Miracle-dz/UniFAR-FTUs huggingface dataset:
Missing Data Problem in Predictive Analytics

A revolution in computational methods and statistics to process and analyse data into insight and knowledge is along with the growth of data. The paradigm of data analytic is changed from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output (predictive analytics). In the cycle of predictive analytics, data preparation is a very important stage. The main challenge faced is that raw data cannot be directly used for analysis and related to the quality of the data. Completeness is arising related to data quality. Missing data is one that often causes data to become incomplete. As a result, predictive analytics generated from these data becomes inaccurate. In this paper, the issues related to the missing data in predictive analytics will be discussed through a literature study from related research. Also, the challenges and direction that might occur in the predictive analytics domain with problems related to missing data will be presented.
An Automatic Analysis Method for Seabed Mineral Resources Based on Image Brightness Equalization

Since the beginning of the 21st century, the exploration of marine resources has become increasingly frequent, it is increasingly recognized that marine resources play a vital role in human development. However, there are still some problems such as real-time, accurancy and validity, and many places worth exploring in depth analysis of seabed mineral resources. The main purpose of this paper is to apply image process and filter technology, and then analysis of seabed image clarity, accurate statistical coverage indicators seabed mineral resources, so as to realize forecasting undersea resources distribution in the area. The focus of this paper is to solve the problem of the coverage accuracy of seabed black connected domain by adjusting the brightness equalization algorithm and setting the Setting Region Of(ROI) area and the window Histogram Equalization(HE). In order to achieve the purpose of evaluation of sea area resources, a series of such as color correction, bilater filter, window HE and binarization processing such as image preprocessing algorithm, accurate statistical coverage of seabed mineral resources. In this article, video image processing based on the qt environment, including export processing of video streams and index data, generate clarity evaluation and black pieces connected domain coverage rate curve, can achieve more accurate and stable the indicators of seabed image detection the prediction of the accurate statistics of image coverage of seabed ore is achieved in the paper, which lays a foundation for the exploration of deep learning in the future.
Deep Chit-Chat: Deep Learning for Chatbots

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.
Web Experience in Mobile Networks: Lessons from Two Million Page Visits

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.
The Importance of Canonical Product Attributes on User Opinions: an Empirical Investigation

When making purchasing decisions, customers usually rely on information from two types of sources: product specifications, provided by manufacturers, and reviews, posted by other customers. Both kinds of information are often available on e-commerce websites. While researchers have demonstrated the importance of product specifications and reviews as separate and valuable sources to support purchase decision-making, a largely uninvestigated issue is what is the relationship between these two kinds of information. In this paper we present an empirical study on the use of direct and indirect mentions to canonical product attributes, that is, those defined by manufactures in product specifications, in the reviews written by customers. For this study, we analyzed more than 1,100,000 opinionated sentences available in about 650,000 user reviews from Amazon.com across five product categories. Our results indicate that user opinions are indeed guided by the attributes from product specifications and highlight the influence of canonical attributes on the user reviews.
Quantifying and Visualizing the Demand and Supply Gap from E-commerce Search Data using Topic Models

The demand generation and assortment planning are two critical components of running a retail business. Traditionally, retail companies use the historical sales data for modeling and optimization of assortment selection, and they use a marketing strategy for demand generation. However, today, most retail businesses have e-commerce sites with rapidly growing online sales. An e-commerce site typically has to maintain a large amount of digitized product data, and it also keeps a vast amount of historical customer interaction data that includes search, browse, click, purchase and many other different interactions. In this paper, we show how this digitized product data and the historical search logs can be used in understanding and quantifying the gap between the supply and demand side of a retail market. This gap helps in making an effective strategy for both demand generation and assortment selection. We construct topic models of the historical search queries and the digitized product data from the catalog. We use the former to model the customer demand and the later to model the supply side of the retail business. We then create a tool to visualize the topic models to understand the differences between the supply and demand side. We also quantify the supply and demand gap by defining a metric based on Kullback-Leibler (KL) divergence of topic distributions of queries and the products. The quantification helps us identifying the topics related to excess or less demand and thereby in designing effective strategies for demand generation and assortment selection. Application of this work by e-Commerce retailers can result in the development of product innovations that can be utilized to achieve economic equilibrium. We can identify the excess demand and can provide insight to the teams responsible for improving assortment and catalog quality. Similarly, we can also identify excess supply and can provide that intelligence to the teams responsible for demand generation. Tools of this nature can be developed to systematically drive efficiency in achieving better economic gains for the entire e-commerce engine. We conduct several experiments collecting data from Walmart.com to validate the effectiveness of our approach.
Automated detection of sleep apnea in infants using minimally invasive sensors

To address the difficult and necessity of early detection of sleep apnea hypopnea syndrome in infants, we present a study into the effectiveness of pulse oximetry as a minimally invasive means of automated diagnosis of sleep apnea in infants. Overnight polysomnogram data from 328 infants were used to extract time-domain based oximetry features and scored arousal data for each subject. These records were then used to determine apnea events and to train a classifier model based on linear discriminants. Performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 68% was achieved, with a specificity of 68.6% and a sensitivity of 55.9%.
Mining Activity Log Data to Predict Student's Outcome in a Course

Use of learning management system (LMS) is very common, which provide support to teaching staff for communication, delivery of resources and in design of learning activities. The wide spread use of technologies like LMS, provide large amount of data. Research shows that higher education institutes can make use of this data to extract data-driven insights to understand the learning process and benefit the students by supporting them in their academics. In this study we used several machine learning algorithms to predict student's outcome in a course using LMS trace data and assessment scores. Selection of the courses is based on the extent the LMS is used and is divided into two categories; distance and internal. This study confirms the importance of LMS data and assessment scores in the prediction of academic performance. However, frequent use of LMS may increase the trace data but it is not necessary improve the predictive accuracy. Predictive models developed for courses, without considering the context of use of LMS data, may not generalize the effects of LMS trace data on student's outcome in the course.
Investigating the Effect of Aging on the Viscosity of Tendon Fascicles and Fibers

In the current work, we investigate the effect of aging on the viscosity of tendon subunits. To that scope, we make use of experimental relaxation curves of healthy and aged tendon fascicles and fibers, upon which we identify the viscosity parameters characterizing the time-dependent behavior of each tendon subunit. We subsequently combine the obtained results with analytical viscoelastic homogenization analysis methods to extract information on the effective viscous contribution of the embedding matrix substance at the fiber scale. The results suggest that the matrix substance plays a significant role in the relaxation process of the upper tendon subunits both for aged and healthy specimens. What is more, the viscosity coefficients computed for the fibrillar components indicate that aging leads to a viscosity reduction that is statistically significant for both fascicles and fibers. Its impact is more prominent for the lower hierarchical scale of fibers. As such, the reduced stress relaxation capability at the tendon macroscale is to be primarily attributed to the modified viscosity of its inner fibrillar subunits rather than to the matrix substance.
Molecular Analysis of the Acinetobacter baumannii Biofilm-Associated Protein

ABSTRACT Acinetobacter baumannii is a multidrug-resistant pathogen associated with hospital outbreaks of infection across the globe, particularly in the intensive care unit. The ability of A. baumannii to survive in the hospital environment for long periods is linked to antibiotic resistance and its capacity to form biofilms. Here we studied the prevalence, expression, and function of the A. baumannii biofilm-associated protein (Bap) in 24 carbapenem-resistant A. baumannii ST92 strains isolated from a single institution over a 10-year period. The bap gene was highly prevalent, with 22/24 strains being positive for bap by PCR. Partial sequencing of bap was performed on the index case strain MS1968 and revealed it to be a large and highly repetitive gene approximately 16 kb in size. Phylogenetic analysis employing a 1,948-amino-acid region corresponding to the C terminus of Bap showed that BapMS1968 clusters with Bap sequences from clonal complex 2 (CC2) strains ACICU, TCDC-AB0715, and 1656-2 and is distinct from Bap in CC1 strains. By using overlapping PCR, the bap MS1968 gene was cloned, and its expression in a recombinant Escherichia coli strain resulted in increased biofilm formation. A Bap-specific antibody was generated, and Western blot analysis showed that the majority of A. baumannii strains expressed an ∼200-kDa Bap protein. Further analysis of three Bap-positive A. baumannii strains demonstrated that Bap is expressed at the cell surface and is associated with biofilm formation. Finally, biofilm formation by these Bap-positive strains could be inhibited by affinity-purified Bap antibodies, demonstrating the direct contribution of Bap to biofilm growth by A. baumannii clinical isolates.
Detection of Foot and Mouth Disease Virus in clinical samples by PCR-Elisa

Foot-and-mouth disease (FMD) is one of the highly contagious diseases of domestic animals. Effective control of this disease needs sensitive, specific, and quick diagnostic tools. In this paper seventy suspected field's samples to FMD were analyzed using virus isolation on cell culture, Enzyme Linked Immunosorbant Assay (ELISA), RT-PCR (Reverse TranscriptionPolymerase Chain Reaction) and PCR-ELISA. In RT-PCR and PCR-ELISA detection of conserved and universal region of the virus was detected. For this purpose annealing primer to 2B region of the FMDV genome was applied. In our study ELISA used as a golden test and sensitivity of the other test was calculated according this test. Sensitivity of virus isolation, RT-PCR and PCR-ELISA was determined 85.7%, 96.6% and 100% respectively. The highest dilution of FMDV A2005IR 10 5.7 TCID50/ml (Tissue Culture Infectious Dose 50%/ Milliliter) that can be detected by ELISA, RT-PCR and PCR-ELISA was found 1/100, 1/10, 1/1000 viral stock dilutions respectively. Specificity of primer and probe that used in RT-PCR and PCR-ELISA evaluated by intact bovine epithelial tissue and after the PCR cycles no amplification was detected.
Hi Doppelgänger : Towards Detecting Manipulation in News Comments

Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.
Trust in the medical profession: conceptual and measurement issues.

OBJECTIVE To develop and test a multi-item measure for general trust in physicians, in contrast with trust in a specific physician. DATA SOURCES Random national telephone survey of 502 adult subjects with a regular physician and source of payment. STUDY DESIGN Based on a multidimensional conceptual model, a large pool of candidate items was generated, tested, and revised using focus groups, expert reviewers, and pilot testing. The scale was analyzed for its factor structure, internal consistency, construct validity, and other psychometric properties. PRINCIPAL FINDINGS The resulting 11-item scale measuring trust in physicians generally is consistent with most aspects of the conceptual model except that it does not include the dimension of confidentiality. This scale has a single-factor structure, good internal consistency (alpha = .89), and good response variability (range = 11-54; mean = 33.5; SD = 6.9). This scale is related to satisfaction with care, trust in one's physician, following doctors' recommendations, having no prior disputes with physicians, not having sought second opinions, and not having changed doctors. No association was found with race/ethnicity. While general trust and interpersonal trust are qualitatively similar, they are only moderately correlated with each other and general trust is substantially lower. CONCLUSIONS Emerging research on patients' trust has focused on interpersonal trust in a specific, known physician. Trust in physicians in general is also important and differs significantly from interpersonal physician trust. General physician trust potentially has a strong influence on important behaviors and attitudes, and on the formation of interpersonal physician trust.
Lightning Talk - Humor Recognition in Russian Language

In this lighting talk paper, we present a dataset of jokes in Russian and deep learning model for solving humor recognition task. The new large dataset was collected from various online resources and complemented carefully with unfunny texts with similar lexical properties. In total, there are more than 300,000 short texts, which is significantly larger than any previous humor-related corpus. Manual annotation of 2,000 items proved the reliability of corpus construction approach. Further, we applied language model fine-tuning for text classification and obtained an F1 score of 0.91, which constitutes a considerable gain over baseline methods.
CIM-SIM: Computation In Memory SIMuIator

Computation-in-memory reverses the trend in von-Neumann processors by bringing the computation closer to the data, to even within the memory array, as opposed to introducing new memory hierarchies to keep (frequently used) data closer to a central processing unit (CPU). In recent years, new non-volatile memory (NVM) technologies, e.g., memristor, PCM, etc., have proven that they can function as memories and perform computations on the stored data as well. In particular, when they are combined with a modest set of (digital) peripheral modules, a wider range of operations can be supported, e.g., vector matrix multiply and Boolean logic. In this paper, we are introducing the CIM-SIM, an open source simulator written in SystemC, which is capable of simulating the functional behaviour of such architectures. The architecture includes the definition of a set of technology-agnostic nano-instructions.
Global Reactions to the Cambridge Analytica Scandal: A Cross-Language Social Media Study

Currently, there is a limited understanding of how data privacy concerns vary across the world. The Cambridge Analytica scandal triggered a wide-ranging discussion on social media about user data collection and use practices. We conducted a cross-language study of this online conversation to compare how people speaking different languages react to data privacy breaches. We collected tweets about the scandal written in Spanish and English between April and July 2018. We used the Meaning Extraction Method in both datasets to identify their main topics. They reveal a similar emphasis on Zuckerberg’s hearing in the US Congress and the scandal’s impact on political issues. However, our analysis also shows that while English speakers tend to attribute responsibilities to companies, Spanish speakers are more likely to connect them to people. These findings show the potential of cross-language comparisons of social media data to deepen the understanding of cultural differences in data privacy perspectives.
Dominin and Segon Form Multiprotein Particles in the Plasma of Eastern Oysters (Crassostrea virginica) and Are Likely Involved in Shell Formation

Dominin and segon are two proteins purified and characterized from the plasma of eastern oysters Crassostrea virginica, making up about 70% of the total plasma proteins. Their proposed functions are in host defense based on their pathogen binding properties and in metal metabolism based on their metal binding abilities. In the present study, the two proteins were further studied for their native states in circulation and extrapallial fluid and their possible involvement in shell formation. Two-dimensional electrophoresis confirmed that the oyster plasma was dominated by a few major proteins and size exclusion chromatography indicated that these proteins were present in circulation in a morphologically homogenous form. Density gradient ultracentrifugation in Cesium Chloride isolated morphologically homogenous particles of about 25 nm in diameter from the plasma and extrapallial fluids. Polyacrylamide gel electrophoresis identified dominin, segon and an unidentified protein as the principal components of the particles and the three proteins likely formed a multiprotein complex that associated to form the particle. Additionally, three major proteins extracted from shell organic matrix were identified based on the apparent molecular weight in SDS-PAGE to correspond to the three major proteins of plasma and protein particles. Moreover, the hemocyte expression of dominin and segon genes measured by real-time RT-PCR increased significantly upon the initiation of shell repair and were significantly greater in younger oysters. These findings suggest that dominin and segon form protein particles by association with each other and perhaps some other major plasma proteins and play a significant role in oyster shell formation.