Miracle-dz/UniFAR-FTUs huggingface dataset:
The complements towards developing a new risk management framework and Its applicability to the Nigerian power sector

The availability of regular supply has been identified as one of the major stimulants for the growth and development of any nation and is thus important for the economic well-being of a nation. The problems of the Nigerian power sector stems from a lot of factors culminating in her slow developmental growth and inability to meet the power demands of her citizens regardless of the abundance of human and natural resources prevalent in the nation. The research therefore had the main aim of investigating the importance and contributions of risk management to the success of projects specific to the power sector. To achieve this aim it was pertinent to examine the efficacy of risk management process in practice and elucidate the various risks typically associated with projects (Construction, Contractual, Political, Financial, Design, Human resource and Environmental risk factors) in the power sector as well as determine the current situation of risk management practice in Nigeria. To address this factors inhibiting the proficiency of the overarching and prevailing issue which have only been subject to limited in-depth academic research, a rigorous mixed research method was adopted (quantitative and qualitative data analysis). A review of the Nigeria power sector was also carried out as a precursor to the data collection stage. Using purposive sampling technique, respondents were identified and a questionnaire survey was administered. The research hypotheses were tested using inferential statistics (Pearson correlation, Chi-square test, t-test and ANOVA technique) and the findings revealed the need for the development of a new risk management implementation Framework. The proposed Framework was tested within a company project, for interpreting the dynamism and essential benefits of risk management with the aim of improving the project performances (time), reducing the level of fragmentation (quality) and improving profitability (cost) within the Nigerian power sector in order to bridge a gap between theory and practice. It was concluded that Nigeria’s poor risk management practices have prevented it from experiencing strong growth and development. The study however, concludes that the successful implementation of the developed risk management framework may help it to attain this status by enabling it to become more prepared and flexible, to face challenges that previously led to project failures, and thus contributing to its prosperity. The research study provides an original contribution theoretically, methodologically and practically which adds to the project risk management body of knowledge and to the Nigerian power sector.
Calibrating Parsimonious Models Of Equity-Linked Default Intensity

Many real-world investment portfolios have a value that is sensitive to both (i) changes in equity price and (ii) risk of default. In some cases, notably convertible bonds, these sensitivities are present in a single instrument. Primitive mixed-discounting approaches are unsatisfactory for both theoretical and practical reasons, yet are common in existing open-source treatments. Elaborating on Andersen and Buffum (2004), this technical report describes a useful stochastic model linking equity and debt, its translation to a grid scheme for pricing various derivatives classes, and best practices for calibrating it. Introduction Financial derivatives generally fall into one of just a few classes: fixed income, equities, foreign exchange, commodities, and energy. Pricing models for these derivatives usually involve standard approaches distinct to each of these asset classes. For example, equity derivatives are priced on variants of the famous BlackScholes model, while tranche protection on mortgage-backed securities uses copulas with marginal Poisson distributions. Convertible bonds are one of the few types of derivative securities straddling asset classes, and whose valuation must be linked to reasonable models of multiple asset types, involving equity valuations, fixed income and sometimes foreign exchange. A convertible bond is similar to a corporate bond1, promising coupons and notional payments at some known set of future dates, but with a twist. The bond holder, who has effectively lent money to the issuer, can choose to convert the bond into equity (subject to some restrictions), in a varying amount known as the conversion value. The bond value therefore depends on three major processes: • equity value affecting conversion value • default of the issuer wiping out equity, coupons and notional • interest rates affecting the discounted value of future coupons and notional Of these processes, the changes in equity value are most important, followed closely by issuer default2. We discuss derivative pricing and calibration based on simply linked models of equities and corporate defaults. Though convertible bonds are our most important case, we also consider mandatories, compound options, and portfolios of equity options and credit derivatives on the same underlying company. Stochastic Model Our basic stochastic model3 links equity values St with hazard rate or default intensity h dSt St = (r + h− q)dt+ σdZ − dJ 1A standard corporate bond is often called a straight bond 2One might wonder how a fixed-income security could be relatively insensitive to stochastic interest rates. Most convertibles are issued in countries with stable economies, so the rates are generally far less variable and far smaller than the credit spreads of the bond issuers. 3More explicitly we could write dSt St = (r(t) + h(St, t) − q(t))dt+ σ(t)dZ − dJ(h(St, t))
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics

Humans can reason about intuitive physics in fully or partially observed environments even after being exposed to a very limited set of observations. This sample-efficient intuitive physical reasoning is considered a core domain of human common sense knowledge. One hypothesis to explain this remarkable capacity, posits that humans quickly learn approximations to the laws of physics that govern the dynamics of the environment. In this paper, we propose a Bayesian-symbolic framework (BSP) for physical reasoning and learning that is close to human-level sample-efficiency and accuracy. In BSP, the environment is represented by a topdown generative model of entities, which are assumed to interact with each other under unknown force laws over their latent and observed properties. BSP models each of these entities as random variables, and uses Bayesian inference to estimate their unknown properties. For learning the unknown forces, BSP leverages symbolic regression on a novel grammar of Newtonian physics in a bilevel optimization setup. These inference and regression steps are performed in an iterative manner using expectation-maximization, allowing BSP to simultaneously learn force laws while maintaining uncertainty over entity properties. We show that BSP is more sample-efficient compared to neural alternatives on controlled synthetic datasets, demonstrate BSP’s applicability to real-world common sense scenes and study BSP’s performance on tasks previously used to study human physical reasoning.1
Geographical variation of prementum size in Iberian Cordulegaster boltonii (Odonata: Cordulegastridae) populations

Within wide geographical areas, Odonata populations can show biometric differences as a consequence of both biotic (e.g., predation, competition) and abiotic factors (mainly temperature). These differences can occur in the larval stage, although reliable characters are needed to detect differences. We analyzed whether Cordulegaster boltonii larvae from 18 Iberian populations differ regarding head width and prementum size (maximum width, minimum width, and maximum length), using measurements taken on final stage exuviae. Prementum length was greater in southern populations than in northern ones. Geographic latitude and temperature were the variables that best explained this variation in females, whereas latitude and altitude above sea level offered the best explanation among males.
Early gastric cancer detection based on the combination of convolutional neural network and attention mechanism

Gastric cancer is one of the three major malignant tumors in the world. The earlier it is found, the higher cure rate get. However, the early clinical diagnosis is still not good enough. Thus, timely and accurate diagnosis of early gastric cancer lesions using computer-aided technology is urgently needed. This paper proposes an early gastric cancer detection (EGCD) model on the basis of CNN and attention mechanism. Due to the irregular shape of early gastric cancer under the endoscopy, the boundary is not obvious. First, improve YOLOv4, and propose E-YOLO with feature layer fusion. Combine CBAM to design EGCD to enhance the characteristic expression of cancer targets in channel and spatial, highlight cancer targets, and suppress background information. The results elaborate specificity of EGCD model is 77.39% and sensitivity is 82.27%. The average accuracy reaches 94.16%, which is 5% higher than YOLOv4. The method proposed has improved results in early gastric cancer detection and has high practical application value.
Roof Supports Measurement in Coal Mining by 3D Registration and Model Fitting

The roof support, a type of special equipment for coal mining, protect miners and other facilities in fully mechanized working face under the ground, and move with shearers when extracting coal. The states of each support, such as the position, height and tilt angle are important for its motion control. Unfortunately, it is difficult to measure these parameters directly in the working face. The novelty of this paper is, we use two LiDARs carried by a rail robot to scan the whole working face for the point cloud and adopt 3D registration and model fitting methods to calculate the key parameters of supports. The experiments show that our method can successfully tackle this problem. Since there are few works that used LiDARs for support measurement, we believe our effort would contribute to the intelligent coal mining.
Application of KNN prediction model in urban traffic flow prediction

Traffic congestion is one of the most important problems of urban traffic. Real time prediction of urban traffic flow can provide data reference to congestion dredging and driving route planning. In order to realize real-time urban traffic flow prediction, an urban traffic flow prediction model based on K nearest neighbor (KNN) model is studied. The experimental results show that the average prediction time of the urban traffic flow prediction model based on KNN is 1.3s and the average prediction accuracy is 91.1%. It can effectively realize the real-time urban bayonet traffic flow prediction efficiently and accurately, and it is of great practical value for the traffic management department to prevent and dredge road congestion and for the driver to choose a smooth driving path.
Adaptive task offloading in V2X networks based on deep reinforcement learning

With the constraints of limited computing and communication resources in V2X networks, V2V communication for task offloading needs to adapt to the dynamic V2X environment while satisfying the requirements of low delay and high reliability. Aiming at balancing the trade-off between delay and power consumption in V2V-enabled task offloading, this paper proposes a V2V-enabled multi vehicle task offloading algorithm based on deep reinforcement learning Deep Deterministic Policy Gradient (DDPG). Firstly, we construct a V2V-enabled multi vehicle task offloading system model, which leverages the surrounding vehicles with computing resources as the relay node. In this model, multi hop communication is used to offload tasks to multiple vehicles for coordinated computation, and we assume the transmission power of tasks is a tunable continuous variable. Then, DDPG algorithm is proposed to deal with the continuous high-dimensional action space in task offloading. The proposed task offloading algorithm based on DDPG is simulated and compared with other deep reinforcement learning algorithms. Experimental results show that the algorithm can effectively balance the delay and power performance while improving the success rate of task offloading.
Non-performing loans and the lending channel of shock transmission across countries

Abstract Recent international macroeconomics literature on global imbalances explains the U.S. persistent current account deficit and emerging countries’ surplus. Little research has been done at the banking-sector level, where U.S. banks are lenders to banks in emerging countries. We build a two-country framework where banks are explicitly modeled to investigate how lending in the banking sector can affect the international macroeconomy during the financial crisis of 2007–2008. In the steady state, banks in the developing country borrow from the U.S. banks. When the borrowers in the U.S. pay back less than contractually agreed and damage the balance sheet of the U.S. banks, with the presence of bank capital requirement constraint, U.S. banks raise lending rates and decrease the loans made to U.S. borrowers as well as banks in the developing country. The results are a sharp increase in the lending spread, a reduction in output and a depreciation in the real exchange rate of the developing country. This is the experience of many emerging Asian markets following the U.S. financial crisis starting in late 2007. Another feature of our model captures an empirical fact, documented by Devereux and Yetman, that across different economies, countries with lower financial ratings can suffer more when the lending country deleverages.
The nature and extent of sexual assault in the sky: Shining a light on a ‘black box’

Strikingly, federal data sources tracking the nature and extent of sexual assaults that occur aboard airlines are incomplete and typically not released to the public. In order to better understand this relatively hidden social phenomenon, we conducted a content analysis of media reports published over approximately a 20-year period (2000–2020). Within these media reports, we analyzed the incident characteristics of in-flight sexual assaults, reporting behaviors of victims, airline responses to victimization, and criminal justice processes such as adjudication. Findings reveal certain patterns in the coverage concerning victim and perpetrator characteristics, offense characteristics, flight information, and post-offense outcomes. In synthesizing study results, we apply tenets of routine activities theory (RAT) and the #MeToo perspective. We outline implications for future study and policy.
Radiomic and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to Detect Breast Malignant Lesions

Purpose:The purpose of this study was to discriminate between benign and malignant breast lesions through several classifiers using, as predictors, radiomic metrics extracted from CEM and DCE-MRI images. In order to optimize the analysis, balancing and feature selection procedures were performed. Methods: Fifty-four patients with 79 histo-pathologically proven breast lesions (48 malignant lesions and 31 benign lesions) underwent both CEM and DCE-MRI. The lesions were retrospectively analyzed with radiomic and artificial intelligence approaches. Forty-eight textural metrics were extracted, and univariate and multivariate analyses were performed: non-parametric statistical test, receiver operating characteristic (ROC) and machine learning classifiers. Results: Considering the single metrics extracted from CEM, the best predictors were KURTOSIS (area under ROC curve (AUC) = 0.71) and SKEWNESS (AUC = 0.71) calculated on late MLO view. Considering the features calculated from DCE-MRI, the best predictors were RANGE (AUC = 0.72), ENERGY (AUC = 0.72), ENTROPY (AUC = 0.70) and GLN (gray-level nonuniformity) of the gray-level run-length matrix (AUC = 0.72). Considering the analysis with classifiers and an unbalanced dataset, no significant results were obtained. After the balancing and feature selection procedures, higher values of accuracy, specificity and AUC were reached. The best performance was obtained considering 18 robust features among all metrics derived from CEM and DCE-MRI, using a linear discriminant analysis (accuracy of 0.84 and AUC = 0.88). Conclusions: Classifiers, adjusted with adaptive synthetic sampling and feature selection, allowed for increased diagnostic performance of CEM and DCE-MRI in the differentiation between benign and malignant lesions.
The Impact of Novelty Seeking on Intention to Visit a Country: The Mediating Role of Overall Cuisine Image

Participation in travel is continuously increasing throughout the world. Seeking novelty, particularly novel cuisine, is one of the motivating factors underlying travel. Image is one of the most important factors that affect intention to visit a country and experience the cuisine. This study examines the mediating effects of Turkish cuisine image on the relationship between novelty seeking and intention to visit. In this regard, Turkish cuisine image and its effect on individuals from the United Kingdom (U.K.) who had eaten at Turkish restaurants was analyzed. Individuals at Turkish restaurants were asked to fill out self-administered questionnaires with 78 questions. An online platform of people was also utilized. It was seen that cuisine image has a significant mediating role on the intention to visit Turkey.
A Pilot Randomized Controlled Trial of Goal Management Training in Canadian Military Members, Veterans, and Public Safety Personnel Experiencing Post-Traumatic Stress Symptoms

Post-traumatic stress disorder (PTSD) is a severe psychiatric illness that disproportionately affects military personnel, veterans, and public safety personnel (PSP). Evidence demonstrates that PTSD is significantly associated with difficulties with emotion regulation (ER) and difficulties with cognitive functioning, including difficulties with attention, working memory, and executive functioning. A wide body of evidence suggests a dynamic interplay among cognitive dysfunction, difficulties with ER, and symptoms of PTSD, where numerous studies have identified overlapping patterns of alterations in activation among neuroanatomical regions and neural circuitry. Little work has examined interventions that may target these symptoms collectively. The primary objective of this pilot randomized controlled trial (RCT) with a parallel experimental design was to assess the effectiveness of goal management training (GMT), a cognitive remediation intervention, in reducing difficulties with cognitive functioning, and to determine its effects on PTSD symptoms and symptoms associated with PTSD, including difficulties with ER, dissociation, and functioning among military personnel, veterans, and PSP. Forty-two military personnel, veterans, and PSP between the ages of 18 and 70 with symptoms of PTSD were recruited across Ontario, Canada between October 2017 and August 2019. Participants were randomized to either the waitlist (WL) (n = 18) or the GMT (n = 22) condition. Participants in both conditions received self-report measures and a comprehensive neuropsychological assessment at baseline, post-intervention, and 3-month follow-up. Following their completion of the 3-month follow-up, participants in the WL condition were given the opportunity to participate in GMT. Assessors and participants were blind to intervention allocation during the initial assessment. A series of 2 (time) × 2 (group) ANOVAs were conducted to assess the differences between the WL and GMT conditions from pre- to post-intervention for the self-report and neuropsychological measures. The results demonstrated significant improvements in measures of executive functioning (e.g., verbal fluency, planning, impulsivity, cognitive shifting, and discrimination of targets) and trending improvements in short-term declarative memory for participants in the GMT condition. Participants in the GMT condition also demonstrated significant improvements from pre- to post-testing in measures of subjective cognition, functioning, PTSD symptom severity, difficulties with ER, dissociative symptom severity, and depression and anxiety symptoms. No adverse effects were reported as a result of participating in GMT. The results of this pilot RCT show promise that GMT may be a useful intervention to improve symptoms of cognitive dysfunction, symptoms of PTSD, and symptoms associated with PTSD within military personnel, veterans, and PSP. Future work is needed to address the small sample size and the durability of these findings.
THE DISCOVERY OF ENTERPRISE NETWORK TOPOLOGY CREATED IN A VIRTUAL ENVIRONMENT WITH SNMPV3

To design network management systems in a best way, network topology have to be discovered with every features of network such as connectivity, device type, etc. Our goal is to discover an enterprise network which is created in a virtual environment (GNS3 and VMWARE Workstation) with SNMPv3. An algorithm which is used in previously academic researchs is redesigned according to features of virtual environments which are used in project.
Consideration of Rotational Motion in the Proper Generalized Decomposition by a Sliding Interface Technique

The simulation of rotating electrical machines, particularly in the development process, involves many parameters, such as the current excitation, permanent magnet materials, and the relative rotor position. To reduce the computational effort related to a large number of degrees of freedom (DOFs), the proper generalized decomposition (PGD) can be employed. Although the mentioned reduction technique is able to efficiently cope with parameter variations, it is limited to conformal meshes. However, the consideration of moving parts in the model for the simulation of rotating electrical machines is a necessity to characterize the machine’s behavior. For this purpose, the sliding interface technique, based on Lagrange multipliers (LMs) can be utilized. In this contribution, the sliding interface technique, which imposes no restrictions on the finite element discretization of the interface between stator and rotor, is combined with the PGD to lift the restriction of the latter to conformal meshes while keeping the symmetry and positive definiteness of the system without the creation of additional elements. This approach enables the parametric simulation of rotating electrical machines by the PGD.