Miracle-dz/UniFAR-FTUs huggingface dataset:
HTTP-Based APT Malware Infection Detection Using URL Correlation Analysis

APT malware exploits HTTP to establish communication with a C & C server to hide their malicious activities. Thus, HTTP-based APT malware infection can be discovered by analyzing HTTP traffic. Recent methods have been dependent on the extraction of statistical features from HTTP traffic, which is suitable for machine learning. However, the features they extract from the limited HTTP-based APT malware traffic dataset are too simple to detect APT malware with strong randomness insufficiently. In this paper, we propose an innovative approach which could uncover APT malware traffic related to data exfiltration and other suspect APT activities by analyzing the header fields of HTTP traffic. We use the Referer field in the HTTP header to construct a web request graph. Then, we optimize the web request graph by combining URL similarity and redirect reconstruction. We also use a normal uncorrelated request filter to filter the remaining unrelated legitimate requests. We have evaluated the proposed method using 1.48 GB normal HTTP flow from clickminer and 280 MB APT malware HTTP flow from Stratosphere Lab, Contagiodump, and pcapanalysis. The experimental results have shown that the URL-correlation-based APT malware traffic detection method can correctly detect 96.08% APT malware traffic, and its recall rate is 98.87%. We have also conducted experiments to compare our approach against Jiang’s method, MalHunter, and BotDet, and the experimental results have confirmed that our detection approach has a better performance, the accuracy of which reached 96.08% and the F1 value increased by more than 5%.
Goal oriented image quality assessment

The area of image quality assessment(IQA) is an active research area in image processing and computer vision. All IQA algorithms reported in literature are attempting to quantify only the visual quality of the images/videos. An interesting question to be answered is that given a goal (task) and an image (with good visual quality), is the image good to achieve the goal by the best possible algorithm. In an attempt to spur the research community to answer this question, a new paradigm of IQA, called goal oriented IQA (GO-IQA), is introduced. GO-IQA is deﬁned as given an image and a goal, predicting how good the image is to achieve that goal by the best possible algorithm. The need for GO-IQA is that if GO-IQA score is less, then any arbitrary algorithm attempting to achieve the goal will eventually fail. In this paper, considering segmentation as goal, a GO-IQA algorithm is proposed to predict how good the image is to do accurate segmentation by the best possible segmentation algorithm. A support vector regression model has been trained with features related to image segmentation along with the accuracies of a class of well-known image segmentation algorithms. In the process, we have obtained encouraging results in terms of the Pearson linear correlation coefﬁcient and mean squared error. The predictions given by the proposed model have been validated with the scores of state of the art segmentation algorithm.
Lessons from a broad survey of tutoring tools: It's a big world out there

ABSTRACT We created a list of more than 140 tools that can be used to create tutoring systems, from complete tutoring systems to low-level tools for preparing instructional materials. Based on this list, we present a preliminary ontology of system dimensions that can serve as a base for a comprehensive review or in building systems. We also note that: (a) such surveys of tutoring systems will always be incorrect because they are incomplete, (b) such reviews are not worth pursing indefinitely (because they will never be complete), and, however, (c) comprehensive reviews, or a “living platform” like a wiki, may be immensely useful for stakeholders. The results provide several insights: (a) the scope of tutoring systems includes a great range of dissimilar tutoring architectures, as well as an even wider range of supplementary tools; (b) systems vary on many dimensions (we provide an initial set); and (c) no tutoring system is likely to gain widespread adoption for all use cases because of the variety of learner and other stakeholder needs. This suggests that a wide variety of tutoring architectures are needed. The ontology and dimensions may be helpful in development, funding, and acquisition decisions.
More farms, less specialized landscapes, and higher crop diversity stabilize food supplies

Theoretical and empirical studies show increased diversity in crops, supply chains, and markets helps stabilize food systems. At the same time global commodity markets and industrial agriculture have driven homogenization of local and regional production systems, and consolidated power in fewer larger specialized farms and distributers. This is a global challenge, with no obvious global solutions. An important question therefore, is how individual countries can build their own resilience through maintaining or increasing diversity within their borders. Here we show, using farm level data from Germany, that spreading production risk by growing the same crops across different farms carries stabilizing benefits by allowing for increased spatiotemporal asynchrony within crops. We also find that increasing asynchrony between the year-to-year production of different crops has stabilizing effects on food supply. Importantly, the benefits of increasing crop diversity are lower in specialized landscapes growing the same crop on large patches. Our results illustrate clear benefits of diversified crops, producers, and agricultural landscapes to buffer supply side shocks, and for incorporation in subsidies and other regulatory measures aimed at stabilizing food systems.
Research on Multimodal Pest Identification System Based on YOLOv5s Algorithm

Pests are inevitable in agricultural production. In recent years, China has lost 40.0 million tons of grain annually due to various pests, accounting for 8.8% of the total. In the nature, there are more than 1 million known insect species, which are complex and diverse. It is inefficient and wasteful to identify pests manually. Using the deep learning network of computer vision to identify pests can effectively solve this problem. This paper mainly introduces the network structure of YOLOv5s algorithm, and designs a multimodal pest identification system based on YOLOv5s algorithm, which can carry out real-time video monitoring and picture recognition. In the training model stage, a variety of data enhancement algorithms are used to train the optimal model, and the average identification accuracy of the optimal pest model can reach 97%.
Intention without action? Differences between whistleblowing intention and behavior on corruption and fraud

Whistleblowing is an effective tool against fraud and corruption in organizations. However, as researchers have struggled to acquire data on actual whistleblowers, research relies on hypothetical intention data and student samples, which is seen as a major limitation. Using a field study of 1,416 employees from China, Germany, and Russia, the purpose of this article is to identify differences and similarities between intention and actual whistleblowing decisions, thus aiding research and interpretation of prior and future studies. I also contribute by analyzing whether findings can be generalized across different cultures and whether status and power influence the whistleblowing process. My results reveal that the key difference between hypothetical and real decisions is not in variables that affect the process, but in effect sizes: Employees underestimate the effect of situational (retaliation) and organizational (compliance measures) variables in hypothetical compared to actual whistleblowing. Thus, reliance on intention research is not inherently problematic, when effect sizes are interpreted with caution. I also find that results are similar across countries and that status and power may not be decisive factors in whistleblowing. My findings should also be of interest to practitioners and policymakers, as they assist in designing effective whistleblowing systems and environments in organizations.
Numerical Investigation of the Influence of the Drill String Vibration Cyclic Loads on the Development of the Wellbore Natural Fracture

Wellbore instability is one of the most serious issues faced in the drilling process. During drilling operations, the cyclic loads applied on the fractured formation progressively modify the initial parameters (i.e., length and width) of the fractured formation, thus resulting into undesirable wellbore instability. In this paper, using a nonlinear finite element software (ABAQUS) as the numerical simulator, a poro-elasto-plastic model has been established which aimed at analyzing the influence of drill string vibration cyclic loads on the development of the wellbore natural fracture. The numerical results showed that the fracture width as a function of time profiles followed a sinusoidal behavior similar to the drill string vibration cyclic load profiles. For different cyclic load magnitudes with constant number of cyclic loads, the highest percentage increase of the fracture width after integration of cyclic loads was 64.77%. Interestingly, the fracture width increased with the fracture length in the near wellbore region while it globally decreased in the region far away from the wellbore. But for constant cyclic load magnitude with different number of cyclic loads, the biggest percentage increase of the fracture width after integration of cyclic loads was slightly lower representing 63.12% while the oscillating period of the fracture width increased with the number of cyclic loads. The parametric study revealed that the drill string vibration cyclic loads were relatively independent of the fracture length and the bottom hole pressure. However, the fracture width and the loss circulation rates were considerably impacted by the drill string vibration and the highest percentage increase of the loss circulation rate after integration of cyclic loads was 14.3%. This study provides an insight into the coupling of the fracture rock development and the continuous cyclic loads generated by drill string vibrations which is an aspect that has been rarely discussed in the literature.
Scaffolding EARGD: On Effective Teaching Writing for EFL

This paper is concerned with the applying scaffolding in learning of writing for English Second Language learners. A quantitative approach used to address the research problem in this paper. The subject is the second semester English Department students of IAIN Takengon, it was purposively sampled for the study. The pre- and post-test responses were evaluated using the same rubric, modified to evaluate students’ writing content, to increase the reliability of the assessment. The same assessment given in the pre-test was used after the treatment. The assessment was used to determine whether students could demonstrate the application of new knowledge, language, and variation in organizational structure and whether scores had improved. The results have shown that the use of scaffolding EARGD is effective to increase students’ writing ability.
Endogenous miR-21 and TIMP-1 Regulate Hepatic Injury and Fibrosis by Bile Duct Ligation in Vivo

TIMP metallopeptidase inhibitor 1 (TIMP-1) has been identified as a multifunctional molecule with divergent functions. It participates in wound healing and regeneration, cell morphology and survival, tumor metastasis, angiogenesis, and inflammatory responses. An imbalance of Matrix Metalloproteinase/TIMP regulation has been implicated in several inflammatory diseases. TIMP-1 could be considered an important regulator in the process of liver fibrosis and bile duct degeneration. Thus, we aimed to determine the role of TIMP-1 in a rat model of Common Bile Duct Ligation (CBDL). Male Sprague-Dawley rats were divided into several groups, including those with/ without CBDL surgery and those with/without amiodarone or simvastatin administration. Amiodarone/simvastatin treatment was given at a daily dose of 15 mg/kg and 18 mg/kg by means of intergalactic gavage, which began 7 days prior to CBDL induction. Two weeks after surgery, the animals in each group were sacrificed and hepatocyte degeneration severity was examined using histological morphologies. Large-scale array for secretory factors is intended for the purpose of finding key functional protein after CBDL. The hepatic level of miR-21 was determined through Taqman miRNA analysis. Furthermore, the TIMP-1 level in liver tissue was also visualized by histological stain. Liver injury and fibrosis were founded in CBDL rats based upon histopathological examination and serum biochemical analysis. Hepatic miR-21 and TIMP-1 were significantly up-regulated in CBDL rats, while being slightly rescued in response to amiodarone or simvastatin treatment. Up-regulation of miR-21 and TIMP-1 may result in the progression of hepatic cirrhosis after bile duct obstruction. Drug intervention for cirrhosis, like the use of statin, may function via similar mechanisms.
Predicting healthcare leaders readiness to lead the implementation of change

A key challenge facing healthcare leaders is how to implement changes successfully. For change to occur in healthcare, leaders must be proactive, and they must have an ability to adapt to new policies and practices. Data from 105 healthcare leaders were used to address the question of to what extent leadership style, leader efficacy, seniority, and gender predict healthcare leaders’ readiness to lead change. The healthcare leaders were panel members of Centiment, an online survey research company that specializes in surveying hard-to-reach participants. A correlation analysis using Spearman’s rho tested the relationship between leadership style and leader efficacy, and multiple regression analyses predicted the readiness of healthcare leaders in leading the implementation of change. The results of the multiple regression analysis show that transformational leadership and leader efficacy are statistically significant in change implementation. However, the results also reveal that transactional and passive leadership, seniority, and gender are not statistically significance. The results suggest that seniority does not moderate the relationship between the leadership styles and organizational readiness for implementing change, and gender does not moderate the interaction between leader efficacy and organizational readiness for implementing change. Healthcare leaders can use the results of this study to mitigate the rate of failure while implementing change. Key words: Organizational change, transformational leadership, transactional leadership, passive-avoidant leadership, leader efficacy, organizational readiness for implementing change, healthcare leadership.
An Interpretable Deep Learning Model for Automatic Sound Classification

Deep learning models have improved cutting-edge technologies in many research areas, but their black-box structure makes it difficult to understand their inner workings and the rationale behind their predictions. This may lead to unintended effects, such as being susceptible to adversarial attacks or the reinforcement of biases. There is still a lack of research in the audio domain, despite the increasing interest in developing deep learning models that provide explanations of their decisions. To reduce this gap, we propose a novel interpretable deep learning model for automatic sound classification, which explains its predictions based on the similarity of the input to a set of learned prototypes in a latent space. We leverage domain knowledge by designing a frequency-dependent similarity measure and by considering different time-frequency resolutions in the feature space. The proposed model achieves results that are comparable to that of the state-of-the-art methods in three different sound classification tasks involving speech, music, and environmental audio. In addition, we present two automatic methods to prune the proposed model that exploit its interpretability. Our system is open source and it is accompanied by a web application for the manual editing of the model, which allows for a human-in-the-loop debugging approach.
Experimental study on seismic performance of shear wall with precast concrete hollow molds

A new type of connection between shear walls is proposed for the shear wall with precast concrete hollow molds (PCHM). The overlapping reinforcements are inserted into the PCHM before pouring concrete into the hollow in this method. Full‐scale test specimens are tested under low cyclic loading test to analyze their seismic performance, such as failure modes, hysteretic characters, stiffness degradation, energy dissipation, and ductility. For the precast specimen, although the penetrating crack appears at the horizontal connection, there is no large slippage leading to anchorage failure and disengagement of interfaces. Generally, compared with cast‐in‐situ shear walls, shear walls with PCHM have no large difference in bearing capacity, failure mode, stiffness degradation, energy dissipation and ductility. Therefore, the proposed shear wall with the new type of connection method is worth popularizing and utilizing in practical engineering projects with similar design procedure to cast‐in‐situ structures.
Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis (P < 0.026, OR = 2.296, 95% confidence interval (CI) 1.1. 02–4.786), major hepatectomy (P=0.031, OR = 2.211, 95% CI 1.077–4.542), ascites (P=0.014, OR = 3.588, 95% 1.299–9.913), intraoperative blood loss (P < 0.001, OR = 4.683, 95% CI 2.281–9.616), PALBI score >−2.53 (, OR = 3.609, 95% CI 1.486–8.764), and FIB-4 score ≥1.45 (P < 0.001, OR = 5.267, 95% CI 2.077–13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.
Novel risk assessment of Cd in earthworms and leeches based on bioavailability through in vitro digestion/Caco-2 and MDCK cells

Little data are available about the extent of bio-accumulation of heavy metals in traditional animal medicines (TAMs). In the present study, the oral bioavailability of cadmium (Cd) in earthworms and leeches was investigated through in vitro PBET digestion/Caco2 and MDKC cell models. We are the first to create an innovative assessment strategy which has capacity to offer a more precise evaluation of Cd-associated health risks in TAMs, by combinational usage of bioavailable Cd levels, the duration and frequency of the exposure to TAMs obtained by questionnaire data, as well as safety factor of TAMs. Our data showed that the percentage of bioavailability for Caco-2 cells in earthworms and leeches ranged from 3.29–14.17% and 4.32–12.61%, respectively. The percentage of bioavailability of MDCK cells in earthworms and leeches ranged from 4.83–15.74% and 6.53–15.04%, respectively. After adjusting by the bioavailability of Cd to target hazard quotient (THQ), excitingly, our findings manifested that the health risks induced by the ingestion of earthworms and leeches were acceptable in the clinic. Our novel strategy has capacity to offer a more realistic and accurate assessment of Cd-associated health risks in TAMs. Besides, our key findings suggest that bioavailability characterization cannot be ruled out and health risks should be assessed on the basis of the bioavailable Cd levels rather than total levels.
Day-90 survival in critically-ill patients with COVID-19 and hydroxychloroquine: a propensity analysis

Background There are limited data on the effect of hydroxychloroquine on medium term outcomes in patients with coronavirus disease 2019 (COVID-19) requiring intensive care. We aimed to evaluate the effects of hydroxychloroquine on day 90 mortality in this specific population. Methods This retrospective, multicenter, propensity matched cohort analysis, used data of adult patients with laboratory confirmed COVID-19 admitted to 3 university affiliated intensive care units between March 7, 2020, to April 7, 2020 in Lyon, France. Patients received either hydroxychloroquine (loading dose of 400 mg twice daily at day 1 followed by 200 mg twice daily from day 2 to day 10) or standard of care without hydroxychloroquine. We compared all-cause mortality at day-90 after ICU admission between propensity score matched groups receiving hydroxychloroquine or standard of care. Results A total of 157 patients were included with a day-28 and day-90 mortality rate of 23.6% and 32.5%, respectively. The median (interquartile) age was 67 years (56–76 years), 105 (66.9%) were men, 65 (41.4%) fulfilled criteria for acute respiratory distress syndrome, and 64 (41%) received hydroxychloroquine (HCQ) for 10 days (4–10 days). In the propensity score matched cohort (59 patients in each group), day-90 mortality was 35.6% for patients who received HCQ and 23.7% for patients who did not (P=0.23). Kaplan Meier survival analysis showed no statistically significant association between HCQ therapy and mortality (P=0.20 by log-rank test). Conclusions In this study, off-label use of HCQ in critically ill patients with COVID-19 was not associated with any significant change in medium-term prognosis, confirming results of studies in less severe patients.