Default Features Research Articles

Assessing Mood With the Identifying Depression Early in Adolescence Chatbot (IDEABot): Development and Implementation Study.

Mental health status assessment is mostly limited to clinical or research settings, but recent technological advances provide new opportunities for measurement using more ecological approaches. Leveraging apps already in use by individuals on their smartphones, such as chatbots, could be a useful approach to capture subjective reports of mood in the moment. This study aimed to describe the development and implementation of the Identifying Depression Early in Adolescence Chatbot (IDEABot), a WhatsApp-based tool designed for collecting intensive longitudinal data on adolescents' mood. The IDEABot was developed to collect data from Brazilian adolescents via WhatsApp as part of the Identifying Depression Early in Adolescence Risk Stratified Cohort (IDEA-RiSCo) study. It supports the administration and collection of self-reported structured items or questionnaires and audio responses. The development explored WhatsApp's default features, such as emojis and recorded audio messages, and focused on scripting relevant and acceptable conversations. The IDEABot supports 5 types of interactions: textual and audio questions, administration of a version of the Short Mood and Feelings Questionnaire, unprompted interactions, and a snooze function. Six adolescents (n=4, 67% male participants and n=2, 33% female participants) aged 16 to 18 years tested the initial version of the IDEABot and were engaged to codevelop the final version of the app. The IDEABot was subsequently used for data collection in the second- and third-year follow-ups of the IDEA-RiSCo study. The adolescents assessed the initial version of the IDEABot as enjoyable and made suggestions for improvements that were subsequently implemented. The IDEABot's final version follows a structured script with the choice of answer based on exact text matches throughout 15 days. The implementation of the IDEABot in 2 waves of the IDEA-RiSCo sample (140 and 132 eligible adolescents in the second- and third-year follow-ups, respectively) evidenced adequate engagement indicators, with good acceptance for using the tool (113/140, 80.7% and 122/132, 92.4% for second- and third-year follow-up use, respectively), low attrition (only 1/113, 0.9% and 1/122, 0.8%, respectively, failed to engage in the protocol after initial interaction), and high compliance in terms of the proportion of responses in relation to the total number of elicited prompts (12.8, SD 3.5; 91% out of 14 possible interactions and 10.57, SD 3.4; 76% out of 14 possible interactions, respectively). The IDEABot is a frugal app that leverages an existing app already in daily use by our target population. It follows a simple rule-based approach that can be easily tested and implemented in diverse settings and possibly diminishes the burden of intensive data collection for participants by repurposing WhatsApp. In this context, the IDEABot appears as an acceptable and potentially scalable tool for gathering momentary information that can enhance our understanding of mood fluctuations and development.

A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning

Marketplace lending has fundamentally changed the relationship between borrowers and lenders in financial markets. As with many other financial products that have emerged in recent years, internet-based investors may be inexperienced in marketplace lending, highlighting the importance of forecasting default rates and evaluating default features such as the loan amount, interest rates, and FICO score. Potential borrowers on marketplace lending platforms may already have been rejected by banks as too risky to lend to, which amplifies the problem of asymmetric information. This paper proposes a holistic data processing flow for the loan status classification of marketplace lending multivariate time series data by using the Bidirectional Long Short-Term Memory model (BiLSTM) to predict “non-default,” “distressed,” and “default” loan status, which outperforms conventional techniques. We adopt the SHapely Additive exPlanations (SHAP) and a four-step ahead model, allowing us to extract the most significant features for default risk assessment. Using our approach, lenders and regulators can identify the most relevant features to enhance the default risk assessment method over time in addition to early risk prediction.

The Status of the Derivative Adjective Forms in the Lexicon: Evidence on Pseudo Default Patterns

This paper investigates the applicability of the plural masculine suffix /-i:n/ as a default inflection marker to nouns having an irregular inflection plural form with /-i:n/ suffix marker. The data taken from Jordanian Arabic (JA) derivatives show that the notion canonical root has a substantial role in the emergence of such a default inflection. The findings also show that there is a tendency for the suffix /-i:n/ to be the marking default as an apparent form among other possible forms in JA despite the fact that they fall outside the ubiquitous regularity domain. These results provide strong evidence on the existence of multiple default systems in JA in addition to the Sound Feminine Plural marker with /a:t/. Finally, these findings conform to the symbolic accounts that rely upon computational mechanisms for the emergence of the default patterns. The findings also draw attention to the systematic suffixes that the derivative forms have /-a:n/ and thus motivate more predictions to the status of the inflected form bearing pseudo default features. Keywords: Pseudo default, Default Patterns, JA, Canonical Root, Symbolic Mechanism, Single Mechanism.

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.

BULWARK: A Framework to Store IoT Data in User Accounts

The explosive growth of the Internet of Things (IoT) devices raises serious concerns for a user’s privacy and security because the existing software framework on these devices often support various default features and generate large data sets. Moreover, many IoT devices incorporate a manufacturer-owned cloud-based back-end support to process and store the generated data while simultaneously sharing with third parties. Clearly, in such an industry-driven environment with the desire to use the IoT data as a revenue stream, it is a challenge for users to control IoT data. Device manufacturers utilize an opaque software design where user data is generated and stored with little transparency. Manufacturers use EULAs as a legal construct to protect a manufacturer’s legal standing and to explain a device’s behavior, however this explanation is vague and lacks the necessary details for a user to determine a device’s acceptable use and it has become increasingly difficult for users to secure and maintain their data. Fortunately, as the privacy minded user base of IoT devices grows, the manufacturers will be forced to implement a new framework that can enable users to have more control on the creation of their IoT data, and to store/disseminate such data in a secure and private manner. In this paper, we address this lack of transparency from manufacturers and address the issues of privacy and security by proposing a new framework called Bulwark, for manufacturer use on IoT devices and mobile applications. Proposed framework enables the user to generate and manage a set of data controlling rules, and store the result in their personal cloud account, while providing a dashboard data reporting tool enabling data transparency and supporting good user choices. The user’s ability to access, disseminate and secure IoT generated data, is now available within our proposed framework. Using reverse engineering, simulation and implementation of open source solutions, we demonstrate support for a set of common devices. Each device executed the framework, while communicating with a mobile application and cloud services. Rules were generated for each message and telemetry was returned to the mobile application for dashboard rendering. We stored generated data in the cloud using our own account, while maintaining the free tier for each of the cloud services. Network usage increased between 4% and 9% while storage size grew between 0% and 2% larger, as compared to using the device without the framework. Our framework demonstrates support for a multitude of devices, by either open source or support for similar feature sets. This framework is easy to integrate and we anticipate wide spread adoption.

Variable Selection and Data Quality Challenges in Impact Assessments

The research is focused on the role of two related key concepts, namely variables and data, in the impact evaluations of public projects. A difficult task of the evaluators and researchers is to select the appropriate variables to ensure the best model of reality and satisfy the evaluation methods' needs. Therefore, the paper aims to look at the current knowledge and discuss how variables and data could be best used to connect the evaluation models, the particularities of the intervention with the potential of the advanced quantitative assessment methods. The results emphasise that evaluations operate with data with different levels of granularity, as required by the intervention logic. Structuring data in clusters and categories, performing evaluability assessments are useful in assessing data quality and limitations and improving them. In line with the existing literature, we demonstrate that data accessibility is a key constraint and imposes adjustment of the desired evaluation model to a feasible one. While Big Data and Open Data systems significantly improved data quality in evaluations in recent years, blockchain, as a ledger technology with default features related to decentralisation and security, is expected to bring large benefits to evaluation. For evaluators and policymakers, blockchain potential is an area of further research looking for additional advantages that could enhance the use of quantitative methods.

Causal structure framework of man-made disaster in construction

Construction accident is a man-made disaster. Causal factors of a construction accident could lie dormant at the project conception, design, and construction stage. Investigating the causal factors is, therefore, inadequate if directed only to operative errors, and the contractor’s management or organisation failures. A systematic investigation should also cover management or organisational failures initiated by the designer, the client’s team, and the client. This would lead to the need for a practical method of the accident investigation by which the operational errors, and management or organisational failures can be revealed comprehensively. This paper presents a constraint-response analysis of causation of construction accidents (CRACCA). It is a model illustrating a generic network of factors in construction accident causation. CRACCA proposes that construction accident causation consist of (1) accident events, (2) proximal factors, and (3) distal factors. The accident events represent accident occurrences and their consequences; the proximal factors represent deficiencies of the construction process; whilst the distal factors represent precursors generating the proximal factors. Both the proximal and distal factors are considered as factors stimulating the increased risk of construction accident. The distal factors are characterised as constraints applied to, and responses by, participants in the construction project. These constraints and responses constitute management actions taken by clients, the client’s team, the designers or the builders. In this way, the model will assist investigators to structure more readily the causal factors, and determine potential or predominant factors leading to construction accidents. In this paper, the constraint-response theory is briefly described. The generic structure of the causal network is presented. The default features of the model are introduced. The paper also illustrates procedures using CRACCA.

Measuring The Impact Of Air Pollution On Health Care Costs.

Air pollution contributes to the development of numerous adverse human health outcomes. The Environmental Protection Agency's Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) tool is widely used in estimating the health care costs of air pollution and in the development of federal and state regulations and policy. Its default features consider only the costs of hospital and emergency department admissions. A more complete accounting of the chain of costs would include ambulatory and other care. In this article we use employer health insurance claims data to infer additional costs that accompany hospitalizations but are not included in BenMAP-CE. Including additional categories increases BenMAP-CE health care cost estimates by approximately 40percent for respiratory and cardiovascular patients. That is, for each dollar of health care costs captured by BenMAP-CE, a more complete accounting would include an additional 40 cents. These results suggest that because such air pollution costs are underestimated, the health care benefits associated with reducing air pollution may be much larger than previously estimated.

Computer Forensic Methodology and Tools

The Computer Forensic has long played a vital role across the domains - Cyber Crimes Investigations, Incident Response, Cyber Threat Intelligence and Corporate Cybersecurity. In the past Digital Evidence lost their validity in the Courtrooms. Prior, similar to some other Physical Evidence, courts used to consider evidence procured from computer systems as genuine. In any case, as time passed, we figured out the fact that it is so natural to corrupt, destroy, or alter computer data, all three stages - storage, network and computation. In the least complex model, if an individual basically opens a computer file, it's absolutely impossible to demonstrate the last date that the document was refreshed aka updated. Computers are intended to record the time and date of an accessed file on its own by default features of an operating system. Examiners later understood that they have to build up the essential devices and procedures to separate the necessary data from computers without influencing the data all the while. With time, specialists concocted systems to securely recover data, preserve and analyze to derive the insights useful in Computer related Crimes. This part of forensic science is currently well known as Computer Forensics.

An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting

As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.

Developing in-vehicular noise robust children ASR system using Tandem-NN-based acoustic modelling

Processing of children's speech is always challenging due to data scarcity and inefficient modelling input feature vectors. Accuracy of the modelling phase is always dependent upon extracted input features. In this paper, posterior probabilities are estimated over a phone set using first discriminatively trained model through neural-net pre-processor. This Neural Network (NN) classifier is first trained on original speech and then context-independent phone posterior probabilities are estimated on Tandem-NN system. The output vectors are employed as default features which are processed on Deep Neural Network-Hidden Markov Model (DNN-HMM) models. The original data-based system performance is improved by extending it using data augmentation. To see the robustness of the augmented speech various in-vehicle data are investigated and found that it is superior to that of other systems. Finally, we combine all augmented data to overcome data scarcity challenges to enhance system performance. It gives a relative improvement of 23.77% over the baseline system.

Application of a content management system for developing equipment safety training courses in surface mining

SYNOPSIS Web-based training (WBT) has become a widely popular training option that can be applied throughout a variety of areas and industries. Mining is a challenging industry that is continuously searching for improvements in its safety training processes. We used the content management system (CMS) WordPress to develop a web-based application to support traditional training in the mining industry. The study focuses on introducing operators of heavy machinery in surface mining operations to pre-shift machine inspections. WordPress does not require technical skills, and in addition to its default features, it offers a vast number of useful plugins. This project implements several plugins to create an application with training courses. In this paper we present both the process of creating a training course and an example of a developed course. Such training applications can be used on the internet or through a local area network. Keywords: web-based training, WordPress, safety training, surface mining.

Developing in-vehicular noise robust children ASR system using Tandem-NN-based acoustic modelling

Processing of children's speech is always challenging due to data scarcity and inefficient modelling input feature vectors. Accuracy of the modelling phase is always dependent upon extracted input features. In this paper, posterior probabilities are estimated over a phone set using first discriminatively trained model through neural-net pre-processor. This Neural Network (NN) classifier is first trained on original speech and then context-independent phone posterior probabilities are estimated on Tandem-NN system. The output vectors are employed as default features which are processed on Deep Neural Network-Hidden Markov Model (DNN-HMM) models. The original data-based system performance is improved by extending it using data augmentation. To see the robustness of the augmented speech various in-vehicle data are investigated and found that it is superior to that of other systems. Finally, we combine all augmented data to overcome data scarcity challenges to enhance system performance. It gives a relative improvement of 23.77% over the baseline system.

Использование текстовых признаков для улучшения качества статистического машинного перевода

In this paper we investigate the technique of extending the Moses Statistical Machine Translation (SMT) system default set of features using shallow linguistic information from source and target phrases. Although a typical SMT system uses a phrase table with 5 default features, most systems are scalable and support any number of additional features. We assume that linguistic information extracted from the source and target phrases can improve the overall translation quality, i. e. make the system more robust and reduce the number of instances of incorrect word choice, punctuation mistakes and other problems SMT systems are prone to. First, we build a baseline SMT system. Then we extract shallow linguistic features directly from source and target phrases of the baseline system’s phrase table. The features are precomputed and stored in the phrase table, so they can be regarded as stateless dense features. We develop and examine 19 features incorporating information from source and target phrases. We explore features commonly used in monolingual and parallel data filtering techniques. The features we investigate include source and target phrase lengths, word, number and punctuation symbol count, word frequencies according to large monolingual corpora etc. For each feature, we build and evaluate a separate SMT system. We conduct a series of experiments on the English-Russian language pair and obtain statistically significant improvements of up to 0.4 BLEU compared to baseline configuration.

Defaulting Retirement Distributions Out of Defined-Contribution Plans: A Role for Managed-Payout Target-Date Portfolios

Default features in defined-contribution plans are designed to improve the retirement security of plan participants. To date, these have focused on the challenge of saving, ignoring the more complex challenge of dissaving. Automatic default provisions for drawing down participant account balances after retirement could be beneficial. Conceptually, the objective is to reframe the Zeitgeist of defined-contribution plans from that of savings plans closer to that of income continuation plans with pension-like features. This could be accommodated though a managed payout feature designed into the plan’s default investment strategy.

Skip, Skip, Skip, Accept!!!: A Study on the Usability of Smartphone Manufacturer Provided Default Features and User Privacy

Abstract Smartphone manufacturer provided default features (e.g., default location services, iCloud, Google Assistant, ad tracking) enhance the usability and extend the functionality of these devices. Prior studies have highlighted smartphone vulnerabilities and how users’ data can be harvested without their knowledge. However, little is known about manufacturer provided default features in this regard—their usability concerning configuring them during usage, and how users perceive them with regards to privacy. To bridge this gap, we conducted a task-based study with 27 Android and iOS smart-phone users in order to learn about their perceptions, concerns and practices, and to understand the usability of these features with regards to privacy. We explored the following: users’ awareness of these features, why and when do they change the settings of these features, the challenges they face while configuring these features, and finally the mitigation strategies they adopt. Our findings reveal that users of both platforms have limited awareness of these features and their privacy implications. Awareness of these features does not imply that a user can easily locate and adjust them when needed. Furthermore, users attribute their failure to configure default features to hidden controls and insufficient knowledge on how to configure them. To cope with difficulties of finding controls, users employ various coping strategies, some of which are platform specific but most often applicable to both platforms. However, some of these coping strategies leave users vulnerable.

SOCCOMAS: a FAIR web content management system that uses knowledge graphs and that is based on semantic programming.

We introduce Semantic Ontology-Controlled application for web Content Management Systems (SOCCOMAS), a development framework for FAIR (‘findable’, ‘accessible’, ‘interoperable’, ‘reusable’) Semantic Web Content Management Systems (S-WCMSs). Each S-WCMS run by SOCCOMAS has its contents managed through a corresponding knowledge base that stores all data and metadata in the form of semantic knowledge graphs in a Jena tuple store. Automated procedures track provenance, user contributions and detailed change history. Each S-WCMS is accessible via both a graphical user interface (GUI), utilizing the JavaScript framework AngularJS, and a SPARQL endpoint. As a consequence, all data and metadata are maximally findable, accessible, interoperable and reusable and comply with the FAIR Guiding Principles. The source code of SOCCOMAS is written using the Semantic Programming Ontology (SPrO). SPrO consists of commands, attributes and variables, with which one can describe an S-WCMS. We used SPrO to describe all the features and workflows typically required by any S-WCMS and documented these descriptions in a SOCCOMAS source code ontology (SC-Basic). SC-Basic specifies a set of default features, such as provenance tracking and publication life cycle with versioning, which will be available in all S-WCMS run by SOCCOMAS. All features and workflows specific to a particular S-WCMS, however, must be described within an instance source code ontology (INST-SCO), defining, e.g. the function and composition of the GUI, with all its user interactions, the underlying data schemes and representations and all its workflow processes. The combination of descriptions in SC-Basic and a given INST-SCO specify the behavior of an S-WCMS. SOCCOMAS controls this S-WCMS through the Java-based middleware that accompanies SPrO, which functions as an interpreter. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required, thereby seamlessly integrating conventional web page technologies with semantic web technologies. SOCCOMAS and the Java Interpreter are available from (https://github.com/SemanticProgramming).

The valuation of privacy premium features for smartphone apps: The influence of defaults and expert recommendations

This study examines the impact of privacy defaults and expert recommendations on smartphone users' willingness to pay for “privacy-enhanced” features on paid applications using a 2 (privacy premium default/no privacy premium default) x 2 (privacy expert recommendation/non-privacy expert recommendation) experimental design. Participants (N = 309) configured four paid apps with respect to privacy features. Selecting premium privacy features was associated with an increased cost, while removing premium privacy features reduced the cost of the application. Replicating findings from behavioral economics on default modes in decision-making, we found that participants presented with apps with privacy premium default features were more likely to retain the more expensive privacy features. However, the recommendation source did not have a significant effect on this relationship. We discuss how these findings extend existing work on users' decision-making process around privacy and suggest potential avenues for nudging users’ privacy behaviors on mobile devices.

SOCCOMAS: A Self-Describing and Content-Independent Application for Semantic Ontology-Controlled Web-Content-Management-Systems

Ontologies are usually utilized for representing knowledge. Here, we extend this use and demonstrate that ontologies also can be used for describing and controlling semantic Web-Content-Management-Systems (WCMS). We call the resulting application SOCCOMAS: a self-describing and content-independent application for semantic ontology-controlled Web-Content-Management-Systems (http://escience.biowikifarm.net/wiki/SOCCOMAS). SOCCOMAS manages the contents of its WCMS through a corresponding knowledge base that is stored in a tuple store. It makes its contents accessible via a flexible and interactive graphical user interface (GUI), utilizing the JavaScript framework AngularJS. Central to SOCCOMAS is a set of application ontologies and a Java middleware, both of which have been developed by us. The ontologies contain the descriptions that control the behavior of the WCMS and the Java middleware interprets these descriptions as an operation-language with commands, attributes and variables. This language is used for describing and therewith defining the function and design of the GUI of the WCMS, including all its user interactions, the underlying data schemes and representations, and all workflow processes of the WCMS. The core application implements a set of default features, such as versioning, provenance and access rights management. These features will be available in all semantic WCMS instances that are based on SOCCOMAS. Because of the ontology-controlled design, SOCCOMAS allows easy customization with a minimum of technical programming background required. Thereby, each WCMS can have its own particular data views, input forms and workflows. The morphological data repository Morph∙D∙Base serves as a first use-case for SOCCOMAS, for which we are implementing ontology-based specimen and media entry representations as well as a new module for the generation of ontology-based morphological descriptions with semantic image annotation.

Performance Analysis of CRT for Image Encryption

With the fast advancements of information technology, the security of image data transmitted or stored over internet is become very difficult. To hide the details, an effective method is encryption, so that only authorized persons can decrypt the image with the keys available. Since the default features of digital image such as high capacity data, large redundancy and large similarities among pixels, the conventional encryption algorithms such as AES, , DES, 3DES, and Blow Fish, are not applicable for real time image encryption. This paper presents the performance of CRT for image encryption to secure storage and transmission of image over internet.