Miracle-dz/UniFAR-FTUs huggingface dataset:
Communities on-line : community-based computer networks

A community network is a network of computers and modems that are interconnected via telephone lines to a central computer. The system provides local information and a means for community residents to communicate electronically. Unlike the similarly named "on-line communities" and "virtual communities", community networks are located in and support a specific physical place. Community-based computer networks have only recently appeared in North America. Their goals are to strengthen the community, increase democratic participation, and ensure that all members of the community have access to the "information highway". Most community networks are rich in local information, ranging from job opportunities to minutes of the city council meetings. But, surprisingly, in spite of the intention to increase a sense of community and democratic participation, many community networks provide limited opportunity for public debate and discussion. In addition, most community networks do not provide electronic access to elected officials or municipal government staff. It may be too soon to evaluate community networks on their long-term goals, but it is certainly appropriate to monitor their progress towards these goals. In the short-term, they must concentrate on finding workable and sustainable models. If community networks are to fulfill their long-term goals, it is essential that more effort be placed on designing systems that encourage public discussion and debate. Further research, involvement of the business community, identification of successful models, improved software design, and a greater emphasis on ensuring access for a wide cross-section of the population, is also needed. The future of community networks is not assured. However, if they continue to tap the energy of their users and volunteers, develop flexible and sustainable mbdels, and stay focused on their long-term goals, they may make an important contribution to their communities. Thesis Supervisor: William J. Mitchell Title: Dean, School of Architecture and Planning 91M-31 i>|J|FrWIMMPE'4]Esi|J! cijp:emesip-le-eyew-masi:Wesee:. mirmsuemana-vasskispearasilm-id-esismiigun-,saias.isi-"nya.F-a-lui-:m-is/gas-;a. ,y. --:s;. r,. "igi.r'/r--r-. -. 9 :-;@%-. ,->. <-->'. 9. ---%1. -:-,. a-2. :-. +. y.1 :4-. e. ,. -w. ,e:1:-year-,W. , :taja. -. 1-ag . w:dar -M -a .
Rhetoric and Rights: Citizenship and the Politics of Persuasion

This article examines the relevance of rhetorical analysis for the theory and practice of rights-claiming. Recent work in the field of human rights proposes that what is important about rights is not what they ‘are’ but what they ‘do’. Utilising performative theory, they suggest that rights-claiming is best understood as a perlocutionary practice of persuasion. The question is, ‘How might rights claims be most persuasive? ’ This article applies insights from the field of rhetoric to investigate how practices of rights-claiming by migrants in France contest French citizenship. It argues that rights claims are ethico-political negotiations of a political situation and that such practices are persuasive when they mobilise transcendent principles embedded within particular political communities. Rhetorical analysis explains how rights can be both inventive and efficacious. In so doing, this article extends the human rights literature by providing a refined rights-claiming analytic.
Methodological assessment and overall confidence in the results of systematic reviews with network meta-analyses in Endodontics.

AIM The aims of the study were to assess the methodological quality of systematic reviews with network meta-analyses (NMAs) in Endodontics using the "A MeaSurement Tool to Assess systematic Reviews" (AMSTAR 2) tool, and to evaluate the overall confidence in the results of the individual reviews included in the analysis. METHODOLOGY Systematic reviews with NMAs within the specialty of Endodontics published in English were identified from the PubMed, EbBSCOhost and SCOPUS databases from inception to July 2021. Two reviewers were involved independently in the selection of the reviews, data extraction, methodological quality assessment and overall confidence rating. Disagreements were resolved by discussion between the reviewers to achieve consensus; if disagreements persisted, a third reviewer made the final decision. The methodological quality of the included NMAs was appraised using the AMSTAR 2 checklist, which contains 16 items. The reviewers scored each item - 'Yes' - when the item was fully addressed, 'Partial Yes' - when the item was not fully addressed, or 'No' - when the item was not addressed. The overall confidence in the results of each review was classified as 'High', 'Moderate', 'Low' or 'Critically low' based on the criteria reported by the AMSTAR 2 developers. RESULTS Twelve systematic reviews with NMAs were included. All the NMAs adequately reported Item 1 ("Did the research questions and inclusion criteria for the review include the components of PICO? "), Item 8 ("Did the review authors describe the included studies in adequate detail? "), Item 9 ("Did the review authors use a satisfactory technique for assessing the risk of bias (RoB) in individual studies that were included in the review? ") and Item 16 ("Did the review authors report any potential sources of conflict of interest, including any funding they received for conducting the review? ") , whereas only one NMA reported Item 10 adequately ("Did the review authors report on the sources of funding for the studies included in the review? "). The overall confidence in the results of eight reviews was categorised as "Critically low", one review was "Low", two reviews were "Moderate" and one review was "High". CONCLUSION The overall confidence in the results for the majority of systematic reviews with NMAs in Endodontics was judged to be 'Critically low' as their methodological quality was below the necessary standard. AMSTAR 2 and PRISMA for NMA guidelines are available to guide authors to produce high quality systematic reviews with NMAs and for Editors and peer-reviewers when assessing submissions to journals.
INNOVATIVE APPROACH ON ABUSIVE SUPERVISION AND SUBORDINATE' PERFORMANCE

Understanding the behavior of employees in working life and the search for building a more peaceful workplace has been the subject of many research and practices for many years. Although the importance of building an environment of peace in working life is known, many undesirable behaviors occur in organizations. Some of these behaviors that negatively affect the organizational climate are caused by the supervisor's behaviors towards the employees. One of them is abusive supervision. It is necessary to carefully analyze the causes and consequences of abusive supervision, which refers to the set of behaviors such as the systematic abuse of the supervisors, the contempt of the employees, and their belittlement. Although many studies have been carried out to understand the causes and consequences of abusive supervision, it has not been sufficiently examined whether the consequences of the perception of abusive supervision differ according to the individual differences of the employees. Since abusive supervision is closely related to subordinates' perceptions, it is assumed that the individual differences of subordinates would play a role in perceiving abusive supervision and its consequences on subordinates' work-related attitudes. The purpose of this study is to investigate the moderating role of general self-efficacy on the relationship between abusive supervision and subordinates' perceived individual performance. Furthermore, it is aimed to discuss the ways to handle destructive outcomes of abusive supervision with an innovative approach. The quantitative research involved the employees working in Istanbul. Scales with previously tested psychometric properties were used to measure abusive supervision, individual performance, and general self-efficacy structures. A total of 304 employees participated in the study. The obtained data were analyzed by referring to the relevant quantitative analyzes (descriptive statistics, reliability, factor, correlation, and moderating analyses). According to the research findings, abusive supervision was negatively correlated with subordinates' perceived individual performance. Subordinates' levels of general self-efficacy did not have a significant moderating role in the relationship between abusive supervision and employees' perceived individual performance. The findings obtained within the scope of the research are discussed in the discussion section, and possible research suggestions that could be carried out in future studies are included in this direction.
Feature Selection Considering Characteristics of Operating Data and Random Cut Trees for Hydroelectric Generator Fault Detection

This paper proposes feature selection (FS) considering characteristics of operating data and Random Cut Trees (RCTs) for hydroelectric generator (HG) fault detection (FD) by Robust Random Cut Forest (RRCF). If the faults are detected using knowledge of experts, huge efforts of the experts are required. Therefore, the faults should be detected using only data without knowledge of experts. The FD method has to treat non-linear correlation of HG operating data. Faults of HGs seldom occur and it is difficult to obtain fault operating data of HGs. Therefore, the faults must be detected by generating models with only normal data. In this paper, the developed HG FD method is assumed to be utilized in a data center for a HG FD service. Therefore, for reduction of communication and CPU costs, only effective features for HG FD should be selected. The proposed method can treat non-linear correlation of HG operating data. Without knowledge of experts, faults can be detected using only data. FD models can be generated with only normal operation data. Effective features for FD of HGs can be selected. The proposed method is compared with a RRCF based method using selected features by Hilbert-Schmidt Independence Criterion and k-nearest neighbor (HSIC-KNN-information coefficient and k-nearest neighbor (MIC-KNN-FS), and Isolation Forest (IF) and RRCF based methods using all features. The proposed method is confirmed to be more effective than other methods with calculation time and the Area Under the Curve (AUC) values. Moreover, the Kruskal-Wallis test and Mann-Whitney U test with Holm correction is applied to verify the results.
Clinical Training in Nursing Study Programs in Latvia and Europe

As a result of the Bologna Process, European countries have been operating common basic requirements for nursing education (NE) programmes for several years. In 2021, reforms have also taken place in Latvia to ensure the effective development of professionals within higher education, who would become autonomous medical staff with a bachelor (Bch) education. Despite the different approaches of each European Union (EU) Member State to implementation of NE programmes, the unifying element is a scope of clinical training, which under EU legislation is at least half of the total scope of study programme. According to the research, these essential requirements have had a positive impact on the development of NE across Europe. Latvian higher education institutions (HEIs) have taken over the positive experience of Europe and have aligned the scope of clinical training with the requirements of regulatory enactments. A HEI has the right to organise clinical training at its discretion, thereby creating an unequal scope of this training in certain parts of the study programme. Some HEIs implement this process as internships, while others integrate it into study courses, supplementing the theoretical knowledge and practical skills acquired in simulation rooms with the acquisition of competencies in the clinical environment (CE). However, despite the organizational differences, the unifying element of these programmes remains the total scope of studies in the CE. The NE programmes in Europe also differ in the use of the term for clinical training. Notwithstanding the designation of this peculiar study form, it is always implemented in a CE. The presented research aims to look at the essence of clinical studies and the usability of the corresponding terms in a framework of study process in a CE, as well as to analyse the differences in the scope of this study form in Bch programmes in nursing in Latvia and Europe. The document analysis method, examining 17 research papers for their compliance with international regulatory enactments, shows that clinical training, nothwithstanding different terminology, is a key component of NE in a high-quality CE. The comparison of Bch’s level nursing studies (NS) at Vilnius University (Lithuania), Riga Stradiņš University (Latvia), Osnabrück University of Applied Sciences (Germany), University of Barcelona (Spain), and Daugavpils University (Latvia) indicates that they generally comply with internationally recognized requirements for the acquisition of the nursing profession.
Examining Effects of Schizophrenia on EEG with Explainable Deep Learning Models

Schizophrenia (SZ) is a mental disorder that affects millions of people globally. At this time, diagnosis of SZ is based upon symptoms, which can vary from patient to patient and create difficulty with diagnosis. To address this issue, researchers have begun to look for neurological biomarkers of SZ and develop methods for automated diagnosis. In recent years, several studies have applied deep learning to raw EEG for automated SZ diagnosis. However, the use of raw time-series data makes explainability more difficult than it is for traditional machine learning algorithms trained on manually engineered features. As such, none of these studies have sought to explain their models, which is problematic within a healthcare context where explainability is a critical component. In this study, we apply perturbation-based explainability approaches to gain insight into the spectral and spatial features learned by two distinct deep learning models trained on raw EEG for SZ diagnosis for the first time. We develop convolutional neural network (CNN) and CNN long short-term memory network (CNN-LSTM) architectures. Results show that both models prioritize the T8 and C3 electrodes and the δ- and γ-bands, which agrees with previous literature and supports the overall utility of our models. This study represents a step forward in the implementation of deep learning models for clinical SZ diagnosis, and it is our hope that it will inspire the more widespread application of explainability methods for insight into deep learning models trained for SZ diagnosis in the future.
Zika Virus Infection Alters Gene Expression and Poly-Adenylation Patterns in Placental Cells

Flaviviruses are small RNA viruses that are mainly transmitted via arthropod vectors and are found in tropic and sub-tropical regions. Most infections are asymptomatic (90–95%), but symptoms can be as severe as hemorrhagic fever and encephalitis. One recently emerged flavivirus is Zika virus (ZIKV), which was originally isolated from rhesus monkeys in Uganda roughly 70 years ago but has recently spread east, reaching S. America in 2015–2016. This outbreak was associated with the development of Guillain–Barré syndrome in adults and microcephaly in infants born to expectant mothers infected early in pregnancy. ZIKV must traverse the placenta to impact the development of the fetus, but the mechanisms responsible are unknown. While flaviviruses are known to disrupt splicing patterns in host cells, little is known about how flaviviruses such as ZIKV impact the alternative polyadenylation (APA) of host transcripts. This is important as APA is well-established as a mechanism in the regulation of mRNA metabolism and translation. Thus, we sought to characterize transcriptomic changes including APA in human placental (JEG3) cells in response to ZIKV infection using Poly(A)-ClickSeq (PAC-Seq). We used our differential Poly(A)-cluster (DPAC) analysis pipeline to characterize changes in differential gene expression, alternative poly-adenylation (APA) and the use of alternative terminal exons. We identified 98 upregulated genes and 28 downregulated genes. Pathway enrichment analysis indicated that many RNA processing and immune pathways were upregulated in ZIKV-infected JEG3 cells. We also updated DPAC to provide additional metrics of APA including the percentage-distal usage index (PDUI), which revealed that APA was extensive and the 3′ UTRs of 229 genes were lengthened while 269 were shortened. We further found that there were 214 upregulated and 59 downregulated poly(A)-clusters (PACs). We extracted the nucleotide sequences surrounding these PACs and found that the canonical signals for poly-adenylation (binding site for poly-A binding protein (PABP) upstream and a GU-rich region down-stream of the PAC) were only enriched in the downregulated PACs. These results indicate that ZIKV infection makes JEG3 cells more permissive to non-canonical poly-adenylation signals.
A novel classification approach for Android malware based on feature fusion and natural language processing

The growing use of Android software has made mobile devices the main platform for information services such as mobile social media and financial services. Mobile software provides great convenience but also brings challenges to the software community. For example, mobile malware, a malicious software specifically designed to target mobile devices, creates security concerns for the business network and the data stored on it. Therefore, it is becoming more and more important to effectively identify and classify malware. Most of the current malware-classification methods rely on the specific (static/dynamic) behaviour information from Android software for improved malware-detection capability. Nevertheless, these methods cannot detect new types of fraud software due to the limited generalisability. To address these issues, this paper proposes the AMC-FN, i.e. an Android-based malware classification method using feature fusion and natural language processing technologies. The proposed AMC-FN aims to improve the dimension and performance of classification and also some specific functions of natural language processing, i.e. mutual information method, n-gram word segmentation and feature mapping. The AMC-FN framework improves the classification dimensions by leveraging the information from Android APK permission, API calls and realistic network traffic. Moreover, the framework also contains a novel multi-level feature fusion algorithm (MFFA) designed to improve the weighted feature fusion. To obtain better fine granularity and generalisability, the fusion features are used by the optimized SVM (Support Vector Machine) classifier for training. Our experimental measurements and comparisons show the improved performance based on the proposed AMC-FN framework.
MiniLearn: On-Device Learning for Low-Power IoT Devices

Recent advances in machine learning enable new, intelligent applications in the Internet of Things. For example, today’s smartwatches use Deep Neural Networks (DNNs) to detect and classify human activities. The training of DNNs, however, is done offline with previously collected and labeled datasets using extensive computational resources such as GPUs on cloud services. Once being quantized and deployed on an IoT device, a DNN commonly remains unchanged. We argue that this static nature of trained DNNs strongly limits their flexibility to adapt to requirements that change dynamically. For example, the device may need to adjust on the fly to the limited memory and energy resources, but only the retraining or pruning of the DNN in the cloud can address these issues. Moreover, the user may need to add new classes or refine existing ones, due to different problem domains materializing dynamically. Retraining DNNs requires a high volume of data collected from IoT devices and trans-mitted to the cloud. However, IoT devices depend on energy-efficient communication with limited reliability and network bandwidth. In addition, cloud storage of extensive IoT data raises significant privacy concerns. This paper introduces MiniLearn that enables re-training of DNNs on resource-constrained IoT devices. MiniLearn allows IoT devices to re-train and optimize pre-trained, quantized neural networks using IoT data collected during deployment of an IoT device. We show that MiniLearn speeds up inference by a factor of up to 2 and requires up to 50% less memory compared to original DNN. In addition, MiniLearn increases classification accuracy for a sub-set by 3% to 9% of the original DNN.
The theory of groups

This is a second edition, accomplished by a photographic process, of Marshall Hall's celebrated text on group theory. It corresponds with the original 1959 edition word for word and symbol for symbol, minor corrections apart. There has been no rewriting, not even of preface or references. Hall's book is a significant and well written text, a fact which was more obviously true in 1959 when the number of books on group theory was much smaller than it is today. Users in schools should be warned: they will find it strong meat. The book is written at the abstract level; even so it goes at a fast pace for a first course, and it makes little concession to concrete matters. There are only twelve diagrams, all of them hardly bigger than a postage stamp, for Hall follows the usual convention that diagrams for abstract theory are strictly a do-it-yourself activity. A feature of the original text was the almost infinite number of misprints, spelling mistakes, etc. To be fair, few of these were seriously misleading. Their provenance was perhaps bad handwriting and hasty proof-reading. They should be charitably regarded—a challenge to the reader, perhaps, or an irritant for reviewers. Though most of them have now gone, comparison with the 1959 edition reveals a sizable residue which it is far beyond the scope of this review to list. By the way the attention of school users might well be directed to Chapter 20, concerning groups in abstract geometry. The undoubted fact that this topic does not usually appear in group theory texts does not detract from either its interest or its importance.
Markov Processes: Characterization and Convergence

Introduction. 1. Operator Semigroups. 2. Stochastic Processes and Martingales. 3. Convergence of Probability Measures. 4. Generators and Markov Processes. 5. Stochastic Integral Equations. 6. Random Time Changes. 7. Invariance Principles and Diffusion Approximations. 8. Examples of Generators. 9. Branching Processes. 10. Genetic Models. 11. Density Dependent Population Processes. 12. Random Evolutions. Appendixes. References. Index. Flowchart.
Maximizing Miner Revenue in Transaction Fee Mechanism Design

Transaction fee mechanism design is a new decentralized mechanism design problem where users bid for space on the blockchain. Several recent works showed that the transaction fee mechanism design fundamentally departs from classical mechanism design. They then systematically explored the mathematical landscape of this new decentralized mechanism design problem in two settings: in the plain setting where no cryptography is employed, and in a cryptography-assisted setting where the rules of the mechanism are enforced by a multi-party computation protocol. Unfortunately, in both settings, prior works showed that if we want the mechanism to incentivize honest behavior for both users as well as miners (possibly colluding with users), then the miner revenue has to be zero. Although adopting a relaxed, approximate notion of incentive compatibility gets around this zero miner-revenue limitation, the scaling of the miner revenue is nonetheless poor. In this paper, we show that if we make a mildly stronger reasonable-world assumption than prior works, we can circumvent the known limitations on miner revenue, and design auctions that generate optimal miner revenue. We also systematically explore the mathematical landscape of transaction fee mechanism design under the new reasonable-world and demonstrate how such assumptions can alter the feasibility and infeasibility landscape.