Miracle-dz/UniFAR-FTUs huggingface dataset:
DOES BANK DIVERSIFICATION SIGNIFICANTLY AFFECT PROFITABILITY? A LONGITUDINAL STUDY ON BANGLADESH

This paper examines the effect of the bank diversification (i.e., income diversification and asset diversification) on the profitability of banks in Bangladesh. We analyze an unbalanced panel data of 32 banks covering 318 bank-year observations from 2007 to 2016 by employing dynamic panel data model with one-step system Generalized Methods of Moments (GMM) and Ordinary Least Squares (OLS) regressions. The results show that income diversification and assets diversification have a significant positive relationship with bank profitability. Thus, the results imply that banks can benefit from the income diversification and assets diversification if diversified activities yield a high return, while it may hurt banks if confronted with low return. This study is one of the unique attempts at examining the relationship between diversification and profitability in the bank industry of Bangladesh. We expect that the findings have significant implications for the bank regulators in Bangladesh and other similar economies.
Stress from Uncertainty Predicts Resilience and Engagement among Subspecialty Medicine Fellows

Background: Burnout is an accelerating phenomenon in the healthcare environment, with implications for physician wellbeing, patient safety, and the quality of healthcare. The ability to manage uncertainty has been identified as a potentially important determinant of burnout in physicians and is now a recognized competency for trainees. No prior studies have evaluated the association of fellows’ reaction to uncertainty with burnout metrics, including resilience and engagement. Methods: Cross-sectional online survey in July 2018 of sub-specialty fellows in the Department of Medicine at Massachusetts General Hospital, a large academic medical center, using validated scales of resilience, engagement, empowerment, burnout, and reaction to uncertainty. Results: Of the 317 eligible participants, 102 (32%) completed the reaction to uncertainty scale. In bivariate analyses, stress from uncertainty was significantly higher among women (44.9 (9.7) vs. 38.1 (11.2); p = 0.002), younger fellows (<= 30 years of age) (47.0 (6.3) vs. 39.6 (11.5); p = 0.006), and graduates of US medical schools (42.3 (10.9) vs. 36.1 (10.6); p = 0.03). Stress from uncertainty was inversely correlated with work engagement (r = -0.26; p = 0.009), work empowerment (r = -0.37; p = 0.003) and resilience (r = -0.38; p < 0.001), and positively correlated with burnout (r = 0.29; p = 0.004). Conclusion: At a time when concern for trainee wellbeing is high, with much speculation about causes of burnout, we find that efforts to decrease stress from uncertainty may pay dividends in reducing burnout, and increasing resilience, engagement and empowerment.
DDOS Anomaly Detection And Mitigation In Software Defined Networking (SDN)

: The introduction of Software Defined Networking as a panacea to the global demand for a more secure and highly dependable internet infrastructure has also brought along security issues. The adoption of OpenFlow Protocol (OFP) by SDN as the way of communication between controllers and switches has not only brought about easy and direct manipulation of data for enhanced packet forwarding policies, but also renders the network vulnerable to security issues (DDOS attacks) since the OF switch has to ask the controller to install new rules for any new incoming packet. In this paper, we prove that SDN is capable of handling security threats that arise from the above vulnerability. We implement a DDOS detection module that detects and blocks SYN flood attacks aimed to destabilize the flow of normal network traffic among users in a software-defined networking environment.
Cytotoxic Effect of Methotrexate on Leishmaniadonovani Promastigotes

Leishmania is auxotroph to folic acid,antifolates drug inhibit the synthesis and conversion of folate derivatives. In this study, cytotoxic effect of methotrexate was investigated on the procyclic promastigotes proliferation of L. donovani.The results showed a significant (p ≥ 0.05) difference in growth of treated groupsat high concentrations (1000, 500, 250, 125.5) μM after 24, 48 hrs. , while at 72 hrs. significant difference was observed at all concentration. The IC50 values was measurable after 24, 48 and 72 hrs. and it was 174.238, 52.283 and 109.175 μM, respectively. The present study showed the cytotoxic effect of methotrexate on the proliferation of promastigotes of the visceral type of Leishmania. ------------------------------------------------------------------------------------------------------------------------------------Date of Submission:30-10-2018 Date of acceptance: 15-11-2018 --------------------------------------------------------------------------------------------------------------------------------------
Exploring the determinants of technical inefficiency in mango enterprise: a case of Muzafargarh, Pakistan

The study purpose is parametric estimation of technical efficiency level of mango growers and exploring those socio management factors responsible for inefficiency in mango production. A Cobb Douglas production function was applied by stochastic frontier analysis. Maximum likelihood was used to check the source of variation in mango yield. Empirical inefficiency model has been developed. Total 110 randomly selected mango growers were personally interviewed by well-designed questionnaire. Results of maximum likelihood shows, 99 percent variation from potential yield level was attributed by technical inefficiency. In result of Cobb Douglas function, the average efficiency level of mango growers was 0.60. It implies that still mango growers have opportunity of reducing the inputs usage by 40 percent without compromising the mango yield. The age of respondents, age of orchard, orchard size and intercropping were contributing in inefficiency of mango growers. On other hand Schooling years, family size, orchard experience, owned power source and access to Exploring the determinants of technical inefficiency in mango enterprise: a case of Muzafargarh, Pakistan Haq, S.U. ; Shahbaz, P.; Biz, I. ; Yildirim, C; Murtaza, M.R. Custos e @gronegócio on line v. 13, Apr/Jun. 2017. ISSN 1808-2882 www.custoseagronegocioonline. com.br 219 extension were significantly reducing the inefficiency level. The efficient farmers’ social profile was much better than moderate and inefficient farmers. The technical efficiency can be increased by providing the better understanding of mango growing technology through extension services in the study area.
Appendix 10C. Sources and methods for arms transfers data

The SIPRI Arms Transfers Project reports on international flows of conventional weapons. Since publicly available information is inadequate for the tracking of all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data are collected from open sources in the SIPRI arms transfers database and presented in a register that identifies the suppliers, recipients and weapons delivered, and in tables that provide a measure of the trends in the total flow of major weapons and its geographical pattern. SIPRI has developed a unique trendindicator value (TIV) system. This value is not comparable to financial data such as gross domestic product, public expenditure and export/import figures. The database covers the period from 1950. Data collection and analysis are continuous processes. As new data become available the database is updated for all years included in the database.1
Building of Classifier and Mining Affiliation Rules – A Research Analysis

: This proposition is given to protection safeguarding characterization and affiliation rules mining over unified information mutilated with randomisation-based techniques which alter singular esteems indiscriminately to give a normal level of security. It is expected that lone contorted esteems and parameters of a mutilating system are known amid the way toward building a classifier and mining affiliation rules. In this proposition, we have proposed the advancement MMASK, which wipes out exponential multifaceted nature of assessing a unique help of a thing set as for its cardinality, and, in outcome, makes the protection saving revelation of incessant thing sets and, by this, association rules attainable. It likewise empowers each estimation of each credit to have diverse mutilation parameters. We indicated tentatively that the proposed advancement expanded the precision of the outcomes for abnormal state of security. We have likewise displayed how to utilize the randomisation for both ordinal and whole number credits to alter their qualities as indicated by the request of conceivable estimations of these ascribes to both keep up their unique space and acquire comparative appropriation of estimations of a property after mutilation. Furthermore, we have proposed security saving strategies for characterization in light of Emerging Patterns. Specifically, we have offered the excited ePPCwEP and languid lPPCwEP classifiers as security safeguarding adjustments of enthusiastic CAEP and apathetic DeEPs classifiers, separately. We have connected meta-figuring out how to protection safeguarding characterization. Have we utilized packing and boosting, as well as we have joined variant likelihood circulation of estimations of properties recreation calculations and remaking sorts for a choice tree keeping in mind the end goal to accomplish higher exactness of order. We have demonstrated tentatively that meta-learning gives higher precision pick up for security saving classification than for undistorted information. The arrangements exhibited in this proposal were assessed and contrasted with the current ones. The proposed strategies got better precision in protection saving affiliation rules mining and arrangement. Besides, they diminished time many-sided quality of finding affiliation rules with safeguarded protection.
Impact of Using a Bilingual Model on Kazakh-Russian Code-Switching Speech Recognition

Due to the prevalence of bilingualism among Kazakh speakers, code-switching to Russian is common in Kazakh speech. That presents a challenge for monolingual Kazakh-language ASR systems that struggle to transcribe the embedded Russian words. This paper attempts to determine the beneﬁt of bilingual training on matrix language (Kazakh) and embedded language (Russian) monolingual data, as opposed to training on code-switched data only. Speciﬁcally, we evaluate the model’s performance on matrix language words and embedded words separately. We make use of two datasets: Kazakh speech with code-switching and Russian speech with no code-switching. We train a monolingual model on each dataset, and a bilingual model on a mixture of the two. The main objective of the experiments is to compare the performance of a model trained on code-switched speech with that of a model trained on full utterances in both languages. Experimental results suggest that bilingual training improves the model’s performance on matrix words, and greatly improves its performance on embedded words. We observe an absolute WER improvement of 14.69% in the code-switched words.
Dissociating identity from gait: A virtual reality study of the role of dynamic identity signatures in person recognition

Studies on person recognition have primarily examined recognition of static faces, presented on a computer screen at a close distance. Nevertheless, in naturalistic situations we typically see the whole dynamic person, often approaching from a distance. In such cases, facial information may be less clear, and the motion pattern of an individual, their dynamic identity signature (DIS), may be used for person recognition. Recently, several studies examined the role of motion in person recognition by presenting videos of people in motion. However, such stimuli do not allow for the dissociation of gait from identity, as different individuals differ both in their gait and their identity. To examine the role of gait independently from person identity in person recognition, we used a virtual environment, which enables presenting the same type of gait across different identities. Using this setting, we assessed the accuracy and distance at which identities are recognized based on their gait, as a function of gait distinctiveness. Furthermore, the virtual environment also enabled us to assess, for the first time, the distance at which a person is recognized as a continuous variable. We find that the accuracy and distance at which people were recognized increased with gait distinctiveness. Importantly, these effects were found when recognizing identities in motion but not from static displays, indicating that DIS rather than attention, enabled more accurate person recognition. Overall these findings highlight an important role for gait in real-life person recognition and stress that gait contributes to recognition independently from the face and body.
Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective

Energy models have been widely applied to the analysis of energy system decarbonisation to assess the options and costs of a transition to a low carbon supply. However, questions persist as to whether they are able to effectively represent and assess heat decarbonisation pathways for the buildings sector. A range of limitations have been identified, including a poor spatio-temporal resolution, limited representation of behaviour, and restricted representation of the full technical option set. This paper undertakes a review of existing energy models for heat decarbonisation in the UK, applying the novel perspective of energy system architecture (ESA). A set of ESA-related features are identified (including evolvability, flexibility, robustness, and feasibility), and models are reviewed against these features. The review finds that a range of models exist that have strengths across different features of ESA, suggesting that multiple modelling approaches are needed in order to adequately address the heat decarbonisation challenge. However, opportunities to improve existing models and develop new approaches also exist, and a research agenda is therefore proposed.
PLASTIC WASTE MANAGEMENT

A trend of significant increase in plastic waste generation has been recorded worldwide. This has been found due to over population growth rate, industrialization, urbanization and economic growth which have ultimately resulted in increased plastic accumulation. In India 70 percent of total plastic consumption is discarded as waste. Around 5.6 million tons annum (TPA) of plastic waste is generated in country, which is about 15,342 tons per day (TPD). An efficacious management needs to be materialized for better planet to live in. The proposed work implements an automated waste segregator household level using microcontroller to control the entire process with ease and simplicity. The sensing unit consists of a capacitive proximity sensor to detect and segregate plastic wastes. The proposed systems would be best applied in industries that handle plastic shipped in from many sources including multiple numbers of municipal recycling facilities. Each of the sorting methods currently being researched would show a great promise in the future.
EE-559 – Deep learning

We present a conceptually simple, ﬂexible, and general framework for object instance segmentation. Our approach efﬁciently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g . , allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. With-out bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. Code has been made available at: https://github.com/ facebookresearch/Detectron .
Computerized neuropsychological rehabilitation of a frontal lobe stroke: Hierarchical attention training

The prefrontal cortex (PFC) has extensive reciprocal connections with nearly all cortical and subcortical structures, placing it in a unique position to orchestrate a wide range of cognitive and affective neural functions. Damage of PFC in humans is associated with profound changes in social and affective behavior. The development of new technologies in the field of cognitive rehabilitation is giving us the opportunity to use computer-based cognitive tools to improve the recovery of patients affected by acquired brain injury.
WASTE VEGETABLE OILS (WVO) AS COMPRESSION IGNITION ENGINE FUEL: A REVIEW

Escalating fuel crisis and environmental pollution have generated interest in alternative fuel. Vegetable oils present promising alternate to diesel, since most of their properties are in good agreement with those of petro-diesel. Obviously, inedible and waste vegetable oils can be seriously considered as fuels for engines as edible vegetable oils are far too expensive. In this work, available information on utilization of waste vegetable oil (WVO) as an alternative fuel in compression ignition engine has been collected and arranged in a systematic order. The present paper discusses method of biodiesel production and characteristics of WVO in comparison with diesel. Engine performance, emission and combustion characteristics using waste vegetable oil as a fuel have been deeply analyzed. In short, a meticulous report has been prepared to give an insight into the progress of research in the field of waste vegetable oil as fuels for compression ignition engine.
Central Bank Communication during Economic Recessions: Evidence from Nigeria

This paper analyses the communication strategy of the Central Bank of Nigeria (CBN) during the 2016 economic recession. Applying text mining techniques, useful insights are derived regarding the linguistic intensity, readability, tone, and topics of published monetary policy communiques. Our results provide evidence of increased central bank communication during the recession. However, the ease of reading the published policy communiques declined, especially at the outset of the recession. In terms of tone, we find that negative policy sentiments were expressed during the 2015-2017 period; reflecting the economic uncertainties that trailed the oil price slump of 2014 and its implications for the domestic economy. The negativity of the policy sentiment score reached its trough in July 2016 and recorded an inflexion; signalling the economy’s turning point towards recovery. Based on the results of the estimated topic model, issues relating to “oil price shocks”, “external reserves”, and “inflation” were of concern to the Monetary Policy Committee (MPC) a few quarters preceding the recession while the topics relating to “exchange rate management” as well as “output growth and market stability” were dominant during the recession. Expectedly, the topic proportion for “prices and macroeconomic policies” remain relatively sizeable across the sample period, reflecting the MPC’s commitment to the CBN’s primary mandate of maintaining price stability.
Supporting the Process of Learning and Teaching Process Models

The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called Model Judge, focused toward the two main actors in the process of learning process model creation: novice modelers and instructors. For modelers, the platform enables the automatic validation of the process models created from a textual description, providing explanations about quality issues in the model. Model Judge can provide diagnostics regarding model structure, writing style, and semantics by aligning annotated textual descriptions to models. For instructors, the platform facilitates the creation of modeling exercises by providing an editor to annotate the main parts of a textual description, which is empowered with natural language processing capabilities so that the annotation effort is minimized. So far around 300 students in process modeling courses of five different universities around the world have used the platform. The feedback gathered from some of these courses shows good potential in helping students to improve their learning experience, which might, in turn, impact process model quality and understandability. Moreover, our results show that instructors can benefit from getting insights into the evolution of modeling processes, including arising quality issues of single students, but also discovering tendencies in groups of students. Although the framework has been applied to process model creation, it could be extrapolated to other contexts where the creation of models based on a textual description plays an important role.
A Genome-Wide Study of Homologous Recombination in Mammalian Cells Identifies RBMX, a Novel Component of the DNA Damage Response

Repair of DNA double-strand breaks is critical to the maintenance of genomic stability, and failure to repair these DNA lesions can cause loss of chromosome telomeric regions, complex translocations, or cell death. In humans this can lead to severe developmental abnormalities and cancer. A central pathway for double-strand break repair is homologous recombination (HR), a mechanism that operates during the S and G2 phases of the cell cycle and primarily utilizes the replicated sister chromatid as a template for repair. Most knowledge of HR is derived from work carried out in prokaryotic and eukaryotic model organisms. To probe the HR pathway in human cells, we performed a genome-wide siRNA-based screen; and through this screen, we uncovered cellular functions required for HR and identified proteins that localize to sites of DNA damage. Among positive regulators of HR, we identified networks of pre-mRNA-processing factors and canonical DNA damage response effectors. Within the former, we found RBMX, a heterogeneous nuclear ribonucleoprotein (hnRNP) that associates with the spliceosome, binds RNA, and influences alternative splicing. We found that RBMX is required for cellular resistance to genotoxic stress, accumulates at sites of DNA damage in a poly(ADP-ribose) polymerase 1-dependent manner and through multiple domains, and promotes HR by facilitating proper BRCA2 expression. Screen data also revealed that the mammalian recombinase RAD51 is commonly off-targeted by siRNAs, presenting a cautionary note to those studying HR with RNAi and highlighting the vulnerability of RNAi screens to off-target effects in general. Candidate validation through secondary screening with independent reagents successfully circumvented the effects of off-targeting and set a new standard for reagent redundancy in RNAi screens.