Miracle-dz/UniFAR-FTUs huggingface dataset:
Cleaning Up the Internet of Evil Things: Real-World Evidence on ISP and Consumer Efforts to Remove Mirai

With the rise of IoT botnets, the remediation of infected devices has become a critical task. As over 87% of these devices reside in broadband networks, this task will fall primarily to consumers and the Internet Service Providers. We present the first empirical study of IoT malware cleanup in the wild -- more specifically, of removing Mirai infections in the network of a medium-sized ISP. To measure remediation rates, we combine data from an observational study and a randomized controlled trial involving 220 consumers who suffered a Mirai infection together with data from honeypots and darknets. We find that quarantining and notifying infected customers via a walled garden, a best practice from ISP botnet mitigation for conventional malware, remediates 92% of the infections within 14 days. Email-only notifications have no observable impact compared to a control group where no notifications were sent. We also measure surprisingly high natural remediation rates of 58-74% for this control group and for two reference networks where users were also not notified. Even more surprising, reinfection rates are low. Only 5% of the customers who remediated suffered another infection in the five months after our first study. This stands in contrast to our lab tests, which observed reinfection of real IoT devices within minutes -- a discrepancy for which we explore various different possible explanations, but find no satisfactory answer. We gather data on customer experiences and actions via 76 phone interviews and the communications logs of the ISP. Remediation succeeds even though many users are operating from the wrong mental model -- e.g., they run anti-virus software on their PC to solve the infection of an IoT device. While quarantining infected devices is clearly highly effective, future work will have to resolve several remaining mysteries. Furthermore, it will be hard to scale up the walled garden solution because of the weak incentives of the ISPs.
The Associations of Active Travel to School With Physical Activity and Screen Time Among Adolescents: Do Individual and Parental Characteristics Matter?

Little is known about the relationship of active travel to school (ATS) with physical activity (PA) and screen time (ST) by individual and parental characteristics among adolescents, especially in China. To address the research gap, this study aimed to explore the difference of sex, age, living environment, parental occupation and education level in the relationship of ATS with PA and ST among students of grades 7–12 (aged 10–18 years) using cross-sectional data. In 13 cities of Hubei province, China, students from 39 public schools were recruited to engage in the survey. In total, 5,898 students (response rate = 89.6%) were invited into this study. Participants were required to report their ATS (including its types), PA and ST as well as sociodemographic information using a validated questionnaire. Descriptive analyses were used to report the information of all variables. Regression models were used to analyse the relationships of ATS and its types with PA and ST. In a total of 4,128 participants (boys: 50.9%; younger adolescents: 61.9%) included in the final analysis, the proportion of those with ATS was 47.3%. Regarding the types of ATS, walking accounted for over 30%, while cycling was 13.2%. Participants with ATS were more likely to have sufficient PA (OR = 1.26, 95% CI: 1.14–1.39), especially among boys, younger adolescents and those with lower parental education level. However, ATS was not associated with ST (OR = 0.94, 95% CI: 0.86–1.01). Participants with cycling had a higher odds ratio of being physically active (OR for cycling = 1.47, 95% CI: 1.27–1.70; OR for walking = 1.18, 95% CI: 1.06–1.32). The association of ATS types with PA and ST differed by gender, age, living environment and parental educational level as well as occupations. ATS may be a useful approach to increase PA among adolescents, but this should be explained by individual and parental characteristics.
The impact of in-depth online recommendation agents on consumer disorientation and cognitive absorption perceptions

ABSTRACT In this study, we leverage valence theory, cognitive absorption theory, and IT adoption literature to investigate the perceptions of consumers towards the use of online Recommendation Agents (RAs) that vary in the number of details they provide in eliciting consumers’ preferences and presenting recommendations accordingly. The research model is empirically validated via an experiment involving 197 online shoppers. Results show that high in-depth RAs are better alternatives to low in-depth RAs in driving consumers’ intention to use RAs in their shopping experience. The findings provide novel insights for researchers and practitioners interested in understanding the proper design for online RAs.
A Big Data implementation of the MANTIS reference architecture for predictive maintenance

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.
Link Prediction Using Multi Part Embeddings

This work has been supported by the TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics at National University of Ireland Galway, Ireland (supported by the Science Foundation Ireland grant 12/RC/2289). The GPU card used in our experiments is granted to us by the Nvidia GPU Grant Program.
Evaluation of Quality of Life among Patients with Multiple Sclerosis and voiding dysfunction: A Cross-Sectional Study

Background: Evaluating the usefulness of treatment requires a direct measurement of the health-related quality of life (QOL). Therefore, this study was carried out aiming to determine the QOL of patients with MS and voiding dysfunction . Materials and Methods: This cross-sectional study was carried out using multi-stage random cluster sampling method on 602 patients with MS in Isfahan, Iran. All data were collected through interviews using standard questionnaires including International Prostate Symptom Score (IPSS), and the Multiple Sclerosis Quality of Life-54 (MSQOL-54). Data were analyzed using descriptive and inferential statistical tests. Results: The prevalence rate of mixed, irritative, and obstructive urinary symptoms was 52.2%, 25.5%, and 6.5%, respectively. The mixed symptom had the highest prevalence among men and women with rates of 56.5% and 51.1%, respectively. The prevalence of irritative and obstructive symptoms was, respectively, higher and statistically significant among women alone and men alone (P < 0.05). The prevalence of irritative symptoms was higher among patients with MS, EDSS score ≤ 3, disease duration of less than 5 years, and with clinically isolated syndrome. In addition, the prevalence of mixed symptoms was higher among patients with MS of over 30 years of age with a Pre-high school degree, severe disability, disease duration of over 10 years, and progressive MS; the difference was statistically significant (P < 0.05). There was a difference in the combined dimensions of physical and mental health of QOL between the two groups with and without urinary symptoms (P < 0.05). Logistic regression analysis revealed that there was a higher probability of a urinary problems among patients with MS and high age [3.273 (1.083-9.860); P = 0.035]. Conclusions: Mixed urinary symptoms are highly prevalent among MS patients and affect QOL dimensions. In order to improve QOL, more attention and focus should be paid to urinary problems in MS patients. Keywords: Multiple sclerosis, urinary symptoms, quality of life
Inversion of Oil-Immersed Paper Resistivity in Transformer Based on Dielectric Loss Factor

The aging of the transformer oil–paper insulation distributes spatially, which results in changes in paper resistivity in different regions. This paper establishes an iterative inversion algorithm using the finite element method to calculate the oil-immersed paper resistivity in different regions of a transformer. This algorithm sets the transformer dielectric loss factor <inline-formula> <tex-math notation="LaTeX">$\tan \delta $ </tex-math></inline-formula> at low frequency, the dielectric parameters of the transformer oil as inputs and the oil-immersed paper resistivity as output. The resistivity obtained from inversion can be used as a reference to access the insulation state of the oil–paper insulation. This paper aims at achieving the nondestructive detection of the partial state of the oil–paper insulation.
Cryptographic Primitives from Physical Variables

In this dissertation we explore a new paradigm emerging from the subtleties of cryptographic implementations and relating to theoretical aspects of cryptography. This new paradigm, namely physical variables (PVs), simply describes properties of physical objects designed to be identical but are not due to manufacturing variability. In the first part of this dissertation, we focus our attention on scenarios which require the unique identification of physical objects and we show how Gaussian PVs can be used to fulfill such a requirement. Using this framework we present and analyze a new technique for fingerprinting compact discs (CDs) using the manufacturing variability found in the length of the CDs’ lands and pits. Although the variability measured is on the order of 20 nm, the technique does not require the use of microscopes or any advanced equipment. Instead, the electrical signal produced by the photo-detector inside the CD reader will be sufficient to measure the desired variability. We thoroughly investigate the new technique by analyzing data collected from 100 identical CDs and show how to extract a unique fingerprint for each
Orientation Invariance Methods for Inertial Gait

Intelligent devices such as smart phones, smart watches, virtual reality (VR) headsets and personal exercise devices have become integral elements of accessories used by many people. The ability of these devices to verify or identify the user could be applied for enhanced security and user experience customization among other things. Almost all these devices have built-in inertial sensors such as accelerometer and gyroscope. These inertial sensors respond to the movements made by the user while performing day to day activities like walking, getting up and sitting down. The response depends on the activity being performed and thus can be used for activity recognition. The response also captures the user’s unique way of doing the activity and can be used as a behavioral biometric for verification or identification. The acceleration (accelerometer) and rate of rotation (gyroscope) are recorded in the device coordinate frame. But to determine the user’s motion, these need to be converted to a coordinate frame relative to the user. In most situations the orientation of the device relative to the user can neither be controlled nor determined reliably. The solution to this problem requires methods to remove the dependence on device orientation while comparing the signals collected at different times. In a vast of majority of research to date, the performance of authentication algorithms using inertial sensors have been evaluated on small datasets with few tens of subjects, collected under controlled placement of the sensors. Very often stand alone inertial sensors have been used to collect
Applications of transit smart cards beyond a fare collection tool: a literature review

Automated fare collection (AFC) systems have been widely implemented around the world. Their main goal is to gather revenue in a more efficient way. In addition to this purpose, they have multiple indirect benefits to transport researchers. This rich data is obtained from daily transactions that mainly occur when a passenger boards or alights a public transit vehicle. They have some features that are investigated in recent studies. Considering volume, longitudinal nature, level of details, and accuracy of the AFC data, this data creates new opportunities for researchers to study public transit systems and travel behaviour of passengers in order to develop various applications, such as origin-destination (OD) estimation, travel pattern mining, trip purpose detection, route choice modelling, transit performance indicators, and identifying policy alternatives. This paper reviews the previous studies on the AFC data focusing on studies after 2010. It will also make suggestions for the direction future studies may wish to take.
Factors Contributing to Alcoholism Among the Youth in New Edubiase of Adansi South District in Ashanti Region-Ghana

The future of every nation lies in the hands of the youth who are expected to be strong and healthy. Most youths put their future into jeopardy by indulging in risky health habits, particularly alcoholism. The purpose of this study was to find out the factors contributing to Alcoholism among the youth in New Edubiase. The study was quantitative with a descriptive cross-sectional design. The study population was a youth in New Edubiase. A multi-stage sampling technique was used to select a sample size of 350. A questionnaire was used for collecting the data. The results were presented in frequencies, percentages and chi-square test. The study found out that the use of alcohol among the youth was low as most of them were uncertain of taking alcoholic beverages. Again, there was high knowledge about alcohol use and its implication on the health of abusers. Further, interventions for alcohol use include punishment of alcohol abusers, enforcement of laws on sales of alcoholic products and the prohibition of an advertisement on alcohol before 9:00 pm. In conclusion, the high knowledge of the youth about alcoholic products and the effect of the health of alcohol abusers could be due to the effort of a health care professional in educating the masses about the health implication. It was recommended that the Food and Drugs Authority must ensure that all media houses adhere to the new directive on the ban of advertisements on alcoholic beverages before 9:00 pm.
Unambiguous Identification of Key Molecular Species in Deposits Responsible for Increased Pollution from Internal Combustion Engines

Clean and efficient internal combustion engine performance will play a significant role in the move to a decarbonized energy system. Currently, fuel deposit formation on engine components negatively impacts CO2 and pollutant emissions, where previous attempts at deposit characterization afforded non-diagnostic chemical assignments. Here, we uncover the identity and 3D spatial distribution of molecular species from gasoline, diesel injector and filter deposits with the 3D OrbiSIMS technique. Alkylbenzyl sulfonates, derived from lubricant oil contamination in the engine fuel cycle, were common to samples, we evidence transformation of the native sulfonate to longer chain species by reaction with fuel fragments in the gasoline deposit. Inorganic salts, identified in both diesel deposits, were prevalent throughout the injector deposits depth. We identified common polycyclic aromatic hydrocarbons up to C66H20, these were prevalent in the gasoline deposits lower depths. This work will enable deposit mitigation by unravelling their chemical composition, spatial distribution, and origins.
Differences in Health Related Quality of Life changes during Pregnancy based on Pregnancy Context: A prospective study

Background The significance of planned pregnancy is an accepted principle for improving the health of pregnant women; and quality of life, as one of the important indicators of women's health, is reduced in high-risk pregnancies. The aim of this research was to investigate the changes in the health related quality of life (HRQL) in low risk pregnancies in different groups based on pregnancy context. Methods The present study was a longitudinal prospective study conducted on 250 pregnant women divided into three groups of women with planned pregnancy, unplanned/wanted pregnancy and unwanted pregnancy. Then, using WHOQOL-26 questionnaire, the quality of life of these women was measured in physical, psychological, social and environmental dimensions at the beginning of pregnancy as well as at the end of the first, second and third trimesters. Results Based on the results, the mean score of environmental-HRQL in women with unwanted pregnancy was significantly lower than the other two groups. All dimensions on HRQL were influenced by time and group. However, changes in the physical, psychological and social dimensions of HRQL varied within the groups. Physical- HRQL changes were different within the groups. The intergroup effect on environmental dimension of quality of life changes was significant. Conclusion It was observed in this study that HRQL in the women with unwanted pregnancy was lower than the women with planned pregnancy and those with unplanned /wanted pregnancy. Moreover, increase in gestational age would lower quality of life, but this decline had a similar pattern in different groups.
Evaluation of Nurses Practices toward Care of Children Undergoing Cardiac Correction “Ventricle Septal Defect” in Baghdad Teaching Hospital at Baghdad City

The study aimed to assess and evaluated nursing practices of the nurses and their demographic characteristics. A descriptive study which initiated from 20 th November 2017 to 25 th of April 2019, carried out in 3 teaching hospitals (Ibn Al - Nafees Teaching Hospital for cardiovascular and thoracic surgery, Ibn AL-Betar Center for Heart Diseases, and Iraqi Center for Heart Diseases) in Baghdad City . The study sample was 50 nurses who were working in the cardiac units of these hospitals. The data was collected through using questionnaire format and the questionnaire consisted of 2 parts. The first one is demographic characteristics and the second part includes observational section of nurses’ practices and this consisted from 4 items pre and post operation. The data were analyzed through the application of descriptive statistic frequency, percentage, and the application of inferential statistical procedures, which include Pearson correlation coefficient and chi-square. The socio-demographic characteristics of 50 nurses included 24 (48%) who were in age group of 19-28 years, 31 (62%) were married, 20 (40%) were graduates of Nursing institute graduate, 28 (56%) had 1-5 years of hospital work and 37 (74%) had 1-5 years of work in the cardiac units. 80% had previous related training courses.
Performance of F1 Piglets of Sows Fed Fermented and Enzyme-Supplemented Cassava Peel Meal Based Diets

The performance of F 1 piglets fed fermented or enzyme-supplemented cassava peels was investigated. Peels from freshly harvested cassava were fermented for four days, sun-dried for 3-5 days, then milled. The cassava peel meal (CPM) replaced maize in grower and finisher diets in T 1 (control), T 2 (fermented CPM) and T 3 (fermented CPM + maxigrain R enzyme) in a completely randomized design. The diets were served at 4% of live body weights daily to 27 Largewhite x Duroc weaner-gilts, aged 8 weeks and weighing 10.61±0.27 kg. The piglets suckled until mid-lactation, then shared finishing diets with the sows in addition. Data was analyzed with one-way ANOVA on SPSS. Average litter sizes, average birth weights, average weaning body weights and mortality rates had no significant (p>0.05) differences. Average daily body weight gains differed significantly (p<0.05) 155.24±0.09g (T 1 ) and 159.29±0.15g (T 3 ) compared to 146.67±0.11g (T 2 ). Fermented cassava peels with or without enzyme supplementation has no deleterious effect on the performance of F 1 piglets.
Human Activity Annotation based on Active Learning

Human activity recognition algorithms have been increasingly sought due to their broad application, in areas such as healthcare, safety and sports. Current works focusing on human activity recognition are based majorly on Supervised Learning algorithms and have achieved promising results. However, high performance is achieved at the cost of a large amount of labelled data required to train and learn the model parameters, where a high volume of data will increase the algorithm’s performance and the classifier’s ability to generalise correctly into new, and previously unseen data. Commonly, the labelling process of ground truth data, which is required for supervised algorithms, must be done manually by the user, being tedious, time-consuming and difficult. On this account, we propose a Semi-Supervised Active Learning technique able to partly automate the labelling process and reduce considerably the labelling cost and the labelled data volume required to obtain a highly performing classifier. This is achieved through the selection of the most relevant samples for annotation and propagation of their label to similar samples. In order to accomplish this task, several sample selection strategies were tested in order to find the most valuable sample for labelling to be included in the classifier’s training set and create a representative set of the entire dataset. Followed by a semi-supervised stage, labelling with high confidence unlabelled samples, and augmenting the training set without any extra labelling effort from the user. Lastly, five stopping criteria were tested, optimising the trade-off between the classifier’s performance and the percentage of labelled data in its training set. Experimental results were performed on two different datasets with real data, allowing to validate the proposed method and compare it to literature methods, which were replicated. The developed model was able to reach similar accuracy values as supervised learning, with a reduction in the required labelled data of more than 89% for the two datasets, respectively.
An Integrated Approach of Transform Techniques & Subspace Model for Web Image Retrieval

In this paper, the problem of efficiently extracting low level features for image representation in content based image retrieval is addressed. Wavelet transform being very popular and effective in representing objects with isolated point singularities, but failed to represent line singularities. We introduce a novel framework to extract appropriate features like color, shape and texture representing edges and other singularities along lines which effectively fills the gap between high level and low level semantics. Ridgelet transform is applied on a segmented image and statistical features such as mean and standard deviation are extracted from each of the Ridgelet sub-bands there by generating feature vectors. PCA is applied to reduce the dimensionality of the data and Euclidian distance is used as similarity measure between database and query images. The algorithm also supports for the retrieval of similar images to the given query image from Google web server. Four benchmarking datasets are considered to conduct the experiment and result shows that the success rate is improved much when compared with traditional methods. Keywords— Low level features, semantic gap, line singularities, Ridgelet transform, feature vectors, Content Based Image Retrieval (CBIR), similarity measures, Principal Component Analysis (PCA).