Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning Classifiers for Predicting Transit Fraud

Fraud related studies cover a wide range of approaches for detection and identifying or mitigating risks associated with it. This study extends the existing body of research and our understanding on fraud with a specific focus on transit media. A large public transit transaction dataset was extracted from production systems of a large U.S Public Transit Authority system. We developed various machine learning models using techniques such as random forests, support vector machines, and artificial neural networks to effectively classify transit fare media fraud into binary groups and compare their relative model performance. We found that random forests and neural networks outperformed support vector machines for transit fraud detection. More data and parameter optimization are needed for improved results.
H2O2 Signature and Innate Antioxidative Profile Make the Difference Between Sensitivity and Tolerance to Salt in Rice Cells

Salt tolerance is a complex trait that varies between and within species. H2O2 profiles as well as antioxidative systems have been investigated in the cultured cells of rice obtained from Italian rice varieties with different salt tolerance. Salt stress highlighted differences in extracellular and intracellular H2O2 profiles in the two cell cultures. The tolerant variety had innate reactive oxygen species (ROS) scavenging systems that enabled ROS, in particular H2O2, to act as a signal molecule rather than a damaging one. Different intracellular H2O2 profiles were also observed: in tolerant cells, an early and narrow peak was detected at 5 min; while in sensitive cells, a large peak was associated with cell death. Likewise, the transcription factor salt-responsive ethylene responsive factor 1 (TF SERF1), which is known for being regulated by H2O2, showed a different expression profile in the two cell lines. Notably, similar H2O2 profiles and cell fates were also obtained when exogenous H2O2 was produced by glucose/glucose oxidase (GOX) treatment. Under salt stress, the tolerant variety also exhibited rapid upregulation of K+ transporter genes in order to deal with K+/Na+ impairment. This upregulation was not detected in the presence of oxidative stress alone. The importance of the innate antioxidative profile was confirmed by the protective effect of experimentally increased glutathione in salt-treated sensitive cells. Overall, these results underline the importance of specific H2O2 signatures and innate antioxidative systems in modulating ionic and redox homeostasis for salt stress tolerance.
Neuroprotective Natural Molecules, From Food to Brain

The prevalence of neurodegenerative disorders is increasing; however, an effective neuroprotective treatment is still remaining. Nutrition plays an important role in neuroprotection as recently shown by epidemiological and biochemical studies which identified food components as promising therapeutic agents. Neuroprotection includes mechanisms such as activation of specific receptors, changes in enzymatic neuronal activity, and synthesis and secretion of different bioactive molecules. All these mechanisms are focused on preventing neuronal damage and alleviating the consequences of massive cell loss. Some neuropathological disorders selectively affect to particular neuronal populations, thus is important to know their neurochemical and anatomical properties in order to design effective therapies. Although the design of such treatments would be specific to neuronal groups sensible to damage, the effect would have an impact in the whole nervous system. The difficult overcoming of the blood brain barrier has hampered the development of efficient therapies for prevention or protection. This structure is a physical, enzymatic, and influx barrier that efficiently protects the brain from exogenous molecules. Therefore, the development of new strategies, like nanocarriers, that help to promote the access of neuroprotective molecules to the brain, is needed for providing more effective therapies for the disorders of the central nervous system (CNS). In order both to trace the success of these nanoplatforms on the release of the bioactive cargo in the CNS and determinate the concentration at trace levels of targets biomolecules by analytical chemistry and concretely separation instrumental techniques, constitute an essential tool. Currently, these techniques are used for the determination and identification of natural neuroprotective molecules in complex matrixes at different concentration levels. Separation techniques such as chromatography and capillary electrophoresis (CE), using optical and/or mass spectrometry (MS) detectors, provide multiples combinations for the quantitative and qualitative analysis at basal levels or higher concentrations of bioactive analytes in biological samples. Bearing this in mind, the development of food neuroprotective molecules as brain therapeutic agents is a complex task that requires the intimate collaboration and engagement of different disciplines for a successful outcome. In this sense, this work reviews the new advances achieved in the area toward a better understanding of the current state of the art and highlights promising approaches for brain neuroprotection.
Parity Decision Tree Complexity is Greater Than Granularity

We prove a new lower bound on the parity decision tree complexity $\mathsf{D}_{\oplus}(f)$ of a Boolean function $f$. Namely, granularity of the Boolean function $f$ is the smallest $k$ such that all Fourier coefficients of $f$ are integer multiples of $1/2^k$. We show that $\mathsf{D}_{\oplus}(f)\geq k+1$. This lower bound is an improvement of lower bounds through the sparsity of $f$ and through the degree of $f$ over $\mathbb{F}_2$. Using our lower bound we determine the exact parity decision tree complexity of several important Boolean functions including majority and recursive majority. For majority the complexity is $n - \mathsf{B}(n)+1$, where $\mathsf{B}(n)$ is the number of ones in the binary representation of $n$. For recursive majority the complexity is $\frac{n+1}{2}$. Finally, we provide an example of a function for which our lower bound is not tight. Our results imply new lower bound of $n - \mathsf{B}(n)$ on the multiplicative complexity of majority.
The Impact of Digitization on Business Models - A Systematic Literature Review

The emergence of digital technologies is disrupting entire industries and businesses face severe shifts in their competitive environment. New competitors with digitally driven business models enter the market and challenge incumbent firms. The concept of business model can help to understand the influence of digitization, but current research efforts do not fully provide a broad understanding. The aim of the paper is to structure the existing literature and provide a conceptual framework for future research. Therefore, a systematic literature review is conducted to gain comprehensive insights. Findings reveal that the impact of digitization on business models can be described through the concept of business model change, namely: creation, termination, revision and extension. This paper provides a conceptual framework based on for future research on the impact of digitization on business models.
Why Do Users Participate in Online Communities? The Effect of Motivational Affordances, Comments, and Peer Contribution on Continuance

Online communities are increasingly being used by technical professionals and aspirants to access information and knowledge, discuss and share experiences, and collaborate in solving problems. Recently, the application of gamification in non-game contexts has attracted the attention of both practitioners and researchers and has generally been found to have a positive effect on user experiences. This research incorporates community design mechanisms and social mechanisms into a comprehensive model to explain the variance in continued online community participation. The research model is tested by using survival analysis models on a dataset collected from an online question and answer community. This research contributes by not only extending our current understanding of participant involvement in online communities, but also by shedding light on the design and management of online communities focused on knowledge building.
Soft Concept Analysis

In this chapter we discuss soft concept analysis, a study which identifies an enriched notion of "conceptual scale" as developed in formal concept analysis with an enriched notion of "linguistic variable" as discussed in fuzzy logic. The identification "enriched conceptual scale" = "enriched linguistic variable" was made in a previous paper (Enriched interpretation, Robert E. Kent). In this chapter we offer further arguments for the importance of this identification by discussing the philosophy, spirit, and practical application of conceptual scaling to the discovery, conceptual analysis, interpretation, and categorization of networked information resources. We argue that a linguistic variable, which has been defined at just the right generalization of valuated categories, provides a natural definition for the process of soft conceptual scaling. This enrichment using valuated categories models the relation of indiscernability, a notion of central importance in rough set theory. At a more fundamental level for soft concept analysis, it also models the derivation of formal concepts, a process of central importance in formal concept analysis. Soft concept analysis is synonymous with enriched concept analysis. From one viewpoint, the study of soft concept analysis that is initiated here extends formal concept analysis to soft computational structures. From another viewpoint, soft concept analysis provides a natural foundation for soft computation by unifying and explaining notions from soft computation in terms of suitably generalized notions from formal concept analysis, rough set theory and fuzzy set theory.
Joint-VAE: Learning Disentangled Joint Continuous and Discrete Representations

We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent distribution of variational autoencoders with a relaxed discrete distribution and controlling the amount of information encoded in each latent unit, we show how continuous and categorical factors of variation can be discovered automatically from data. The learned model also contains an inference network which can infer quantities such as angle and width of objects from image data in a completely unsupervised manner. Our experiments show that the framework disentangles continuous and discrete generative factors on various datasets, including disentangling digit type from stroke thickness, angle and width on MNIST, chair type from azimuth and width on the Chairs dataset and age from azimuth on CelebA.
Inconsistency Investigation between Online Review Content and Ratings

Despite the tremendous role of online consumer reviews (OCRs) in facilitating consumer purchase decision making, the potential inconsistency between product ratings and review content could cause the uncertainty and confusions of prospect consumers toward a product. This research is aimed to investigate such inconsistency so as to better assist potential consumers with making purchase decisions. First, this study extracted a reviewer’s sentiments from review text via sentiment analysis. Then, it examined the correlation and inconsistency between product ratings and review sentiments via Pearson correlation coefficients (PCC) and box plots. Next, we compared such inconsistency patterns between fake and authentic reviews. Based on an analysis of 24,539 Yelp reviews, we find that although the ratings and sentiments are highly correlated, the inconsistency between the two is more salient in fake reviews than in authentic reviews. The comparison also reveals different inconsistency patterns between the two types of reviews.
What Does it Mean to be Autistic? Examining How Identity Threats and Coping Strategies Influence the ASD Identity through Analysis of Identity Talk in Online Communities

This study employs a grounded theory approach to examine how autism spectrum disorder (ASD) individuals make sense of their identity through online communities. Findings indicate that the autism diagnosis and perceptions of the diagnosis create identity threats which ASD individuals cope with in constructing and managing their identity. The autism diagnosis establishes a prescribed identity (i.e., identity an individual or social group is told they have) while others’ perceptions of the diagnosis create a stereotype identity (i.e., identity an individual or social group is assumed to have). These threaten the identity motives of belongingness, distinctiveness, self-esteem, and self-control/regulation. In order to manage these threats, ASD individuals employ the coping strategies of acceptance, online social support, digital insulation, positive distinctiveness, disidentification, concealment, and fantasy. We explore the impacts of these threats and coping strategies on autism identity sensemaking and explicate the implications for practice and research.
Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification

Background Instagram, with millions of posts per day, can be used to inform public health surveillance targets and policies. However, current research relying on image-based data often relies on hand coding of images, which is time-consuming and costly, ultimately limiting the scope of the study. Current best practices in automated image classification (eg, support vector machine (SVM), backpropagation neural network, and artificial neural network) are limited in their capacity to accurately distinguish between objects within images. Objective This study aimed to demonstrate how a convolutional neural network (CNN) can be used to extract unique features within an image and how SVM can then be used to classify the image. Methods Images of waterpipes or hookah (an emerging tobacco product possessing similar harms to that of cigarettes) were collected from Instagram and used in the analyses (N=840). A CNN was used to extract unique features from images identified to contain waterpipes. An SVM classifier was built to distinguish between images with and without waterpipes. Methods for image classification were then compared to show how a CNN+SVM classifier could improve accuracy. Results As the number of validated training images increased, the total number of extracted features increased. In addition, as the number of features learned by the SVM classifier increased, the average level of accuracy increased. Overall, 99.5% (418/420) of images classified were correctly identified as either hookah or nonhookah images. This level of accuracy was an improvement over earlier methods that used SVM, CNN, or bag-of-features alone. Conclusions A CNN extracts more features of images, allowing an SVM classifier to be better informed, resulting in higher accuracy compared with methods that extract fewer features. Future research can use this method to grow the scope of image-based studies. The methods presented here might help detect increases in the popularity of certain tobacco products over time on social media. By taking images of waterpipes from Instagram, we place our methods in a context that can be utilized to inform health researchers analyzing social media to understand user experience with emerging tobacco products and inform public health surveillance targets and policies.
Predicting the Performance of Basketball Players Using Automated Personality Mining

The Big Five personality traits, which provide a guide to the comprehensive assessment of individuals, have been found to be a valid predictor of behavior performance in the athletic context. However, traits have commonly been inferred via self-report questionnaires, which are criticized for reflecting only limited aspects of personality. Numerous studies have shown that human language reflects personality based on the frequency with which certain categories of words are used. Thus, we used an automated language-based measurement of personality and identified important traits of Basketball players - a combination of high conscientiousness and high agreeableness - that may be of significance for our prediction model of individual performance. Our findings are supported by prior research conducted on the prototypical "athletic personality profile". Predicting players' performance would not only further our understanding of competitive behavior in an athletic context but would also facilitate athlete selection and the design of training programs.
What Drives Paid Search Success? A Systematic Literature Review

Whilst paid search has received much scholarly attention across various research fields, the findings have not yet been synthesized. As user behavior in paid search is influenced by such a wide range of factors, synthesizing the findings would, however, avoid ‘omitted variable biases’. In our systematic literature review we analyzed over 1,000 sources and identified 34 relevant research papers. These are organized around the three main decisions every advertiser faces when approaching paid search: which keywords to use, which ad positions to target, and how to craft ad copies. We then draw out empirical generalizations and outline a research agenda. In particular, we propose a research framework that researchers and practitioners alike can employ to avoid the risk of omitting variables when analyzing the success factors of paid search.
Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Attribute-aware CF models aim at rating prediction given not only the historical rating given by users to items but also the information associated with users (e.g., age), items (e.g., price), and ratings (e.g., rating time). This paper surveys work in the past decade to develop attribute-aware CF systems and finds that they can be classified into four different categories mathematically. We provide readers not only with a high-level mathematical interpretation of the existing work in this area but also with mathematical insight into each category of models. Finally, we provide in-depth experiment results comparing the effectiveness of the major models in each category.
Comparing the Use of Agile Methodologies in the Classroom

There is ample research on when Agile methodologies should and should not be implemented in software development team projects. This stream of research also extends into how to implement Agile methodologies (Abrahamsson et al., 2009; Yu and Petter, 2014) and which Agile practices work best within existing teams (Drury-Grogan, 2014; Vijayasarathy and Butler, 2016). Yet, research on information and resources on how to incorporate the practice of Agile training into the classroom or which Agile methodology is best suited for information systems development education are scarce (Kropp et al., 2016; Landry and McDaniel, 2016). Throughout a computer science or management information systems curriculum, Agile methodologies are referenced and taught frequently; however, students are seldom required to develop in Agile environments in the classroom as part of learning best development practices. This lack of practical application and exposure might be due to limited opportunities for students to meet outside of class hours in conjunction with possible departmental limitations. Yet, with the rapid adoption and proliferation of Agile methodologies throughout the software development industry, the absence of this practical experience and subsequent cursory knowledge puts students at a competitive disadvantage in the workplace. This research compares the performance of two popular Agile methodologies by investigating how information systems students learn to develop software in teams using these methodologies within their curriculum. Both Meier et al (2016) and Kropp et al (2016) investigated the teaching of agile collaboration and values in student teams in the classroom, with each investigation demonstrating that incorporating agile methodologies into the software engineering education leads to positive student outcomes. This research investigates which Agile methodologies can be most effectively applied within the academic arena in order to give students the experience they need to be more successful in a professional workplace setting through applied learning based on employer desired skill sets.
Is Information Systems Late to the Party? The Current State of DevOps Research in the Association for Information Systems eLibrary

With the recent growth and popularity of DevOps in industry, practitioner outlets, and publications within the areas of computer science and software engineering, the aim of this paper is to address the question, “Is Information Systems late to the party? ” In hopes of shedding light on this question, the authors conducted a thorough and systematic review of the publications contained in the Association for Information Systems eLibrary to examine the current state of DevOps research within the area of Information Systems. By following guidelines established for conducting reviews of literature in Information Systems, the authors identified 42 papers containing the keyword DevOps or Devops. Further analysis of each paper revealed only six papers that directly addressed the topic of DevOps. The authors conclude that Information Systems needs to fully “join the party” and that DevOps represents a fertile ground for increased Information Systems research.
Field Of Interest Proposal for Augmented Mitotic Cell Count: Comparison of two Convolutional Networks

Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will often almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic detection of mitotic figures can't yet be considered reliable enough for clinical application. In order to aid the work of the human expert and at the same time reduce variance in tumor grading, it is beneficial to assess the whole slide image (WSI) for the highest mitotic activity and use this as a reference region for human counting. For this task, we compare two methods for region of interest proposal, both based on convolutional neural networks (CNN). For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity. The first method performs a segmentation at the original image resolution, while the second approach performs a segmentation operation at a significantly reduced resolution, cutting down on processing complexity. We evaluate the approach using a dataset of 32 completely annotated whole slide images of canine mast cell tumors, where 22 were used for training of the network and 10 for test. Our results indicate that, while the overall correlation to the ground truth mitotic activity is considerably higher (0.94 vs. 0.83) for the approach based upon the fine resolution network, the field of interest choices are only marginally better. Both approaches propose fields of interest that contain a mitotic count in the upper quartile of respective slides.