Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Machine Learning for Data Encoding applied to Ovarian Cancer Transcriptomes

Machine learning algorithms are revolutionising how information can be extracted from complex and high-dimensional data sets via intelligent compression. For example, unsupervised Autoen-coders train a deep neural network with a low-dimensional “bottlenecked” central layer to reconstruct input vectors. Variational Autoencoders (VAEs) have shown promise at learning meaningful latent spaces for text, image and more recently, gene-expression data. In the latter case they have been shown capable of capturing biologically relevant features such as a patients sex or tumour type. Here we train a VAE on ovarian cancer transcriptomes from The Cancer Genome Atlas and show that, in many cases, the latent spaces learns an encoding predictive of cisplatin chemotherapy resistance. We analyse the effectiveness of such an architecture to a wide range of hyperparameters as well as use a state-of-the-art clustering algorithm, t-SNE, to embed the data in a two-dimensional manifold and visualise the predictive power of the trained latent spaces. By correlating genes to resistance-predictive encodings we are able to extract biological processes likely responsible for platinum resistance. Finally we demonstrate that variational autoencoders can reliably encode gene expression data contaminated with significant amounts of Gaussian and dropout noise, a necessary feature if this technique is to be applicable to other data sets, including those in non-medical fields.
Quality Assurance Mechanisms in an E-commerce Platform Sales: The Moderating Effect of Quality Disclosure Transparency

Based on the two-dimensional classification of quality assurance mechanisms, this study capitalizes on an adjustment event of quality disclosure strategy on Ctrip.com in June 2017, and uses the sales data of online outbound package tour products to investigate the impact of transparency of quality disclosure on sales, as well as its moderating effect on the relationship between eWOM valence and sales. We uncover following interesting findings as follows. First, the valence of eWOM has a curvilinear (inverted U-shaped) relationship with sales. Second, the transparency of quality disclosure has a positive moderating effect. Our study reveals how the information transparency strategy influence the development of the platform ecosystem.
On constrained Volterra cubic stochastic operators

We consider constrained Volterra cubic stochastic operators and construct several Lyapunov functions for the constrained Volterra cubic stochastic operators. We prove that such kind operators do not have periodic trajectories. Finally, we show that the set of all constrained Volterra cubic stochastic operators is a convex compact set and find the extreme points of this set.
Machine learning for IoT network monitoring

The growing Internet of Things (IoT) market introduces new security challenges for network administrators. Most IoT devices are poorly configured making them a target of choice for attackers. Mirai botnet illustrates the threat posed by IoT devices. In this context, Machine Learning techniques can be leveraged to detect attacks in IoT networks. Indeed, contrary to desktop computers or laptops, IoT devices are used for very specific tasks. Therefore, the generated network traffic follows a predictable pattern making data analysis techniques well suited to detect a deviation from the expected behavior. In this paper, we present machine learning based techniques for IoT network monitoring. We first built an experimental smart home network to generate network traffic data. The network traffic is described using features, such as the size of the first N packets sent and received along with the corresponding inter-arrival times. We then train and test classification algorithms for devices recognition purposes. We also describe how to use autoencoders for anomaly detection in IoT networks.
Inserting and Encircling Two complementary immersive strategies for mixed-reality applied to cultural heritage *

To accomplish the aims of a three-year research project we are developing, connected to cultural heritage, we became interested in the fusion of Virtual Reality and Augmented Reality, two emergent development fields that gave birth to what was coined as Mixed Reality. Both dimensions have intricate connections with hardware and software improvements related with the so called "4th Industrial Revolution". Virtual Reality (VR), an interactive experience generated by a computer, takes place inside of simulated environments, which can be analogous to the real world or which can be created as imaginary contexts. On the other hand, Augmented Reality (AR) is always based in an interactive experience inside a tangible environment where the elements of that reality are nurtured with digital information, across several senses, to empathize certain aspects of reality. Our research combines both VR and AR to empathize sensory and intellectual experience. To do so, several senses, mainly visual and auditory, are stimulated.We therefore explore two Case-Studies from our research project in order to show two different strategies. The intention of both situations is to create immersive mixed reality environments where the fusion of the digital and analogue elements can be persistently sustained by the visual outputs.
The Character of an Effective Nation Leader According to the Old Testament to Prepare the Young Leaders of the Present Are Visionary and Effective for Urban Communities(Main Samples of the City of JAKARTA and its surroundings)

"We form our buildings, but after that, they will shape us (Winston Churchill). "There are no plans; then the future will also not exist. " for the city of Jakarta too. Years later, Ali Sadikin confirmed his policy: "I acted realistically and was not hypocritical. When I became Governor of DKI Jakarta (1966-1977), he agreed to legalized gambling because the city government did not have enough funds" Looking at the situation in Jakarta from time to time the time is getting worse the author has the desire to do research that is more profound and more widespread and will begin since 2012. So much degradation has occurred in Jakarta for several years, especially with the fading and alienation of humanity and the lives of its people, especially young people in Jakarta which in the future will be very important in the progress of the Indonesian people, especially Jakarta and its surroundings. Before everything is too late The author takes a small step to develop and transform this situation with an empathetic management and leadership approach to change the existence of young people and future generations of Indonesia. The plan is to look at some literature relating to Management and Leadership and an in-depth approach to verses and stories in the Old Testament that are very relevant to this context. Towards what is aspired by our Father of the Nation, even more so, the Almighty God, Our Father Who Is In Heaven
Trend of Supervised LearningModels Based Articles

Supervised learning techniques also known as classification techniques are an important aspect of data mining techniques that assist us in classifying the object based upon certain additional information like labels. The objective of this study is to present the Google Scholar’s publication trend of supervised learning based article published in the domain of marketing, healthcare, text processing as well as in agriculture. The statistics reveal that most of the supervised learning techniques based article has been published in healthcare followed by marketing.
PET-CT Based Non-Invasive Radiomic Biomarkers as a Part of Precision Medicine Initiative to Predict Local Recurrences in Oral Cancers

Oral cancer is the sixth most common cancer worldwide and continues to be most prevalent cancer related to the consumption of tobacco, alcohol and other carcinogenic products. Nevertheless, significant reduction in mortality has been achieved by advances in early diagnosis and implementation of multidisciplinary treatment programs leading to improvement of survivorship and better quality of life [1]. Even with recent advances in cancer diagnosis and therapy, treatment outcomes for many cancers remain dismal. Patients often show different response to the same therapy regimen, supporting the development of precision medicine. There has been increased use of positron emission tomography (PET) using [18F] Fluoro-2-deoxy-d-glucose (FDG),a glucose metabolism analog, for prediction of outcomes and guiding the precision medicine in tumor detection, staging, and treatment planning and surveillance post therapy in head and neck cancer sites [2]. Accumulating evidence of recent literature suggests that pre-treatment FDG uptake could be used as a prognostic factor for predicting treatment outcomes and is motivated by the fact that radiotracer uptake in the tumor is dependent on the characteristics of the microenvironment, quantitative analysis of FDG uptake is based on changes in the standardized uptake value (SUV) [3], However, sole SUV measurements are potentially impacted by the initial FDG uptake kinetics and radiotracer distribution and there is significant inter and intra observer variability in interpreting the results [4]. Recent development in Radiomics provides a promising objective way for tumor assessment, which uses computerized tools to extract a large number of image features that capture additional information not currently used in clinic that has prognostic value [5]. Radiomics is a process designed to extract a large number of quantitative features from digital images, place these data in shared databases, and subsequently mine the data for hypothesis generation, testing, or both. Radiomics is designed to develop decision support tools; therefore, it involves combining radiomic data with other patient characteristics, as available, to increase the power of the decision support models [6].
A Case Study of New England Open Data Portals

Open government data has proliferated across every level of government in the 2010s, but research has focused primarily on national or municipal portals, which may obscure the challenges faced in providing open government data in less densely populated areas. This research focuses on the cases of three US statesMaine, New Hampshire, and Vermont. We examine the stated goals of each portal and any policies related to their establishment or upkeep. We then examine the portals with regard to updating, reuse, organization and other factors. Of the three cases, Vermont’s portal is moderately successful and continues to be used. New Hampshire’s strategy of linking to data on agency websites is inconsistent, but the state law requiring data published to be in open formats does mean data is more open when it is provided. Maine’s portal went dormant soon after its initial creation, and was fully taken down in the timeframe of this research. These cases illustrate that the establishment of a state portal alone does not guarantee that the portal will support the desired outcomes.
Science Education for Citizenship: A Case Study

The aim of this study is to discover the topics/contexts covered in the science course with respect to citizenship education. This study was carried out using a case study design, a qualitative research approach. Since this study was designed as a case study, the primary data collection technique was the participant observation technique. Critical case sampling was used to collect qualitative data during the observations. The observational data were obtained from two classes in 2009 during the fall semester. Also, several semi-structured interviews were conducted with the teachers in 2010 during the spring semester to support observation data and increase the credibility of the study. Criterion sampling was used to determine the teachers to enroll in the study, and a total of 24 teachers participated in the interviews. The study data were analyzed through content analysis. In the content analysis, a conceptual framework was formed in light of the literature on the relationship between citizenship education and citizenship education within a science course. The coding stage in this study was based on this conceptual framework. The results showed that, as a part of citizenship education, science course could deal with the topics of Science-Technology-Society-Environment, Scientific Literacy, Socio-Scientific Issues, Rights and Duties.
Frequency of Maternal Risk Factors and Neonatal Complications of Premature Rupture of Membranes

BACKGROUND AND OBJECTIVE: Premature rupture of membranes (PROM) is a serious complication during pregnancy, which is accompanied by prenatal, neonatal and maternal complications including premature delivery, infection and fetal death. The Purpose of this research is to study the frequency of maternal risk factors and neonatal complications of premature rupture of membranes. METHODS: This cross-sectional study was conducted on 309 infants with premature rupture of membranes (more than 18 hours before delivery) from 2009 to 2015. After confirming premature rupture of membranes based on history and speculum examination, a questionnaire containing pregnancy information, delivery information and infant profile was completed and assessed. FINDINGS: Maternal risk factors included diabetes (23 cases, 12.7%), hypertension (17 cases, 9.5%), smoking (16 cases, 8.9%), history of premature rupture of membrane (16 cases, 8.9%), urinary tract infection (13 cases, 7.2%), thyroid disorders (9 cases, 5%), previous premature birth (8 cases, 4.4%) and cerclage (7 cases, 3.8%). Delivery complications included cesarean (117 cases, 38.1%), oligohydramnios (61 cases, 33.9%), chorioamnionitis (14 cases, 11.6%), placental abruption (17 cases, 9.5%), fetal distress (6 cases, 3.3%), intrapartum fever (6 cases, 3.3%) and placenta praevia (5 cases, 2.8%). Neonatal complications included immaturity (165 cases, 73%), jaundice (108 cases, 59%), neonatal infection (94 cases, 52%), respiratory distress syndrome (65 case, 30.4%) and asphyxia (52 cases, 17%). There was a significant difference between infants with infection and without infection in terms of first and fifth minute Apgar scores, erythrocyte sedimentation, platelet and gestational age (p=0.000). CONCLUSION: Results of the study demonstrated that diabetes and hypertension are the most frequent maternal risk factors of premature rupture of membranes. Immaturity, respiratory disorders, asphyxia and infection are the most serious associated problems. It may be possible to reduce neonatal problems with proper measures and management.
Specificity of Diabetes in Central Hospital Nampula, Mozambique

The present study found that educational intervention of diabetes had the highest prevalence of diabetes in 41 to 60 years of age group (54.5%), the percentage of patients with male higher (56%) then female (42.1%) , diabetes mellitus type 2 had the highest prevalence (87.5%) compared to diabetes mellitus type 1 (5.1%), Hypertension was the most frequently complication of diabetes mellitus (39.7%); the least frequently complication was diabetic gastropathy (0.2%) and oral anti-diabetic medications (28.9%) more frequent used compared to insulin (15.0%) and diet-alone for control of diabetes(7.4%).
Identification of Tomato Diseases using Skip-gram and LSTM Based on QA(Question-Answer) System

In the field of agricultural information processing, automatic identification and diagnosis of common diseases of tomatoes play an important role. Deep learning is a hot research topic in the field of pattern recognition and machine learning. It can effectively solve some problems of vegetable pathology, such as disease identification, automatic control and production prediction. In this paper, the tomato pest and disease experiments were carried out with natural language data set of 1.12G pathological and healthy tomatoes crawled from Doctors of Agriculture Website as source data. Five common tomato diseases’ symptoms were identified by training Skip-gram algorithm. Finally, symptoms identified above can be classified using LSTM algorithm with classifiers. The research shows that the tomato pest and disease corpus identification models based on LSTM algorithm with classifiers achieve an average accuracy which are over 60%. The simulation results of tomato diseases identification show feasibility and effectiveness of the methods. This article aims at integrating our methodology into working systems that can be used in the identification fields.
International Public Sector Accounting Standards (IPSAS) Adoption and Implementation in Nigerian Public Sector

This study evaluated the relationship between IPSAS adoption and financial reporting quality in South West, Nigeria. Specifically, it analysed the effect of IPSAS adoption on credibility and comparability of financial statements. Additionally; salient factors influencing IPSAS implementation were investigated. Primary data collected from one hundred and eighty accountants in South West Nigeria were analysed using tabulation, graphs, factor analysis, and Goodman and Kruskal’s gamma statistics.The empirical results indicated that IPSAS adoption exerted significant and positive relationships with financial reporting quality, credibility and comparability of financial statements. Decisively, discoveries from this study reflect that implementation cost, staff training, technological factor, IPSAS knowledge and awareness and availability of expertise significantly affect IPSAS implementation. However, findings further revealed that IPSAS implementation is not significantly influenced by institutional commitment, cultural, sociological, legal, political and environmental factors. Based on these findings, the authors recommend that considerable amount of money should be set aside for full adoption and implementation of IPSAS in Nigeria.
Veränderungen in der Anwendung und beim Outcome von Chemotherapie beim frühen Mammakarzinom in der letzten Dekade (2003 – 2014)

Wie hat sich die Anwendung von Chemotherapie beim primären Mammakarzinom in der klinischen Routine in Deutschland über die letzten 10 Jahre verändert? Diese Frage stellt sich nicht nur vor dem Hintergrund eines wachsenden Verständnisses des Mammakarzinoms als heterogene Systemerkrankung, sondern auch unter Berücksichtigung des Ziels, Chemotherapie möglichst selektiv zur Vermeidung von Übertherapie nur den Patientinnen anzubieten, bei denen ein Nutzen zu erwarten ist [1]. Für das frühe Mammakarzinom zeigen aktuelle Ergebnisse aus klinischen Registern, dass ein brustkrebsspezifisches Überleben bei invasiven Primärfällen über alle Tumorsubtypen hinweg von mehr als 90% über 5 Jahre erreicht werden kann [2]. Die Verbesserungen beim Outcome von Patientinnen mit frühem Mammakarzinom sind dabei zu einem großen Teil auf Verbesserungen im Bereich der Systemtherapie zurückzuführen [3]. Während der letzten Dekade entwickelte sich die neoadjuvante Anwendung von Chemotherapie (NACT) zur gleichwertigen Alternative gegenüber der adjuvanten Anwendung (ACT). Sie besitzt dabei Vorteile, die über eine mögliche alleinige Verkleinerung des Tumors im Sinn einer verbesserten Operabilität hinausgehen. Die Vorteile der neoadjuvanten Therapie liegen vor allem in den Prinzipien der In-vivo-Sensitivitätstestung und der Vorbereitung auf eventuelle post-neoadjuvante Therapieansätze. Darüber hinaus gewinnt man Zeit vor der Operation, die im Bedarfsfall genutzt werden kann für genetische Testung oder rekonstruktive Beratung. Zudem zeigt sich, dass das Erreichen einer pathologischen Komplettremission (pCR) nach NACT eine prognostische Aussagekraft besitzt und vor allem für HER2-positive und triple-negative Tumorsubtypen mit einem signifikanten Vorteil bzgl. krankheitsfreiem und
Joint Random Subcarrier Selection and Channel-Based Artificial Signal Design Aided PLS

A novel method for providing physical layer security (PLS) depending on the randomness of wireless channel is proposed. Specifically, a channel-based joint random subcarrier selection and artificial signal design are introduced to protect the communication in the presence of a strong passive eavesdropper. Our analysis assumes a window-based subcarrier selection method in which the strongest subcarriers in each window are selected. Chosen subcarriers are considered for secret sequence extraction. In addition the generated channel dependent secret sequence is used for both random subcarrier selection and artificial signal design. We evaluate the efficiency of the proposed method through some representative metrics, such as secret sequence disagreement rate (SSDR), throughput and bit error rate (BER), in both perfect and imperfect channel estimation cases. Simulation results are presented and insightful discussions are drawn.
DETECTION OF PUMP FAULTS BASED ON SOUND SIGNALS USING NON-NEGATIVE MATRIX FACTORIZATION

Maintenance is very closely related to the performance of the production process. An alternative method that can be used to determine the damage to the engine is from the analysis of the sound pattern produced. If the sound source is more than one, then there will be signal mixing, and it will be a challenge in detecting damage to the engine. In this study, mixed signals will be separated. Separation of mixed sound signals was done using non-negative matrix factorization (NMF) method. Overall this study is aimed at detecting unbalance, misalignment, and bearing faults at pumps with microphones as sensors. The pumps used in this study were three pumps, where each pump had different conditions (unbalance, misalignment, and bearing fault). All three pumps have 3000 rpm. In this study, the recording process was carried out for 5 s. In this study, we also compare the location of the instantaneous frequency in full spectrum and corresponding frequency in local spectrum, and the distance between the spectra via the log spectral distance from the baseline signal and the estimated signal. Based on the instantaneous frequency approach, no error was found because of the instantaneous frequency suitability of the unbalanced machine condition with the estimated signal in the mixing configuration of three sources with two sensors. From the log spectral instance (LSD) results, the smallest value was obtained the smallest value in estimation 2, which tends to approach the unbalance condition with the LSD value of 1.0889. The most significant relative error is the estimated misalignment signal with a value of 11.2. However, overall damage can still be identified based on the pattern formed and some statistical parameters.