Miracle-dz/UniFAR-FTUs huggingface dataset:
Third-Party Trackers in COVID-19 Mobile Applications Can Enable Privacy Leaks

Since 2019, the world has been facing an unprecedented pandemic. Governments and various organizations have made great efforts to develop applications that aid in controlling the spread of COVID-19 and improve people’s quality of life. However, our research has discovered that many of these applications contain third-party libraries, including trackers, which can impact user privacy. Out of the 595 applications we reviewed, 402 included at least one tracker, and sensitive information was being transferred to these tracker servers. From a privacy perspective, including trackers in sensitive applications must be reconsidered, primarily when governments sponsor them.
A Minimum Offloading Capacity Guarantee Strategy for RIS-assisted MEC System

In this paper, we propose a novel offloading scheme for edge computing, termed Minimum Offloading Capacity Guarantee (MOCG) strategy, specifically, we investigate how to ensure that devices with poor communication channels in Reconfigurable Intelligent Surface (RIS)-assisted Mobile Edge Computing (MEC) system can effectively utilize edge servers to complete their computational tasks. RIS has been shown to improve the computational performance of MEC system, however, in existing studies, in order to maximize the overall system computational performance, we have to give less resources to those devices with poor communication quality, which obviously leads to the inability of these devices to complete the predefined tasks. To solve this problem, we propose a new resource allocation strategy, specifically, nodes that are unable to complete their computational tasks will search and request those neighboring nodes with good channels and apply to use them as relays to complete the offloading of computational tasks to the edge servers. The main advantage of the proposed strategy is that it takes into account the needs of those nodes who have difficulty communicating directly with the RIS without affecting the overall computational efficiency of the system, and is more suitable for practical application scenarios. The numerical evaluation confirms the effectiveness of the proposed strategy and analyzes the impact of different parameters on the system performance.
FlexConnect: Mobile Computational Offloading

Edge computing is increasing its popularity also in non-computing specific environments. In particular, Makerspaces are facing the issue of providing cognitive-edge to cloud computational power to their users, for running their 3D modeling and optimizations. However, while the edge-to-cloud offloading might be a traditional issue in edge computing systems, some countries require Makerspaces to provide their service only during operational hours, and therefore to switch off their machines, including their servers, during the night. This calls for the need for flexible computational offloading, enabling to checkpoint of current tasks and restoring them during opening hours. This work examines the feasibility of designing and implementing such a system, to facilitate computational offloading with checkpointing to increase the availability of computational power. We propose and prototype the implementation for a system that enables a provider to provide computational power through their edge with a checkpoint and restore feature to prevent loss of progress while the system works on a computation. A client-side application was designed and implemented to enable users to access the computational offloading service from a provider at a given location directly from their smartphone. Results show that it is possible to implement a continuous edge-to-cloud computational platform to enable full checkpoint and restore of computational tasks while providing a good quality of service and using a controlled amount of energy.
Reconnaissance d'états émotionnels par analyse visuelle du visage et apprentissage machine. (Recognition of emotional states by visual facial analysis and machine learning)

Dans un contexte presentiel, un acte de communication comprend des expressions orales et emotionnelles. A partir de l’observation, du diagnostic et de l’identification de l’etat emotionnel d’un individu, son interlocuteur pourra entreprendre des actions qui influenceraient la qualite de la communication. A cet egard, nous pensons ameliorer la maniere dont les individus percoivent leurs echanges en proposant d’enrichir la CEMO (communication ecrite mediatisee par ordinateur) par des emotions ressenties par les collaborateurs. Pour ce faire, nous proposons d’integrer un systeme de reconnaissance, en temps reel, des emotions (joie, peur, surprise, colere, degout, tristesse, neutralite) a la plate-forme pedagogique “Moodle”, a partir de l’analyse des expressions faciales de l’apprenant a distance lors des activites collaboratives. La reconnaissance des expressions faciales se fait en trois etapes. Tout d’abord, le visage et ses composants (sourcils, nez, bouche, yeux) sont detectes a partir de la configuration de points caracteristiques. Deuxiemement, une combinaison de descripteurs heterogenes est utilisee pour extraire les traits caracteristiques du visage. Finalement, un classifieur est applique pour classer ces caracteristiques en six emotions predefinies ainsi que l’etat neutre. Les performances du systeme propose seront evaluees sur des bases publiques d’expressions faciales posees et spontanees telles que Cohn-Kanade (CK), Karolinska Directed Emotional Faces (KDEF) et Facial Expressions and Emotion Database (FEED).
Adverse Drug Reaction from Social Media by Cascade Feature Extraction and Meta-Learner

Adverse drug reaction (ADR) is one of the most important public health problems. Social media has encouraged more patients to share their medication experiences and has become a major source of unreported ADRs in the testing industry. However, since a large number of user posts do not mention any ADRs, it is difficult to accurately detect the presence of ADRs in each user post. An ADR extraction (ADRE) method from social media based on cascade feature extraction and meta-learner (CFEML) is proposed, including text pre-processing, word embedding, feature extraction and feature cascade, and classification by meta-learner. The experimental results show that ADRE based on CFEML can achieve good performance in the classification task of confirming the relationship between the "ADR" pairs in the description of ADRs, and the final model achieves accuracy of 85.52%. The results validate that the proposed method can help evaluators quickly obtain all possible ADRs from social media, and can be used to provide more research means for drug safety evaluation studies.
ADARES: A Single-cell Classification Model Based on Adversarial Data Augmentation and Residual Networks

Single-cell RNA sequencing (scRNA-seq) helps researchers identify the expression status of genes from the bulk level to the single-cell level, where the recognition of cell types plays an important role in this area. However, due to the heterogeneity and complexity of single-cell data, there are some limitations to the performance of traditional classification methods, such as the neglect of higher-order expression between cells and the subpar classification results on large-scale datasets, etc. In this paper, we propose a novel deep learning model combining residual network and adversarial data augmentation for cell types classification (ADARES). Specifically, we first construct a gene expression matrix and a gene adjacency matrix through gene filtering. Next, these two matrices are fed into the residual network to extract the embedding of nodes and a softmax feature aggregation layer is adopted to fuse the embedding. In addition, we introduce a perturbation with gradient in the process of training to further enhance node features and improve the robustness of our model. Extensive experiments demonstrate that ADARES has achieved satisfactory results on several benchmark single-cell datasets, our proposed model can be used as an effective method for recognizing cell types.
Semi-supervised Multimodal Emotion Recognition with Consensus Decision-making and Label Correction

Multimodal emotion recognition is the task of identifying and understanding emotions by integrating information from multiple modalities, such as audio, visual, and textual data. However, the scarcity of labeled data poses a significant challenge for this task. To this end, this paper proposes a novel approach via a semi-supervised learning framework by incorporating consensus decision-making and label correction methods. Firstly, we employ supervised learning on the trimodal input data to establish robust initial models. Secondly, we generate reliable pseudo-labels for unlabelled data by leveraging consensus decision-making and label correction methods. Thirdly, we train the model in a supervised manner using both labeled and pseudo-labeled data. Moreover, the process of generating pseudo-labels and semi-supervised learning can be iterated to refine the model further. Experimental results on the MER 2023 dataset show the effectiveness of our proposed framework, achieving significant improvement on the MER-MULTI, MER-NOISE, and MER-SEMI subsets, respectively.
A Survey on Searchable Symmetric Encryption

Outsourcing data to the cloud has become prevalent, so Searchable Symmetric Encryption (SSE), one of the methods for protecting outsourced data, has arisen widespread interest. Moreover, many novel technologies and theories have emerged, especially for the attacks on SSE and privacy-preserving. But most surveys related to SSE concentrate on one aspect (e.g., single keyword search, fuzzy keyword search, etc.) or lack in-depth analysis. Therefore, we revisit the existing work and conduct a comprehensive analysis and summary. We provide an overview of state of the art in SSE and focus on the privacy it can protect. Generally, (1) we study the work of the past few decades and classify SSE based on query expressiveness. Meanwhile, we summarize the existing schemes and analyze their performance on efficiency, storage space, index structures, etc.; (2) we complement the gap in the privacy of SSE and introduce in detail the attacks and the related defenses; (3) we discuss the open issues and challenges in existing schemes and future research directions. We desire that our work will help novices to grasp and understand SSE comprehensively. We expect it can inspire the SSE community to discover more crucial leakages and design more efficient and secure constructions.
Test Data Selection Based on Applying Mutation Testing to Decision Tree Models

Software testing is crucial to ensure software quality, verifying that it behaves as expected. This activity plays a crucial role in identifying defects from the early stages of the development process. Software testing is especially essential in complex or critical systems, such as those using Machine Learning (ML) techniques, since the models can present uncertainties and errors that affect their reliability. This work investigates the use of mutation testing to support the validation of ML applications. Our approach involves applying mutation analysis to the decision tree structure. The resulting mutated trees are a reference for selecting a test dataset that can effectively identify incorrect classifications in machine learning models. Preliminary results suggest that the proposed approach can successfully improve the test data selection for ML applications.
What Makes A Good Course and Professor: Through The Lens of RateMyProfessor Website

What makes a good professor? Do the courses a professor teach and their difficulty levels influence a professor’s overall evaluation? What topics do the students mostly care about when evaluating a professor and a course? What emotions are pervasive in student reviews? Do universities from different locations, and Carnegie classification levels exhibit different rating? Using a large compilation of the RateMyProfessor (RMP) dataset, we proceed to explore answers to these questions in this paper by performing a comprehensive data-driven analysis. We first explore the distribution of good professors for university locations and classifications and understand whether a professor’s perceived difficulty level affects their RMP rating. Then, for a granular review level investigation, we analyze the relationship between a course’s difficulty level and student ratings and investigate the main topics and emotions prevalent in student reviews.
Predictive Health: Chronic Kidney Disease Case Study

Kidney failure is a silent chronic disease, without symptoms, and develops into an acute stage quickly. Unfortunately, the majority of patients discover this disease in its advanced stages where hemodialysis or transplant becomes a necessity. Predicting kidney failure will make controlling the progression of the disease possible and can even stabilize the state of patients. Data Analytics and data mining techniques, as well as machine learning proved a great success in information and knowledge discovery from medical big data to help decision making. Indeed, all the fields of the medical sector are paramount by the application of these technologies, given the useful knowledge extracted from collected data. Such knowledge will contribute to improve decision-making and save human lives. In this paper, we will present the big role of data mining technics in medical field in matters of predicting chronic diseases. Also we will show how these technics are very crucial to extract useful knowledge, and that by using linear regression model to predict glomerular filtration rate values (GFR) as the best indicator of kidney failure. The main purpose is to allow doctors to help patients, predict kidney failure and control this unwanted disease.
Prediction of Chlorophyll-a Content Base on Multi-module One Dimensional Convolutional Neural Network

Aiming at the problem of red tide control in Bohai Bay, a multi-module one dimensional convolutional neural network (M-1DCNN) model was proposed to predict the content of chlorophyll-a in multi-step. Firstly, the dynamic standardization method was used to extract the data waveform characteristics, and then a general module structure suitable for the prediction of chlorophyll-a content in each step was designed, after that through the improved deep deterministic policy gradient (DDPG) algorithm divided the prediction task into multiple modules, meanwhile optimized the parameters of each module, and finally combines all modules to complete the training. The results show that the improved DDPG algorithm can complete the parameter search efficiently and stably, and the prediction results of M-1DCNN in each step are stronger than the comparison model. M-1DCNN has excellent short-term and medium-term prediction capabilities for chlorophyll-a content, which can provide a reference for early warning of eutrophication and red tide phenomena in the Bohai Bay waters.
3-D crustal structure along the North Anatolian Fault Zone in north-central Anatolia revealed by local earthquake tomography

SUMMARY 3-D P-wave velocity structure and Vp/Vs variations in the crust along the North Anatolian Fault Zone (NAFZ) in north-central Anatolia were investigated by the inversion of local P- and S-wave traveltimes, to gain a better understanding of the seismological characteristics of the region. The 3-D local earthquake tomography inversions included 5444 P- and 3200 S-wave readings obtained from 168 well-located earthquakes between 2006 January and 2008 May. Dense ray coverage yields good resolution, particularly in the central part of the study area. The 3-D Vp and Vp/Vs tomographic images reveal clear correlations with both the surface geology and significant tectonic units in the region. We observed the lower limit of the seismogenic zone for north-central Anatolia at 15 km depth. Final earthquake locations display a distributed pattern throughout the study area, with most of the earthquakes occurring on the major splays of the NAFZ, rather than its master strand. We identify three major high-velocity blocks in the mid-crust separated by the Izmir-Ankara-Erzincan Suture and interpret these blocks to be continental basement fragments that were accreted onto the margin following the closure of Neo-Tethyan Ocean. These basement blocks may have in part influenced the rupture propagations of the historical 1939, 1942 and 1943 earthquakes. In addition, large variations in the Vp/Vs ratio in the mid-crust were observed and have been correlated with the varying fluid contents of the existing lithologies and related tectonic structures.
A multi-scale evaluation of eastern hognose snake (Heterodon platirhinos) habitat selection at the northern extent of its range

A complex interaction of biotic and abiotic variables structure landscapes into a hierarchal assemblage of habitats. Species respond to this environmental hierarchy by selecting habitat based upon a set of ecological variables occurring across a range of organizational levels. However, as the criteria for selection may be scaledependent, it is vital to quantify the influence these variables have on species distribution at each spatial scale. Two years of telemetry data from 17 individuals were used to examine the multi-scale selection process in the northern population of Heterodon platirhinos on the New Boston Air Force Station in New Boston, New Hampshire. Thermal quality, habitat structure, prey availability, and predator avoidance were predicted to be the primary influential variables dictating the selective process in these ectothermic organisms, with the thermal environment being of particular importance. Statistical comparisons and modeling results revealed that snakes were selective at all three spatial scales, with thermal extremes and habitat cover being the dominant influential variables. At the landscape level, mixed forest maintaining environmental temperatures above thermal minima (7.0°C) were highly selected whereas at the home-range level, hemlock forests that did not exceed thermal maxima (40.5°C) were preferred. Overall optimal habitat was identified as having the following characteristics: 1) mixed and hemlock forests having continuous canopy and understory architecture interspersed with fine-scale openings; 2) close proximity to wetlands; 3) high density of leaf litter, debris, and rocks; and 4) homogeneous surface temperatures within critical thermal limits. Together, this structural configuration likely maximizes thermoregulatory precision while still conferring the secondary biological needs of predator avoidance and suitable prey availability.
How to understand a research question—a challenging first step in setting up a statistical model

How to understand a research question—a challenging first step in setting up a statistical model Peter A. Edelsbrunner , Simone Sebben , Lisa K. Frisch, Victoria Schüttengruber, John Protzko, and Christian M. Thurn Department of Humanities, Social and Political Sciences, ETH Zurich, Zurich, Switzerland; Department of Psychology, University of Zurich, Zurich, Switzerland; Department of Psychological Sciences, Central Connecticut State University, New Britain, United States
Use of in-situ surfactant-based solid phase extraction for determination of low concentrations of CrIII in infusions of medicinal plants

Abstract A new solid phase extraction procedure, known as in situ surfactant-based solid phase extraction, has been proposed to detect the presence of CrIII in infusions of medicinal plants. In this work, a small amount of cetyltrimethylammonium bromide was injected into a sample containing CrIII, which was complexed by 1-(2-pyridylazo)-2-naphthol. After shaking, a small volume of NaClO Was added to the solution as an ion-pairing agent. After preconcentration, the settled phase was dissolved in a specific volume of HCl and then analyzed by Flame Atomic Absorption Spectrometry. A Doehlert matrix was used to optimize the CrIII complex formation conditions, such as the chelating agent concentration (0.18 mmol L-1) and pH of 9.5 in the reaction medium. A 23 factorial design was used to screen the variables that influence the extraction of the complex. The concentration of ionic-pairing agent (0.26 mol L-1) and the vortex mixer agitation time (7.51 minutes) were significant and optimized using response surface methodology. The limit of detection, limit of quantification, and preconcentration factor were 1.66 μg L-1, 5.5 μg L-1 and 12, respectively. The linear range was 5.5-5000 μg L-1. The method proved accuracy by the comparison with certified reference material APS-1071 (p>0.05), and the optimized conditions were successfully applied for the determination of CrIII in samples of medicinal plant infusions. GRAPHICAL ABSTRACT
Chitosan-based electrochemical biosensors for lung cancer detection: A mini-review

Abstract Lung cancer is among the most life-threatening diseases, as it is by far the leading cause of death around the globe. However, plentiful studies have been reported with satisfactory results employing electrochemical biosensors, which make this method popular in lung cancer diagnosis. In assembling the sensing platforms, different materials of the sensor interfaces are utilized in electrode surface modification to enhance performance. A natural biopolymer chitosan (CS) is an outstanding material for the development of electrochemical biosensors. The unique physiochemical features exhibited by CS facilitate the preservation of its innovative traits and promote its secure adhesion onto the electrode surface. It also has inherent characteristics, such as high adsorption ability, good biocompatibility, eco-friendliness nature, greater heat stability, improved permeability, mechanical potency, outstanding layer-forming capability, and ease of availability. In combination with conductive materials, CS enables the modified electrodes to perform the best analytical results. This review is focused on the importance of CS in sensor configuration, advancements of CS-based electrochemical biosensors for detecting lung cancer biomarkers, and future prospects. GRAPHICAL ABSTRACT
Delineating yeast cleavage and polyadenylation signals using deep learning

3’-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared to well-defined regulatory elements in mammals. Especially, recent deep sequencing experiments showed extensive cleavage heterogeneity for some mRNAs in Saccharomyces cerevisiae and uncovered the polyA motif differences between S. cerevisiae vs. Schizosaccharomyces pombe. The findings raised the fundamental question of how polyadenylation signals are formed in yeast. Here we addressed this question by developing deep learning models to deconvolute degenerate cis-regulatory elements and quantify their positional importance in mediating yeast polyA site formation, cleavage heterogeneity, and strength. In S. cerevisiae, cleavage heterogeneity is promoted by the depletion of U-rich elements around polyA sites as well as multiple occurrences of upstream UA-rich elements. Sites with high cleavage heterogeneity show overall lower strength. The site strength and tandem site distances modulate alternative polyadenylation (APA) under the diauxic stress. Finally, we developed a deep learning model to reveal the distinct motif configuration of S. pombe polyA sites which show more precise cleavage than S. cerevisiae. Altogether, our deep learning models provide unprecedented insights into polyA site formation across yeast species.