Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Ordinal Suicide Ideation Detection on Social Media

The rising ubiquity of social media presents a platform for individuals to express suicide ideation, instead of traditional, formal clinical settings. While neural methods for assessing suicide risk on social media have shown promise, a crippling limitation of existing solutions is that they ignore the inherent ordinal nature across fine-grain levels of suicide risk. To this end, we reformulate suicide risk assessment as an Ordinal Regression problem, over the Columbia-Suicide Severity Scale. We propose SISMO, a hierarchical attention model optimized to factor in the graded nature of increasing suicide risk levels, through soft probability distribution since not all wrong risk-levels are equally wrong. We establish the face value of SISMO for preliminary suicide risk assessment on real-world Reddit data annotated by clinical experts. We conclude by discussing the empirical, practical, and ethical considerations pertaining to SISMO in a larger picture, as a human-in-the-loop framework
Online Post-Processing in Rankings for Fair Utility Maximization

We consider the problem of utility maximization in online ranking applications while also satisfying a pre-defined fairness constraint. We consider batches of items which arrive over time, already ranked using an existing ranking model. We propose online post-processing for re-ranking these batches to enforce adherence to the pre-defined fairness constraint, while maximizing a specific notion of utility. To achieve this goal, we propose two deterministic re-ranking policies. In addition, we learn a re-ranking policy based on a novel variation of learning to search. Extensive experiments on real world and synthetic datasets demonstrate the effectiveness of our proposed policies both in terms of adherence to the fairness constraint and utility maximization. Furthermore, our analysis shows that the performance of the proposed policies depends on the original data distribution w.r. t the fairness constraint and the notion of utility.
An Improved Feature Extraction Approach for Web Anomaly Detection Based on Semantic Structure

Anomaly-based Web application firewalls (WAFs) are vital for providing early reactions to novel Web attacks. In recent years, various machine learning, deep learning, and transfer learning-based anomaly detection approaches have been developed to protect against Web attacks. Most of them directly treat the request URL as a general string that consists of letters and roughly use natural language processing (NLP) methods (i.e., Word2Vec and Doc2Vec) or domain knowledge to extract features. In this paper, we proposed an improved feature extraction approach which leveraged the advantage of the semantic structure of URLs. Semantic structure is an inherent interpretative property of the URL that identifies the function and vulnerability of each part in the URL. )e evaluations on CSIC-2020 show that our feature extraction method has better performance than conventional feature extraction routine by more than average dramatic 5% improvement in accuracy, recall, and F1-score.
Data-Driven Approach to Identification of Latin Phrases in Russian Web-Crawled Corpora

Latin phrases are an integral part of the language of educated speakers in many (European) languages. Besides lexical units of Latin origin that have been already adapted to the orthography of the respective host language and calques, phrases retaining the original form and orthography can also be found in many texts. Due to the rather low frequency of the phenomenon, however, any systematic attempt of its analysis was a real challenge before the advent of very large (multi-Gigaword) corpora. Our paper presents a method of semi-automatic detection of Latin phrases in a Russian web corpus based on applying a Latin tagger and a series of filtrations performed by standard Linux utilities. The preliminary analysis of the resulting candidate list is shown in the concluding part of the paper.
Processo de formação de crostas superficiais em razão de sistemas de preparo do solo e chuva simulada

A chuva provoca a modificacao da estrutura superficial do solo levando ao desenvolvimento de crostas. Este estudo avaliou a dinâmica da formacao de crostas superficiais por meio da analise e descricao micromorfologica de lâminas delgadas. O experimento foi conduzido em 2009 e 2010. Em parcelas de 15 x 30 m, foram implantados os diferentes sistemas de preparos do solo: preparo convencional (PC), preparo reduzido (PR) e sem preparo (SP). Dentro das parcelas instalaram-se as microparcelas de 1 x 1m e aplicaram-se diferentes lâminas de chuva simulada (0, 27, 54 e 80 mm) com intensidade de 80 mm h-1. Nas microparcelas, coletou-se uma amostra indeformada em que consistia em um bloco de solo de 0,07 x 0,05 x 0,12 m. Os blocos foram impregnados com resina e apos endurecimento e secagem total foram confeccionadas lâminas delgadas (4,5 x 8 cm), descritas, utilizando microscopio optico. Foi confeccionada uma lâmina delgada para cada chuva (0, 27, 54 e 80 mm) e para cada preparo do solo. Todos os sistemas de preparo avaliados demonstraram presenca de crostas; entretanto, o processo de formacao das crostas ocorreu com dinâmica diferenciada em cada sistema de preparo. O preparo convencional apresentou formacao de crostas a partir de 27 mm de chuva aplicada. O aumento da lâmina de chuva aplicada causou maior degradacao da estrutura superficial e maior espessura da camada encrostada. O preparo reduzido e o sistema sem preparo apresentaram condicoes de superficie irregulares com as chuvas aplicadas, ou seja, nao demonstraram modificacoes constantes da superficie do solo e na formacao das crostas, com o aumento da chuva, como constatado no preparo convencional. A analise das lâminas delgadas e a descricao micromorfologica permitiram satisfatoria observacao e conclusao dos processos e da dinâmica envolvida na formacao de crostas.
Fractional Determination of Nanogram Amounts of Vanadium(IV) and Vanadium(V) in Natural Water by a Catalytic Method Combined with Solvent Extraction

A simple and sensitive method has been developed for the fractional determination of nanogram amounts of vanadium(IV) and vanadium(V) in natural waters. It is based on the solvent extraction of these ions with N- cinnamoyl- N -(2,3-xylyl)hydroxylamine into toluene at different pH values, followed by back-extraction into a sodium hydroxide solution and on the determination of vanadium by a catalytic method using the oxidative coupling reaction of 4-aminoantipyrine with N , N -dimethylaniline. Vanadium(IV) and vanadium(V) at the 0.1-1.0 ngml^-1 level can be determined. The proposed method suffers few interferences and can be successfully applied to the fractional determination of vanadium in natural water.
Fine-Grained Task Access Control System for Mobile Crowdsensing

Mobile crowdsensing enables people to collect and process a massive amount of information by using social resources without any cost on sensor deployment or model training. Many schemes focusing on the problems of task assignment and privacy preservation have been proposed so far. However, the privacy-preserving of requesters and task access control, which are vital to mobile crowdsensing, is barely considered in the literature. To address the aforementioned issues, a fine-grained task access control system for mobile crowdsensing is proposed. In particular, the requester can decide the group of task performers who can access the task by utilizing attribute-based encryption technology. T he untrusted crowdsensing platform cannot obtain any sensitive information concerning the requester or the task, while the qualified task performers are capable of retrieving tasks within 0.85 ms. Security analysis and experimental results are presented to show the feasibility and efficiency of the proposed system.
Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition

Department of Computer Science & IT, Sarhad University of Science and Information Technology, Peshawar, Pakistan School of Information and Electronics, Beijing Institute of Technology, Beijing, China Department of IT & Computer Science, Pak-Austria Fachhoschule: Institute of Applied Sciences and Technology, Haripur, Pakistan Department of Computer Science, University of Swabi, Swabi, Pakistan Department of Accounting & Information Systems, College of Business & Economics, Qatar University, Doha, Qatar
An Efficient HPRA-Based Multiclient Verifiable Computation: Transform and Instantiation

Choi, Katz, Kumaresan, and Cid put forward the conception of multiclient noninteractive verifiable computation (MVC), enabling a group of clients to outsource computation of a function of f. CKKC’s MVC is impractical due to their dependence on fully homomorphic encryption (FHE) and garbled circuits (GCs). In this paper, with the goal of satisfying practical requirements, a general transform is presented from the homomorphic proxy re-authenticator (HPRA) of Deler, Ramacher, and Slamanig to MVC schemes. MVC constructions in this particular study tend to be more efficient once the underlying HPRA avoids introducing FHE and GCs. By deploying the transform to DRS’s HPRA scheme, a specific MVC scheme for calculating the linear combinations of vectors has been proposed. It can be understood that it is the first feasible and implementableMVC scheme so far, and the instantiation solution has a great advantage in efficiency compared with related works.
Weighted Nuclear Norm Minimization on Multimodality Clustering

Generally, multimodality data contain different potential information available and are capable of providing an enhanced analytical result compared to monosource data. *e way to combine the data plays a crucial role in multimodality data analysis which is worth investigating. Multimodality clustering, which seeks a partition of the data in multiple views, has attracted considerable attention, for example, robust multiview spectral clustering (RMSC) explicitly handles the possible noise in the transition probability matrices associated with different views. Spectral clustering algorithm embeds the input data into a lowdimensional representation by dividing the clustering problem into k subproblems, and the corresponding eigenvalue reflects the loss of each subproblem. So, the eigenvalues of the Laplacian matrix should be treated differently, while RMSC regularizes each singular value equally when recovering the low-rankmatrix. In this paper, we propose a multimodality clustering algorithmwhich recovers the low-rank matrix by weighted nuclear normminimization. We also propose a method to evaluate the weight vector by learning a shared low-rank matrix. In our experiments, we use several real-world datasets to test our method, and experimental results show that the proposed method has a better performance than other baselines.
ScholarWorks @ UTRGV ScholarWorks @ UTRGV Influence of microbial priming and seeding depth on germination Influence of microbial priming and seeding depth on germination and growth of native wildflowers and growth of native wildflowers

Background: Using native wildflowers for restoring marginal lands has gained considerable popularity. Establishment of wildflowers can be challenging due to several environmental factors. Restoring the microbial community in degraded habitats can potentially result in the native plant performance and habitat restoration. This study was conducted to investigate the impact of native soil microbes and seeding depth on germination of south Texas native wildflowers. Two wildflower species, Ratibida columnifera (Nutt.) (Mexican Hat) and Verbesina encelioides (Cav.) (cowpen daisy), were treated with microbial wash extracted from native soils, and germination rate was recorded for 14-day period. We further analyzed the growth, biomass allocation, and root colonization by mycorrhizal fungi in these two plants growing them in a plant growth chamber for 6 weeks. To determine the impact of seeding depth, we planted the seeds of the two plant species at 2-cm, 6-cm, and 12-cm depth and monitored germination and plant growth. Results: The two species responded differently to the seeding depth and microbial wash treatments. Microbial wash treatment resulted in higher germination rate in R. columnifera compared to control, while it did not have any impact on V. encelioides seed germination. While microbial treatment did not influence the total biomass, it had a significant impact on the biomass allocation in both the plant species. R. columnifera seeds germinated at both 2-cm and 6-cm depth and did not germinate at 12 cm, while the V. encelioides seeds germinated only at 2 cm and did not germinate at 6-cm or 12-cm seeding depth. Conclusions: While our results are species specific, our results indicate that native soil microbes can potentially improve the seed germination and growth of wildflowers. Our results also indicate the importance of specific seeding depth when sowing wildflower seeds for habitat restoration.
Web App Design for Cryptocurrency Transaction System

The project provides a new idea to design a crypto-transaction application. There are two main goals we want to achieve in this project. The ﬁrst one is that we want provide an easier conversion between physical and digital money. The second goal is to build a pure token-based transaction system. We print digital cryptocurrency into physical notes, which means the physical notes store the value of cryptocur-rency directly. In order to achieve these goals, we designed an Android App and a Web App. This report will focus on the design and development of the Web App as well as its function and interaction with database.
Critical Success Factors in Implementing Employee Information System BasedOn E-Government in The Bureau of Public Affairs at The Regional SecretariatOf West Java Province

This research aimed to identify critical success factors in the implementation of EGovernment based on employee management information system in the Bureau of Public Affairs at the Regional Secretariat of West Java Province. Employee management information system is a process on managing information through the use of available resources within the organization. The formulation of the problem in this study is in its implementation in which various kinds of obstacles were found such as the difficulties in operating digital-based information system applications by each employee, also the inaccuracy of available data in information system. Qualitative method was used in this research by collecting data through literature review study, field research and interviewing four informants. They were the Head of the Public Affairs Bureau at the Regional Secretariat of West Java Province, the Head of Household Administration and Staffing, the administrator staff for management information system and a civil servant within the General Affairs Bureau. The research shows that the implementation of employee management information system based on e-government in the Bureau of Public Affairs at the Regional Secretariat of West Java province is not optimal because it has not been supported by competent human resources in the implementation of technology-based management information system therefore data accuracy is difficult to be achieved.
Master degree in Nanotechnologies for ICTs

After the discovery of graphene, the field of 2D materials has awaken an impressive amount of interest in the scientific community. These materials stable in 2D, are layered materials, where each layer stacks on the others interacting by van der Waals (vdW) interactions. Due to the weak nature of this interaction, they can be grown epitaxially in presence of large mismatch and it is possible to combine them in heterostructures. The family includes a wide range of properties, including metals, insulators, topological insulators, semiconductors, superconductors. The class of 2D ferromagnetic materials has been intensively investigated only recently, as most articles have been published just in the last years, from 2017. Among these, the vast majority are based on mechanical exfoliation, obtaining μm-size flakes. Our goal is to develop the epitaxy of van der Waals materials and heterostructures on cm2 areas. The field of spintronics regards the information processing based on the electron spin instead of its charge. 2D materials have been identified as high potential materials for spintronics and are currently being investigated. In this work, done in the ”2D and semiconductor spintronics” team of Spintec (Univ. Grenoble Alpes/CEA/CNRS), we investigate the epitaxial growth of ferromagnet Fe3GeTe2 by molecular beam epitaxy (MBE), which is a fundamental step to pave the way for studies of heterostructures and devices, also combining other 2D van Der Waals materials with exotic properties, for instance topological insulators. In the following chapters scientific background and motivations will be discussed first, second the experimental techniques used will be elaborated, in the third experimental results will be detailed, and conclusions in the last chapter.
Human Resource Management for Increasing Employees’ Satisfaction and Enhancing Competitive Strengths

Purpose: This study aims to discuss impacts of human resource management practice in efforts to achieve competitive strengths by means of employees’ mediation within the context of five-stars hotels in Jakarta. Design/Methodology/Approach: The study used a quantitative approach by examining the hypothesis and applying SEM structural equation modelings. The data is collected from 191 employees by way of questionnaires distributed to respondents from Tourism Sector, particularly, those five-stars hotels located in Jakarta. Finding: This study revealed a significant influence on competitive strength brought about by human resources practices. Yet, human resources management practice unlikely led to significant influence on employees’ job satisfaction. Whereas, satisfaction on work performance produced significant positive impacts on competitive strength. Furthermore, it was clear that human resource management practice unlikely mediated impacts of human resources practice on competitive strengths of the employees of five-stars hotels in Jakarta. Practical Implications: The results are interesting from the practical aspect to be applied for the improvement of the enterprise job satisfaction in the tourism sector. Originality/value: This paper examines job satisfaction when human resource practices increase competitive advantage.
A study of the effects of work-family conflict, family-work conflict, and work-life balance on Saudi female teachersâ•Ž performance in the public education sector with job satisfaction as a moderator

Purpose: The main objective of this paper is to investigate the effects of work-family conflict, family-work conflict, and work-life balance on female teachers in the public education sector in Saudi Arabia as well as to analyze the impact of job satisfaction as a moderator for the independent variables. Methodology: This paper is based on quantitative research methods that included a survey of employees. The constructs of work-family conflict, family-work conflict, and work-life balance were formed using the bivariate and correlation analysis done in SPSS. A scatterplot was used to identify the relationships between the moderating factor of job satisfaction with the independent variables. Finally, multiple regression was done to understand the impact of work-family conflict, family-work conflict, and work-life balance on employee performance among female Saudi teachers in the public education sector. A linear regression analysis was applied to test the research hypotheses. Findings: The results showed that there is a positive and highly significant impact on employee performance by work-life balance and job satisfaction, whereas there is a positive but not significant effect when it comes to work-family conflict. However, family-work conflict revealed a significant negative impact on employee performance.
A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. In order to reduce the complexity of the feature-level fusion, we propose a modified chaotic binary particle swarm optimization (MCBPSO) algorithm to select features. It uses kernel extreme learning machine (KELM) as a fitness function and chaotic binary sequences to initialize particle swarms. After the global optimal position (Gbest) is generated in each iteration, the position of Gbest is varied by using chaotic binary sequences, which is useful to realize chaotic local search and avoid falling into the local optimal position. The experiments are conducted on CASIA multimodal iris and face dataset from Chinese Academy of Sciences.The experimental results demonstrate that the proposed system can not only reduce the number of features to one tenth of its original size, but also improve the recognition rate up to 99.78%. Compared with the unimodal iris and face system, the recognition rate of the proposed system are improved by 11.56% and 2% respectively. The experimental results reveal its performance in the verification mode compared with the existing state-of-the-art systems. The proposed system is satisfactory in addressing face-iris multimodal biometric identification.
PROBING LUNAR POLAR IMPACT CRATERS USING FULL-POL L-BAND CHANDRAYAAN-2 DUAL- FREQUENCY SAR (DFSAR)

Introduction: The L&S band Dual-Frequency Synthetic Aperture Radar (DFSAR) onboard Chandrayaan-2 orbiter, is the first fully (quad) polarimetric SAR system in the lunar orbit [1]. The architecture of this radar instrument supports multiple polarimetric modes of operation. DFSAR observations contribute significantly towards understanding the origin and evolution of lunar impact craters [2]. In the present study, we exhibit some initial results obtained from DFSAR L-band Full-Polarimetric (FP) observations towards understanding physical behaviour of two different groups of impact craters. Study Area and Data Used: L-band FP SAR observations obtained at 26o incidence angle over secondary craters situated on the floor of Peary Crater (87.9oN, 35.3oE) in the lunar north polar region are analysed. This region consists of one young, fresh crater and two craters that were classified as CPR anomalous craters in previous studies [3] (Fig. 1). For comparison, CPR product derived from S-band zoom mode SAR data of the same region acquired by LRO Mini-RF are used.