analyze an arbitrary recording, we need to train our classifiers with multiple examples. The test data was created from recordings independent of the training data in order to simulate the real-life situation of encountering …
Transcanalicular Laser-Assisted Dacryocystorhinostomy With Endonasal Augmentation in Primary Nasolacrimal Duct Obstruction: Our Experience.

PURPOSE To evaluate and compare the success rate of transcanalicular laser-assisted dacryocystorhinostomy with endonasal augmentation, with and without intubation, in patients suffering from primary acquired nasolacrimal duct obstruction, at 1 year of follow up. METHODS A prospective, randomized interventional pilot study was conducted at a tertiary care center, in accordance with the guidelines of Declaration of Helsinki. Sixty eyes of 60 adult patients with primary acquired nasolacrimal duct obstruction were included. The participants were divided randomly into 2 equal groups (A and B-without and with bicanalicular intubation, respectively). An osteotomy was first created using 980 nm diode laser (set at 8W continuous mode) transcanalicularly and then enlarged intranasally using Blakesley's nasal forceps, followed by bicanalicular silicon intubation in group B patients. The tubes were removed at the end of 8 weeks. The ostium size was assessed endoscopically at 8 weeks and again at the end of follow up, at 1 year. A successful outcome was defined in terms of ostium patency at the end of 1 year. The results were analyzed at the end of a follow up of 1 year, using various statistical tests (p < 0.05). RESULTS The mean age of the patients was 35.3 ± 15.89 years, with 23 male and 37 female patients, the 2 groups having a similar male:female ratio. An overall success rate of 90% was achieved at the end of 1 year with no statistically significant difference between the groups. Postoperative complications like tube displacement and punctal, canalicular injury were more in the intubated group. The average osteotomy size was 8.06 ± 5.4 mm at the end of 1 year. CONCLUSIONS Transcanalicular laser-assisted dacryocysto rhinostomy, with endonasal augmentation, is a scarless, effective, daycare procedure, for treatment of primary acquired nasolacrimal duct obstruction with no additional advantage offered by silicone intubation.
Local dynamics of gap-junction-coupled interneuron networks

Interneurons coupled by both electrical gap-junctions (GJs) and chemical GABAergic synapses are major components of forebrain networks. However, their contributions to the generation of specific activity patterns, and their overall contributions to network function, remain poorly understood. Here we demonstrate, using computational methods, that the topological properties of interneuron networks can elicit a wide range of activity dynamics, and either prevent or permit local pattern formation. We systematically varied the topology of GJ and inhibitory chemical synapses within simulated networks, by changing connection types from local to random, and changing the total number of connections. As previously observed we found that randomly coupled GJs lead to globally synchronous activity. In contrast, we found that local GJ connectivity may govern the formation of highly spatially heterogeneous activity states. These states are inherently temporally unstable when the input is uniformly random, but can rapidly stabilize when the network detects correlations or asymmetries in the inputs. We show a correspondence between this feature of network activity and experimental observations of transient stabilization of striatal fast-spiking interneurons (FSIs), in electrophysiological recordings from rats performing a simple decision-making task. We suggest that local GJ coupling enables an active search-and-select function of striatal FSIs, which contributes to the overall role of cortical-basal ganglia circuits in decision-making.
Membrane-Mediated Interaction between Strongly Anisotropic Protein Scaffolds

Specialized proteins serve as scaffolds sculpting strongly curved membranes of intracellular organelles. Effective membrane shaping requires segregation of these proteins into domains and is, therefore, critically dependent on the protein-protein interaction. Interactions mediated by membrane elastic deformations have been extensively analyzed within approximations of large inter-protein distances, small extents of the protein-mediated membrane bending and small deviations of the protein shapes from isotropic spherical segments. At the same time, important classes of the realistic membrane-shaping proteins have strongly elongated shapes with large and highly anisotropic curvature. Here we investigated, computationally, the membrane mediated interaction between proteins or protein oligomers representing membrane scaffolds with strongly anisotropic curvature, and addressed, quantitatively, a specific case of the scaffold geometrical parameters characterizing BAR domains, which are crucial for membrane shaping in endocytosis. In addition to the previously analyzed contributions to the interaction, we considered a repulsive force stemming from the entropy of the scaffold orientation. We computed this interaction to be of the same order of magnitude as the well-known attractive force related to the entropy of membrane undulations. We demonstrated the scaffold shape anisotropy to cause a mutual aligning of the scaffolds and to generate a strong attractive interaction bringing the scaffolds close to each other to equilibrium distances much smaller than the scaffold size. We computed the energy of interaction between scaffolds of a realistic geometry to constitute tens of kBT, which guarantees a robust segregation of the scaffolds into domains.
Adaptive Graph Learning for Multimodal Conversational Emotion Detection

Multimodal Emotion Recognition in Conversations (ERC) aims to identify the emotions conveyed by each utterance in a conversational video. Current efforts encounter challenges in balancing intra- and inter-speaker context dependencies when tackling intra-modal interactions. This balance is vital as it encompasses modeling self-dependency (emotional inertia) where speakers' own emotions affect them and modeling interpersonal dependencies (empathy) where counterparts' emotions influence a speaker. Furthermore, challenges arise in addressing cross-modal interactions that involve content with conflicting emotions across different modalities. To address this issue, we introduce an adaptive interactive graph network (IGN) called AdaIGN that employs the Gumbel Softmax trick to adaptively select nodes and edges, enhancing intra- and cross-modal interactions. Unlike undirected graphs, we use a directed IGN to prevent future utterances from impacting the current one. Next, we propose Node- and Edge-level Selection Policies (NESP) to guide node and edge selection, along with a Graph-Level Selection Policy (GSP) to integrate the utterance representation from original IGN and NESP-enhanced IGN. Moreover, we design a task-specific loss function that prioritizes text modality and intra-speaker context selection. To reduce computational complexity, we use pre-defined pseudo labels through self-supervised methods to mask unnecessary utterance nodes for selection. Experimental results show that AdaIGN outperforms state-of-the-art methods on two popular datasets. Our code will be available at https://github.com/TuGengs/AdaIGN.