Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving the Multi-class Classification Task in Spiking Neural Network by using Supervised Spiking Learning Rule with a Consistent Competitive Mechanism

In recent years, spiking neural networks (SNNs), a computing model inspired by the brain's ability to code and process information in the time domain with great computational power, has drawn a lot of attention from researchers for learning applications. For training in SNNs, several supervised spiking learning rules have been proposed, however, applying these learning algorithms to real-world problems yet remains an open issue. For this reason, this paper presents a new spiking neural network for the handwritten digit dataset classification problem. Our proposed network is trained by using the spike-based NormAD algorithm with a consistent winner-take-all mechanism. The experiment has shown a promising performance just after one epoch passing over the test dataset.
Evading a Machine Learning-based Intrusion Detection System through Adversarial Perturbations

Machine-learning based Intrusion Detection and Prevention Systems provide significant value to organizations because they can efficiently detect previously unseen variations of known threats, new threats related to known malware or even zero-day malware, unrelated to any other known threats. However, while such systems prove invaluable to security personnel, researchers have observed that data subject to inspection by behavioral analysis can be perturbed in order to evade detection. We investigated the use of adversarial techniques for adapting the communication patterns between botnet malware and control unit in order to evaluate the robustness of an existing Network Behavioral Analysis solution. We implemented a packet parser that let us extract and edit certain properties of network flows and automated an approach for conducting a grey-box testing scheme of Stratosphere Linux IPS. As part of our implementation, we provided several techniques for providing perturbation to network flow parameters, including a Simultaneous Perturbation Stochastic Approximation method, which was able to produce sufficiently perturbed network flow patterns while adhering to an underlying objective function. Our results showed that network flow parameters could indeed be perturbed to ultimately enable evasion of intrusion detection based on the detection models that were used with the Intrusion Detection System. Additionally, we demonstrated that it was possible to combine evading detection with techniques for optimization problems that aimed to minimize the magnitude of perturbation to network flows, effectively enabling adaptive network flow behavior.
4MuLA: A Multitask, Multimodal, and Multilingual Dataset of Music Lyrics and Audio Features

We present a new benchmark dataset of songs with structured information to be applied in various machine learning tasks. The data comes from a platform focused on lyrics information, but contain several other annotations provided by their users. Our dataset, called 4MuLA (Multitask, Multimodal, and Multilingual Music Lyrics and Audio features dataset), includes features extracted from 96,458 songs distributed by 15,310 artists in 76 genres. In particular, our dataset contains latin music genres that are often overlooked in other benchmark datasets. For each track, we make available various acoustic features, extracted tags, and lyrics in English, Portuguese, or Spanish. With these features, researchers can use our dataset for, at least, lyrics-, audio- or multimodal-based genre classification, music and artist similarity, and popularity regression. Moreover, we can perform cross- or multilingual text analysis on lyrics, such as discourse analysis or measuring the differences between emotion transmitted by audio and lyrics.
Modifications using Circular Shift for a Better Bloom Filter

The Bloom filter is a hash-based data structure that facilitates membership querying. Computation speed of Bloom filter is affected by hash functions that produce hash outputs. Basically, two operations: 'add' and 'query', consists of the Bloom filter. Previous researches have shown advanced computation speed of Bloom filter since the standard Bloom Filter is published. For example, Double Hash Bloom filter, Single Hash Bloom filter, etc. We propose a system that uses less computation than previous works with similar false positive and which is implemented by using a circular bit shift. This method was implemented with faster calculation speed, compared with previous works. Furthermore, experiments which were compared with previous researches and standard Bloom filter. Therefore, we demonstrate that the proposed system computes faster than previous studies with similar false positive rate.
The impact of first recommendations based on exploration or exploitation approaches in recommender systems' learning

The current advances have recognized the online recommendation task as a Multi-Armed Bandit (MAB) problem and proposed several algorithms by combining it with Collaborative Filtering (CF). In general, their effectiveness is related to their practical ability to handle the trade-off between selecting the most relevant item (exploitation) or choose an unexpected item to add more knowledge to the system (exploration). However, we observed that a naive failure has been introduced in the main CF bandit models, which have intrinsically reduced itself to pure exploitation of the information known a prior by the system (e.g., items' popularity) or a pure exploration of the items in the first user's interaction. Conversely, we hypothesize this trade-off should be considered since the first user's interactions to improve the system's effectiveness. Thus, we propose a new methodology to measure the impact of this problem. This methodology has two main phases. In phase 1, traditional non-personalized methods resemble pure-exploitation and pure-exploration to feed the learning of all MAB models. Then, analyzing the MAB effectiveness in phase 2, we may determine the best approach to be considered in the first user's interactions. Moreover, we also propose and add a new strategy that combines exploration and exploitation in phase 1. Indeed, our experimental results show that the combination of exp-exp at first user's interactions improves the system's effectiveness without adding more effort to users.
A Crowdsourcing Method for Sign Segmentation in Brazilian Sign Language Videos

Like the spoken languages, sign languages are not universal and vary in different countries. LIBRAS (Brazilian Sign Language) is the second official language of Brazil and it is the language adopted by Brazilian Deaf's community to communicate. The signs of LIBRAS are composed of hand configurations, facial expressions and are affected by space and intensity modifiers, which makes their recognition more complicated than the simple identification of hand signs. The signs are arranged, according to a grammar, respecting form phrases, clauses, and sentences like any other spoken or sign language. The automatic machine translation of a sign language typically includes an initial phase for detecting sign boundaries. In this paper, we apply a crowdsourcing method to identifying signs boundaries present in pre-recorded videos those features LIBRAS interpreters. The limits or boundaries of the signs in the videos were established from the processing of contributions from workers from different countries, who have supposedly never heard of LIBRAS nor any other sign languages. To evaluate the segmentation process, we compared the sign boundaries identified by the crowd with the ground truth provided by a team of LIBRAS experts, who also assessed the quality of the delimitation of the identified signs. Our analysis showed that our crowdsourcing method was able to get 93.75% of the sign boundaries successfully.
WikiGaze: Gaze-based Personalized Summarization of Wikipedia Reading Session

Wikipedia is an open-content encyclopedia that receives billions of page views per month. It has been observed that in a single reading session, Wikipedia users visit multiple articles. To reduce the problems of overload and loss of information, there has been a growing interest in the research community to develop new approaches to present the only necessary information to the users. Automatically generation of personalized summaries is a proven remedy for the information overload problem. In this paper, we propose a technique to generate personalized summaries for Wikipedia articles by analyzing the reading patterns of users. To perform reading pattern analysis, we track eye gaze during the article reading session. Eye gaze analysis helps in identifying the attention distribution of a reader over an article. We extend the proposed approach to generate a summary for multiple articles visited during a user's Wikipedia reading session. We capture a dataset representing the reading pattern of Wikipedia users. We make this dataset publicly available for research community1.
A study of the estimation of Stroke ASPECTS Scores based on NCCT brain scan images using deep learning

Stroke is a high-risk disease causing death, permanent disability in patients, and is the leading cause of death worldwide. Stroke can be quickly examined for disease through CT, an imaging diagnostic tool. However, the diagnosis of Ischemic Stroke using a CT image has the advantage of being able to take a picture in a short time with less restrictions in place, but there is a problem that diagnosis through an image is very difficult. In this paper, we propose a deep learning system capable of learning and classifying ischemic stroke diseases that are small datasets and difficult to learn about image data. We propose a preprocessing algorithm optimized for ischemic stroke based on Non-Contrast CT data in Middle Cerebral Artery (MCA) area.
Developing IoT Applications Using Spiking Neural Networks Framework

With the recent progress in SNN(Spiking Neural Networks), IoT applications require more intelligent data processing that can operate on neuromorphic hardware. However, as there is no development environment where SNN frameworks can be applied to existing IoT applications, it is difficult for IoT developers to utilize neuromorphic hardware. In this paper, we present a new development framework of IoT applications using SNN as a machine learning solution, called NA-Designer. It has GUI based neural network editor to quickly generate SNN components that can be run on various neuromorphic hardware. In particular, this component is designed to interwork with other IoT framework such as Node-RED. We implement NA-Designer and core neural networks libraries and measure its performance. Our framework can be used to develop Edge computing applications that utilize neuromorphic hardware to reduce energy consumption and latency.
Modeling User Loyalty for Korean Political YouTube Channels

In this paper, we propose a model based on user loyalty to understand user behavior. User loyalty is defined based on three factors, which are coverage, duration, and enthusiasm. In particular, we focus on a case study of user loyalty involving South Korean politics in online social networks. Our purpose is to understand user behaviors to help governments and politicians in decision-making. We deploy a web-based system (called TubePlunger) to collect information from Youtube videos and corresponding comments. Our system collects approximately 3M comments of more than 300K users from six channels for the initial dataset and 23 channels for testing the model. Firstly, we separate six channels into two sides: left-wing and right-wing. Based on their comments information in videos of the channel, we recognize the loyalty distribution of users who engaged in online political platforms is sharply polarized. In this step, we only consider the usernames instead of video and comment contents. Secondly, we apply the user loyalty model not only to define which channels of 23 testing channels belong to left-wing or right-wing but also to present the user loyalty distribution. The experimental results show the absolute consistency in user loyalty distribution with left-wing and right-wing in all three mentioned factors.
EDAroid: An Efficient Dynamic Analysis Tool for Android Applications

A dynamic Android application(app) analysis tool is as known as useful for detecting errors or vulnerabilities in the Android apps at runtime by observing the internal behavior changes like specific method calls which can make malicious behaviors. However, existing Android app analysis tools not only cannot extract all methods used in the Android app but also cannot extract code blocks that can check the branch condition of the program. In this paper, we propose EDAroid, an efficient dynamic analysis tool for Android apps. The proposed EDAroid can dynamically extract not only the core system's methods but also the user-defined methods in Android apps. The proposed EDAroid can also represent the extracted methods and code blocks in Android apps in a graph. We also evaluate the functionality and performance of the proposed EDAroid and the evaluation results show that the proposed EDAroid is more efficient than the existing Android app analysis tool.
Triangulating Multi-Method Assessments of Parental Support for Early Math Skills

Past research has examined parental support for math during early childhood using parent-report surveys and observational measures of math talk. However, since most studies only present findings from one of these methods, the construct (parental support for early math) and the method are inextricably linked, and we know little about whether these methods provide similar or unique information about children’s exposure to math concepts. This study directly addresses the mono-operation bias operating in past research by collecting and comparing multiple measures of support for number and spatial skills, including math talk during semi-structured observations of parent–child interactions, parent reports on a home math activities questionnaire, and time diaries. Findings from 128 parents of 4-year-old children reveal substantial within-measure variability across all three data sources in the frequency of number and spatial activities and the type and content of parent talk about number and spatial concepts. Convergence in parental math support measures was evident among parent reports from the questionnaire and time diaries, such that scale composites about monthly number activities were related to number activities on the previous work day, and monthly spatial activities were correlated with spatial activities the prior non-work days. However, few parent report measures from the survey or time diary were significantly correlated with observed quantity or type of math talk in the semi-structured observations. Future research implications of these findings are discussed.
Teachers' Accuracy in Estimating Social Inclusion of Students With and Without Special Educational Needs

It is unclear to what extent teachers can accurately assess the social inclusion of their students with and without SEN. The study aims to shed light on these desiderata. Students (N = 1.644) with SEN (learning, behavior, and language problems) and without SEN and their teachers (N = 79) participated in the study. Sociometric peer nominations, students' self-perceived social inclusion, and teachers' assessments regarding students' social inclusion and self-perceived social inclusion were administered. The results suggest that teachers are moderately accurate in identifying social acceptance and social rejection, while accuracy is low when assessing students' self-perceived social inclusion. That said, rating accuracy varied strongly between teachers, ranging from no agreement to a perfect concordance. Teachers seem to be more accurate in estimating the social acceptance of students with learning problems. The results emphasize the importance of differentiating between various social inclusion criteria (i.e., students' self-report vs. peer nominations) and accounting for inter-individual differences in teachers' rating accuracy.
Crystal Structure Prediction of Binary Alloys via Deep Potential

Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg12Al8 shows excellent ductility and Mg5Al27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.
A New Dichoptic Training Strategy Leads to Better Cooperation Between the Two Eyes in Amblyopia

Recent clinical trials failed to endorse dichoptic training for amblyopia treatment. Here, we proposed an alternative training strategy that focused on reducing signal threshold contrast in the amblyopic eye under a constant and high noise contrast in the fellow eye (HNC), and compared it to a typical dichoptic strategy that aimed at increasing the tolerable noise contrast in the fellow eye (i.e., TNC strategy). We recruited 16 patients with amblyopia and divided them into two groups. Eight patients in Group 1 received the HNC training, while the other eight patients in Group 2 performed the TNC training first (Phase 1) and then crossed over to the HNC training (Phase 2). We measured contrast sensitivity functions (CSFs) separately in the amblyopic and fellow eyes when the untested eye viewed mean luminance (monocularly unmasked) or noise stimuli (dichoptically masked) before and after training at a particular frequency. The area under the log contrast sensitivity function (AULCSF) of masked and unmasked conditions, and dichoptic gain (the ratio of AULCSF of masked to unmasked condition) were calculated for each eye. We found that both dichoptic training paradigms substantially improved masked CSF, dichoptic gain, and visual acuity in the amblyopic eye. As opposed to the TNC paradigm, the HNC training produced stronger effects on masked CSFs, stereoacuity, dichoptic gain, and visual acuity in the amblyopic eye. Interestingly, the second-phase HNC training in Group 2 also induced further improvement in the masked contrast sensitivity and AULCSF in the amblyopic eye. We concluded that the HNC training strategy was more effective than the TNC training paradigm. Future design for dichoptic training should not only focus on increasing the tolerable noise contrast in the fellow eye but should also “nurture” the amblyopic eye under normal binocular viewing conditions and sustained interocular suppression.
Chronic Atrazine Exposure Beginning Prenatally Impacts Liver Function and Sperm Concentration With Multi-Generational Consequences in Mice

Atrazine is a commonly used herbicide frequently detected in waterways and drinking water around the world. Worryingly, atrazine is an endocrine and metabolic disruptor but there is a lack of research regarding the effects of long-term exposure beginning in utero. In this study we investigated how chronic exposure to atrazine (5 mg/kg bw/day) in drinking water from E9.5 until 12 or 26 weeks of age affected metabolic and reproductive characteristics in male mice. We then examined whether mating these males to unexposed females altered in vitro embryo characteristics. Atrazine exposure caused a decrease in liver weight and changes in both liver and testis gene expression, specifically in genes involved in lipid uptake and fatty acid metabolism in the liver, as well as androgen conversion in the testis. Notably, atrazine exposure decreased epididymal sperm concentration and subsequent embryo cell numbers generated from the 12-week cohort males. Collectively, these data suggest that atrazine exposure, beginning prenatally, affects both metabolic and reproductive characteristics, and highlights the importance of assessing atrazine effects at different life stages and over multiple generations. The continued widespread use of atrazine warrants further studies, as it is essential to understand the health risks for all species, including humans.
Cobalt Resistance via Detoxification and Mineralization in the Iron-Reducing Bacterium Geobacter sulfurreducens

Bacteria in the genus Geobacter thrive in iron- and manganese-rich environments where the divalent cobalt cation (CoII) accumulates to potentially toxic concentrations. Consistent with selective pressure from environmental exposure, the model laboratory representative Geobacter sulfurreducens grew with CoCl2 concentrations (1 mM) typically used to enrich for metal-resistant bacteria from contaminated sites. We reconstructed from genomic data canonical pathways for CoII import and assimilation into cofactors (cobamides) that support the growth of numerous syntrophic partners. We also identified several metal efflux pumps, including one that was specifically upregulated by CoII. Cells acclimated to metal stress by downregulating non-essential proteins with metals and thiol groups that CoII preferentially targets. They also activated sensory and regulatory proteins involved in detoxification as well as pathways for protein and DNA repair. In addition, G. sulfurreducens upregulated respiratory chains that could have contributed to the reductive mineralization of the metal on the cell surface. Transcriptomic evidence also revealed pathways for cell envelope modification that increased metal resistance and promoted cell-cell aggregation and biofilm formation in stationary phase. These complex adaptive responses confer on Geobacter a competitive advantage for growth in metal-rich environments that are essential to the sustainability of cobamide-dependent microbiomes and the sequestration of the metal in hitherto unknown biomineralization reactions.