Miracle-dz/UniFAR-FTUs huggingface dataset:
Intimate partner violence and resilience in women from the western Brazilian Amazon

Objective: To verify the prevalence of violence against women perpetrated by the intimate partner, to identify the predominant factor of resilience, to verify whether intimate partner violence influences resilience. Methods: Cross-sectional analytical epidemiological research with a total of 291 women between 18 and 59 years old, users of Primary Health Care, from April to July 2018 in a city in the western Brazilian Amazon. Instruments: socioeconomic questionnaire; violence tracker validated by Schraiber et al.; resilience scale validated by Pesce et al. Results: Prevalence of violence in the last 12 months: 53.3%. The highest concentration of participants was in Factor I of resilience with 55% of participants. There is a statistically significant relationship between the variables of physical violence and resilience (p=0.023). Conclusion: More than half of the participants suffered intimate partner violence, mainly psychological violence, physical and sexual violence. Resilient Factor of perseverance good humor and predominated in the participants. Physical violence influenced the development self-confidence and adaptability, making these less flexible to change, more less self-confidence.
W HAT DOES H YPERBOLIC S PACE R OLES FOR G RAPH L EARNING ?

Models, like graph neural networks, built upon hyperbolic spaces have achieved great success in tree-structured data, but it is not clear in what aspect the hyperbolic space plays an effective role. In this paper, we take the recommender system, a user-item graph-structured networks, as an example for an in-depth analysis. Given that the prevalence of the power-law distribution in user-item graph-structured networks, hyperbolic space has attracted considerable attention and achieved impressive performance recently. The advantage of hyperbolic recommendation lies in that its exponentially increasing capacity is well-suited to describe the power-law distributed user-item network whereas the Euclidean equivalent is deficient in. Nonetheless, it remains unclear which aspects are effective or counterproductive with hyperbolic models. To address the above concerns, we take the one of the most popular recommendation techniques, collaborative filtering, as the medium, to investigate the behaviors of hyperbolic and Euclidean graph models. The results reveal that tail nodes get more emphasise in the hyperbolic model than that built upon Euclidean space, but there is still ample room for improvement; head nodes receive modest attention in hyperbolic space, which could be considerably improved; and nonetheless, the hyperbolic models show more competitive performance.
MACHINE LEARNING AND ITS POTENTIAL APPLICATIONS IN THE INDEPENDENT EVALUATION UNIT OF THE GREEN CLIMATE FUND: A SCOPING STUDY

This paper explores the extent to which and how machine learning can support the evaluation function of the Independent Evaluation Unit (IEU) and, more broadly, how it can support project development at the Green Climate Fund (GCF). The paper suggests machine learning can be an effective tool for optimizing the identification and selection of beneficiaries, generating necessary data and information for proposal development that can be used within feasibility studies, and identifying the factors most relevant for project approval. The paper also suggests that machine learning can contribute to the measurement of outcomes from GCF investments, by monitoring outcomes during project implementation and filling data gaps when measuring outcomes. In addition, machine learning can improve the accuracy of specific counterfactual evaluation techniques. The paper explains the increasing role of machine learning in evidence reviews and illustrates the variety of text-mining approaches for document screening and synthesis, that can expedite the review of evidence on a specific thematic area. The paper concludes by highlighting the vital ethical dimensions of using machine learning for evaluation, and how the IEU can learn from the existing approaches to machine learning used by other multilateral agencies.
Linking Entities across Relations and Graphs

This paper proposes a notion of parametric simulation to link entities across a relational database <tex>$\mathcal{D}$</tex> and a graph <tex>$G$</tex>. Taking functions and thresholds for measuring vertex close-ness, path associations and important properties as parameters, parametric simulation identifies tuples <tex>$t$</tex> in <tex>$\mathcal{D}$</tex> and vertices <tex>$v$</tex> in <tex>$G$</tex> that refer to the same real-world entity, based on topological and semantic matching. We develop machine learning methods to learn the parameter functions and thresholds. We show that parametric simulation is in quadratic-time, by providing such an algorithm. Putting these together, we develop HER, a parallel system to check whether <tex>$(t,v)$</tex> makes a match, find all vertex matches of <tex>$t$</tex> in <tex>$G$</tex>, and compute all matches across <tex>$\mathcal{D}$</tex> and <tex>$G$</tex>, all in quadratic-time. Using real-life and synthetic data, we empirically verify that HER is accurate with <tex>$\mathbf{F}$</tex> -measure of 0.94 on average, and is able to scale with database <tex>$\mathcal{D}$</tex> and graph <tex>$G$</tex>.
An Unsupervised Probabilistic Approach for the Detection of Outliers in Corpora

Many applications of computational linguistics are greatly influenced by the quality of corpora available and as automatically generated corpora continue to play an increasingly common role, it is essential that we not overlook the importance of well-constructed and homogeneous corpora. This paper describes an automatic approach to improving the homogeneity of corpora using an unsupervised method of statistical outlier detection to find documents and segments that do not belong in a corpus. We consider collections of corpora that are homogeneous with respect to topic (i.e. about the same subject), or genre (written for the same audience or from the same source) and use a combination of stylistic and lexical features of the texts to automatically identify pieces of text in these collections that break the homogeneity. These pieces of text that are significantly different from the rest of the corpus are likely to be errors that are out of place and should be removed from the corpus before it is used for other tasks. We evaluate our techniques by running extensive experiments over large artificially constructed corpora that each contain single pieces of text from a different topic, author, or genre than the rest of the collection and measure the accuracy of identifying these pieces of text without the use of training data. We show that when these pieces of text are reasonably large (1,000 words) we can reliably identify them in a corpus.
Speech Interference Generated by Proverbs and Phraseological Units of the Russian and Chinese Languages

To express their thoughts in Russian, students should have language resources in their active vocabulary, the consolidation of which in memory is advisable based on the skills of their native language. The most important role in teaching students a non-native language is played by transfer, which is one of the central categories of psychology. Russian and Chinese mentalities have different, sometimes opposite features, which make it difficult for Chinese students to learn Russian. The phenomenon of transfer occurs and exists during language contact regardless of the will and desire of bilinguals. As a result of the transfer phenomenon, interference and its cause are that certain areas in the linguistic picture of the world are preserved, to which the proverb and phraseology belong. The authors believe that proverbs should be attributed to the category of fixed expressions of the language and studied from the standpoint of general, particular, and comparative phraseology.
Long-term stability of the inversion process for sugar and ethanol production in an existing Japanese sugar mill

In order to utilize sugarcane with a high reducing-sugar content as the raw material for sugar production, a new technology called the ‘inversion process’ has been developed. This new technology aims to enhance raw sugar yield via removal of reducing sugars through selective ethanol fermentation using an invertase-defective yeast, prior to sugar crystallization. To assess the feasibility of the inversion process technology in an existing sugar mill, a test of continuous and repeated-batch fermentation using clear juice was undertaken at a pilot-scale facility at the Shinko Sugar Mill in Japan. Batch fermentation trials were performed at 35°C for 1.5-3 h and repeated 70 times in a 2000 L fermenter using the same culture of invertase-defective yeast strain GYK-10. To confirm the long-term stability of the selective fermentation, the concentrations of saccharides and ethanol in the fermenter were measured every hour and the residual sucrose ratio and the reducing sugars removal ratio were calculated. Each batch was checked for the presence of contaminating bacteria. The results showed that 88.4% of reducing sugars were converted to ethanol and 99.5% of sucrose remained throughout the 70 consecutive batch fermentations. This indicates that the saccharometabolism selectivity of GYK-10 is stable. Although contamination by some microorganisms, such as Clostridium beijerinckii, Bacillus simplex, and Bacillus brevis, was observed, this had little influence on the fermentation outcomes. This paper reports on the feasibility of using the inversion process in an existing sugar mill.
Chapter 20: An Introduction to Fairness, Absence of Bias, and Equity in Learning Analytics

In this chapter, we examine the ways educational justice has been and may be taken up in learning analytics research. To do so, we ﬁrst outline how we see equity as playing a necessary role in the future development of the learning analytics community. Next, we review how equity has been explored in this area heretofore, focusing on notions of algorithmic fairness and absence of bias. Then, we turn to newer political approaches to the study of learning that are emerging in the learning sciences. We summarize trends in this research’s conceptualizations of equity and the political dimensions of learning. Finally, we connect these related ways of thinking about social justice with respect to learning analytics, and examine the tensions and possibilities at their intersection. We close with some recommendations for the learning analytics ﬁeld to ensure that it contributes to positive educational change moving into the future.
EnKhCorp1.0: An English–Khasi Corpus

In machine translation, corpus preparation is one of the crucial tasks, particularly for low­ resource pairs. In multilingual countries like India, machine translation plays a vital role in communication among people with vari­ ous linguistic backgrounds. There are avail­ able online automatic translation systems by Google and Microsoft which include vari­ ous languages which lack support for the Khasi language, which can hence be consid­ ered low­resource. This paper overviews the development of EnKhCorp1.0, a corpus for English–Khasi pair, and implemented base­ line systems for English­to­Khasi and Khasi­ to­English translation based on the neural ma­ chine translation approach.
Price Discovery and Cross-Market Informational Flow in High-Yield Systematic CDS and Equity Markets: Out-of-Sample Evidence

We contribute to the literature on the efﬁciency of CDS and equity markets by rigorously conducting out-of-sample analysis in order to determine the true predictive power of cross-market informational ﬂow in the systematic high yield sector. Extant studies have been based on in-sample analysis only and reported average results across the examined time period, thereby leaving the question of true and persistent predictive power unanswered. Interestingly, we ﬁnd that both markets are useful in forecasting future values of the other on an out-of-sample basis, indicating that each is more efﬁcient in pricing in certain types of information. However, the CDS market has an informational advantage over the equity market which has increased with time, something not previously documented in the closely related literature. We attribute this ﬁnding to the development of the CDS market into more of an index based vs. a single name market as well as the relative lower level of volatility and investor fear characterizing the back end of our sample.
THE IMPACT OF TEMPERATURE INCREASE IN GREECE ON THE ENERGY DEMAND OF BUILDINGS

The increase of the ambient air temperatures in urban areas during the past few decades, due to the heat island phenomenon and the warming of the lower atmosphere, has strong impact on the energy profile of buildings, the comfort conditions, the air pollution and the indoor environment, especially in the Mediterranean regions with hot climate conditions. The present paper focuses on the investigation of the impact of the temperature increase in nine Greek cities over the last forty years on the energy demand of buildings. This research is very crucial in order to understand climatic future trends, to establish the development of new technologies and techniques (such as ventilation strategies and smart materials) aiming to reduce the energy consumption of buildings, to improve the urban microclimate, as well as to steer specific policy actions. The methodology developed for this research includes: a) Analysis of climatic data: hourly data series of air temperature and relative humidity covering the forty year period 1970-2010 from nine Greek meteorological stations of the Hellenic National Meteorological Service (HNMS) are presented and analysed. b) Simulation studies: In order to evaluate the potential impact of temperature variation on the energy demand of buildings, specific energy simulation studies have been performed using the Virtual Building Dataset (VBD) tool. The simulation process includes the determination of typical building for the simulations, the development of the required climate data files and the performance of 369 simulations using the VBD tool, which has been developed by coupling TRNSYS with Matlab. c) Analysis of the simulation results: the annual heating and cooling loads of the typical building for the nine Greek cities and for the period 1970-2010. The analysis showed that for the period in question the heating load in the Greek building sector has decreased by about 1 kWh/m 2 per decade, while the cooling load has increased by about 5 kWh/m 2 per decade. This phenomenon has major environmental, economic and social consequences, which will be amplified in the upcoming decades in view of the expected man-made climatic changes in this geographic area.
Minefield: A Software-only Protection for SGX Enclaves against DVFS Attacks

Modern CPUs adapt clock frequencies and voltage levels to workloads to reduce energy consumption and heat dissipa-tion. This mechanism, dynamic voltage and frequency scaling (DVFS), is controlled from privileged software but affects all execution modes, including SGX. Prior work showed that manipulating voltage or frequency can fault instructions and thereby subvert SGX enclaves. Consequently, Intel disabled the overclocking mailbox (OCM) required for software undervolting, also preventing benign use for energy saving. In this paper, we propose Mineﬁeld, the ﬁrst software-level defense against DVFS attacks. The idea of Mineﬁeld is not to prevent DVFS faults but to deﬂect faults to trap instructions and handle them before they lead to harmful behavior. As groundwork for Mineﬁeld, we systematically analyze DVFS attacks and observe a timing gap of at least 57 . 8 µs between every OCM transition, leading to random faults over at least 57 000 cycles. Mineﬁeld places highly fault-susceptible trap instructions in the victim code during compilation. Like redundancy countermeasures, Mineﬁeld is scalable and enables enclave developers to choose a security parameter between 0 % and almost 100 %, yielding a ﬁne-grained security-performance trade-off. Our evaluation shows a density of 0 . 75, i.e. , one trap after every 1-2 instruction, mitigates all known DVFS attacks in 99 % on Intel SGX, incurring an overhead of 148 . 4 % on protected enclaves. However, Mineﬁeld has no performance effect on the remaining system. Thus, Mineﬁeld is a better solution than hardware- or microcode-based patches disabling the OCM interface.
Chapter 9: Learning Analytics for Understanding and Supporting Collaboration

Collaboration is an important competency in the modern society. To harness the intersection of learning, work, and collaboration with analytics, several fundamental challenges need to be addressed. This chapter about collaboration analytics aims to highlight these challenges for the learning analytics community. We ﬁrst survey the conceptual landscape of collaboration and learning with a focus on the computer-supported collaborative learning (CSCL) literature while attending to perspectives from computer supported cooperative work (CSCW). Grounded in the conceptual exploration, we then distinguish two salient strands of collaboration analytics: (a) computational analysis of collaboration that applies computational methods to examining collaborative processes; and (b) analytics for collaboration which is primarily concerned with designing and deploying data analytics in authentic contexts to facilitate collaboration. Examples and cases representing different contexts for learning and analytical frames are presented, followed by a discussion of key challenges and future directions.
EXPLORING THE MULTIGENERATIONAL WORKFORCE’S PERSONAL AND WORK VALUES: THE FUTURE RESEARCH AGENDA

Purpose. The purpose of the paper is to review the main contributors to the development of the topic of generational differences at workplaces and to explore emerging trends in the future research agenda. Design/methodology/approach. The literature review, based on conceptual and relational content analyses, has been carried out to annotate and critique the literature on the topic of generational differences as a source of conflict or productivity. The paper develops three research questions and advances three hypotheses to prove or disprove for future research. Findings. The paper provides scientific discussion and recommendations on literature sources that might be valuable for similar research on generational difference issues. The paper develops a theoretical framework for understanding the values of different generations and how they are formed and changed and provides a new conceptual model of future research. Originality/value. The paper identifies four further streams of research on the correlation between personal and work values in the context of generational differences in the workplace; therefore, the paper can potentially contribute to the current scientific discussion on the multigenerational workforce’s personal and work values .
A compact multi‐layer phase correcting lens to improve directive radiation of Vivaldi antenna

Gain of the conventional Vivaldi antenna (CVA) decreases at higher frequencies. A phase correcting lens (PCL) made up of nonresonant rectangular patch unit cells (UCs) is proposed to correct this problem. The layout of the UCs is specially designed taking into account the actual phase error at antenna aperture. The detailed analysis, design and discussion for the CVA with the proposed PCL are performed. The CVA, CVA with one‐layer PCL (CVA‐OPCL), and three‐layer PCL (CVA‐TPCL) are fabricated and measured. The measured results agree well with the simulated ones. The measured results show that the proposed CVA‐TPCL provides a gain variation of 5.75‐14.9 dBi in the wide operating range from 3.5 to 16 GHz, which leads to 1.1‐6.1 dBi gain enhancement than that of the CVA. In addition, the E‐plane half power beamwidths become narrowed significantly at higher frequencies. An average improvement of 4.54 dB is achieved in terms of the front‐to‐back ratio. The sidelobe levels, on average, are reduced by 4.1 dB and 2.8 dB for E‐plane and H‐plane, respectively.
Identification of critical production factors in planted forests of southern Brazil

This work has analyzed the forest-based production chain in the state, especially in the segment consisting of pinus and eucalyptus producing companies, under the prism of the principles of public policy development. The critical factors of the forest production link were detected and prioritized, using the component factor analysis to determine the critical factors and the Friedman test to verify the existence of differences between factors and order them. The Wilconxon test was used to verify the existence of differences in the perception of integrated and non-integrated companies in relation to critical factors. The use of the content analysis technique, theoretically based on the Pestel framework, allowed us to analyze the external environment through the identification of challenges and opportunities and their respective generating macro-environments. Based on the analysis of this compilation of critical factors identified in bibliographical research, 30 critical factors related to forest production were structured. The use of factor analysis and its subsequent interpretation provided the reduction of 30 critical factors in 10 factors. These were considered the critical performance factors of the forest production activity. Based on the analyzes carried out, the great relevance of the application of specific statistical analyzes is verified, aiming at the compilation of critical factors, which are added to the decision-making and articulations that favor forest planning. The critical factors for this study were technology, macroeconomics, logistical and social costs, and legislation. In this context, it is recommended to encourage small and medium-sized forestry companies to take the interactions between the critical factors into account. This needs the forestry-based segment to follow through the development of a joint forestry and industrial policy. In addition, it is recommended that the articulation between the private and government sectors in the search for the development of actions take advantage of the current context of pursuit of sustainability by the end consumer and the state's forestry potential.
Does the application of herbicides with distinct mechanisms of action change enzymatic activity and grain yield of Clearfield ® canola?

Herbicides have a high potential for phytotoxicity and generate oxidative stress, even on tolerant crops. The canola hybrid Hyola 571 CL obtained through Clearfield ® technology shows tolerance to imidazolinones. Given the importance of this hybrid, this work assessed the effect of using herbicides with different mechanisms of action on the activity of antioxidant enzymes, ALA-D, gas exchange, and grain yield of canola hybrid tolerant to imidazolinones. Treatments applied to Hyola 571 CL canola hybrid were: control without herbicides, diclosulam, imazaquim, sulfentrazone, sulfentrazone + diuron pendimenthalin, flumioxazin, imazethapyr + imazapic, imazapic + imazapyr, imazethapyr, imazamox, nicosulfuron, chlorimuron, netsulfuron-methyl, iodosulfuron, and pyroxsulam. Seven, 14, 21, and 28 days after application we assessed herbicide phytotoxicity to canola. Antioxidant enzyme systems, variables related to plant physiology, and grain yield of Hyola 571 CL were also determined. It was observed that the hybrid, even tolerant, showed sensitivity to herbicides. Pendimethalin, flumioxazin, chlorimuron-ethyl, metsulfuron-methyl, and iodosulfuron were the most harmful to Hyola 571 CL. Commercial mixtures by [imazethapyr + imazapic] and [imazapic + imazapyr], as well as imazamox, were the most selective ones. Catalase was inhibited by most treatments. Sulfentrazone promoted the greatest negative interference along with its mixture with diuron and nicosulfuron, chlorimuron, and metsulfuron-methyl. The hybrid showed sensitivity to herbicides and the differences in gas exchange rates and other analyzes for the types of treatment do not result in productivity.